The quality of an interferogram, which is limited by various phase noise, will greatly affect the further processes of InSAR, such as phase unwrapping. Interferometric SAR (InSAR) geophysical measurements’, such as height or displacement, phase filtering is therefore an essential step. In this work, an improved Goldstein interferogram filter is proposed to suppress the phase noise while preserving the fringe edges. First, the proposed adaptive filter step, performed before frequency estimation, is employed to improve the estimation accuracy. Subsequently, to preserve the fringe characteristics, the estimated fringe frequency in each fixed filtering patch is removed from the original noisy phase. Then, the residual phase is smoothed based on the modified Goldstein filter with its parameter alpha dependent on both the coherence map and the residual phase frequency. Finally, the filtered residual phase and the removed fringe frequency are combined to generate the filtered interferogram, with the loss of signal minimized while reducing the noise level. The effectiveness of the proposed method is verified by experimental results based on both simulated and real data. As an all-weather all-time remote sensing technique, the synthetic aperture radar (SAR) is of great importance in many fields, such as natural hazards’ monitoring, ocean investigation, geographic mapping, and so on ,2,3. SynIn recent years, various filtering methods were proposed to improve the quality of the interferometric phase. The simplest ones are the mean filter in the spatial domain and the low-pass filter in the frequency domain. As an extension, in the spatial domain, the fringe adaptive smoothing approach, proposed by Lee et al. (commonly referred to as the Lee filter) , has beeIn the frequency domain, the coherent signal in the filtering patch accumulates and forms a dominant peak, while the power of uncorrelated noises disperses stochastically in different directions ,21. BaseTo address the aforementioned problem, in this work, an enhanced Goldstein filtering method is proposed to preserve the fringes by removing the local fringe frequency before phase filtering. Firstly, a mean-filter moving patch is utilized to prefilter the pixels before fringe frequency estimation. The size of the prefilter window is adjusted with the mean coherence value and the PSD (phase standard deviation) in each filtering patch. In this way, the areas with low coherence or high noise level are filtered with larger windows. Since the phase precision decreases sharply when the size of mean filtering approaches to or exceeds the critical averaging look numbers , the sizThe remainder of this paper is organized as follows. The improved Goldstein filter based on local frequency estimation is introduced in The Goldstein filter smooths the intensity of Fourier-transformed samples of small, overlapping interferogram patches. The spectrum of the filtered interferogram can be expressed as :(1)Hσ=∑Nessed as :(7)nr=⌊As can be seen, the areas of lower coherence or more noise will be prefiltered with larger windows, while the low noise region will be prefiltered with smaller windows.Next, fringe frequency estimation is performed after the above prefiltering operation. According to the model of the noisy phase described in , the powIn this part, the residual noisy phase, containing the noise and the residual phase component, is further discussed. In an ideal interferogram without noise, the residue phase without fringe frequency approaches zero in the frequency domain. Nevertheless, the frequencies of the residual phase are not close to zero in practice due to different types of phase noise. Therefore, a change to the Goldstein parameter This modification ensures that the incoherent or noisy areas are filtered more than coherent or low-noise ones. The filtering operation is implemented by combining Equations (1) and (12). To express it more clearly, we define the filtered residual complex phase as In this section, the performance of the proposed method is studied using simulated and real data. The effect of the three proposed modifications to the standard Goldstein filter is demonstrated by simulated data; both the simulated and the real dataset are used to show the effectiveness of the proposed method in phase noise reduction and fringe preservation in comparison with several existing methods.The interferogram is derived from the simulated complex SAR image pairs through interferometry. The SAR images with 150 × 150 samples are initially simulated according to the SAR geometry and the imaging area . SubsequWe filter the simulated data using five algorithms: the reference Goldstein interferogram filter , the impIn order to evaluate the performance of our modifications in a quantitative way, the phase residue number, the edge preservation index (EPI) and the mean-square errors (MSE) between the filtered and the true phase are calculated. The number of residues determines the effectiveness of eliminating phase noise, and the EPI, indicating the capability of fringe and edge preservation, is given by :(14)EPIfined as :(15)MSEThe filtered interferograms are evaluated using the aforementioned criteria. The results are shown in In this part, the performance of our method is evaluated with the simulated dataset. As a comparison, the reference Goldstein filter , the topAs shown in The calculated quantitative results are shown in Then, in order to validate the robustness of our method in filtering the transitional region and phase jumping region, two cross-sections (A in the azimuth direction and B in the range direction) where phase distortion often occurs are extracted from As show in Finally, the density function of phase errors  within the range In this part, the interferogram of the SIR C-SAR data  is used to investigate the performance of the proposed method. A typical area of the experimental results with a 400 × 400 sample dimension is selected.In Overall, we can conclude that the proposed interferogram filter based on local frequency estimation has consistently outperformed the existing ones.The adaptive prefiltering operation based on phase standard deviation and coherence can effectively improve the accuracy of local fringe frequency estimation for areas incoherent or with a high level of noise without reducing the resolution of the interferogram.The fringe frequency estimation and slope compensation before applying the Goldstein filter can significantly enhance its performance in edge preservation.The modified Goldstein parameter Fringe frequency compensation and residual phase filtering are combined to reduce the number of phase residues significantly while preserving the fringe details well, even for fringes with strong curvatures.In this paper, a modified Goldstein filtering method to reduce the phase noise has been proposed. As demonstrated by experimental results based on both simulated data and real data, it can suppress noise effectively while preserving the fringe details well. In detail, we have the following four major findings:As a result, the filtered interferogram can keep its fringe frequency components and also benefit from the reduced noise level provided by the Goldstein filter, leading to an improved performance by the proposed method.


The public health initiatives for obesity prevention are increasingly exploiting the advantages of smart technologies that can register various kinds of data related to physical, physiological, and behavioural conditions. Since individual features and habits vary among people, the design of appropriate intervention strategies for motivating changes in behavioural patterns towards a healthy lifestyle requires the interpretation and integration of collected information, while considering individual profiles in a personalised manner. The ontology-based modelling is recognised as a promising approach in facing the interoperability and integration of heterogeneous information related to characterisation of personal profiles.The presented ontology captures individual profiles across several obesity-related knowledge-domains structured into dedicated modules in order to support inference about health condition, physical features, behavioural habits associated with a person, and relevant changes over time. The modularisation strategy is designed to facilitate ontology development, maintenance, and reuse. The domain-specific modules formalised in the Web Ontology Language (OWL) integrate the domain-specific sets of rules formalised in the Semantic Web Rule Language (SWRL). The inference rules follow a modelling pattern designed to support personalised assessment of health condition as age- and gender-specific. The test cases exemplify a personalised assessment of the obesity-related health conditions for the population of teenagers.The paper addresses several issues concerning the modelling of normative concepts related to obesity and depicts how the public health concern impacts classification of teenagers according to their phenotypes. The modelling choices regarding the ontology-structure are explained in the context of the modelling goal to integrate multiple knowledge-domains and support reasoning about the individual changes over time. The presented modularisation pattern enhances reusability of the domain-specific modules across various health care domains. Overweight and obesity are estimated to result in the deaths of about 320 000 people in western Europe every year . The preThe task of representing personal profiles in a model that integrates diverse kinds of data provided by various sources motivates the employment of Semantic Web technologies . In partSeveral studies report the use of ontology and semantic technologies to target obesity e.g. , 17, 18), 1817, 1The relevant literature addressing the issues related to obesity mostly refers to specific scenarios, focusing either on adults with certain diseases  or on chSince the public health concerns related to obesity  include Formalisation of generic knowledge related to obesity.Specialisation of the generic model into a teenager tailored model.Modularisation to support integration of generic and specific knowledge.Changes of personal features over time.Automatic inference of personal health status that is relevant for obesity assessment and prevention.In order to face the need to model complex and diverse aspects of obesity-related knowledge, this paper in particular deals with:The possibility of having both a general and a specific model is achieved by adopting a modular design strategy. The core ontology module specifies certain generic classes that are applicable to any human being and a generic characterisation of individual health conditions. The domain-specific ontology modules (applicable to any person) provide obesity-related classifications. The core module as well as the domain-specific ontology modules are formalised in OWL (see the following sections). The modules specifying sets of rules are modelled in SWRL and explicitly provide reference values that support inference and classification of personal profiles for the population of teenagers. In particular, the ontology design is driven by the need to track changes in health condition over time .personal profiles on a generic level to support specific inference within a comprehensive ontology model of the obesity-related knowledge. The developed ontology formalises information about obesity-related human features, enables reasoning, and enables information flow and interoperability between the technological tools and platforms employed to monitor the changes of health status, behaviour, and nutritional habits of humans in general, and adolescents in particular.Thus, the ontology model presented in this paper addresses the problem that was only partially addressed in the models previously described in the literature i.e. , 17, 18), 1817, 1The development of this ontology was initiated within the European research project named PEGASO  whose maIn the following sections we first outline the theoretical and practical context that is relevant in explaining and justifying the decisions taken during the ontology design phase. We discuss the modular structure of the ontology as related to the methodological approach that considers ontology-design from two perspectives:(1) task dependent modelling that faces a particular application scenario and (2) extrapolation of general modelling patterns that can be used in a broad context that goes beyond a single application task. We specifically describe an ontology module that captures the physical domain and classifies health conditions based on the assessment of body constitution. In the second part of the paper we present the inference patterns that are used in the current version of the ontology. In order to exemplify the employment of reasoning patterns, we provide the case of reasoning over a personal assessment of health condition by combining OWL and SWRL rules. The concluding remarks outline some advantages of the modular structure and inference patterns, discussing the potential of their reuse in other application scenarios.ontology as an engineering artifact that is useful to model some aspects of the world. In other words, we accept the position that in Artificial Intelligence Systems, “what exists” is what can be represented , goal(s), and context of current scientific knowledge, i.e. theories used to define the key concepts relevant for capturing obesity-related knowledge in a comprehensive manner. Since the domain problem covers several fields of knowledge that are related to the problem of obesity, the specific fields are identified as distinct sub-domains of knowledge. The identified fields are later used to structure knowledge into the dedicated ontology modules, each of which can exist independently as the modules capture field-specific aspects of human features that are relevant for the modelling task and are also applicable to a wide scope of related scenarios. The methodology is in line with the tradition that considers esented  the specification of the ontology goal and scope, (2) the methodology for the ontology design, and (3) the justification of the representational choices regarding the study of obesity and its prevention. The impact of cross-disciplinary studies on the ontology design are considered in the context of background knowledge and theories that the ontology needs to capture formally. The explication of the design-rationales aims at reducing opacity of the developed ontology and increasing its re-usability While dealing with the problem of obesity, its characterisation and prevention, it is important to consider several factors such as physical (in)activity, physiological (dys)function, (un)healthy eating habits, social and psychological problems . In someVirtual Individual Model (VIM) [health status of a person with reference to overweight and obesity, focusing on adolescents in particular. Since the VIM captures obesity-related knowledge by common representational means, e.g. natural language definitions, tables and graphs (readable to competent human experts), the presented information was not specified in a formal language. In other words, the VIM lacks a formal semantics and explicitness that would disambiguate its terminological and ontological assumptions in order to structure the concepts and relations in a comprehensive and machine-readable form.Lafortuna et al. , Guarnerel (VIM) , 23 thatAn elaboration of the contents of the VIM led to the identifications of the key targets of the ontology-model: (1) capture health conditions of individual teenagers; (2) detect personal obesity-related risk factors; and (3) optimise the information structure in order to provide a personalised feedback that can motivate behavioural changes towards a healthy lifestyle.ontology goal, the next step in the ontology design was to partition the modelling domain by specifying the most relevant fields of knowledge that can be formalised as independent sub-domain ontologies, i.e. the modules that can be later integrated into the final ontology model (see the following section).After the identification of the (i)the physical-physiological level ,(ii)the nutritional level , and(iii)the psycho-social level .The identification of knowledge sub-domains started with the analysis of the VIM that characterises human individual on three levels:distinct, coherent and complementary segments of knowledge, the characterisation of the levels (i-iii) is insufficient as it lacks a clear demarcation criterion necessary for the development of independent ontology modules. For instance, level (ii) includes the characterisation of dietary habits and as such it partly intersects with level (iii), which also  aims at targeting behavioural habits, e.g. fruit and vegetable intake. On the other hand, level (i) deals with both physical and physiological aspects that are closely related, but nonetheless  distinct. In addition, the physical description in (i), besides body constitution, also includes the characterisation of habits such as physical activity that actually represents a behaviour. As such, the characterisation of physical activity captures features that are distinct from those used in the description of some physical parameters related to body constitution. For the requirements of an ontologically clean and coherent model that follows proper classification criteria [Each of these three levels of the VIM addressed the problem of obesity and its prevention from diverse disciplinary perspectives, thus allowing the domain specialists to contribute with their expertise to a comprehensive view on the problem. However, since we aim at characterising the knowledge sub-domains as criteria , 29, 30,discipline we consider any field of study that is covered by the current educational system . The identification of a meta-topic can be illustrated by the previous example of physical activity and physical features of humans that can be the topics of study addressed by psychologists, nutritionists, general practitioner, and so on. As a selected topic of study might target ontologically distinct objects of interest, we used a meta-topic characterisation to discriminate between static and dynamic parameters, features changeable over time vs. rigid features, etc. For instance, while a living being will necessarily have weight and height, their values will change over time. Likewise, the date of birth can be considered as a rigid parameter bound for a person, while the age of an individual changes over time. Also the characterisation of physical activity might be considered as a topic that includes the description of physical features, but from the meta-topic point of view the description of the activity captures behaviour and not some static physical features. The description of physical features might complement the description of physical activity, but the two concepts have different meanings as they capture diverse aspects of the physical reality. Thus, the demarcation of the topic of interest was performed according to an onto-sensitive approach that was used jointly with the disciplinary criterion to define the ontology modules, as described in the following section.The task at hand is to specify sub-domains of knowledge in a way that can support a sustainable ontology development, thus following a coherent modularisation approach. Accordingly, we define and combine the topic-centred and discipline-oriented demarcation criteria, where by ducation ). On theducation . Thus, tOntology modularisation is recognised as an important topic especially regarding the implementation, maintenance, and reuse of ontology –34. DespEven so, the opportunity to modularise an ontology already in its early developmental stages provides numerous advantages related to its evolution, maintenance and reuse.Concerning the most appropriate modularisation strategy for the task at hand as well as for potential future applications , 38, 39,the ontology modules are identified and separated from the whole;ontology maintenance is enhanced by enabling independent work on single modules;modules can evolve independently and new modules can be added with minimal side effects;the differences between different domain-specific categories are represented explicitly, thus enabling both human understanding and formal machine inference.First, the linearisation methodology for robust modular implementation of ontologies  is takenvalidation criterion  aiterion  aMultidimensional Modularisation Methodology (MMM)  Fig.  that idedisciplinary perspectives; (b) the meta-topic coherence view, i.e. the criterion used to define (b1) the specific Topic that should be captured within an ontology module in an onto-sensitive and coherent manner, thus narrowing down the scope of a disciplinary perspective and domain coverage; and (b2) features that can be either dynamic or static (see the previous section). The meta-topic view is also used to specify the scope of (c) the integrative-view criterion that is used to identify common concepts shared across-domains, thus supporting the inter-module integration .(a) the criterion of validation criterion focused on Formal Specification, jointly with (b), is used to distinguish the ontology segments modelled in OWL (T1-T6) from the rule segments modelled in SWRL (R1-R5)  PhysicalStatus that captures physical features of the human body.(O2) PhysicalActivity that captures the physical behaviour and habits.(O3) PhysiologicalStatus that captures certain physiological parameters.(O4) NutritionalBehaviour, capturing nutritional habits and behaviour.(O5) ApplicationContext that specifies the contextual information relevant for potential application scenarios, e.g. geographical location.(O6) Common module captures cross-domain information to support the interoperability across the modules (O1-O5)., formalised in OWL, consists of a TBox that provides terminological contents and the most generic specification of classes and relations that are common for all other modules (O1-O5). On the other hand, the domain-specific ontology modules (O1-O5) are composed of a TBox component formalised in OWL (see T1-T6) and an RBox component containing only domain-specific SWRL rules (see R1-R5) specified as extension of the corresponding TBox modules. In particular, the following RBox modules are used for personalised inference and classification of individuals within the target population of teenagers:R1) PhysicalStatus Rules - used for personalised assessment of health conditions as based on the obesity classification.(R2) PhysicalActivity Behaviour Rules - used for the assessment related to behavioural habits, e.g. sedentariness.(R3) PhysiologicalStatus Rules - related to the assessment of health conditions based on physiological parameters, e.g. metabolism rate.(R4) NutritionalBehaviour Rules - used for the assessment of nutritional characterisation of individuals as based on the food and drink intake, e.g. breakfast skipper.(R5) ApplicationContext Rules - used for the context-dependent assessment to characterise conditions that vary across socio-cultural contexts, e.g. modifying the assessment based on the information about geographical location. reference tables for the population of adolescents , and theCommon and PhysicalStatus ontology modules, specifying the most relevant body features as related to measurements and to several other classes of health conditions that are used to define the obesity-related status and potential risk factors.This section focuses on the ontology content. While presenting the modelling patterns, we describe the obese based on his/her characteristics, description of which  fits to the description of an obese phenotype that is typical for every person of a certain gender and age range. We define typical features of an obese phenotype in terms of a conventional agreement at the current stage of knowledge. The reference system that we use to characterise the physical features of an obese phenotype is provided by the World Health Organisation [health condition.In general terms, a description of a person (e.g. a teenager) via some structural, functional, and behavioural characteristics is actually capturing aspects considered to be relevant to describe his/her phenotype (that might be a teenager-specific phenotype). The main focus is on the phenotypic features describing the class to which a person belongs as determined by the characterisation of his/her physical and behavioural features . Thus, wnisation  and it inisation ). MoreovCommon module , the obesity classification is captured within the PhysicalStatus module which defines further the subclasses of HealthCondition as the hierarchy of PhysicalConstitutionCondition  in one l weight . Accordicentral obesity can be assessed by providing information on either (2) or (3) or (4); gynoid and android status is assessed based on (2); obesity status is assessed as one of the body constitution classes as based on (1); adiposity status is assessed based on (5).In other words, PhysicalStatus module) because all of them satisfy the common criterion of describing phenotype via characterisation of physical constitution.In addition, the classification is annotated with the reference sources and relevant scientific literature providing evidence for the classificatory choices. Finally, the classification Fig.  capturesteenagers, the inference rules, together with the reference values, are defined within the domain-specific sets of SWRL rules. The following subsections present how the ontology is used in practice to personalise, and automatically asses, an obesity-related health condition. The personalised inference is achieved by combining OWL-TBoxes and SWRL-RBoxes that specify inference rules, particularly considering the population of teenagers.Regarding the above mentioned specialisation of the model to characterise health conditions specifically for the population of x1 - Ax10). SWRL supports only the conjunctive form, and it does not support negated atoms or disjunction [SWRL is an expressive DL-based rule language that allows specification of rules expressed in terms of OWL concepts while enhancing the deductive reasoning capabilities , 42. A Sjunction . The preIn order to personalise inference about someone’s health condition, the classification of individuals is performed by combining the defined SWRL rules with the OWL declarations that formalise the domain-specific yet generic classifications of health condition and the facts asserted about instances of classes Person and HealthCondition.Common module (O1) provides the classes used in the instantiation of basic personal information, including the link between the person and their health condition. Precisely, the Common module (O1) specifies Person and HealthCondition as two key OWL classes. These two classes are linked by a restriction involving the object property isInHealthCondition  enables the assignment of diverse health conditions to a person, thus capturing diverse physical and behavioural features of an individual while tracking the evolution of the captured health conditions and associated features over time . In ordapproach  as the fThe inference patterns are modelled further as the rules that classify personal health conditions as belonging to some of the HealthCondition subclasses see Fig. . In othethe body mass index (BMI) defined via the data property isCharacterizedByBodyMassIndex (see 1 in (C1) and Fig. The asserted functional and structural features as well as age are directly associated with a health condition. For example gender is defined by instantiating a Person as belonging to one of its subclasses, i.e. classes Male and Female within the Common module  utilises the SWRL built-ins defined for various numeric types [The age calculation rule (Aic types . Such a x1 - Ax10) exemplify the sets of SWRL rules that are defined according to the domain-specific criteria used to asses some health condition as based on age and gender. The axioms labelled with the odd subscripts are specifying inference-rules for the male population, while the even-subscript axioms define the rules to classify health conditions associated with female individuals. The examples are just a fragment of the rule sets formalised within the PhysicalStatus RBox (see R1).The following axioms , while gender is specified as a predicate  associated with ‘p’. For instance, Axiom 10 can be interpreted in natural language as a conditional declaring that for any female person of age between 13 and 17, who is also in health condition that is characterised by waist to hip ratio greater or equal 0.85, we can infer that the asserted health condition of that person is AndroidCondition. In this way the rules lead to new knowledge, thus expanding the Knowledge Base with new information that classifies health conditions associated with persons based on the information describing particular phenotype.Generally speaking, whenever the conditions specified in the antecedent hold, then the conditions specified in the consequent must also hold . The lisThe following subsection presents how the above-introduced classes are instantiated in practice and explains the reasoning steps that exploit specific information at the time of assessment, date of birth, specific characterisation of physical features that are all together used to infer a personal health condition.The ontology validation was performed on the test cases designed to capture diverse profiles of teenagers by instantiating classes Person and HealthCondition and assigning data values to the instances in a realistic manner across the domain- specific modules. This section illustrates the employment of the ontology in the reasoning over instances by means of an example of the obesity assessment that provides an explanation of the above-presented design patterns on the application level.x0 and Ax5) automatically infers that the condition TomCondition1 associated with Tom is an ObeseCondition. This fact presents the new information inferred via ontology which ABox previously contained only the facts about Tom’s birth data, gender, and the data used to characterise his health condition (i.e. BMI).Figure x1 - Ax10), given that Tom’s age changes while his body mass index stays unchanged, only TomCondition1 is inferred to be ObeseCondition, while TomCondition2 is classified as OverWeightCondition.Since the goal of the ontology is to capture changes of phenotypes associated with a person over time, consider the scenario in which Tom is assigned two assessments of his health condition at two different time points: TomCondition1 (assessed in November 2014), and TomCondition2 (assessed in November 2015) see Fig. . Since tThe following specification captures this in OWL’s Manchester syntax .The facts resulting from the reasoning can be saved into the ontology, thus actually enriching the Knowledge Base. The inferred facts can be safely added to the Knowledge Base because the addition of new instances of the assessed health conditions will not invalidate the previous inferences, thanks to the adopted modelling pattern. Moreover, the analysed ontology provides the explanation that Tom’s condition TomCondition2 is assessed as OverWeightCondition in November 2015 based on BMI. On the other hand, the ontology explains that Tom’s ObeseCondition is assessed in November 2014 based on the BMI that characterises TomCondition1.The same reasoning can be performed with persons of different ages and genders. The domain-specific RBoxes specify the inference rules with the reference values relevant for assessing the obesity-related categories of a person’s health condition  , 49 archAccordingly, access to the stored data can be managed separately on each of the three levels. Any assertion of the facts about instances requires a concurrent access to both ABoxes that will only jointly associate some health status with appropriate instances representing persons. The partition of ABoxes is motivated by ethical concerns and it aims to support privacy of the health-related data also on the modelling level.Common TBox, thus enabling the merging of sub-domain ontologies into the integrated and populated ontology. Alternatively, the modular structure enables independent development, employment, re-use, and evolution of the domain-specific segments. For example, Fig. physical constitution  and Person ABox;between the HealthCondition ABox.between the domain-specific ontology modules (O1-O5) and the Nonetheless, independently of the modelling task, the module-integration patterns HealthCondition ABox. The integrated ABox brings together personal information and generic obesity-related knowledge (captured as the OWL classes (T1-T5) and SWRL reasoning rues (R1-R5)) as it stores both asserted and inferred facts, including the links that hold between instances of the classes Person and HealthCondition.In other words, the domain-specific ontology modules (O1-O5) are used to classify individual health conditions, while the assertions of the facts about the conditions are stored in the corresponding This paper described the ontology that captures several obesity-related knowledge-domains: Physical Status Domain, Physical Activity Behaviour-Domain, Physiological Status Domain, and Nutritional Habits Domain. The ontology is designed to support flexible use and reuse of captured information, the interoperability between technological devices, the integration of collected information, and automated inference about personal status over time. The modular structure is adopted in order to enable independent development, maintenance, evolution, and validation, as well as the integration of diverse domain-specific modules. The modular design enables the use and combination of the modules according to the needs of a particular task, while each of the modules can be used separately from others and the ontology can be extended with new modules that can be added at a later stage.Common ontology module was developed to support the integration and interoperability between the domain-specific modules as well as tracking the evolution of personal health condition over time. The combination of two formal languages motivated partition of the modules into TBoxes specified in OWL and RBoxes specified in SWRL, while the ethical concerns motivated partition of the ABox into the segments that store separately facts about persons and those about health conditions.Besides the validation and domain-coverage criteria, the modularisation methodology included the criterion that distinguishes disciplinary perspectives, the meta-topic criterion, and the integrative-view criterion. In particular, the In particular, the paper illustrated how health conditions are associated with the physical constitution (i.e. obesity-related) classes, and are then employed to infer automatically personal health status as age- and gender-dependent.The forthcoming task is to perform the ontology testing within a Semantic repository that will be developed and populated with real data (i.e. instances representing adolescents and their phenotypic features) acquired through the pilot studies of the PEGASO project ( p. 19). Regarding the interoperability and integration with other ontologies, future work will examine possible links and alignments , 53 withThe further ontology development will include additional specification of the relevant information, e.g. the reasoning about the behavioural patterns and interventional strategies, the formal specification of other relevant phenotypic features and target behaviours relevant for the motivation of healthy lifestyle.Healthy food. Extending the ontology with the module that makes explicit links with diseases is of a high relevance and the existing ontologies  require abstention from certain types of food that would otherwise be recommended as beneficial to any other individual who is not diagnosed with the condition. Thus, the model of an interventional strategy regarding nutrition should explicate the information about particular disease-related health conditions that might impact the interpretation of the concept es (e.g. ) will bees (e.g. , 17 is t


Twelve GPS Block IIF satellites, out of the current constellation, can transmit on three-frequency signals . Taking advantages of these signals, Three-Carrier Ambiguity Resolution (TCAR) is expected to bring much benefit for ambiguity resolution. One of the research areas is to find the optimal combined signals for a better ambiguity resolution in geometry-free (GF) and geometry-based (GB) mode. However, the existing researches select the signals through either pure theoretical analysis or testing with simulated data, which might be biased as the real observation condition could be different from theoretical prediction or simulation. In this paper, we propose a theoretical and empirical integrated method, which first selects the possible optimal combined signals in theory and then refines these signals with real triple-frequency GPS data, observed at eleven baselines of different lengths. An interpolation technique is also adopted in order to show changes of the AR performance with the increase in baseline length. The results show that the AR success rate can be improved by 3% in GF mode and 8% in GB mode at certain intervals of the baseline length. Therefore, the TCAR can perform better by adopting the combined signals proposed in this paper when the baseline meets the length condition. At the time of writing, three-frequency signals are available from all 12 GPS Block IIF satellites. One of the new generation of GPS satellites, GPS III or GPS Block IIIA, is planned to be initially launched on 3 May 2017, bringing another signal, L1C. The additional signals are anticipated to bring significant improvements to the efficiency and reliabilities of carrier phase ambiguity resolution (AR) by forming more extra-wide-lane (EWL) and wide-lane (WL) combinations. The selection of optimal signal combinations has become a popular topic since optimal signal combinations can not only eliminate or mitigate bias terms in the mathematical model but also alleviate the computational burden of processing multi-frequency data  and HatcAlthough early TCAR can only resolve integer ambiguities using a GF bootstrapping methods , it has The purpose of this study is to identify the optimal signal combinations for GF and GB TCAR separately with regards to different baseline lengths, which usually mean different levels of biases including both ionospheric biases and tropospheric biases. This study is significant for the following two reasons. Most of the research described above selected only one optimal signal combination with the longest wavelength, the lowest noise, the lowest total noise level or the lowest ionospheric scale factor (ISF) for each step of AR, expecting this combination could work well in all conditions ,18. HoweThis paper is organized as follows. In i, j and k are the coefficients, which must be integer numbers in order to maintain the integral property of the ambiguity; c is the speed of light in vacuum; ε represents the observation noise; Generally, both GF and GB TCAR methods adopted in this study are based on a group of selected linear combinations of the multi-frequency observations. A general form of GPS triple-frequency signal combinations for double-difference (DD) carrier phase  follows .(1)∇▵Φ .Step 1. Fixing the EWL ambiguities in a GF model. In this step, instead of using a GB model, a GF model (Equations (10)–(12)) is usually adopted, as there is no difference in the success rate between them and it is more convenient to conduct AR in a GF model .Step 2. Fixing the ambiguities of the second EWL/WL signals.Step 3. Fixing the ambiguities of the NL signal.where A is the linear coefficient matrix; L is the observation vector; B is the coefficient matrix; X is the unknown parameter vector including both the baseline parameters and the ambiguities, which need to be estimated; V is the residual vector. Equation (21) can be solved by least-squares (LSQ) adjustment, where the LAMBDA search method can be used to calculate the fixed solutions. With regards of the correlation of DD measurements and the correlation of the two combined signals, the variance-covariance matrix (P) and the stochastic model of the estimated parameters –) is usuaGF TCAR can eliminate most of the effect of the error sources, such as the satellite orbit error and the tropospheric bias, by adopting a pair of combined signals in each step. Taking advantage of the triple-frequency signals, the optimal combined EWL and WL signals can significantly enhance the efficiency and reliability of the AR ,34. HoweAs combined signal pairs of different wavelengths are needed in different steps of GF TCAR, we will select two groups of combined signal pairs, EWL or WL, whose ambiguities are to be determined. The numerous selected signal pairs will be refined with the sum of ISFs and the measurement noise . These combined signals can be chosen from EWL, WL, middle-lane  interpolation method will be used to obtain the possible changes of the success rate with the increase of the baseline length. The reason why this study adopts PCHIP interpolation method is that the generated lines are easier to see the fluctuations than those from the linear interpolation and the interpolated values are more reasonable than those from higher order non-linear interpolation such as Lagrange, which usually provides too big fluctuations. The success rates of all the combined signal pairs involving EWL a are theR), whose value is larger than a validation threshold  are larger than those obtained by another combined signal , from the percentage of the epochs, where R values of signal B are larger than those of signal A, is called the reliability ratio of A to B. If the reliability ratio is positive, this means the percentage of epochs whose ambiguities can be more reliable using signal A compared to signal B.The theoretically selected combined signals for GB TCAR will be analyzed in this section based on two criteria, the AR success rate and the reliability ratio. The AR success rate of GB TCAR in this study is calculated by the percentage of  can provide higher success rates by a maximum number of 8% for the baselines whose length are shorter than 200 km or over 400 km, while for other lengths of baselines, ones with WL can perform better compared to the traditional signal pairs, which work with WL. The ambiguity corrected EWL carrier phase signals perform better than any combined code signals in AR when fixing ambiguities of WL signals. When working with the EWL corrected signal, all the WL signal pairs proposed by this paper can have the same success rate regardless of the baseline length.In GB TCAR, the WL signal selected in this paper has higher probability to provide more reliable ambiguities especially when the baseline length is short, compared to the traditional EWL signal. The combined signal NL performs more stable than the traditional NL, while NL always shares the same success rate with NL.In GF TCAR, although the results obtained by the theoretical section meet the results using the real data, the assumed success rates using pure carrier phase combined signal pairs will be lower than those using the real data, while the success rates using signal pairs involving both carrier phase and code will be higher than those using real data. To the best of our knowledge, this might be because of the slightly incorrect assumptions of the effect of the error sources and the imprecision of the mathematic model used in the theoretical analysis. However, the real reason needs further research.In GB TCAR, although the proposed theoretical method can be used to analyze the relationship between the signals and the noise level, there was not showing a strong correlation between the wavelength to total noise ratio using the theoretical model and the success rate using the real data, which means there might be still some unselected signals with a good success rate when only the wavelength to total noise ratio was considered. Therefore, future improvement could be on developing a theoretical method to demonstrate the GB success rate directly in order to discover all the signals with high performance.In this paper, we proposed a theoretical and empirical combined method to select the optimal combined signals for GF TCAR and GB TCAR separately. For GF TCAR, four criteria, including wavelength, the sum of ISFs and the measurement noise, wavelength to total noise ratio and the theoretical success rate, were considered to select the possible optimal combined signal pairs. The theoretically selected combined signal pairs were refined with the real triple-frequency GPS data by considering the AR success rates based on the theory of statistics. For GB TCAR, the possible combined signals were first theoretically selected with regards to three criteria, wavelength, ISF and the measurement noise, and the wavelength to total noise ratio. These signals were refined using the same data sample as GF with regards to the AR success rate and the reliability ratio. The main conclusions and the future research aspects are listed as follows.


Historical land use information is essential to understanding the impact of anthropogenic modification of land use/cover on the temporal dynamics of environmental and ecological issues. However, due to a lack of spatial explicitness, complete thematic details and the conversion types for historical land use changes, the majority of historical land use reconstructions do not sufficiently meet the requirements for an adequate model. Considering these shortcomings, we explored the possibility of constructing a spatially-explicit modeling framework . Then a three-map comparison method was adopted to validate the projected reconstruction map. The reconstruction suggested that the HLURM model performed well in the spatial reconstruction of various land-use categories, and had a higher figure of merit (48.19%) than models used in other case studies. The largest land use/cover type in the study area was determined to be grassland, followed by arable land and wetland. Using the three-map comparison, we noticed that the major discrepancies in land use changes among the three maps were as a result of inconsistencies in the classification of land-use categories during the study period, rather than as a result of the simulation model. Decadal-centennial land use and land cover change (LUCC) has been considered as a vital driver of global environmental change123468111314Due to a lack of available adequate historical datasets, the reconstruction of historical land use/cover relies on existing databases containing local/regional level statistics or records, population statistics, historical maps and model assumptions171819202122232425262728293031Model-based reconstructions of land-use change are rapidly advancing as anthropogenic land-use change is one of the most important drivers of environmental change. As the accumulation of human activities’ impact on the earth’s surface, present land use pattern contains the essential information about historical land use patterns. Meanwhile, remote-sensing technique makes it feasible and convenient to observe the global characterization of land use/cover with high resolution and thus the present land-use spatial pattern is the most crucial information for historical land reconstruction. In general, the spatial allocation method is under the assumption of similarities between historical land use spatial patterns and present spatial patterns. Related models have been divided into three types according to the various effects on controlling the similarities: totally dependent, partially dependent and dynamically dependent36As the third largest and most populated country, China has a long history of agricultural civilization and its land use has undergone great changes owing to significant transformation caused by human activities, natural factors and their impact on the climate and environment384142Zhenlai County . A series of topographic maps from the 1930 s  were also collected and digitized. The historical spatiotemporal distribution of land use and land cover in 1954 was also reconstructed by consulting and incorporating information relating to terrain, climate, geology, soil, vegetation, hydrology and socioeconomic statistical data from topographic maps and physical environmental background maps44Local Record of Zhenlai County”. A slope raster was generated using a 90 m raster DEM obtained from the Shuttle Radar Topography Mission (SRTM).One Landsat MSS  and two Landsat TM (Thematic Mapper) images were selected for the years 1976, 2000 and 2005. These remote sensing images were used to interpret and vectorize land use data obtained from the United States Geological Survey  provides a high-resolution reconstruction of historical land use spatial distributions. The model outputs are based on the assumption that the present spatial pattern of land use is intrinsically dependent on a historical pattern.Considering the natural, social and economic predictors of changes in land use and land cover, HLURM takes into account regional land use changes since the advent of remote sensing. This was undertaken by taking the grid as the research unit, calculating the probability of each land type in each grid to determine the most likely land-use category and then using the land type with the largest probability to reveal the land use in the research units. A cellular automata (CA) model was then used to produce a backward simulation as a carrier.The HLURM model consists of four main modules: quantity control module (QM), spatial conversion rule module (CM), probability module (PM) and spatial allocation module (AM) .Quantity control module (QM), also known as the demand-constraint module, is the basis of HLURM model and also the premise of the spatial reconstruction. This module can be operated and run independently of the HLURM model. In general, total quantity of land use can be determined in two ways: (a) collecting historical documents and calibrating the available data; and (b) modeling quantitatively from the simple historical trend extrapolation method to the relatively complicated economic model. Appropriate model of backward projection for land use quantity should be chosen according to the actual circumstance.Spatial conversion rule module (CM) establishes historical rules for land use and land cover change. This module contains analysis of both land use conversion sensitivity and land use conversion sequence. Land use conversion sensitivity monitors the conversion characteristics and trends among land-use categories during each time interval; obtains the transformation probabilities for land dynamics in the study period; and clarifies the trajectories of land use changes. These factors provide fundamental basis for analyzing the land use conversion sequence. CM will then reveal land dynamic characteristics and provide essential data for the following probability module, as well as producing the constraints for the spatial allocation module.Probability module (PM) calculates the probability of each land type in each grid based on probability theory. By analyzing the relationships between both various natural environmental conditions and socio-economic conditions and land-use distributions, a land suitability evaluation map is produced to exhibit the possible spatial distribution of historical land cover. According to current research about land use and land cover change, three assumptions of HLURM are proposed to operate the PM module: 1) The current spatial land use pattern is intrinsically dependent on a historical pattern; 2) The boundary of historical land use with human activities, such as arable land and settlements, does not exceed the union range of each land use type in the study period; 3) The natural environmental factors of land suitability have not changed over time during the past century due to the limitation of data collection.Spatial allocation module (AM) distributes the quantity of each land use into the geographical locations and spatially explicit outcomes according to certain principles or approaches. It then realizes the backward projection of historical land use. For specific spatial allocation, downscaling can be used in the descending order of distribution probability under the control of total land-cover area and other distribution factors.According to the influence of human activities on land use and the characteristics of land use, the reconstruction order for land-use categories in this study is as follows: settlements, arable land, water, wetland, forestland, grassland and other unused land. Different spatial constrains are established for land-use categories and then spatial grid allocation is operated under these constrains.The maps used for the three-map validation of the LUCC model included reference time 1, reference time 2 and simulation time 2. Comparisons between the maps of reference time 1 and reference time 2 characterized the observed changes in the maps, reflecting the dynamics of the land use. Comparisons between the maps of reference time 1 and simulated time 2 characterized the predicted changes, demonstrating the model behavior. Model validation estimates the agreement (or error) stemming from the comparison between the maps of simulation time 2 and the reference  time 2. Here, the model accuracy was assessed by determining the four components of accuracy and error, namely, null successes (accuracy of observed versus predicted persistence), hits (accuracy of observed versus predicted change) misses (error due to observed change predicted as persistence) and false alarms (error due to observed persistence predicted as change). The summary statistics for error due to quantity EQ,  and erro49where, H, M and F are the hits, misses and false alarms; HOC, MOC and FOC are, respectively, the ratio of hits, misses and false alarms to the observed change, which is the summation of the hits and misses.Based on available historical data, the total quantity of each land use was calculated according to the weights of various data sources, such as historical documents, Markov model results and vector data derived from topographic maps .t − 1 to t might not be totally consistent with those in the period between t and t + 1. Thus, the transition matrices were modified by analyzing land use changes over the last few decades, and were also used to define the rules for AM, assuming that current spatial pattern of land use is inherently dependent on the historical pattern. In this context, in order to account for the period between 1932 and 1954, transition matrices were revised between 1954 and 1976 according to the rules governing LUCC in the study area from 1954 to 2005. The analysis of land use changes indicated that nearby forests and wetlands would not be utilized until all of the grassland suitable for farming was reclaimedThe spatiotemporal Markov Chain model uses transitional probabilities to model change over time among land-use categories, and is able to simulate several land-use categories simultaneously. However, due to temporal changes in the various land-use categories, transition matrices describing various types of land cover from Weight is often used to develop a set of relative weights for a group of factors in a multi-criteria evaluation. Here, the analytic hierarchy process (AHP) approach, calculating from “WEIGHT-AHP weight derivation” module of the IDRISI Selva software, was used to determine the weights of land-use categories from different data sources in the 1930 s based on the area of various land use from multi-source data and the reaction characteristics of land-use categories in these different data sources. Under the control of 531606.14 ha of the total study area, the areas of land-use categories in the 1930 s were obtained. Research regarding the spatial conversion rule module was comprehensively explained and discussed in the spatiotemporal changes’ analysis from 1954 to 2005 in the study area47Factors and constraints were the two types of criteria used in this study ; a factoHere, two constraints were considered: water and unchanged land-cover during 1954 and 2005. Based on the previous three assumptions of the PM, we concluded that the unchanged land use from 1954–2005 had existed in 1932. Considering that the other six land cover types do not typically occur on bodies of water, water layers digitized from the 1930 s topographic maps  were subWe made use of the MCE process involving criteria of varying importance in accordance with decision makers along with information regarding the relative importance of the criteria. It is generally obtained by assigning a weight to each factor. The weights given to different factors in this study were generated using Saaty’s Analytical Hierarchy Process (AHP), where a larger weight denotes a more important criterion in terms of overall utility . The proThis module, based on the algorithm of the cellular automata (CA) model, has two kinds of spatial constraints: (1) interpretation results of topographic maps; and (2) the spatial probability of each land-use category. The specific spatial allocation process is shown in The spatial allocation module is programmed in Matlab software and the 1930 s land-use map simulated by the HLURM model could be output by spatial allocation module . It can Through producing a backward projection, the HLURM model creates a high-resolution historical reconstruction of land use and land cover in Zhenlai County in the 1930 s. A common method to test the reliability of historical reconstructions involves comparing with information from a variety of independent sourcesThe comparison of observed and predicted change is shown in 4852The analysis illustrated that the major differences among the three maps are less concerned with the simulation model; rather, they are related to the discrepancies in land category delineation between 1954 and 1932. The applications with a large FOM are those using the correct, or near-correct, net quantities for the categories in the prediction map. The large number of hits in the HLURM model might be explained by the fact that different definitions exist between the reference map of 1932 and the simulated map of 1932. It is possible that any precise measurement of simulation accuracy is unattainable due to time point inconsistencies concerning the definitions of land use categories. The majority of the topographic maps used in this study were produced between 1932 and 1935 for the Japanese military, while some were produced during the Manchukuo era for the Chinese military. Thus, these maps are limited primarily by the fact that they were created with a specific military purpose in mind. The maps also define certain types of land cover, such as arable land, settlements and water, however, other land-use types, such as unused land, are not clearly reflected. The land cover classes in each map are indicated based on their purpose and criteria. Accordingly, the grassland and unused land types drawn in the 1930 s topographic maps are distinct from those derived from remote sensing images as a result of their different intended uses. For example, grassland is often combined with other land covers in the topographic maps, and thus its boundary is not easily determined. In addition, a large proportion of the grassland in these maps was categorized as wildland, resulting in limited areas of grassland being depicted on the maps. Rainfed lands and blank areas lacking a symbol are often difficult to decipher from grassland and wildland, making it challenging to extract and digitize grassland data. In the context of modern cartographic conventions, unused land is generally under-represented on historical maps. Thus, historical maps possess numerous limitations that must be considered in order to accurately interpret changes in land use. It is also necessary to recognize that discrepancies exist between the topographic maps and remote sensing images in respect to the information depicted. For instance, the period from 1900–1935 has been demonstrated to be a dry period in the history of northeastern China53The HLURM model, consisting of four main modules , had a good performance in the spatial reconstruction of various land cover types. A CA model was also used to produce a backward simulation as a carrier.The results of the historical reconstruction revealed that the largest percentage of the study area was grassland, followed by arable land and wetland. The remaining land-use categories comprised relatively smaller areas. Most of the arable land was located in the flat regions in the central and western areas, as well as in the north of the study area. Wetland areas were mainly distributed to the east and south along the Nenjiang and Tao’er Rivers.The total area change in the reference change map between 1954 and 1932 was 306,161.00 ha, while it was 195,500.47 ha for the simulated change map. Gross losses and gains in land-use categories were primarily concentrated in the central areas as well as those areas in the vicinity of rivers and lakes. The expansion of arable land at the expense of grassland was most probably due to the fast population growth experienced over the last few decades. The relative proportion of water bodies increased marginally as a result of increased precipitation. Between 1932 and 1954, a large proportion of grassland was transformed into unused land in both the change maps, especially in the reference change map, indicating that substantial environmental degradation had occurred.The figure of merit of the model was 48.19%, which is higher than that estimated in several other case studies. Error due to allocation was 11.91%, while error due to quantity was 21.01%; this is likely as a result of the inconsistencies concerning category definitions between the maps. The major differences observed among the three maps are less concerned with the simulation model, but are more associated with the inconsistencies regarding how the land-use categories were defined during the study period, especially with regards to grassland and unused land types. For instance, in the topographic maps, grassland is often combined with other land covers, making its boundaries difficult to determine. Furthermore, a substantial portion of grasslands on the maps were often categorized as wildlands, resulted in difficulty extracting and digitizing the spatial extent of the grassland data. Therefore, it is crucial to select a reference map that has achieved high accuracy in the model validation for use in the three-map comparison. Unfortunately, due to the limited availability of historical data, it is often challenging to obtain a suitable reference map to be used to validate a reconstruction model.Historical land use information is essential in understanding how anthropogenic land use and land cover change has influenced the temporal dynamics of environmental and ecological issues. To satisfy the growing demand for harmonized, spatially explicit and high resolution land use change products, a spatial-explicit modeling framework  was explored and proposed in this research to enhance our understanding of the degree and extent of global and regional anthropogenic changes in land use patterns. And then this modeling framework was applied to the historical reconstruction of spatial distribution of land use/cover in the early reclaimed time (1930 s) of Zhenlai County, northeastern China to check its modeling behavior. This was achieved by building a HLURM backward projection model in 90 m × 90 m spatial resolution based on three assumptions: that presently established spatial patterns of land use are intrinsically dependent on the historical patterns; that the boundary of historical land use with human activities does not exceed the union range of each land use type; and that factors relating to land suitability do not change over time. A three-map comparison methodology was then used to validate the projected reconstruction. The main conclusions are:How to cite this article: Yang, Y. et al. Analyzing historical land use changes using a Historical Land Use Reconstruction Model: a case study in Zhenlai County, northeastern China. Sci. Rep.7, 41275; doi: 10.1038/srep41275 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Sandwiched piezoelectric transducers are widely used, especially in high power applications. For more convenient analysis and design, a PSpice lossy model of sandwiched piezoelectric ultrasonic transducers in longitudinal vibration is proposed by means of the one-dimensional wave and transmission line theories. With the proposed model, the resonance and antiresonance frequencies are obtained, and it is shown that the simulations and measurements have good consistency. For the purpose of further verification the accuracy and application of the PSpice model, a pitch-catch setup and an experimental platform are built. They include two sandwiched piezoelectric ultrasonic transducers and two aluminum cylinders whose lengths are 20 mm and 100 mm respectively. Based on this pitch-catch setup, the impedance and transient analysis are performed. Compared with the measured results, it is shown that the simulated results have good consistency. In addition, the conclusion can be drawn that the optimal excitation frequency for the pitch-catch setup is not necessarily the resonance frequency of ultrasonic transducers, because the resonance frequency is obtained under no load. The proposed PSpice model of the sandwiched piezoelectric transducer is more conveniently applied to combine with other circuits such as driving circuits, filters, amplifiers, and so on. The sandwiched piezoelectric ultrasonic transducer vibrating in longitudinal mode is also called the Langevin composite transducer. It consists of metal front and back masses, piezoelectric ceramic stack, metal electrodes and the prestressed bolt. Based on the conventional design theory about the sandwiched piezoelectric transducers vibrating in longitudinal mode as shown in It is well known that the theory analysis for piezoelectric components is a vital basis for the design of piezoelectric transducers. So far there are a few methods used in analyzing piezoelectric transducers. Among the methods, the equivalent circuit method is widely used, because it is brief and clear in its physical meaning. In addition, mechanical and dielectric losses can be easily taken into account using the equivalent circuit method. The piezoelectric ceramic disks or rings vibrating in the thickness direction have been successfully modeled by using equivalent circuit methods which mainly include Mason’s , RedwoodFor the sandwiched piezoelectric ultrasonic transducer form in For establishing the PSpice model of the sandwich piezoelectric ultrasonic transducer vibrating in longitudinal direction, the one-dimensional wave and transmission line theories for the piezoelectric vibrational mechanism in thickness mode are illustrated in this section.To obtain the proper parameters used in simulations, the comparison of wave propagation is carried out in electrical transmission lines and acoustical medium, respectively. Consider a different length Note that To obtain the above parameters, Kirchhoff’s voltage law is used in the circuit from With the limit as Similarly, by using Krichhoff’s current law to the node Then letting We call Equations (3) and (5) the general transmission-line equations, which are first-order partial differential equations based on The general transmission line equations on the basis of Equations (8) and (9) are called as time-harmonic transmission line equations which simplify to the following Equations (10) and (11) under the lossless conditions d2V is denoted asThe time dependence for Equation (15) can be got by means of multiplying Equation (16) illustrates two traveling waves. One travels in the positive obtained :(17)∂2pThe general solution for Equation (17) is expressed asThe characteristic impedance given as (23)Zel=The characteristic acoustic impedance Now we take small but non-negligible losses into consideration and suppose The relationship of the low-loss characteristic impedance Equation (27) is used to obtain the following expressionsThe real part of Equation (26) is called as the attenuation constant Corresponding to Equation (30), the classical theory relationship of acoustic attenuation is obtained asAccording to Equations (28)–(30), we can derive the following expressionsBecause of the materials used in the sandwich piezoelectric ultrasonic transducer having a low heat conductance, the loss resulting from the thermal conductance can be neglected. Then letting the conductance Therefore, Equation (32) can be rewritten asFinally, the parameters of the acoustical lossy transmission line can be derived as follows:The thickness direction vibration of the thickness poled piezoelectric ceramic rings is a general vibration mode. For this kind of thickness poled piezoelectric ceramic ring, its thickness is much lower than diameter. It is assumed that it works in the thickness mode. For the piezoelectric ring with fixed or free ends vibrating in thickness mode, it has a fundamental resonant frequency :(37)f=vFor modeling a sandwiched piezoelectric ceramic transducer vibrating in longitudinal mode, the piezoelectric ring is modeled using Leach’s equivalent circuit model  as shownIt consists of the clamped capacitance For establishing the PSpice model of the sandwiched piezoelectric ceramic ultrasonic transducer vibrating in longitudinal direction, the piezoelectric ceramic stack from For simplifying the ultrasonic transducer model, the metal front and back masses and metal electrodes are modeled by using the lossless transmission line model from In the simulation using PSpice, the parameters of the lossless transmission line mainly include the resonance frequency given as :(39)F=NLThe single piezoelectric ceramic ring is modeled using the PSpice model from The parameters of the lossy transmission line are obtained from Equation (36), and the loss resistance given as (41)R0=1sistance  R1 has tIn The piezoelectric rings located in the piezoelectric ceramic stack are connected together mechanically in series as shown in On the basis of the above analysis about the modeling of the piezoelectric stack and the metal masses, we can obtain the PSpice model of the sandwich piezoelectric ultrasonic transducer from In he value  Rair1=RaThe sandwich piezoelectric ceramic ultrasonic transducer is mentioned in this paper and the piezoelectric material selects PZT-4 , and theThe impedance analysis is used to research the frequency response and derive the resonance and antiresonance frequencies. Moreover, the measured impedance results are obtained by utilizing the impedance analyzer PV520A which is made by BEIJING BAND EAR CO , LTD as shown in There are several factors that can well explain frequency error or difference in frequencies in In order to investigate the vibrational velocity ratio between the front and back masses, the transient analysis is carried out using the 8 cycle single tone signal by modulated Hanning window as shown in In general, if the materials of the front and back masses are chosen as aluminum and steel respectively, the vibrational velocity ratio between the front mass and the back mass is 3:1 for sandwich piezoelectric ultrasonic transducers. From To demonstrate the application of the proposed model, a pitch-catch setup is built as shown in Here, the resistance In the first place, for the purpose of obtaining the resonance frequencies of the pitch-catch setup, the AC analysis is performed under the conditions of the length of the transmission medium having the value 20 mm and 100 mm. But the excitation source ①the length of the transmission medium is 20 mm and the excitation signal frequencies are 23.309 and 21.098 in kHz.②the length of the transmission medium is 100 mm and the excitation signal frequencies are 23.309 and 24.363 in kHz.In the next place, based on the pitch-catch setup from Then, to verify the accuracy of the simulated results, the pitch-catch experimental platform is built from The voltage signals are received by the ultrasonic transducer under the conditions that the aluminum cylinders are 20 mm and 100 mm in length respectively. Simultaneously, these signal data are recorded by the oscilloscope. By using these recorded data, the signal waveforms received in the time domain are given as shown in 2mV. represent the simulated and measured voltage values from the received ultrasonic transducer respectively and At the same time, the voltage values received are specifically listed in There are several factors that can well explain amplitude error or difference in the received voltage in The PSpice model of the sandwiched piezoelectric ceramic ultrasonic transducer in longitudinal vibration is proposed in this work. It is mainly for providing convenience for the design and analysis of the sandwiched ultrasonic transducers. Compared with the theory analysis method based on the Mason’s equivalent circuit of the sandwiched piezoelectric ultrasonic transducers, the proposed PSpice model has the following advantages. First, it is very easy to access the parameters such as the input electrical impedance and reactance, the vibrational velocity, the resonance and anti-resonance frequencies. Second, the proposed transducer model has great flexibility and strong expansion. Moreover, it can easily combine with the excitation, filter and amplifier circuits, which provides help to improve and optimize these circuits. Third, on the basis of the model proposed, the wireless power or data transmission system based on the sandwiched ultrasonic transducer in longitudinal or thickness vibration can be easily built; the parameters for the system are easily obtained by the AC and transient analysis, and so on.The effect of the losses including mechanical and dielectric on the transducer performance parameters such as resonance frequency, electrical quality factor and electro-acoustical efficiency, needs be considered in practical transducers. However, these mechanical losses, such as the metal front and back masses, and the metal electrodes, are ignored in the proposed PSpice model. Therefore, for the purpose of improving the sandwiched piezoelectric ceramic transducer in longitudinal or thickness vibration, the effect of the losses for the transducer performance should be analyzed by using the lossy transmission line. Moreover, the effect of the sealed epoxy resin layer is not considered in the transducer PSpice model. For the formulation of the sealed epoxy resin layer, it can be regarded as the lossy transmission line. It is strongly suggested that the effect of the prestressed bolt can be analyzed using the finite element method. These issues are expected to be further investigated in our subsequent work.(1)The comparisons of the measured results and simulated values of the sandwiched piezoelectric ultrasonic transducer indicate the accuracy of the proposed lossy model.(2)The PSpice model has been successfully applied to the pitch-catch setup. It is shown that the experimental results and the simulated values have good consistency. Simultaneously, we can find that the optimal excitation frequency of the sandwiched ultrasonic transducer is not necessarily the resonance frequency for the pitch-catch setup.(3)The accomplishment in PSpice can provide convenient analysis for the sandwiched piezoelectric transducers in time and frequency domains. Compared with the sandwiched piezoelectric transducer model based on Mason’s equivalent circuit, the proposed model may be more easily used to investigate the sandwiched transducers.(4)The proposed PSpice model of sandwich piezoelectric transducers can be more conveniently used to combine with other circuits such as driving circuits, filters, amplifiers, and so on.In this paper, based on Leach’s equivalent circuit and lossless transmission line, a PSpice model of the sandwiched piezoelectric ultrasonic transducer in longitudinal vibration is proposed, and the resonance and antiresonance frequencies are obtained. To further verify the accuracy and application of the proposed model, a pitch-catch setup and an experimental platform are built; the resonance frequency is obtained and the simulated results are compared with the measured ones. In summary, based on the analysis mentioned above, some conclusions can be drawn as follows:


Many correlations are available in the literature to predict the higher heating value (HHV) of raw biomass using the proximate and ultimate analyses. Studies on biomass torrefaction are growing tremendously, which suggest that the fuel characteristics, such as HHV, proximate analysis and ultimate analysis, have changed significantly after torrefaction. Such changes may cause high estimation errors if the existing HHV correlations were to be used in predicting the HHV of torrefied biomass. No study has been carried out so far to verify this. Therefore, this study seeks answers to the question: “Can the existing correlations be used to determine the HHV of the torrefied biomass”? To answer this, the existing HHV predicting correlations were tested using torrefied biomass data points. Estimation errors were found to be significantly high for the existing HHV correlations, and thus, they are not suitable for predicting the HHV of the torrefied biomass. New correlations were then developed using data points of torrefied biomass. The ranges of reported data for HHV, volatile matter (VM), fixed carbon (FC), ash (ASH), carbon (C), hydrogen (H) and oxygen (O) contents were 14.90 MJ/kg–33.30 MJ/kg, 13.30%–88.57%, 11.25%–82.74%, 0.08%–47.62%, 35.08%–86.28%, 0.53%–7.46% and 4.31%–44.70%, respectively. Correlations with the minimum mean absolute errors and having all components of proximate and ultimate analyses were selected for future use. The selected new correlations have a good accuracy of prediction when they are validated using another set of data (26 samples). Thus, these new and more accurate correlations can be useful in modeling different thermochemical processes, including combustion, pyrolysis and gasification processes of torrefied biomass. However, for the proper use of biomass resources, their physical, chemical and thermodynamic properties play an essential role in designing energy systems [Biomass is widely-available renewable energy resource with balanced CO systems . For ins systems . Therefo systems . However systems ,10,11,12To devise HHV predicting empirical correlations using multiple variables’ linear or non-linear regression analysis of many data points, one can consider HHV as a dependent parameter and the components of proximate  and ultimate analyses  as independent parameters. Akkaya  has adopHowever, one may argue that the selection of correlations with all components of proximate and ultimate analyses may not be the correct approach, as the fixed carbon content or the oxygen content may be replaced by the linear combination of other components. As it will not improve the prediction power of correlation, such correlations with all components of proximate and ultimate analyses can be avoided. Another aspect of developing empirical correlations is the use of a wide range of data points. As the basis of the derivation of HHV predicting correlations is only a statistical analysis of the data points incorporated in the analysis, such correlations will not be valid beyond the range of the used data points. In addition, the correlation derived for a limited type of species or materials will not be applicable for other types of materials. This study, thus, excludes the correlations with all of the components of proximate and ultimate analyses and uses only the data points collected for the torrefied biomass produced from various biomass species.Recently, research on biomass torrefaction that can enhance the biomass fuel characteristics has increased significantly. Many studies have shown that the torrefaction process, a thermal pretreatment method, increases the HHV, hydrophobicity, grindability and combustion properties. Studies have found that the torrefaction of biomass has changed components of both the proximate and ultimate analyses. Typically, the dry torrefaction process is carried out at atmospheric pressure condition in a temperature range of 200–300 °C and in an inert environment, whereas the wet torrefaction process deploys a reactor with highly pressurized water in relatively low-temperature conditions (180–230 °C) . The chaWhile torrefaction reduces the percentage of volatile matter, it increases the percentage of the fixed carbon and ash contents in the torrefied biomass. This increases the fuel ratio (fixed carbon to volatile matter contents) of biomass and decreases the char reactivity . This coGiven the major changes in the proximate and ultimate analyses of the torrefied biomass, it may be erroneous to use the existing HHV correlations, which were developed using raw biomass, for predicting the HHV of the torrefied biomass. Though one may argue that the existing expressions, which are valid for a wide range of biomass materials, may also be useful to determine the HHV of the torrefied biomass, this study thus examines and confirms if the existing correlations based on the proximate and ultimate analyses can be used or not. At the time of writing this paper, no such correlations, which use HHV, proximate analysis and ultimate analysis of torrefied biomass, are also reported in the literature. This study, therefore, includes (i) reviewing the published literature on biomass torrefaction and collects the information on proximate and ultimate analyses, (ii) reviewing the published correlations to predict the HHV of raw biomass, (iii) examining if the currently available correlations can be used to predict HHV of the torrefied biomass or not, (iv) developing new forms of correlations for predicting HHV using a large number of published data points of the proximate and ultimate analyses for the torrefied biomass and (v) validating the selected correlations with another set of data.Different published papers were reviewed to gather the information on proximate, ultimate and heating value analyses of both the raw and torrefied biomass materials. The collected information (in dry basis) from the literature for torrefied and raw biomass materials are summarized in In order to validate if the existing correlations can be deployed or not, only a few selected existing HHV correlations were tested to predict the HHV of the torrefied biomass from There could be a number of possible new forms of correlations that can predict the HHV of torrefied biomass. Thus, the authors have used various new forms of correlations to incorporate the individual and combination effects of different components of the proximate and ultimate analyses. The correlation is said to be the best-fitted regression line if the error of the estimation tends to zero . Howeverively. N 46 is the studies ,10,11,12The variations of the HHV of both the raw and torrefied biomass with respect to the compositions of the ultimate analysis are shown in In addition to this, one can see from From Proximate analysis-based correlations have more estimation errors compared to the ultimate analysis-based correlations. Moreover, one can confirm that the average biased error (ABE) was found to be negative for all reported correlations based on the proximate analysis. This confirms that the existing proximate analyses-based HHV correlations underestimate the higher heating value of torrefied biomass. The best correlation among the tested ultimate analysis-based correlations was the correlation presented  of torrefied biomass are changed significantly, it is essential to find the new correlations for predicting the HHV, which is applicable mainly for the torrefied biomass. This section provides the probable estimation errors of different possible forms of the new correlations presented in Among the studied new forms of proximate analysis-based HHV correlations, PSP4 (present study proximate), PSP11, and PSP12  at different operating conditions , the selected empirical correlations may also lead to a greater prediction error if they needed to predict HHV of torrefied biomass produced from a new technology.The torrefaction process changes the properties of biomass. The changes in the properties affect the existing HHV predicting correlations. Results showed that not all existing HHV correlations could be deployed to predict the HHV of torrefied biomass. Estimation errors of correlations based on the proximate analysis were found significantly higher compared to the ultimate analysis-based HHV correlations. New correlations were then determined using the least sum square error method in Microsoft Excel. Comparing the MAE, AAE and ABE, new correlations with the least MAE value are selected to predict the HHV of torrefied biomass. The newly-selected correlations for predicting the HHV of torrefied biomass are:The newly-selected correlations were then validated using another set of data (26 torrefied biomasses). They have good prediction accuracy within the error band of ±10% and are better than the existing correlations. Therefore, they could be used to predict the HHV of torrefied biomass.These correlations would be of a great interest in the present context where research is growing on biomass torrefaction, and such correlations can help in reducing the cost of experimental tests and in saving testing time. The authors would like to declare here that though these new correlations could predict the HHV of torrefied biomass with a good accuracy, this paper does not indicate that the existing correlations are not suitable for predicting the HHV of raw biomass.


This paper presents a fixed-switching-frequency model predictive current controller using multiplexed current sensor for switched reluctance machine (SRM) drives. The converter was modified to distinguish currents from simultaneously excited phases during the sampling period. The only current sensor installed in the converter was time division multiplexing for phase current sampling. During the commutation stage, the control steps of adjacent phases were shifted so that sampling time was staggered. The maximum and minimum duty ratio of pulse width modulation (PWM) was limited to keep enough sampling time for analog-to-digital (A/D) conversion. Current sensor multiplexing was realized without complex adjustment of either driver circuit nor control algorithms, while it helps to reduce the cost and errors introduced in current sampling due to inconsistency between sensors. The proposed controller is validated by both simulation and experimental results with a 1.5 kW three-phase 12/8 SRM. Satisfied current sampling is received with little difference compared with independent phase current sensors for each phase. The proposed controller tracks the reference current profile as accurately as the model predictive current controller with independent phase current sensors, while having minor tracking errors compared with a hysteresis current controller. Due to advantages such as simple and rugged motor construction, high working temperature, and low cost, switched reluctance machine (SRM) drives are becoming viable candidates for industry applications ,2. The pCurrent profiling is a frequently used method to minimize the torque ripple of SRMs. The current profiles can either be determined through the torque waveform of each phase according to torque-sharing functions ,6, or deCurrent sensor multiplexing technologies have been used in drive systems for permanent magnet synchronous motors, brushless dc motors, and induction motors ,18,19. MThis paper focuses on implementing accurate current control under an MPC scheme with reduced current sensors through sensor multiplexing. The proposed method utilizes the feature of independent phase switching control in MPC to realize sensor multiplexing in an alternate sampling manner. Current sensor multiplexing is achieved without additional calculation in MPC scheme. In the two-phase excitation region, the two excited phases have same control frequency but with a skewing of half period; analog-to-digital (A/D) conversions are triggered by period interrupts of PWM counters under continuous-increase-decrease-counting mode, the duty ratio of PWM unions are limited to ensure that one phase is turned off when another phase started A/D conversion. The simulation and experiments are carried out to confirm the feasibility of the proposed method.The contribution of the manuscripts is the realization of current sensor multiplexing in MPC scheme, individual phase is controlled using single-step predictive model, the predictive control steps of adjacent phases are staggered. Current sensor multiplexing is integrated in predictive control without additional control algorithm. The proposed method performs as well as MPC with independent sensors, while achieves lower costs.The current sensor multiplexing is realized with minor adjustment of the SRM drive. The topology of the drive is changed so that no freewheeling and discharging current flowed through current sensor, and the sampling time of each phase was staggered. This section describes the implementation of model predictive current control with only one current sensor in the drive.The operation fundamental of SRMs is different from other types of motors, such as permanent magnet motors, brushless DC motors, and induction motors. For SRM drive, each phase is independently controlled. Thus, asymmetrical bridge topology is commonly used in SRM converters . Figure The phase branch is controlled independently by two switches and operates under three modes, which are named charging, freewheeling, and discharging, represented as mode I, mode II, and mode III, respectively. An abbreviated current path of phase A under each mode is shown in In mode I, SIn conventional converters, each phase is configured with one current sensor. For the purpose of current sensor multiplexing, the topology is modified to distinguish freewheeling and discharging current from exciting current during the sampling period, as discussed in . The insIn the modified converter, all emitters of the down switches are connected to the negative DC bus through a current sensor. The phases of SRM are excited alternately to carry out continuous rotation; generally, no more than two phases conduct simultaneously. With this configuration, sampling current passes through the bus sensor belongs to one phase if only one phase was excited, while it is a combination of two phases in the commutation stage. The operation of SRM can be categorized into two stages: single-phase excitation and simultaneously two-phase excitation, as illustrated in In the two-phase excitation stage, the contents of the sampling current depend on the states of both phases. Either the outgoing phase or the incoming phase are in mode II or mode III, and the sampling currents of the sensor are all derived from another phase. All possible combinations of sampling current when phase A and phase B are simultaneously excited and corresponding phase states are summarized in In single-phase excitation stage, the sampling current of the sensor is equal to phase current of the single excited phase when down-switch is on, thus, the result can be used directly in control algorithms. In two-phase excitation stage, the sampling current of the sensor may be the sum of phase currents in two adjacent phases. However, in digital current control, the phase current is sampled with certain frequency instead of continuous sampling, and one can switch one phase to mode II shortly to sampling another phase; that is the basic principle of multiplexing current sensor between two phases. High-frequency pulse injection  was deveU is the phase voltage applied on the phase winding, i is the phase current, R is the winding resistance, i. The model in the discrete-time domain was derived asModel predictive control (MPC) predicts the future phase current in the next several control steps according to the discrete model of SRM, with average phase voltage as the input. Then, it makes use of predictive behavior to evaluate the predefined cost function, and finally selects the optimal average voltage. The average voltage is implemented through the adjusted duty ratio of PWM union in each control step. The phase voltage equation of an SRM can be given ash periods, and h is the predictive horizon.The predictive model for multiple steps is given in matrix form asGenerally, a current reference is predetermined to optimize performance criteria . To minimize torque ripple, a convenient solution for SRMs in instantaneous-torque-control mode is to coordinate the torque production of the individual phases so that the total torque tracks the reference value , namely given by  and 7)..(4)Uk+1∣given by , and theIn the modified converter, the up-switches of excited phase remain open, and the states of down-switches are controlled by the PWM (Pulse width modulation) signal. The PWM union works in continuous-up-down mode, the duty ratios are updated when the PWM counters decrease to zero. The predictive control algorithm executes within period interrupt. The period register value Avoid errors due to current ringings. Current ringings are generated at a switching instant due to parasitics and nonidealities of the switching devices and the inverter. These ringings introduce errors in the sampled current. Current sampling at the middle of a time period leaves enough time for current ringing to decay.Leave enough time for calculating duty ratio using predictive model before compare register updates.In the single-phase excitation region, the current flow through the bus sensor equals to the current of the excited phase if down-switch of the excited phase is open, while no current pass through the bus sensor when down-switch of the excited phase is turned off. The sampling current can be used directly in control loops; however, the sampling time should be selected skillfully. In the proposed method, the sampling time is arranged in the middle of a time period. The benefits of this arrangement are:ved from  to receiI or mode When the outgoing phase starts sampling, the incoming phase has just finished the previous period. If the duty ratio of the incoming phase is less than 100%, the down-switch of the incoming phase is turned off when the outgoing phase starts sampling, the sampling current at this moment identifies the phase current of the outgoing phase, which can be used directly in control loops. It is the same case when the incoming phase starts sampling. If we keep the duty ratio of both phases less than 100%, the multiplexing of the current sensor is realized. If the original duty ratio calculated from  equals 1The current sampling using multiplexed current sensor is integrated into predictive current control. The predictive current control model for single step is given in Equation , the parThe feasibility of the proposed current control method is evaluated in simulation and experimental platform. The current sampling result with multiplexed sensor is evaluated. Current waveforms in three control methods—MPC with independent sensors, MPC with multiplexed sensors, and traditional hysteresis control—are compared. A 1.5 kW 12/8 pole SRM was employed in the simulation and experiment, and mechanical parameters of the studied SRM are summarized in The flux linkage and torque data of the motor obtained using Maxwell software are used, and the electromagnetic characteristic of the studied SRM is shown in To ensure that the simulation is precise, a co-simulation platform, using Matlab, Simplorer and Maxwell, was employed. The 2D finite element model of SRM is built in Maxwell, the power converter and mechanical load is built using multiple-domain simulation software Simplorer, and the control algorithm was executed in Matlab/Simulink to provide the switch signals of the converter. Real-time interaction was undertaken between all software packages. A diagram of the simulation platform is shown in The current sampling process with multiplexed sensor in two-phase excitation region is shown in The performances of current control using three methods are compared, as shown in A 1.5 kW SRM prototype with the same geometric parameters as the simulation model is employed to build the experimental system, and various experimental results are provided to evaluate the effectiveness of the proposed method. The proposed control method is achieved in Digital Signal Processor (DSP) TMS320F2812 from Texas Instruments , which work at a frequency of 150 MHZ. A modified asymmetric bridge inverter is used; however, both independent phase current sensors and a multiplexed bus current sensor are deployed simultaneously in the converter. The bus current sensor is sampled by the built-in A/D converters of the DSP and used directly in the control algorithm. The current data are also saved in the in-board ROM for further verification. The independent phase current sensors are sampled through a data acquisition union (DAQ) from National Instruments. The dc bus voltage was set to 200 V. A 1.5 kW DC motor is used as the mechanical load. The rotor position is obtained by using an incremental encoder with resolution of 1024.The practical laboratory setup of the experimental system is shown in The performance of three control methods, MPC with independent current sensors, MPC with multiplexed current sensor and hysteresis control with independent current sensors, are compared in different condition. A current control method of SRM using MPC scheme with sensor multiplexing method was proposed. The proposed method exploits the flexibility of MPC in sampling time. The principle of current sensor multiplexing was introduced. The implementation of MPC with a multiplexed sensor was detailed, and the topology of a converter was modified to distinguish currents from simultaneously excited phases. MPC was executed in parallel in adjacent phases; however, the control steps of the two phases were staggered. The duty ratio of PWM was limited to make enough time for sampling. Both simulation and experiment were done to validate the effectiveness of the proposed method. The results showed that sampling current from a multiplexed current sensor was coincident with that from independent phase current sensors. MPC using a multiplexed current sensor has similar performance as using independent phase current sensors in phase current control. The switching frequency is fixed when using MPC. In simulation and experimental tests, both MPC with independent sensors and MPC with multiplexed sensor have minor current ripple than hysteresis current control in tracking reference current. MPC using a multiplexed current sensor was an improved method to reduce the cost with guaranteed performance.