Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset . Results show that the proposed method has superior performance in comparison with state-of-the-art approaches. The task of determining the meaning of words automatically in computational linguistics is reflected in word sense disambiguation (WSD). WSD has been a vital issue in natural language processing (NLP) for years, and it has been applied in various NLP tasks, such as information retrieval, machine translation, and automatic summarization. The most important clue for WSD is the context of an ambiguous word. Feature words are selected from the context to determine the right sense of the ambiguous word. Knowledge-based WSD usually selects words in a certain length of a window as feature words. Then, according to the relatedness between feature words and each sense of ambiguous word, the sense with maximum relatedness is selected as the right sense. Word ambiguity is categorized into polysemous and homonymy words. Polysemous words refer to words that have multiple senses with subtle differences, whereas homonymy words are words that have multiple senses, with each sense relating to a specific domain. Disambiguating homonymy words is easier than disambiguating polysemous words. Disambiguation requires an accurate quantifier that can measure the semantic relation between any two senses. Without such quantifier, a large annotated corpus, which is an expensive and time-consuming effort, is needed for the disambiguation process.Several studies proposed different techniques for WSD. Such techniques are mainly based on annotated corpuses –3, semanThe main factor in the semantic disambiguation is how the similarity or relatedness is measured. In this paper, the main contribution is to provide accurate evaluator that can estimate the semantic value for the inputted senses. This evaluator represents the core part of the proposed PSO, where it is used as fitness function of the algorithm. Also, we enforce the search process of the PSO by including local search algorithm. Incorporating such algorithm is frequently used to help the main search method in intensifying the search around a single solution. Using local search too often during the search process can lead to premature convergence. The combination of the two meta-heuristic approaches, namely, PSO  and SA  algorithms, increases the coverage of the proposed hybrid PSO. Meanwhile, the balance of exploitation and exploration leads to the global optimum of the search space and avoids the local minima . Hence, We also proposed a novel combination of the extended Lesk (e-Lesk) algorithm and JCN method for all parts of speech (POSs). Our approach aims to combine the high-performance JCN for measuring the similarity of nouns and verbs and the e-Lesk algorithm that covers all POSs. The e-Lesk algorithm is considered as an effective measurement method for all POSs , 21.This article is organized as follows. Section 2 presents the related studies in the field of WSD. Section 3 provides the proposed methodology involving language resource, semantic measures, and hybridized PSO and SA. Section 4 discusses the results of the proposed method, which is described and experimented with different window sizes. Section 5 analyzes the results of our model relative to other methods from the literature and our model limitations. Finally, Section 6 presents the conclusion of this work, along with final remarks.This section reviews the related state-of-the-art WSD studies. The proposed WSD model falls in the meta-heuristic method. Hence, this section focuses on the search methods that use semantic similarity or relatedness.Unsupervised approaches can avoid the knowledge acquisition bottleneck  23], i., i.23], In the context of unsupervised WSD, a co-occurrence graph is a method that suggests a different view of word sense discrimination, and it has been recently explored with certain success . The metIntelligence search techniques are most similar to co-occurrence graphs. First, meta-heuristic approaches to solving the WSD problem has been successfully applied using the SA algorithm . This SAIn recent years, many meta-heuristic algorithms have been applied for WSD due to the success achieved in the reference . These aThe genetic algorithm (GA) has been investigated for solving WSD , 36 8, , 38. GA In the context of swarm intelligence methods, PSO has been successfully applied to NLP tasks . For exaThe model design of this study consists of four main phases.Phase 1: This phase involves reading SemCor files and mapping fine-grained POS tags to coarse-grained POS tags.Phase 2: This phase involves the use of hybrid PSO  search, which is an efficient meta-heuristic algorithm, to maximize the entire semantic relatedness on a set of parsed words. This phase includes the following steps.Step 1: This step presents the data. Each word in the sentence is mapped to a number that denotes the number of senses of the word being disambiguated.Step 2: The fitness function that combines two semantic relatedness measures is implemented.Step 3: The best solution among all pre-generated solutions is identified using the hybrid PSO search algorithm.Phase 3: This phase involves the assessment of the quality of the final solution with regard to the benchmark corpus.These phases are illustrated in WordNet is a common language resource that finds the taxonomic and networked relationships of English concepts, arranges these related concepts, and provides groups of synonyms that represent the concepts , 52. AllSemantic measures can be categorized into two main classes, namely, semantic similarity and semantic relatedness. Semantic similarity indicates the dependencies among two concepts with respect to the related information of their hierarchy. Such measure can be accomplished by either the path length between two concepts in a hierarchy , 54 or tThe JCN measurement , 60 methOne of the significant drawbacks of the JCN method is the discarding of the similarity score between two concepts with different POS tags. For this purpose, this study aims to utilize the extended Lesk gloss overlap as another measurement method. The key characteristic of gloss overlap methods is inspired by the idea of WordNet relations (has-part and is-a), which does not cover all possible relations between two concepts being measured.The input of the extended Lesk algorithm comprises two concepts that need to be measured, and the output is a relatedness volume, which is shown as a numeric value. The The solution in this study is evaluated by assessing the semantic similarity and semantic relatedness for the required text. The objective function identifies the semantic coherence among every pair of senses . The criterion for evaluating the solution is to quantify the semantic similarity and relatedness for the target text. In this work, the cost of the solution indicates the semantic coherence between each pair of senses (pair of decision variables in the solution). Hence, these methods work as an objective function for the proposed algorithm by finding the total semantic coherence of a sentence. For example, the extended Lesk algorithm measures the solution cost as follows:The objective function is obtained by computing the quality of the created solutions. In this study, the extended Lesk algorithm finds the relatedness value of PSO solutions on the basis of The proposed method employs two types of meta-heuristic search algorithms, with each type being characterized by special search ability. This section explains the mechanism of each algorithm and how they work together for achieving a high-quality search method.pbest and gbest, which denote the best solution achieved and the best value gained by any particle in the population, respectively. All particles have objective values that are obtained via the objective function to be improved. They also have velocities in the direction of their inertia. Hence, the best objective value and best location are induced from the population at the end of the search.PSO was first developed by Kennedy and Eberhart 1995) and is based on the swarm intelligence manner that mimics birds flocking in nature. Potential solutions in PSO, termed as particles, fly over the problem space by going after the two best values, as shown in Eqs  and is bIn WSD, each word has a specific number of senses with regard to the used lexical database. A word in a given sentence corresponds to a solution variable in PSO. Thus, each variable in the solution has different boundaries. The disambiguation process begins with the deployment of random guesses for each sentence related to variable limits, as shown in lines 3–8 of SA is a meta-heuristic search algorithm that mimics metal cooling operations . This alΔS denotes the difference between the current and new solution costs.SA interprets slow cooling as a slow decrease in the probability of accepting poor solutions. Hence, the above condition relies on the temperature value (T) to accept a poor solution. Meanwhile, This study hybridized a population-based algorithm called PSO with a local search algorithm called SA algorithm. This hybridization aims to achieve an accurate search algorithm that can identify a set of appropriate senses for ambiguous words in a given text. SA incorporates PSO algorithm to intensify the search process for a candidate solution.In our proposed method, the global best of the PSO is passed to SA to search the space around this solution . The locThe experimental results of the proposed method are based on the standard metrics shown in The evaluation of the designed model in WSD is based on specific data that reflect the accuracy of the proposed model. Two types of datasets are used to evaluate WSD systems. The datasets designed to examine the methods for one-word targets consist of a number of contexts that include a single word to be disambiguated.Machine learning approaches target the aforementioned data. However, the proposed model is designed to tackle ambiguous words in a given text. Hence, this method is examined using other types of datasets. These datasets should consist of sentences that require disambiguation. Thus, each word in a sentence, excluding the stopping words, is ambiguous. Therefore, the designed model must assign the appropriate sense for each word. Moreover, the examined model must consider all possible senses for each word.The designed model is experimented on the basis of the semantic concordance (SemCor) corpus, which is part of the Brown corpus . Such cohttps://projects.csail.mit.edu/jwi/. This library provides wide usage functions that can extract each part of a line and return it separately. For instance, it returns POS, lemma, and number of senses separately .In the given example, the italicized word is the target ambiguous word, whereas the underlined words are the window context used to disambiguate the target word. This example shows a window of three words, including the target word. Certain words are neglected during the selection of the window as they are stop words , where ΔS is the difference between the new solution and the previous solution, T is the current temperature, and r is a random number between 0 and 1. PSO enables the global search of the problem space to find various solutions of different qualities. A local search algorithm, on the contrary, intensifies the search and processes a promising solution to be improved by searching its neighborhood. The local search method used in this model is the SA algorithm. This algorithm accepts unsatisfactory moves to break out of the local optima in the problem space of WSD. However, accepting non-improving moves is limited by a stochastic condition that considers the SA parameter called temperature. exp. The proposed PSO utilizes a local search algorithm using SA to improve the search of a neighborhood. In addition, we investigated the effect of window size on the disambiguation process. Hence, we presented experimental results for each window size to highlight their impact on our model. We also proposed a novel combination of semantic similarity and relatedness methods. The results of these methods and of the final model were presented independently. The final results of our model were shown in comparison with the results of related studies. This comparison was based on the same metrics applied on a three-benchmark dataset . Certain related works have presented their results on the basis of nouns only. Thus, we compared our results on the basis of this POS. Our experiments in the SemCor 3.0 dataset showed that the F-measure of the hybrid PSO is close to the best results of the related work, whereas our technique yielded the highest precision of 67.44% in terms of all POSs. Our proposed technique significantly outperforms other state-of-the-art techniques implemented with SensEval-2 and SensEval-3 datasets based on all POSs. Experimental results show that our novel combination of semantic measures along with the meta-heuristic hybrid PSO achieves the best results for varying datasets. In addition, the proposed method effectively improves WSD in comparison with other meta-heuristics approaches.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.


Using perfectly matched layers for the computation of resonances in open systems typically produces artificial or spurious resonances. We analyze the dependency of these artificial resonances with respect to the discretization parameters and the complex scaling function. In particular, we study the differences between a standard frequency independent complex scaling and a frequency dependent one. While the standard scaling leads to a linear eigenvalue problem, the frequency dependent scaling leads to a polynomial one. Our studies show that the location of artificial resonances is more convenient for the frequency dependent scaling than for a standard scaling. Moreover, the artificial resonances of a frequency dependent scaling are less sensitive to the discretization parameters. Hence, the use of a frequency dependent scaling simplifies the choice of the corresponding discretization parameters. In this paper, we study acoustic resonances in open systems. In this context an open system is an unbounded domain of wave activity, allowing for the existence of complex resonances . TherefA popular numerical approach to such resonance problems is the use of boundary element methods , 31. TheOther methods, including the technique of complex scaling, make use of the decomposition of the unbounded domain into a bounded interior and an unbounded exterior domain. While for the bounded interior problem standard finite elements can be used, the exterior domain needs particular attention because of its unboundedness. Possible ways to treat the problem in the exterior domain include so-called infinite element methods , 18 baseThe complex scaling method was introduced amongst others by Simon in the 70s .When treating time depending problems with complex scaling, it is common to choose a frequency dependent damping parameter (see e.g. ) to guarThe remainder of this paper is organized as follows: Radial complex scaling for Helmholtz resonance problems is introduced in Sect. We study the dependency of artificial resonances on two different types of radial complex scalings. Namely a frequency independent linear complex scaling, and a frequency dependent one.Let n. For the details we refer to [u given by . Bamentclass2pt{minimformulation of a complex scaled resonance problem [see ],truncation of }ΩT [see ], andfinite element discretization of ΩT) [see ].Note that the second step is not necessary for complex scaling methods with singular damping profiles . Startisee e.g. ).Based on this damping function, we introduce a complex scaled variableu of  eigenvalue problems arising from  and the X, let For some open set ollowing , we defiLet tor, and  is a weaFor a frequency independent scaling shown in  that \dofer with ).p as well since p has compact support by assumption.By a similar reasoning as in ).Focusing on spurious resonances, we observe similar effects as we did studying the Bessel equations. In the frequency independent case Fig.  we see tFor the frequency dependent scaling we study the effect of the meshsize on the location of the spurious resonances in Fig. Overall, the frequency dependent complex scaling produces a larger quantity of correct resonances than the frequency independent complex scaling does since in the frequency independent case the exterior spurious resonances pollute the domain of interest. In principle, all remarks to the one-dimensional results remain valid for this two-dimensional problem as well.We have studied the effects of the various parameters and sub-steps in the discretization process of a complex scaling method on the computed resonances of the Helmholtz equation in one and two dimensions and have classified the occurring spurious resonances into three categories: truncation resonances, which are an approximation to the essential spectrum, and interior and exterior spurious resonances, generated by the discretization of the interior and exterior domain respectively. In applications where resonances near the real axis are sought-after, the interior and exterior spurious resonances are the ones raising problems, especially since the location of the exterior spurious resonances delicately depends on the choice of the damping parameter.Moreover, we have in the context of resonance problems introduced and studied the use of a frequency dependent damping parameter resulting in a polynomial eigenvalue problem. In this case the same classes of spurious resonances appear, but take different shapes than in the frequency independent case. The exterior spurious resonances keep a constant distance to the real axis, since the exponential decay of the absolute value of the complex scaled resonance functions in the exterior domain does not depend as heavily on the frequency. Thus, resonances near the real axis are not polluted by exterior spurious resonances. Moreover, if the damping parameter is chosen within reasonable bounds, it has no impact on the number of well approximated correct resonances.If solving the non-linear eigenvalue problems should be avoided, the frequency dependent scaling can still be used as a filter for the resonances computed with frequency independent scaling. Plugging the results of the linear eigenvalue problem into the discretization matrices of the frequency dependent scaling should lead to large condition numbers, if the result is the approximation of a resonance to the non-linear, frequency scaled resonance problem. So truncation and exterior spurious resonances can be detected by this procedure. Of course for this application a precise knowledge of the spectral properties of the non-linear eigenvalue problem resulting from frequency dependent complex scaling is crucial. In this way this paper might help to construct a reliable filter for spurious resonances as well.


In the last decade, interest in stretchable electronic systems that can be bent or shaped three-dimensionally has increased. The application of these systems is that they differentiate between two states and derive there from the requirements for the materials used: once formed, but static or permanently flexible. For this purpose, new materials that exceed the limited mechanical properties of thin metal layers as the typical printed circuit board conductor materials have recently gained the interest of research. In this work, novel electrically conductive textiles were used as conductor materials for stretchable circuit boards. Three different fabrics  made of silver-plated polyamide fibers were investigated for their mechanical and electrical behavior under quasi-static and cyclic mechanical loads with simultaneous monitoring of the electrical resistance. Thereto, the electrically conductive textiles were embedded into a thermoplastic polyurethane dielectric matrix and structured by laser cutting into stretchable conductors. Based on the characterization of the mechanical and electrical material behavior, a life expectancy was derived. The results are compared with previously investigated stretchable circuit boards based on thermoplastic elastomer and meander-shaped conductor tracks made of copper foils. The microstructural changes in the material caused by the applied mechanical loads were analyzed and are discussed in detail to provide a deep understanding of failure mechanisms. Stretchable circuit boards are required for applications where an applied single or repeated mechanical load results in deformation of the system without loss of the desired functionality. In the first case, electronic components are integrated onto two-dimensional substrates that are easier to manufacture than complex three-dimensional ones, before the final shaping. In these forming processes, one-time loads act on the circuit carriers . In the As is known, commonly used textile fibers show no intrinsic electrically conductive properties. However, the raw fiber textile can be processed into an electrically conductive material by plating a thin metal layer onto their surface, in which gold, silver or copper is usually applied . These fThus, we propose the use of metallized polymer-based fabrics, which, due to textile processing techniques, produce a material with higher ductility than a single yarn- or fabric-based material on metal wires. The investigation of the mechanical and electrical properties of these materials, structured into conductor tracks and embedded in an elastic matrix, is the object of this article. In All selected fabrics are made of silver-plated polyamide (PA 6.6) fibers, which are embedded in thermoplastic polyurethane (TPU). Important material properties are summarized in Thermoplastic polyurethane in film form is used as a substrate material. TPU combines the properties of thermoplastics and elastomers. At temperatures above the glass transition temperature (at −40 °C), TPU has an elastic behavior. This feature is required for later application as a stretchable circuit board. In contrast to elastomers, the selected TPU film also has a melting point (at 160 °C). Above this temperature, it can be thermoformed .In the textiles industry, polyamide is the second most used man-made fiber . It is aThe fabrics, made from polyamide fibers, are plated with a thin silver layer. Silver is chosen because of its high electrical conductivity and chemical resistance . In addiFor electrical insulation and protection against external agents, the conductor tracks are embedded in a matrix of TPU. 2-laser (see step 2). The excess material is removed, so only the structure of the conductive pattern remains on the carrier (see step 3). Afterwards, a second TPU film is laminated as a cover layer (see step 4). Then the sample outline is laser-cut. Finally, the stretchable composite can be lifted from the rigid carrier (see steps 5 and 6). In A rigid carrier is used to improve handling during manufacturing. A TPU film and a conductive fabric are fixed and bonded at an elevated temperature and pressure onto the carrier see , step 1.It has been observed that the lamination of conductive fabrics has a significant impact on the electrical and mechanical properties of the material. In general, the metallized polyamide filaments are contacted to each other selectively, not over their entire surface. The number of contact points per surface again differs between the woven, the knitted and nonwoven fabric, depending on the voids and type of the thread intersections. Depending on the textile processing, the resulting contact pressure between the filaments plays an important role in the electrical resistance according to Holm’s contact theory . FurtherTemperatures far below the melting temperature of silver, which also occur while laminating the samples, have a great influence on the silver layer. The individual silver nanoparticles sinter together to form a more compact layer see , wherebyThe sheet resistance is used to compare the electrical resistances of two-dimensional conductors with different width and lengths, such as electrically conductive fabrics. It is calculated from the measured resistance and the quotient of conductor width and length  is used for the uniaxial tensile test. Simultaneously, the electrical resistance of the conductor tracks is measured. For this purpose, the test samples are contacted with crimp connectors and connected to a digital multimeter Fluke 175 . The boundary conditions for these tests are listed in The cyclic mechanical load, which acts on the stretchable circuit boards in many dynamic applications, is simulated by repeated uniaxial loading and relaxation. In order to characterize the change of the electrical behavior during the cyclic mechanical load test, the stretch tester developed and described by Born  is used.The results of the tested mechanical and electrical properties of the three elastic textile/polymer composite samples are listed in In the case of woven and knitted structures, the mechanical damage causes the electrical failure. When the elastic textile/polymer composite breaks at about 38% and 72%, respectively, the silver structure also breaks. For nonwovens, the loosely connected filaments are pulled apart. Only a small amount of mechanical load acts on the fabrics. From an elongation of approx. 75%, the sample is no longer electrically conductive, as the filaments have completely separated from each other. Furthermore, the TPU matrix holds the composite together. The samples with conductor tracks made of nonwovens have a high initial resistance with great fluctuations. While quasi-static mechanical loading, they show the highest relative increase in resistance of the investigated structures. Due to their inconstant mechanical and electrical properties, the nonwoven fabrics enable no reliable use of them, so consequently they are excluded from further investigations. The knitted fabric shows the lowest initial and breaking resistance, as well as the lowest relative increase in resistance under quasi-static mechanical load. Likewise, it has the highest elongation at break of the tested structures. It is, regarding the mechanical and electrical properties, the most suitable structure for single forming.As expected, the elongation at break of the threads  is loweThe stretchable circuit boards have an average elongation at break point of about 72%. Knitted fabric samples that have undergone the same temperature and pressure conditions as the processed circuit boards, but which have not been embedded in TPU, have an elongation at break point of 68%. The non-tempered knit fabric is stretchable up to 55%. The increase in stretchability by heat and pressure treatment is due to a reduction of the degree of stretching . An equivalent evaluation of the results has shown that processing the fibers to woven structures increases the stretchability only by 2%. With 38% (woven-) to 72% (knitted fabric), the stretchable conductors made of textile structures have a higher elongation at break point than the silver-plated polyamide yarns  with an untested sample.In order to observe the internal structure of the untested samples, transversal microsections were made and analyzed with a visible light microscope (VLM) see .Unlike the composite with conductive woven fabric, the knitted sample is not in the center of the TPU matrix and thus not in the neutral fiber. During the first lamination, the TPU layer melts and the knitted material is pressed into the TPU. In the second lamination step, TPU flows into the remaining gaps and bonds to the first TPU layer. However, the knit is pressed until the edge of the material compound. As a result, some polyamide threads are exposed, and they are no longer isolated and can react with the environment.The exposed polyamide threads of the knit-based composite in the TPU layer can also be seen on the SEM images see , left.Energy-dispersive X-ray spectroscopy shows that the silver layer on the polyamide filaments is still intact see , left. IIn order to investigate the internal structure of the composite material, micro computed tomography (micro CT) is used. In the areas between the loops, even in the parts of their cross-connections, the untested fiber surfaces are still largely smooth. In the tested sample it can be seen that in the area of the loops, the previously smooth surfaces become rougher see .Knowing that the greatest damage to the silver layer occurred in the contact areas of the loops, transverse microsections of an untested and a tested sample were prepared and analyzed using a focused ion beam (FIB) and SEM in the contact area. The silver nanoparticles on the filaments are sintered together to form a continuous, no uniform layer. This layer is around 50 nm to 200 nm thick  embedded in a thermoplastic elastomer matrix.It was observed that after lamination, the sheet resistances of all tested fabrics were reduced due to a sintering of silver nanoparticles and an increase in the contact points of the conductive fibers with each other. The silver-plated knit shows the best properties in the results of this investigation. It has the lowest initial and the least increase in sheet resistance under quasi-static mechanical load. In particular, the metallized knit fabric has the highest life expectancy under a cyclic mechanical load and thus exceeds the life expectancy of commercially available stretchable circuit boards based on thermoplastic polyurethane and meander-shaped conductor tracks made of copper foil.With a cyclic mechanical tensile load of the textile–elastomer composite, no classical conductor cracks could be observed. Instead, the silver-plated polyamide fibers rub against each other at the loop of courses and wales intersections, so that over time the thin silver layer partially flakes off from the filament, thereby decreasing the electrical conductivity in the loop running direction. However, due to the presence of secondary filaments that transverse the loop running direction, which are subjected to lower linear mechanical load and friction, the electrical conductivity is maintained but decreases with mechanical load over time.Due to the relatively high sheet resistances of conductive fabrics and the described electrical behavior under a mechanical load, an application, in particular, in the field of body-near textile sensors and electrodes is conceivable. Therefore, a life expectancy of a typical textile-integrated electrode with high mechanical strain as it occurs for example in knitted shirts was presented.In the next step, the lamination of the conductive textiles into the elastomer matrix has to be optimized in order to prevent out-of-plane displacement and thus substantial damage to the surface metallization of the filaments. A further optimization of the mechanical load capacity is expected by a meandering design of the tracks, analogous to the currently used concepts for copper foil based stretchable conductor structures. Due to the mechanical failure behavior of the tested materials, no conductor breaks occur up to a high load, and we expect a good washing resistance of the textile/ elastomer-based circuit board. In conjunction with conductive textiles with a thicker surface metallization, the novel composite and manufacturing concept presented here can be used for most e-textile and smart clothing applications as well as multi-layer textile circuit boards for the realization of more complex textile-integrated electronic systems. Further fields of application are conceivable in the field of soft robotics. Pneumatically controlled, active morphing soft elastomer surfaces can be extended with conductive textiles to an electronic skin with sensor functions.


Micro-structural analyses are an important tool to understand material behavior on a macroscopic scale. The analysis of a microstructure is usually computationally very demanding and there are several reduced order modeling techniques available in literature to limit the computational costs of repetitive analyses of a single representative volume element. These techniques to speed up the integration at the micro-scale can be roughly divided into two classes; methods interpolating the integrand and cubature methods. The empirical interpolation method (high-performance reduced order modeling) and the empirical cubature method are assessed in terms of their accuracy in approximating the full-order result. A micro-structural volume element is therefore considered, subjected to four load-cases, including cyclic and path-dependent loading. The differences in approximating the micro- and macroscopic quantities of interest are highlighted, e.g. micro-fluctuations and stresses. Algorithmic speed-ups for both methods with respect to the full-order micro-structural model are quantified. The pros and cons of both classes are thereby clearly identified. In recent years the use of composite or multi-phase, high performance steels and hybrid materials in advanced structures has increased significantly. Due to the ability to tailor their mechanical properties, these materials are applied in high-tech systems and structures where optimal material properties such as a high strength to weight ratio are essential. To achieve these excellent properties, the underlying microstructures in these heterogeneous materials tend to become increasingly complex, with strongly non-linear behavior of the constituents.As a consequence, the material testing procedures have become intricate, time-consuming and expensive. The number of required experiments can be reduced drastically by reliable modeling of the material behavior. Computational homogenization is one of the most accurate modeling techniques currently available to analyze and design the material behavior of heterogeneous materials on a structural scale. Microstructures of heterogeneous materials can be modeled adequately, but it remains a computationally demanding problem when applied on materials with a large scale separation between the structural dimensions and the dimensions of the material heterogeneities.The early concepts of computational homogenization were introduced by Suquet  and extea et al. , composia et al.  among mamentclasspt{minimaa et al. .The common denominator in the aforementioned models is that the material behavior on the macroscale is obtained from a detailed model of the microstructure of the material. When the microscopic model is sufficiently large and detailed to accurately capture the homogenized behavior of the material it is referred to as a representative volume element (RVE). In solving a macroscopic problem one can use the computationally homogenized model of the representative volume element or in materials design analysis one can use the RVE to analyze the resulting macroscopic response of different microstructures. This requires the same microscopic RVE problem to be resolved numerous times for different load-cases.One approach to decrease the required computation time is to increase the efficiency of solving the microscopic system of equations, Michel et al.  used a FThis reduced order modeling technique was originally proposed in the late 70’s begin 80’s with Almroth et al. and Noor et al. , 17. TheThe number of global basis functions required to capture the solution accurately is typically orders of magnitude smaller than the number of local basis functions used in classical discretization techniques such as the Finite Element Method. Rewriting the problem onto the global basis reduces the size of the algebraic system of equations that needs to be resolved at every iteration. A detailed overview of the advances in reduced order modeling is presented in  and 28]28].Empirical Interpolation Methods (EIM) and (2) models reducing the costs of the integration scheme, minimizing the number of evaluations of the non-linear term required, i.e. Cubature Methods.As pointed out by Rathinam and Petzold  the redud Suquet  to accoud Suquet , hyper-rEmpirical interpolation of the nonlinear stress is done by projecting locally sampled values, also known as gappy data, onto a basis for the nonlinear term. The reconstruction of gappy data using modes is pioneered by Everson and Sirovich [Sirovich  from whiSirovich , and latSirovich , MissingSirovich , DiscretSirovich . A compaSirovich . This recubature methods reduce the cost of the integration of the nonlinear integrand by using a reduced set of integration points (or elements) with weights that minimize the integration error. The first cubature method was proposed by An et al. [Energy-Conserving Sampling and Weighting (ECSW) hyper reduction method in the field of computational mechanics.On the other hand, n et al.  and laten et al.  introducThis paper studies the accuracy and efficiency of hyper-reduction techniques in the context of microstructural volume element analysis. To this purpose, a detailed comparison will be made between the Empirical Interpolation Method on the one hand and the Cubature Method on the other hand. We will compare two specific implementations of these methods, namely the High-Performance Reduced Order Modeling technique (HP-ROM)  and the In this comparison emphasis is given on the reduction of the computational costs of evaluating the nonlinear stress, the efficiency in terms of computational time and accuracy in resolving the RVE problem. In particular, this comparison will focus on the path-dependency originating from the elasto-viscoplastic material behavior.The paper is organized as follows. In Sect. In this section, the two-scale computational homogenization procedure will be outlined briefly. In the computational homogenization framework a high-fidelity problem at the macroscopic length scale mentclass2pt{minimAt the macroscale, where the variables and fields are denoted by subscript The macroscopic problem is defined as a material body To characterize the microstructure of the material a Representative Volume Element The homogenization is based on two principles. The first principle is the volume average of the stress or the strain, which should match the macroscopic strain and stress respectively.ondition  which prensation .Both the macroscopic and microscopic problems, Eqs.  and 6)  6) can bThe trial functions ium Eqs.  and 8)..\documenApplying computational homogenization to a high-fidelity problem with ample microscopic details entails a significant increase of the required time and computational resources. Despite the reductions achieved by the homogenization procedures, the size of the macroscopic problem that can be computed within a feasible amount of time and computational resources is still limited for RVEs with nonlinear constituents.The term FEThe same scaling relation of the number of floating point operations holds for the number of evaluations of the ordinary differential equation in the material model. The ordinary differential equation in the material model has to be solved in every integration point at the microscale. The number of integration points on the macro- and microscale are denoted by As shown in the previous section, the computational cost of the homogenization scales with the number of DOFs Using a reduced basis, the finite element discretization is mapped onto a global basis. In most physical problems this yields a significant reduction of the required number of DOFs After the reduction of the number of DOFs the number of integration points The number of DOFs If there is prior knowledge of the kinematics occurring in the RVE, it is possible to construct a basis that captures the kinematics more efficiently with a limited number of DOFs. Since Computational Homogenization often involves repetitive calculations of an RVE, it calls for an additional optimization step. The extra computational costs for identifying a Reduced Basis for the problem are justified by the higher costs for solving the RVE multiple times.k. In total there are snapshot-matrixTo find a more optimal basis for the microscale problem, prior knowledge of the relevant kinematics needs to be obtained. This is done by repetitively solving the RVE-problem under different macroscopic strains An orthonormal basis is derived from the snapshot matrix mentclass2pt{minimBased on their eigenvalues the most important modes can be selected to construct a Reduced Basis for the microscale problem. The eigenvalues and their corresponding eigenvectors are sorted in descending order, allowing to construct the basis from the first entclass1pt{minimaentclass1pt{minimaAfter determining the number of required modes  balance  the follThe Empirical Interpolation Method (EIM)  projectsAlternatively, the integration scheme can be reduced directly. This approach is known in literature as Energy-Conserving Sampling and Weighting (ECSW) hyper reduction method or Empirical Cubature Method (ECM) . The intIn this section two approaches to reduce the costs of evaluating the nonlinear internal force are reviewed:In the Empirical Interpolation Method, the stress field is approximated by interpolating between weighted stress modes The weighted strain modes e et al. .14\docummentclasspt{minimaThe stress- and strain basis are composed out of stress- and strain-fields resulting from the converged linear momentum balance . This letated in .In order to solve this problem, the Expanded Basis Approach (EBA) is applied, where the stress-basis gappy stresses in Voigt notation hyper-reduced problem [The coefficients for the expanded basis  \documenshown by , the coe problem .To complete the hyper-reduced model a set of integration points hot data .To improve the stability of the system of equations, the selected points are complemented with a second set of points selected from the remaining integration points. These points are selected using the Greedy algorithm using a criterion that aims at optimizing the conditioning of the resulting reduced tangent stiffness matrix.Accuracy points Hernández et al. [truncation error that represents the error introduced due to the use of a reduced stress basis and a reconstruction errorz et al.  select tz et al.  into a tStability points Hernández reported that using integration points that are only selected on the basis of accuracy, provides a system of equations that is not unconditionally stable. Therefore, the set of selected integration points needs to be complemented with a set of extra points to ensure the stability of the system. The same Greedy selection procedure is used with a criterion that aims to optimize the positive-definiteness of the tangent stiffness matrix. To ensure maximum stability, the criterion given in [given in  that neeweighted reconstruction matrix.The resulting weighted stress field in the RVE is given by the Empirical Interpolation . The coepression  by substTo reconstruct the macroscopic stress, the reconstructed stress-field Instead of approximating the stress field using a Proper Orthogonal Basis constructed from the stress snapshots, the expensive integral of the internal forces can be approximated using the Empirical Cubature Method.For a polynomial FEM basis one can construct an exact quadrature scheme. This classical Gaussian quadrature scheme approximates the integral summing up weighted samples of the integrand at specific points, the Gauss integration pointsWhen rewriting the problem using the reduced-order basis to  one can The method will be outlined briefly in two steps. First the determination of the integration weights is discussed; next the selection procedure for the integration points is presented.To determine the weights, the internal force contributions at each integration point Note that problem  is again problem  yields tent}|Vm| .36\documIt is intractable to evaluate all the The criterion used to identify candidate points, selects the point that is currently most aligned to the residual of the snapshot integrals. This procedure repeats until enough integration points are selected to drop the residual under the required tolerance for the integration accuracy or the maximum number of gappy integration points It is not trivial to reconstruct the microscopic stress field from the sampled stresses as the Empirical Cubature Method does not use stress-modes. One can however follow the approach proposed by  to reconThe linear momentum balance with reduced internal force  is solveTo assess the performance of the hyper-reduced models, a critical comparison is made between the empirical models, the reduced order models and the full-order model of a RVE. Focus is put on the suitability for the hyper-reduced models as a stand-in replacement of the complex full-order model of the RVE in a computational homogenization framework. Small plane-strain is assumed in the model of the RVE. The RVE consists of fibers in a soft matrix. The matrix is modeled using a small-strain elasto-viscoplastic material constitutive law of De Souza Neto et al. , p. 148.In which The dimensions and topology of the composite microstructure are shown in Fig. The reduced order models are initialized using an orthogonal set of macroscopic strains (computed off-line). The full-order model results for the microfluctuations and stresses are stored in the snapshot matrix for each time-increment. The loading paths chosen to initialize the model run from 0 to The microfluctuation and stress-modes are determined from the snapshot matrices using POD. The eigenvalues that correspond to these modes are shown in Fig. ernández , the numTo generate the empirical cubature scheme, a cut-off tolerance on the integration error is required. The order of magnitude of the interpolation error is predicted by the sum of the squared eigenvalues of the truncated modes of the stress basis. These interpolation errors In the remainder of this paper, three different techniques will be compared: (1) standard reduced order (with full integration), 2) empirical interpolation and (3) empirical cubature models. A summary of all the specifications of the (Hyper-)Reduced Order Models in terms of modes and sampling points are given in Table  empiricaIn order to assess the achieved approximative qualities of the reduced models, the microfluctuations and micro- and macroscopic stresses are compared to the full-order model. In the reduced order model there is no loss of information present during the integration of the stress-field to obtain the internal forces. Therefore, this model can be regarded as the best-approximation for the applied reduced basis and serves as a reference model to investigate the performance of the hyper-reduced models. The achieved time reduction will be evaluated by comparing the CPU-time required to perform the computations to the CPU-time required to solve the original full-order model.The accuracy of the empirical methods are first investigated for one of the load-cases used to generate the snapshots reduced order models are plotted in Figs. The error in the microfluctuations of the hyper-reduced models , without parallelization to keep the comparison clear. The speed-up with respect to the full-order model are presented in Table The computation times for all models are assessed on a single CPU reduced models for the path-dependent case is presented in Table xx-direction, shifting its history increasingly further away from its virgin state. The loading direction corresponds to the snapshots, excluding the influence of any interpolation in between different loading directions.Finally, the behavior of the models under repetitive loading is analyzed. The RVE is exposed to a cyclic load in the The stress–strain curve is shown in Fig. The speed-up achieved by the (hyper-)reduced models for the cyclic load-case are presented in Table Both the empirical interpolation method and empirical cubature method yield a significant reduction in computation time and memory footprint compared to traditional ROM. Both models are capable of interpolating between the sampled deformations as shown for the biaxial load-case. When the load-case includes unsampled states of the history parameters, the empirical interpolation method result in errors above 1% for the macroscopic stress with respect to the full-order model. The empirical cubature method is less sensitive to the extrapolation of the snapshot space, since it only depends on the strain modes and not on the stress-modes.The main asset of the ECM method is that it naturally preserves the stability of the full-order model and therefore does not require the added stabilization needed for the EIM method. However, with the stabilizing integration points added, the EIM models showed good convergence for all the cases demonstrated in Sect. The empirical cubature method requires more computational time and has a larger memory footprint than the empirical interpolation method due to the extra integration points needed to accurately capture the behavior of the full-order model. The EIM method outperforms the ECM method when on-line computational efficiency is considered due to the extra information it has available in the stress-modes. However, this dependence on stress-modes makes EIM also more vulnerable for ill-sampled path dependent problems, as demonstrated in the path-dependent and cyclic examples.The largest reduction in required memory and computation time can therefore be achieved using the empirical interpolation method in a computational homogenization setting, due to the smaller amount of memory required to store the history of the sampling. To get accurate results, the high-dimensional snapshot space needs to be sampled sufficiently well such that the paths found in the macroscopic problem can be interpolated sufficiently accurate by the EIM model.Herein lies an open challenge, since the material model used in the above examples requires 7 history parameters and 3 strain parameters to calculate the macroscopic stress, the sample space of the model is 10 dimensional. It is therefore not trivial to obtain a sufficiently dense sampling of the parameter-space to capture the path-dependent macroscopic stress.In the considered computational homogenization problems, the microscopic RVE has to be evaluated numerous times in the deformation and history space. It is therefore necessary to construct an accurate yet compact snapshot space. The empirical interpolation method is expected to be sufficiently accurate for many engineering purposes (within 10% error). However, when a higher accuracy is required, ECM methods are more promising since they are less prone to produce large interpolation errors. In problems with a strong path-dependence, it is more straightforward to construct an accurate snapshot space for ECM models.


A significant proportion of newborn and maternal deaths can be prevented through simple and cost-effective strategies. The main aim of this study was to evaluate the impact of the PRONTO obstetric-emergency management training for improving evidence-based birth attendance practices among providers attending the training at 12 hospitals in three states of Mexico from 2010 to 2012, and to estimate dissemination of the training within the hospitals.The average treatment on the treated effect of the PRONTO intervention for the probability of performing certain practices during birth attendance was estimated in a sample of 310 health providers. Impact estimates were obtained by performing provider-level matching using a mixed Mahalanobis distance one-to-one nearest-neighbor and exact matching approach. A secondary analysis estimated the positive externalities caused by the intervention in the treated hospitals using the same analytical approach. Provider-level fixed effects regression models were used to estimate the rate of decay of the probability of performing the examined practices.Providers attending the PRONTO training showed significant increases in the probability of performing the complete active management of the third stage of labor, especially the first and third steps, and skin-to-skin-contact. There was a negative and significant effect on the probability of performing uterine sweeping. Providers who did not attend the training in treated hospitals also showed marked significant changes in the same practices, except for uterine sweeping. There was no evidence of a significant decay of the probability of performing the routine practices over time among the treated providers.PRONTO is efficacious in changing trained providers’ behavior, but not on all practices, suggesting that some practices are deeply ingrained. The results also suggest that information on practices is effectively transmitted to peers within treated hospitals. Previous findings of the dilution of the effect of PRONTO on some practices seem to be more related to the rotation of personnel (mainly interns) rather than providers returning to their former habits.NCT01477554. Registered on November 18, 2011; retrospectively registered.The online version of this article (10.1186/s12884-018-1872-4) contains supplementary material, which is available to authorized users. Cost-effective health strategies are known to save women’s lives and to protect their newborns . In factMaternal and newborn care is dependent on effective multiprofessional teams of health care providers. The current challenge for training programs is to increase the capacity and experience of these multidisciplinary teams . ObstetrMany studies on maternal mortality have indicated that the quality of obstetric services, as well as the provision of timely and adequate care for obstetric emergencies, is key in reducing institutional maternal morbidity and mortality –12. ModeAn overview of interventions aimed at improving the performance of health professionals in low-income countries suggests that the simple dissemination of written guidelines is often ineffective, whereas educational outreach visits and audits with feedback are generally effective , 15. AddThe present study built on the previous report of the average treatment effect (ATE) of PRONTO on routine practices. We sought to deepen the understanding of the mechanisms explaining the dilution of the effect by incorporating newly obtained information and a different analytical strategy. Instead of comparing practices performed in treated vs. untreated hospitals as a whole, we focused on the participants who actually received the treatment  to estimate the average treatment on the treated (ATT) effect. The ATT estimation allowed us to approximate how large the total ATE would be if a larger proportion of personnel had taken the training. Additionally, by comparing the effect on the trainees to the effect observed on those who did not actually attend the training but there were working side by side who those who did indeed attend (Average Treatment effect on the Non-Treated or ATNT), it can also provide insights on how well the competencies acquired through training are transmitted among peers in health institutions.Our analysis addressed two key questions left unanswered by the previous ATE analysis of the impact of PRONTO on routine practices during delivery : 1) WhatThe PRONTO simulation-based training, a training course on the management of obstetric emergencies, was developed and piloted in Mexico in 2009. Although the training is not oriented toward the management of normal deliveries, that topic is included in several sessions of the curriculum . A clustData collection began in August 2010, and the follow-up concluded in March 2013 in the 24 participating hospitals. Five field workers were instructed to conduct a structured observation of births from the beginning of the second stage of labor until 10–20 min after the third stage was completed, using a paper-based checklist to collect the data. During each data collection period , the goal was to observe at least 10 vaginal births attended by different providers on different shifts at each hospital over a maximum 5-day observation period. Prior to the observation of births, both the health provider and the laboring woman provided oral consent. The observers were not blinded to the treatment allocation, but they were blinded to the treatment status of individual providers. A more detailed explanation is provided elsewhere .The original dataset analyzed by Fritz, et al. (2017) was a set of 641 observed deliveries . HoweverA uniformization of the providers’ names was performed using the Stata command STRGROUP , which mth provider at the tth stage of the study, for a final analytic sample size of 356 observations, of which 50 corresponded to observations of trained providers, 141 were observations of non-trained providers in treated hospitals, and 165 were potential controls . The study flow chart is included as supplementary material [see Additional file The original dataset of 641 observed deliveries was collapsed by hospital and stage  to a dataset of 310 providers who attended at least one delivery after the time of the training. We summarized the outcomes as the proportion of attended deliveries in which routine practice was followed. The unit of analysis was the iThe main outcomes were the routine practices performed during delivery. Three practices can improve patient outcomes and prevent significant childbirth complications, according to the WHO : 1) ActiThe main explanatory variable was participation in the PRONTO training (yes/no). Important covariates at the individual level were the health provider’s gender, shift, and profession.δ1), which can be understood as the expected difference on the performance of routine practices  between those in the intervention group and those who were trained:The following equation is the mathematical approach used to estimate the ATT effect of the PRONTO training , given that the participant was actually trained. The ATT impact estimators were obtained by comparing the prevalence of practices performed by participants with the prevalence among non-participants who would have taken the training had they been offered the opportunity to do so, the prevalence among this non-participants serves as a proxy of the counterfactual: the prevalence on those who were indeed trained in the hypothetical situation were the training never took place. To accomplish this, and because participants in the training were not randomly selected within the treated hospitals, we matched each trained provider with a non-trained provider from the control hospitals, this procedure relies on the assumption that that conditional on the matched covariates, the trained providers and non-trained providers are exchangeable [tebalance Stata routine. We chose the one-to-one matching procedure, because it provided more than twice the bias reduction (in terms of the average difference in covariates between the groups) yielded by the use of two or more neighbors.In this equation, yangeable . We perfangeable , which wTo estimate the possible effect of PRONTO on those who were not directly trained , we performed an estimation of the ATE for non-treated providers (ATNT), matching those who did not attend the training but were working in treated hospitals to those working in control hospitals, otherwise following the same approach described above. Finally, to test for possible decay (linear trend) of the probability of treated providers performing the examined practices in the post-training period, we fitted a provider-level fixed effects regression model with robust standard errors and clustering correction at the hospital level, including time after training as a continuous variable with time units of four months (time elapsed between observation rounds). All analyses were performed with Stata 14.0 . All relevant data is included in anonymized form as a supplementary file [see Additional file Descriptive statistics on providers who attended the training, providers who did not attend but who worked in treated hospitals, and providers working in control hospitals are presented in Table Approximately 46% of the health providers who attended deliveries in the control hospitals were interns, whereas interns comprised only 10% of the providers attending the training. Most providers taking the training and attending deliveries were either general practitioners (66%) or obstetricians (20%). The proportion of treated providers who were obstetricians was very similar to the overall proportion of delivery-attending providers made up by obstetricians in the control hospitals. No treated residents attended deliveries, whereas 5.5% of the providers attending deliveries in control hospitals were residents. The proportion of treated providers who were nurses was quite low, but it was higher than the proportion of providers attending deliveries who were nurses in the control hospitals (4 vs. 1.2%).We found that providers trained by PRONTO showed significant increases in the probability of performing the complete AMTSL (21 percentage point (p.p.) increase) and especially the first and third steps , as well as skin-to-skin-contact (26 p.p. increase). We also found a negative and significant effect of the PRONTO training on the probability of performing uterine sweeping (26 p.p. decrease)  over time among the treated providers. Most time trend coefficients indicated very close to zero change, except for two practices: uterine sweeping and 1st step of AMTSL, in which changes (non-significant) occurred in the expected direction (negative and positive respectively) Table .Table 4LIn this study, we used a matching procedure to estimate the ATT impact of receiving the intervention (PRONTO training) on the probability of performing routine practices. This approach may allow us to estimate the effect of the intervention if all providers at the trained facilities had attended the program. In a previous impact evaluation of PRONTO, significant changes in the probability of routine practices being performed were found, but it was not known if these were diluted because of the relatively small fraction of eligible health providers who were trained or to what extent there was a spillover effect . Our anaThat no effect was observed for several of the practices, namely episiotomy and fundal pressure, reflects how deeply engrained these practices are in the culture of care and underscores the importance of interventions designed to impact the use of these practices earlier in the providers’ careers, even during medical school. The results also suggest that the previously observed dilution of the effect of the intervention might be more related to the rotation of personnel than to providers returning to their old habits. This suggestion is supported by more than 40% of deliveries being attended by interns, who only spend 2–4 months on the obstetrics rotation.Several previous studies have estimated the treatment effects of a program evaluation approach associated with maternal health outcomes. Kaul et al. used propensity score matching to estimate ATT associated with deliveries at health care institutions for women in India . HabibovRenfrew and colleagues established a comprehensive list of beneficial and harmful practices during intrapartum care . IncludeA group of researchers who conducted a study in Latin America reported that, within institutions, interventions founded in evidence-based medicine were generally underused, while ineffective or even harmful practices continue to be used. This study showed that the intervention, carried out within a randomized trial and relying on a combination of practice and knowledge was significant in terms of changing delivery practices in favor of those based on evidence .Several study limitations related to our analysis must be acknowledged. As the study was not originally designed to examine providers, we were only able to match on a limited number of available covariates. Data on provider gender was missing for a sizeable fraction of the original sample of observed deliveries. We were unable to identify gender in 12% of the original sample, although most of these providers were interns, who constituted only a small proportion of the treated providers. Gender was identified for 100% of the trained sample. In addition, the sample size of providers who actually received the training was quite small, which reduced the power of the study. Nonetheless, this sample size was large enough for us to detect significant effects for most of the indicators examined.The analysis of the impact of PRONTO training on providers yields new insights on the true efficacy of this approach and, through comparing the estimated effect on the treated vs. that on the non-treated providers, the extent to which knowledge and abilities acquired through this training are disseminated within hospitals. It also further highlights the importance of continuous, ongoing training to counter the dilution effect, which is probably caused by the rotation of personnel within health institutions in Mexico, to ensure the provision of quality maternal and newborn care.Additional file 1:Study Flow Chart. The process from hospital-level randomization leading to the provider-level analytic sample is described in this chart. (DOC 41 kb)Additional file 2:Provider-level dataset. This dataset includes all relevant anonymized information of the study participants’ as well as the birth observation variables during baseline and follow ups. (XLS 169 kb)


Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model. Recently, location-based services such as real-time tracking, security alerts, informational services, and entertainment applications are becoming important in wireless communication infrastructures. Global positioning systems (GPSs) are the most commonly used outdoor location sensing technology . HoweverUWB systems use an exceedingly wide band of the radio frequency (RF) spectrum to achieve higher temporal resolution and robustness to multipath fading ,4. AlthoReceived signal strength indicator (RSSI)-based techniques for WLANs have been proposed for localization services ,6. RSSI-To improve WLAN localization performance, several studies have, over time, investigated how to distinguish between LOS and NLOS using handcrafted features by a series of CSI ,13,14,15In this paper, we propose a new LOS and NLOS classification method for WLANs based on RSSI and CSI. The proposed convolutional neural network LSTM (CNNLSTM) model combines the advantages of a CNN by reducing the variance of CSI data and the ability of LSTM in modeling long-range dependencies of sequential data in a unified framework. Compared to the LSTM model , the proThe rest of this paper is structured as follows. In Deep learning was constituted by neural network models with deep hidden layer architectures . A deep A CNN was developed by preserving the spatial structure of the input data for object recognition tasks such as handwritten digit recognition , computeAn RNN is specially developed to solve problems related to sequential data such as language modeling  and speeA CNNLSTM model is designed with both spatial and temporal features in mind by combining convolutional layers for latent feature extraction on input data and LSTM layers to support sequence prediction ,29,30. IIn this section, we consider a system model and experimental data for commodity WLANs, where a receiver obtains the RSSI at each transmission and estimates the frequency-domain CSI of the subcarriers.L is the number of multipath taps. The frequency- domain CIR for the N is the fast Fourier transform (FFT) size, K is the fast Fourier transform (FFT) size, K is the number of used subcarriers. At the receiver, the channel state information (CSI) for the Let deled as (1)H(k)=ce of N0 .In IEEE 802.11 WLANs, RSSI is provided for upper layer information. At each transmission, the RSSI is used as an indication of the received power level. RSSIs for the LOS condition are concentrated at a high value, while RSSIs for the NLOS condition are distributed over a wide range .For performance comparison with the previous result, we exploited data collected at Seoul National University . Figure The measurement took 4300 s to complete. During the measurement campaigns, the transmitter sent sounding packets every 10 ms and the receiver measured RSSI and CSI per packet transmission. For signal transmission, IEEE 802.11n protocol with a 20-MHz bandwidth was used, and therefore, total To classify LOS and NLOS in WLANs, we propose a novel CNNLSTM model. L convolutional layers, and one output vector with size U), etc. . Withoutussed in .L layers and we finally stress out the data to a vector with size As shown in p, the equations below describe the internal structure of the LSTM module:The RNN model is composed of LSTM modules and an output layer for classification. The input vector for the LSTM module is defined as P is the number of packet transmissions. The output b is a bias factor. In(9), the sigmoid function Finally, for the NLOS and LOS condition decision, we put the feature vector N is the batch size of model, We set To minimize the loss function, many variants of the gradient-descent method such as AdaGrad, AdaDelta, and Adam optimizers have been studied. These optimizers adaptively change the learning rate to properly minimize the loss function. In this study, we applied the Adam optimization algorithm to train our proposed CNNLSTM model as the Adam optimizer is straight-forward and saves memory and computational resources.The process starts with random initialization of all the model parameters. During the training phase, the weight update takes place after a whole sequence has been propagated forward through the network. The error signals are calculated with respect to the Mean of Cross Entropy Losses cost function. The loss function was chosen as the natural cost function for the sigmoid output layer with the aim of maximizing the likelihood of classifying the input data correctly.Once every parameter in the proposed CNNLSTM model is adjusted appropriately, the model can identify the channel condition based on the following simple hypothesis testIn this section, we will discuss the results of the proposed scheme using CSI and RSSI data with a total 100,000 packet transmissions. We assess several numbers of packet transmissions, The model was trained by truncated backpropagation through time  with AdaL of convolution layers to extract the implicit features of data. We applied the CNN models with a different number of In the CNN segment, we used hybrid hyper-parameters settings for the CNNs with high numbers, In the RNN segment, we also set different the number of units in LSTM layer, X symbol in the figure.P for the test set. It can be seen that the best outcome was obtained at P is increased to 100, the performance shows a decreasing trend for all models. Hence, in the results shown below, we use In this paper, we also implemented CNN methods and compared them with the conventional method LSTM method. The CNN model was also optimized similarly to the proposed CNN segment, except here, instead of the LSTM layer, we added the Flatten layer after the final CNN layer. The LSTM model only learns time relative sequence information whereas the CNN model focuses on extracting implicit features that contain space information, while our proposed model offers both of these advantages. P. Note that CNNLSTM* denotes a model that applies the absolute values of CSI data. It offers slightly better performance than the LSTM and CNN models while using simpler data, so we can consider it as a choice when generating data from practical instruments. The input signal for the CNN segment in this case can be written asIn To further understand what is inside the model, we analyze the internal representations of the trained network. Following the training procedure, the hidden state vector of the last LSTM module was used to visualize the trained network. In this paper, we proposed a deep learning model to identify channel conditions by combining CNN and RNN. In the proposed CNNLSTM model, the CNN captured the feature from frequency-domain characteristics of CSIs and then LSTMs extracted the temporal feature from RSSI and the output of CNN. In addition, a CNNLSTM model with the absolute value of CSIs was proposed to reduce the complexity with slightly better performance than the conventional models. The proposed methods were verified under indoor environments for WLANs and achieved higher accuracy than the conventional LSTM model in classifying LOS and NLOS. In future work, we would like to investigate the performance of the proposed CNNLSTM models in outdoor environments to expand the range of applications of the algorithm.


Microwave-assisted sintering materials have been proven to deliver improvements in the mechanical and physicochemical properties of the materials, compared with conventional sintering methods. Accurate values of dielectric properties of materials under high temperatures are essential for microwave-assisted sintering. In view of this, this paper, proposes an on-line system to measure the high temperature dielectric properties of materials under microwave processing at a frequency of 2450 MHz. A custom-designed ridge waveguide is utilized, where samples are heated and measured simultaneously. An artificial neural network (ANN) trained with the corresponding simulation data is integrated into this system to reverse the permittivity of the measured materials. This whole system is tested at room temperature with different materials. Accuracies of measuring dielectric property with an error lower than 9% with respect to theoretical data have been achieved even for high loss media. The functionality of the dielectric measurement system has also been demonstrated by heating and measuring Macor and Duran ceramic glass samples up to 800 °C. All the preliminary experiments prove the feasibility of this system. It provides another method for dielectric property measurement and improves the understanding of the mechanism between microwave and media under high temperatures, which is helpful for optimizing the microwave-assisted sintering of materials. They obtained LiBa1−xPO4:xTm3+ phosphors with more uniform grain size distributions and enhanced emission intensity [Considerable interest has been drawn to the processes of materials sintering by microwave power and many potential merits have been recognized in applications ,3,4,5,6.ntensity . Leonellntensity .Unfortunately, microwave processing materials usually encounters well-known issues like hot spot and thermal runaway ,12. ReseResearchers have investigated the designs of high-temperature permittivity measurement systems. M. Arai et al. have developed two techniques to obtain permittivity measurements up to about 1200 °C ; G. F. GIn view of these disadvantages, some researchers proposed to heat the measurement device filled with samples, raising the temperature of tested material by heat conduction ,20,23,24D. Couderc  and José10 at 2450 MHz) was utilized, where heating and measuring were performed simultaneously. An artificial neural network trained with the corresponding simulation data was used to reverse the permittivity. The whole system was tested at room temperature with different materials. Measurements on dielectric properties of ceramic and glass were also performed in order to evaluate the performance of the setup under high temperatures. The preliminary experiments prove the feasibility of this system.This paper, to reduce the complexity of dual operation, proposes the transmission/reflection method to determine the dielectric properties of materials under high temperatures in real time, due to the system using transmission/ reflection method is simpler, easy-controlling and more accurate . A custoRidge waveguide can focus the electromagnetic energy in a small space between two ridges, which makes it capable of heating the sample quicker and more sensitive to the permittivity change of the material under test (MUT). Moreover, since the space between two ridges for placing MUT is narrower, this design needs less sample amount in each measurement. Therefore, a ridge waveguide (based on the BJ22 waveguide) is designed and chosen here as the heating and measuring part. Its schematic photo is shown in Other two cut-off waveguides are introduced on the side surfaces of ridge waveguide for the observation of sample change during the heating. The dimensions and positions of access holes in the waveguide are designed to ensure that they do not disturb the field inside and to prevent microwave leakage. The thickness of metal walls is 2 mm.Success of permittivity measurement depends upon the theoretical model accurately relating the measured quantities to the complex permittivity of the sample. However, since the custom-designed ridge waveguide is not a standard element, the theoretical relation between measured S parameters and complex permittivities for waveguide is not reliable any more here. Modern approaches extend into modeling and an optimization technique based on artificial neural networks (ANNs) ,30. ThisThe models of this ridge waveguide with different dielectric inclusions in a cylindrical quartz tube were simulated at 2450 MHz to obtain the corresponding S parameters.It may encounter the problem of multi-value when using ANN to do the inversion: different complex permittivity corresponds the same or very close S parameters. This will lead to wrong measured results. To solve this problem, the ridge waveguide model was optimized to get one-to-one correspondence from complex permittivity to S parameters. With the simulated data, a two-input-two-output ANN was constructed to create the relation network between The input of the network is an N × 2 matrixIt contains all the values of simulated reflection coefficients and transmission coefficients at frequency of 2450 MHz. The output of network is thus the N × 2 matrix of corresponding formulas ,35:(2)εIn the formula, Normally, for a given input cell −3. To check the reverse error for certain inputs, random data  were also picked to reverse their corresponding complex permittivity. Parts of the original and reversed complex permittivity are demonstrated in The errors depend on the training method as well as on the number of hidden neurons. To optimize the network and minimize the errors, the number of neurons was determined by using a simple trial-and-error process and the backpropagation technique. It appeared that the network with 10 or more hidden neurons generated very acceptable results. With the numerical testing, the parameters for training the network were chosen like those shown below in This solution has been programed. A module of data collection was coded and applied to simultaneously acquire the information of temperature and S parameters. Integrating the ANN code into this data collection module, we programed a graphical user interface (GUI) interactive interface to realize auto measurement, making this system friendly to users.The accuracy of reversed dielectric property reached by the transmission /reflection method with a custom-designed ridge waveguide must be evaluated first. To verify the accuracy of the tested dielectric property, a number of different samples in wide range of dielectric constants and losses were measured at room temperature with the measurement system described above. All the samples were filled in a 2.2 mm thick quartz tube with inner diameter of 18 mm in the experiments. Results were compared with the theoretical ones or those measured by other researchers. The comparisons are tabulated in The easy-to-access analytical pure reagents like methanol, ethanol and different concentrations of methanol-ethanol mixtures and methanol-n-butanol mixtures were measured separately at room temperature. All the measured data were compared with those obtained by others  and listIt is obvious that the accuracy of the ridge waveguide system is quite good, with the maximum discrepancies between theoretical and measured results less than 9% even for high loss materials. It seems that the accuracies of measurements for dielectric constant larger than 14 are respectively higher, while those for the dielectric loss show a wavy tendency. One can also observe that the estimation of dielectric properties of single reagent is better than that of reagent mixtures. Researchers reported that the conventional mixture formulas cannot describe the dielectric property of mixed reagents appropriately, since the mixture may present an anomaly in its dielectric property ,39. TherMany other factors may cause the measured errors. During the experiment, we found that the heights of samples were strongly related to the measured S parameters (dielectric property). The higher the loss tangent is, the stronger dependence of S parameters (dielectric property) is on the height of sample. Therefore, one of the main source introducing errors in this measurement system may lay on the small variation of height of sample in experiments. Another main error source may be the trained ANN. Normally, there exist difference between the original data and the reversed data obtained by the trained ANN, due to the non-infinite data used for training. This is already revealed in the last section. Different range of permittivity may need different density samples to train the network to guarantee the reverse accuracy. This partially expresses why the measured accuracies for different dielectric constant and loss factor ranges alter.The accuracy of permittivity measurement at high temperature by this system is estimated to be very close to that of the measurement at room temperature, because the thermal expansion of the testing waveguide is negligible due to the thermal cooling and the rapid measurement. Besides, it is reported that the dielectric property of quartz tube remain practically unaltered under 1000 degrees ,40, whicThe final goal of this paper is to propose an on-line high-temperature dielectric property measurement system. Its feasibility of permittivity measurement under dynamic temperatures must be checked. The temperature dependence of dielectric property of Macor glass and Duran glass samples were measured in this section to test the performance of the method aforementioned. An infrared temperature sensor was used to measure the temperature changes of the samples through the top hole. It is difficult to obtain the relatively accurate bulk temperature of the sample by multi-point measurement in microwave environment. Since the sample size is very small, its temperature distribution under conventional heating is also relatively uniform, as that under microwave heating. The bulk temperature of media with microwave heating is thus calibrated with the measured results of the conventional heating. Samples having similar thermal properties were heated by conventional heating, with the top surface of the sample exposing to the ambient. Three thermocouples were employed to measure the temperatures of the top surface, side surface and the center of the sample. The temperatures of side surface and center were averaged to obtain the bulk temperature of the sample since the sample size was small and its temperature distribution was approximately uniform. A relation between the bulk temperature and the top surface temperature under conventional heating was established, which was then applied to reverse and estimate the bulk temperature of a sample with the top surface temperature under microwave heating. Rod-shape Macor glass sample and Duran glass fragment sample were respectively filled in a quartz tube and placed between two ridges. The measured consequences are compared with those obtained by M. Arai et al.  and JoséIt is seen from To double check the system, Duran glass fragment was employed to measure its relative permittivity, since we cannot find the solid rod Duran sample. The black lines in The main reason to cause the discrepancy is that the sample used in our paper is Duran glass fragment, while that in the reference paper is in solid rod. The sample is in fact a mixture with majority of glass fragment and a little of air. According to the Bruggeman formula, their dielectric property is between the pure Duran glass and air, which accords very well the comparison shown in In both experiments, no size changes in the dielectric samples were observed during heating. Therefore, the accuracy of dielectric calculations, as stated in room temperature measurement section, is estimated to be less than 9% for the dielectric property in the entire measured range.In this paper, to obtain the dielectric property variation of microwave sintering materials, an on-line high temperature dielectric property measurement system at frequency of 2450 MHz is successfully established. This system mainly consists of two parts: a ridge waveguide where samples are heating and measuring simultaneously; a trained artificial neural network (ANN) for reversing the dielectric property of measured samples. A module of data collection was integrated into this system to realize auto measurement, making this system user friendly. The functionality of the dielectric measurement system has been demonstrated by heating and measuring two different glasses ceramic samples up to 800 °C. Accuracies of the permittivity obtained by this ridge waveguide based system have been verified by comparing measured results of different materials with those acquired by other researchers.This system provides another way to measure the dielectric property of materials at high temperature, which may help improve the understanding of the mechanism between microwave and media, design microwave devices for materials sintering and facilitate comparisons with conventionally heated materials.