Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis models that have been developed for classifying various fault types in gearboxes. However, the classification results of the conventional fault classification models degrade when they are applied to gearbox systems with multi-level tooth cut gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in discriminating the gear fault types. Due to the improved computational capabilities of modern systems, the application of deep neural networks (DNNs) is getting popular in a variety of research fields, such as image and natural language processing. DNNs are capable of improving the classification results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this research, an adaptive noise control (ANC) and a stacked sparse autoencoder–based deep neural network (SSA-DNN) are used to construct a sensitive fault diagnosis model that can diagnose a gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicatedness. An ANC is applied to gear vibration characteristics to remove a significant level of noise along the frequency spectrum of vibration signals to fix the most fault-informative components of each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault-informative components to separate the fault types considered in this study. Furthermore, the implementation of the SSA-DNN is substituted for feature extraction, feature selection, and the classification processes in traditional fault diagnosis schemes by high-performance unity. The experimental results show that the proposed model outperforms conventional methodologies with higher classification accuracy. Different types of gearboxes are used in various equipment such as vehicles, industrial machinery, and electrical generators. However, they are prone to defects due to harsh and continuous working conditions. Gear defects can lead to damage of the gearbox system and become a root cause of damaging the whole mechanical device, which may lead to serious economic losses and the threat of personal safety. Hence, the condition monitoring of gearboxes is essential, and it would be beneficial if the gear defects in gearboxes can be detected in the early stages. The general non-destructive method for condition monitoring of gearboxes is based on sensing the vibration characteristics which contain the fault-related components . The comRecently, many digital signal processing techniques have been developed by researchers that can be applied in different domains  by employing a variety of advanced approaches such as Fourier transforms, short-time Fourier transforms, Hilbert transforms, wavelet transforms, Hilbert-Huang transform-based empirical mode decomposition ,12,13,14Considering the feature engineering and classification processes, the traditional gearbox fault diagnosis methods include feature pool configuration (feature extraction and feature selection) and fault classification by machine learning algorithms such as k-nearest neighbors (k-NN), support vector machines (SVMs), and artificial intelligence networks (ANNs) ,20,21,22The major contributions of this study are summarized as follows: (1) an adaptive noise control approach is designed for de-noising and preserving fault-related elements of raw vibration signals to obtain the optimized subbands on its outputs which mostly contain the essential informative components of vibration signals, and (2) the SSA-DNN utilizes the optimized subbands for identifying the MTCG defect types. The efficiency of the proposed model is evaluated by applying it to the vibration dataset collected from the MTCG gearbox that contains signals collected under six levels of tooth cut fault, such as 6.6%, 10%, 20%, 30%, 40%, and 50% cut as well as signals collected under normal operating conditions. The experimental dataset was collected under variable shaft rotating speeds, such as 300 RPM, 600 RPM, 900 RPM, and 1200 RPM, respectively. The results demonstrate the improved fault classification performance in comparison with the existing models. The rest of this paper is organized as follows. k-th meshing frequency harmonics , installation defects , and defects caused by long-term operation . In this work, the MTCG defects were created to simulate the operated defects as the multi-level depth of a tooth cut seeded in the gear wheel of the gearbox system. The vibration characteristics of a gearbox system are analyzed in the cases of a healthy gear (a defect-free gear) and a defect gear for identifying the informative fault-related components in the vibration signal. The vibration signal of a defect-free gear represents a linear and periodical signal that is calculated using the following formula :(1)ynt =Compared to a vibration signal of a normal gear, a signal of a defected gear is more complex due to the occurrence of impulsive vibrations when the motion is transferred from the DS to the NDS by rotating a pinion wheel through a gear wheel at a defective tooth position during one rotation cycle. Those periodical impulsive vibrations create the non-linear and non-stationary vibration signal formed as the amplitude and phase modulation signal in the point of view in the signal processing zone . The faui-th sideband, respectively, roundly k-order meshing the frequency tone of the vibration signal Here, The proposed sensitive and speed invariant model for diagnosing gearbox faults is presented in 1.Generating the reference signal to supply to the reference input of an ANC:ANC is a signal processing method used for reducing noise and preserving the fault-related informative elements in gearbox vibration characteristics. The ANC approach is a self-constructed and time-varying system that uses a recursive algorithm for optimizing its parameters for obtaining the desired optimized signal in its output . GeneralMainly, there are two types of noise present in the vibration signal: white noise and band noise. Hence, the reference signal generator creates the output signal behavior which is homologous with those such as Gaussian signals and white noise signals, as illustrated in requency . Concret(1)the mean value (2)the standard deviation of the Gaussian windows is selected in the following range:Also, the number of sideband segments fined in , first, By limiting the adjusting values of the coefficient quencies . 2.The construction of an adaptive filter0, c1, …, cN-1]T) and a least mean square (LMS) adaptive algorithm. The reference signals are used as the input to the digital filter and its output signals are summed with the vibration subbands to calculate the output error signals. Based on this error, the LMS adaptive algorithm tunes the coefficient vectors according to the convergence criterion of the least mean square error for determining the optimal coefficient vector (c0) and then identifying the local optimal subbands. The operation of an adaptive filter is functionally described in The adaptive filter is formed by combining the N-tap FIR digital filter (the coefficient vector as c(n) ≡ [c3.The optimization process for selecting the optimal vibration subbandEach vibration subband, processed by an adaptive filter with the input reference of a parameter- adjustable Gaussian reference signal, results in many subbands in its output  corresponding to the set of specific values of parameters and appropriate optimal coefficient vectors. At this step, the ANC selects the subband which has a minimum mean squared value as an output result of the optimization process (termed as an optimized subband) illustrated in In fact, the signal portions, which reflect the gear states , are represented mostly in the frequency domain as magnitudes, tones amplitudes, oscillations, frequencies, and the ratios between them. Thus, it is suitable to use the frequency spectrum of the optimized subband as the input data to the SSA-DNN so the deep network can explore and automatically extract the defect characteristic features from its inputs. Additionally, the usage of the frequency spectrum of the vibration signal reduces the complexity of the DNN. Therefore, in this paper, the frequency spectrum of the optimized vibration subband calculated by Fourier transform  is used N dimensions) is encoded to a lower-dimensional space K dimensions), producing the output vector known as a latent space. The encoder function or the latent space can be represented as follows:A stacked autoencoder is a type of DNN, with a number of hidden layers greater than one, formed by stacking simple autoencoders for feature discrimination and classification. To understand the concept of a stacked autoencoder, a simple autoencoder should be discussed first. It is an unsupervised DNN based on a three-layer symmetrical architecture for learning the representation of high-level data . An autoHere ollowing : (11)Ls,In this paper, the feature engineering and classification path of the sensitive and speed invariant gearbox fault diagnosis model is constructed by stacking multiple sparse autoencoders as a stacked sparse autoencoder (SSA) for determining the small differences of features between gear defect types which are the basis components for improving classification accuracy. In the next subsection, the sparse autoencoder algorithm is explained.Sparsity is a special parameter of autoencoders, which puts a constraint onto the hidden layer and causes activation of inactive hidden units to discover the tiny differences in decimated features of data representation more sensitively and robustly than the simple autoencoder architecture . The conIn Equation (12),  follows : (13)ϕweThis Equation (14) is known as Kullback-Leibler divergence . ΦsparseTo establish the SSA, several numbers of sparse autoencoders, which have been individually trained, are stacked and positioned in a form such the input layer is placed before the series of hidden layers, and a SoftMax classifier  represenTo validate the effectiveness of feature engineering and classification by the SSA-DNN in the proposed model, we perform a set of four experiments listed in The parameters of the DNA play an important role in classification performance, so that the tuning process for selecting the optimal values has to be performed . To consThe length of the recipient input is the size of a single sample which is inputted to the DNA, it is also known as the value of higher-dimensional representation of the input layer. According to , this paSimilarly, the number of neurons in the hidden layers also influences the performance of the DNA. Although there are no exact guidelines for selecting the number of neurons for a hidden layer of an autoencoder, this parameter directly impacts the process of feature extraction. Based on the functionality of the autoencoder, the number of nodes in the first hidden layer has to be lesser than the length of the recipient input for compressing the higher-dimensional data. To adjust the parameters of node number and sparsity, in this paper we create a fine-tuning dataset which is formed by randomly picking 100 data instances corresponding to each class under each rotation speed condition. Hence, the fine-tuning dataset consisted of The sparsity penalty can be used for improving the forward learning process of an unsupervised autoencoder, whose purposive activity orients to manifest the highly representative features. To evaluate the effect of the sparsity penalty, the reconstruction error is mostly considered for the experiment the value of sparsity penalty parameter in the first autoencoder (the first hidden layer is selected with number hidden nodes as 3000). The number of hidden layers plays an important role in the learning process. There exists a general opinion that a higher number of hidden layers results in better accuracy, but also reduces the generalization ability of the network . In thisThe complexity of computation of the architecture, in general, can be measured as an average time required for one training cycle of DNA. Through the experiments in the previous subsection, it was observed that with the increase of DNA architecture complexity, the reconstruction error was getting smaller while the time needed for training the deep architecture was increasing. However, from The main function of the ANC is to perform noise reduction and to preserve the fault-related useful components existing in the vibration signals. To collect the informative content of the vibration sample, where the content represents numerous fault-related components that are useful for designing the sensitive fault diagnosis model, the analog signals from the vibration acceleration sensor were digitized with a high sampling frequency of 65,536 Hz every one second. Thus, a 1-sec length data sample is used to monitor several rotation cycles  to collect fault-related vibration characteristics with some special oscillations. After data collection, the digitized vibration samples were filtered by a digital low-pass filter with the cut-off frequency of 10,000 Hz accompanied with the down-sampling process to remove the high-frequency components  which are out of operation range of the acceleration sensor, and to preserve the vibration components with intrinsic fault-informative features following realistic operation of a gearbox system. That is the first step for preprocessing data to remove the redundancy in the raw vibration signals. The vibration subbands output from a low-pass filter are inputted into the ANC module for a fine-optimizing process for noise reduction. In the range of the frequency spectrum less than 10 kHz, the ANC uses adaptive windows to access and remove white noise and band noise remaining between two consecutive sideband frequencies along the frequency spectrum. These results are presented in These results can be observed because many fault-related components stay hidden in the background noise which can only be detected by the application of signal processing methods. Regarding model 4, the raw 1-sec vibration signal with 65,536 points was firstly down-sampled by four times with a 10 kHz low-pass filter integrated for antialiasing to obtain the vibration subband with 16,384 data points. Then this subband is segmented in series without overlap by using windows of 1024-point size to attain sixteen segments of 1024-point vibration subbands. Then, each 1024-point window containing the vibration subband is transformed from the time domain to the frequency domain by FFT to obtain a 513-point sized vibration frequency spectrum. This process was repeated eight times by randomly picking eight segments of 1024-point vibration subbands from sixteen segments. These spectrums were stacked to form the 513 × 8 grayscale image corresponding to each raw 1-sec vibration sample. This image was later converted to a binary image by an 8 × 4 sized filter and the threshold (0.7). Hence, the binary image containing 4014 components in the frequency domain was used as the input to the ANN with three layers  for classification. The fault classification results of model 4 on the dataset used in this paper were about 45.17% ± 6% during four experiments. By analyzing the experimental results of the referenced models, it can be seen that the sensitive and speed invariant fault diagnosis model proposed in this study outperformed their fault diagnosis performance with results around 97% ± 2% during four experiments showing small accuracy deviations when alternating the shaft rotational speeds of the MTCG gearbox system.Additionally, to verify the stability of the proposed algorithm, the experiments described above have been performed five times. The classification accuracies and their averages computed over five experimental trials are presented in Controlling the noise embedded in the vibration signals is essential for the sensitive detection of multi-level cut tooth faults in gearbox systems. The presence of a high noise level can cause misidentifications of fault types and thus reduce the fault classification accuracy. Noise reduction is a complex problem, and it is not always possible to completely resolve this issue by signal processing or feature engineering techniques. Therefore, simultaneous usage of the ANC and SSA-DNN methods is an efficient approach for significant noise reduction while preserving the original fault-related information of the gear vibration characteristic, which is useful for fault identification. The design of a sensitive and speed invariant model requires exploration of the representative features that can be used for discrimination of multi-level tooth cut gear defects and maintaining its reliable performance under the operating speed fluctuation conditions in the gearbox system. In general, the manual feature extraction methods cannot satisfy those requirements, thus the unsupervised approaches based on deep neural networks are well-suitable for extracting the latent representative features by the process of minimizing reconstruction errors during the operation of a back-propagation algorithm in the DNA. The SSA-based DNN constructed in this research satisfies the requirements for constructing the proposed model, such as extracting the representative feature space, selecting the most defect-related useful features for classification, and finally, achieving high fault classification results.This study presents a novel method which combines an ANC and an SSA-DNN to utilize their advantages for constructing a sensitive and speed invariant fault identification model for gearbox systems with multi-level tooth cut gear defects. The ANC technique is created based on the analysis of vibration characteristics of a gearbox system to generate the speed-dependent reference window signals with adjustable parameters, according to the noise types presenting in the raw vibration signals. Then, these generated window series were adaptively adjusted to access the space between two consecutive defect-related frequency tones and remove the noise along the whole frequency range of vibration signals. The ANC optimizes the input vibration signal for outputting the optimal subband which contains mostly the defect-related frequency tones with the integration of low-level background noise. Then, the frequency spectra of these optimal subbands are used as the input to the deep network architecture. This network is built up by stacking sparse autoencoders as the hidden layers of the network and using a Softmax activation function at the output layer for extracting latent representative feature spaces and selecting the most defect-related discriminative features for identifying the multi-level tooth cut fault types under the condition of various shaft rotational speeds. The effectiveness of the proposed model is validated by experiments performed using the vibration dataset containing MTCG gearbox defects collected under four different rotational speeds. To validate the property of speed invariance for the proposed model, the experiment was arranged as four sub-experiments using the datasets corresponding to each rotational speed. Each sub-experiment uses a one-speed dataset to construct and train the model. Then this given model is used for fault identification using two datasets collected under other speed conditions. This procedure was performed four times using the different speed datasets for building the model in each. The average classification result achieved over four experiments was 97%, which outperforms the techniques used for comparison. Moreover, the classification results shown by the proposed model did not fluctuate significantly (2–3%) when applied to different speed datasets, which evidences that the prosed model is speed invariant and can be used for identifying multi-level tooth cut defects in a gearbox system under varying rotational speeds.


The Single Europe Sky Air Traffic Management Research (SESAR) program develops and implements innovative technological and operational solutions to modernize European air traffic management and to eliminate the negative environmental impacts of aviation activity. This article presents our developments within the SESAR Solution “Safety Support Tools for Avoiding Runway Excursions”. This SESAR Solution aims to mitigate the risk of runway excursion, to optimize airport operation management by decreasing the number of runway inspections, to make chemical treatment effective with respect to the environment, and to increase resilience, efficiency and safety in adverse weather situations. The proposed approach is based on the enhancement of runway surface condition awareness by integrating data from various sources. Dangerous windy conditions based on Lidar measurements are also discussed as another relevant factor in relation to runway excursions. The paper aims to explore four different data mining methods to obtain runway conditions from the available input data sources, examines their performance and discusses their pros and cons in comparison with a rule-based algorithm approach. The output of the SESAR Solution is developed in compliance with the new Global Reporting Format of the International Civil Aviation Organization for runway condition description to be valid from 2020. This standard is expected to provide concerned stakeholders with more precise information to enhance flight safety and environmental protection. The European Air Traffic Management (ATM) system plays an important role in both European and global aviation, handling around 26,000 flights daily . NeverthOne of the SESAR Solutions is Pj03b-06, named “Safety Support Tools for Avoiding Runway Excursions”. Excursions are among the most frequent runway safety accidents. According to the International Air Transport Association Safety Report, runway excursions caused 22% of all accidents over the 2010–2014 period [The new regulatory framework of the International Civil Aviation Organization (ICAO) requires Airport Operators (AOs) to report runway surface conditions to flight crews in a standardized manner  in order to determine aircraft take-off and landing performance more accurately. AOs will have to assess the runway surface condition by using a Runway Condition Assessment Matrix (RCAM). As a result, AOs will assign a Runway Condition Code (RWYCC) to each third of each runway at the airport, based on the type, depth and coverage of water or contaminants. The established link between the runway surface conditions and the aircraft performance resolves the current problem of missing reference for friction measuring devices.Our study attempts to address both the topic of the runway excursions and the implementation of the new reporting format in the framework of a novel approach, in order to increase the aviation safety and environmental protection. The new conceptual method involves the integration and processing of various input data sources, which results in an estimation of the RWYCC as a main output. One of the major novelties of our method is that the traditional approach of numerical modeling  is replaced/supplemented by models based on data mining (DM) methods. We compared the performance of four different DM methods to calculate the RWYCC. Strong wind is another contributing factor to runway excursions along with the runway surface condition, represented by the RWYCC. Therefore, we also applied DM methods to handle the automated detection of wind shear based on similarities between the current Lidar 3D field of radial velocities and a conceptual model of microbursts. The motivation of this effort originated in the development of a fog prediction model based on DM , which hIn 2008, the ICAO Friction Task Force commenced its work to address the shortfalls in the accuracy and timeliness of the assessment and reporting methods provided for in the present ICAO provisions and guidance material. Their goal was to develop provisions for the reporting of runway surface conditions and develop guidance on the operational requirements for airplane performance and for the assessment of runway surface conditions, including friction level. This resulted in the introduction of the new GRF transitioning from assessing the surface friction characteristics to runway surface condition assessment with consistent relation to aircraft braking performance . A fundamental change in the GRF is the introduction of the RWYCC—a code number describing the runway surface condition—reflecting the runway braking capability as a function of the surface conditions. The RCAM see  is a bas1)An agreed set of criteria used in a consistent manner for runway surface condition assessment, aeroplane (performance) certification and operational performance calculation;2)a unique RWYCC, linking the agreed set of criteria with the aircraft landing and takeoff performance table, and related to the BA experienced and eventually reported by the flight crews;3)reporting of the contaminant type and depth that is relevant to take-off performance;4)a standardized common terminology and phraseology for the description of runway surface conditions that can be used by AO inspection personnel, air traffic controllers (ATCOs), aircraft operators and flight crew; and5)globally harmonized procedures for the establishment of the RWYCC with a built-in flexibility to allow for local variations to match the specific weather, infrastructure and other particular conditions. The concept of the RCR is premised on:The scope of the change induced by the implementation of the GRF is illustrated by the fact that at least ten ICAO documents have been amended in the discussed context: Annex 3, Annex 6, Annex 8, Annex 14 Volume I, Annex 15, Doc 9981, Doc 10066, Doc 4444, Doc 10064 and Doc 9137 [The SESAR Solution may be divided into two distinctive parts: the airborne and the ground-based parts. The validation exercise related to the first, airborne part was conducted by Dassault with the support of the Direction des Services de la Navigation Aérienne (French Air Navigation Service Provider). Since one of the key topics of the current paper is related to the second, the ground-based part of the SESAR Solution, the next paragraphs will introduce it in more detail. The Runway Condition Awareness Management System (RCAMS), developed within this research, supports the staff responsible for runway assessment and maintenance. Unlike the current method of discontinuous visual inspections of runway conditions, the RCAMS enhances the awareness of runway conditions in a non-disruptive and continuous (uninterrupted) manner. It contributes to an optimization of airport operation management by decreasing the number of runway inspections, supports decision making for effective chemical treatment and increases resilience and safety in adverse weather situation. Polish Air Navigation Services Agency and Warsaw University at Gdansk Lech Walesa Airport,Air Navigation Services of the Slovak Republic and MicroStep-MIS at Poprad–Tatry Airport,Aéroports de Paris at Paris Charles de Gaulle Airport.The ground-based part of the SESAR Solution  was validated by three different partners at three different sites:The RCAMS validation exercises were supplemented and enriched by an On-board Braking Action Computation System (OBACS) prototype developed and validated by Airbus. In order to cover all 13 validation objectives allocated to this SESAR Solution, a workshop was organized to enhance the AO awareness for cases when the runway surface condition presents a critical status, which may impact airport operations. Moreover, the workshop involved Airspace Users and ATCOs to define runway excursion risk alert mechanisms and the alert interoperability with flight crews’ alerts.1)Sensors and data acquisition: The system acquired the data necessary to run the calculation of the RWYCC. The available Computed BA reported by OBACS was used as a potential downgrade criterion for the calculated RWYCC.2)Processing algorithms: The collected data were processed to calculate the RWYCC.3)Prediction: The current runway surface condition and the output from both the runway surface condition model and the numerical weather prediction model were processed to calculate the predicted RWYCC.4)Interpretation of outputs: Human–Machine Interfaces (HMIs) presented relevant information to stakeholders: AOs  and ATCOs .The RCAMS provides the automatic means for the AOs to continuously assess a RWYCC on each third of each runway of the airport. One RCAMS platform was installed and validated at Poprad–Tatry Airport, which is a regional single runway airport located in the north of Slovakia. Due to its location near to the highest mountains of Slovakia and elevation exceeding 700 m above the sea level, this airport provides a wide range of adverse runway conditions. The validation exercise of the RCAMS at Poprad–Tatry Airport addressed four system components:As a main means of validation, the RWYCC computed by the system was compared to the manually recorded RWYCC value based on disruptive runway inspection and friction measurement summarized in the SNOWTAM message . The conThis paper processes data from a SESAR validation exercise period from mid-February to April 2018, and also from the following winter season (November 2018–April 2019) at Poprad–Tatry Airport. The collected dataset consists of data of different origins, including data from Runway Surface Condition sensors, meteorological observations, and the previous SNOWTAMs containing information about the runway surface condition based on the manual inspection of the runway. In order to compute the RWYCC using DM models, we derived the RWYCC as a target attribute from human observations in SNOWTAMs. The observed contaminant type and depth was translated in compliance with the GRF described in The basic tool for the implementation of the GRF is the RCAM . HoweverThe baseline for the calculation of the RWYCC by a rule-based algorithm is built on the last available observation of the contaminant type and depth and the resulting RWYCC (an application of the RCAM on the observations). The reliability of this information decreases with time. Therefore, the persistence of the last observed runway condition (and the related RWYCC) is continuously crosschecked by data from the sensors. An important RCAMS component for runway condition monitoring is a pair of passive and active sensors embedded into the runway at each third of the runway. From passive sensors, the measurement of water film height is used as the first criterion to confirm the persistence of the last observed runway condition or to indicate the trend associated with the information regarding the sensor contaminant type. Since the runway built-in sensors provide only point measurements at three specific locations on the runway, they lack information about spatial variations. Moreover, sensor capabilities are limited from the aspect of full RCAM scope and reliability of measurements .Therefore, the next criterion for the confirmation of persistence or the indication of a change is the measurement of precipitation, since type and intensity of precipitation significantly impact the state of the runway surface. The precipitation indicator sensor provides a reliable binary parameter and if it indicates precipitation, its type (phase) and intensity are retrieved from a disdrometer. This information is further crosschecked by a rain gauge and METAR messages. A confrontation of the last observed contaminant type and the resulting RWYCC based on precipitation requires the assessment of freezing conditions. The current runway temperature and the current freezing point temperature, measured by the active runway sensor, are compared. This information also enables us to identify potential phase changes of contaminants . High relative humidity (near 100%) indicates the possibility of dew or frost formation on a previously dry runway. Finally, the runway (or air) temperature distinguishes between RWYCC 3 and 4 for compacted snow on the runway (according to the RCAM). These additional criteria support the rule-based algorithm to identify and eliminate the obvious inconsistences between the sensor contaminant type and depth and the observed weather conditions at the airport , contributing to situational awareness.Considering the last observation crosschecked by all available and relevant inputs for a particular weather situation, the rule-based algorithm assigns some probability to each RWYCC from 6 to 0 (based on expert judgment). The final RWYCC  is then the one with the highest probability. The RWYCC is supplemented by information about a consistency check against the last manually observed contaminant type, depth and the associated RWYCC in order to support decision making for the runway inspection execution or the application of the mechanical/chemical treatment of the runway.Data mining (DM) is a general approach where extensive databases are analyzed with the goal of obtaining new and potentially useful information on the future behavior of the investigated phenomenon, which is also called the target event. Tan et al.  describeData preparation and cleaning are important phases of the CRISP-DM methodology that impact the final success of the models. All measured values were subjected to quality checks  before the learning process. Manually observed numerical values were checked against textual logs of the runway status. We identified very low amount (less than 0.1 %) of records with missing data, and these were excluded from further processing. Due to the negligibly low percentage of missing records, we didn’t expect any significant influence of the data exclusion on the final results.Classification treeLinear Discriminant Analysis (LDA)K-nearest Neighbors (KNN)Artificial Neural Network (ANN).In this paper, we applied supervised machine learning models implemented in the 64-bit edition of the open source programming language R, version 3.5.1 . The modrpart [method set to class and the splitting index set to information, which meant building of classification trees where the information gain served as the splitting measure.A classification tree is a set of computer-generated rules, usually based on comparing features with thresholds. It consists of nodes of two types: terminal and non-terminal. Each non-terminal node has exactly two descendants and one parent, except of the root node with no parents. Comparing the entropy of the parent node with the entropy of the descendant nodes, one obtains the information gain , or, in rpart  with them distinct categories. LDA assumes that feature vectors have a multivariate Gaussian distribution with the same non-singular covariance matrix. The training dataset consists of feature vectors n is total number of feature vectors and known classifications p is the dimension of feature vectors.:LDA aims at creating a classification rule for resolving objects into In (1) MASS [Equation (2) is then the discriminant equation with matrix notation . We utilMASS  with defK nearest classified feature vectors to the yet non-classified object. Then the object is classified according to the maximal occurrence of categories among the K nearest neighboring feature vectors. KNN is considered as one of the simplest machine learning methods [class [k equal either to 1 or to 3, respectively, helped us to create two versions of the KNN model.The KNN method is based on finding the  methods ,19. The s [class  with thennet [The ANN method is inspired by biological neural networks. An ANN is like a group of connected nodes  that can transmit signals. When the weighted sum of the input signals to the neuron is greater than the threshold, the neuron will fire and send a signal to a further neuron (node). The aim of ANN is to find weights and thresholds for each neuron (node) and its synapses (connections) ,22. We annet . The amoWhen a wet/contaminated runway occurs simultaneously with a gusty wind, wind shear or strong tail/cross wind , the risk of adverse effects to the plane’s braking performance increases. The wind field poses separate safety issues in itself; some major aircraft accidents have been caused by adverse wind conditions ,25. To iComputationally, the detection/identification of microbursts is a complex problem. Because of the relatively short time that this phenomenon endures, in order to be handled by an alerting system, it must be recognized in its early phases. The detection of wind shear on the basis of radial velocities from Lidar is not straightforward—it requires trained personnel constantly monitoring the scans. In principle, it should be possible to automate this by means of pattern recognition  and While the introduction of the new GRF (described in As depicted in The AO responsible for monitoring and reporting of the runway condition has the possibility to adjust the information to be disseminated according to his experience. The scheme of sharing and utilizing the information about the runway surface condition (contained in RCR) in Accuracy (ACC), see Equation (3)Heidke skill score (HSS), see Equation (4)Hanssen-Kuipers discriminant (HK), sometimes also termed as Pierce skill score, see Equation (5)Kuiper skill score (KSS), see Equation (6).In the SESAR validation exercise, we implemented a rule-based algorithm for the RWYCC calculation . The utilization of DM methods represents another approach to estimate the RWYCC from the available data sources. Instead of the direct application of the RCAM onto input data, we trained DM models using historical data sets containing available input sources from sensors. Human observations by professional AO staff served as the target attribute, as we assumed the correct application of the RCAM´s full scope. The subdivision of data into a training set and a test set that was excluded from the training process enabled the evaluation of the DM models. We applied four different DM methods, as described in i, Oj)nN(F and the observed events in category j is j)N, i = jn = 0n(F for i ≠ j. K is the number of categories. These scores are derived from a multi-category contingency table . In TablFollowing the convention used in In the following five subsections (3.2.1‒3.2.5), we will present the most relevant statistics , variability (Var), minimum (Min), maximum (Max), median, skewness and kurtosis) of the scores related to the individual DM methods. An inter-comparison of the individual methods  and their comparison with the performance of the Persistence model (assuming the persistence of the previous status) is introduced in This subsection provides the statistics for the scores of the LDA method. The RWYCC, based on the SNOWTAM from Poprad–Tatry Airport, was split into a training set (approx. 60%) and a test set (40%). The multi-category contingency table contains mean values based on 1000 LDA models on test data. The statistics resulting from the scores for each of 1000 models using the LDA method are summarized in K = 1. The training set comprised 60% of the available data set‒the rest were test data.K = 3). The statistics for the scores (This subsection presents the scores for the same DM method (KNN), but considers three nearest neighbors  method achieved the best performance among the examined methods. Its mean accuracy of 90.1% and other scores with means over 0.8 based on 1000 models (with different splitting of the dataset into training and test data) makes this DM method the most successful and meaningful for the RWYCC calculation. This performance stems also from the resistance of the KNN (K = 1) method to unbalanced input data, since, for the categorization of new measurements, only information from the nearest neighbor is necessary. The mean accuracy of all other methods ranged from 65% to 75%, except for the Classification tree method, reaching the mean accuracy only slightly higher than the Persistence model (56.9%). This DM method is probably the most affected by an unequal distribution of the RWYCCs, and thus, is rather not suitable for our purposes. A complete evaluation of the performance of the examined DM methods and their comparison with the Persistence model is discussed in All investigated DM methods for the RWYCC calculation reached better scores in comparison with the accuracy (54.8%) of the Persistence model . The KNN  number of some categories  from input parameters with appropriate weighting factors. This enables a DM model to assign low priority to unreliable parameters from the runway built-in sensors see . On the The demand for an increase in safety motivated ICAO to introduce a new GRF for the observation and reporting of runway surface conditions with a closer relation to aircraft performance than the current methods. Coping with the full RCAM´s scope requires the visual inspection of the runway, which is associated with runway closure. In order to optimize the number of runway inspections and to support decision making for runway maintenance and chemical treatment, the current paper examined different methods for enhanced automatic means of continuous assessment of the runway surface condition. This contributes to increasing flight safety and environmental protection.K‒nearest neighbor method with K = 1 showed the best scores, with mean accuracy of 90.1% and a mean Heidke skill score, Hanssen‒Kuipers discriminant and Kuiper skill score over 0.8. All DM methods outperformed the Persistence model , except for the Classification tree. The DM methods showed resiliency regarding the reliability of the input data, as they inherently used a poorly correlating input with less influence on the final result. We presented the core results of the SESAR project Pj03b-06 “Safety Support Tools for Avoiding Runway Excursions”, which defines a new conceptual method based on the integration of multiple data sources. The RCAMS developed and validated within this SESAR Solution represents such a decision support tool for the staff responsible for runway assessment and maintenance to improve awareness of runway condition. The HMI of the RCAMS displays all the necessary information, including the calculated RWYCC characterizing the runway surface condition. In order to obtain the RWYCC, we examined four different DM methods, from which the The more precise the available information in the GRF is, the higher the increase in resilience, efficiency and safety in adverse weather situations (with rapidly changing conditions) can be expected. The quality of the information may be increased by qualitative and quantitative extensions of observation and calculation methods; both ways will be summarized below in more detail. The performance of DM methods grows as the available database of training data enlarges. Each extension of the validation period and the available input data brings potential improvement to DM models and increases the reliability of the results. The database can be extended temporally into the coming years and spatially by merging data from several airports. The latter, however, has to be done with care, as merging airports that are too dissimilar from climatic point of view can deteriorate the learning process. We plan to reexamine the results after each winter season and extend the number of testing sites, too.A rule-based algorithm is another approach for the RWYCC calculation consisting of a physical model assessing all available input data with regards to the RCAM. In comparison with the DM methods, the performance of the rule-based algorithm turned out to be insufficient, due to the unreliability of the assessment of the contaminant type by runway built-in sensors having an undesirably large influence on the final output. Therefore, the rule-based algorithm should be modified so that the importance of sensor reported contaminant type will be suppressed in comparison to other inputs. On the other hand, the performance of the rule-based algorithm was not negatively affected by the uneven distribution of the reference measurements typical for rare phenomena. In conclusion, the most advantageous approach appears to be a combination of both the DM methods and the rule-based algorithm. DM methods would be utilized in common and frequent situations with a sufficient amount and distribution of data, and an upgraded rule-based algorithm  in rare conditions, when DM methods are limited by a lack of training data.As a qualitative extension of the input database, we are also going to investigate the integration of new data sources, e.g., outputs of mobile runway surface condition sensors, surveillance data from an ADS-B receiver, on-line data of transmitted braking performance from landing aircrafts, processed imagery of ground-based cameras and cameras on aircraft. Such integrated validation exercise is planned for 2021 in Poland.Furthermore, we plan to further apply our experiences with DM methods also to Lidar-based wind flow information to generate appropriate wind shear warnings. Finally, runway surface condition assessment and adverse wind condition detection together with a fog prediction model ,5 would


As the communication competencies of physicians are crucial for providing optimal patient care, their assessment in the context of the high-stakes Objective Structured Clinical Examination (OSCE) is of paramount importance. Despite abundant literature on the topic, evidence-based recommendations for the assessment of communication competencies in high stakes OSCEs are scarce. As part of a national project to improve communication-competencies assessments in the Swiss licensing exam, we held a symposium with national and international experts to derive corresponding guidelines.Experts were invited on account of their recognized expertise either in teaching or assessing communication competencies, or in conducting national high-stakes OSCEs. They were asked to propose concrete solutions related to four potential areas for improvement: the station design, the rating tool, the raters’ training, and the role of standardized patients. Data gene.rated in the symposium was available for analysis and consisted of video recordings of plenary sessions, of the written summaries of group work, and the cards with participants’ personal take-home messages. Data were analyzed using a thematic analysis approach.Nine major suggestions for improving communication-competencies assessments emerged from the analysis and were classified into four categories, namely, the roles of the OSCE scenarios, rating tool, raters’ training, and simulated patients.In the absence of established evidence-based guidelines, an experts’ symposium facilitated the identification of nine practical suggestions for improving the assessment of communication competencies in the context of high-stakes OSCEs. Further research is needed to test effectiveness of the suggestions and how they contribute to improvements in the quality of high-stakes communication-competencies assessment. On thnication . During nication , 20, whiThese reflections led to the question of how to improve assessments of communication competencies in the Swiss FLE. The Swiss Federal Office of Public Health funded a national project in 2014 to address the challenge of improving the assessment of communication competencies. The first step of the project consisted of a nationwide survey; collecting data from instructors and students on how Swiss medical schools train students to apply communication skills . The secTwenty-nine communication and assessment experts met for a 2-day symposium held in February 2016 in Bern, Switzerland. The four international experts  were recognized for their expertise and research activity either in teaching or assessing physician-patient communication competencies, or in conducting national high-stakes OSCEs. The twenty-five experts from the five Swiss medical schools were communication instructors and researchers, members of the experts’ group in charge of the conceptualization and quality improvement of the national OSCE, and faculty members in charge of the communication-competencies curricula.The symposium started with a presentation of the above-mentioned national survey. Each of the international experts then presented their perspective on the assessment of communication-competencies. Participants were then divided into small groups and discussed possible improvements regarding the four predefined areas . At the end of every day, each group presented and discussed its achievements in plenary sessions. The last part of the symposium consisted of a plenary discussion of the main lessons learned. Participants were asked to propose and discuss concrete implications for the future communication skills assessment within the Swiss Clinical Skills-FLE. The plenary sessions were video-recorded. At the end of the symposium, we asked each participant to write down a personal take-home message.Data generated in the symposium were available for qualitative analysis and consisted of video recordings of the two plenary sessions (2 h 55 min), of the written summaries of group work, and of the cards with personal take-home messages. Data were analyzed using a five-phase thematic analysis approach . ThematiThe thematic analysis helped us highlight nine major themes, which were classified according to the four pre-established areas Table . StatemeScenarios measuring the adequacy of examinees’ responses to a pre-specified patient situation with emotional distressThe variety of contexts to which candidates are exposed during the FLE includes a large sample of situations. However, to better discriminate the levels of communication competence, participants also suggested designing stations with a specific focus on communication:enriching some of the traditional stations with specific communication challenges, for example, by introducing specific emotional cues or concerns in the OSCE case”. These cases aim to measure the appropriateness of the examinee’s responses to emotional distress portrayed by the SP. “The emotional cues or concerns could be expressed verbally or non-verbally and should be related to the medical problem” or its perceived consequences. A suggestion was made to use the Verona Coding Definitions of Emotional Sequence (VRCoDES) [if the examinees expected an emotional agenda in every patient encounter”, and consequently, would adopt a non-authentic, test-induced communication style. Hence, participants suggested limiting the number of stations with specific emotional stimuli and varying the types and intensity of the emotional states to be portrayed .2)Scenarios with the main focus on specific communication situationsParticipants suggested “VRCoDES) , 26 as adeveloping specific OSCE scenarios where communication would be the core of the clinical encounter”. For such stations, specific communication situations  were proposed. To ensure content validity, participants proposed the development of OSCE scenarios that were built on validated communication models  [“a platform for the communication instructors from the five medical schools to exchange information and impressions about such models”.3)Involvement of communication experts in the development of the OSCE stationsParticipants suggested “ad news) . The impnot all clinicians would be familiar with these concepts”. Therefore, a recommendation was made to pair communication experts with clinical experts for the case-writing process.4)Balance between authenticity and standardizationGiven the specificity of the communication models used in the new OSCE stations, the participants anticipated that “need to strive for high levels of authenticity and standardization in the context of high-stakes assessments”. They pointed out the difficult trade-off between these two characteristics. For example, if, in the attempt to achieve higher standardization, case writers develop very detailed SP scenarios, the SPs would have less flexibility in adjusting their role-plays to correspond to the quality of the examinee’s communication. “A one-fits-all response of the SP to all examinees’ interactions would, therefore, decrease the authenticity of the experience”. For this reason, some participants proposed allowing some flexibility in the SP’s portrayal, based on whether or not the examinee adopted the expected communication attitude.The participants stressed the “5)Ensure the presence of items that capture case-specific communication outcomesMedical content  is actually assessed using a case-specific checklist, while communication competencies are assessed at all stations using a global rating scale derived from the Analytic Global OSCE Ratings , 20.5)Eensuring that items on the checklists and the global rating scale systematically capture the specific communication goals of each station”. From the perspective of a licensing examination, participants pointed out how it might be more meaningful to assess the “outcome of the encounter” , than the technique the candidate used to achieve the results .6)Having a global rating scale for the assessment of general physician-patient communication competenciesParticipants stressed the importance of “having all faculty members in the undergraduate curriculum use this scale for both summative and formative assessments”.Given the concerns about the inability of raters to differentiate between the four dimensions of the Analytic Global OSCE Ratings, participants proposed a focus on familiarizing raters with the scale, rather than changing it. To achieve this, they proposed “7)Additional assessment of communication by SPsassessments by SPs could be complementary to those of physician raters because they perceive other dimensions of communication”. The feasibility of this proposal depends on the time interval between the stations. A short time  might prevent SPs from conducting a thorough evaluation.8)Adapting SPs’ training to the new stations for the assessment of communication competenciesSome participants suggested involving SPs in the evaluation of communication competencies in order to achieve a more accurate discrimination of good communication. They argued that “standardization can be ensured by providing SPs with examples of how to respond to “unsatisfactory, intermediate, and good” responses by students/candidates” and train them to react differently to such behaviors.Participants expressed concern that the introduction of the scenarios in which SPs have to portray pre-determined verbal and non-verbal emotional hints at a pre-defined level of intensity would increase the complexity of the SP’s role-play and training. The standardization imperatives of the OSCE require SPs to provide the same information to each candidate, regardless of the quality of their communication competencies. Hence, some participants suggested allowing greater flexibility in the SPs’ role-plays. Even if the role-play and the SP-training are challenging, “9)Adapt the raters’ training to the stations dedicated to the assessment of communication competenciesWith the introduction of OSCE stations dedicated to communication competencies, raters’ training must address all aspects related to the assessments conducted at such stations. In particular, raters should know how to use specific assessment criteria  and models, especially if they are not communication experts. As mentioned previously, frequent use of the same rating scale for the clinical skills component of the FLE throughout the undergraduate curriculum could also be considered as a type of training for raters. Finally, participants emphasized the importance of keeping rating scales as simple and intuitive as possible, thereby simplifying the raters’ training.Our symposium identified significant elements for improvement, mainly concerning the design and development of OSCE cases and the assessment instruments.Quality in the assessment of physician-patient communication competencies at the Swiss FLE has so far been ensured by at least three elements: the blueprint of OSCE stations providing a large sample of clinical situations  in different clinical settings  and disciplines, the systematic assessment of general communication competencies at each OSCE station and the monitoring of psychometric properties of the rating tool over the years . This, hSix-function model of medical communication [explicitness of the response (explicitly or not explicitly referred to as the cue/concern) and provision of space (the response provides or reduces space for further disclosure of the cue/concern) [Our suggestion to introduce concrete elements in OSCE scenarios, so as to allow candidates to be exposed to specific communication aspects (Suggestions 1 and 2) is corroborated by a recent analysis by the National Board of Medical Examiners (NBME) which reviewed all components of the United States Medical Licensing Examination (USMLE) . This annication , which pnication , or the nication , 26. Theconcern) , 26. Zhoconcern) . To ensuConcerning the importance of ensuring authenticity in the SPs’ portrayals and the validity of the assessment, we already develop scenarios inspired by real patient narratives  which weAnother condition evoked to ensure validity of the assessment of communication competencies, is the selection of an appropriate rating tool (Suggestion 5 and 6). There is an ongoing debate in the literature in relation to the topic of which type of rating tool best suits the assessment of communication competencies in the OSCE setting. Recent research has suggested some advantages of using global rating scales over checklists, such as greater internal consistency , 29, 30 Our suggestion is consistent with the finding that combining the global rating scale and checklists increases the reliability and content validity of the communication-competencies assessment , 29, 30.As for the appropriateness to keeping the Analytic Global OSCE Ratings , 20 (SugOur suggestions is that a well-suited evaluation tool for communication-competencies assessments in the licensing OSCE setting should be a validated scale with good psychometric properties, which is well known and accepted by raters, feasible in the exam setting, with dimensions that are consistent with instruction, and items that are able to capture the communication goals of the station.The symposium was not aimed nor empowered to produce consensus statements. Indeed, agreement about the results of the thematic analysis was obtained only from the authors of this article and not from the other participants. Further, the results have not been additionally reviewed by external experts. That is why we thought it appropriate to talk in terms of suggestions and not recommendations. However, we believe that our conclusions are credible for the following reasons. Firstly, all the authors actively participated in the symposium and were therefore able to capture the essence of the discussions. Secondly, the material was analyzed separately by different authors and comes from original and verifiable sources such as video recordings of the plenary discussions. Thirdly, in the analysis we also took into account the individual “take-home massages” of each participant. Such a symposium can provide major directions; however, detailed concepts have to be developed based on these proposals. Nevertheless, to our knowledge, this is one of the few publications of this type, and the results should be useful to all professionals involved in the assessment of communication skills.This article offers nine practical suggestions, at both structural and process levels, to improve the assessment of communication competencies in high-stakes licensing OSCEs. Further research is needed to test to what extent the implementation of all these suggestions will effectively contribute to improvements in the quality of communication-competencies assessments.


Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression method, space–time adaptive processing (STAP) degrades in shipborne HFSWR. To deal with this problem, an innovative training sample acquisition method is proposed, in the area of joint domain localized (JDL) reduced-rank STAP. In this clutter-suppression method, based on a single range of cell data, the unscented transformation is introduced as a preprocessing step to obtain adequate homogeneous secondary data and roughly estimated clutter covariance matrix (CCM). The accurate CCM is calculated by integrating the approximate CCM of different range of cells. Compared with existing clutter-suppression algorithms for shipborne HFSWR, the proposed approach has a better signal-to-clutter-plus-noise ratio (SCNR) improvement tested by real data. In shipborne high-frequency surface-wave radar (HFSWR), the main detection background clutter for targets of low-velocity is the spreading first-order sea clutter caused by the platform motion. Under this circumstance, it is necessary to suppress the spread clutter for target detecting.The effect of platform motion on the spectra of the received signals has been noted and some analyses have been carried out . Xie, YuDue to limited qualified training samples, the performance of STAP algorithms encounters a major challenge, that is, the estimated CCM is inaccurate. In order to degenerate this performance degradation, recent developments mainly focused on two approaches, one is looking for efficient training samples selecting methods to pick out samples most similar to the CUT. These selected samples are used to estimate the CCM, such as the generalized inner product (GIP) method , the powFor shipborne HFSWR, there are not enough training samples for several reasons. First, constrained by the size, the detecting range is small. In addition, HFSWR mainly focuses on Doppler frequency resolution and its range resolution is always low, which will lead to a small quantity of training samples. Even worse, the first-order sea clutter echo spreads and occupies a wide range of Doppler frequencies. Different from onshore HFSWR, clutter energy is not concentrated on a narrow Bragg frequency. As a result, the statistical characteristics of the clutter vary significantly in range dimension, which means that the training samples in range dimension are not IID. Thus, not only the quantity of the training samples is small but also the quality is bad. To deal with this problem, a single-dataset-based preprocessing JDL algorithm is proposed in this study. which uses an innovative training sample acquisition method. Like the conventional JDL method, the received array data are transformed into the angle-Doppler domain and the size of the local processing region (LPR) is determined according to statistical characteristics. It is worth mentioning that this part is not the focus of this paper, therefore, this paper directly uses the conclusions drawn in our previous conference paper . After tThe structure of this paper is organized as follows. In JDL processing is one of the reduced-dimension STAP methods which is used to suppress clutter in homogeneous environments. To facilitate subsequent analysis, the signal model in conventional JDL is illustrated. This model can be found in ,44,45,46N isotropic monopole antennas in the entire coherent processing interval (CPI) of M pulses, the data of the ith range cell isnth channel and mth pulse. According to the echo component, this vector Among them, In which In conventional JDL processing, a transformation matrix Take a The transformed data vector Then the CCM is estimated asThe adaptive weights can be obtained from the following equationThe output after clutter-suppression isIn this study, decision statistics are calculated using the modified sample matrix inversion (MSMI) statistics,The premise to use JDL for clutter-suppression is that the clutter is homogeneous. In the study , no analAn experiment was conducted and real data were recorded from the shipborne HFSWR system in the Yellow Sea of China in September 1998. As described in ,19,24, tThe receiving array of the shipborne HFSWR system consists of 7 receiving antennas which form a ULA. The entire system is mounted on a barge which is tugged by a tugboat. Like the side-looking airborne radar, the radar points perpendicular to the direction of the flight. In shipborne HFSWR, the principal component of the detecting background for low velocity targets is the spreading first-order sea clutter. Xie, Yuan and Liu  analyzedThe premise to obtain CCM using other range cells is the clutter is homogeneous in range dimension. However, this assumption fails in shipborne HFSWR.When the platform stays still, like onshore HFSWR, the first-order sea clutter concentrates to the Bragg frequency and can be seen as an approximately straight line in the range-Doppler map. Considering that the strength of the first-order sea clutter is so high that the clutter takes the dominant position and their statistic characteristics can be seen as homogeneous. For an intuitive standpoint, The reason for this may be as follows. The sea clutter from a range cell can be modeled as the superposition of a large number of independent clutter patches that are evenly distributed in azimuth . When thkth LPR. The covariance matrix Vectorize the data Eigen-decompose Compute the correlation coefficients by the following equation To evaluate the homogeneity of the spreading first-order sea clutter in range dimension, the correlation coefficients between the CUT and training samples from other range cells are calculated. Here, we choose the correlation analysis method proposed in . The recThe correlation coefficients between the CUT and other range bins are shown in In conclusion, the first-order sea clutter in shipborne HFSWR is broaden and has a specific space-time distribution. It is nonhomogeneous in range dimension. Clutter-suppression methods need to consider these features.N is often two to dozens and the number of pulses M is often thousands to several tens of thousands. For example, the number of pulses is 1024 to achieve a long CIT for higher Doppler resolution and the number of channels is 7 in the shipborne HFSWR system used in this paper. In this scenario, at least 14,336 training samples are needed for fully STAP, which is unavailable.STAP is an efficient method to deal with space–time coupled clutter. The fully STAP utilizes the CCM of the CUT to calculate the optimal weight. Generally, the CCM is unknown and needs to be estimated from training samples. For shipborne HFSWR, the number of receiving channel Therefore, dimensional-reduction STAP methods are introduced. The JDL algorithm is one of the suboptimum STAP methods which decrease the amount of training samples through transforming the data to the angle-Doppler domain. It is widely used in finite training samples situation. Like the other suboptimum STAP algorithms, the training samples which are used to estimate the CCM should be target free and contain clutter with the same statistical properties as the CUT. The performance of the JDL algorithm degrades drastically when the estimation of the CCM is inaccurate. The minimum number of samples required to achieve a robust performance is twice the degree of freedom of the CUT, according to the RMB criterion . In thisN + 1 secondary training samples based on an N dimensional primary data. It is worth mentioning that the generated secondary samples cannot replace the primary samples. However, it can improve the performance to some extent.To deal with this problem, a single-dataset-based preprocessing JDL algorithm is proposed here. The unscented transformation is introduced to preprocess the primary data to obtain adequate homogeneous secondary data and approximate the CCM initially. Then the rough approximation of CCM from different range bins is integrated together to obtain a more accurate result which will also increase the rank of the CCM since it can be seen as a way of smoothing. The proposed data acquisition method can generate 2 In nonhomogeneous scenario like shipborne HFSWR, the lack of qualified training samples is the major factor which influences the accuracy of CCM estimation. Using the adjacent range cells directly to estimate the CCM will not get a good result. If we can obtain some homogeneous secondary data from the CUT data through a transformation and approximate the covariance of the CUT by using the produced secondary data, the accuracy of the estimation will be improved. This procedure can be seen as a special transformation. The input data are the CUT and the expected output is more available training data. Thus, the unscented transformation is suitable here.N sigma points The unscented transformation is a method to estimate the statistic parameters of a nonlinear transform . It apprDenoted the weight with the covariance as n + 1 sigma points are used, which follows the formulaFor general unscented transformation, 2The weights With unscented transformation, more training data can be obtained based on the primary data. However, the definite form of this nonlinear transformation is undetermined, we must assume one. Considering that the predicted secondary data need to be IID with the CUT, a linear transformation Determine the size of the LPR and transform the receiving data to the angle-Doppler domain using Equations (6) and (7).For transformed Set the transformation Estimate the CCM Calculate the adaptive weights as Equation (11) denotes and calculate the output statistic by using Equation (12).The procedure of the proposed single-dataset-based preprocessing JDL algorithm is described as follows:The second step and third step mitigate the influence of the nonhomogeneity and are based on the single data-set. This preprocessing approximates the CCM of the CUT through unscented transformation. Then the forth step integrates the effect of different range bins as a way of smoothing to increase the rank of the CCM, which will further decrease the effect of nonhomogeneity.In this section, the experimental results are shown using measured data to evaluate the proposed approach. The performance comparison with other existing clutter-suppression method for shipborne HFSWR proposed before is also given. The Results have verified that the proposed method outperforms other tested methods.To validate the performance of the proposed method for multitarget situations, three simulated targets are injected to the real clutter data, the detailed information is shown in As mentioned above, the unscented transformation is conducted on the primary data first. To prove the validity of the generated secondary samples, the correlation coefficients are calculated using the azimuth–Doppler joint eigenvectors analysis method listed in Some of the correlation coefficients are much lower than others. The reason is that the clutter to noise ratio (CNR) decreases in these selected LPR. In other words, the proportion of the first-order sea clutter is lower than other range bins. The proposed data acquisition method can be considered as a predictive modeling. The data in a certain LPR consists of three parts, target, clutter and noise. For the training samples, it is assumed that there are no targets. The correlation coefficients are determined mainly by the clutter since that the correlation of noise is much weaker than the first-order sea clutter. Hence, if the CNR decreases, the correlation coefficients will be unstable and some of them may be smaller.After the first step and second step, the third step and fourth step are performed. To evaluate the performance of the algorithm, the SCNR improvement, defined as the improvement of the SCNR before and after clutter-suppression, is given as follows:As shown in A non-cooperative target is selected to investigate the effectiveness of the proposed method. It is a passenger liner named Changsong which was receding from the platform with the following parameters: range 78 km, azimuth e stable .The processing procedure is the same as simulated targets. The Doppler profile is shown in N + 1 training samples based on an N dimensional primary data. These generated training samples are highly correlated with the initial data that tested by the correlation coefficients, which means they are statistically similar. As shown in the result, the proposed algorithm performs well for clutter-suppression and increases the SCNR ratio, which will lead to better detection performance. Compared with other existing clutter-suppression methods like conventional JDL, improved orthogonal weighting, improved oblique projecting, the proposed algorithm has a greater effect on improving SCNR. The methods in previous work have an assumption that the spreading first-order sea clutter is statistically uniform in range dimension and ignores the heterogeneity of the clutter, our method analyses this property and provides a solution.As depicted by simulation results given in Furthermore, it is not only applicable for shipborne HFSWR, the proposed data acquisition method can also be used in all cases where the qualified training samples are limited. The proposed clutter-suppression method can be taken as an approach to solve nonhomogeneous clutter. However, the unscented transformation is used for nonlinear transform and here we use linear instead since the special nonlinear transform is unknown. If the statistics of the CUT is known, then how to find the most appropriate transformation needs further study.In this paper, a single-data-based preprocessing JDL algorithm is proposed to suppress the spread first-order sea clutter in shipborne HFSWR. To deal with the impact of the nonhomogeneous characteristics and limited number of training samples, an innovative training sample acquisition method is proposed using the unscented transformation. Then, the CCM of the CUT is estimated by integrating different space–time snapshots, which will increase the rank of the CCM and mitigate the effect of the heterogeneity in range dimension. The real data are used to test the performance of the proposed algorithm. Experimental results show that the proposed algorithm has a higher SCNR improvement compared with FFT–DBF, improved orthogonal weighting, improved oblique projecting and conventional JDL algorithm and is applicable for first-order sea clutter-suppression in shipborne HFSWR.


Polyisocyanurate foam (PIF) has been adopted as a liquefied natural gas (LNG) insulating material owing to its various mechanical merits such as high structural stability and mechanical strength, and excellent insulating ability. In an attempt to increase the mechanical strength of PIF, chopped-glass-fiber-reinforced polyisocyanurate foam (CGR-PIF) was synthesized by adding chopped glass fibers to polyol and isocyanate, which are the raw materials used in the polymerization process for producing PIF. The main objective is to closely observe the compression material characteristics of PIF and CGR-PIF in terms of the cryogenic temperature. Therefore, compressive tests were conducted at cryogenic temperature including low temperatures, and microscopic images were obtained to analyze the cell size and distribution that affects the mechanical and thermal properties of the foam. Furthermore, recovery ratio and weight loss which are important factors of brittle fracture were evaluated, and the applicability of the foams to a cryogenic environment was evaluated. Finally, thermal conductivity, an important parameter of insulation, was evaluated. The obtained results confirm that the compressive strength of CGR-PIF significantly increases at cryogenic temperatures; moreover, a relatively higher thermal conductivity was observed in the case of CGR-PIF as compared to that of PIF owing to the chopped glass fibers. The global energy demand is increasing owing to the growing population and expanding economies. Furthermore, natural gas is already an essential green energy source for fulfilling the global energy demand. The natural gas demand is expected to grow by 4.4% each year till 2030, which will make it the most popular fossil fuel . The liqPolymeric foams are widely used as insulation materials in sandwich structures because of their low density, excellent energy absorption ability, low moisture permeability and good insulation performance ,6. RigidPIF has been developed to have superior thermal stability and flammability characteristics. PIF can be synthesized without the use of polyol but requires the use of isocyanate, a catalyst, a blowing agent, and a foam stabilizer (surfactant). Instead of reacting with the polyol, the isocyanate reacts with itself to form a highly cross-linked thermosetting neutralized body with a cyclic structure. Owing to this isocyanurate bonding as shown in Owing to the excellent physical and mechanical properties of polymeric foam, several research studies have been conducted on the mechanical properties of PUF. The room-temperature mechanical properties of closed-cell polyurethane foam were measured at various values of density, which influences its mechanical performance ,13. ParkHowever, the problem of breakage of the polymeric foam used in the insulation system continues to occur, and studies are underway for enhancing the performance of polymeric foams. Generally, the method used for improving the performance of a polymer foam includes adding fibers and particulates to the foam. Zhao et al. analyzed the mechanical and thermal performances of granular silica aerogel reinforced PIF. It was found that the higher the silica aerogel ratio was, the lower its thermal conductivity was, and its compressive strength was slightly improved . NeverthAlthough research has been conducted on the performance of PUF with the addition of glass fibers, the synthesis of glass fibers for PIF, which has different molecular structure, and its mechanical performance evaluation at various temperatures has not been conducted. The majority of the studies on this subject are focused on the mechanical performance of the foam at room temperature or a low temperature and have not evaluated its mechanical performance at cryogenic temperatures, such as those used for storing LNG.When a polymeric foam having a relatively high density is used, the weight of the hull is increased, thus resulting in economic loss. In order to overcome this insulation system problem, it is necessary to improve the mechanical strength per unit density and thermal performance of the polymeric foam. Although PIFs are widely used in insulation systems, a few studies have been conducted on the effects of glass-fiber reinforcement and no cryogenic mechanical performance evaluations have been performed when PIFs are reinforced with glass fiber .In this present study, the main objective is to closely observe the compression material characteristics of PIF and chopped-glass-fiber-reinforced PIF (CGR-PIF) in terms of the cryogenic temperature. Therefore, compressive tests were conducted with PIF and CGR-PIF at various temperatures, and SEM micrographs were also obtained in order to analyze the cell shape and distribution that affects the mechanical and thermal properties of the foam. In addition, the recovery ratio and weight loss were evaluated, and the applicability of the foams to a cryogenic environment was evaluated. Finally, the thermal conductivity, as an important parameter of insulation, was evaluated.The polyol mixture was used to prepare PIFs with commercially available polyols from BASF company —Lupraphen 1901/1 . Properties of the polyol mixture used in this study are summarized in 3) to produce free-rise foam. One half was poured in an empty mold to produce pure PIF. The other half of the mixture was poured into a mold with chopped glass fibers at its bottom. The liquids were then pressed to mix with the chopped glass fibers and to align the fibers in the horizontal direction. After 24 h at an ambient temperature of 20 °C, the polymerization reaction was completed.PIF is a cellular, thermoset plastic formed when the two liquids polyol and M50 are combined. In this study, CGR-PIF is fabricated to investigate the effect of chopped glass fibers in PIF. The main constituents of the CGR-PIF are polyol, M50, and chopped glass fibers. 3) and CGR-PIF (mass density = 75 kg/m3) were subjected to compression tests and a brick-type specimen of size 50(B) 3 was machined in accordance with the Korean Industrial Standard (KS M ISO844). In addition, compression test specimens were machined in the foam rising direction, which is the main direction in which the self-gravity of the LNG and the sloshing load act. 3 and that of the CGR-PIF was approximately 75 kg/m3. For the field emission SEM (FE-SEM) analysis, the X–Y plane (perpendicular to the foam rising direction) and Z-plane  of the PIF and CGR-PIF which is not conducted the compression test were sampled and analyzed. The test specimens for the thermal property analysis were fabricated with a size of 1(B) 3. Five specimens were randomly selected in bulk, and a graphite coating was applied on them in order to enhance their light absorption.In this study, a homogenizer was used to mix the polyol and M50 at 6000 rpm. As shown in The rigidity of the polymeric matrix and the cellular structures of the foam influence the mechanical and thermal properties of the polymeric foam . PolymerWhen a load is applied to the polymeric foam, work is done by the forces acting on it, which is called specific energy. The work per unit volume (specific energy) used to deform it to a densification strain is represented by the area under the stress–strain curve up to ficiency , wherebytion (1) ,31,32.(As the main loading component of LNG cargo containment system is the compressive load (sloshing load and self-gravity) applied to the rise direction of the polymeric foam, five compression tests were conducted in each case to obtain a reliable stress–strain relationship. The average value of the three nearest data in five experiments was used as a representative value for each scenario.After the polymeric foam is compressively deformed, recovery occurs in the direction in which the deformation occurs. This can cause dimensional instability under LNG loading (compressively deformed) and unloading states (recovery) in LNG insulation system owing to a permanent deformation resulting in strength reduction due to a gap in the insulation structure. In this study, when the load of the universal testing machine applied to the testing sample reached 5 kN, the compressive loading was terminated. As soon as the test was terminated, the maximum deformation height of the tested sample was measured to calculate the maximum deformation ratio. After 24 h at ambient temperature with no external loading, the permanent deformation height of the tested sample was measured to calculate recovery ratio. Brittle fracture occurs when the compressive load is applied to the polymeric foam at a cryogenic temperature. The brittle fracture destroys the side of the polymeric foam specimen such that the degree of brittle fracture can be measured by evaluating the weight loss after the compressive test. Thermal conductivity is an important characteristic of a thermal insulation material . The heaThe test was performed five times for each specimen, and tructure ,42.(2)αIn terms of a comparison of the shapes of the cells of the synthesized CGR-PIF and PIF, the cells of the PIF were found to be relatively uniform in shape, and the cells of the closed form were completely present. However, in the case of CGR-PIF, it is observed that the cell growth around the glass fiber was hindered by the glass fiber, and consequently, the cell size around the chopped glass fiber was relatively small.In terms of mechanical behavior of PIF and CGR-PIF, the difference in compressive strength increases as the temperature decreases. Considering that the density is the important parameter when the volume of LNG cargo containment system (CCS) is fixed, the mechanical performance of CGR-PIF at −163 °C is the most superior in terms of compressive strength, Young’s modulus, and specific energy. In fact, when a large load is applied to the PIF used in an LNG fuel CCS, it is better to use CGR-PIF without increasing the thickness of the PIF in terms of mechanical performance.In terms of failure characteristics, generally, the recovery ratio of CGR-PIF was higher than that of PIF. Especially at −163 °C, which is the temperature at which LNG is stored, it was confirmed that CGR-PIF recovered 20% whereas PIF recovered only slightly. Furthermore, in the evaluation of the degree of brittle fracture, the weight loss of PIF is found to be two times higher than that of CGR-PIF. Chopped glass fibers can compensate for the disadvantage of PIF becoming friable at −163 °C.In terms of thermal conductivity, it was confirmed that the insulation performance of PIF was lowered owing to the addition of chopped glass fibers.In this study, CGR-PIF was synthesized based on the amount of glass fiber that is generally added to commercially available R-PUF to investigate the effect of chopped glass fibers on the mechanical and thermal performance of PIF. The results obtained in this study are summarized below.


The hydrogen diffusion process in ferritic steel during thermal desorption tests was simulated using the finite element method based on the two-dimensional diffusion-trapping coupled model. This model was first verified by experimental data to obtain a physically meaningful combination of trap/lattice parameters. Then, the effect of specimen geometry was studied by varying the height of cylindrical specimens with other parameters fixed at constant values. Simulation of desorption spectra with different specimen geometries indicates that the measurement of hydrogen concentration is not affected by the change in specimen geometry due to the mass conservation law, for original thermal desorption spectra (TDS), which are, however, unlikely to be detected in traditional experiments due to the necessity of specimen transfer procedures. Considering the hydrogen escape during rest time (specimen preparation/transfer/evacuation), the measured TDS curves are expected to be strongly dependent on the specimen geometry. The effect of specimen geometry on desorption spectra is more pronounced for smaller specimens, resulting in the dramatic decrease in peak flux and the increased error of Kissinger method in the determination of trap deactivation energy. The present study may contribute to better understanding and more reliable interpretation of the TDS curves by considering the size effect. Hydrogen is one of the most promising clean energy sources due to its features of zero emission and no pollution. However, the safety of hydrogen transportation and storage remains a challenging problem especially for hydrogen gas pressure vessels, since the presence of hydrogen can result in dramatic material degradation and consequently unexpected failures of structures ,2,3. PreR is the gas constant. The good linear relationship between The popular method for precisely measuring the trapping parameters of hydrogen for a specific material is thermal desorption spectroscopy (TDS), during which the hydrogen atoms in trap sites gradually escape with the increase in temperature, diffuse out of the specimen through lattice sites and are detected by the instrument. The features of flux–temperature curves, i.e., the position , it has been proved by Kirchheim  through Experiments show that the TDS curves are strongly dependent on the test parameters, such as specimen geometry, transfer time, etc., which implies that the TDS measurement and parameter extraction may be affected by these factors in testing procedures. Escobar et al.  experimeT, lattice activation energy T is a function of time: K is determined by the trap binding energy: The hydrogen transport process during TDS tests can be described by the diffusion-trapping coupled model based on local equilibrium theory , as showap sites . TherebyEquation (2) was solved using the COMSOL Multiphysics  software by analogy to the stabilized convection–diffusion model as shown in u [3, The partial differential equation becomes nonlinear when the coefficients are dependent on the variable u . The con = 293 K . The inius study . The typFirst of all, the numerical model was verified by comparing with experimental data in literature. There are many studies in literature involving the TDS tests using different specimens. However, these studies involve different materials with a wide range of trap parameters. Verification of the model by comparing with the experimental data for different geometries in one paper is somewhat difficult, due to undetermined trap binding energy in the experiment. Note that good fitting can be achieved for a single TDS curve by using different combinations of trap parameters and lattice parameters. Instead, the simulation was compared with multiple desorption spectra at different heating rates, to obtain a physically meaningful combination of trap/lattice parameters, as shown in 2 environment at 85 °C for 210 h [3. For the consideration of computation efficiency, the two-dimensional symmetrical geometry with a diameter D = 6.3 mm and height H = 25 mm was considered in the simulation, instead of the 3D geometry utilized in the experiment. As will be shown shortly, the decrease in the specimen size has some influence on the desorption spectra, resulting in the negative shift of peak temperature and peak flux. Therefore, the estimated trap activation and trap density are expected to be slightly lower than the ‘real’ ones estimated from the 3D model, although the true values are difficult to obtain due to other uncertainties. As can be seen in The experimental TDS curves were obtained at three heating rates, i.e., 50, 100, and 200 K/h, for 4130X steel after thermal charging in 100 MPa Hor 210 h . In the al peaks , which sH and D were simulated, among which the combination of H = 20 mm and D = 4 mm was the closest to the experimental condition.The effect of specimen geometry on the TDS curves was studied by changing the ratio of specimen height/diameter from 0.1 to 10, using the same parameters in It is observed from R = 1). This is because the hydrogen escapes faster along the axial direction (the amount of hydrogen lost per unit time is higher) than the radial direction due to the shorter distance from the center of specimen to the surface and the fluxes at the top/bottom surfaces dominate the total flux. For a sufficiently small specimen height, i.e., H = 0.4 mm, the flux can be barely detected. This is attributed to the significant amount of hydrogen lost during the rest time, i.e., For the rod specimens, the influence of specimen geometry on the desorption spectra is small if the specimen height reaches a sufficiently large value, e.g., H > 10 mm. This is because the flux for the long-rod specimens is mainly composed of the fluxes at the side surfaces, while the fluxes at the top and bottom surfaces only occupy a small fraction. In this way, the 2D cylindrical problem is similar with the 1D problem except that the axial coordinates are used in the 2D model. However, a dramatic decrease of To rule out the influence of hydrogen loss, the dependence of the original TDS curves (rest time is zero) on specimen geometry is presented in It is seen that the peak temperature shifts negatively while the peak flux increases apparently with the decrease in specimen height, while the total hydrogen concentration remains constant. However, the hydrogen loss is much faster in the smaller specimen (H = 1 mm) than the larger specimen (H = 10 mm), as indicated by the significant decrease in flux from R for different rest times are shown in For clarity, the variations of R for all rest times. The variation of peak flux Consistent with the results in It is expected that the specimen geometry would affect not only the measurement of hydrogen concentration but also the determination of trap parameters. Since the validity of Kissinger theory is probably dependent on trap parameters themselves, as addressed by Raina et al. , only thThe simulated desorption spectra for different specimen geometries at four heating rates from 200 to 600 K/h are shown in There is an apparent increase in peak flux when the geometry changes from rod to disk specimens, while the shape of desorption spectra is not affected by the change in specimen geometry. The trap deactivation energy can be determined from the slope of the Choo–Lee plot by Equation (1), as discussed in previous sections. The predicted values of It is seen that the deviation of the predicted values from the true values is fairly small for large R values, indicating the validity of Kissinger theory. It was verified through simulation that further increase in the mesh number along the radial direction does not apparently improve the accuracy. The minor deviation is probably due to the error of Kissinger theory instead of the finite element model. According to Kirchheim , the govThe error of Kissinger theory increases dramatically with the decrease in specimen geometry for small disk-shaped specimens. The Choo–Lee plots for different specimen geometries and the parameters are shown in R2, decreases with specimen geometry, which implies the increased error of Kissinger theory. The intercept of the linear fitting also shifts apparently with the decrease in specimen size, which can be explained from the analytical approximation shown in Equation (3), indicating dependence of the intercept (second term on the right) on specimen length.The estimated trap deactivation energy increases with the decrease in specimen size, indicated by the change in slope of the linear fitting. For a large specimen height, a very good linear fitting can be obtained in the Choo–Lee plot. However, the goodness of linear fitting, as indicated by the values of The previous results show the effect of specimen geometry on desorption spectra, with the specimen diameter fixed at constant value while the specimen volume is varied greatly. In this section, the desorption spectra were simulated by changing both the diameter and height, with the specimen volume fixed at constant value.The simulated desorption spectra for two typical specimen geometries are shown in R values (H < D), the characteristic length (H) is small, resulting in faster hydrogen release. Accordingly, decreased hydrogen concentration is measured after a considerable period of rest time. This phenomenon is similar for very large R values (H > D), since the characteristic length (D) becomes small too. The hydrogen loss along the characteristic length increases sharply in these cases, which results in the dramatic decrease of Further investigation on the effect of the specimen ratio on The effect of specimen geometry on the measurement of TDS curves and the determination of trap parameters using Kissinger theory was studied using a two-dimensional finite element model based on the Oriani’s local equilibrium theory, taking ferritic steel as an example. The diffusion-trapping coupled partial differential equation was solved by analogy to the stabilized convection–diffusion model in COMSOL software. The numerical model was first verified by the experimental data from literature to determine the ‘effective’ combination of trap/lattice parameters. Then the geometry parameters were varied with other parameters fixed at meaningful values. The simulation indicates significant influence of specimen geometry on the desorption spectra, indicated by the change in peak temperature and peak flux with the variation of specimen size. When the specimen size reaches a sufficiently small value, it would be difficult to detect hydrogen flux in practical experiments due to the significant hydrogen loss during specimen transfer. Although the mass conservation law holds for different specimen geometries, it is expected that the measured hydrogen concentration is dependent on the specimen size, especially for the small disk-shaped specimens. This is due to the dependence of hydrogen loss on specimen thickness. Regarding the trap energy, the Kissinger theory holds well for long rod-shaped specimens, while the error of Kissinger theory increases sharply as the specimen changes from rod to disk specimens. The specimen geometry affects both the slope and intercept of the Choo–Lee plots, which greatly influences the determination of trap energy. In both diameter-constant and volume-constant cases, the effect of geometry on spectra is mainly controlled by the hydrogen loss during specimen transfer. For diameter-constant cases, the hydrogen loss is mainly dependent on the change in specimen volume, while for volume-constant cases, the hydrogen loss mainly depends on the characteristic length. Therefore, it is recommended to use specimens with sufficiently large volume and characteristic length to prevent hydrogen loss during rest time, and to obtain more reliable information on the hydrogen trapping behavior through TDS tests.