Pub Date : 2014-12-09DOI: 10.1109/CIES.2014.7011835
V. Stanković, J. Liao, L. Stanković
Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches.
{"title":"A graph-based signal processing approach for low-rate energy disaggregation","authors":"V. Stanković, J. Liao, L. Stanković","doi":"10.1109/CIES.2014.7011835","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011835","url":null,"abstract":"Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121459396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011824
Afshin Gholamy, V. Kreinovich
In many engineering applications ranging from engineering seismology to petroleum engineering and civil engineering, it is important to process seismic data. In processing seismic data, it turns out to be very efficient to describe the signal's spectrum as a linear combination of Ricker wavelet spectra. In this paper, we provide a possible theoretical explanation for this empirical efficiency. Specifically, signal propagation through several layers is discussed, and it is shown that the Ricker wavelet is the simplest non-trivial solution for the corresponding data processing problem, under the condition that the described properties of the approximation family are satisfied.
{"title":"Why Ricker wavelets are successful in processing seismic data: Towards a theoretical explanation","authors":"Afshin Gholamy, V. Kreinovich","doi":"10.1109/CIES.2014.7011824","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011824","url":null,"abstract":"In many engineering applications ranging from engineering seismology to petroleum engineering and civil engineering, it is important to process seismic data. In processing seismic data, it turns out to be very efficient to describe the signal's spectrum as a linear combination of Ricker wavelet spectra. In this paper, we provide a possible theoretical explanation for this empirical efficiency. Specifically, signal propagation through several layers is discussed, and it is shown that the Ricker wavelet is the simplest non-trivial solution for the corresponding data processing problem, under the condition that the described properties of the approximation family are satisfied.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121055918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011827
A. Buck, J. Keller
This paper presents a novel method for visualizing vectors of fuzzy numbers. The proposed approach is an extension of the standard polar area diagram and can be applied to a single uncertain vector or a fuzzy weighted graph with vectors of fuzzy attributes on the vertices and/or edges. The resulting diagrams are intuitive to understand and do not require an extensive background in fuzzy set theory. By visualizing uncertain vectors in this way, the viewer can easily compare and contrast sets of fuzzy numbers. This can be useful in the context of decision support systems, particularly those involving multi-criteria decision making. We demonstrate our approach on the problem of finding a least-cost path through an uncertain environment.
{"title":"Visualizing uncertainty with fuzzy rose diagrams","authors":"A. Buck, J. Keller","doi":"10.1109/CIES.2014.7011827","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011827","url":null,"abstract":"This paper presents a novel method for visualizing vectors of fuzzy numbers. The proposed approach is an extension of the standard polar area diagram and can be applied to a single uncertain vector or a fuzzy weighted graph with vectors of fuzzy attributes on the vertices and/or edges. The resulting diagrams are intuitive to understand and do not require an extensive background in fuzzy set theory. By visualizing uncertain vectors in this way, the viewer can easily compare and contrast sets of fuzzy numbers. This can be useful in the context of decision support systems, particularly those involving multi-criteria decision making. We demonstrate our approach on the problem of finding a least-cost path through an uncertain environment.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116397982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011823
Quentin Brefort, L. Jaulin, M. Ceberio, V. Kreinovich
Constraints are ubiquitous in science and engineering. Constraints describe the available information about the state of the system, constraints describe possible relation between current and future states of the system, constraints describe which future states we would like to obtain. To solve problems from engineering and science, it is therefore necessary to process constraints. We show that if we treat constraints as hard (crisp), with all the threshold values exactly known, then in the general case, all the corresponding computational problems become algorithmically unsolvable. However, these problems become algorithmically solvable if we take into account that in reality, constraints are soft: we do not know the exact values of the corresponding thresholds, we do not know the exact dependence between the present and future states, etc.
{"title":"If we take into account that constraints are soft, then processing constraints becomes algorithmically solvable","authors":"Quentin Brefort, L. Jaulin, M. Ceberio, V. Kreinovich","doi":"10.1109/CIES.2014.7011823","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011823","url":null,"abstract":"Constraints are ubiquitous in science and engineering. Constraints describe the available information about the state of the system, constraints describe possible relation between current and future states of the system, constraints describe which future states we would like to obtain. To solve problems from engineering and science, it is therefore necessary to process constraints. We show that if we treat constraints as hard (crisp), with all the threshold values exactly known, then in the general case, all the corresponding computational problems become algorithmically unsolvable. However, these problems become algorithmically solvable if we take into account that in reality, constraints are soft: we do not know the exact values of the corresponding thresholds, we do not know the exact dependence between the present and future states, etc.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130136936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011829
D. N. Coelho, G. Barreto, Cláudio M. S. Medeiros, J. Santos
This paper aims at the detection of short-circuit incipient fault condition in a three-phase squirrel-cage induction motor fed by a sinusoidal PWM converter. In order to detect this fault, different operation conditions are applied to an induction motor, and each sample of the real data set is taken from the line currents of the PWM converter aforementioned. For feature extraction, the Motor Current Signature Analysis (MCSA) is used. The detection of this fault is treated as a classification problem, therefore different supervised algorithms of machine learning are used so as to solve it: Multi-layer Perceptron (MLP), Extreme Learning Machine (ELM), Support-Vector Machine (SVM), Least-Squares Support-Vector Machine (LSSVM), and the Minimal Learning Machine (MLM). These classifiers are tested and the results are compared with other works with the same data set. In near future, an embedded system can be equipped with these algorithms.
{"title":"Performance comparison of classifiers in the detection of short circuit incipient fault in a three-phase induction motor","authors":"D. N. Coelho, G. Barreto, Cláudio M. S. Medeiros, J. Santos","doi":"10.1109/CIES.2014.7011829","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011829","url":null,"abstract":"This paper aims at the detection of short-circuit incipient fault condition in a three-phase squirrel-cage induction motor fed by a sinusoidal PWM converter. In order to detect this fault, different operation conditions are applied to an induction motor, and each sample of the real data set is taken from the line currents of the PWM converter aforementioned. For feature extraction, the Motor Current Signature Analysis (MCSA) is used. The detection of this fault is treated as a classification problem, therefore different supervised algorithms of machine learning are used so as to solve it: Multi-layer Perceptron (MLP), Extreme Learning Machine (ELM), Support-Vector Machine (SVM), Least-Squares Support-Vector Machine (LSSVM), and the Minimal Learning Machine (MLM). These classifiers are tested and the results are compared with other works with the same data set. In near future, an embedded system can be equipped with these algorithms.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133329264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011843
Shawn C. Eastwood, S. Yanushkevich
Risk profiler in this paper is understood as the tool for risk assessment in automated human authentication systems. Authentication of a biometric enable e-passport/ID and the holder of this document (traveler) are the most important functions of such systems. Risks in this procedure are related to both technical and human factors. We developed a profiler tool which can measure the system performance at an arbitrary state of the authentication process. The tool is based on modeling modules, and each is represented by a Belief network and interfacing between the modules. A collection of small modules with reasonably chosen parameters is given as an example, and some examples demonstrating how the modules can be used for inference is also given.
{"title":"Risk profiler in automated human authentication","authors":"Shawn C. Eastwood, S. Yanushkevich","doi":"10.1109/CIES.2014.7011843","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011843","url":null,"abstract":"Risk profiler in this paper is understood as the tool for risk assessment in automated human authentication systems. Authentication of a biometric enable e-passport/ID and the holder of this document (traveler) are the most important functions of such systems. Risks in this procedure are related to both technical and human factors. We developed a profiler tool which can measure the system performance at an arbitrary state of the authentication process. The tool is based on modeling modules, and each is represented by a Belief network and interfacing between the modules. A collection of small modules with reasonably chosen parameters is given as an example, and some examples demonstrating how the modules can be used for inference is also given.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121406567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011839
Lingyu Lyu, M. Kantardzic, Elaheh Arabmakki
Predicting of global solar irradiance is very important in applications using solar energy resources. This research introduces a new methodology to estimate the solar irradiance. Denoising based on wavelet transformation as a preprocessing step is applied to the time series meteorological data. Artificial neural network and support vector machine are then used to make predictive model on Global Horizontal Irradiance (GHI) for the three cities located in California, Kentucky and New York, individually. Detailed experimental analysis is presented for the developed predictive models and comparisons with existing methodologies show that the proposed approach gives a significant improvement with increased generality.
{"title":"Solar irradiance forecasting by using wavelet based denoising","authors":"Lingyu Lyu, M. Kantardzic, Elaheh Arabmakki","doi":"10.1109/CIES.2014.7011839","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011839","url":null,"abstract":"Predicting of global solar irradiance is very important in applications using solar energy resources. This research introduces a new methodology to estimate the solar irradiance. Denoising based on wavelet transformation as a preprocessing step is applied to the time series meteorological data. Artificial neural network and support vector machine are then used to make predictive model on Global Horizontal Irradiance (GHI) for the three cities located in California, Kentucky and New York, individually. Detailed experimental analysis is presented for the developed predictive models and comparisons with existing methodologies show that the proposed approach gives a significant improvement with increased generality.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134334364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011840
Liam A. Comerford, M. Beer, I. Kougioumtzoglou
A compressive sensing (CS) based approach is developed in conjunction with an adaptive basis reweighting procedure for stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. To this aim, an ensemble of stochastic process realizations is often assumed to be available. Relying on this attribute an adaptive data mining procedure is introduced to modify harmonic basis coefficients, vastly improving on standard CS reconstructions. The procedure is shown to perform well with stationary and non-stationary processes even with up to 75% missing data. Several numerical examples demonstrate the effectiveness of the approach when applied to noisy, gappy signals.
{"title":"Compressive sensing based power spectrum estimation from incomplete records by utilizing an adaptive basis","authors":"Liam A. Comerford, M. Beer, I. Kougioumtzoglou","doi":"10.1109/CIES.2014.7011840","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011840","url":null,"abstract":"A compressive sensing (CS) based approach is developed in conjunction with an adaptive basis reweighting procedure for stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. To this aim, an ensemble of stochastic process realizations is often assumed to be available. Relying on this attribute an adaptive data mining procedure is introduced to modify harmonic basis coefficients, vastly improving on standard CS reconstructions. The procedure is shown to perform well with stationary and non-stationary processes even with up to 75% missing data. Several numerical examples demonstrate the effectiveness of the approach when applied to noisy, gappy signals.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129709482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011825
J. Herten, D. Deschrijver, T. Dhaene
When approximating complex high-fidelity black box simulators with surrogate models, the experimental design is often created sequentially. LOLA-Voronoi, a powerful state of the art method for sequential design combines an Exploitation and Exploration algorithm and adapts the sampling distribution to provide extra samples in non-linear regions. The LOLA algorithm estimates gradients to identify interesting regions, but has a bad complexity which results in long computation time when simulators are high-dimensional. In this paper, a new gradient estimation approach for the LOLA algorithm is proposed based on Fuzzy Logic. Experiments show the new method is a lot faster and results in experimental designs of comparable quality.
{"title":"Fuzzy local linear approximation-based sequential design","authors":"J. Herten, D. Deschrijver, T. Dhaene","doi":"10.1109/CIES.2014.7011825","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011825","url":null,"abstract":"When approximating complex high-fidelity black box simulators with surrogate models, the experimental design is often created sequentially. LOLA-Voronoi, a powerful state of the art method for sequential design combines an Exploitation and Exploration algorithm and adapts the sampling distribution to provide extra samples in non-linear regions. The LOLA algorithm estimates gradients to identify interesting regions, but has a bad complexity which results in long computation time when simulators are high-dimensional. In this paper, a new gradient estimation approach for the LOLA algorithm is proposed based on Fuzzy Logic. Experiments show the new method is a lot faster and results in experimental designs of comparable quality.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122264647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011833
Jin Huang, Xibin Zhao, Xinjie Chen, Jiaguang Sun, Qinwen Yang
The control sequence optimization problem is difficult to solve due to its high nonlinearity, various constraints and the possible changes in the sequence of comprising elements at any instant of time. The optimization of train trip running profile is a typical control sequence optimization problem, whose optimization object is to minimize the energy consumption as well as the time deviation under various constraints. Engineers always have to face the trade-off between the optimization performance and calculation time for an onboard control system for such problems. This paper mainly proposed a framework of an offline to onboard system solution for control sequence optimization problems, specifically using on the train trip profile optimization problems. The framework choose the parameter-decision tree solution for the onboard control system, and then a series of offline procedures including sequence mining, optimal computation, and machine learning is proposed for getting the parameter-decision tree. The framework inherits the good optimization performance of offline systems, as well as guaranteed the onboard calculation time for real-time control. Performance on using such a framework for solving train trip profile optimization problems is shown in the literature, which shows the potentials of using such frameworks on solving related control sequence optimization problems.
{"title":"From offline to onboard system solution for a control sequence optimization problem","authors":"Jin Huang, Xibin Zhao, Xinjie Chen, Jiaguang Sun, Qinwen Yang","doi":"10.1109/CIES.2014.7011833","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011833","url":null,"abstract":"The control sequence optimization problem is difficult to solve due to its high nonlinearity, various constraints and the possible changes in the sequence of comprising elements at any instant of time. The optimization of train trip running profile is a typical control sequence optimization problem, whose optimization object is to minimize the energy consumption as well as the time deviation under various constraints. Engineers always have to face the trade-off between the optimization performance and calculation time for an onboard control system for such problems. This paper mainly proposed a framework of an offline to onboard system solution for control sequence optimization problems, specifically using on the train trip profile optimization problems. The framework choose the parameter-decision tree solution for the onboard control system, and then a series of offline procedures including sequence mining, optimal computation, and machine learning is proposed for getting the parameter-decision tree. The framework inherits the good optimization performance of offline systems, as well as guaranteed the onboard calculation time for real-time control. Performance on using such a framework for solving train trip profile optimization problems is shown in the literature, which shows the potentials of using such frameworks on solving related control sequence optimization problems.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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