Pub Date : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch009
Flávio C. A. Teixeira, A. Romariz
This chapter presents the application of a comprehensive statistical analysis for both algorithmic performance comparison and optimal parameter estimation on a multi-objective digital signal processing problem. The problem of designing optimum digital finite impulse response (FIR) filters with the simultaneous approximation of the filter magnitude and phase is posed as a multiobjective optimization problem. Several computational-intelligence-based algorithms for solving this particular optimization problem are presented: genetic algorithms (GA), particle swarm optimization (PSO) and simulated annealing (SA) with multi-objective scalarization methods. Algorithms with Pareto sampling methods, namely non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective simulated annealing (MOSA) are also applied as a way of dealing with multi-objective optimization. Instead of using a process of trial and error, a statistical exploratory analysis is used to estimate optimal parameters. A comprehensive statistical comparison of the applied algorithms is addressed, which indicates a particularly strong performance of NSGA-II and pure GA with weighting scalarization.
{"title":"Statistical Analysis of Computational Intelligence Algorithms on a Multi-Objective Filter Design Problem","authors":"Flávio C. A. Teixeira, A. Romariz","doi":"10.4018/978-1-60566-798-0.ch009","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch009","url":null,"abstract":"This chapter presents the application of a comprehensive statistical analysis for both algorithmic performance comparison and optimal parameter estimation on a multi-objective digital signal processing problem. The problem of designing optimum digital finite impulse response (FIR) filters with the simultaneous approximation of the filter magnitude and phase is posed as a multiobjective optimization problem. Several computational-intelligence-based algorithms for solving this particular optimization problem are presented: genetic algorithms (GA), particle swarm optimization (PSO) and simulated annealing (SA) with multi-objective scalarization methods. Algorithms with Pareto sampling methods, namely non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective simulated annealing (MOSA) are also applied as a way of dealing with multi-objective optimization. Instead of using a process of trial and error, a statistical exploratory analysis is used to estimate optimal parameters. A comprehensive statistical comparison of the applied algorithms is addressed, which indicates a particularly strong performance of NSGA-II and pure GA with weighting scalarization.","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130731639","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch002
L. Ang, K. Lim, K. Seng, Siew Wen Chin
This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems. DOI: 10.4018/978-1-60566-798-0.ch002
{"title":"A Lyapunov Theory-Based Neural Network Approach for Face Recognition","authors":"L. Ang, K. Lim, K. Seng, Siew Wen Chin","doi":"10.4018/978-1-60566-798-0.ch002","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch002","url":null,"abstract":"This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems. DOI: 10.4018/978-1-60566-798-0.ch002","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130604669","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch006
T. Weise, R. Chiong
The ubiquitous presence of distributed systems has drastically changed the way the world interacts, and impacted not only the economics and governance but also the society at large. It is therefore important for the architecture and infrastructure within the distributed environment to be continuously renewed in order to cope with the rapid changes driven by the innovative technologies. However, many problems in distributed computing are either of dynamic nature, large scale, NP complete, or a combination of any of these. In most cases, exact solutions are hardly found. As a result, a number of intelligent nature-inspired algorithms have been used recently, as these algorithms are capable of achieving good quality solutions in reasonable computational time. Among all the nature-inspired algorithms, evolutionary algorithms are considerably the most extensively applied ones. This chapter presents a systematic review of evolutionary algorithms employed to solve various problems related to distributed systems. The review is aimed at providing an insight of evolutionary approaches, in particular genetic algorithms and genetic programming, in solving problems in five different areas of network optimization: network topology, routing, protocol synthesis, network security, and parameter settings and configuration. Some interesting applications from these areas will be discussed in detail with the use of illustrative examples.
{"title":"Evolutionary Approaches and Their Applications to Distributed Systems","authors":"T. Weise, R. Chiong","doi":"10.4018/978-1-60566-798-0.ch006","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch006","url":null,"abstract":"The ubiquitous presence of distributed systems has drastically changed the way the world interacts, and impacted not only the economics and governance but also the society at large. It is therefore important for the architecture and infrastructure within the distributed environment to be continuously renewed in order to cope with the rapid changes driven by the innovative technologies. However, many problems in distributed computing are either of dynamic nature, large scale, NP complete, or a combination of any of these. In most cases, exact solutions are hardly found. As a result, a number of intelligent nature-inspired algorithms have been used recently, as these algorithms are capable of achieving good quality solutions in reasonable computational time. Among all the nature-inspired algorithms, evolutionary algorithms are considerably the most extensively applied ones. This chapter presents a systematic review of evolutionary algorithms employed to solve various problems related to distributed systems. The review is aimed at providing an insight of evolutionary approaches, in particular genetic algorithms and genetic programming, in solving problems in five different areas of network optimization: network topology, routing, protocol synthesis, network security, and parameter settings and configuration. Some interesting applications from these areas will be discussed in detail with the use of illustrative examples.","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130903233","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch003
M. Albertini, R. Mello
Machine learning is a field of artificial intelligence which aims at developing techniques to automatically transfer human knowledge into analytical models. Recently, those techniques have been applied to time series with unknown dynamics and fluctuations in the established behavior patterns, such as humancomputer interaction, inspection robotics and climate change. In order to detect novelties in those time series, techniques are required to learn and update knowledge structures, adapting themselves to data tendencies. The learning and updating process should integrate and accommodate novelty events into the normal behavior model, possibly incurring the revaluation of long-term memories. This sort of application has been addressed by the proposal of incremental techniques based on unsupervised neural networks and regression techniques. Such proposals have introduced two new concepts in time-series novelty detection. The first defines the temporal novelty, which indicates the occurrence of unexpected series of events. The second measures how novel a single event is, based on the historical knowledge. However, current studies do not fully consider both concepts of detecting and quantifying temporal novelties. This motivated the proposal of the self-organizing novelty detection neural network architecture (SONDE) which incrementally learns patterns in order to represent unknown dynamics and fluctuation of established behavior. The knowledge accumulated by SONDE is employed to estimate Markov chains which model causal relationships. This architecture is applied to detect and measure temporal and nontemporal novelties. The evaluation of the proposed technique is carried out through simulations and experiments, which have presented promising results. DOI: 10.4018/978-1-60566-798-0.ch003
{"title":"A Self-Organizing Neural Network to Approach Novelty Detection","authors":"M. Albertini, R. Mello","doi":"10.4018/978-1-60566-798-0.ch003","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch003","url":null,"abstract":"Machine learning is a field of artificial intelligence which aims at developing techniques to automatically transfer human knowledge into analytical models. Recently, those techniques have been applied to time series with unknown dynamics and fluctuations in the established behavior patterns, such as humancomputer interaction, inspection robotics and climate change. In order to detect novelties in those time series, techniques are required to learn and update knowledge structures, adapting themselves to data tendencies. The learning and updating process should integrate and accommodate novelty events into the normal behavior model, possibly incurring the revaluation of long-term memories. This sort of application has been addressed by the proposal of incremental techniques based on unsupervised neural networks and regression techniques. Such proposals have introduced two new concepts in time-series novelty detection. The first defines the temporal novelty, which indicates the occurrence of unexpected series of events. The second measures how novel a single event is, based on the historical knowledge. However, current studies do not fully consider both concepts of detecting and quantifying temporal novelties. This motivated the proposal of the self-organizing novelty detection neural network architecture (SONDE) which incrementally learns patterns in order to represent unknown dynamics and fluctuation of established behavior. The knowledge accumulated by SONDE is employed to estimate Markov chains which model causal relationships. This architecture is applied to detect and measure temporal and nontemporal novelties. The evaluation of the proposed technique is carried out through simulations and experiments, which have presented promising results. DOI: 10.4018/978-1-60566-798-0.ch003","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122798070","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch012
P. Renna
These days competition is played in an environment characterized by high market shifting, rapid development as well as introduction of new technologies, global competition and customer needs focalization. Therefore, manufacturing environments are becoming more dynamic and turbulent than ever before. Traditional manufacturing facilities, however, are not able to cope with such environments, as no single aBsTracT
{"title":"A Performance Comparison between Efficiency and Pheromone Approaches in Dynamic Manufacturing Scheduling","authors":"P. Renna","doi":"10.4018/978-1-60566-798-0.ch012","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch012","url":null,"abstract":"These days competition is played in an environment characterized by high market shifting, rapid development as well as introduction of new technologies, global competition and customer needs focalization. Therefore, manufacturing environments are becoming more dynamic and turbulent than ever before. Traditional manufacturing facilities, however, are not able to cope with such environments, as no single aBsTracT","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124753808","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch001
A. Upegui
During the last few years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bit-string, providing high architectural flexibility, while guaranteeing high performance. On the other hand, we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse approaches like evolvable hardware, neural hardware, and fuzzy hardware. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in the early stages and they are not well supported by FPGA vendors, thus making their inclusion difficult in existing bio-inspired systems. This chapter presents a set of methodologies and architectures for exploiting the reconfigurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures are neural networks, spiking neuron models, fuzzy systems, cellular automata and Random Boolean Networks.
{"title":"Dynamically Reconfigurable Hardware for Evolving Bio-Inspired Architectures","authors":"A. Upegui","doi":"10.4018/978-1-60566-798-0.ch001","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch001","url":null,"abstract":"During the last few years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bit-string, providing high architectural flexibility, while guaranteeing high performance. On the other hand, we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse approaches like evolvable hardware, neural hardware, and fuzzy hardware. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in the early stages and they are not well supported by FPGA vendors, thus making their inclusion difficult in existing bio-inspired systems. This chapter presents a set of methodologies and architectures for exploiting the reconfigurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures are neural networks, spiking neuron models, fuzzy systems, cellular automata and Random Boolean Networks.","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132639481","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch011
M. Boryczka
{"title":"Ant Colony Programming","authors":"M. Boryczka","doi":"10.4018/978-1-60566-798-0.ch011","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch011","url":null,"abstract":"","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127618333","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch004
A. K. Palit, W. Anheier
An ideal linear sensor is one for which input and output values are always proportional. Typical sensors are, in general, highly nonlinear or seldom sufficiently linear enough to be useful over a wide range or span of interest. Due to the requirement of tedious effort in designing sensor circuits with sufficient linearity for some applications, the word nonlinearity has acquired a pejorative connotation. Hence, a computationally intelligent tool for extending the linear range of an arbitrary sensor is proposed. The linearization technique is carried out by a very efficiently trained neuro-fuzzy hybrid network which compensates for the sensor’s nonlinear characteristic. The training algorithm is very efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than any first order training algorithm. Linearization of a negative temperature coefficient thermistor sensor with an exponentially decaying characteristic function is used as an application example, which demonstrates the efficacy of the procedure. The proposed linearization technique is also applicable for any nonlinear sensor (such as J-type thermocouple or pH sensor), whose output is a monotonically increasing/decreasing function.
{"title":"Efficient Training Algorithm for Neuro-Fuzzy Network and its Application to Nonlinear Sensor Characteristic Linearization","authors":"A. K. Palit, W. Anheier","doi":"10.4018/978-1-60566-798-0.ch004","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch004","url":null,"abstract":"An ideal linear sensor is one for which input and output values are always proportional. Typical sensors are, in general, highly nonlinear or seldom sufficiently linear enough to be useful over a wide range or span of interest. Due to the requirement of tedious effort in designing sensor circuits with sufficient linearity for some applications, the word nonlinearity has acquired a pejorative connotation. Hence, a computationally intelligent tool for extending the linear range of an arbitrary sensor is proposed. The linearization technique is carried out by a very efficiently trained neuro-fuzzy hybrid network which compensates for the sensor’s nonlinear characteristic. The training algorithm is very efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than any first order training algorithm. Linearization of a negative temperature coefficient thermistor sensor with an exponentially decaying characteristic function is used as an application example, which demonstrates the efficacy of the procedure. The proposed linearization technique is also applicable for any nonlinear sensor (such as J-type thermocouple or pH sensor), whose output is a monotonically increasing/decreasing function.","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121285299","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch005
S. García, J. Cano, F. Herrera
{"title":"A Review on Evolutionary Prototype Selection","authors":"S. García, J. Cano, F. Herrera","doi":"10.4018/978-1-60566-798-0.ch005","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch005","url":null,"abstract":"","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"327 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115966169","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 : 1900-01-01DOI: 10.4018/978-1-60566-798-0.ch007
G. Yen
Humans and computers form teams in complex environments such as in aviation, glass cockpit, nuclear power plants, manufacturing lines, and command and control scenarios. The computers generally undertake the automation part while the human is responsible for the supervision of the overall task or interrupts the process at the higher level. Task sharing is generally done at design time, using Fitts list (Fitts, 1951). Automation was thought to be the remedy to the problems resulting from human errors. aBsTracT
{"title":"Evolutionary Based Adaptive User Interfaces in Complex Supervisory Tasks","authors":"G. Yen","doi":"10.4018/978-1-60566-798-0.ch007","DOIUrl":"https://doi.org/10.4018/978-1-60566-798-0.ch007","url":null,"abstract":"Humans and computers form teams in complex environments such as in aviation, glass cockpit, nuclear power plants, manufacturing lines, and command and control scenarios. The computers generally undertake the automation part while the human is responsible for the supervision of the overall task or interrupts the process at the higher level. Task sharing is generally done at design time, using Fitts list (Fitts, 1951). Automation was thought to be the remedy to the problems resulting from human errors. aBsTracT","PeriodicalId":325405,"journal":{"name":"Intelligent Systems for Automated Learning and Adaptation","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129298287","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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