Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557694
Feng Xie, A. Uitdenbogerd, A. Song
A novel approach is proposed in this study, which is to evolve visual inspection programs for automatic defect detection on populated printed circuit boards. This GP-based method does not require knowledge of the layout design of a board, nor relevant domain knowledge such as lighting conditions and visual characteristics of the components. Furthermore, conventional image operators are not required to perform the detection. The experiments show that these evolved GP programs can identify all the faults while some suspicious areas are also highlighted. By this GP approach, manual inspection effort can be dramatically reduced. In addition, an evolved GP detection program can readily work on different types of boards without re-training.
{"title":"Detecting PCB component placement defects by genetic programming","authors":"Feng Xie, A. Uitdenbogerd, A. Song","doi":"10.1109/CEC.2013.6557694","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557694","url":null,"abstract":"A novel approach is proposed in this study, which is to evolve visual inspection programs for automatic defect detection on populated printed circuit boards. This GP-based method does not require knowledge of the layout design of a board, nor relevant domain knowledge such as lighting conditions and visual characteristics of the components. Furthermore, conventional image operators are not required to perform the detection. The experiments show that these evolved GP programs can identify all the faults while some suspicious areas are also highlighted. By this GP approach, manual inspection effort can be dramatically reduced. In addition, an evolved GP detection program can readily work on different types of boards without re-training.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126542904","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557855
Rui Wang, Zhang Tao, Bo Guo
Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has become increasingly popular in solving multi-objective problems (MOPs). In MOEA/D, weight vectors are responsible for maintaining a nice distribution of Pareto optimal solutions. Often, we expect to obtain a set of uniformly distributed solutions by applying a set of uniformly distributed weight vectors in MOEA/D. In this paper, we argue that uniformly distributed weights do not produce uniformly distributed solutions, however, uniformly distributed search directions do. Moreover, we propose to perform a pre-organization procedure before running MOEA/D. The procedure matches each weight to its closet candidate solution. Experimental results show (i) MOEA/D with uniformly distributed search directions would exhibit a better diversity performance, and (ii) MOEA/D with the pre-organization procedure performs better, especially for the convergence performance.
{"title":"An enhanced MOEA/D using uniform directions and a pre-organization procedure","authors":"Rui Wang, Zhang Tao, Bo Guo","doi":"10.1109/CEC.2013.6557855","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557855","url":null,"abstract":"Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has become increasingly popular in solving multi-objective problems (MOPs). In MOEA/D, weight vectors are responsible for maintaining a nice distribution of Pareto optimal solutions. Often, we expect to obtain a set of uniformly distributed solutions by applying a set of uniformly distributed weight vectors in MOEA/D. In this paper, we argue that uniformly distributed weights do not produce uniformly distributed solutions, however, uniformly distributed search directions do. Moreover, we propose to perform a pre-organization procedure before running MOEA/D. The procedure matches each weight to its closet candidate solution. Experimental results show (i) MOEA/D with uniformly distributed search directions would exhibit a better diversity performance, and (ii) MOEA/D with the pre-organization procedure performs better, especially for the convergence performance.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126566336","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557846
Khalid M. Salama, A. Freitas
Learning classifiers from datasets is a central problem in data mining and machine learning research. ABC-Miner is an Ant-based Bayesian Classification algorithm that employs the Ant Colony Optimization (ACO) meta-heuristics to learn the structure of Bayesian Augmented Naive-Bayes (BAN) Classifiers. One of the most important aspects of the ACO algorithm is the choice of the quality measure used to evaluate a candidate solution to update pheromone. In this paper, we explore the use of various classification quality measures for evaluating the BAN classifiers constructed by the ants. The aim of this investigation is to discover how the use of different evaluation measures affects the quality of the output classifier in terms of predictive accuracy. In our experiments, we use 6 different classification measures on 25 benchmark datasets. We found that the hypothesis that different measures produce different results is acceptable according to the Friedman's statistical test.
{"title":"Investigating the impact of various classification quality measures in the predictive accuracy of ABC-Miner","authors":"Khalid M. Salama, A. Freitas","doi":"10.1109/CEC.2013.6557846","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557846","url":null,"abstract":"Learning classifiers from datasets is a central problem in data mining and machine learning research. ABC-Miner is an Ant-based Bayesian Classification algorithm that employs the Ant Colony Optimization (ACO) meta-heuristics to learn the structure of Bayesian Augmented Naive-Bayes (BAN) Classifiers. One of the most important aspects of the ACO algorithm is the choice of the quality measure used to evaluate a candidate solution to update pheromone. In this paper, we explore the use of various classification quality measures for evaluating the BAN classifiers constructed by the ants. The aim of this investigation is to discover how the use of different evaluation measures affects the quality of the output classifier in terms of predictive accuracy. In our experiments, we use 6 different classification measures on 25 benchmark datasets. We found that the hypothesis that different measures produce different results is acceptable according to the Friedman's statistical test.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126597108","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557778
U. Johansson, Rikard König, Tuwe Löfström, Henrik Boström
In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification, this translates to that the probability of excluding the correct class is lower than a predefined significance level, in the long run. Since the error rate is guaranteed, the most important criterion for conformal predictors is efficiency. Efficient conformal predictors minimize the number of elements in the output prediction sets, thus producing more informative predictions. This paper presents one of the first comprehensive studies where evolutionary algorithms are used to build conformal predictors. More specifically, decision trees evolved using genetic programming are evaluated as conformal predictors. In the experiments, the evolved trees are compared to decision trees induced using standard machine learning techniques on 33 publicly available benchmark data sets, with regard to predictive performance and efficiency. The results show that the evolved trees are generally more accurate, and the corresponding conformal predictors more efficient, than their induced counterparts. One important result is that the probability estimates of decision trees when used as conformal predictors should be smoothed, here using the Laplace correction. Finally, using the more discriminating Brier score instead of accuracy as the optimization criterion produced the most efficient conformal predictions.
{"title":"Evolved decision trees as conformal predictors","authors":"U. Johansson, Rikard König, Tuwe Löfström, Henrik Boström","doi":"10.1109/CEC.2013.6557778","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557778","url":null,"abstract":"In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification, this translates to that the probability of excluding the correct class is lower than a predefined significance level, in the long run. Since the error rate is guaranteed, the most important criterion for conformal predictors is efficiency. Efficient conformal predictors minimize the number of elements in the output prediction sets, thus producing more informative predictions. This paper presents one of the first comprehensive studies where evolutionary algorithms are used to build conformal predictors. More specifically, decision trees evolved using genetic programming are evaluated as conformal predictors. In the experiments, the evolved trees are compared to decision trees induced using standard machine learning techniques on 33 publicly available benchmark data sets, with regard to predictive performance and efficiency. The results show that the evolved trees are generally more accurate, and the corresponding conformal predictors more efficient, than their induced counterparts. One important result is that the probability estimates of decision trees when used as conformal predictors should be smoothed, here using the Laplace correction. Finally, using the more discriminating Brier score instead of accuracy as the optimization criterion produced the most efficient conformal predictions.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122188386","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557697
Feng Xie, A. Song, V. Ciesielski
Recognition of activities such as sitting, standing, walking and running can significantly improve the interaction between human and machine, especially on mobile devices. In this study we present a GP based method which can automatically evolve recognition programs for various activities using multisensor data. This investigation shows that GP is capable of achieving good recognition on binary problems as well as on multi-class problems. With this method domain knowledge about an activity is not required. Furthermore, extraction of time series features is not necessary. The investigation also shows that these evolved GP solutions are small in size and fast in execution. They are suitable for real-world applications which may require real-time performance.
{"title":"Activity recognition by smartphone based multi-channel sensors with genetic programming","authors":"Feng Xie, A. Song, V. Ciesielski","doi":"10.1109/CEC.2013.6557697","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557697","url":null,"abstract":"Recognition of activities such as sitting, standing, walking and running can significantly improve the interaction between human and machine, especially on mobile devices. In this study we present a GP based method which can automatically evolve recognition programs for various activities using multisensor data. This investigation shows that GP is capable of achieving good recognition on binary problems as well as on multi-class problems. With this method domain knowledge about an activity is not required. Furthermore, extraction of time series features is not necessary. The investigation also shows that these evolved GP solutions are small in size and fast in execution. They are suitable for real-world applications which may require real-time performance.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114183976","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557914
Aneta Stevanovic, Bing Xue, Mengjie Zhang
In this paper, we propose two new methods for feature selection based on particle swarm optimisation and a probabilistic rough set model called decision-theoretic rough set (DTRS). The first method uses rule degradation and cost properties of DTRS in the fitness function. This method focuses on the quality of the selected feature subset as a whole. The second method extends the first one by adding the individual feature confidence to the fitness function, which measures the quality of each feature in the subset. Three learning algorithms are employed to evaluate the classification performance of the proposed methods. The experiments are run on six commonly used datasets of varying difficulty. The results show that both methods can achieve good feature reduction rates with similar or better classification performance. Both methods can outperform two traditional feature selection methods. The second proposed method outperforms the first one in terms of the feature reduction rates while being able to maintaining similar or better classification rates.
{"title":"Feature selection based on PSO and decision-theoretic rough set model","authors":"Aneta Stevanovic, Bing Xue, Mengjie Zhang","doi":"10.1109/CEC.2013.6557914","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557914","url":null,"abstract":"In this paper, we propose two new methods for feature selection based on particle swarm optimisation and a probabilistic rough set model called decision-theoretic rough set (DTRS). The first method uses rule degradation and cost properties of DTRS in the fitness function. This method focuses on the quality of the selected feature subset as a whole. The second method extends the first one by adding the individual feature confidence to the fitness function, which measures the quality of each feature in the subset. Three learning algorithms are employed to evaluate the classification performance of the proposed methods. The experiments are run on six commonly used datasets of varying difficulty. The results show that both methods can achieve good feature reduction rates with similar or better classification performance. Both methods can outperform two traditional feature selection methods. The second proposed method outperforms the first one in terms of the feature reduction rates while being able to maintaining similar or better classification rates.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121063389","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557633
W. Raffe, Fabio Zambetta, Xiaodong Li
This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players.
{"title":"Neuroevolution of content layout in the PCG: Angry bots video game","authors":"W. Raffe, Fabio Zambetta, Xiaodong Li","doi":"10.1109/CEC.2013.6557633","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557633","url":null,"abstract":"This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123743340","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557602
Dumidu Wijayasekara, M. Manic, M. McQueen
Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexity is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.
{"title":"Information gain based dimensionality selection for classifying text documents","authors":"Dumidu Wijayasekara, M. Manic, M. McQueen","doi":"10.1109/CEC.2013.6557602","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557602","url":null,"abstract":"Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexity is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123774925","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557797
Benjamin Lacroix, D. Molina, F. Herrera
In this paper, we present a memetic algorithm which combines in a local search chaining framework, a steady-state genetic algorithm as evolutionary algorithm and a CMA-ES as local search method. It is an extension of an already presented algorithm which uses a region-based niching strategy and which has proven to be very efficient on real parameter optimisation problems. In this new version, we propose to dynamically update the niche size in order to make it less dependent to such critical parameter. In addition, we used an automatic configuration tool to optimise its parameters, and show that the optimised version of this algorithm is significantly better than with its default parameters. We tested this algorithm on the Special Session and Competition on Real-Parameter Optimization of the IEEE Congress on Evolutionary 2013 benchmark.
{"title":"Dynamically updated region based memetic algorithm for the 2013 CEC Special Session and Competition on Real Parameter Single Objective Optimization","authors":"Benjamin Lacroix, D. Molina, F. Herrera","doi":"10.1109/CEC.2013.6557797","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557797","url":null,"abstract":"In this paper, we present a memetic algorithm which combines in a local search chaining framework, a steady-state genetic algorithm as evolutionary algorithm and a CMA-ES as local search method. It is an extension of an already presented algorithm which uses a region-based niching strategy and which has proven to be very efficient on real parameter optimisation problems. In this new version, we propose to dynamically update the niche size in order to make it less dependent to such critical parameter. In addition, we used an automatic configuration tool to optimise its parameters, and show that the optimised version of this algorithm is significantly better than with its default parameters. We tested this algorithm on the Special Session and Competition on Real-Parameter Optimization of the IEEE Congress on Evolutionary 2013 benchmark.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124921390","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557795
S. Elsayed, R. Sarker, T. Ray
The performance of Differential Evolution (DE) algorithms is known to be highly dependent on its search operators and control parameters. The selection of the parameter values is a tedious task. In this paper, a DE algorithm is proposed that configures the values of two parameters (amplification factor and crossover rate) automatically during its course of evolution. For this purpose, we considered a set of values as input for each of the parameters. The algorithm has been applied to solve a set of test problems introduced in IEEE CEC'2013 competition. The results of the test problems are compared with the known best solutions and the approach can be applied to other population based algorithms.
{"title":"Differential evolution with automatic parameter configuration for solving the CEC2013 competition on Real-Parameter Optimization","authors":"S. Elsayed, R. Sarker, T. Ray","doi":"10.1109/CEC.2013.6557795","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557795","url":null,"abstract":"The performance of Differential Evolution (DE) algorithms is known to be highly dependent on its search operators and control parameters. The selection of the parameter values is a tedious task. In this paper, a DE algorithm is proposed that configures the values of two parameters (amplification factor and crossover rate) automatically during its course of evolution. For this purpose, we considered a set of values as input for each of the parameters. The algorithm has been applied to solve a set of test problems introduced in IEEE CEC'2013 competition. The results of the test problems are compared with the known best solutions and the approach can be applied to other population based algorithms.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114295348","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：