Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721995
Hao Zheng, Degang Wang, Wei Zhou
In this paper, a stochastic configured Bayesian neural network (SCBNN) is proposed for solving regression and classification problems. Firstly, stochastic configuration network (SCN) is applied to extract feature. Then, the stochastic configured scheme is applied to Bayesian neural network (BNN) for obtaining the appropriate structure. The extracted features are combined with the original features to compute the output of the network. Further, an integration strategy of the Bayesian model average (BMA) is considered to improve the performance of the network. Some experimental results demonstrate the validity of the proposed method.
{"title":"A modified Bayesian neural network integrating stochastic configuration network and ensemble learning strategy","authors":"Hao Zheng, Degang Wang, Wei Zhou","doi":"10.1109/ICCSS53909.2021.9721995","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721995","url":null,"abstract":"In this paper, a stochastic configured Bayesian neural network (SCBNN) is proposed for solving regression and classification problems. Firstly, stochastic configuration network (SCN) is applied to extract feature. Then, the stochastic configured scheme is applied to Bayesian neural network (BNN) for obtaining the appropriate structure. The extracted features are combined with the original features to compute the output of the network. Further, an integration strategy of the Bayesian model average (BMA) is considered to improve the performance of the network. Some experimental results demonstrate the validity of the proposed method.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125234806","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 : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721959
Youdam Chung, Wen-kai Lu, X. Tian
In this paper, we aim to solve problems in interactive segmentation, a technique which is widely used for data labeling tasks. It requires the user to provide clicks for the objects of interest. The user-provided clicks are transformed into the distance map, which plays an important role in the interactive segmentation. Therefore, we propose a novel distance map that is obtained by combining the automatic segmentation result with the user-provided clicks. Since we have validated that better automatic segmentation result leads to better interactive segmentation result, we concatenate the original image with its LOG (Laplacian of Gaussian) filter image to improve the automatic segmentation results. Besides, given that its successful implementation requires correct labels so as to enable the computer to simulate the user interaction, a data cleansing technique is applied to filter out samples with inaccurate labels also known as noisy labels. The effectiveness of our proposed method is assessed using the Kaggle’s TGS Salt Identification Challenge dataset. The obtained results indicate that when using the proposed algorithm, the average IoU reaches 91.81% for only one user-provided click.
{"title":"Interactive Segmentation Using Prior Knowledge-Based Distance Map","authors":"Youdam Chung, Wen-kai Lu, X. Tian","doi":"10.1109/ICCSS53909.2021.9721959","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721959","url":null,"abstract":"In this paper, we aim to solve problems in interactive segmentation, a technique which is widely used for data labeling tasks. It requires the user to provide clicks for the objects of interest. The user-provided clicks are transformed into the distance map, which plays an important role in the interactive segmentation. Therefore, we propose a novel distance map that is obtained by combining the automatic segmentation result with the user-provided clicks. Since we have validated that better automatic segmentation result leads to better interactive segmentation result, we concatenate the original image with its LOG (Laplacian of Gaussian) filter image to improve the automatic segmentation results. Besides, given that its successful implementation requires correct labels so as to enable the computer to simulate the user interaction, a data cleansing technique is applied to filter out samples with inaccurate labels also known as noisy labels. The effectiveness of our proposed method is assessed using the Kaggle’s TGS Salt Identification Challenge dataset. The obtained results indicate that when using the proposed algorithm, the average IoU reaches 91.81% for only one user-provided click.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131576904","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}
With the continuous expansion of the software industry, the problem of software defects is receiving more and more attention. There has been a series of machine learning methods applied to the field of software defect prediction (SDP) as a way to ensure the stability of software. However, SDP suffers from the imbalance problem. To solve this problem, we first propose a class-specific broad learning system (CSBLS), which assigns a specific penalty factor to each class in accordance with the data distribution. Then we design a class-specific kernel-based broad learning system (CSKBLS), which adopts kernel mapping instead of random projection. This additive kernel scheme takes into account both outliers and noise in the data set. Extensive experiments on the real-world NASA datasets show that CSKBLS outperforms the comparison methods on the tasks of software defect prediction.
{"title":"Kernel-based Class-specific Broad Learning System for software defect prediction","authors":"Wuxing Chen, Kaixiang Yang, Yifan Shi, Qiying Feng, Chengxi Zhang, Zhiwen Yu","doi":"10.1109/ICCSS53909.2021.9721979","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721979","url":null,"abstract":"With the continuous expansion of the software industry, the problem of software defects is receiving more and more attention. There has been a series of machine learning methods applied to the field of software defect prediction (SDP) as a way to ensure the stability of software. However, SDP suffers from the imbalance problem. To solve this problem, we first propose a class-specific broad learning system (CSBLS), which assigns a specific penalty factor to each class in accordance with the data distribution. Then we design a class-specific kernel-based broad learning system (CSKBLS), which adopts kernel mapping instead of random projection. This additive kernel scheme takes into account both outliers and noise in the data set. Extensive experiments on the real-world NASA datasets show that CSKBLS outperforms the comparison methods on the tasks of software defect prediction.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130489164","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 : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721956
Ying Hou, Yilin Wu, Hong-gui Han
Multi-objective vehicle routing problem with time windows (MOVRPTW) is a canonical logistics problem widely existing in supply chain. It is challenging to obtain the feasible solutions with fast convergence and well diversity due to the constraint of time windows. To address this issue, a solution evaluation-oriented multi-objective differential evolution (SE-MODE) algorithm is presented in this paper. First, a solution evaluation mechanism based on constraint dominance principle is developed to evaluate the dominance degree of feasible solutions and infeasible solutions quantitatively. Second, infeasible solutions with less dominance degree are utilized to generate solutions in the early stage of evolution adopting a memetic algorithm framework. Third, a feasible solution-oriented differential mutation strategy is developed to increase the probability of generating feasible solutions and improve the convergence of the population. Finally, the proposed SE-MODE algorithm is evaluated on the RC instances from Solomon, experimental results show that SE-MODE algorithm is promising in solving MOVRPTW.
{"title":"Solution Evaluation-Oriented Multi-objective Differential Evolution Algorithm for MOVRPTW","authors":"Ying Hou, Yilin Wu, Hong-gui Han","doi":"10.1109/ICCSS53909.2021.9721956","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721956","url":null,"abstract":"Multi-objective vehicle routing problem with time windows (MOVRPTW) is a canonical logistics problem widely existing in supply chain. It is challenging to obtain the feasible solutions with fast convergence and well diversity due to the constraint of time windows. To address this issue, a solution evaluation-oriented multi-objective differential evolution (SE-MODE) algorithm is presented in this paper. First, a solution evaluation mechanism based on constraint dominance principle is developed to evaluate the dominance degree of feasible solutions and infeasible solutions quantitatively. Second, infeasible solutions with less dominance degree are utilized to generate solutions in the early stage of evolution adopting a memetic algorithm framework. Third, a feasible solution-oriented differential mutation strategy is developed to increase the probability of generating feasible solutions and improve the convergence of the population. Finally, the proposed SE-MODE algorithm is evaluated on the RC instances from Solomon, experimental results show that SE-MODE algorithm is promising in solving MOVRPTW.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130727907","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 : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721941
Ailin Xue, Xiaoli Li, Chunfang Liu
In human-robot cooperation, it is a challenge thing that the robot should perform to convert humans' natural languages to continuous action sequences, which is necessary for completing complex collaborative tasks. In this paper, firstly, a new knowledge base is built for encoding different features of movements, objects and relations; then, a hierarchical motion sequences retrieval algorithm is presented by combining our knowledge base with Deep Q-learning. Finally, the experiments verify that the developed reasoning system is effective and accomplishes to manipulate the objects to reach target statuses.
{"title":"A Hierarchical Motion Retrieval Algorithm for Complex Manipulation Tasks Planning with An Encoded Knowledge Base","authors":"Ailin Xue, Xiaoli Li, Chunfang Liu","doi":"10.1109/ICCSS53909.2021.9721941","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721941","url":null,"abstract":"In human-robot cooperation, it is a challenge thing that the robot should perform to convert humans' natural languages to continuous action sequences, which is necessary for completing complex collaborative tasks. In this paper, firstly, a new knowledge base is built for encoding different features of movements, objects and relations; then, a hierarchical motion sequences retrieval algorithm is presented by combining our knowledge base with Deep Q-learning. Finally, the experiments verify that the developed reasoning system is effective and accomplishes to manipulate the objects to reach target statuses.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131040334","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 : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721957
Yibo Wang, Chao Shang, Dexian Huang
Soft sensors have been widely applied in many different industrial fields to predict the values of quality variables, which cannot be measured online. However, it is likely that most of processes are affected greatly by time-varying changes. Thus, the bias updating mechanism is frequently introduced into the maintenance of soft sensors in industrial processed. However, the soft sensors models are developed in a static sense, and it is questionable that their performance is optimal under bias update. To address this issue, we propose an optimal design of soft sensors and bias updating scheme based on rank-constrained optimization. To efficiently solve the optimization problem, an algorithm based on the difference-of-convex programming is proposed. Compared with classical static least squares equipped with bias update, the new approach turns out to more accurate and robust, which is demonstrated by a simulation study.
{"title":"Optimal design of soft sensors and bias updating scheme based on rank-constrained optimization","authors":"Yibo Wang, Chao Shang, Dexian Huang","doi":"10.1109/ICCSS53909.2021.9721957","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721957","url":null,"abstract":"Soft sensors have been widely applied in many different industrial fields to predict the values of quality variables, which cannot be measured online. However, it is likely that most of processes are affected greatly by time-varying changes. Thus, the bias updating mechanism is frequently introduced into the maintenance of soft sensors in industrial processed. However, the soft sensors models are developed in a static sense, and it is questionable that their performance is optimal under bias update. To address this issue, we propose an optimal design of soft sensors and bias updating scheme based on rank-constrained optimization. To efficiently solve the optimization problem, an algorithm based on the difference-of-convex programming is proposed. Compared with classical static least squares equipped with bias update, the new approach turns out to more accurate and robust, which is demonstrated by a simulation study.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"14 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132914294","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}
In this paper, we propose a coverage control method based on the community discovery algorithm. In the traditional coverage control, the Voronoi partition method is used to divide the target region. However, it cannot be applied in the concave area of the plane or the high-dimensional space. Hence, we propose a coverage control method based on the community discovery algorithm, which can be applied in discrete, concave, and high-dimensional areas. In addition, we introduce the method of Delaunay triangulation to generate the topological relationship between different agents. As a result, the coverage control method of a set of points with internal connections is solved. And the coverage control method is proved to be effective by two examples in simulation.
{"title":"Multi-agent coverage control based on improved community discovery algorithm","authors":"Hongyan Li, Shengjin Li, Zhen Wang, Chong Li, Shan Gao, Dengxiu Yu","doi":"10.1109/ICCSS53909.2021.9722022","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722022","url":null,"abstract":"In this paper, we propose a coverage control method based on the community discovery algorithm. In the traditional coverage control, the Voronoi partition method is used to divide the target region. However, it cannot be applied in the concave area of the plane or the high-dimensional space. Hence, we propose a coverage control method based on the community discovery algorithm, which can be applied in discrete, concave, and high-dimensional areas. In addition, we introduce the method of Delaunay triangulation to generate the topological relationship between different agents. As a result, the coverage control method of a set of points with internal connections is solved. And the coverage control method is proved to be effective by two examples in simulation.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133567183","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 : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722023
Zhiqiang Geng, Qi Wang, Yongming Han
Modular multilevel converter (MMC) is a new type of the voltage source converter, which is widely used in the flexible DC transmission and motor drive. However, the MMC is composed of a large number of sub-modules, which poses a huge difficulty for accurately locating the specific sub-module that has a fault. Therefore, this paper proposes an improved support vector machine (SVM) based on the overlapped wavelet packet transform (MODWPT) to diagnose the open circuit fault of the insulated gate bipolar transistor (IGBT) of the MMC sub-module. The MODWPT is used for the feature extraction, then the k-fold cross-validation can group fault feature data sets to evaluate the performance of SVM classifiers, which can effectively reduce the generalization error of the fault diagnosis model. Based on the MMC fault simulation model of the PSCAD platform, the experimental results show that the average fault diagnosis accuracy of the improved SVM based on the MODWPT is 99.78%, which has better classification accuracy and reliability than the traditional SVM, the back propagation neural network and Bayesian.
{"title":"IGBT Open Circuit Fault Diagnosis Based on Improved Support Vector Machine","authors":"Zhiqiang Geng, Qi Wang, Yongming Han","doi":"10.1109/ICCSS53909.2021.9722023","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722023","url":null,"abstract":"Modular multilevel converter (MMC) is a new type of the voltage source converter, which is widely used in the flexible DC transmission and motor drive. However, the MMC is composed of a large number of sub-modules, which poses a huge difficulty for accurately locating the specific sub-module that has a fault. Therefore, this paper proposes an improved support vector machine (SVM) based on the overlapped wavelet packet transform (MODWPT) to diagnose the open circuit fault of the insulated gate bipolar transistor (IGBT) of the MMC sub-module. The MODWPT is used for the feature extraction, then the k-fold cross-validation can group fault feature data sets to evaluate the performance of SVM classifiers, which can effectively reduce the generalization error of the fault diagnosis model. Based on the MMC fault simulation model of the PSCAD platform, the experimental results show that the average fault diagnosis accuracy of the improved SVM based on the MODWPT is 99.78%, which has better classification accuracy and reliability than the traditional SVM, the back propagation neural network and Bayesian.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132898569","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}
This paper proposes a finite-time consensus tracking algorithm for a large-scale multi-motor system (LSMMS), which is extremely meaningful for modern automatic product lines to run continuously with high precision and efficiency. A second-order communication topology inspired by the social structure is proposed to reduce the computational complexity of the system and make it more suitable for product lines with a vast amount of motors that need to be controlled. Then, the finite-time consensus controller based on second-order communication topology for LSMMS is designed using the backstepping method, making the time costs in the tracking errors of position and velocity converging to zero finite. Simulation is given to illustrate the effectiveness of the proposed approach.
{"title":"Finite-time consensus tracking for large-scale multi-motor system based on second-order communication topology","authors":"Taoyuan Zhang, Dengxiu Yu, Zhen Wang, Hao Xu, Shengjin Li, Jia Long","doi":"10.1109/ICCSS53909.2021.9721992","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721992","url":null,"abstract":"This paper proposes a finite-time consensus tracking algorithm for a large-scale multi-motor system (LSMMS), which is extremely meaningful for modern automatic product lines to run continuously with high precision and efficiency. A second-order communication topology inspired by the social structure is proposed to reduce the computational complexity of the system and make it more suitable for product lines with a vast amount of motors that need to be controlled. Then, the finite-time consensus controller based on second-order communication topology for LSMMS is designed using the backstepping method, making the time costs in the tracking errors of position and velocity converging to zero finite. Simulation is given to illustrate the effectiveness of the proposed approach.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116645055","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 : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721997
Shuyuan Wang, Chuandong Li
Spiking neural networks (SNN) are known as the third generation neural network, which can simulate biological neural networks signals and has stronger computing power. In contrast to the model classification tasks previously mentioned in machine learning, the Tempotron algorithm is a biologically rational and temporal coding supervised synaptic learning rule that enables neurons to efficiently learn a wide range of decision rules. Embedding information in the space-time structure of spikes rather than simply the average spike emission frequency. In this paper, we adopt Tempotron algorithm to perform binary classification task on the imported Fashion MNIST dataset and adopt gradient descent algorithm to update the synaptic weight during the training process. The two conditions of sending spikes and no sending spikes are taken as the classification standard. The experimental results show that this method has high learning accuracy and efficiency can classify the dataset accurately, and solve complex and real-time problems better.
{"title":"A Supervised Learning Algorithm to Binary Classification Problem for Spiking Neural Networks","authors":"Shuyuan Wang, Chuandong Li","doi":"10.1109/ICCSS53909.2021.9721997","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721997","url":null,"abstract":"Spiking neural networks (SNN) are known as the third generation neural network, which can simulate biological neural networks signals and has stronger computing power. In contrast to the model classification tasks previously mentioned in machine learning, the Tempotron algorithm is a biologically rational and temporal coding supervised synaptic learning rule that enables neurons to efficiently learn a wide range of decision rules. Embedding information in the space-time structure of spikes rather than simply the average spike emission frequency. In this paper, we adopt Tempotron algorithm to perform binary classification task on the imported Fashion MNIST dataset and adopt gradient descent algorithm to update the synaptic weight during the training process. The two conditions of sending spikes and no sending spikes are taken as the classification standard. The experimental results show that this method has high learning accuracy and efficiency can classify the dataset accurately, and solve complex and real-time problems better.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132316619","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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