Pub Date : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642212
Hongzong Li, Jun Wang
The traveling salesman problem is known to be NP-hard and has numerous areas of applications. This paper proposes a collaborative neurodynamic optimization algorithm based on Boltzmann machines for solving the traveling salesman problem. A population of Boltzmann machines is employed for local search, and their initial states are repeatedly reinitialized by using the particle swarm optimization update rule for global repositioning. The efficacy of the proposed collaborative neurodynamic optimization algorithm is substantiated on four traveling salesman problem benchmark instances.
{"title":"A Collaborative Neurodynamic Optimization Algorithm Based on Boltzmann Machines for Solving the Traveling Salesman Problem","authors":"Hongzong Li, Jun Wang","doi":"10.1109/ICICIP53388.2021.9642212","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642212","url":null,"abstract":"The traveling salesman problem is known to be NP-hard and has numerous areas of applications. This paper proposes a collaborative neurodynamic optimization algorithm based on Boltzmann machines for solving the traveling salesman problem. A population of Boltzmann machines is employed for local search, and their initial states are repeatedly reinitialized by using the particle swarm optimization update rule for global repositioning. The efficacy of the proposed collaborative neurodynamic optimization algorithm is substantiated on four traveling salesman problem benchmark instances.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115057045","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 study evaluates Moderate resolution Imaging Spectroradiometer (MODIS) aboard terra Collection 6(C6) Dark Target (DT) Aerosol Optical Depth (AOD) products at 10 km (MOD04_10k) and 3 km (MOD04_3k) spatial resolution against ground-based AOD observations over Yuan Island in the North Yellow Sea. MOD04_3k retrievals show better performance, with larger number of collocations, larger percentage of observations falling within EE, smaller RMSE and MAE, especially for spring. MODIS comparability with ground-based data is not monotonic with Quality Assured Confidence(QAC) value, valid data(QAC >0) are the most accurate data set for both MOD04_10k and MOD04_3k. For seasonal analysis, trends of MOD04_10k and MOD04_3k are similar, MODIS AOD data for autumn perform best, whereas data quality for spring is the worst, which might be due to the dust aerosol effect. In summary, Terra/MODIS AOD data(QAC >0) at 3km are slightly more reliable than data at 10km over Yuan Island in the North Yellow Sea.
本研究将terra Collection 6(C6)上的中分辨率成像光谱仪(MODIS)在10 km (MOD04_10k)和3 km (MOD04_3k)空间分辨率下的暗目标(DT)气溶胶光学深度(AOD)产品与北黄海元岛的地面AOD观测结果进行了比较。MOD04_3k检索表现出更好的性能，搭配数量更多，在EE范围内的观测值百分比更大，RMSE和MAE更小，特别是在春季。MODIS与地面数据的可比性不是单调的，具有质量保证置信度(QAC)值，有效数据(QAC >0)是MOD04_10k和MOD04_3k最准确的数据集。MOD04_10k和MOD04_3k的季节变化趋势相似，秋季MODIS AOD数据表现最好，春季MODIS AOD数据质量最差，这可能与沙尘气溶胶的影响有关。综上所述，北黄海元岛3km波段的Terra/MODIS AOD数据(QAC >0)比10km波段的数据更可靠。
{"title":"Validation of Terra/MODIS 3KM and 10KM Aerosol Optical Depth Over Yuan Island in the North Yellow Sea","authors":"Yujuan Ma, Jianchao Fan, Yanlong Chen, Jianli Zhang","doi":"10.1109/ICICIP53388.2021.9642173","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642173","url":null,"abstract":"This study evaluates Moderate resolution Imaging Spectroradiometer (MODIS) aboard terra Collection 6(C6) Dark Target (DT) Aerosol Optical Depth (AOD) products at 10 km (MOD04_10k) and 3 km (MOD04_3k) spatial resolution against ground-based AOD observations over Yuan Island in the North Yellow Sea. MOD04_3k retrievals show better performance, with larger number of collocations, larger percentage of observations falling within EE, smaller RMSE and MAE, especially for spring. MODIS comparability with ground-based data is not monotonic with Quality Assured Confidence(QAC) value, valid data(QAC >0) are the most accurate data set for both MOD04_10k and MOD04_3k. For seasonal analysis, trends of MOD04_10k and MOD04_3k are similar, MODIS AOD data for autumn perform best, whereas data quality for spring is the worst, which might be due to the dust aerosol effect. In summary, Terra/MODIS AOD data(QAC >0) at 3km are slightly more reliable than data at 10km over Yuan Island in the North Yellow Sea.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115481731","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642165
Y. Kong, Jiajia Wu, Shiyong Chen, Junwen Zhou
A distributed manipulability optimization (DMO) scheme based on a finite time neural network is proposed in this paper to solve the cooperative motion planning of redundant manipulators. In this proposed kinematic scheme, the end-effectors of the manipulators can complete the specific task in a cooperative manner under peer-to-peer communication and the optimal kinematic time of redundant manipulators has achieved. The DMO scheme is formulated into a quadratic program and is solved by Lagrange multiplier theorem. The stability and finiteness of the proposed DMO scheme have been proved in theory. Simulation results on three redundant manipulators show the validity and accuracy of this new DMO scheme. method
{"title":"Distributed Manipulability optimization in a Finite Time Neural Network for Redundant Manipulators","authors":"Y. Kong, Jiajia Wu, Shiyong Chen, Junwen Zhou","doi":"10.1109/ICICIP53388.2021.9642165","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642165","url":null,"abstract":"A distributed manipulability optimization (DMO) scheme based on a finite time neural network is proposed in this paper to solve the cooperative motion planning of redundant manipulators. In this proposed kinematic scheme, the end-effectors of the manipulators can complete the specific task in a cooperative manner under peer-to-peer communication and the optimal kinematic time of redundant manipulators has achieved. The DMO scheme is formulated into a quadratic program and is solved by Lagrange multiplier theorem. The stability and finiteness of the proposed DMO scheme have been proved in theory. Simulation results on three redundant manipulators show the validity and accuracy of this new DMO scheme. method","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121907039","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}
The excellent performance of deep learning depends on the strong representation ability of its backbone. As a conventional means of most backbones, pretraining can make the model obtain high accuracy, but it also brings some disadvantages that can not be ignored: first, the structures of the backbones need pretraining are fixed, they are difficult to modify and migrate across tasks; second, the pretraining process needs to consume huge computing power. To solve this problem, we propose a backbone named RVNet (Residual VGGNet), which can make the model converge quickly without pretraining. The design of RVNet is divided into the following two steps: firstly, the residual convolutional layer (RCL) is designed by referring to the residual skill and BN layer, which can prevent the gradient from disappearing and restrain the data distribution. At the same time, The introduced 1* 1 convolution layer can improve the nonlinearity of the model while controlling the number of feature maps’ channels; then, based on VGGNet-19, the designed RCLs replace the original 3* 3 convolution layer to improve the representation ability of the backbone. We take the person re-identification (Re-ID) task as the research object, and prove the effectiveness and superiority of RVNet through a series of ablation experiments.
{"title":"Design of A Backbone without Pretraining","authors":"Shaoqi Hou, Wenyi Du, Yiyin Ding, Yuhao Zeng, Chunyu Wang, Guangqiang Yin","doi":"10.1109/ICICIP53388.2021.9642216","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642216","url":null,"abstract":"The excellent performance of deep learning depends on the strong representation ability of its backbone. As a conventional means of most backbones, pretraining can make the model obtain high accuracy, but it also brings some disadvantages that can not be ignored: first, the structures of the backbones need pretraining are fixed, they are difficult to modify and migrate across tasks; second, the pretraining process needs to consume huge computing power. To solve this problem, we propose a backbone named RVNet (Residual VGGNet), which can make the model converge quickly without pretraining. The design of RVNet is divided into the following two steps: firstly, the residual convolutional layer (RCL) is designed by referring to the residual skill and BN layer, which can prevent the gradient from disappearing and restrain the data distribution. At the same time, The introduced 1* 1 convolution layer can improve the nonlinearity of the model while controlling the number of feature maps’ channels; then, based on VGGNet-19, the designed RCLs replace the original 3* 3 convolution layer to improve the representation ability of the backbone. We take the person re-identification (Re-ID) task as the research object, and prove the effectiveness and superiority of RVNet through a series of ablation experiments.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"30 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125874170","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}
Text classification is a classical and basic task of natural language processing (NLP). In recent years, machine learning has been widely used for text classification. However traditional machine learning methods depend heavily on high quality feature engineering. Recently, deep learning methods have contributed to improving text classification performance. Graph convolution network (GCN) has been proved to be able to capture spatial feature of documents. However, the ability of GCN to capture sentence local features and context information is limited. In this paper, we propose a novel method using local features to enhance GCN for text classification. The combination methods based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) (such as Bi-LSTM, C-LSTM and ServeNet) are used to capture local features to enrich feature information, and a weight value is used to adjust the intensity of enhancement. We conducted extensive experiments on 5 benchmark datasets (WSDataset, Ohsumed, R52, R8, 20NG), proving the proposed method significantly outperforms the baseline deep learning methods.
{"title":"A Novel Method Using Local Feature to Enhance GCN for Text Classification","authors":"Chunlian Yang, Yuchen Guo, Xiaowei Li, Benhui Chen","doi":"10.1109/ICICIP53388.2021.9642171","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642171","url":null,"abstract":"Text classification is a classical and basic task of natural language processing (NLP). In recent years, machine learning has been widely used for text classification. However traditional machine learning methods depend heavily on high quality feature engineering. Recently, deep learning methods have contributed to improving text classification performance. Graph convolution network (GCN) has been proved to be able to capture spatial feature of documents. However, the ability of GCN to capture sentence local features and context information is limited. In this paper, we propose a novel method using local features to enhance GCN for text classification. The combination methods based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) (such as Bi-LSTM, C-LSTM and ServeNet) are used to capture local features to enrich feature information, and a weight value is used to adjust the intensity of enhancement. We conducted extensive experiments on 5 benchmark datasets (WSDataset, Ohsumed, R52, R8, 20NG), proving the proposed method significantly outperforms the baseline deep learning methods.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125157489","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642223
B. Zhang, X. Lan, Ye Li, Xi Yu Zhang
In view of the problem of recognition of active motion intention of human upper limb, based on the EMG signal of the upper limb surface, this paper proposes a method of predicting the angle of upper limb joint based on RBF neural network. The motion intention of shoulder joint, elbow joint and wrist joint in sagittal plane of human body is predicted and recognized effectively. The simulation results show that the RBF method proposed in this paper can better predict the angle of the upper limb, and verified that the RBF neural network method proposed in this paper can improve the accuracy of the angle prediction of the upper limb joint, which lays the algorithm framework and theoretical foundation for the human-computer interaction control of the upper limb rehabilitation robot.
{"title":"A novel RBF neural network based recognition of human upper limb active motion intention","authors":"B. Zhang, X. Lan, Ye Li, Xi Yu Zhang","doi":"10.1109/ICICIP53388.2021.9642223","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642223","url":null,"abstract":"In view of the problem of recognition of active motion intention of human upper limb, based on the EMG signal of the upper limb surface, this paper proposes a method of predicting the angle of upper limb joint based on RBF neural network. The motion intention of shoulder joint, elbow joint and wrist joint in sagittal plane of human body is predicted and recognized effectively. The simulation results show that the RBF method proposed in this paper can better predict the angle of the upper limb, and verified that the RBF neural network method proposed in this paper can improve the accuracy of the angle prediction of the upper limb joint, which lays the algorithm framework and theoretical foundation for the human-computer interaction control of the upper limb rehabilitation robot.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"70 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114130664","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642215
Keyi Chen, Hangjun Che, Xuanhao Yang, Man-Fai Leung
Graph nonnegative matrix factorization (GNMF) is superior for mining the intrinsic geometric structure embedded in high-dimensional data. As the sparsity of the factorized matrices is crucial for clustering, the l0 norm is commonly used in the formulated optimization problem to enforce the sparseness which makes the problem NP-hard and discontinuous. In this paper, the sparse graph nonnegative matrix factorization (SGNMF) is formulated as a global optimization problem by using the sum of inverted Gaussian functions to approximate the l0 norm, the multiplicative update rules are developed to solve the problem with guaranteed convergence. The superior performance of the proposed approach is substantiated by clustering tests on four public datasets.
{"title":"Sparsity-constrained Graph Nonnegative Matrix Factorization for Clustering","authors":"Keyi Chen, Hangjun Che, Xuanhao Yang, Man-Fai Leung","doi":"10.1109/ICICIP53388.2021.9642215","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642215","url":null,"abstract":"Graph nonnegative matrix factorization (GNMF) is superior for mining the intrinsic geometric structure embedded in high-dimensional data. As the sparsity of the factorized matrices is crucial for clustering, the l0 norm is commonly used in the formulated optimization problem to enforce the sparseness which makes the problem NP-hard and discontinuous. In this paper, the sparse graph nonnegative matrix factorization (SGNMF) is formulated as a global optimization problem by using the sum of inverted Gaussian functions to approximate the l0 norm, the multiplicative update rules are developed to solve the problem with guaranteed convergence. The superior performance of the proposed approach is substantiated by clustering tests on four public datasets.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116030663","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642172
Zheqi Zhu, Yingjia Gao, Shenshen Gu
With the development of artificial intelligence, the utilization of robots based on AI is widespread in our daily life, especially in the area of sports. In the aspect of tennis, collecting tennis balls on the ground after a fierce match or training would be tiresome work, so an automatic tennis ball picking robot becomes useful. Three main aspects should be considered in the research of the tennis ball collection robot: the recognition and localization of tennis balls, path planning for collecting every tennis ball, and the global positioning and navigation of the robot. Firstly, computer vision based on deep learning algorithms has excellent reliability, and the MobileNet-SSD model can be quantized and deployed on Raspberry Pi. Therefore, we choose the MobileNet-SSD model with a monocular camera catching pictures to recognize tennis balls. Secondly, perspective transformation is used to get the precise location of the target tennis ball. We propose a regional traversal algorithm to plan the path to collect as many tennis balls as possible. Thirdly, we utilize ultra-wide-band (UWB) supplemented by triangle centroid methods to locate the robot in a global position. After proper training, the tennis ball collection robot performs well and has excellent potential.
{"title":"Tennis Ball Collection Robot Based on MobileNet-SSD","authors":"Zheqi Zhu, Yingjia Gao, Shenshen Gu","doi":"10.1109/ICICIP53388.2021.9642172","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642172","url":null,"abstract":"With the development of artificial intelligence, the utilization of robots based on AI is widespread in our daily life, especially in the area of sports. In the aspect of tennis, collecting tennis balls on the ground after a fierce match or training would be tiresome work, so an automatic tennis ball picking robot becomes useful. Three main aspects should be considered in the research of the tennis ball collection robot: the recognition and localization of tennis balls, path planning for collecting every tennis ball, and the global positioning and navigation of the robot. Firstly, computer vision based on deep learning algorithms has excellent reliability, and the MobileNet-SSD model can be quantized and deployed on Raspberry Pi. Therefore, we choose the MobileNet-SSD model with a monocular camera catching pictures to recognize tennis balls. Secondly, perspective transformation is used to get the precise location of the target tennis ball. We propose a regional traversal algorithm to plan the path to collect as many tennis balls as possible. Thirdly, we utilize ultra-wide-band (UWB) supplemented by triangle centroid methods to locate the robot in a global position. After proper training, the tennis ball collection robot performs well and has excellent potential.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122953767","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 : 2021-12-03DOI: 10.1109/ICICIP53388.2021.9642179
Yunong Zhang, Jielong Chen, Haosen Lu
Future prediction is a branch of information processing. An attempt to predict some future event year is presented in this work via combining the addition-subtraction frequency (ASF) method, i.e., ASF algorithms with 3 inputs, and multiple mathematical modeling methods (e.g., polynomial curve fitting, exponential curve fitting, and smoothing spline). The 3-input ASF algorithms using full-traversal, equal-half-traversal, and unequal-half-traversal are applied in the numerical experiments. The difficult challenge we face is that the raw data set size is small, i.e., only 4. Thus, we process the limited information in a variety of ways, i.e., we handle the small data set by using multiple methods. We finally predict that 2021, 2022, or 2027 is of relatively high possibility to be a future year of such a small-data sequence. There may be errors of one to two years, and it may be avoided if some proper measures are taken.
{"title":"Predicting Future Event via Small Data (e.g., 4 Data) by ASF and Curve Fitting Methods","authors":"Yunong Zhang, Jielong Chen, Haosen Lu","doi":"10.1109/ICICIP53388.2021.9642179","DOIUrl":"https://doi.org/10.1109/ICICIP53388.2021.9642179","url":null,"abstract":"Future prediction is a branch of information processing. An attempt to predict some future event year is presented in this work via combining the addition-subtraction frequency (ASF) method, i.e., ASF algorithms with 3 inputs, and multiple mathematical modeling methods (e.g., polynomial curve fitting, exponential curve fitting, and smoothing spline). The 3-input ASF algorithms using full-traversal, equal-half-traversal, and unequal-half-traversal are applied in the numerical experiments. The difficult challenge we face is that the raw data set size is small, i.e., only 4. Thus, we process the limited information in a variety of ways, i.e., we handle the small data set by using multiple methods. We finally predict that 2021, 2022, or 2027 is of relatively high possibility to be a future year of such a small-data sequence. There may be errors of one to two years, and it may be avoided if some proper measures are taken.","PeriodicalId":435799,"journal":{"name":"2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"2004 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127309747","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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