To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene.
{"title":"UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism","authors":"Sugang Ma, Zixian Zhang, Zhixian Zhao, Xiaobao Yang, Zhiqiang Hou","doi":"10.1145/3573942.3574035","DOIUrl":"https://doi.org/10.1145/3573942.3574035","url":null,"abstract":"To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124706587","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 output of the Micro Electro-mechanical System (MEMS) gyroscope is susceptible affected by temperature drift, which reduces the measurement accuracy of the gyroscope. In this paper, a gyroscope temperature compensation method based on sparrow search algorithm (SSA) and radial basis function (RBF) neural network is proposed to reduce the temperature drift error of gyroscope. Firstly, we utilize the RBF neural network to establish the model of temperature error on the original output of gyroscope; then SSA is employed to find the optimal parameters of the RBF neural network in order to improve its search speed and generalization performance; finally, the optimized RBF neural network is applied to the temperature compensation of the gyroscope. The numerical simulation and comparison results under different temperatures demonstrate that, compared with polynomial and RBF neural network, the SSA-RBF neural network compensation method has superior compensation accuracy and faster convergence speed, which significantly reduces the maximum error, mean value and the standard deviation of gyroscope. Thus, the proposed SSA-RBF method can obtain more accurate fitting performance, effectively compensate the temperature error of MEMS gyroscope, and improve the MEMS gyroscope measurement accuracy.
{"title":"MEMS Gyroscope Temperature Compensation Based on SSA-RBF Neural Network","authors":"Yuanhua Liu, Ziwei Wang, Xinliang Niu","doi":"10.1145/3573942.3573959","DOIUrl":"https://doi.org/10.1145/3573942.3573959","url":null,"abstract":"The output of the Micro Electro-mechanical System (MEMS) gyroscope is susceptible affected by temperature drift, which reduces the measurement accuracy of the gyroscope. In this paper, a gyroscope temperature compensation method based on sparrow search algorithm (SSA) and radial basis function (RBF) neural network is proposed to reduce the temperature drift error of gyroscope. Firstly, we utilize the RBF neural network to establish the model of temperature error on the original output of gyroscope; then SSA is employed to find the optimal parameters of the RBF neural network in order to improve its search speed and generalization performance; finally, the optimized RBF neural network is applied to the temperature compensation of the gyroscope. The numerical simulation and comparison results under different temperatures demonstrate that, compared with polynomial and RBF neural network, the SSA-RBF neural network compensation method has superior compensation accuracy and faster convergence speed, which significantly reduces the maximum error, mean value and the standard deviation of gyroscope. Thus, the proposed SSA-RBF method can obtain more accurate fitting performance, effectively compensate the temperature error of MEMS gyroscope, and improve the MEMS gyroscope measurement accuracy.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258745","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 view of the importance of Age of Information (AoI) in delay sensitive applications of Wireless Sensor Networks (WSNs), an improved gray wolf algorithm (POPAGA) based on the combination of particle swarm optimization possibility fuzzy C-mean clustering is proposed. POPAGA is optimized from the clustering stage and the path planning stage. In the clustering stage, the particle swarm optimization algorithm is first used to optimize the possibility fuzzy hybrid clustering algorithm, which not only overcomes the problem that the fuzzy C-means is sensitive to the initial clustering center, but also avoids the poor initialization effect of the possibility fuzzy c-means clustering, so as to determine the Hovering Collection Data points (HCD) and their associated Sensor Nodes (SNs). In the path planning stage, based on the hover collection data points obtained in the previous stage, the improved gray wolf optimization algorithm (GWO) is used to find the optimal path to minimize the maximum AoI and the average AoI. The simulation results show that POPAGA can obtain the global minimum AoI optimal value, whether compared with the traditional genetic algorithm (GA) and simulated annealing algorithm (SA) for solving TSP problem, or compared with the genetic algorithm (GA) and greedy algorithm based on AoI.
{"title":"An Age-Based Data Collection and Path Planning Algorithm in UAV-Assisted Wireless Sensor Networks","authors":"Chi Sun, De Wei","doi":"10.1145/3573942.3573981","DOIUrl":"https://doi.org/10.1145/3573942.3573981","url":null,"abstract":"In view of the importance of Age of Information (AoI) in delay sensitive applications of Wireless Sensor Networks (WSNs), an improved gray wolf algorithm (POPAGA) based on the combination of particle swarm optimization possibility fuzzy C-mean clustering is proposed. POPAGA is optimized from the clustering stage and the path planning stage. In the clustering stage, the particle swarm optimization algorithm is first used to optimize the possibility fuzzy hybrid clustering algorithm, which not only overcomes the problem that the fuzzy C-means is sensitive to the initial clustering center, but also avoids the poor initialization effect of the possibility fuzzy c-means clustering, so as to determine the Hovering Collection Data points (HCD) and their associated Sensor Nodes (SNs). In the path planning stage, based on the hover collection data points obtained in the previous stage, the improved gray wolf optimization algorithm (GWO) is used to find the optimal path to minimize the maximum AoI and the average AoI. The simulation results show that POPAGA can obtain the global minimum AoI optimal value, whether compared with the traditional genetic algorithm (GA) and simulated annealing algorithm (SA) for solving TSP problem, or compared with the genetic algorithm (GA) and greedy algorithm based on AoI.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128407001","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}
A single feature cannot adapt to the dynamic changes of the scene during video target tracking. This paper, to address this issue, first studies the tracking algorithm of multi-feature fusion, which uses the complementarity between different features to better adapt to the scene changes. On this basis, the APCE anti-occlusion criterion is added to enable the algorithm to resist the influence of target occlusion on tracking to a certain extent. The experimental results show that the average tracking accuracy of the proposed algorithm is about 0.779, which is about 2% higher than that of the SAMF algorithm, and the tracking success rate can be as high as 72%.
{"title":"Research and Implementation of Multi-feature Tracking Algorithms","authors":"Xinyue Zhang, Yao Tang","doi":"10.1145/3573942.3574040","DOIUrl":"https://doi.org/10.1145/3573942.3574040","url":null,"abstract":"A single feature cannot adapt to the dynamic changes of the scene during video target tracking. This paper, to address this issue, first studies the tracking algorithm of multi-feature fusion, which uses the complementarity between different features to better adapt to the scene changes. On this basis, the APCE anti-occlusion criterion is added to enable the algorithm to resist the influence of target occlusion on tracking to a certain extent. The experimental results show that the average tracking accuracy of the proposed algorithm is about 0.779, which is about 2% higher than that of the SAMF algorithm, and the tracking success rate can be as high as 72%.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128557896","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}
An improved residual network model is proposed to deal with the complex and changeable characteristics of one-dimensional electrocardiogram. In this model, firstly, in order to avoid the network degradation problem of the model along with the deepening of the number of layers, when extracting various deep-level features of ECG signals using multiple convolution layers in CNN, the residual module is integrated into the network, and an appropriate shortcut connection is selected to connect the input with the superposition output of the corresponding convolution layer to construct a deep residual network to extract more abstract signal features. Secondly, the output of the last residual module is sent to the GAP layer, and the parameters of this layer are greatly reduced compared with those of the full connection layer, which is equivalent to the compression of the model, and thus the over-fitting of the model is avoided to a certain extent. Finally, the original ECG signals were automatically classified based on the PCinCC2017 database to complete the recognition of atrial fibrillation. Experimental results show that the proposed algorithm has a classification accuracy of 86% and a F1 measure of 83%, which prove the feasibility of the model and the effectiveness of the algorithm.
{"title":"Improved atrial fibrillation recognition algorithm based on residual network","authors":"Zhiqiang Bao, Ting Ai, Ying Bai","doi":"10.1145/3573942.3574118","DOIUrl":"https://doi.org/10.1145/3573942.3574118","url":null,"abstract":"An improved residual network model is proposed to deal with the complex and changeable characteristics of one-dimensional electrocardiogram. In this model, firstly, in order to avoid the network degradation problem of the model along with the deepening of the number of layers, when extracting various deep-level features of ECG signals using multiple convolution layers in CNN, the residual module is integrated into the network, and an appropriate shortcut connection is selected to connect the input with the superposition output of the corresponding convolution layer to construct a deep residual network to extract more abstract signal features. Secondly, the output of the last residual module is sent to the GAP layer, and the parameters of this layer are greatly reduced compared with those of the full connection layer, which is equivalent to the compression of the model, and thus the over-fitting of the model is avoided to a certain extent. Finally, the original ECG signals were automatically classified based on the PCinCC2017 database to complete the recognition of atrial fibrillation. Experimental results show that the proposed algorithm has a classification accuracy of 86% and a F1 measure of 83%, which prove the feasibility of the model and the effectiveness of the algorithm.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129896713","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}
Network traffic features change with time and network environment, creating a concept drift problem that leads to a decrease in the accuracy of machine learning-based network traffic classification methods. This is because the traditional network traffic classifiers are static models that cannot adapt to the changes in data distribution. Therefore, we proposed a concept drift detection approach based on Jensen–Shannon divergence, named CDJD. The method uses a double-layer window mechanism to detect changes in data distribution based on the Jensen-Shannon divergence, and thus detects concept drift. After detecting concept drift, the Jensen-Shannon divergence is used to check whether the current concept is a recurrence of the past concept and thus decide whether to reuse the old classifier. The method is experimentally compared with common concept drift detection methods, and the experimental results show that the method can effectively detect concept drift and showing better classification performance.
{"title":"A Concept Drift Detection Approach Based on Jensen-Shannon Divergence for Network Traffic Classification","authors":"Wujun Yang, Rui Su, Yuanzheng Cheng, Juan Guo","doi":"10.1145/3573942.3573979","DOIUrl":"https://doi.org/10.1145/3573942.3573979","url":null,"abstract":"Network traffic features change with time and network environment, creating a concept drift problem that leads to a decrease in the accuracy of machine learning-based network traffic classification methods. This is because the traditional network traffic classifiers are static models that cannot adapt to the changes in data distribution. Therefore, we proposed a concept drift detection approach based on Jensen–Shannon divergence, named CDJD. The method uses a double-layer window mechanism to detect changes in data distribution based on the Jensen-Shannon divergence, and thus detects concept drift. After detecting concept drift, the Jensen-Shannon divergence is used to check whether the current concept is a recurrence of the past concept and thus decide whether to reuse the old classifier. The method is experimentally compared with common concept drift detection methods, and the experimental results show that the method can effectively detect concept drift and showing better classification performance.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129914075","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 the environment of the 5G era, with the advancement of communication technology and the continuous improvement of people's living and work needs, users' demand for network access bandwidth is increasing. Orthogonal Frequency Division Multiplexing-Radio Frequency over Optical (OFDM-RoF) system is an Internet solution with high spectrum utilization, large bandwidth and fast transmission data rate. The chromatic dispersion (CD) and polarization mode dispersion (PMD) existing in the system will affect the transmission performance of the OFDM-RoF system. In this paper, the artificial neural network algorithm is applied to the field of channel estimation. Reduce the effect of dispersion on the system by estimating the activation function of the channel. Simulation results show that compared with the frequency domain least squares (FDLS) method, this algorithm can improve the system performance and improve the bit error rate optimization ability by an order of magnitude.
{"title":"Channel Estimation Algorithm of OFDM-RoF System in 5G Mobile Front-end Network Based on Artificial Neural Network","authors":"Yun Zhang, Siyuan Liang, Chunting Wang, Feng Zhao","doi":"10.1145/3573942.3574000","DOIUrl":"https://doi.org/10.1145/3573942.3574000","url":null,"abstract":"In the environment of the 5G era, with the advancement of communication technology and the continuous improvement of people's living and work needs, users' demand for network access bandwidth is increasing. Orthogonal Frequency Division Multiplexing-Radio Frequency over Optical (OFDM-RoF) system is an Internet solution with high spectrum utilization, large bandwidth and fast transmission data rate. The chromatic dispersion (CD) and polarization mode dispersion (PMD) existing in the system will affect the transmission performance of the OFDM-RoF system. In this paper, the artificial neural network algorithm is applied to the field of channel estimation. Reduce the effect of dispersion on the system by estimating the activation function of the channel. Simulation results show that compared with the frequency domain least squares (FDLS) method, this algorithm can improve the system performance and improve the bit error rate optimization ability by an order of magnitude.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120985769","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}
Due to VANET (vehicle ad-hoc network, VANET) has the characteristics of fast node movement and unstable network topology, the data transmission in the network faces the problems of disconnection of communication links and difficult to guarantee delay. Therefore, it is very important to design a routing algorithm that can ensure the stability of the communication link and the efficient data transmission. Based on the traditional GPSR protocol (greedy perimeter stateless routing, GPSR), this paper proposes an improved VANET routing method CL-GPSR, which makes forwarding decisions based on the established link connection time prediction model and delay estimation model. Simulation results show that the proposed CL-GPSR routing method can provide higher packet delivery rate and lower average delay.
{"title":"Routing Method Based on Connectivity and Latency in VANET","authors":"Hua Liu, Wujun Yang, Zhixian Chang, Min Shi","doi":"10.1145/3573942.3573991","DOIUrl":"https://doi.org/10.1145/3573942.3573991","url":null,"abstract":"Due to VANET (vehicle ad-hoc network, VANET) has the characteristics of fast node movement and unstable network topology, the data transmission in the network faces the problems of disconnection of communication links and difficult to guarantee delay. Therefore, it is very important to design a routing algorithm that can ensure the stability of the communication link and the efficient data transmission. Based on the traditional GPSR protocol (greedy perimeter stateless routing, GPSR), this paper proposes an improved VANET routing method CL-GPSR, which makes forwarding decisions based on the established link connection time prediction model and delay estimation model. Simulation results show that the proposed CL-GPSR routing method can provide higher packet delivery rate and lower average delay.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123396947","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}
Most of the image description generation methods in the attention-based encoder-decoder framework extract local features from images. Despite the relatively high semantic level of local features, it still has two problems to be solved, one is object loss, where some important objects may be lost when generating image descriptions, and the other is prediction error, as an object may be identified in the wrong class. In this paper, a G-AoANet model is proposed to solve the above problems. The model uses an attention mechanism to combine global features with local features. In this way, our model can selectively focus on both object and contextual information, improving the quality of the generated descriptions. Experimental results show that the model improves the initially reported best CIDEr-D and SPICE scores on the MS COCO dataset by 9.3% and 5.1% respectively.
{"title":"Research on Image Description Generation Method Based on G-AoANet","authors":"Pi Qiao, Ruixue Shen, Yuan Li","doi":"10.1145/3573942.3574072","DOIUrl":"https://doi.org/10.1145/3573942.3574072","url":null,"abstract":"Most of the image description generation methods in the attention-based encoder-decoder framework extract local features from images. Despite the relatively high semantic level of local features, it still has two problems to be solved, one is object loss, where some important objects may be lost when generating image descriptions, and the other is prediction error, as an object may be identified in the wrong class. In this paper, a G-AoANet model is proposed to solve the above problems. The model uses an attention mechanism to combine global features with local features. In this way, our model can selectively focus on both object and contextual information, improving the quality of the generated descriptions. Experimental results show that the model improves the initially reported best CIDEr-D and SPICE scores on the MS COCO dataset by 9.3% and 5.1% respectively.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127607955","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}
Firstly, an improved ant colony algorithm (QCANT) is proposed to optimize quantum connectivity, and the entanglement example distribution node deployment in quantum wireless multi-hop networks is studied and analyzed. On this basis, this paper combined genetic algorithm with improved ant colony algorithm (GA-QCANT), which can effectively alleviate the problem of low efficiency of ant colony algorithm due to the lack of initial pheromone. Simulation results show that both QCANT and GA-QCANT improves quantum connectivity significantly, and GA-QCANT improves quantum connectivity by an average of 32.1% compared to QCANT.
{"title":"Research on Multi-hop Transmission in Quantum Wireless Communication Networks Based on Improved Ant Colony Algorithm","authors":"Xinyuan Mao, Min Nie, Guang Yang","doi":"10.1145/3573942.3573985","DOIUrl":"https://doi.org/10.1145/3573942.3573985","url":null,"abstract":"Firstly, an improved ant colony algorithm (QCANT) is proposed to optimize quantum connectivity, and the entanglement example distribution node deployment in quantum wireless multi-hop networks is studied and analyzed. On this basis, this paper combined genetic algorithm with improved ant colony algorithm (GA-QCANT), which can effectively alleviate the problem of low efficiency of ant colony algorithm due to the lack of initial pheromone. Simulation results show that both QCANT and GA-QCANT improves quantum connectivity significantly, and GA-QCANT improves quantum connectivity by an average of 32.1% compared to QCANT.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133104268","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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