With the development of computer and network technology, industrial control systems are connecting with the Internet and other public networks in various ways, viruses, trojans and other threats are spreading to industrial control systems, industrial control system information security issues are becoming increasingly prominent. Under this background, it is necessary to construct the network security evaluation model of industrial control system based on the safety evaluation criteria and methods, and complete the safety evaluation of the industrial control system network according to the design scheme. Based on back propagation (BP) neural network's evaluation of the network security status of industrial control system, this paper determines the number of neurons in BP neural network input layer, hidden layer and output layer by analyzing the actual demand, empirical equation calculation and experimental comparison, and designs the network security evaluation index system of industrial control system according to factors affecting industrial control safety, and constructs a safety rating table. Finally, by comparing the performance of BP neural network and multilinear regression to the evaluation of the network security status of industrial control system through experimental simulation, it can be found that BP neural network has higher accuracy for the evaluation of network security status of industrial control system.
{"title":"Evaluation of Network Security State of Industrial Control System Based on BP Neural Network","authors":"Daojuan Zhang, Peng Zhang, Wenhui Wang, Minghui Jin, Fei Xiao","doi":"10.1109/wsai55384.2022.9836386","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836386","url":null,"abstract":"With the development of computer and network technology, industrial control systems are connecting with the Internet and other public networks in various ways, viruses, trojans and other threats are spreading to industrial control systems, industrial control system information security issues are becoming increasingly prominent. Under this background, it is necessary to construct the network security evaluation model of industrial control system based on the safety evaluation criteria and methods, and complete the safety evaluation of the industrial control system network according to the design scheme. Based on back propagation (BP) neural network's evaluation of the network security status of industrial control system, this paper determines the number of neurons in BP neural network input layer, hidden layer and output layer by analyzing the actual demand, empirical equation calculation and experimental comparison, and designs the network security evaluation index system of industrial control system according to factors affecting industrial control safety, and constructs a safety rating table. Finally, by comparing the performance of BP neural network and multilinear regression to the evaluation of the network security status of industrial control system through experimental simulation, it can be found that BP neural network has higher accuracy for the evaluation of network security status of industrial control system.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114307744","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 : 2022-06-23DOI: 10.1109/wsai55384.2022.9836381
Tianyu Wang, Yuhang Ye, Zihan Zhang, Haoran Zhang, Zonghan Yang
With the development of automatic driving, fast and safe motion planning algorithms are in need. However, data transferred to the motion planning part may be noisy, and some obstacles are unnecessary for later processing. This paper proposes a focus layer and a DQN agent to select necessary barriers and submit them to the motion planning algorithms. The Focus layer ignores some obstacles that are not likely to impact the ego vehicle and focuses attention on those critical obstacles. Note to Practitioners: This paper is motivated by the heavy computation time in automatic driving when planning a trajectory. Constraints such as obstacles along the road affect the efficiency of the planning methodology. Existing research conducts experiments on capturing drivers' facial expressions or eye contact when driving on the road. However, such research cannot fit into the automatic driving algorithms. Thus, we propose a method to reduce unnecessary obstacles in a simulation environment, which is similar to focusing on the essential elements for drivers. Our process generates a layer to focus ego vehicles' attention on critical obstacles before the trajectory planning algorithm and can easily fit in all trajectory planning algorithms.
{"title":"Focus Layer - Drawing Attention to Necessary Obstacles","authors":"Tianyu Wang, Yuhang Ye, Zihan Zhang, Haoran Zhang, Zonghan Yang","doi":"10.1109/wsai55384.2022.9836381","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836381","url":null,"abstract":"With the development of automatic driving, fast and safe motion planning algorithms are in need. However, data transferred to the motion planning part may be noisy, and some obstacles are unnecessary for later processing. This paper proposes a focus layer and a DQN agent to select necessary barriers and submit them to the motion planning algorithms. The Focus layer ignores some obstacles that are not likely to impact the ego vehicle and focuses attention on those critical obstacles. Note to Practitioners: This paper is motivated by the heavy computation time in automatic driving when planning a trajectory. Constraints such as obstacles along the road affect the efficiency of the planning methodology. Existing research conducts experiments on capturing drivers' facial expressions or eye contact when driving on the road. However, such research cannot fit into the automatic driving algorithms. Thus, we propose a method to reduce unnecessary obstacles in a simulation environment, which is similar to focusing on the essential elements for drivers. Our process generates a layer to focus ego vehicles' attention on critical obstacles before the trajectory planning algorithm and can easily fit in all trajectory planning algorithms.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127504296","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 : 2022-06-23DOI: 10.1109/wsai55384.2022.9836393
Junyu Lu, Yuxin Liu, Pingjian Zhang
Substitution of electronic components is an important research topic in the supply chain management of design and manufacture of electronic products. Previous studies mainly use simulation technology and case study, the system is complex and unable to comprehensively evaluate the different properties of components in each application environment. In this paper, we propose the Electronic Component Knowledge Graph (ECKG), which helps to discover knowledge from a large amount of data and assist in the substitution of electronic components. The ECKG integrates the electronic component data from different manufacturers and contains substitution relations labeled by domain expert experience. The ECKG contains two types of nodes: the central node is the representation of electronic components, and the peripheral node contains the attribute values that provides semantic support for the central node, which helps learning the structural knowledge. Moreover, we present the Self-adaptive Knowledge Embedding (SAKE) approach that integrates the semantic information of peripheral nodes into their corresponding central node. The SAKE is pre-trained on our large-scale ECKG with a knowledge-based attention mechanism to obtain the contextual representation of the central nodes. Experiment results show that SAKE outperforms other counterparts on the entity typing and link prediction tasks.
{"title":"Self-Adaptive Knowledge Embedding for Large-Scale Electronic Component Knowledge Graph","authors":"Junyu Lu, Yuxin Liu, Pingjian Zhang","doi":"10.1109/wsai55384.2022.9836393","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836393","url":null,"abstract":"Substitution of electronic components is an important research topic in the supply chain management of design and manufacture of electronic products. Previous studies mainly use simulation technology and case study, the system is complex and unable to comprehensively evaluate the different properties of components in each application environment. In this paper, we propose the Electronic Component Knowledge Graph (ECKG), which helps to discover knowledge from a large amount of data and assist in the substitution of electronic components. The ECKG integrates the electronic component data from different manufacturers and contains substitution relations labeled by domain expert experience. The ECKG contains two types of nodes: the central node is the representation of electronic components, and the peripheral node contains the attribute values that provides semantic support for the central node, which helps learning the structural knowledge. Moreover, we present the Self-adaptive Knowledge Embedding (SAKE) approach that integrates the semantic information of peripheral nodes into their corresponding central node. The SAKE is pre-trained on our large-scale ECKG with a knowledge-based attention mechanism to obtain the contextual representation of the central nodes. Experiment results show that SAKE outperforms other counterparts on the entity typing and link prediction tasks.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125143133","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 : 2022-06-23DOI: 10.1109/wsai55384.2022.9836400
Yuxin Liu, Junyu Lu, Pingjian Zhang
In the electronic component supply chain system, manually built knowledge graph usually lacks the alternative relations among the electronic components. Prevalent graph embedding approaches exhibit strong capability in representing graph elements. However, it's difficult to generalize to never-seen elements due to the graph incompleteness, and the Laplacian-based convolution of GCN limits the information propagation to immediate neighbors. In contrast, the pre-trained encoder have stronger ability to extract semantic information. In this paper, we propose a hybrid encoding approach SiGeTR: Similarity-based Graph Enhanced Text Representation. Based on the approach of structural encoding, it incorporates the textual encoding which employs the text of triples in the graph and contextualized repre-sentations. Meanwhile, we propose to use node similarity based convolution matrices in the GCN to compute node embeddings. In experiments, our methods obtain state-of-the-art performance on the electronic components knowledge graph benchmark dataset and achieve significant results with low resources.
{"title":"Similarity-Based Graph Enhanced Text Representation Learning for Electronic Component Knowledge Graph Completion","authors":"Yuxin Liu, Junyu Lu, Pingjian Zhang","doi":"10.1109/wsai55384.2022.9836400","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836400","url":null,"abstract":"In the electronic component supply chain system, manually built knowledge graph usually lacks the alternative relations among the electronic components. Prevalent graph embedding approaches exhibit strong capability in representing graph elements. However, it's difficult to generalize to never-seen elements due to the graph incompleteness, and the Laplacian-based convolution of GCN limits the information propagation to immediate neighbors. In contrast, the pre-trained encoder have stronger ability to extract semantic information. In this paper, we propose a hybrid encoding approach SiGeTR: Similarity-based Graph Enhanced Text Representation. Based on the approach of structural encoding, it incorporates the textual encoding which employs the text of triples in the graph and contextualized repre-sentations. Meanwhile, we propose to use node similarity based convolution matrices in the GCN to compute node embeddings. In experiments, our methods obtain state-of-the-art performance on the electronic components knowledge graph benchmark dataset and achieve significant results with low resources.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134486364","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 progress of science and technology, artificial intelligence has emerged and received widespread attention. At present, it has been applied in many fields. In order to realize the prediction of power grid construction project duration, this paper proposes a prediction model of power grid construction project duration based on BP neural network. Firstly, the characteristics of the power grid project are analyzed and the influencing factors that have a great influence on the project duration are summarized. Secondly, according to the construction characteristics of the power grid project, the whole project is divided into several stages, and each stage is subdivided into several processes. Thirdly, according to the construction stage of the power grid project and the division of the process, the number of nodes in each layer of the BP neural network is designed, and the effectiveness of the method is demonstrated by engineering examples. Finally, it is concluded that the model has certain value in the prediction of the duration of the power grid project.
{"title":"Prediction Model of Power Grid Project Duration Based on BP Neural Network","authors":"Baogang Chen, Jing Mo, Zhanghai He, Qinghe Zeng, Zhilong Weng, Xiangbiao Leng, Haixiang Yu","doi":"10.1109/wsai55384.2022.9836417","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836417","url":null,"abstract":"With the continuous progress of science and technology, artificial intelligence has emerged and received widespread attention. At present, it has been applied in many fields. In order to realize the prediction of power grid construction project duration, this paper proposes a prediction model of power grid construction project duration based on BP neural network. Firstly, the characteristics of the power grid project are analyzed and the influencing factors that have a great influence on the project duration are summarized. Secondly, according to the construction characteristics of the power grid project, the whole project is divided into several stages, and each stage is subdivided into several processes. Thirdly, according to the construction stage of the power grid project and the division of the process, the number of nodes in each layer of the BP neural network is designed, and the effectiveness of the method is demonstrated by engineering examples. Finally, it is concluded that the model has certain value in the prediction of the duration of the power grid project.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123209659","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 : 2022-06-23DOI: 10.1109/wsai55384.2022.9836355
C. Peng, Guangjian He, Lihua Cai
Finite-time super-twisting trajectory tracking control for a coaxial twelve-rotor unmanned flying robot (UFR) is investigated under un-modeled dynamics and external disturbance. The coaxial twelve-rotor UFR as the nested closed-loop control system is divided into outer loop and inner loop. The integral sliding mode controller is adopted for the outer loop, and finite-time super-twisting sliding mode controller is proposed for the inner loop. A finite-time extended state observer (ESO) is designed to effectively estimate un-modeled dynamics and external disturbance. Then, the stability of the closed- loop system is proved by Lyapunov stability theorem. Finally, numerical simulation experiments demonstrate the effectiveness and superiority of the proposed control strategy.
{"title":"Finite-Time Super-Twisting Trajectory Tracking Control for a Coaxial Twelve-Rotor Unmanned Flying Robot","authors":"C. Peng, Guangjian He, Lihua Cai","doi":"10.1109/wsai55384.2022.9836355","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836355","url":null,"abstract":"Finite-time super-twisting trajectory tracking control for a coaxial twelve-rotor unmanned flying robot (UFR) is investigated under un-modeled dynamics and external disturbance. The coaxial twelve-rotor UFR as the nested closed-loop control system is divided into outer loop and inner loop. The integral sliding mode controller is adopted for the outer loop, and finite-time super-twisting sliding mode controller is proposed for the inner loop. A finite-time extended state observer (ESO) is designed to effectively estimate un-modeled dynamics and external disturbance. Then, the stability of the closed- loop system is proved by Lyapunov stability theorem. Finally, numerical simulation experiments demonstrate the effectiveness and superiority of the proposed control strategy.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129416154","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 : 2022-06-23DOI: 10.1109/wsai55384.2022.9836399
C. Peng, Lihua Cai, Guanyu Qiao, Xun Gong
The actuator saturation tends to occur in the yaw movement of the coaxial eight-rotor unmanned flying robot under external disturbances, for the reason that the yaw movement is much weaker than the pitch and roll movement. For this problem, a dynamic anti-windup compensator based on linear active disturbance rejection controller(LADRC) is proposed from the perspective of practical engineering application. LADRC is easy to adjust in engineering, and can estimate and compensate external disturbances in real time. On this basis, a dynamic anti-windup compensator is devised to prevent actuator saturation in the yaw movement. Then, the stability of the yaw control system with dynamic anti-windup compensator based on LADRC is proved. Finally, the validity and robustness of the proposed algorithm are verified via numerical simulations and coaxial eight-rotor unmanned flying robot experiment.
{"title":"Dynamic Anti-windup Compensation Control of Yaw Movement for a Coaxial Eight-Rotor Unmanned Flying Robot","authors":"C. Peng, Lihua Cai, Guanyu Qiao, Xun Gong","doi":"10.1109/wsai55384.2022.9836399","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836399","url":null,"abstract":"The actuator saturation tends to occur in the yaw movement of the coaxial eight-rotor unmanned flying robot under external disturbances, for the reason that the yaw movement is much weaker than the pitch and roll movement. For this problem, a dynamic anti-windup compensator based on linear active disturbance rejection controller(LADRC) is proposed from the perspective of practical engineering application. LADRC is easy to adjust in engineering, and can estimate and compensate external disturbances in real time. On this basis, a dynamic anti-windup compensator is devised to prevent actuator saturation in the yaw movement. Then, the stability of the yaw control system with dynamic anti-windup compensator based on LADRC is proved. Finally, the validity and robustness of the proposed algorithm are verified via numerical simulations and coaxial eight-rotor unmanned flying robot experiment.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130588914","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 : 2022-06-23DOI: 10.1109/wsai55384.2022.9836397
Li Qun, Zhu He, Yuan Peng, Zheng Yu, Zhao Dan, G. Feng, Zhu Hao Ran, Zhu Jinfu, Liao Hanliang
In order to study the thermal mechanical characteristics of the broken line of the connecting pipe and pipeline joint of the transmission line, the tensile test of the steel cored aluminum strand steel wire, the thermodynamic analysis of the broken line joint of the connecting pipe and the metallographic test of the steel core of the broken line joint were carried out successively. The tensile strength of the steel core of the steel cored aluminum strand, the temperature load curve at different times and the metallographic test results of the steel core of the broken line joint were obtained, Finally, the exposed section of steel core is tested by metallography. The results show that the tensile strength of steel cored aluminum strand meets the standard, the heating of connecting pipe caused by conductor current will affect the calculated breaking force of steel core, and the exposed section of steel core has been running at high temperature for a period of time before being pulled off.
{"title":"Operation Characteristics and Thermal Stability of Conductor Splice Tube under Overheat Operation Fatigue Damage Simulation Analysis","authors":"Li Qun, Zhu He, Yuan Peng, Zheng Yu, Zhao Dan, G. Feng, Zhu Hao Ran, Zhu Jinfu, Liao Hanliang","doi":"10.1109/wsai55384.2022.9836397","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836397","url":null,"abstract":"In order to study the thermal mechanical characteristics of the broken line of the connecting pipe and pipeline joint of the transmission line, the tensile test of the steel cored aluminum strand steel wire, the thermodynamic analysis of the broken line joint of the connecting pipe and the metallographic test of the steel core of the broken line joint were carried out successively. The tensile strength of the steel core of the steel cored aluminum strand, the temperature load curve at different times and the metallographic test results of the steel core of the broken line joint were obtained, Finally, the exposed section of steel core is tested by metallography. The results show that the tensile strength of steel cored aluminum strand meets the standard, the heating of connecting pipe caused by conductor current will affect the calculated breaking force of steel core, and the exposed section of steel core has been running at high temperature for a period of time before being pulled off.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121616099","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 : 2022-06-23DOI: 10.1109/wsai55384.2022.9836412
Dongting Xu, Zhisheng Zhang, Jinfei Shi
Big loss is caused by the failures in complex manufacturing process or in a production line. The design of the efficient and effective failure detection and prediction algorithms is the key for reducing the loss, and more and more algorithms rely on advanced machine learning technologies. The design of failure detection and prediction algorithms is however particularly challenging due to the high dimensionality, extremely imbalanced classes and the non-stationary distribution of the multivariate time series. For multivariate time series in real complex manufacturing process, it's really hard to decide whether the variable is dependent or independent because there is always variation along the production line. In this study, a novel failure prediction approach which combines gated recurrent unit and autoencoder is designed to improve the performance of imbalanced learning. The failure prediction algorithm is applied in a real pulp and paper mill to detect and predict the sheet break during the production. The results show that the proposed method can perform better than other related work.
{"title":"Failure Prediction Using Gated Recurrent Unit and Autoencoder in Complex Manufacturing Process","authors":"Dongting Xu, Zhisheng Zhang, Jinfei Shi","doi":"10.1109/wsai55384.2022.9836412","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836412","url":null,"abstract":"Big loss is caused by the failures in complex manufacturing process or in a production line. The design of the efficient and effective failure detection and prediction algorithms is the key for reducing the loss, and more and more algorithms rely on advanced machine learning technologies. The design of failure detection and prediction algorithms is however particularly challenging due to the high dimensionality, extremely imbalanced classes and the non-stationary distribution of the multivariate time series. For multivariate time series in real complex manufacturing process, it's really hard to decide whether the variable is dependent or independent because there is always variation along the production line. In this study, a novel failure prediction approach which combines gated recurrent unit and autoencoder is designed to improve the performance of imbalanced learning. The failure prediction algorithm is applied in a real pulp and paper mill to detect and predict the sheet break during the production. The results show that the proposed method can perform better than other related work.","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132635690","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 : 2022-06-23DOI: 10.1109/wsai55384.2022.9836367
{"title":"Copyright and Reprint Permission","authors":"","doi":"10.1109/wsai55384.2022.9836367","DOIUrl":"https://doi.org/10.1109/wsai55384.2022.9836367","url":null,"abstract":"","PeriodicalId":402449,"journal":{"name":"2022 4th World Symposium on Artificial Intelligence (WSAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133551253","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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