The task of Knowledge Base Question Answering (KBQA) is to answer a question in natural language over a Knowledge Base. And multi-hop KBQA aims to reason over multiple hops of facts in KB to answer a complex question. Step-wised reasoning has been an important schema to solve multi-hop KBQA. But previous approaches suffer from lacking reasoning paths, causing models may answer in an incorrect way. To address the issue, we present a novel approach to enhance the KBQA model by leveraging consistency between different views of the data, with few intermediate-relation-labeled data. Previous retrieval-based methods proceeded by utilizing the data view of (question, intermediate entities, answer entities). In our method, we introduce the data view of (question, intermediate relations) and enhance the KBQA model through the consistency of different data views. Concretely, we first implement a question-to-intermediate relations(Q2R) model to obtain intermediate relations’ distributions. By utilizing a pretrained text generation model, it performs well using a small part of relation-labeled data. Then we devise a map function to map distributions of intermediate entities to distributions of intermediate. Finally, a constraint that metrics the consistency between the intermediate path distributions obtained from the Q2R model and the original KBQA model is constructed to enhance the KBQA model. Experiments over three datasets of multi-hop KBQA are conducted, and the results demonstrate the effectiveness of our method.
{"title":"Multi-view consistency for multi-hop knowledge base question answering","authors":"Xin Wang","doi":"10.1117/12.2689748","DOIUrl":"https://doi.org/10.1117/12.2689748","url":null,"abstract":"The task of Knowledge Base Question Answering (KBQA) is to answer a question in natural language over a Knowledge Base. And multi-hop KBQA aims to reason over multiple hops of facts in KB to answer a complex question. Step-wised reasoning has been an important schema to solve multi-hop KBQA. But previous approaches suffer from lacking reasoning paths, causing models may answer in an incorrect way. To address the issue, we present a novel approach to enhance the KBQA model by leveraging consistency between different views of the data, with few intermediate-relation-labeled data. Previous retrieval-based methods proceeded by utilizing the data view of (question, intermediate entities, answer entities). In our method, we introduce the data view of (question, intermediate relations) and enhance the KBQA model through the consistency of different data views. Concretely, we first implement a question-to-intermediate relations(Q2R) model to obtain intermediate relations’ distributions. By utilizing a pretrained text generation model, it performs well using a small part of relation-labeled data. Then we devise a map function to map distributions of intermediate entities to distributions of intermediate. Finally, a constraint that metrics the consistency between the intermediate path distributions obtained from the Q2R model and the original KBQA model is constructed to enhance the KBQA model. Experiments over three datasets of multi-hop KBQA are conducted, and the results demonstrate the effectiveness of our method.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124918548","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 applications of wireless sensor networks (WSNs), it is important to locate an object of interest. However, the intensive measurement noise that contaminates the observations from each sensor node, may impair the localization performance. The widely studied adaptive and cooperative schemes combat the noise via reliable cooperation and adaption strategies with the neighborhoods. However, they underestimate the smooth correlations of the object’s movements, thereby remaining space for improvement. In this paper, we focus on improving the existing cooperative schemes by prefiltering its contaminated observations on each node. By exploiting the smooth correlations of the object’s mobility, we design a sequential pre-filter, which is capable of using the previously estimated information as a priori to overcome the intensive noise. As such, it helps to derive a less-noisy observation on each node, and therefore improves the localization accuracy of the cooperative schemes. Numerical simulations demonstrate the effect of the proposed sequential pre-filter, which can indeed better the cooperative schemes and gain a more promising localization performance.
{"title":"Sequential pre-filter assisted cooperative scheme for localization in wireless sensor networks","authors":"Wei Wei, Liu Zhang","doi":"10.1117/12.2689521","DOIUrl":"https://doi.org/10.1117/12.2689521","url":null,"abstract":"In the applications of wireless sensor networks (WSNs), it is important to locate an object of interest. However, the intensive measurement noise that contaminates the observations from each sensor node, may impair the localization performance. The widely studied adaptive and cooperative schemes combat the noise via reliable cooperation and adaption strategies with the neighborhoods. However, they underestimate the smooth correlations of the object’s movements, thereby remaining space for improvement. In this paper, we focus on improving the existing cooperative schemes by prefiltering its contaminated observations on each node. By exploiting the smooth correlations of the object’s mobility, we design a sequential pre-filter, which is capable of using the previously estimated information as a priori to overcome the intensive noise. As such, it helps to derive a less-noisy observation on each node, and therefore improves the localization accuracy of the cooperative schemes. Numerical simulations demonstrate the effect of the proposed sequential pre-filter, which can indeed better the cooperative schemes and gain a more promising localization performance.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114943975","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}
Zhiguo Yang, Xiaoming Yang, Tianqian Li, Wentao Peng, Yang Zhou, Fangmin Liao, Jing Tan, Zhengjiang Tang, Baiqiang Li, Bide Zhang, Xuan Lin
In order to improve the efficiency and decision-making ability of airport operation support, the realization of estimation about service time of flight ground support can reduce the time and economic losses caused by flight delays. Considering the complexity and particularity of the service process, this article started from the analysis of the flight ground support process and constructed a mathematical model of the service time. The method of Principal Component Analysis (PCA) was used to reduce the correlation between variables, and a service time prediction model of flight ground support based on Deep Neural Network (DNN) was established. Finally, the flight support operation data of an airport were selected for simulation and verification. Experimental results show that the average absolute error of service time prediction can reach 2.709 min, the proposed model can effectively estimate the service time of flight support and has higher accuracy.
{"title":"Estimation about service time of flight ground support based on deep neural network","authors":"Zhiguo Yang, Xiaoming Yang, Tianqian Li, Wentao Peng, Yang Zhou, Fangmin Liao, Jing Tan, Zhengjiang Tang, Baiqiang Li, Bide Zhang, Xuan Lin","doi":"10.1117/12.2689371","DOIUrl":"https://doi.org/10.1117/12.2689371","url":null,"abstract":"In order to improve the efficiency and decision-making ability of airport operation support, the realization of estimation about service time of flight ground support can reduce the time and economic losses caused by flight delays. Considering the complexity and particularity of the service process, this article started from the analysis of the flight ground support process and constructed a mathematical model of the service time. The method of Principal Component Analysis (PCA) was used to reduce the correlation between variables, and a service time prediction model of flight ground support based on Deep Neural Network (DNN) was established. Finally, the flight support operation data of an airport were selected for simulation and verification. Experimental results show that the average absolute error of service time prediction can reach 2.709 min, the proposed model can effectively estimate the service time of flight support and has higher accuracy.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121912571","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}
THz metasurfaces that reflect radiation back can be applied in various fields such as imaging, biosensing, and optical communications. However, the conventional THz metasurfaces have limitations due to their inflexible electromagnetic responses and complex structures. In this paper, a voltage-controlled dual-polarization metasurface based on vanadium dioxide (VO2) is proposed, which can achieve the polarization conversion and transmission by controlling the conductivity of VO2 in the surface. The results show that when the VO2 is in the metal state, the metasurface can realize the circular polarization mode conversion in the range of 1.2-1.4 THz. While insulated, it will switch to the low-efficiency transmission conversion mode. Then, A broadband voltage-controlled OAM beam generator metasurface is designed, providing a method for realizing high performance multifunctional tunable metasurfaces in the THz band. This work has the potential to expand the practical applications of metasurfaces.
{"title":"Broadband multifunctional metasurface based on voltage-tunable VO2","authors":"Lunhao Hu, Hao Xu","doi":"10.1117/12.2689769","DOIUrl":"https://doi.org/10.1117/12.2689769","url":null,"abstract":"THz metasurfaces that reflect radiation back can be applied in various fields such as imaging, biosensing, and optical communications. However, the conventional THz metasurfaces have limitations due to their inflexible electromagnetic responses and complex structures. In this paper, a voltage-controlled dual-polarization metasurface based on vanadium dioxide (VO2) is proposed, which can achieve the polarization conversion and transmission by controlling the conductivity of VO2 in the surface. The results show that when the VO2 is in the metal state, the metasurface can realize the circular polarization mode conversion in the range of 1.2-1.4 THz. While insulated, it will switch to the low-efficiency transmission conversion mode. Then, A broadband voltage-controlled OAM beam generator metasurface is designed, providing a method for realizing high performance multifunctional tunable metasurfaces in the THz band. This work has the potential to expand the practical applications of metasurfaces.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122067203","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}
Dong Chen, Limin Cai, Peizhi Zhao, Hao Wei, Zhongyuan Lai
In the production process of viscose filament, broken filament inspection is the most important part of detecting filament defects. To solve the problem of low speed and accuracy of broken filament detection and improve the online quality inspection system. In this paper, we design a broken filament detection method for viscose filaments based on the improved YOLOv5 algorithm. The GhostNet network structure is introduced to replace and modify the backbone network layer of YOLOv5 to reduce the complexity and computation of the structure and realize the light weight of the overall network structure; the ECA attention mechanism is introduced in the backbone network to enhance the feature perception of the broken filament target and increase the mobility of the feature information in the deep network. The improved YOLOv5 algorithm achieves an average detection accuracy of 93.9% and an average detection speed of 64 FPS in the final experimental results, which is better than the traditional methods of image recognition detection and can meet the realtime detection requirements of broken filament detection in practical engineering.
{"title":"Study on the detection of viscose filament defects based on improved YOLOv5","authors":"Dong Chen, Limin Cai, Peizhi Zhao, Hao Wei, Zhongyuan Lai","doi":"10.1117/12.2689415","DOIUrl":"https://doi.org/10.1117/12.2689415","url":null,"abstract":"In the production process of viscose filament, broken filament inspection is the most important part of detecting filament defects. To solve the problem of low speed and accuracy of broken filament detection and improve the online quality inspection system. In this paper, we design a broken filament detection method for viscose filaments based on the improved YOLOv5 algorithm. The GhostNet network structure is introduced to replace and modify the backbone network layer of YOLOv5 to reduce the complexity and computation of the structure and realize the light weight of the overall network structure; the ECA attention mechanism is introduced in the backbone network to enhance the feature perception of the broken filament target and increase the mobility of the feature information in the deep network. The improved YOLOv5 algorithm achieves an average detection accuracy of 93.9% and an average detection speed of 64 FPS in the final experimental results, which is better than the traditional methods of image recognition detection and can meet the realtime detection requirements of broken filament detection in practical engineering.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128401185","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}
Ground Penetrating Radar (GPR) data requires a significant amount of network bandwidth and storage space for transmission and storage due to the large number of channels and vast amount of data. In this paper, we propose an improved method for compressing GPR data. Firstly, we analyze and preprocess the features of the data to enhance its compression potential. Secondly, we introduce convolutional layers into the AutoEncoder to improve its generalization ability. We then use multiple-level compression to further compress the data based on the radar data's features. Finally, we introduce range encoding for secondary compression. Simulation experiments demonstrate that our proposed algorithm can effectively compress radar data while maintaining high compression ratios and speed.
{"title":"A radar data compression method based on autoencoder neural network and range encoding","authors":"Zelong Hu, Feng Yang, Xu Qiao, Fanruo Li","doi":"10.1117/12.2689784","DOIUrl":"https://doi.org/10.1117/12.2689784","url":null,"abstract":"Ground Penetrating Radar (GPR) data requires a significant amount of network bandwidth and storage space for transmission and storage due to the large number of channels and vast amount of data. In this paper, we propose an improved method for compressing GPR data. Firstly, we analyze and preprocess the features of the data to enhance its compression potential. Secondly, we introduce convolutional layers into the AutoEncoder to improve its generalization ability. We then use multiple-level compression to further compress the data based on the radar data's features. Finally, we introduce range encoding for secondary compression. Simulation experiments demonstrate that our proposed algorithm can effectively compress radar data while maintaining high compression ratios and speed.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124304112","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}
To achieve that the mobile warehouse robot follows the given desired path quickly and smoothly, the MPC and LQR steering control algorithms are applied based on the lateral kinematic constraints of the vehicle. First, the Ackermann kinematic modelling of the mobile platform is performed. The nonlinear model is linearized and discretized to create a discrete state space model of the mobile robot. Under the same conditions, a lateral control system based on MPC and LQR is designed for the mobile robot. A performance comparison of parameters such as different vehicle speeds, straightline trajectory tracking, curve trajectory tracking and algorithm consumption time is performed. The simulation shows that the LQR and MPC controllers can calculate the vehicle's steering angle in real time according to the road curvature and drive according to the preset desired path.
{"title":"Research on path tracking control of mobile storage robot based on model predictive control and linear quadratic regulator","authors":"Hao Chen, Xuelin Wang, Lide Zhao, Ru Jiang, Baigunchekov Zhumadil","doi":"10.1117/12.2689391","DOIUrl":"https://doi.org/10.1117/12.2689391","url":null,"abstract":"To achieve that the mobile warehouse robot follows the given desired path quickly and smoothly, the MPC and LQR steering control algorithms are applied based on the lateral kinematic constraints of the vehicle. First, the Ackermann kinematic modelling of the mobile platform is performed. The nonlinear model is linearized and discretized to create a discrete state space model of the mobile robot. Under the same conditions, a lateral control system based on MPC and LQR is designed for the mobile robot. A performance comparison of parameters such as different vehicle speeds, straightline trajectory tracking, curve trajectory tracking and algorithm consumption time is performed. The simulation shows that the LQR and MPC controllers can calculate the vehicle's steering angle in real time according to the road curvature and drive according to the preset desired path.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125659557","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 high proportion of renewable energy access puts new requirements on the adequacy of grid capacity, requiring the power system to have sufficient confidence capacity to accommodate the fluctuation and stochasticity of renewable energy generation. For the nonlinear relationship between power planning and power system confidence capacity, it is difficult for the traditional power planning methods to accurately estimate the confidence capacity of the power system, and it is also impossible to determine the confidence capacity adequacy constraint of the power system. Based on the consideration of thermal power generation, renewable energy, energy storage, and demand-side response, an 8760-based annual production and operation simulation model is constructed to ensure sufficient system resilience and to optimize the capacity of demand response resources and energy storage. Based on this, a new iterative method is proposed to solve the nonlinear problem of energy storage confidence capacity, and an example analysis is carried out with a regional grid. It is found that the flexibility constraint is the dominant influence in high percentage renewable energy systems, and the system cost can be significantly reduced by introducing a small amount of demand-side response resources, thus opening a new way for future planning problems of high percentage renewable energy systems.
{"title":"A new power system power planning method based on demand response and energy storage capacity","authors":"Jingying Yang, Dexin Li, Chang Liu, Zixin Yan, Mingyang Zhu","doi":"10.1117/12.2690116","DOIUrl":"https://doi.org/10.1117/12.2690116","url":null,"abstract":"The high proportion of renewable energy access puts new requirements on the adequacy of grid capacity, requiring the power system to have sufficient confidence capacity to accommodate the fluctuation and stochasticity of renewable energy generation. For the nonlinear relationship between power planning and power system confidence capacity, it is difficult for the traditional power planning methods to accurately estimate the confidence capacity of the power system, and it is also impossible to determine the confidence capacity adequacy constraint of the power system. Based on the consideration of thermal power generation, renewable energy, energy storage, and demand-side response, an 8760-based annual production and operation simulation model is constructed to ensure sufficient system resilience and to optimize the capacity of demand response resources and energy storage. Based on this, a new iterative method is proposed to solve the nonlinear problem of energy storage confidence capacity, and an example analysis is carried out with a regional grid. It is found that the flexibility constraint is the dominant influence in high percentage renewable energy systems, and the system cost can be significantly reduced by introducing a small amount of demand-side response resources, thus opening a new way for future planning problems of high percentage renewable energy systems.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125910855","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}
Aiming at the problem that the estimation accuracy of the direct position determination (DPD) method based on subspace is not high under the condition of small number of snapshots and low signal-to-noise ratio (SNR), a noncircular signal DPD method based on off-grid sparse reconstruction is proposed. This method combines the non-circular characteristics of the signal to expand the received data and then expand the array aperture. Then, based on the spatial sparsity of the target location, an ultra-complete dictionary set is constructed by discretizing the location area grid, and the problem of target position estimation is transformed into the problem of spatial signal sparse reconstruction. At the same time, considering the signal model that the target is not on the grid point, the joint optimization problem is solved by the alternating iteration method to obtain the estimated value of the target position. Finally, the experimental simulation shows that the method has better positioning performance.
{"title":"Direct position determination of non-circular signals based on off-grid sparse reconstruction","authors":"Jie Deng, Jie-xin Yin, B. Yang, Tiantian Chen","doi":"10.1117/12.2689790","DOIUrl":"https://doi.org/10.1117/12.2689790","url":null,"abstract":"Aiming at the problem that the estimation accuracy of the direct position determination (DPD) method based on subspace is not high under the condition of small number of snapshots and low signal-to-noise ratio (SNR), a noncircular signal DPD method based on off-grid sparse reconstruction is proposed. This method combines the non-circular characteristics of the signal to expand the received data and then expand the array aperture. Then, based on the spatial sparsity of the target location, an ultra-complete dictionary set is constructed by discretizing the location area grid, and the problem of target position estimation is transformed into the problem of spatial signal sparse reconstruction. At the same time, considering the signal model that the target is not on the grid point, the joint optimization problem is solved by the alternating iteration method to obtain the estimated value of the target position. Finally, the experimental simulation shows that the method has better positioning performance.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134233388","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}
High-dimensional time series anomaly detection has always been an important challenge in the field of system security. Most existing methods are dedicated to modelling the temporal variation of features to capture anomalous moment points, however as features become more high-dimensional, the associations between features take on a complex spatial structure. This spatial structure information will compensate for the constraints of unsupervised training conditions, and guide the model to be more fully trained. In this study, we propose a detection model that incorporates spatial supervision signals. The model not only simultaneously models the temporal and spatial dependencies, but also simulates the topological structure and physical characteristics of data in the real world through graph structure learning and contrastive learning, providing guidance for anomaly detection. We conducted experiments on two real-world datasets and demonstrated that our model outperforms the baseline. Finally, we conducted detailed data analysis to provide interpretability for the model.
{"title":"Deep spatial-constraints networks for unsupervised anomaly detection in multivariate time series data","authors":"Yanwen Wu, Di Ge, Y. Cheng","doi":"10.1117/12.2689395","DOIUrl":"https://doi.org/10.1117/12.2689395","url":null,"abstract":"High-dimensional time series anomaly detection has always been an important challenge in the field of system security. Most existing methods are dedicated to modelling the temporal variation of features to capture anomalous moment points, however as features become more high-dimensional, the associations between features take on a complex spatial structure. This spatial structure information will compensate for the constraints of unsupervised training conditions, and guide the model to be more fully trained. In this study, we propose a detection model that incorporates spatial supervision signals. The model not only simultaneously models the temporal and spatial dependencies, but also simulates the topological structure and physical characteristics of data in the real world through graph structure learning and contrastive learning, providing guidance for anomaly detection. We conducted experiments on two real-world datasets and demonstrated that our model outperforms the baseline. Finally, we conducted detailed data analysis to provide interpretability for the model.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132948331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：