Environmental sensing is an essential aspect of autonomous driving systems, with millimeter wave radar currently gaining attention in academic circles due to its unique physical properties that complement optical sensing techniques such as vision. Compared to cameras and LIDAR, millimeter wave radar is not limited by light and meteorological conditions, boasts high penetration capabilities, and can operate around the clock to identify objects. However, the larger wavelengths of millimeter wave signals present significant challenges such as sparse point clouds and multipath effects, resulting in lower accuracy in environmental sensing. To address this issue, this paper proposes a point cloud enhancement method based on a GAN-LSTM network that converts the sparse point cloud data into semantically informative RF images, thereby improving object recognition accuracy. The proposed method is evaluated on the CARRADA dataset, and the experimental results demonstrate an improvement in object classification accuracy by 7.86% compared to the current state-of-the-art methods. This approach can significantly enhance the accuracy of millimeter wave radar-based environmental sensing in autonomous driving systems, enabling safer and more reliable vehicle operation.
{"title":"Enhanced radar for object recognition based on GANs","authors":"Guowei Lu, Zhenhua He, Yi Zhong, Yi Han","doi":"10.1117/12.2689832","DOIUrl":"https://doi.org/10.1117/12.2689832","url":null,"abstract":"Environmental sensing is an essential aspect of autonomous driving systems, with millimeter wave radar currently gaining attention in academic circles due to its unique physical properties that complement optical sensing techniques such as vision. Compared to cameras and LIDAR, millimeter wave radar is not limited by light and meteorological conditions, boasts high penetration capabilities, and can operate around the clock to identify objects. However, the larger wavelengths of millimeter wave signals present significant challenges such as sparse point clouds and multipath effects, resulting in lower accuracy in environmental sensing. To address this issue, this paper proposes a point cloud enhancement method based on a GAN-LSTM network that converts the sparse point cloud data into semantically informative RF images, thereby improving object recognition accuracy. The proposed method is evaluated on the CARRADA dataset, and the experimental results demonstrate an improvement in object classification accuracy by 7.86% compared to the current state-of-the-art methods. This approach can significantly enhance the accuracy of millimeter wave radar-based environmental sensing in autonomous driving systems, enabling safer and more reliable vehicle operation.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"1 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":"128884918","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}
Speaker recognition, also known as voiceprint recognition, is a biometric technology with wide practicability at present. This paper summarizes and compares and analyzes the main research methods of speaker recognition at home and abroad at this stage, and proposes an improved ECAPA_TDNN algorithm. It is proved by experiments that the improved ECAPA_TDNN algorithm in this paper is superior to the classical algorithm in terms of accuracy and loss.
{"title":"Speaker recognition based on improved ECAPA-TDNN network","authors":"Hongyan Chen, Xiaoming Ling, Xiaoyu Zhang, Zhen Zhang, Zeifei Dang, Xiang Ji","doi":"10.1117/12.2689511","DOIUrl":"https://doi.org/10.1117/12.2689511","url":null,"abstract":"Speaker recognition, also known as voiceprint recognition, is a biometric technology with wide practicability at present. This paper summarizes and compares and analyzes the main research methods of speaker recognition at home and abroad at this stage, and proposes an improved ECAPA_TDNN algorithm. It is proved by experiments that the improved ECAPA_TDNN algorithm in this paper is superior to the classical algorithm in terms of accuracy and loss.","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":"125292109","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 expansion of power grid scale, the performance bottleneck of traditional reliability assessment methods has gradually become prominent. How to improve the efficiency of reliability assessment has become an urgent problem to be solved. At the same time, the widespread access of distributed generation in the distribution network also affects the reliability of the distribution network. In view of these problems, this paper proposes an improved Warhall topology analysis method and an improved Monte-Carlo method to record fault sections, which improve the efficiency of reliability evaluation. On the basis of the improved method, the reliability index of a distribution network with DG access is calculated through a specific example, and the influence of DG access and access location on system reliability is further analyzed.
{"title":"Distribution network reliability evaluation","authors":"X. Jin, Bin Han, Yan Ma, Chang Che, Daokuan Bian","doi":"10.1117/12.2689683","DOIUrl":"https://doi.org/10.1117/12.2689683","url":null,"abstract":"With the expansion of power grid scale, the performance bottleneck of traditional reliability assessment methods has gradually become prominent. How to improve the efficiency of reliability assessment has become an urgent problem to be solved. At the same time, the widespread access of distributed generation in the distribution network also affects the reliability of the distribution network. In view of these problems, this paper proposes an improved Warhall topology analysis method and an improved Monte-Carlo method to record fault sections, which improve the efficiency of reliability evaluation. On the basis of the improved method, the reliability index of a distribution network with DG access is calculated through a specific example, and the influence of DG access and access location on system reliability is further analyzed.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"10 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":"125345975","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 bat algorithm (BA) was a kind of meta-heuristic algorithm that was simple and efficient in optimization, it had been widely applied in various fields. To improve the capability of the original BA, two methods used frequently in improvements were proposed in this paper: the gradient and sub-gradient methods, together with the Levy flights. Simulation experiments were carried out on the representatives of unimodal and multimodal benchmark functions. Results confirmed that not all traditional improvements were effective, some of the improved BA even work worse than the original one. However, the Levy flights could be considered a better replacement of randomness in applications.
{"title":"Research on the improved bat algorithms","authors":"Yantao Tao, Z. Gao","doi":"10.1117/12.2689366","DOIUrl":"https://doi.org/10.1117/12.2689366","url":null,"abstract":"The bat algorithm (BA) was a kind of meta-heuristic algorithm that was simple and efficient in optimization, it had been widely applied in various fields. To improve the capability of the original BA, two methods used frequently in improvements were proposed in this paper: the gradient and sub-gradient methods, together with the Levy flights. Simulation experiments were carried out on the representatives of unimodal and multimodal benchmark functions. Results confirmed that not all traditional improvements were effective, some of the improved BA even work worse than the original one. However, the Levy flights could be considered a better replacement of randomness in applications.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"12748 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":"130041045","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 development of economy and society, people's requirements for power quality and reliability are gradually increasing, so it is necessary to carry out risk assessment on power system. Monte Carlo method is a common method for power system risk assessment, but there is a contradiction between the calculation speed and accuracy. In order to improve the calculation accuracy, a large number of state samples need to be calculated, which takes a long time, and there are many repetitive states. For this reason, an improved Monte Carlo method based on Huffman code and state identification is proposed, which can significantly improve the efficiency of power grid risk assessment from two aspects. The first is to uniquely identify the system states through Huffman code and record relevant data, so as to avoid recalculation of the same states. The second is the fast and efficient identification of system state based on the shortest weighted path length of Huffman code. The effectiveness of the method is verified by an example, and several factors that may affect the effectiveness of the method are analyzed.
{"title":"Fast assessment method of power grid risk based on Huffman code and state identification","authors":"Chang Che, Y. Wang, Wenbo Liu, Guozheng Zhang","doi":"10.1117/12.2689697","DOIUrl":"https://doi.org/10.1117/12.2689697","url":null,"abstract":"With the development of economy and society, people's requirements for power quality and reliability are gradually increasing, so it is necessary to carry out risk assessment on power system. Monte Carlo method is a common method for power system risk assessment, but there is a contradiction between the calculation speed and accuracy. In order to improve the calculation accuracy, a large number of state samples need to be calculated, which takes a long time, and there are many repetitive states. For this reason, an improved Monte Carlo method based on Huffman code and state identification is proposed, which can significantly improve the efficiency of power grid risk assessment from two aspects. The first is to uniquely identify the system states through Huffman code and record relevant data, so as to avoid recalculation of the same states. The second is the fast and efficient identification of system state based on the shortest weighted path length of Huffman code. The effectiveness of the method is verified by an example, and several factors that may affect the effectiveness of the method are analyzed.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"62 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":"127929347","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}
For the purpose of the present study of lithium battery SOC estimation, fractional-order calculus theory and the fact that the real capacitance is fractional-order in nature mean that integer-order modeling yields incorrect methods. To improve the accuracy of lithium battery state-of-charge (SOC) estimation, a fractional-order traceless Kalman filter technique is proposed with a second-order RC fractional-order model, and a least-squares approach with a variable forgetting factor is utilized to determine battery parameters. The system gives real-time updates to the battery condition and settings through recursive estimation of state and parameter variables. Simulation analysis is performed using experimental data and UDDS operating parameters. The traceless Kalman filter method's simulated values are compared to the simulation outcomes. These results show that the method beats the integer-order traceless Kalman algorithm and that the maximum estimation error of battery SOC can be maintained below 2%. This proves that the proposed approach works as intended.
{"title":"SOC estimation of lithium battery based on fractional order model","authors":"Yuhang Chen, Yi Guo","doi":"10.1117/12.2689433","DOIUrl":"https://doi.org/10.1117/12.2689433","url":null,"abstract":"For the purpose of the present study of lithium battery SOC estimation, fractional-order calculus theory and the fact that the real capacitance is fractional-order in nature mean that integer-order modeling yields incorrect methods. To improve the accuracy of lithium battery state-of-charge (SOC) estimation, a fractional-order traceless Kalman filter technique is proposed with a second-order RC fractional-order model, and a least-squares approach with a variable forgetting factor is utilized to determine battery parameters. The system gives real-time updates to the battery condition and settings through recursive estimation of state and parameter variables. Simulation analysis is performed using experimental data and UDDS operating parameters. The traceless Kalman filter method's simulated values are compared to the simulation outcomes. These results show that the method beats the integer-order traceless Kalman algorithm and that the maximum estimation error of battery SOC can be maintained below 2%. This proves that the proposed approach works as intended.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"19 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":"116530095","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 essay investigates the lateral dynamics control problem of an intelligent car with respect to bounded disturbances. A sliding mode controller is designed to address the above issue by introducing an event triggering mechanism. Compared with existing lateral motion adjustment algorithms with periodic control, unnecessary signal samplings, transmissions, computations and actuations are avoided, which indirectly saves the limited energy. In addition, finite-time convergence performance is achieved due to the sliding mode. Finally, numerical simulation experiments are used to illustrate the theoretic results
{"title":"Finite-time control for the lateral dynamics of intelligent car: a sliding mode event triggering approach","authors":"Qingshen Yu, Zhi Liu, Xing Chu","doi":"10.1117/12.2689596","DOIUrl":"https://doi.org/10.1117/12.2689596","url":null,"abstract":"This essay investigates the lateral dynamics control problem of an intelligent car with respect to bounded disturbances. A sliding mode controller is designed to address the above issue by introducing an event triggering mechanism. Compared with existing lateral motion adjustment algorithms with periodic control, unnecessary signal samplings, transmissions, computations and actuations are avoided, which indirectly saves the limited energy. In addition, finite-time convergence performance is achieved due to the sliding mode. Finally, numerical simulation experiments are used to illustrate the theoretic results","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"413 1 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":"116556481","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 increasing automation of aircraft ground assembly integration testing, the progress of aircraft ground function testing is severely impacted by the multitude of devices, types, and interfaces present at civil aircraft final assembly sites. Some devices even lack communication interfaces. Embedded system-based interlayer cooperative interaction is a general trend and the future development direction. It is urgent to enable data interaction between the hardware and software layers using advanced embedded microprocessor technology. To address this challenge, this paper proposes a Mirco XRCE-DDS (EXtremely Resource Constrained Environments DDS) solution based on eProsima that adheres to the OMG (Object Management Group) specification and is suitable for resource-limited equipment, to facilitate the design of civil aircraft ground function testing systems
{"title":"Design of civil aircraft ground function test system based on DDS communication","authors":"Bo Zhang, Changfa Wang, Qinghua Yang","doi":"10.1117/12.2689793","DOIUrl":"https://doi.org/10.1117/12.2689793","url":null,"abstract":"With the increasing automation of aircraft ground assembly integration testing, the progress of aircraft ground function testing is severely impacted by the multitude of devices, types, and interfaces present at civil aircraft final assembly sites. Some devices even lack communication interfaces. Embedded system-based interlayer cooperative interaction is a general trend and the future development direction. It is urgent to enable data interaction between the hardware and software layers using advanced embedded microprocessor technology. To address this challenge, this paper proposes a Mirco XRCE-DDS (EXtremely Resource Constrained Environments DDS) solution based on eProsima that adheres to the OMG (Object Management Group) specification and is suitable for resource-limited equipment, to facilitate the design of civil aircraft ground function testing systems","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"152 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":"128194360","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}
Tianyu Zhu, Qiang Ye, Jiaqi Yang, Chaoyue Gao, Xinnuo Li, Dan Wang
This paper proposes a wind vector prediction method based on long-short term memory neural network (LSTM). The correlation between wind speed and direction is analyzed from the perspective of feature engineering. The results show that they contain different feature information and can be used as input variables to train the model at the same time. On the other hand, the above analysis also provides a basis for selecting the time length of input variables. The wind vector is decomposed into two orthogonal one-dimensional variables of east-west and north-south wind speeds based on wind direction to prevent the complexity of the algorithm from being increased by multi-dimensional variables. The LSTM algorithm is used to train the prediction model for the wind speed in both directions, and finally the wind vector prediction data containing the wind speed and direction are restored. Without increasing the complexity of the algorithm, the information density contained in the model is increased. One month's second level data of a wind farm in Hebei and Gansu provinces are selected for verification. The results show that the proposed hybrid prediction algorithm can better capture the information about wind speed and direction, and the error range of wind speed and direction prediction is reduced to 1m/s and 5° respectively, with an accuracy rate of more than 90%
{"title":"A wind vector prediction method based on LSTM algorithm","authors":"Tianyu Zhu, Qiang Ye, Jiaqi Yang, Chaoyue Gao, Xinnuo Li, Dan Wang","doi":"10.1117/12.2689499","DOIUrl":"https://doi.org/10.1117/12.2689499","url":null,"abstract":"This paper proposes a wind vector prediction method based on long-short term memory neural network (LSTM). The correlation between wind speed and direction is analyzed from the perspective of feature engineering. The results show that they contain different feature information and can be used as input variables to train the model at the same time. On the other hand, the above analysis also provides a basis for selecting the time length of input variables. The wind vector is decomposed into two orthogonal one-dimensional variables of east-west and north-south wind speeds based on wind direction to prevent the complexity of the algorithm from being increased by multi-dimensional variables. The LSTM algorithm is used to train the prediction model for the wind speed in both directions, and finally the wind vector prediction data containing the wind speed and direction are restored. Without increasing the complexity of the algorithm, the information density contained in the model is increased. One month's second level data of a wind farm in Hebei and Gansu provinces are selected for verification. The results show that the proposed hybrid prediction algorithm can better capture the information about wind speed and direction, and the error range of wind speed and direction prediction is reduced to 1m/s and 5° respectively, with an accuracy rate of more than 90%","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"60 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":"130589419","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}
Ziquan Liu, N. Yao, Qing Fan, Xueqiong Zhu, Hai Xue
In order to cope with the increasingly complex situation of safe operation of power grid, this paper proposes fault event reasoning of substation power grid based on knowledge map technology. By using the method of knowledge map, the logical relationships such as co-reference relationship, causality relationship and time sequence relationship among substation monitoring events are established, and the rules and patterns among the events are described. Based on the power grid equipment entity and concept map, business logic map and historical case map, and according to the key information flow after fault signal analysis, the auxiliary decision of fault handling operation mode is made by using power grid operation and control logic, rules and experience knowledge. Realize the substation power grid fault analysis and processing function, and further improve the intelligent level of fault management.
{"title":"Reasoning simulation of substation power grid fault events based on knowledge map technology","authors":"Ziquan Liu, N. Yao, Qing Fan, Xueqiong Zhu, Hai Xue","doi":"10.1117/12.2690053","DOIUrl":"https://doi.org/10.1117/12.2690053","url":null,"abstract":"In order to cope with the increasingly complex situation of safe operation of power grid, this paper proposes fault event reasoning of substation power grid based on knowledge map technology. By using the method of knowledge map, the logical relationships such as co-reference relationship, causality relationship and time sequence relationship among substation monitoring events are established, and the rules and patterns among the events are described. Based on the power grid equipment entity and concept map, business logic map and historical case map, and according to the key information flow after fault signal analysis, the auxiliary decision of fault handling operation mode is made by using power grid operation and control logic, rules and experience knowledge. Realize the substation power grid fault analysis and processing function, and further improve the intelligent level of fault management.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"10 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":"131961536","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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