Recently, Low Earth Orbit (LEO) satellite constellations with low-latency and high-bandwidth attract extensive research. However, most available studies focused on the field of satellite network routing algorithms, ignoring the impact of topology on the efficiency of inter-satellite networking and the quality of inter-satellite communication. In this paper, we propose a satellite network topology design method based on deep reinforcement learning (DRL), with the goal of reducing the latency of the entire satellite network. To achieve this goal, we first model the satellite network communication scene and formulate the topology optimization problem as a Markov decision process (MDP). Then, we further propose the idea of backbone-point satellites and use DRL to optimize the topology structure. Finally, we conduct extensive experiments on different performances of satellite topology, and we conclude that the network topology constructed in this way can provide lower latency communications than the motif and +Grid topologies, optimized by 8.48% and 42.86% respectively.
{"title":"A topology design method for satellite networks based on deep reinforcement learning","authors":"Yuning Zheng, Yifeng Lyu, Y. Wang, Xiufeng Sui, Liyue Zhu, Shubin Xu","doi":"10.1117/12.2682444","DOIUrl":"https://doi.org/10.1117/12.2682444","url":null,"abstract":"Recently, Low Earth Orbit (LEO) satellite constellations with low-latency and high-bandwidth attract extensive research. However, most available studies focused on the field of satellite network routing algorithms, ignoring the impact of topology on the efficiency of inter-satellite networking and the quality of inter-satellite communication. In this paper, we propose a satellite network topology design method based on deep reinforcement learning (DRL), with the goal of reducing the latency of the entire satellite network. To achieve this goal, we first model the satellite network communication scene and formulate the topology optimization problem as a Markov decision process (MDP). Then, we further propose the idea of backbone-point satellites and use DRL to optimize the topology structure. Finally, we conduct extensive experiments on different performances of satellite topology, and we conclude that the network topology constructed in this way can provide lower latency communications than the motif and +Grid topologies, optimized by 8.48% and 42.86% respectively.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130327728","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 classification plays an important role in network resource management and security. The application of encryption techniques and the rapid increase in the size of network traffic have placed higher demands on traffic classification. In this paper, we design multi-headed attention (MHA) and deep metric learning (DML) in our model for network traffic classification. In addition, MHA-DML also extracts more subtle and highly differentiated features through the improved triplet measurement loss. Experimental results demonstrate that the model achieves the best classification on all three publicly available web traffic datasets. The MHA-DML guarantees detection accuracy even when facing a classification task with many categories.
{"title":"Network traffic classification based on multi-head attention and deep metric learning","authors":"Zhuo-Hang Lv, Bin Lu, Xue Li, Zan Qi","doi":"10.1117/12.2682521","DOIUrl":"https://doi.org/10.1117/12.2682521","url":null,"abstract":"Network traffic classification plays an important role in network resource management and security. The application of encryption techniques and the rapid increase in the size of network traffic have placed higher demands on traffic classification. In this paper, we design multi-headed attention (MHA) and deep metric learning (DML) in our model for network traffic classification. In addition, MHA-DML also extracts more subtle and highly differentiated features through the improved triplet measurement loss. Experimental results demonstrate that the model achieves the best classification on all three publicly available web traffic datasets. The MHA-DML guarantees detection accuracy even when facing a classification task with many categories.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"399 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113997258","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}
Xuesong Su, Shanshan Huang, Jia Liu, Mei Wang, Xingsha Yang, Kaijian Wang
With the development of deep learning technology, image-based object detection algorithms have been widely used in oilfield safety behavior regulation. However, the accuracy of identifying safety warning bands in oilfields is low, mainly due to the extreme aspect ratios. To solve the above problems, this paper proposes an improved YOLOv5-based method for detecting rotating targets of oilfield warning bands. By adding an additional angle prediction task to the original object detection framework and using a cyclic smooth labelling algorithm to transform the angle regression problem into a classification problem, the horizontal and predicted angle decoders can be combined to obtain the rotation bounding box of the target. This provides a more accurate spatial position representation of the warning band target, making it easier for the network to extract strong discriminative features of the target. Compared with traditional object detection algorithms annotated with horizontal bounding boxes, the rotation bounding box annotated object detection algorithm proposed in this paper significantly improves the recognition performance of safety warning bands and meets practical application requirements.
{"title":"Improved YOLOv5-based detection method for oilfield safety warning bands","authors":"Xuesong Su, Shanshan Huang, Jia Liu, Mei Wang, Xingsha Yang, Kaijian Wang","doi":"10.1117/12.2682501","DOIUrl":"https://doi.org/10.1117/12.2682501","url":null,"abstract":"With the development of deep learning technology, image-based object detection algorithms have been widely used in oilfield safety behavior regulation. However, the accuracy of identifying safety warning bands in oilfields is low, mainly due to the extreme aspect ratios. To solve the above problems, this paper proposes an improved YOLOv5-based method for detecting rotating targets of oilfield warning bands. By adding an additional angle prediction task to the original object detection framework and using a cyclic smooth labelling algorithm to transform the angle regression problem into a classification problem, the horizontal and predicted angle decoders can be combined to obtain the rotation bounding box of the target. This provides a more accurate spatial position representation of the warning band target, making it easier for the network to extract strong discriminative features of the target. Compared with traditional object detection algorithms annotated with horizontal bounding boxes, the rotation bounding box annotated object detection algorithm proposed in this paper significantly improves the recognition performance of safety warning bands and meets practical application requirements.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"12715 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129114234","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 order to improve the accuracy of heart disease prediction models and address the lack of interpretability in traditional machine learning models, this paper proposes a heart disease prediction method based on random forests and SHAP value. This method first preprocesses the dataset by encoding the data, filling in missing values, and removing outliers. It then uses recursive feature elimination and cross-validation to remove irrelevant features and select relevant features for further model training. The results, compared with other methods using accuracy, precision, recall, and F1 score, show that the proposed method outperforms other models. The interpretable model constructed based on SHAP value reflects the effect of feature values on prediction model results and provides a ranking of feature importance. The experimental results show that the method can effectively improve the accuracy of heart disease prediction, and provide a clear interpretation of the model prediction results. It can be an aid in the treatment and prevention of heart disease.
{"title":"Interpretable prediction of heart disease based on random forest and SHAP","authors":"Lin Wu","doi":"10.1117/12.2682322","DOIUrl":"https://doi.org/10.1117/12.2682322","url":null,"abstract":"In order to improve the accuracy of heart disease prediction models and address the lack of interpretability in traditional machine learning models, this paper proposes a heart disease prediction method based on random forests and SHAP value. This method first preprocesses the dataset by encoding the data, filling in missing values, and removing outliers. It then uses recursive feature elimination and cross-validation to remove irrelevant features and select relevant features for further model training. The results, compared with other methods using accuracy, precision, recall, and F1 score, show that the proposed method outperforms other models. The interpretable model constructed based on SHAP value reflects the effect of feature values on prediction model results and provides a ranking of feature importance. The experimental results show that the method can effectively improve the accuracy of heart disease prediction, and provide a clear interpretation of the model prediction results. It can be an aid in the treatment and prevention of heart disease.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129168567","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 paper is aimed at improving the previous experiments of duffing oscillator detecting weak signals. The influence of phase transition direction, signal phase, frequency and amplitude on Duffing system is analyzed and a new method for detecting weak signal by Duffing Oscillator is proposed. This method detects weak signals by the way of reverse detection, and calculates the threshold through Poincare section, so that the detection is more accurate.
{"title":"An improved method of weak signal erasometry based on Duffing oscillator","authors":"J. Ma, Ying-juan Zhao, Haimei Du, Jianqiang Zhang","doi":"10.1117/12.2682513","DOIUrl":"https://doi.org/10.1117/12.2682513","url":null,"abstract":"This paper is aimed at improving the previous experiments of duffing oscillator detecting weak signals. The influence of phase transition direction, signal phase, frequency and amplitude on Duffing system is analyzed and a new method for detecting weak signal by Duffing Oscillator is proposed. This method detects weak signals by the way of reverse detection, and calculates the threshold through Poincare section, so that the detection is more accurate.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123648292","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}
Nowadays,more and more enterprises have begun to treat it as the core part of the security infrastructure and apply automation to help solve the problem of “security, cost and efficiency” difficult to balance in enterprise security operations. But the traditional network security is protected based on a concept of stacking security devices, many types of security devices have their security capabilities overlapped. This paper discusses how the atomic device control strategy can be used to standardize the management of network security devices, guide the planning of device deployment and implement automatic security emergency response on various SOAR platforms. For a certain enterprise, its internal network security devices are limited in types and the overall workload is acceptable.
{"title":"Application of atomization management scheme based on network security technology with SOAR","authors":"Dong Bin, Chunyan Yang, Songming Han","doi":"10.1117/12.2682470","DOIUrl":"https://doi.org/10.1117/12.2682470","url":null,"abstract":"Nowadays,more and more enterprises have begun to treat it as the core part of the security infrastructure and apply automation to help solve the problem of “security, cost and efficiency” difficult to balance in enterprise security operations. But the traditional network security is protected based on a concept of stacking security devices, many types of security devices have their security capabilities overlapped. This paper discusses how the atomic device control strategy can be used to standardize the management of network security devices, guide the planning of device deployment and implement automatic security emergency response on various SOAR platforms. For a certain enterprise, its internal network security devices are limited in types and the overall workload is acceptable.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"38 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131468593","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 main factors limiting the long-distance application of vortex beam are the low receiving power and the wavefront phase distortion caused by atmosphere turbulence. Coherent beam combining (CBC) technology is an effective way to generating high power vortex beams. However, most common coherent combined vortex (CCV) fiber array is currently based on a single-ring structure with limited output power enhancement. In this paper, a dual-ring fiber array is developed to achieve higher output power and improved stochastic-parallel-gradient-descent (SPGD) correction accuracy. To improve SPGD correction speed, cross-grouping method is used. The results show that CCV beam in dual-ring structure can maintain good intensity distribution and mode distribution after SPGD correction.
{"title":"Optimization for coherent combined vortex fiber array with phase correction","authors":"Guangwei Qin, Tao Yu, Qiao Xie","doi":"10.1117/12.2682530","DOIUrl":"https://doi.org/10.1117/12.2682530","url":null,"abstract":"The main factors limiting the long-distance application of vortex beam are the low receiving power and the wavefront phase distortion caused by atmosphere turbulence. Coherent beam combining (CBC) technology is an effective way to generating high power vortex beams. However, most common coherent combined vortex (CCV) fiber array is currently based on a single-ring structure with limited output power enhancement. In this paper, a dual-ring fiber array is developed to achieve higher output power and improved stochastic-parallel-gradient-descent (SPGD) correction accuracy. To improve SPGD correction speed, cross-grouping method is used. The results show that CCV beam in dual-ring structure can maintain good intensity distribution and mode distribution after SPGD correction.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"267 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120976098","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 stock market is usually regarded as a barometer of the economy, while the stock index can reflect the ups and downs, as well as trend changes of the stock market, to a certain extent. In recent years, the long short-term memory neural network model (LSTM model) has been widely used in the forecasting of stock prices due to its effectiveness. Nonetheless, few studies have focused on the forecasting ability of the LSTM model based on stock-index prices, with the effectiveness of this field still needing to be further explored. Against this background, this paper first constructs and designs the LSTM model of deep learning. Secondly, through the Min-Max normalization method to the data of three kinds of China A-share stock market indexes collected by Python, this paper carries out algorithm training for the LSTM model. Furthermore, based on the cleaned data, this paper conducts an empirical analysis of the price forecasting ability of the LSTM model, thus testing the accuracy of the LSTM model forecasting through the difference between the predicted and the true price curves. In closing, the paper draws relevant conclusions and puts forward targeted recommendations for improvement. Regarding research significance, the greatest contribution of this paper is to improve the stock-index price forecasting system and the research related to the defect system of the LSTM model.
{"title":"Research on the forecast ability of long short-term memory neural network model","authors":"Xiaolei Ding, Lingwei Zhang, Biyuan Yang","doi":"10.1117/12.2682465","DOIUrl":"https://doi.org/10.1117/12.2682465","url":null,"abstract":"The stock market is usually regarded as a barometer of the economy, while the stock index can reflect the ups and downs, as well as trend changes of the stock market, to a certain extent. In recent years, the long short-term memory neural network model (LSTM model) has been widely used in the forecasting of stock prices due to its effectiveness. Nonetheless, few studies have focused on the forecasting ability of the LSTM model based on stock-index prices, with the effectiveness of this field still needing to be further explored. Against this background, this paper first constructs and designs the LSTM model of deep learning. Secondly, through the Min-Max normalization method to the data of three kinds of China A-share stock market indexes collected by Python, this paper carries out algorithm training for the LSTM model. Furthermore, based on the cleaned data, this paper conducts an empirical analysis of the price forecasting ability of the LSTM model, thus testing the accuracy of the LSTM model forecasting through the difference between the predicted and the true price curves. In closing, the paper draws relevant conclusions and puts forward targeted recommendations for improvement. Regarding research significance, the greatest contribution of this paper is to improve the stock-index price forecasting system and the research related to the defect system of the LSTM model.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129473002","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}
Minhui Hu, Jianhua Fan, Yongyang Hu, Rui Xu, Yang Guo
Due to its efficiency, energy-saving, and abundant data reuse, systolic array has been a popular choice for Convolutional Neural Network (CNN) accelerators. Dataflow of the systolic array defines computation mapping strategy and memory access and it is one of the most important design points of accelerators. Most conventional accelerator designs choose a single dataflow and optimize around it. This may influence the Processing Element (PE) utilization rate and cause waste of computing resources and energy. This work introduces a self-paced method to alleviate this problem. We analyse and quantify the PE utilization rate related to the three basic dataflows and build a model called PEU-sim to explore workload-oriented flexible dataflow. Experiments show by combining three dataflows, we are able to raise more than 10% of PE utilization rate for most neural networks and we get the highest of 12.4% for MobileNet.
{"title":"Modeling and optimizing PE utilization rate for systolic array based CNN accelerators","authors":"Minhui Hu, Jianhua Fan, Yongyang Hu, Rui Xu, Yang Guo","doi":"10.1117/12.2682498","DOIUrl":"https://doi.org/10.1117/12.2682498","url":null,"abstract":"Due to its efficiency, energy-saving, and abundant data reuse, systolic array has been a popular choice for Convolutional Neural Network (CNN) accelerators. Dataflow of the systolic array defines computation mapping strategy and memory access and it is one of the most important design points of accelerators. Most conventional accelerator designs choose a single dataflow and optimize around it. This may influence the Processing Element (PE) utilization rate and cause waste of computing resources and energy. This work introduces a self-paced method to alleviate this problem. We analyse and quantify the PE utilization rate related to the three basic dataflows and build a model called PEU-sim to explore workload-oriented flexible dataflow. Experiments show by combining three dataflows, we are able to raise more than 10% of PE utilization rate for most neural networks and we get the highest of 12.4% for MobileNet.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"12715 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129948078","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}
As Moore’s law indicates, the number of transistors on a chip doubles every 18 months, which guarantees many resourcedemanding applications can be implemented on these advanced chips. In order to fulfill this purpose, CAD tools should be precise and efficient. In this paper, we dig into FPGAs, which unavoidably require CAD tools to be configured. A new timing database construction method mainly focusing on reformatting the timing models of programmable interconnections and routing wires is proposed to improve routing efficiency for FPGAs. A contrast experiment has been carried out to compare routing efficiency with original and new database. The results of our experiment show that routing with this new database can implement circuits of high quality (1.000× critical path delay) within less time (0.994× original routing time). And it can at most route resource-demanding circuits within 0.787× original routing time.
{"title":"A timing library construction method aimed at improving routing efficiency for modern FPGAs","authors":"Gang Liao, Jun Yu","doi":"10.1117/12.2682408","DOIUrl":"https://doi.org/10.1117/12.2682408","url":null,"abstract":"As Moore’s law indicates, the number of transistors on a chip doubles every 18 months, which guarantees many resourcedemanding applications can be implemented on these advanced chips. In order to fulfill this purpose, CAD tools should be precise and efficient. In this paper, we dig into FPGAs, which unavoidably require CAD tools to be configured. A new timing database construction method mainly focusing on reformatting the timing models of programmable interconnections and routing wires is proposed to improve routing efficiency for FPGAs. A contrast experiment has been carried out to compare routing efficiency with original and new database. The results of our experiment show that routing with this new database can implement circuits of high quality (1.000× critical path delay) within less time (0.994× original routing time). And it can at most route resource-demanding circuits within 0.787× original routing time.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"598 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132764953","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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