Shunda Xun, Pengcheng Zhu, Binghua Yang, Jin Xiong
This paper proposes a self-attention LSTM (SALSTM) model for ship motion prediction, which combines the advantages of LSTM and self-attention mechanisms. The model also introduces the concept of attention gate. The paper studies the influence of forecast lead time on the prediction accuracy of three degrees of freedom: roll, surge and heave. The paper compares the SALSTM model with a baseline LSTM model on a ship motion data set under different forecast durations and lead times. The paper evaluates the performance of the SALSTM model using four metrics and verifies its effectiveness under three representative working conditions. The paper also gives the applicable conditions of the SALSTM model for ship motion prediction
{"title":"Multi-direction prediction based on SALSTM model for ship motion","authors":"Shunda Xun, Pengcheng Zhu, Binghua Yang, Jin Xiong","doi":"10.1117/12.2690178","DOIUrl":"https://doi.org/10.1117/12.2690178","url":null,"abstract":"This paper proposes a self-attention LSTM (SALSTM) model for ship motion prediction, which combines the advantages of LSTM and self-attention mechanisms. The model also introduces the concept of attention gate. The paper studies the influence of forecast lead time on the prediction accuracy of three degrees of freedom: roll, surge and heave. The paper compares the SALSTM model with a baseline LSTM model on a ship motion data set under different forecast durations and lead times. The paper evaluates the performance of the SALSTM model using four metrics and verifies its effectiveness under three representative working conditions. The paper also gives the applicable conditions of the SALSTM model for ship motion prediction","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"39 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113999680","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 recent years, information security has become more and more important. Image encryption technology based on chaos theory has become one of the hot issues in this field. Chaotic system has the characteristics of initial value sensitivity and sequence ergodicity, which is very suitable for image encryption. In this paper, the folded surface is generated by connecting the randomly generated points on the bottom surface and the top surface in the unit space, and the surjective binary function is further constructed. Use this type of function and functions such as planes to construct discrete dynamical system. It is experimentally analyzed that the function has good chaotic characteristics by drawing bifurcation diagram and Lyapunov exponent diagram. The chaotic sequence of the discrete dynamic system is used for image encryption, and its information entropy and correlation coefficient before and after encryption are calculated. It is proved that this kind of system has good chaotic characteristics. This is a new type of chaotic system, which needs further research, analysis and expansion.
{"title":"Application of two-dimensional linear piecewise full mapping function in chaotic image encryption","authors":"Wanbo Yu, Q. Hou, Zhenzhen hu","doi":"10.1117/12.2689598","DOIUrl":"https://doi.org/10.1117/12.2689598","url":null,"abstract":"In recent years, information security has become more and more important. Image encryption technology based on chaos theory has become one of the hot issues in this field. Chaotic system has the characteristics of initial value sensitivity and sequence ergodicity, which is very suitable for image encryption. In this paper, the folded surface is generated by connecting the randomly generated points on the bottom surface and the top surface in the unit space, and the surjective binary function is further constructed. Use this type of function and functions such as planes to construct discrete dynamical system. It is experimentally analyzed that the function has good chaotic characteristics by drawing bifurcation diagram and Lyapunov exponent diagram. The chaotic sequence of the discrete dynamic system is used for image encryption, and its information entropy and correlation coefficient before and after encryption are calculated. It is proved that this kind of system has good chaotic characteristics. This is a new type of chaotic system, which needs further research, analysis and expansion.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"80 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":"114810660","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 based telemetry stations are usually used to acquire real-time information of flight vehicles, and monitor the flying states in order to guarantee the safety of flight tests. However, when the telemetry ground station tracks the aerial vehicle, some wild values are inevitably included in the received telemetry data due to various interference factors, which can seriously affect the interpretation of the telemetry data and the evaluation of the vehicle performance. In order to make up for the shortage of existing telemetry data wild value elimination algorithms, this paper uses the wavelet transform threshold method to eliminate the wild values in telemetry data based on the principle of wavelet transform, and introduces the swarm intelligence optimization algorithm to obtain the optimal thresholds for different telemetry data adaptively. The optimal threshold and threshold function coefficients are obtained for different telemetry data to achieve better filtering effect. Corresponding results show that the proposed method can effectively eliminate the wild values in the telemetry data and realize the filtering of the telemetry data.
{"title":"Research on filtering method of telemetry data based on whale optimization and wavelet transform","authors":"Cheng-Fei Li, Zhang Wang, Fang Pu, Maolin Chen, Li-Yu Daisy Liu","doi":"10.1117/12.2689586","DOIUrl":"https://doi.org/10.1117/12.2689586","url":null,"abstract":"Ground based telemetry stations are usually used to acquire real-time information of flight vehicles, and monitor the flying states in order to guarantee the safety of flight tests. However, when the telemetry ground station tracks the aerial vehicle, some wild values are inevitably included in the received telemetry data due to various interference factors, which can seriously affect the interpretation of the telemetry data and the evaluation of the vehicle performance. In order to make up for the shortage of existing telemetry data wild value elimination algorithms, this paper uses the wavelet transform threshold method to eliminate the wild values in telemetry data based on the principle of wavelet transform, and introduces the swarm intelligence optimization algorithm to obtain the optimal thresholds for different telemetry data adaptively. The optimal threshold and threshold function coefficients are obtained for different telemetry data to achieve better filtering effect. Corresponding results show that the proposed method can effectively eliminate the wild values in the telemetry data and realize the filtering of the telemetry data.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"63 3 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":"124095303","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 the complexity of electricity consumption increases, the traditional distribution network is increasingly unable to cope with the complex distribution network load. In order to improve the economic efficiency of the distribution network, this paper proposes an active distribution network economic optimisation dispatching method based on an improved particle swarm algorithm for accessing virtual power plants. After combining the actual situation of a region, the combination of the types as well as the number of distributed power sources and energy storage systems within the virtual power plant is comprehensively considered. The IEEE33 node system is introduced for simulation analysis, and constraints are set and modelled according to active power, reactive power and load demand. By improving the particle swarm algorithm, the selection of inertia weight is optimised to control the scheduling of internal and external power output of the virtual power plant for the twenty-four hours of the day. At the same time, power is purchased from the upper grid in combination with the tariff, and finally the minimum daily operating cost of the distribution network is obtained. This method reduces costs by 11.4% compared to the non-optimised period. It also improves the speed of convergence and perfects the composition of the active distribution network.
{"title":"Optimal scheduling method of virtual power plant based on improved particle swarm algorithm","authors":"Lihan Yu, Ru Hong, Yiqian Yao, Jiaping Chen, Guoning Chen","doi":"10.1117/12.2689494","DOIUrl":"https://doi.org/10.1117/12.2689494","url":null,"abstract":"As the complexity of electricity consumption increases, the traditional distribution network is increasingly unable to cope with the complex distribution network load. In order to improve the economic efficiency of the distribution network, this paper proposes an active distribution network economic optimisation dispatching method based on an improved particle swarm algorithm for accessing virtual power plants. After combining the actual situation of a region, the combination of the types as well as the number of distributed power sources and energy storage systems within the virtual power plant is comprehensively considered. The IEEE33 node system is introduced for simulation analysis, and constraints are set and modelled according to active power, reactive power and load demand. By improving the particle swarm algorithm, the selection of inertia weight is optimised to control the scheduling of internal and external power output of the virtual power plant for the twenty-four hours of the day. At the same time, power is purchased from the upper grid in combination with the tariff, and finally the minimum daily operating cost of the distribution network is obtained. This method reduces costs by 11.4% compared to the non-optimised period. It also improves the speed of convergence and perfects the composition of the active distribution network.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"139 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":"124375485","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 study is devoted to a description of a loop closure detection framework, in which the leveraging of a VGGNet-19 and a K-means cluster enables a practical, autonomous feature learning-based detecting. The principal components analysis (PCA) for dimension reduction is also investigated, guaranteeing the algorithm optimization in both accuracy and efficiency. In terms of benchmark dataset tests, the results are compared against bag-of-words (BoW) model, AlexNet and VGGNet-16, revealing our proposed design significantly outperforms others in Precision-Recall. The calculated cosine similarities and the detected closed-loop frames are simultaneously provided.
{"title":"The leveraging of a VGGNet-19 and a K-means cluster in visual loop closure detection tasks","authors":"Linlin Xia, Yu Wang, Zhuo Wang, Yue Meng","doi":"10.1117/12.2689495","DOIUrl":"https://doi.org/10.1117/12.2689495","url":null,"abstract":"This study is devoted to a description of a loop closure detection framework, in which the leveraging of a VGGNet-19 and a K-means cluster enables a practical, autonomous feature learning-based detecting. The principal components analysis (PCA) for dimension reduction is also investigated, guaranteeing the algorithm optimization in both accuracy and efficiency. In terms of benchmark dataset tests, the results are compared against bag-of-words (BoW) model, AlexNet and VGGNet-16, revealing our proposed design significantly outperforms others in Precision-Recall. The calculated cosine similarities and the detected closed-loop frames are simultaneously provided.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"59 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":"121820490","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}
Mingcheng Ling, Wei-min Qi, Di Chang, Xia Zhang, Chi Zhang
As the security issues of Internet of Things (IoTs) are rapidly evolving, machine learning techniques are increasingly adopted for detecting and preventing cyber threats. Recent machine learning based approaches (e.g., anomaly detection, intrusion detection, and predictive analytics) are being utilized in IoTs security. With the proliferation of IoTs devices, it is crucial to develop scalable and effective security solutions to keep pace with the changing threat landscape. This paper proposes a novel NSM (Network Sparsification Modeling) approach for identifying and categorizing cybersecurity threats in the cloud and IoTs environment. The proposed NSM algorithm is to optimize the Kullback-Leilber divergence based on higher-order spanning k-tree modeling process. The NSM model is capable of detecting cybersecurity threats in the cloud and IoTs setting by converting raw data into a meaningful format. The performance of the NSM model was evaluated using CICIDS 2017 dataset. The testing results prove that NSM model is state-of-the-art by outperforming others. Future deep-learning approaches are capable to integrate in the ML-based NSM model for further enhancement.
{"title":"Machine-learning-based network sparsification modeling for IoTs security analysis","authors":"Mingcheng Ling, Wei-min Qi, Di Chang, Xia Zhang, Chi Zhang","doi":"10.1117/12.2690061","DOIUrl":"https://doi.org/10.1117/12.2690061","url":null,"abstract":"As the security issues of Internet of Things (IoTs) are rapidly evolving, machine learning techniques are increasingly adopted for detecting and preventing cyber threats. Recent machine learning based approaches (e.g., anomaly detection, intrusion detection, and predictive analytics) are being utilized in IoTs security. With the proliferation of IoTs devices, it is crucial to develop scalable and effective security solutions to keep pace with the changing threat landscape. This paper proposes a novel NSM (Network Sparsification Modeling) approach for identifying and categorizing cybersecurity threats in the cloud and IoTs environment. The proposed NSM algorithm is to optimize the Kullback-Leilber divergence based on higher-order spanning k-tree modeling process. The NSM model is capable of detecting cybersecurity threats in the cloud and IoTs setting by converting raw data into a meaningful format. The performance of the NSM model was evaluated using CICIDS 2017 dataset. The testing results prove that NSM model is state-of-the-art by outperforming others. Future deep-learning approaches are capable to integrate in the ML-based NSM model for further enhancement.","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":"115300110","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 development of image processing technology has provided strong support for image recognition technology. At present, image recognition technology has gradually broken through the concept limit and has been applied to many fields. Image recognition technology is an innovation and upgrading of image processing technology, which is mainly used to collect and transmit various information through computer operation. At present, image processing technology mostly adopts the method based on depth learning model, but the traditional depth learning model has many disadvantages in image processing. In order to solve the problems of a single depth learning model, an image recognition system design based on depth learning hybrid model is propose.
{"title":"Design of image recognition system based on deep learning hybrid model","authors":"Ping-Shen Huang, Weixi Feng, Haiyuan Xu","doi":"10.1117/12.2690006","DOIUrl":"https://doi.org/10.1117/12.2690006","url":null,"abstract":"The development of image processing technology has provided strong support for image recognition technology. At present, image recognition technology has gradually broken through the concept limit and has been applied to many fields. Image recognition technology is an innovation and upgrading of image processing technology, which is mainly used to collect and transmit various information through computer operation. At present, image processing technology mostly adopts the method based on depth learning model, but the traditional depth learning model has many disadvantages in image processing. In order to solve the problems of a single depth learning model, an image recognition system design based on depth learning hybrid model is propose.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"321 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":"122569526","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}
Conventional algorithms typically rely on system identification techniques to estimate the inertia of power systems online. However, selecting an appropriate model order can be challenging, and an incorrect choice can lead to significant errors. To address this issue, we propose an algorithm based on Long Short-Term Memory Network (LSTM) deep learning networks for power system inertia identification. In our approach, we preprocess and input frequency and power deviation data obtained from monitoring into the LSTM model for learning. Additionally, we utilize the multi-sampling point method to reduce errors introduced by approximation algorithms. Once we obtain the inertia time constant for each unit, we calculate the system's overall inertia. Finally, we build a simulation system using MATLAB/Simulink to demonstrate the effectiveness and accuracy of our proposed method.
{"title":"Online evaluation of power system inertia based on LSTM deep-learning network","authors":"Xin-Qiang Cai","doi":"10.1117/12.2689541","DOIUrl":"https://doi.org/10.1117/12.2689541","url":null,"abstract":"Conventional algorithms typically rely on system identification techniques to estimate the inertia of power systems online. However, selecting an appropriate model order can be challenging, and an incorrect choice can lead to significant errors. To address this issue, we propose an algorithm based on Long Short-Term Memory Network (LSTM) deep learning networks for power system inertia identification. In our approach, we preprocess and input frequency and power deviation data obtained from monitoring into the LSTM model for learning. Additionally, we utilize the multi-sampling point method to reduce errors introduced by approximation algorithms. Once we obtain the inertia time constant for each unit, we calculate the system's overall inertia. Finally, we build a simulation system using MATLAB/Simulink to demonstrate the effectiveness and accuracy of our proposed method.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"5 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":"125047315","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}
Muons produced by cosmic rays can be used to reconstruct images by analyzing their energy and angle information after passing through a medium. These subatomic particles have strong penetrating ability and are sensitive to high-Z (high atomic number) materials, making them ideal for large-scale structural imaging and nuclear material detection, which is critical for maintaining nuclear safety. However, muon tomography faces challenges such as low natural muon flux and difficulties in image reconstruction. Therefore, developing effective imaging reconstruction algorithms is crucial for muon tomography. In this study, we present modifications to the ASR algorithm, then apply the modified version to experimental data. Our results show that the images reconstructed using the modified ASR algorithm exhibit good quality, indicating the algorithm's effectiveness.
{"title":"Application experiment and partial improvement of traditional muon scattering imaging algorithm","authors":"Jinye Wang, Yunping Qi, Liangwen Chen","doi":"10.1117/12.2689701","DOIUrl":"https://doi.org/10.1117/12.2689701","url":null,"abstract":"Muons produced by cosmic rays can be used to reconstruct images by analyzing their energy and angle information after passing through a medium. These subatomic particles have strong penetrating ability and are sensitive to high-Z (high atomic number) materials, making them ideal for large-scale structural imaging and nuclear material detection, which is critical for maintaining nuclear safety. However, muon tomography faces challenges such as low natural muon flux and difficulties in image reconstruction. Therefore, developing effective imaging reconstruction algorithms is crucial for muon tomography. In this study, we present modifications to the ASR algorithm, then apply the modified version to experimental data. Our results show that the images reconstructed using the modified ASR algorithm exhibit good quality, indicating the algorithm's effectiveness.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"268 1-2 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":"123714260","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 problems of uneven distribution of ground users, reliability fluctuation of nodes and links and frequent switching of controller groups in software-defined satellite networks, a multi-controller deployment strategy for LEO satellites based on nearest neighbor propagation is proposed. The strategy aims to reduce the delay, balance the network load, improve the reliability of nodes and links and extend the effective duration of the controller group. The control domain is divided by the nearest neighbor propagation clustering algorithm and the controller group is selected. Then the simulated annealing algorithm is used to iteratively select a better performance scheme. Experiments show that the algorithm can effectively reduce the delay in the control domain, improve the link reliability, and ensure the stability of the controller group under the condition of guaranteeing the load balance of the whole network.
{"title":"Multi-controller deployment strategy for LEO satellite based on affinity propagation algorithm","authors":"Zuoren Yan, Debin Wei, Weiwei Qiao","doi":"10.1117/12.2689820","DOIUrl":"https://doi.org/10.1117/12.2689820","url":null,"abstract":"Aiming at the problems of uneven distribution of ground users, reliability fluctuation of nodes and links and frequent switching of controller groups in software-defined satellite networks, a multi-controller deployment strategy for LEO satellites based on nearest neighbor propagation is proposed. The strategy aims to reduce the delay, balance the network load, improve the reliability of nodes and links and extend the effective duration of the controller group. The control domain is divided by the nearest neighbor propagation clustering algorithm and the controller group is selected. Then the simulated annealing algorithm is used to iteratively select a better performance scheme. Experiments show that the algorithm can effectively reduce the delay in the control domain, improve the link reliability, and ensure the stability of the controller group under the condition of guaranteeing the load balance of the whole network.","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":"129767714","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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