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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Huang, Yu Zhang, Huiyan Chen, Zixian Zou, Jiangyan Chongqing Technology and Business University School of Mechanical Engineering Chongqing400067 China Chongqing Technology and Business University National Research Base of Intelligent Manufacturing Service Chongqing400067 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. Chongqing400707 China Chongqing Technology and Business University School of Artificial Intelligence Chongqing400067 China

ISBN: (纸本)9798350363173

Under different communication topologies, this paper investigates the consensus problem for multi-agent systems with general linear dynamics under leader following. A distributed dynamic event-triggered control strategy with output feedback adjustment is proposed by using adaptive control technology of auxiliary variables, and it is applied to different follower communication topologies. The simulation results show that the leader-following systems under different communication modes of the followers can solve the completely distributed consensus problem. Moreover, the Zeno behavior of each multi-agent can be excluded. Finally, the effectiveness of the proposed control strategy is verified by taking multi-vehicle cooperative tracking control as an example. © 2024 IEEE.

关键词： Multi agent systems

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Double-differential Ionospheric Delay Simulation Analysis Based Onboard Single-frequency GPS of Space-aircraft 2

Double-differential Ionospheric Delay Simulation Analysis Ba...

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2nd International Conference on Geographic Information and Remote Sensing technology, GIRST 2023

作者： Zhang, Bin Chen, Yuanzhu Li, Yonghua Changjiang Spatial Information Technology Engineering Co. Ltd Hubei Water Conservancy Information Perception and Big Data Engineering Technology Research Center National Dam Safety Engineering Technology Research Center Wuhan China

ISBN: (纸本)9781510668478

Three types of formation flight operational schemes are designed. They are combined with the IRI2001 model, to study the effect of ionospheric delay in relative positioning by onboard GPS of space-aircraft. Deep analyses also make for double-differential ionospheric delay of single frequency GPS onboard LEO satellite. Some useful conclusions are drawn for ambiguity solution in carrier phase positioning in such a case that two spacecraft with 1100km high from the Earth and a distance from 150km to 200km. The residuals of double-differential ionospheric delay usually don’t affect the solution of the ambiguity. © 2023 SPIE.

关键词： Global positioning system

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Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1452-1463页

作者： Mei Liu Xiaoyan Zhang Mingsheng Shang Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Shenzhen Research Institute Lanzhou UniversityShenzhen 518063China

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)*** obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA ***,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ***,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

关键词： Consensus filter k-winners-take-all(kWTA) multirobot coordination noise resistance

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Implications of Preference and Reference Effects on A Green Supply Chain Considering Subsidy Withdrawal 12

Implications of Preference and Reference Effects on A Green ...

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12th International Symposium on Project Management, ISPM 2024

作者： Chen, Shan Fang, Meiqi Wang, Linlin Su, Jiafu Chongqing Industrial Big Data Innovation Center Co. Ltd. Chongqing China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China School of Management Science and Engineering Chongqing Technology and Business University Chongqing China Western Branch of China Academy of Information and Communications Technology Chongqing China International College Krirk University Bangkok Thailand

ISBN: (纸本)9798331302344

In order to examine the impact of the withdrawal of government green subsidies on the green market, this paper formulates a two-period model to examine a green supply chain composed of a government and two manufacturers. We construct a basic model of government green subsidies in the first period, and formulate four scenarios in the second period considering the market expansion effect, green preference effect, price reference effect, and quality reference effect. We examine the impacts of the above effects on equilibrium decision-making and profits of the green supply chain through comparing and numerical analysis. We find that: 1) Only the market size enlarges to a certain degree, the profit of green manufacturer would increase after subsidy withdrawal. 2) Both green and traditional manufacturers can benefit from the consumers’ green preference, but it will hinder some traditional manufacturers from investing in green production. 3) The consumer price reference effect will damage green manufacturers' profits and reduce the greenness of products, while the traditional manufacturer could benefit from it under certain conditions. 4) Appropriate consumer green quality preferences are beneficial to the green market and can increase the profits of green manufacturers, but they will also provide free-riding opportunities for traditional manufacturers. © 2024 ISPM. All Rights Reserved.

关键词： Green manufacturing

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TSKE: A Dual-Branch Model for Knowledge Graph Embedding with Joint Textual and Structural Information 11th

TSKE: A Dual-Branch Model for Knowledge Graph Embedding wit...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Deng, Weibin Xiong, Yuanyuan Jiang, Ziqun Yu, Hong Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China Smart Posts Engineering Technology Research Center Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

ISBN: (纸本)9783031927430

Knowledge graphs (KGs) serve as critical structured representations of the real world, modeling entities and their complex relational patterns. While traditional knowledge graph embedding (KGE) methods excel at capturing structural regularities through geometric or tensor operations, they have limitations in effectively integrating structural and textual information. To address this limitation, we propose TSKE, a novel dual-branch architecture that effectively integrates both textual and structural information. The text branch employs language models to encode textual descriptions, generating context-aware semantic representations. Simultaneously, the structure branch introduces a multi-scale dilated attention mechanism that captures local neighborhood patterns and global graph dependencies through hierarchical receptive fields. The efficient channel attention further enhances feature selectivity by adaptively filtering noise and emphasizing feature relevance. TSKE incorporates an adaptive fusion module that dynamically adjusts the contribution weights of textual and structural features. Experimental results demonstrate TSKE’s superior performance in KGE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism 5

Image Denoising Method Based on Channel Compression and Spli...

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5th International Conference on Computer engineering and intelligent Control, ICCEIC 2024

作者： Sun, Hao Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Shandong Yantai China

ISBN: (纸本)9798331507992

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks. © 2024 IEEE.

关键词： Restoration

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An online monitoring method for embedded time-gate displacement sensor operation state base on CNN and LSTM 11

An online monitoring method for embedded time-gate displacem...

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11th International Symposium on Precision Mechanical Measurements

作者： Gou, Li Wang, Minxuan Peng, Donglin Fan, Wanlin Engineering Research Center of Mechanical Testing Technology and Equipment Ministry of Education Chongqing University of Technology Chongqing40054 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd Chongqing400700 China Chongqing Gearbox Co. Ltd. Chongqing402263 China Chongqing Key Laboratory of Time-grating Sensing and Advanced Testing Technology Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9781510680296

In view of the problems that embedded time-gate displacement sensor (hereinafter referred to as time-gate), which has severe working conditions, high-precision state is difficult to maintain for a long time, and the operating state is difficult to monitor online. this paper proposes an online monitoring method of time-gate operating state, based on a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). Firstly, feature information was obtained from the geometric, operating, electromagnetic simulation and environmental characteristics of the time-gate displacement sensor. Then, A time-gate operating state prediction model was constructed by feeding feature information into CNN-LSTM model and verified by test set, The CNN layers Perform feature learning and extraction and the LSTM layers Process timing data. Finally, the relevant real-time data are fed into the above operating state monitoring system, which achieve the real-time online Monitoring of the time-gate operation state. The experimental results show that, the extreme prediction errors such as abnormal and no alarm were avoided by this method in the test set, and the recognition accuracy of the state is up to 91.1% by this prediction model. Effectively guarantee the safe and stable operation of the time-gate and the equipment in which it is located. © 2024 SPIE.

关键词： Convolutional neural networks

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace Information research Institute.

关键词： Support vector machines

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research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model 14th

Research on Zenith Tropospheric Delay Model Based on TCN Imp...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Shi, Danyang Zhao, Jumin Wu, Fanming Yan, Liangquan Feng, Ran Zhang, Xinfang Zhao, Jinhua College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3 (GPT3) model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3_ZTD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network (TCN) for improving the hourly global pressure and temperature 2 (HGPT2) model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2_ZTD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCN_ZTD obtained from the proposed model is higher than GPT3_ZTD and HGPT2_ZTD when using the GNSS_ZTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious. © 2024, Aerospace Information research Institute.

关键词： Fourier analysis

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information research Institute.

关键词： Long short-term memory

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