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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Sun, Jia Hao Liu, Zeng Li School of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9798350316728

Underwater information is mainly acquired through hydroacoustic networks, and routing protocols, as an important part of hydroacoustic networks, are related to the reliable transmission of data and the lifetime of the overall network. Depth-based routing protocols are more widely used because they do not need to obtain the 3D position information of nodes. However, relying only on depth information for routing results in null phenomenon and redundant forwarding during transmission. For the above two phenomena, the ENDOR protocol is proposed by improving the depth-based routing protocol, which is improved for the route selection and coordination phases by adding the node degree of the nodes to the selection criteria, and preventing the generation of the null phenomenon by selecting the nodes with high node degree. For redundant forwarding phenomenon is by optimizing the waiting time to inhibit nodes with low priority from forwarding the same packet, it is verified by simulation that the protocol can effectively reduce the occurrence of the nulling phenomenon and redundant forwarding phenomenon, save the node's energy, and prolong the survival cycle of the network. © 2023 IEEE.

关键词： Routing protocols

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ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data

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IEEE Access 2025年 13卷 100920-100933页

作者： Xianguang Jia Weijie Fang Yingying Lyu Jinke Zhang Mengyi Guo Dong Li Jie Qu Fengxiang Guo Faculty of Transportation Engineering Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Aiming at the challenges of spatio-temporal heterogeneity and dynamic correlation in the prediction of travel origins and destinations for urban residents, this study invokes a spatio-temporal graph attention network (ST-GAT) OD prediction model based on cell phone signaling data. This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. The data cleaning innovatively uses the DTW data cleaning model, and MSE, MAE, RMSE are used as evaluation indexes. In this paper, this model is compared with the existing ST-GCN, DMS, GMM, PSAM-CNN, ST-Transformer, and COMD models, and the experimental results show that the predicted values of the ST-GAT model have a significant improvement in the prediction accuracy compared to other models. The research in this paper provides an effective solution for the application of Intelligent Transportation System (ITS) and demonstrates the potential of Deep Learning (DL) in urban transportation planning.

关键词： Predictive models Data models Computational modeling Cellular phones Adaptation models Feature extraction Transportation Attention mechanisms Deep learning Data mining

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A low-complex one-bit DOA estimation algorithm based on DTFT 16

A low-complex one-bit DOA estimation algorithm based on DTFT

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhu, Guanghao Zhao, Xuanzhi Liu, Zengli Zeng, Sai Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Shanghai Marine Electronic Equipment Research Institute Shanghai201108 China

ISBN: (纸本)9781510689251

The existing one-bit direction of arrival (DOA) estimation methods based on sparse restoration or subspace have problems such as high computational cost, high estimation accuracy depending on the mesh size or the number of snapshots, etc., when used in massive uniform linear arrays (MULA). This paper considers the low-complexity one-bit DOA estimation problem of MULA with a single snapshot. Firstly, the initial DOA estimation is carried out by discrete Fourier transform (DFT). Then, the initial estimation of the angle is searched for the left and right intervals according to a certain method, and then the frequency refinement of the interval is carried out by discrete time Fourier transform (DTFT) to obtain accurate estimates. The resulting method is called a one-bit DTFT. The method is straightforward and easy to implement. The high-precision DOA estimation of MULA can be obtained through one snapshot, and the computational complexity is significantly lower than that of the existing one-bit DOA estimation methods. In addition, the proposed method can be extended not only to multiple snapshot scenarios, but also to other array structures such as nested arrays. The simulation results show the effectiveness of the one-bit DTFT method. © 2025 SPIE.

关键词： Discrete Fourier transforms

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A novel deep domain-adversarial method based on DRN-DATL for rolling bearing 43

A novel deep domain-adversarial method based on DRN-DATL for...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Zhuorui Wang, Xiaodong Ma, Bao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581581

In recent years, a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research orientation. However, there is still a gap between the existing diagnostic model and the practical application, i.e., 1) due to the variability of working conditions, the domain shift problem between source domain and the target domain have led to the obvious degradation of the diagnostic performance. 2) It is time-consuming and expensive to label a mass of monitoring data in practical situation. To address this problem, a domain-adversarial transfer learning method based on deep residual network (DRN-DATL) for rolling bearing fault diagnosis is proposed. Firstly, DRN is designed to extract features from raw vibration signal, which can avoid gradient problems. Then, the APReLU activation function is adopted to improved diagnostic accuracy under different working conditions. Meanwhile, domain-adversarial training is utilized to minimize distribution differences between source domain and target domain. Finally, seven comparison methods are selected for systematic analysis, and the results show that the proposed method can effectively improve the overall performance of the diagnosis model, which has achieved higher classification accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Adversarial machine learning

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Investigations and Time Estimation on Federated Learning for Future Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17387-17398页

作者： Fukumoto, Shun Li, Ruidong Zeng, Kai Nan, Haihan Su, Zhou Kanazawa University Intelligent Computation and Network Laboratory Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan Kunming University of Science and Technology Faculty of Information Engineering Automation Kunming066000 China Kanazawa University Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan George Mason University Electrical and Computer Engineering Department FairfaxVA22030 United States Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work. © 2014 IEEE.

关键词： Stochastic systems

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Effect of loads on temperature distribution characteristics of oil-immersed transformer winding

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Distributed Generation and Alternative Energy Journal 2022年第2期37卷 237-254页

作者： Zhao, Zhengang Jiang, Zhangnan Li, Yang Li, Chuan Zhang, Dacheng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650000 China Yunnan Key Laboratory of Computer Technology Applications Kunming650000 China

The temperature of the hot-spots on windings is a crucial factor that can limit the overload capacity of the transformer. Few studies consider the impact of the load on the hot-spot when studying the hot-spot temperature and its location. In this paper, a thermal circuit model based on the thermoelectric analogy method is built to simulate the transformer winding and transformer oil temperature distribution. The hot-spot temperature and its location under different loads are qualitatively analyzed, and the hot-spot location is analyzed and compared to the experimental results. The results show that the hot-spot position on the winding under the rated power appears at 85.88% of the winding height, and the hot-spot position of the winding moves down by 5% in turn at 1.3, 1.48, and 1.73 times the rated power respectively. © 2021 River Publishers

关键词： Temperature distribution

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Heat Transfer Performance of Copper Powder-Copper Mesh Composite Wick Flat-Plate Micro Heat Pipe

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Journal of Thermal science and engineering Applications 2025年第6期17卷 061003页

作者： Zhao, Zhengang Zhu, Chenyu Cai, Shuiyan Luo, Chuan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming 650500 China

Flat-plate micro heat pipe (FMHP) plays an important role in the field of heat dissipation of electronic devices through vapor–liquid two-phase flow. Improving the heat transfer performance of FMHP has become a hot topic. This article analyzes the heat transfer principle of the FMHP to discover that balancing the capillary pressure and permeability of the wick is crucial to the heat transfer performance of the FMHP. Therefore, we propose a kind of copper powder and three kinds of copper powder-copper mesh integrated sintered wicks, build an experimental device, and analyze and research their heat transfer performance. The results show that the wick sintered from two layers of copper mesh and copper powder can better balance the capillary pressure and permeability, and the prepared FMHP has the best heat transfer performance. The maximum thermal power is 30 W and the minimum thermal resistance is 0.27 °C/W at the optimum filling ratios.

关键词： flat-plate micro heat pipe Energy dissipation wick Permeability Heat Design heat transfer enhancement vapor–liquid two-phase flow evaporation heat pipes Thermal energy Thermal resistance heat transfer performance Flat plates two-phase flow and heat transfer Evaporation Heat transfer Vapors Fluids gasification heat transfer Theoretical analysis Copper Copper powders Two-phase flow Pressure Heat pipes heat and mass transfer Composite materials

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Unveiling first self-healing in metallised film capacitor:A macro-micro analysis

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High Voltage 2025年第2期10卷 362-373页

作者： Yushuang He Feipeng Wang Guoqiang Du Lei Pan Jian Li Hongming Yang Xiao Zhang Zhicheng Zhang Kaizheng Wang State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqingChina School of Electrical&Information Engineering Changsha University of Science and TechnologyChangshaChina National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of EngineeringWuhanChina Xi'an Key Laboratory of Sustainable Energy Material Chemistry School of ChemistryXi'an Jiaotong UniversityXi'anChina Faculty of Electrical Engineering Kunming University of Science and TechnologyKunmingChina

Metallised film capacitors(MFCs)are renowned for their unique self-healing(SH)properties,which bestow them with exceptional reliability and stability in the face of intense electric fields,high voltages,and pulse power ***,the exploration of SH characteristics concerning single-layer dielectric film remains insufficient for advancing MFC reliability *** establish the theoretical correlation of SH characteristics from the device to the film in the MFCs,this work developed a simulation model to analyse the SH dynamic behaviour in the *** effects of coupling capacitors,arc resistance and insulation resistance on the macroscopic characteristics(voltage drop and pulse current)are focused during the SH process in *** results indicate that SH is primarily associated with the voltage drop duration rather than the sampling ***,the SH process in MFC is characterised as an abrupt decrease in voltage to its minimum *** refinement enhances the SH energy dissipation model of *** quantified relationship between the macroscopic characteristics and microstructure evolution(polypropylene decomposition and aluminium electrode vaporisation)is established in MFCs under diverse SH energy *** SH energy and duration increase,the proportion of energy attributed to polypropylene decomposition increases,resulting in multi-layer ablation and adhesion within the metallised film and a pronounced deterioration in MFC electrical *** examination of macro-micro perspectives sheds new light on the intricate mechanisms governing the SH behaviour in MFCs,offering valuable insights for the advancement of their design,reliability evaluation,and performance optimisation in diverse electrical applications.

关键词： pulse current reliability evaluation polypropylene decomposition aluminium electrode vaporisation voltage drop metallised film capacitors macro micro analysis metallised film capacitors mfcs

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Fault diagnosis model based on adaptive generalised morphological filtering and LLTSA-ELM

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International Journal of information and Communication technology 2023年第1期22卷 1-14页

作者： Xiao, Jie Li, Jingtao Deng, Chao Luo, Zhigang Lin, Huafeng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Electrical Engineering Co. Ltd. China Railway 12th Bureau Group Tianjin300000 China

It is difficult for a single feature to contain all the information needed to describe the running state of the equipment. Though multi-features can contain more information about running state, the redundancy between high-dimension features can easily reduce the accuracy of the classifier. Aimed at that, a fault diagnosis method for rolling bearings combining adaptive generalised morphological filter, linear local tangent space alignment and extreme learning machine (LLTSA-ELM) is proposed. Firstly, the rolling bearing vibration signals are filtered by an adaptive generalised morphological filter. Secondly, the multi-domain features are extracted from filtered signal to construct high-dimensional features set of bearing. Thirdly, the dimension of high-dimensional features is reduced by maximum likelihood estimation (MLE) and LLTSA. Finally, the bearing condition monitoring model based on ELM is constructed by the reduced dimension features, and then is used to analyse and diagnose the running state of bearing. Experiments show that this method can effectively filter noise, reduce the redundancy caused by high-dimensional features, and improve the recognition accuracy of the running state of bearings. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Failure analysis

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Residual and Attention Mechanism-Based Network for Aluminum-Silicon Alloy Microstructure Image Classification 11

Residual and Attention Mechanism-Based Network for Aluminum-...

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11th International Conference on information Systems and Computing technology, ISCTech 2023

作者： Yang, Xiao Shen, Tao Yin, Jiancheng Liu, Yingli Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Materials Science and Engineering Kunming650093 China

ISBN: (纸本)9798350342406

To enhance the accuracy of aluminum-silicon alloy microstructure image classification, we propose an aluminum-silicon alloy microstructure image classification model based on an improved residual network, incorporating attention mechanisms and multi-scale thinking. The model is built upon the ResNet18 architecture, utilizing enhanced basic residual blocks to select informative features. Additionally, a multi-scale attention module is incorporated into the network to fuse multi-scale features and further extract effective features. Experimental results on an aluminum-silicon alloy microstructure image classification dataset demonstrate that compared to the best-performing model currently available, the proposed model achieves an increase in classification accuracy, precision, recall, and F1 score by 3.17%, 0.74%, 5.53%, and 3.26%, respectively, reaching 94.97%, 96.39%, 93.97%, and 95.17%. These results indicate that the proposed model exhibits strong capabilities in classifying aluminum-silicon alloy microstructure images and can facilitate further research on the properties of aluminum-silicon alloys. © 2023 IEEE.

关键词： Image classification

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