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2023 IEEE International Conference on Power Science and Technology, ICPST 2023

作者： Zhao, Wenqing Wang, Jifa Chen, Rui Liu, Liang Xu, Lijiao North China Electric Power University School of Control and Computer Engineering Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding China North China Electric Power University School of Control and Computer Engineering Baoding China

ISBN: (纸本)9798350311358

With refinement of power equipment, the identification and classification of its operation states is quite important for the stable operation of the system. As the power equipment states are rather complicated, it is difficult to realize general classification due to the limitation of data types and model characteristics when using deep learning. In the light of this, based on the time series data of various power equipment, an equipment state classification method is proposed by combining Recurrent Neural Network (RNN), Long short-term memory (LSTM) and attention mechanism. Firstly, RNN is used as the bottom frame network, and the LSTM core network integrating attention mechanism is added to construct the complex Feature Extraction Module (FEM). Secondly, main feature extraction is implemented in the LSTM network integrated with attention mechanism. Then, the extracted features are input into the complex FEM model and trained to match the evaluation state and obtain the evaluation results. In this paper, confusion matrix, F1micro and accuracy are used as evaluation indexes. Three cases were selected to verify the method and five groups of comparison models were set for each case. Results show that the proposed method has good accuracy in power equipment state classification and also has strong generalization ability in other data classification. © 2023 IEEE.

关键词： Long short-term memory

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Chinese Image Description Evaluation Method Based on Target Domain Semantic Constraints 3

Chinese Image Description Evaluation Method Based on Target ...

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3rd International Conference on Image Processing and Intelligent control, IPIC 2023

作者： Wang, Zhenhao Sun, Wenyi Wang, Zhengsong Yang, Le School of Computing and Communication Engineering Northeastern University at Qinhuangdao Haigang Hebei Qinhuangdao China School of Control Engineering Northeastern University at Qinhuangdao Haigang Hebei Qinhuangdao China

ISBN: (纸本)9781510668140

To address the problems of insufficient accuracy and difficulty of application in the current Chinese image description field, this paper proposes an evaluation method based on semantic constraints in the target domain. Unlike previous research, this method acts on the output stage of the model, and based on the extraction of key semantics in the target application domain, it is constrained by the macroscopic semantic space of that domain or by introducing external semantic information from other visual tasks. The experiments show that the proposed method effectively improves the semantic coherence between the model output description sentences and the input images in the target domain, and is helpful for the practical application of image description in specific domains. © 2023 SPIE.

关键词： Semantics

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Secure control and filtering for industrial metaverse

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Frontiers of Information Technology & Electronic engineering 2024年第2期25卷 179-181页

作者： Qing-Long HAN Derui DING Xiaohua GE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China

Metaverse can be regarded as a socialized and virtualized cyberspace parallel yet interactive to the real *** from the rapid development of cloud computing and digital twins,metaverse is transforming traditional control and filtering para‐digms of industrial automation systems into cyberphysical social *** this scenario,future industrial automation systems could be an integration of real-world ones and virtual twins combined with great computational power within a certain temporal and spatial range.

关键词： interactive filtering Secure

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Dual-Scale Cellular Traffic Prediction Based on FastDTW for Graph Neural Network 16

Dual-Scale Cellular Traffic Prediction Based on FastDTW for ...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Jia, Junrong Zhang, Haixia Zhou, Xiaotian School of Control Science and Engineering Shandong University Jinan250061 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Jinan250061 China

ISBN: (纸本)9798350390643

Cellular traffic prediction is crucial to the network efficiency as it provides prior knowledge to guide the wireless resource allocation. The artificial intelligence (AI) based traffic prediction can provide better prediction performance and has drawn increasing interests. Nevertheless, most existing works utilized Euclidean structure data, which can provide traffic prediction to only certain grid area rather than specific spot, such as each base station (BS). To give more precise BS level prediction, in this work we propose a dual-scale spatiotemporal prediction framework based on fast dynamic time warping (FastDTW) algorithm. The proposed framework can capture both the overall neighborhood relationship of historical traffic and the local neighborhood relationship graph to improve the prediction accuracy. More importantly, due to the utilization of FastDTW, it solves the disadvantage of the high complexity of the dynamic time warping (DTW) algorithm and completes the prediction at the BS level more efficiently. Simulation results confirms the generality and performance of the proposed method, which outperforms the benchmark algorithms. © 2024 IEEE.

关键词： Prediction models

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Joint Task Offloading and Service Caching for Vehicular Edge computing with Energy Constraints 16

Joint Task Offloading and Service Caching for Vehicular Edge...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Gao, Yifan Zhang, Haixia Liu, Qianqian Deng, Yiqin School of Control Science and Engineering Shandong University Jinan250061 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Jinan250061 China

ISBN: (纸本)9798350390643

Vehicular edge computing (VEC) enables vehicles to offload their tasks to idle vehicles for processing. To process tasks, the needed service model should be stored ahead. Considering the storage of vehicles is limited and downloading the service model upon tasks causes repeated data transmission, it is necessary to determine which service models should be cached and which tasks should be offloaded. For vehicles, the task completion time is crucial due to their mobility nature. Therefore, balancing the completion time and energy consumption for vehicles with limited battery capacity in highly dynamic system remains a significant challenge. To address this, we jointly designed task offloading and service caching scheme to minimize the tasks completion time in a cache-assisted VEC scenario while satisfying the long-term average energy constraints. We adopt Lyapunov optimization and Deep reinforcement learning (D RL) based Task offloading and Service caching (LDTS) to solve the proposed problem. Specifically, the Lyapunov optimization is used to tackle the issues of long-term energy consumption constraints by transforming the original problem into per-slot optimization problems that can be resolved using a model-free DRL method. Simulation results demonstrate that the LDTS method can minimize the completion time while satisfying the constraints of the long-term energy consumption budget. The results indicate that the LDTS method outperforms other benchmark methods. © 2024 IEEE.

关键词： Budget control

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Accelerating heterogeneous federated learning with closed-form classifiers 24

Accelerating heterogeneous federated learning with closed-fo...

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Proceedings of the 41st International Conference on Machine Learning

作者： Eros Fanì Raffaello Camoriano Barbara Caputo Marco Ciccone Department of Computing and Control Engineering Polytechnic University of Turin Italy Department of Computing and Control Engineering Polytechnic University of Turin Italy and Istituto Italiano di Tecnologia Genoa Italy Department of Computing and Control Engineering Polytechnic University of Turin Italy and CINI Consortium Rome Italy

Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, particularly pronounced in the final classification layer, negatively impacting convergence speed and accuracy. To address this issue, we introduce Federated Recursive Ridge Regression (FED3R). Our method fits a Ridge Regression classifier computed in closed form leveraging pre-trained features. FED3R is immune to statistical heterogeneity and is invariant to the sampling order of the clients. Therefore, it proves particularly effective in cross-device scenarios. Furthermore, it is fast and efficient in terms of communication and computation costs, requiring up to two orders of magnitude fewer resources than the competitors. Finally, we propose to leverage the FED3R parameters as an initialization for a softmax classifier and subsequently fine-tune the model using any FL algorithm (FED3R with Fine-Tuning, FED3R+FT). Our findings also indicate that maintaining a fixed classifier aids in stabilizing the training and learning more discriminative features in cross-device settings. Official website: https://***/.

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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OAFuser: Toward Omni-Aperture Fusion for Light Field Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6225-6239页

作者： Teng, Fei Zhang, Jiaming Peng, Kunyu Wang, Yaonan Stiefelhagen, Rainer Yang, Kailun Hunan University School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Karlsruhe76131 Germany Institute for Visual Computing ETH Zurich Zurich8092 Switzerland

Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: 1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. 2) A relative displacement difference exists in the data collected by different microlenses. To address these issues, we propose an omni-aperture fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from subaperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective subaperture fusion module (SAFM). This module efficiently embeds subaperture images in angular features, allowing the network to process each subaperture image with a minimal computational demand of only (∼1\rm GFlops). Furthermore, to address the mismatched spatial information across viewpoints, we present a center angular rectification module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +3.69%. The source code for OAFuser is available at https://***/FeiBryantkit/OAFuser. © 2020 IEEE.

关键词： Semantic Segmentation

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Simplified control Strategies for Power Converters in a Grid-Connected Hybrid DC-AC Microgrid

Simplified Control Strategies for Power Converters in a Grid...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Busarello, Tiago Davi Curi Simões, Marcelo Godoy Laaksonen, Hannu Kauhaniemi, Kimmo Federal University of Santa Catarina Department of Control Automation and Computing Engineering Blumenau Brazil University of Vaasa School of Technology and Innovations Vaasa Finland

ISBN: (纸本)9798350332308

Many typical converter control strategies used in microgrids are complex or require modifications in the power structure. The complexities are due coupling effects among controllers and time-delay caused in exchanging information in the system. This paper proposes simplified control strategies for power converters in a grid-connected hybrid DC-AC microgrid, aiming to ensure precise voltage and current regulation. The used controllers are proportional-resonant, integral-double-lead, proportional-integral and single integral controller. In this paper, the methodology for tuning each controller is carefully described in detail and recommendation for choosing their bandwidth is given. Then, the designed controllers are discretized through the Backward-Euler approximation. The power converters of the microgrid are an inverter, a Buck, a Boost and a bidirectional DC-DC. A case study is conducted through simulations with Matlab/Simulink. Results show that the controlled variables follow their reference signals with negligible steady-state error, proving the efficacy of the simplified control strategies and allowing an stable operation of the microgrid. © 2023 IEEE.

关键词： controllers

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Aerodynamic/control integrated optimization method for unpowered high-speed vehicle configuration design

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Chinese Journal of Aeronautics 2024年第7期37卷 153-167页

作者： Xin PAN Linlin WANG Li LI Lulu JIANG Gang CHEN State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace EngineeringXi’an Jiaotong UniversityXi’an 710049China Shannxi Key Laboratory for Environment and Control of Flight Vehicle School of Aerospace EngineeringXi’an Jiaotong UniversityXi’an 710049China Beijing Institute of Space Long March Vehicle Beijing 100076China AVIC Xi’an Aeronautics Computing Technique Research Institute Xi’an 710068China

The unpowered high-speed vehicle experiences a significant coupling between the disciplines of aerodynamics and control due to its characteristics of high flight speed and extensive maneuverability within large *** conventional aircraft conceptual design process follows a sequential design approach,and there is an artificial separation between the disciplines of aerodynamics and control,neglecting the coupling effects arising from their *** a result,this design process often requires extensive iterations over long periods when applied to high-speed vehicles,and may not be able to effectively achieve the desired design *** enhance the overall performance and design efficiency of high-speed vehicles,this study integrates the concept of Active control Technology(ACT)from modern aircraft into the philosophy of aerodynamic/control integrated *** integrated optimization strategies,with differences in coupling granularity,have been ***,these strategies are put into action on a biconical vehicle that operates at Mach *** results reveal that the comprehensive performance of the synthesis optimal model derived from the aerodynamic/control integrated optimization strategy is improved by 31.76%and 28.29%respectively compared to the base model under high-speed conditions,demonstrating the feasibility and effectiveness of the method and optimization strategies ***,in comparison to the single-stage strategy,the multi-stage strategy takes into deeper consideration the impact of control *** a result,the control performance of the synthesis opti-mal model derived from the multi-stage strategy improves by 13.99%,whereas the single-stage strategy only achieves a 5.79%*** method enables a fruitful interaction between aerodynamic configuration design and control system design,leading to enhanced overall performance and design ***,it improves the controllability of high-sp

关键词： Aerodynamic/control Integrated Optimization MDO High-speed vehicle Shape Optimization controllability

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