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Review of data security within energy blockchain:A comprehensive analysis of storage,management,and utilization

High-Confidence Computing 2024年第3期4卷 82-102页

作者： Yunhua He Zhihao Zhou Yan Pan Fahui Chong Bin Wu Ke Xiao Hong Li School of Information Science and Technology North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China Key Laboratory in Blockchain and Data Security by Ministry of Industry and Information Technology Beijing 100040China Institute of Information Engineering Chinese Academy of Sciences Beijing 100195China

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.

关键词： Energy blockchain Data security Storage Management Utilization

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Dual-Path GAN:A Method for Enhancing Small-scale Defect Detection on Metal Images 41

Dual-Path GAN:A Method for Enhancing Small-scale Defect Dete...

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第41届中国控制会议

作者： Zhuoxun Ye Meiqin Liu Senlin Zhang Ping Wei State Key Laboratory of Industrial Control Technology Zhejiang University College of Electrical Engineering Zhejiang University Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Automated surface inspection(ASI) is an important research content in computer *** recent years,with the application of deep learning models represented by convolutional neural networks(CNN) in computer vision,surface defect detection based on computer vision has made impressive ***,compared with a few or dozens of pictures in real industrial scenes,traditional deep learning methods require a large amount of annotation data for training,so it is quite difficult to adapt to the complex industrial scenarios with varying surface profiles,lighting conditions,imaging angles and *** these difficulties,there is still a wide gap in the performance between the detection of small-scale and large-scale *** Generative Adversarial Nets(GANs) based augmentation method can only be used for classification networks or unsupervised learning,and if applied to detection networks,they need to be labeled,which requires much labor and *** order to solve these problems,a metal surface small-scale defect images augmentation method is *** method contains two parts:generation part and augmentation *** the generation part,two pairs of generators and discriminators are used to generate the defect areas and the background areas of the image,which can not only generate more realistic defect images than others but also save the training process for surface *** the augmentation part,the real defect images and the generated defect images are concatenated before copy-pasting them on background images to generate annotated dataset for *** conduct experiments on our metal surface *** experimental results show that our method can generate highquality defect samples and background samples,which greatly enriches the original *** evaluate different augmentation strategies,and ultimately,we achieve 6.2% improvement on baseline and 4.4% on copy-pasting method.

关键词： Generative adversarial nets automated surface inspection data augmentation few-shot learning small-scale object detection

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MPC-Based Trajectory Tracking control Strategy Incorporating Autonomous Lane-Changing Mechanism for Intelligent Vehicles

MPC-Based Trajectory Tracking Control Strategy Incorporating...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Dongxue Zhang Huiwen Liu Xiaohong Jiao Ting Zhang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

Navigating the intricate traffic environment, lane-changing has emerged as a frequent and essential driving maneu-ver for intelligent vehicles. However, it is challenging to guarantee the safety of the intelligent vehicle and the trajectory tracking accuracy against the external environment during lane-changing. Considering these challenges to overcome, the model predictive control (MPC)-based trajectory tracking strategy incorporating an autonomous lane-changing mechanism is proposed in this paper. The autonomous lane-changing mechanism considering lane-changing intention is proposed to ensure that the intelligent vehicle generates safe lane-changing decisions and trajectories. Subsequently, the MPC strategy that considers the physical char-acteristics of the intelligent vehicle is proposed to guarantee the accuracy and smoothness of trajectory tracking. The validation of its effectiveness and advantages is established on the PreScan and MATLAB/Simulink co-simulation platforms.

关键词： Accuracy Intelligent vehicles Trajectory tracking Navigation Vehicle safety Trajectory MATLAB Predictive control

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Predictive Cruise control for Connected Autonomous Vehicles Considering Communication Delay in Mixed Traffic

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IFAC-PapersOnLine 2024年第29期58卷 172-177页

作者： Xinyu Li Jin Zhang Jiaqi Xue Lin Qi Wenbin Tang Xiaohong Jiao Institute of Electrical Engineering Yanshan University Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 Hebei Province China

The stochastic behaviors of human-driven vehicles (HDVs) and the communication delay of Vehicle-to-Everything (V2X) technique present challenges to the advancement of connected autonomous vehicles (CAVs) in mixed traffic comprising CAVs, uncertain numbers of HDVs and connected HDVs (CHDVs). Consequently, a predictive cruise control strategy based on V2X communication is proposed for CAVs in a mixed platoon. Firstly, a long-short-term memory (LSTM) network combined with a conditional linear Gaussian (CLG) model is adopted for speed prediction of the preceding vehicle considering the communication delay. Subsequently, the CAV's speed planning is conducted for passing continuous signalized intersections without stopping. Then, the planned speed is tracked in energy-efficient, safe, and comfortable manners under a stochastic model predictive control (SMPC) framework. The simulation results validate the effectiveness and advantages of the proposed strategy.

关键词： Connected autonomous vehicles Mixed traffic Communication delay Predictive cruise control

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Multi-object Crowd Real-time Tracking in Dynamic Environment Based on Neural Network

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 386-394页

作者： Zhang, Fu-Quan Ma, Lin-Juan College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In this paper, it proposes a new lightweight neural network detection model and tracking algorithm that enables us to perform multi-object crowd tracking. The technique applied in our study is known as object detection and tracking which is based on neural network and can be calculated and reproduced intelligently according to the movement and behavior of the tracked crowd. Also, a number of experiments have been carried out to test the validity of the accuracy in the algorithm. And it is indicated that the results of the experiments have better performance and can contribute to multi-object crowd real-time tracking with high accuracy, which is similar to the state-of-the-art. All our preliminary results throw light on the nature of detection and tracking and result in a solution of multi-object crowd tracking, and it proves to be fruitful and encouraging. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Object detection

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A generative deep learning framework for airfoil flow field prediction with sparse data

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Chinese Journal of Aeronautics 2022年第1期35卷 470-484页

作者： Haizhou WU Xuejun LIU Wei AN Hongqiang LYU MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Aerodynamic Noise Control Mianyang 621000China State Key Laboratory of Aerodynamics Mianyang 621000China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Deep learning has been probed for the airfoil performance prediction in recent *** with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper ***,effective training of the data-driven models in deep learning severely hinges on the data in diversity and *** this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse *** presented approach consists of two modules,pre-training module and fine-tuning *** pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training *** the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the *** use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training *** results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.

关键词： CFD Flow field Generative adversarial networks(GANs) Sparse data Supercritical airfoil

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Bimodal Fusion Network for Basic Taste Sensation Recognition from Electroencephalography and Electromyography

Bimodal Fusion Network for Basic Taste Sensation Recognition...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Han Gao Shuo Zhao Huiyan Li Li Liu You Wang Ruifen Hu Jin Zhang Guang Li State Key Laboratory of Industrial Control Technology Zhejiang University School of Computer and Communication Engineering Changsha University of Science and Technology

Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals in taste sensation recognition. This paper proposes a bimodal fusion network (Bi-FusionNet) for recognizing basic taste sensations (sour, sweet, bitter, salty, umami, and blank). Two convolutional backbones with similar structures are designed to separately extract the single-modal features of EEG and EMG. Then, EEG and EMG features are concatenated for bimodal interaction and complementarity. Finally, three loss functions are adopted: a center loss for aggregating intra-class samples, a mean squared error loss for sequence positions for minimizing the difference between signals during the stimulation, and a softmax loss for minimizing the entropy of prediction and true labels. The results on the taste sensation dataset show that bimodal fusion improves recognition performance, and Bi-FusionNet outperforms single-modal methods and other fusion methods. Bi-FusionNet paves the way for the application of multimodal fusion in taste sensation recognition.

关键词： Convolution Speech recognition Brain modeling Feature extraction Electromyography Electroencephalography Entropy

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Eigenvalue-Oriented Data-Driven Small-Signal Stability Assessment for DC Microgrids

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IEEE Transactions on Power Systems 2024年

作者： Deng, Ruilong Jiang, Qiliang Zhou, Xu Wang, Yuhong Sun, Mingyang Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China Sichuan University College of Electrical Engineering Chengdu610065 China Peking University Department of Industrial Engineering and Management College of Engineering Beijing100091 China

The stability issues of DC microgrids (DCmGs) are becoming increasingly important due to the widespread deployment of renewable distributed energy resources (DERs), which has led to a growing demand for DCmGs. Existing smallsignal white-box and gray-box models of DCmGs require system parameters and are severely limited in practical applicable scenarios, while black-box models cannot effectively assess DCmGs' stability. To this end, this paper proposes a novel eigenvalueoriented small-signal stability assessment approach for DCmGs, which achieves small-signal modeling and stability assessment of DCmGs through measurements of converters' interface. The contributions of this paper are as follows: 1) The Fed-Koopman network is proposed, mapping a multi-scenario DCmG to highdimensional linear space via a federated learning algorithm, and establishing the black-box small-signal model. 2) An improved elastic net regression (ENR) algorithm, named NeuENR is proposed, identifying the system matrix Asys. 3) Considering measurement errors during Asys identification, the small-signal stability criterion and stability margin are defined. The approach is implemented on the commercial electromagnetic transient simulation platform, CloudPSS, demonstrating its applicability to different operation conditions of DCmGs and superior assessment performance. © 1969-2012 IEEE.

关键词： Microgrids

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Distributed model predictive control based on adaptive sampling mechanism

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Chinese Journal of Chemical engineering 2021年第11期34卷 193-204页

作者： Zhen Wang Aimin An Qianrong Li College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou 730050China National Experimental Teaching Center of Electrical and Control Engineering Lanzhou 730050China

In this work,an adaptive sampling control strategy for distributed predictive control is *** to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation ***,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period *** the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is *** an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled *** can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

关键词： Chemical process Distributed model predictive control Adaptive sampling mechanism Optimal sampling interval System dynamic behavior

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