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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Hu, Tingting Yang, Zeyu Ye, Lingjian Zhou, Zhe Yao, Le Shen, Bingbing School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou313000 China School of Mathematics Hangzhou Normal University Hangzhou311121 China

ISBN: (纸本)9798350361674

Stacked autoencoder (SAE) has been applied to the prediction of manufacturing process data quality owing to the excellent feature selection capabilities. Nevertheless, a challenge arises in the accumulation of losses from the lowest to the highest level when minimizing reconstruction losses at each layer. Additionally, many SAE models struggle to extract sophisticated features linked to quality crucial for output predictions. This article delves into a supervised stacked dual-guided autoencoder (SDAE) proposed to describe complex data patterns and learn quality-related features better. It reconstructs the same original input data for each dual-guided autoencoder (DAE) and measures the variables at the input layer related to quality. A new pretraining objective function aims to supervise the network's pre-training process, ensureing that the extracted features include more quality prediction information. Lastly, the efficiency of the introduced SDAE is validated on the CO2 absorption column process. © 2024 IEEE.

关键词： Deep learning

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems Science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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A signature extracting method for inductive online oil debris sensors based noise-assisted local mean decomposition and kurtosis mode selection

A signature extracting method for inductive online oil debri...

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第35届中国过程控制会议

作者： Ning Wang Minglv Jin Jinglun Xu Ye Zheng Baiyu Zhu Yunqi Cao Pingjie Huang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University

The inductive wear debris sensor is extensively utilized for online measurement of metal particles in lubricating oil, enabling the assessment of the operational state of rotating machinery. Given the harsh working environment of the debris sensor, particularly the susceptibility of small-sized debris signals to be overwhelmed by background noise, the challenge lies in effectively processing the signal to eliminate noise and extract debris signatures. A noise-assisted local mean decomposition(LMD) method that combining kurtosis mode selection and mode cell thresholding denoising is investigated. This approach selects modal components containing characteristic debris signals based on kurtosis, mitigates mode mixing by incorporating complementary noise, and ultimately performs noise reduction and signal reconstruction through mode cell thresholding. Through experimental analysis of both simulated signals and actual signals, the proposed method demonstrates its effectiveness in detecting weak debris signals amidst strong background noise, providing critical support for subsequent feature processing.

关键词： oil debris sensor Local mean decomposition kurtosis noise assistance signal reconstruction

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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Hierarchical Energy Management Strategy of Hybrid Electric Truck Fleet under Connected Environment 43

Hierarchical Energy Management Strategy of Hybrid Electric T...

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43rd Chinese control Conference, CCC 2024

作者： Lu, Qizhe Tang, Wenbin Pang, Zitong Zhang, Yahui Jiao, Xiaohong Yanshan University School of Electrical Engineering Qinhuangdao066004 China Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Hebei Province Qinhuangdao066004 China Yanshan University School of Mechanical Engineering Qinhuangdao066004 China

ISBN: (纸本)9789887581581

Considering the slow-moving and energy-consuming issues that tend to occur when truck fleets drive, this paper proposes a hierarchical energy management strategy (EMS) for the hybrid electrical truck (HET) fleet, including speed planning and energy management. The speed threshold range that allows the whole fleet to pass through the traffic light is computed in the upper-layer speed planning. Within the model predictive control framework, a multi-objective optimization function is constructed for speed planning to find the optimum solution. In the lower-layer energy management, an offline model is established for power allocation between the Integrated Starter Generator and the Electric Motor and gear shift selection of automated manual transmission to reduce the dimensionality of the control variables. Then, an adaptive equivalent consumption minimum strategy (A-ECMS) is designed based on the fleet's optimal speed provided. In the offline part, the transformation relationship between the co-state and equivalence factor is established based on Pontryagin's minimum principle (PMP), and PMP obtains the globally optimal control sequence in the historical driving cycle to train a back-propagation neural network (BPNN). The BPNN model predicts the current optimal co-state in the online part, transformed into a real-time optimal equivalence factor to guide the A-ECMS online application. The simulation results indicate that the fuel economy of the proposed EMS is comparable to that of the global optimization method PMP and superior to that of Fixed EF-ECMS and PI-ECMS. Additionally, it enables the HET fleet to pass through traffic lights without stopping, thereby ensuring queue continuity and improving the passage efficiency of the fleet. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Predictive control systems

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Semantic Correspondence with Peripheral Position Coding 13

Semantic Correspondence with Peripheral Position Coding

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Chen, Jinjian Li, Zuoyong Lai, Taotao Xie, Huosheng College of Computer and Data Science Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350319156

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM. © 2023 IEEE.

关键词： Image enhancement

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Deep convolutional autoencoder thermography for artwork defect detection

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Quantitative InfraRed Thermography Journal 2024年第6期21卷 367-383页

作者： Liu, Yi Wang, Fumin Liu, Kaixin Mostacci, Miranda Yao, Yuan Sfarra, Stefano Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan China Professional Restorer Celano Italy Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Infrared thermography is a cost-effective non-destructive evaluation technique that plays a critical role in extracting information about defects in cultural heritage such as works of art. However, in-depth studies on the internal structure of contemporary artworks using infrared thermography remain lacking, and therefore, a deep convolutional autoencoder thermography (DCAT) data analysis method is proposed for defect detection of contemporary artworks. In the proposed method, original data are enhanced by convolution to reduce the noise and inhomogeneous background in the original thermal images;subsequently, a deep autoencoder is used to extract nonlinear features from the enhanced thermographic data, and the results of the hidden layer are visualised. These visualised images highlight information regarding the internal structure and defects of the artwork. The case study on a handmade replica of Picasso’s ‘La Bouteille de Suze’, reproduced exactly as the original, has shown that DCAT significantly improved the accuracy of defect detection when compared with that of commonly used methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Hierarchical Policy Optimization for Cooperative Multi-Agent Reinforcement Learning

Hierarchical Policy Optimization for Cooperative Multi-Agent...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： He, Shunfan Zheng, Ronghao Zhang, Senlin Liu, Meiqin College of Electrical Engineering Zhejiang University Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an710049 China

ISBN: (纸本)9781665410205

To handle the non-stationarity of the environment and the curse of dimensionality issues in multi-agent reinforcement learning, gathering information through communication is a critical part. Existing frameworks have proposed centralized or distributed structures to deal with the problem. However, they either have problems of robustness or problems of high communication costs. This paper adopts a hierarchical zeroth-order policy optimization (HZOPO) algorithm for cooperative multi-agent reinforcement learning (MARL) problems. The agents are divided into different groups with high-and low-level entities. A hierarchical communication structure is implemented to reach global consensus. It is shown that the HZOPO algorithm can balance both convergence and communication efficiency in cooperative MARL environments. The convergence of the algorithm is also proved. © 2024 IEEE.

关键词： Adversarial machine learning

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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