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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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A boundary evidence controlled level set inference method for nuclei instance segmentation in histopathology images

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Multimedia Tools and Applications 2024年 1-31页

作者： Vatani, Amir Song, Jie Xiao, Liang School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

Periodic B-spline (PBS) has been a successfully used technique in histopathology image segmentation. However, it is limited when dealing with multi-instance objects in real-world biomedical applications. Moreover, its segmentation results are quite sensitive to the initial settings of the model and highly depend on the selection of control points. To address these issues and boost the canonical PBS to a new level of adaptive fitting, we present Level-Set B-Spline (LSBS), a novel definition of nuclear contour inference that employs B-splines under the energy minimization of a variational level set functional, which can promote each other. To this end, we adopt a two-stage approach: The first stage solves the marker detection and image segmentation problem and the second stage solves the joint modeling of the missing contour and the latent shape. To do so, we propose the use of modified concavity measurement and optimal boundary-to-marker association as an efficient basis for computing a periodic set of knots to drive the LSBS. For any probe image, our approach enables to analysis of free-lying and overlapping cell nuclei with weak boundaries. We demonstrated the superiority of the proposed LSBS by evaluating it on various datasets, including the challenging TCGA KIRC and Kumar nuclei, where the consistently state-of-the-art performances were achieved for comparison against state-of-the-art image analysis-based methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Multi-Fault Diagnosis, Quantitative Analysis and Warning Strategy for Lithium-ion Battery System: When LSTM Meets the Fuzzy Logic Theory

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8224-8235页

作者： Chen, Hui Tian, Engang Wang, Licheng Zheng, Yuejiu University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai University of Electric Power College of Automation Engineering Shanghai200090 China University of Shanghai for Science and Technology College of Mechanical Engineering Shanghai200093 China

This paper is concerned with the multi-level secure warning problem for lithium-ion battery systems subject to multi-fault scenarios. First, the long short-term memory (LSTM) network is employed to design a series of cell voltage estimators. In comparison with the estimation of the cell voltage and its measurement, the estimation residual is constructed in terms of the Euclidean distance, which plays an essential index for the multi-fault detection issue. Furthermore, in combination of the voltage envelope relationship, a specific type of the faults is subsequently identified. Then, for the subsequent fault warning purpose, a novel fault quantitative analysis method is proposed to further determine not only the fault amplitude but also its duration. In addition, on the basis of specific characteristics of the fault such as the type, count, duration and the amplitude, a fuzzy-logic-based multi-level secure warning strategy is put forward. Finally, the effectiveness of the developed multi-level fault warning approach is substantiated through a comprehensive experimental validation. © 2015 IEEE.

关键词： Fuzzy logic

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Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1566-1578页

作者： Yuhan Zhang Zidong Wang Lei Zou Yun Chen Guoping Lu the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China IEEE the Department of Computer Science Brunel UniversityUB83PHUnited Kingdom the College of Information Science and Technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the School of Automation Hangzhou Dianzi UniversityHangzhou 310018China the School of Electrical Engineering Nantong UniversityNantong 226019China

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication *** transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear *** the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

关键词： Buffer-aided strategy neural networks nonlinear control output-feedback control unreliable communication channel

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Saliency Feature Learning for Multi-modality Person Retrieval in Visual Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 15883-15896页

作者： You, Shuai Feng, Yujian Wang, Shitao Wu, Fei Sha, Yuchen Ji, Yimu Jing, Xiao-Yuan School of Internet of Things Nanjing China Jiangsu University of Technology School of Computer Engineering Changzhou China NJUPT School of Internet of Things Nanjing China NJUPT School of Computer Science Nanjing China NJUPT College of Automation College of Artificial Intelligence Nanjing China Wuhan University School of Computer Wuhan430072 China

In the Visual Internet of Things (VIoT), smart surveillance is an important component of the Multi-modality Person Retrieval (MPR) task. Capturing discriminative pedestrian information from images aids in identifying target individuals through sketch or text descriptions, improving the reliability of VIoT systems. Current granularity-level matching methods face challenges with information redundancy and modality differences in MPR tasks. In this paper, we propose a novel approach for intra-and inter-modality saliency feature learning by Multi-granularity Feature Selection and Relation (MFSR). Firstly, to reduce redundant information within each modality, the Intra-modality Feature Selection Module (IFSM) employs the adaptive weighting mechanism to enhance salient pedestrian features while suppressing irrelevant features. Secondly, the Multi-granularity Feature Relation Module (MFRM) aligns cross-modality salient person features by increasing the similarity scores of local and global features across modalities, to reduce differences between multi-modality visual-text pairs. Finally, the Cross-modality Similarity Matching (CSM) loss is designed to enhance consistency in visual-text pairs of the same identity, ensuring compact intra-class features by minimizing discrepancies between cross-modality similarity distributions and identity-matching distributions. Experimental results show that our approach achieves state-of-the-art performance on benchmark datasets. © 2014 IEEE.

关键词： Feature Selection

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YOLOv5 improved algorithm and its application in real-time inventory of corrugated paper

YOLOv5 improved algorithm and its application in real-time i...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wu, Qian Kang, Qinhao Yang, Yuxiang Li, Lin Han, Yu College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China College of Automation Chongqing University Chongqing400044 China

ISBN: (纸本)9781510681514

To detect corrugated paper quickly and accurately, a YOLOv5-based algorithm has been created. In order to improve the fusion of lower layer feature information, the operation of GSConv layer is introduced in the feature enhancement network algorithm. for better utilizing feature semantic information, the CARAFE upsampling operator is used instead of the original upsampling. MobileNetv3 was used to replace its backbone network to reduce computing costs and achieve lightweighting. Using the corrugated paper dataset constructed by the authors as an example, the improved algorithm was found to have a parameter size of 1.3 M based on the results., computational complexity of 1.8 GFLOPS, and weight size of 2.9 MB. Compared to the original YOLOv5n algorithm, these values decreased by 27.8%, 56.1%, and 21.6%. Its key technical indicators mAP@0.5 and FPS can be as high as 88.0% and 68.5 respectively. These results show that the improved algorithm satisfies the requirements for on-site deployment on mobile devices. The research shows that applying improved algorithms to conduct real-time and accurate inventory of corrugated packaging is a feasible approach. © 2024 SPIE.

关键词： Signal detection

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Research on surface defect detection algorithm of steel pipe weld seam based on deep learning

Research on surface defect detection algorithm of steel pipe...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wu, Qian Lu, Zhiyong Li, Lin Liu, Jiangbo Han, Yu College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China College of Automation Chongqing University Chongqing400044 China

ISBN: (纸本)9781510681514

An improved YOLOv5s model was proposed to address the issue of poor efficiency in steel pipe weld defect detection. First, the network's feature learning and localization capabilities are enhanced by introducing a coordinate attention mechanism. Secondly, compress the model through the lightweight Ghost module to effectively get fewer network parameters. Thirdly, standard convolution and depth-separable convolution are combined in the Neck module for convolution operations, aiming to reduce model complexity while maintaining accuracy. Finally, introduced a new WIoU loss function to speed up model convergence. Based on a self-created dataset, the detection of three types of surface defects, including overlap, spatter, and undercut, is conducted. The experimental findings demonstrate that the improved model achieves a 2.4% increase in mAP. Furthermore, the model also achieved a 22.4% reduction in parameter count. These findings indicate that the improved model can effectively meet the practical detection requirements. The experimental comparison confirms the feasibility and effectiveness of the proposed improved algorithm for detecting surface defects in steel pipe welds. © 2024 SPIE.

关键词： Convolution

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A Deep Echo State Network with Scaling Factor Activation Functions for Fault Diagnosis of Electrical Drive Systems

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7874-7884页

作者： Gong, Yuanpeng Jiang, Yulian Cheng, Chao Chen, Hongtian Wang, Shenquan Changchun University of Technology College of Electrical and Electronic Engineering Changchun130012 China Changchun University of Technology School of Computer Science and Engineering Changchun130012 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

Deep echo state networks (Deep-ESNs) play an important role in fault diagnosis. However, due to its limitation in the iterative process of dealing with nonlinear data, the accuracy of fault diagnosis is relatively low. In order to improve the fault diagnosis accuracy of electrical drive systems, this paper proposes a novel Deep-ESN based on the synergistic effect of golden jackal optimization (GJO), variational mode decomposition (VMD) and scaling factor activation function, called GVSD-ESN. The main work of this study contains: 1) the novel scaling factor activation function is proposed to solve the gradient vanishing problem in the Deep-ESN model;2) GJO is used to solve high-dimensional optimization problems of VMD;3) the power spiral curve is proposed to optimize the position update equation of GJO, which solves the problem of falling into the local optimal;4) adding a sparse regularization layer between reservoirs can enhance the class definition of GVSD-ESN. Finally, the effectiveness of the proposed method is verified in an electrical drive systems. © 2015 IEEE.

关键词： Variational mode decomposition

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Pixel-level Monocular Depth Estimation Based on Global-local Feature Fusion 6

Pixel-level Monocular Depth Estimation Based on Global-local...

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6th International Conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Tian, Daoqiang Wang, Bo Xie, Liping College of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan China Chinese Academy of Sciences Institute of Automation Beijing China

ISBN: (纸本)9798350353174

To address the issue of insufficient extraction of global features in CNN monocular depth estimation networks, leading to scale ambiguity and object edge ambiguity in predicted depth maps, this paper presents a pixel-level monocular depth estimation method based on global-local feature fusion. Multiple pre-trained Transformer blocks are employed as encoders to extract global features at multiple scales. Additionally, a global area enhancement module based on pyramid pool is designed to increase the receptive field of the network, providing effective global context prior for in-depth scene analysis and enhancing the network's ability to obtain feature information. Furthermore, a feature fusion module based on large kernel convolution attention is proposed to better utilize global and local features such as shape and texture, refining semantic scenes and improving the global contours of depth maps. Finally, an adaptive distribution depth interval and estimation module are designed according to the requirements of depth features in different regions of the scene. The depth information is estimated by the probability distribution of pixels and linear combination of discrete pixels, resulting in clearer predicted depth maps with detailed and smooth object outlines. © 2024 IEEE.

关键词： Convolution

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EMD decomposition-based signal denoising method for DC electrical methods

EMD decomposition-based signal denoising method for DC elect...

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2023 International Conference on Electronic Information Engineering and Data Processing, EIEDP 2023

作者： Huo, Jingri Wang, Guofu Li, Hongli College of Automation Electronic and Computer Science Guangxi University of Science and Technology Liuzhou China

ISBN: (纸本)9781510666160

The DC resistivity method uses the inherent conductive differences of subsurface materials as the basis for exploration to observe the earth's electric field, while human activities, nature and solar storms can cause changes in the earth's electric field, which in turn seriously affects the accuracy of exploration. Today the DC resistivity method is used for monitoring hazards such as landfill seepage, coal mine seepage, and slope seepage. The above hazards change slowly, and the monitored change signal is weak, and if there is earth current interference around it will bury the hazard signal. Therefore, it makes sense to noise cancel the signal for DC method. In this paper, empirical mode decomposition (EMD), sliding average and least squares fitting are combined to propose a noise-canceling method for DC electrical law signals. In this paper, the effectiveness of the method is verified by theoretical simulations. © 2023 SPIE.

关键词： Hazards

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