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2022 International Siberian Conference on Control and Communications, SIBCON 2022

作者： Vladimirov, Mikhail V. Senchenko, Pavel V. Sidorov, Anatoly A. Tomsk State University of Control Systems and Radioelectronics Department of Data Processing Automation Tomsk Russia

ISBN: (纸本)9781665476287

The article provides an analysis of the possibilities of using Blockchain technology in the framework of programs for economic digitalization and development of the educational sector. Three main areas of blockchain use are highlighted: cryptocurrencies, tracking the uniqueness of items and smart-contracts. A classification of blockchain-based systems dividing existing systems by scope has also been proposed. The article discusses the current methods of using this technology for the implementation of various tasks within the framework of individual enterprises, states and the world. A promising solution in accordance with the current trends and directions of development has also been proposed. The problems associated with the implementation of blockchain systems are also described. © 2022 IEEE.

关键词： Blockchain

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Enhancing topological information of the Lyapunov-based distributed model predictive control design for large-scale nonlinear systems (vol 25, pg 1476, 2023)

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ASIAN JOURNAL OF CONTROL 2023年第4期25卷 3286-3286页

作者： He, W. Li, S. Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information processing Shanghai Jiao Tong University Shanghai China Contribution: Conceptualization Data curation Formal analysis Investigation Methodology Project administration Software Supervision Validation Visualization Shaoyuan Li Department of Automation Shanghai Jiao Tong University Shanghai 200240 China. Contribution: Funding acquisition Resources Supervision Validation Visualization

In this paper, a distributed model predictive control architecture based on graph theory for integrated large-scale nonlinear process systems is proposed. This architecture is first agglomerated using popular community detection techniques and then organized on account of the relative master–slave relationship according to the ample information of interactions among separate subsystems. Both sequential and iterative distributed nonlinear model predictive coordination forms are considered for the reduction of the communication and computational burden within an acceptable loss of performance. Furthermore, the control performance of the large-scale integrated system could be improved to some extent under the architecture and the communication strategy we propose, whereby a brave exploration is made on the relationship between the control structure and the control performance, ulteriorly obtaining some notable results towards the untapped territory. The effectiveness of the proposed coordination method is evaluated by a standard reactor-separator process system.

关键词： Graph theory

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Terahertz Spectroscopy of blood plasma for cancer diagnosis

Terahertz Spectroscopy of blood plasma for cancer diagnosis

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2024 International Conference Laser Optics, ICLO 2024

作者： Cherkasova, O.P. Nikolaev, N.A. Institute of Automation and Electrometry Sb RAS Ia &e Sb Ras Laboratory of Terahertz Photonics Novosibirsk Russia Novosibirsk State Technical University Nstu Department of Data Acquisition and Processing Systems Novosibirsk Russia Institute of Laser Physics Sb RAS Ilp Sb Ras Laboratory of Laser Biophysics Novosibirsk Russia

ISBN: (纸本)9798350390674

Early cancer detection can be achieved by analyzing blood plasma. Here we propose ways to improve the sensitivity of terahertz time-domain spectroscopy to cancer diagnosis. Two approaches are being considered to lessen the effect of water, which determines the spectrum of blood plasma: lyophilization and numerical processing. Additionally, several machine learning algorithms are proposed to process measured spectra. And the final approach is based on the use of nanoantennas to diagnose tumor marker concentrations. © 2024 IEEE.

关键词： Terahertz spectroscopy

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The Strength of Structural Diversity in Online Social Networks

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Research 2021年第1期2021卷 1203-1212页

作者： Yafei Zhang Lin Wang Jonathan JHZhu Xiaofan Wang Alex‘Sandy’Pentland Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China Department of Media and Communication and School of Data Science City University of Hong KongHong Kong S.A.R.China Department of Automation Shanghai UniversityShanghai 200444China Media Lab Massachusetts Institute of TechnologyCambridgeMA 02139USA

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective *** a large-scale dataset from a knowledge-sharing website,this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social *** capture the structural diversity of an individual,we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into *** show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation,and the inclusion of a coexposure network provides an additional ingredient to achieve that *** synthetically controlling several possible confounding factors through matching experiments,structural diversity still plays a nonnegligible role in the prediction of personal online social *** work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains,such as social influence and collective intelligence studies.

关键词： collective networks founding

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Application of Blockchain Technology for Verification of State Documents in the Russian Federation

Application of Blockchain Technology for Verification of Sta...

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Siberian Conference on Control and Communications (SIBCON)

作者： Mikhail V. Vladimirov Pavel V. Senchenko Anatoly A. Sidorov Department of Data Processing Automation Tomsk State University of Control Systems and Radioelectronics Tomsk Russian Federation

ISBN: (纸本)9781665476294

关键词： Training Economics Instruments Bitcoin Market research Blockchains Behavioral sciences

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method

arXiv

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arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Wang, Kaijie Huang, Xiaolin Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University 800 Dongchuan RD Shanghai200240 China STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Oude Markt 13 Leuven3000 Belgium

data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputation is helpful for learning performance, and it follows that the pursuit of better classification can guide the data imputation process. While some works consider using label information to assist in this task, their simplistic utilization of labels lacks flexibility and may rely on strict assumptions. In this paper, we propose a new framework that effectively leverages supervision information to complete missing data in a manner conducive to classification. Specifically, this framework operates in two stages. Firstly, it leverages labels to supervise the optimization of similarity relationships among data, represented by the kernel matrix, with the goal of enhancing classification accuracy. To mitigate overfitting that may occur during this process, a perturbation variable is introduced to improve the robustness of the framework. Secondly, the learned kernel matrix serves as additional supervision information to guide data imputation through regression, utilizing the block coordinate descent method. The superiority of the proposed method is evaluated on four real-world data sets by comparing it with state-of-the-art imputation methods. Remarkably, our algorithm significantly outperforms other methods when the data is missing more than 60% of the features. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Development of Software for Automated Marking of Vertebral Bodies

Development of Software for Automated Marking of Vertebral B...

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2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022

作者： Piletskii, D. Gladkov, A. Yakimenko, A. Novosibirsk State Technical University Faculty of Automation and Computer Engineering Novosibirsk Russia Novosibirsk State Technical University Department of Data Acquisition and Processing Systems Novosibirsk Russia Institute of Computational Mathematics and Mathematical Geophysics SB RAS Laboratory of Geophysical Informatics Novosibirsk Russia

ISBN: (纸本)9781665464802

in order to create a digital twin of the spine to simulate the behavior of the spine after various interventions, the task of automating the marking of the vertebral bodies on radiographs was formulated. The presented work describes the developed approaches to solving the problem of marking the vertebral bodies on radiographs, and also describes algorithms for extracting images of the spine and images of individual vertebrae. For the detection of images of each individual vertebra, the YOLOv5 model was used, and an ensemble of regression models was created to solve the problem of marking the image of each individual vertebra. After a series of experiments, marking errors were estimated at 4.6% using the ratio of coordinates obtained using trained models and correctly marked data. Theories were also put forward about the possible improvement of the created software by dividing the vertebrae into two subclasses in order to reduce the markup error to the desired 0.8%. © 2022 IEEE.

关键词： Computer vision

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