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14th Asian control Conference, ASCC 2024

作者： Zhou, Wenhao Li, Yingxuan Zhao, Jiancheng Zhao, Chunhui College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9789887581598

Weakly supervised video anomaly detection is chal-lenging due to the lack of frame-level labels in abnormal videos. Previous work generally formulates it as a multiple instance learning (MIL) problem, in which abundant snippets especially including hard snippets in abnormal videos are underutilized, causing the training process to be dominated by redundant easy snippets. In this work, we introduce a semi-supervised learning perspective and aim to provide reliable snippet-level labels for abnormal videos to fully leverage them. Specifically, we propose a relabeling strategy by utilizing the Label Propagation Network (LPN) to propagate labels from confident snippets to hard snippets. Considering label propagation tends to be fragile and inaccurate in this case due to incorrect pseudo labels, we develop a feature relation module to enhance the intra-class compactness and inter-class dispersion, as well as a label separation module to enlarge the discrepancy of propagated labels. The overall model is designed to be concise and universal, which can be applied to existing weakly supervised methods to improve performance. Extensive experiments demonstrate that our method achieves state-of-the-art results on three benchmark datasets. © 2024 Asian control Association.

关键词： Self-supervised learning

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Development of a Compact Optical Layout for a Miniature Fluorescence Spectrometer: In-Situ Detection of Organic Pollutants in Aquatic Environment

Development of a Compact Optical Layout for a Miniature Fluo...

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2024 China Automation Congress, CAC 2024

作者： Fan, Xingyu Yu, Jie Zhang, Qiuhao Cao, Shuai Zhang, Lingke Xue, Fangjia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

In recent years, the increasing presence of emerging organic pollutants in rivers, lakes, and other aquatic environments has posed significant threats to human health and ecological safety. Current monitoring technologies for these pollutants face several challenges, including the high cost of equipment and the scarcity of trained professionals. Furthermore, existing portable fluorescence sensors are limited by inadequate data, unsuitability for in-situ water environments, and the ability to detect only specific pollutants. This paper proposes a novel design for an in-situ miniature fluorescence spectrometer, utilizing a compact optical layout to monitor organic pollutants in water. The design features a high-power UV LED combined with a lens group to enhance the fluorescence excitation effect of low-concentration organic pollutants. The system components are integrated into a compact layout centered around a flow cell, significantly reducing the device's size. In experimental validation, vitamin B was used as a model organic pollutant to test the prototype's detection capabilities. Within the concentration range of 1 - 100 μg/L, the fluorescence intensity exhibited a strong linear correlation with the vitamin B concentration, achieving an R2 value of 0.9945, comparable to that of commercial spectrometers. This portable device demonstrates excellent performance in detecting organic matter and offers significant advantages for in-situ monitoring applications, making it well-suited for real-time pollutant detection in rivers, lakes, and other aquatic environments. © 2024 IEEE.

关键词： Lake pollution

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Identification of Mixed Organic Pollutants in River Water Based on Graph Convolutional Networks

Identification of Mixed Organic Pollutants in River Water Ba...

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2024 China Automation Congress, CAC 2024

作者： Cao, Shuai Yu, Jie Fan, Xingyu Zhang, Qiuhao Xue, Fangjia Hou, Dibo State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

In the application of three-dimensional (3-D) fluorescence spectroscopy for river water pollutant detection, traditional feature extraction models often neglect wavelength information and complex intermolecular interrelationships. Moreover, experiments are frequently limited to single-substance classifications. This research addresses these limitations by proposing a novel model for organic pollutant recognition based on graph neural networks (GNNs). The proposed method begins by using the excitation-emission matrix and the wavelength information corresponding to each fluorescence intensity value as inputs. A convolutional neural network (CNN) is initially employed to extract preliminary features from the three-dimensional fluorescence spectra. Subsequently, the initial spectral features obtained during the pre-training stage are used to construct a graph neural network model. This model represents each category of organic pollutants as nodes within the graph, capturing the intrinsic connections between different pollution samples through node interactions. Finally, the model outputs a score vector for each category to classify the pollutants. To validate the proposed model, datasets comprising twelve common organic pollutants, both as single substances and mixtures, were collected. Comparative analysis with other methods demonstrates that the proposed approach achieves a high accuracy rate. ©2024 IEEE.

关键词： Graph neural networks

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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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Directed Acyclic Graphs With Tears

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 972-983页

作者： Chen, Zhichao Ge, Zhiqiang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Peng Cheng Laboratory Shenzhen518000 China

Bayesian network is a frequently used method for fault detection and diagnosis in industrial processes. The basis of Bayesian network is structure learning which learns a directed acyclic graph (DAG) from data. However, the search space will scale super-exponentially with the increase of process variables, which makes the data-driven structure learning a challenging problem. To this end, the DAGs with NOTEAR methods are being well studied not only for their conversion of the discrete optimization into continuous optimization problem but also their compatibility with deep learning framework. Nevertheless, there still remain challenges for NOTEAR-based methods: 1) the infeasible solution results from the gradient descent based optimization paradigm;2) the truncation operation to promise the learned graph acyclic. In this work, the reason for challenge 1) is analyzed theoretically, and a novel method named DAGs with tears method is proposed based on mix-integer programming to alleviate challenge 2). In addition, prior knowledge is able to incorporate into the new proposed method, making structure learning more practical and useful in industrial processes. Finally, a numerical example and an industrial example are adopted as case studies to demonstrate the superiority of the developed method. © 2020 IEEE.

关键词： Numerical methods

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RAGAN: Regression Attention Generative Adversarial Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1549-1563页

作者： Jiang, Xiaoyu Ge, Zhiqiang Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Peng Cheng Laboratory Shenzhen518000 China

Despite surrounding by Big Data, we still need to learn from insufficient data in many scenarios. Building an accurate regression model for a small amount of data is a pretty tricky and exciting problem. At present, it is a promising solution to augment limited real data by generating data through generative adversarial networks (GANs). However, when GAN is used to generate labeled data in regression modeling, it lacks attention to the relationship between independent and dependent variables, resulting in poor performance of regression modeling. This article proposes a novel regression attention GAN (RA-GAN) for augmented regression modeling. Regression attention mechanisms are introduced into network parameters learning of both generator and discriminator in RA-GAN to establish a known relationship between variables. This makes RA-GAN restore the regression information during data generation. In addition, an indicator called cross regression score is designed to describe the quality of the generated data before augmented regression modeling, effectively evaluating data augmentation performance in advance. The effectiveness and superiority of the proposed methods are verified in an actual industrial soft-sensing case and a diabetes prediction case through data augmentation regression applications. © 2020 IEEE.

关键词： Generative adversarial networks

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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Improved Residual Reinforcement Learning for Dynamic Obstacle Avoidance of Robotic Arm 13

Improved Residual Reinforcement Learning for Dynamic Obstacl...

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

作者： Liu, Zhenting Liu, Shan College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

This paper proposes an improved residual deep reinforcement learning method for robot arm dynamic obstacle avoidance and position servo. The proposed method first simplifies the state space by constructing key points and pre-trains a model capable of completing obstacle avoidance tasks using the simplified state. Then, when training with real state information, a guiding network is used to help accumulate good samples, which improves the training efficiency. To overcome the convergence difficulty of residual DQN in robot arm obstacle avoidance, this paper incorporates the action of the feedback controller into the action space and uses incremental reward values to evaluate the actions. Simulation results demonstrate that the proposed method can effectively achieve robot arm dynamic obstacle avoidance and position servo. © 2024 IEEE.

关键词： Deep reinforcement learning

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An improved adaptive minimum entropy deconvolution method in bearing fault detection 4

An improved adaptive minimum entropy deconvolution method in...

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4th International Conference on Mechanical, Electronics, and Electrical and Automation control, METMS 2024

作者： Liao, Zihao Xu, Jinglun Jin, Minglv Cao, Yuqi Cao, Yunqi Hou, Dibo Huang, Pingjie State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

Bearings, a crucial element in rotating machinery, are most susceptible to failure during operation, making up over half of all malfunctions. Detecting bearing faults in a timely manner can be quite challenging due to the frequent influence of strong noise on the fault features. To address the issue, an adaptive parameter optimization based on Minimum Entropy Deconvolution (MED) method in conjunction with Infogram is proposed. First, the Infogram is created through spectral negentropy, followed by the construction of a filter that utilizes the center frequency and the frequency band exhibiting the highest spectral negentropy. This filter is then applied to the original vibration signal in order to isolate the frequency band that contains the most significant bearing fault information. Then, based on MED method, the autocorrelation function and L1 norm are introduced to evaluate the filtered signal to realize the adaptive search of deconvolution filter length. Finally, the characteristic frequency components are extracted by envelope spectrum analysis. The validation of the effectiveness of the proposed method is conducted on a public dataset across various bearing operating conditions. The verification results show that this method can accurately detect the conditions of bearings, and extract more distinctive characteristic frequency components compared with some relevant techniques. © 2024 SPIE.

关键词： Spectrum analysis

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Class-Imbalance and Client-Imbalance Federated Learning for Fault Diagnosis 13

Class-Imbalance and Client-Imbalance Federated Learning for ...

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

作者： Sun, Shu Qian, Jinchuan Zhang, Xinmin Song, Zhihuan College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

With the development of big data, data-driven fault diagnosis models have experienced rapid growth. However, as the complexity of models increases, there is a corresponding rise in the demand for data. To obtain a substantial amount of data, traditional approaches involve centralizing datasets from multiple factories for training. However, due to heightened emphasis on data privacy from enterprises and regulatory bodies, this method becomes more challenging to implement and poses risks of data leakage. In response to this challenge, this paper designs a novel federated learning-based fault diagnosis modeling approach. Each local device trains its model on its local dataset and subsequently uploads updated model parameters to a central server for global model aggregation and updating. However, it is worth noting that in industrial processes, there may be concurrent class imbalance and data client imbalance, which brings challenges to fault diagnosis modeling based on federated learning. To address this challenge, we further propose a dynamic client aggregation fault diagnosis method based on category loss in federated learning. Experimental results indicate that the proposed approach not only protects data privacy in imbalanced class and client scenarios but also achieves outstanding fault recognition accuracy and robustness. © 2024 IEEE.

关键词： Federated learning

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