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Annual Conference of the IEEE Industrial Electronics Society

作者： Yiting Wang Wangli He Ministry of Education The Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

This paper investigates impulsive consensus of nonlinear multi-agent systems in the leader-following framework subject to denial-of-service (DoS) attacks. The case that the communication network suffers from DoS attacks is considered, which will destroy the communication link, resulting in switching topologies. Based on the assumption that the system can recover from DoS attacks, sufficient conditions on the design of the impulse interval and the impulse attack ratio that the system can sustain is provided. A numerical example is given to validate the effectiveness of our theoretical results.

关键词： Multi-agent systems Topology Switches Denial-of-service attack Network topology Communication networks

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Automatic Sleep Stage Classification by a Density-Distance-Based K-means Clustering Algorithm with Amendments

Automatic Sleep Stage Classification by a Density-Distance-B...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics

作者： Ying Yu Bei Wang Jing Jin Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai China

Sleep staging has important significance for the monitoring, prevention and treatment of sleep disorders. In this study, the clustering algorithm of K-means is investigated in order to realize the automatic sleep stage classification. Unlike the traditional K-means clustering algorithm, density-distance-based processing procedures were presented to determine the cluster centers. Additionally, the clustering results were amended based on the regularity of sleep stages during one's overnight sleep process. The presented algorithm was tested and analyzed in detail with six subjects from the Sleep-EDF database. The averaged accuracy is about 74%. Comparing with the traditional K-means clustering algorithm, the developed algorithm achieved more accurate and reasonable sleep staging results which can be an assistant tool for clinical inspection.

关键词： Sleep Clustering algorithms Electroencephalography Support vector machines Signal processing algorithms Electrodes Databases

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Unsymmetric Protonation Driven Highly Efficient H2O2Photosynthesis in Supramolecular Photocatalysts via One-Step Two-Electron Oxygen Reduction

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Angewandte Chemie 2024年第10期137卷

作者： Dr. Wenting Li Bing Han Yuhan Liu Dr. Jingyi Xu Huarui He Gege Wang Prof. Dr. Junshan Li Prof. Dr. Yaxin Zhai Prof. Dr. Xiaolin Zhu Prof. Dr. Yongfa Zhu Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education) School of Chemistry and Chemical Engineering Shaanxi Normal University Xi'an 710119 P. R. China Department of Chemistry Tsinghua University Beijing 100084 P. R. China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Hunan Normal University Changsha 410081 China Institute for Advanced Study Chengdu University Chengdu 610106 P. R. China

Photocatalytic hydrogen peroxide (H 2 O 2 ) production has emerged as an attractive alternative to the traditional anthraquinone process. However, its performance is often hindered by low selectivity and sluggish kinetics of oxygen reduction reaction (ORR). Herein, we report an anthrazoline-based supramolecular photocatalyst, SA - SADF - H + , featuring an unsymmetric protonation structure for H 2 O 2 photosynthesis from water and air. The introduction of unsymmetric protonation disrupts the initial mirror symmetry of SADF , significantly enhancing the molecular dipole and facilitating efficient charge separation and electron transfer. Additionally, this modification increases the hydrophilicity of SA - SADF - H + , enabling the interaction of water and dissolved oxygen with the catalytic sites. The altered electron density distribution creates numerous dual active sites for Yeager-type O 2 adsorption, facilitating an efficient ORR towards H 2 O 2 via a direct one-step two-electron pathway. Notably, SA-SADF - H + achieves an outstanding photocatalytic H 2 O 2 production at a rate of 4666.7 μmol L −1 h −1 , with a remarkable solar-to-chemical conversion (SCC) of 1.35 %, surpassing most organic photocatalytic systems. Furthermore, SA - SADF - H + demonstrates remarkable photocatalytic antibacterial activity, achieving 100 % antibacterial efficiency against Staphylococcus aureus within 60 min.

关键词： Supramolecular Photocatalyst Unsymmetric Protonation O2 Adsorption H2O2 production One-step two-electron ORR

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Data-Driven Model Predictive Monitoring for Dynamic processes

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IFAC-PapersOnLine 2020年第2期53卷 98-103页

作者： Qingchao Jiang Huaikuan Yi Xuefeng Yan Xinmin Zhang Jian Huang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237 P.R. China Department of Control Science and Engineering Zhejiang University Hangzhou 310027 P.R. China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 P.R. China

process monitoring plays an important role in maintaining favorable process operation conditions and is gaining increasing attention in both academic community and industrial applications. This paper proposes a data-driven model predictive fault detection method to achieve efficient monitoring of dynamic processes. First, a measurement sample is projected into a dominant latent variable subspace that captures main variance of the process data and a residual subspace. Then the dominant latent variable subspace is further decomposed as a dynamic feature subspace and a static feature subspace. A fault detection residual is generated in each subspace, and corresponding monitoring statistic is established. By using the model predictive monitoring scheme, not only the status of a process but also the type of a detected fault, namely a dynamic feature fault or a static feature fault, can be identified. Effectiveness of the proposed data-driven model predictive monitoring scheme is tested on a lab-scale distillation process.

关键词： Model predictive process monitoring data-driven process monitoring dynamic processes canonical correlation analysis fault detection

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Object Detection Algorithm based on Dense Connection

Object Detection Algorithm based on Dense Connection

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IEEE advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： PAN Zhihao CHEN Ying Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University

The way that information propagates in neural networks is of great importance. In this paper, we propose a connectivity pattern: dense connection, aiming to solve object detection algorithm YOLO-Tiny with less convolutional layers, low feature utilization rate, low precision and poor detection of small objects. We integrate dense connection into YOLO-Tiny, increasing its convolutional layers and improving the feature extraction network. Improved network extracts feature maps and fuses the feature maps by using the Dense Block module. Detection network completes the classification and location at different scales with different anchor boxes. We tested improved network on the Pascal VOC dataset. The experimental results show that our network has improved accuracy by 15% compared with the original algorithm. Although the detection speed has increased, it can still meet the requirements of real-time detection. Compared with the YOLO-Tiny model, our model size only increases by 9.8. MB, compared to the YOLO model, the model size is about 1/5 of the original.

关键词： Feature extraction Object detection Classification algorithms Data mining Convolution Convolutional neural networks Computational modeling

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Automatic determination of optimal spectral peaks for classification of Chinese tea varieties using laser-induced breakdown spectroscopy

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International Journal of Agricultural and Biological Engineering 2018年第3期11卷 154-158页

作者： Hongyang Zhang Qibing Zhu Min Huang Ya Guo Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi 214122JiangsuChina

The accurate identification of tea varieties is of great significance to ensure the interests of tea producers and *** a non-destructive or micro damage detection method,laser-induced breakdown spectroscopy(LIBS)has been widely used in the quality detection or classification of agricultural products and *** objective of this research was to automatically select optimal spectral peaks from the full LIBS spectra,and develop effective classification model for identifying tea *** LIBS spectra covering the region 200-500 nm were measured for 600 Chinese tea leaves including six varieties(*** green tea,Jinhao black tea,Tie Guanyin,Huang Jinya,White peony tea,and Anhua dark tea).A total of 50 optimal spectral peaks were automatically selected from full LIBS spectra(6102)by using the uninformative variable elimination(UVE)and partial least squares projection analysis,and the selected spectral peaks mainly represent the elemental difference in C,Fe,Mg,Mn,Al and *** Least Squares Discriminant Analysis(PLS-DA)was used for developing classification model using selected optimal spectral peaks,and yielded the 99.77%classification accuracy for 300 test samples was *** results indicate that the proposed method can be used to identify leaf varieties in various tea products.

关键词： LIBS tea varieties classification feature selection PLS projection algorithm UVE PLSDA

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When autonomous systems meet accuracy and transferability through AI: A survey

arXiv

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arXiv 2020年

作者： Zhang, Chongzhen Wang, Jianrui Yen, Gary G. Zhao, Chaoqiang Sun, Qiyu Tang, Yang Qian, Feng Kurths, Jürgen Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74075 United States Potsdam Institute for Climate Impact Research Potsdam14473 Germany Institute of Physics Humboldt University of Berlin Berlin12489 Germany

With widespread applications of artificial intelligence (AI), the capabilities of the perception, understanding, decision-making and control for autonomous systems have improved significantly in the past years. When autonomous systems consider the performance of accuracy and transferability, several AI methods, like adversarial learning, reinforcement learning (RL) and meta-learning, show their powerful performance. Here, we review the learning-based approaches in autonomous systems from the perspectives of accuracy and transferability. Accuracy means that a well-trained model shows good results during the testing phase, in which the testing set shares a same task or a data distribution with the training set. Transferability means that when a well-trained model is transferred to other testing domains, the accuracy is still good. Firstly, we introduce some basic concepts of transfer learning and then present some preliminaries of adversarial learning, RL and meta-learning. Secondly, we focus on reviewing the accuracy or transferability or both of them to show the advantages of adversarial learning, like generative adversarial networks (GANs), in typical computer vision tasks in autonomous systems, including image style transfer, image super-resolution, image deblurring/dehazing/rain removal, semantic segmentation, depth estimation, pedestrian detection and person re-identification (re-ID). Then, we further review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation and robotic manipulation. Finally, we discuss several challenges and future topics for using adversarial learning, RL and meta-learning in autonomous systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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Leader selection in multi-agent networks with switching topologies via submodular optimization

arXiv

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arXiv 2019年

作者： Chen, Kaile He, Wangli Tang, Yang Zhang, Wenle Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

In leader-follower multi-agent networks with switching topologies, choosing a subset of agents as leaders is a critical step to achieve desired performances. In this paper, we concentrate on the problem of selecting a minimum-size set of leaders that ensure the tracking of a reference signal in a high-order linear multi-agent network with a set of given topology-dependent dwell time (TDDT). First, we derive a sufficient condition that guarantees the states of all agents converging to an expected state trajectory. Second, by exploiting submodular optimization method, we formulate the problem of identifying a minimal leader set which satisfies the proposed sufficient condition. Third, we present an algorithm with the provable optimality bound to solve the formulated problem. Finally, several numerical examples are provided to verify the effectiveness of the designed selection scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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Defect detection of yarn-dyed shirts based on denoising convolutional self-encoder 8

Defect detection of yarn-dyed shirts based on denoising conv...

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8th IEEE Data Driven control and Learning Systems Conference, DDCLS 2019

作者： Zhang, Hongwei Tang, Wenbo Zhang, Lingjie Li, Pengfei Gu, De Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou Zhejiang Province310027 China Xi'An Polytechnic University Xi'an ShanXi710048 China Key Laboratory of Advanced Process Control for Light Industry Institute of Automation Jiangnan University Ministry of Education Wuxi214122 China

ISBN: (纸本)9781728114545

A defect detection algorithm for dyed shirts with supervised framework relies on a large number of labeling samples and high modeling cost. This paper proposes an automatic detection and location method for color fabric defects based on unsupervised denoising convolution self-encoder. Firstly, a shirt image data set containing 66 kinds of yarn-dyed patterns and a total of 11900 pieces was constructed. Then, Gaussian noise was added to the defect-free samples, and a depth-deconvolution convolution self-encoder was used to construct the image of the yarn-dyed shirt piece. The denoising reconstruction model performs reconstructive repair on the noise interference;Then, the residual of the image is tested and the reconstructed image is calculated, and the slice defect region is detected and located using a mathematical morphology algorithm. The experimental results show that the image reconstruction model and residual image analysis algorithm based on denoising convolution self-encoder can effectively detect and locate the defective area of the dyed shirt piece without relying on the label sample. © 2019 IEEE.

关键词： Yarn

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Impact of turbulence model in coupled simulation of ethylene cracking furnace

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Huagong Xuebao/CIESC Journal 2019年第2期70卷 450-459页

作者： Ni, Chengzhen Du, Wenli Hu, Guihua School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Key Laboratory of Advanced Process Control and Optimization Technology of Ministry of Education East China University of Science and Technology Shanghai200237 China

The ethylene cracking furnace equipped with the bottom burner and the side wall burner is more and more widely used. Different combustion modes affect the turbulent flow state in the furnace. Considering the turbulent flow in the cracking furnace and the gas injection, the combustion and heat transfer are strong. The nonlinear coupling effect, for this purpose, explores the influence of different turbulence models in the cracking furnace/reactor coupling simulation is critical for the precise design and optimization of the cracking furnace. In this paper, a coupling simulation for a 100000 t industrial ethylene cracking furnace was carried out for different turbulence models. The turbulent flow model established by the standard k-Ε model, RNG k-Ε and Realizable k-Ε model was evaluated by CFD numerical simulation. The simulation results of the three turbulence models are compared with the industrial data. The distribution of velocity, temperature and turbulence capacity in the cracking furnace is analyzed. The results show that the Realizable k-Ε model is superior to the other two models in flame stability and reaction efficiency. And based on the Realizable k-Ε turbulence equation, the calculation results of the heat flux and the outer wall temperature distribution of the furnace tube are closer to the actual working conditions. © All Right Reserved.

关键词： Computational fluid dynamics

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