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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _optimization -norm usually achieve better performance than the convex case (e.g., \ell _optimization -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： optimization

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Study of Photocatalytic Activity of a Nanostructured Composite of ZnS and Carbon Dots

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Advances in Nanoparticles 2022年第4期11卷 111-128页

作者： Irié Bi Irié Williams Essy Kouadio Fodjo Pomi Bi Boussou Narcisse Aka Alla Martin Koffi Koffi Kra Sylvestre Trokourey Albert Zhen Gu Laboratory of Constitution and Reaction of Matter UFR SSMT Université Felix Houphouet Boigny Abidjan Cote d’Ivoire Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

Environmental pollution jeopardizes our existence. For this purpose, research is moving more and more towards the search for economic means and green chemistry to curb this phenomenon. In this context, the photocatalytic activity of zinc sulfide nanoparticles (ZnS NPs) and nanostructured composite ZnS/carbon dots (ZnS/CDs) was evaluated after their synthesis. The results of X-ray diffraction (XRD) analysis indicate that the crystal structure of ZnS/CDs is identical to that of the cubic phase structure of ZnS, revealing that the cubic phase structure of ZnS was not altered in the presence of CDs. Indeed, there is no additional peak in the crystal structure of ZnS/CDs, revealing that the crystalline structure of ZnS is not responsible for the difference in photocatalytic activity between ZnS/CDs and ZnS NPs. Moreover, analysis performed by transmission electron microscopy (TEM) shows aggregation of the synthesized ZnS and ZnS/CDs nanoparticles with an average size estimated around 10 nm and 12 nm, respectively. In addition, the reflectance study in the visible range shows a reduction in the sunlight reflection intensity using ZnS/CDs compared to the capability of ZnS NPs. Photocatalytic degradation tests reveal that ZnS/CDs have the best methylene blue (MB) degradation rate. Indeed, under the optimal conditions, the photocatalytic activity can reach 100% efficiency within 100 min and 240 min of sunlight exposure for the degradation of 7.5 mg/L MB using ZnS/CDs and ZnS, respectively. This improvement in photocatalytic activity of ZnS/CDs may be due to the presence of CDs which can permit to undergo a reduction of reflection properties of ZnS NPs in the visible range. These results show that CDs can play a key role in enhancing the photocatalytic activity of ZnS, and suggest that ZnS/CDs could be used as eco-friendly composite materials for the degradation of organic pollutants of similar structures in the aquatic environment under solar irradiation.

关键词： Fluorescent Carbon Dots (CDs) Photocatalytic Degradation of Methylene Blue Sunlight Reflectance Property Synthesis of ZnS Nanoparticles

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Intelligent scheduling and flexible operation for the commercial-scale coal-fired power plant integrated with post-combustion carbon capture

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IFAC-PapersOnLine 2022年第9期55卷 553-558页

作者： Peizhi Liao Xiao Wu Meihong Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and Environment Southeast University Nanjing 210096 China

This paper develops a two-level control structure for commercial-scale coal-fired power plant integrated with post-combustion carbon capture (CFPP-PCC). In high-level scheduler, an economic objective function with the consideration of grid demand, price conditions and system constraints is developed. Deep belief network is utilized to develop the steady-state model for CFPP-PCC process. Intelligent scheduling of electricity generation and capture level is then implemented using Bayesian optimization. Given the optimal setpoints, model predictive controller (MPC) is applied to achieve flexible operation in the low-level supervisory control. The simulation results show that the proposed control structure specifies an economically attractive operation profile for the CFPP-PCC process.

关键词： Coal-fired power plant Post-combustion carbon capture Deep belief network Optimal scheduling Model predictive control

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Physical Safety and Cyber Security Analysis of Multi-Agent Systems:A Survey of Recent Advances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 319-333页

作者： Dan Zhang Gang Feng Yang Shi Dipti Srinivasan the Research Center of Automation and Artificial Intelligence Zhejiang University of TechnologyHangzhou310023 Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of EducationShanghai 200237China the Department of Biomedical Engineering City University of Hong KongHong KongChina the Department of Mechanical Engineering University of VictoriaCanada the Department of Electrical and Computer Engineering National University of SingaporeSingapore

Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making ***,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation *** of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation ***,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of *** paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber ***,the paper concludes with some potential future research topics on the security issues of MASs.

关键词： Consensus deception attack deny-of-service(DoS)attack fault detection fault estimation fault tolerant control multiagent systems

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Intelligent droplet tracking with correlation filters for digital microfluidics

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Chinese chemical Letters 2021年第11期32卷 3416-3420页

作者： Libin Li Zhen Gu Jia-Le Zhou Bingyong Yan Cong Kong Hua Wang Hui-Feng Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and TechnologyShanghai 200237China Key Laboratory of East China Sea Fishery Resources Exploitation Ministry of Agriculture and Rural AffairsEast China Sea Fisheries Research InstituteChinese Academy of Fishery SciencesShanghai 200090China Department of Laboratory Medicine Renji HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai 200137China

Tracking the movement of droplets in digital microfluidics is essential to improve its control stability and obtain dynamic information for its applications such as point-of-care testing,environment monitoring and chemical ***,an intelligent,accurate and fast droplet tracking method based on machine vision is developed for applications of digital *** continuously recognize the transparent droplets in real-time and avoid the interferes from background patterns or inhomogeneous illumination,we introduced the correlation filter tracker,enabling online learning of the multi-features of the droplets in Fourier *** show the proposed droplet tracking method could accurately locate the *** also demonstrated the capacity of the proposed method for estimation of the droplet velocity as faster as 20 mm/s,and its application in online monitoring the Griess reaction for both colorimetric assay of nitrite and study of reaction kinetics.

关键词： Digital microfluidics Correlation filter trackers Machine vision Reaction kinetics Colorimetric assay

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Graph-based semisupervised learning for icing fault detection of wind turbine blade

Graph-based semisupervised learning for icing fault detectio...

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作者： Yi, Huaikuan Jiang, Qinchao Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

The detection of blade icing faults in wind farms is an important task in improving the reliability and safety of wind power systems. Detection is primarily achieved through supervised learning, using labeled samples. However, labeling a large volume of data often consumes lots of manpower, which means that semisupervised learning is usually required. This study proposes a graph-based semisupervised learning system to achieve efficient fault detection in wind turbines (WTs). The proposed method detects abnormal samples by optimizing a small amount of labeled and a large amount of unlabeled data. Firstly, feature selection based on mutual information is performed on supervisory control and data acquisition data, so as to eliminate redundant variables. Secondly, a graph is constructed, using the limited labeled data to indicate the connections between samples. Finally, the initial class information is propagated to the entire data set by iteratively updating the input of the graph model. Experimental results based on a selection of benchmark data sets and real WT data demonstrate the feasibility of the proposed method. © 2020 IOP Publishing Ltd.

关键词： Wind turbines

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A novel chemical composition estimation model for cement raw material blending process

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Chinese Journal of chemical Engineering 2019年第11期27卷 2734-2741页

作者： Yaoyao Bao Yuanming Zhu Weimin Zhong Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.

关键词： Raw material blending process chemical component estimation Modulus prediction System identification

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Robust multi-layer extreme learning machine using bias-variance tradeoff

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Journal of Central South University 2020年第12期27卷 3744-3753页

作者： YU Tian-jun YAN Xue-feng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ***,ELM with a single hidden layer structure often fails to achieve good results when faced with large-scale multi-featured *** resolve this problem,we propose a multi-layer framework for the ELM learning algorithm to improve the model’s generalization ***,noises or abnormal points often exist in practical applications,and they result in the inability to obtain clean training *** generalization ability of the original ELM decreases under such *** address this issue,we add model bias and variance to the loss function so that the model gains the ability to minimize model bias and model variance,thus reducing the influence of noise signals.A new robust multi-layer algorithm called ML-RELM is proposed to enhance outlier robustness in complex *** results show that the method has high generalization ability and strong robustness to noise.

关键词： extreme learning machine deep neural network robustness unsupervised feature learning

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Oxidation of emerging organic contaminants by in-situ H_(2)O_(2) fenton system

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Green Energy & Environment 2024年第3期9卷 417-434页

作者： Yuqin Ni Chuxiang Zhou Mingyang Xing Yi Zhou State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes School of Resources&Environmental EngineeringEast China University of Science and TechnologyShanghai200237China Key Laboratory for Advanced Materials and Institute of Fine Chemicals National Engineering Laboratory for Industrial Wastewater TreatmentSchool of Chemistry and Molecular EngineeringEast China University of Science and Technology130 Meilong RoadShanghai200237China Department of Applied Natural Sciences Technische Hochschule LubeckMonkhofer Weg 23923562 LubeckGermany

The existence and risk of emerging organic contaminants(EOCs)have been under consideration and paid much effort to degrade these *** system is one of the most widely used technologies to solve this *** original Fenton system relies on the hydroxyl radicals produced by Fe(Ⅱ)/H_(2)O_(2) to oxidize the organic ***,the application of the Fenton system is limited by its low iron cycling efficiency and the high risks of hydrogen peroxide transportation and *** introduction of external energy(including light and electricity etc.)can effectively promote the Fe(Ⅲ)/Fe(Ⅱ)cycle and the reduction of oxygen to produce hydrogen peroxide in *** review introduces three in-situ Fenton systems,which are electro-Fenton,Photo-Fenton,and chemical *** mechanism,influencing factors,and catalysts of these three in-situ Fenton systems in degrading EOCs are discussed *** review strengthens the understanding of Fenton and in-situ Fenton systems in degradation,offering further insight into the real application of the in-situ Fenton system in the removal of EOCs.

关键词： In-situ H_(2)O_(2)production Fenton Emerging organic contaminants Photocatalysis Electrocatalysis

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Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

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Computer Modeling in Engineering & Sciences 2019年第10期121卷 123-144页

作者： Yanzhen Wang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai200237China

A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between *** contrast,ensemble models can effectively solve this *** key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel,the diversity between subsample sets and the optimal ensemble *** study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the *** proposed method first uses a bagging algorithm to generate multiple subsample ***,an indicator vector is defined to describe these subsample ***,subsample sets are selected on the basis of the results of agglomerative nesting clustering on indicator vectors to maximize the diversity between ***,these subsample sets are placed in a stacked autoencoder for ***,XGBoost algorithm,rather than the traditional simple average ensemble method,is imported to ensemble the model during *** machine learning public datasets and atmospheric column dry point dataset from a practical industrial process show that our proposed method demonstrates high precision and improved prediction ability.

关键词： Ensemble model deep learning bagging stacked autoencoder XGBoost

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