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Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

Journal of Systems Engineering and Electronics 2018年第1期29卷 166-175页

作者： LIU Lu SHAN Liang DAI Yuewei LIU Chenglin QI Zhidong School of Automation Nanjing University of Science and Technology Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population *** tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

关键词： bacterial foraging algorithm fractional-order quantum rotation gate proportion integration differentiation(PID) servo system

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Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

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Chinese Journal of chemical Engineering 2017年第8期25卷 983-991页

作者： Lili Tao Bin Xu Zhihua Hu Weimin Zhong College of Engineering Shanghai Polytechnic University School of Mechanical Engineering Shanghai University of Engineering Science Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemical process in industry [ 1 ]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta- neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob- lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm （SADE） to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm （ISADE）. optimization results indicate that application oflSADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

关键词： p-Xylene oxidation Operation condition optimization Multi-objective optimization Self-adaptive differential evolution

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Zoning search using a hyper-heuristic algorithm

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Science China(Information Sciences) 2019年第9期62卷 193-195页

作者： Qinqin FAN Ning LI Yilian ZHANG Xuefeng YAN Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai Jiao Tong University Logistics Research Center Shanghai Maritime University Key Laboratory of Marine Technology and Control Engineering Ministry of CommunicationsShanghai Maritime University Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Dear editor,With the development of high-performance computing techniques, run time of meta-heuristic algorithms can be reduced effectively. However, improved computation power has not been indirectly converted into search capability in most of previous studies [1]. Moreover, the solution precision of an optimization problem is directly determined

关键词： pro CEC Zoning search using a hyper-heuristic algorithm

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Neural network-based source tracking of chemical leaks with obstacles

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Chinese Journal of chemical Engineering 2021年第5期34卷 211-220页

作者： Qiaoyi Xu Wenli Du Jinjin Xu Jikai Dong Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China

The leakage of hazardous gases poses a significant threat to public security and causes environmental *** effective and accurate source term estimation(STE)is necessary when a leakage accident ***,most research generally assumes that no obstacles exist near the leak source,which is inappropriate in practical *** solve this problem,we propose two different frameworks to emphasize STE with obstacles based on artificial neural network(ANN)and convolutional neural network(CNN).Firstly,we build a CFD model to simulate the gas diffusion in obstacle scenarios and construct a benchmark ***,we define the structure of ANN by searching,then predict the concentration distribution of gas using the searched model,and optimize source term parameters by particle swarm optimization(PSO)with well-performed cost ***,we propose a one-step STE method based on CNN,which establishes a link between the concentration distribution and the location of ***,we propose a novel data processing method to process sensor data,which maps the concentration information into feature *** comprehensive experiments illustrate the performance and efficiency of the proposed methods.

关键词： Obstacle optimization Neural networks Feature extraction Source term estimation Computational fluid dynamics (CFD)

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Distillation Sequence optimization Considering Extractive Distillation under Multiple Conditions: A Methanol to Propylene Case Study

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China Petroleum processing & Petrochemical Technology 2019年第4期21卷 88-93页

作者： Yu Jing Qian Feng Wang Jiming Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237 Coteva AgriscienceTM Shanghai 201203 School of Chemical Engineering East China University of Science and TechnologyShanghai 200237

This study provides insights into the distillation sequence optimization of refinery system in a methanol to propylene plant with extractive distillation under multiple conditions. The simulated annealing algorithm(SA) with relative cost function was used to solve a meaningful optimization problem. It was observed that different conditions had differed on the flowsheet. Case study shows the effectiveness of the proposed method.

关键词： distillation sequence optimization simulated annealing algorithm MATLAB multiple conditions

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Adaptive Neural Output Feedback control for Nonstrict-Feedback Nonlinear Systems with Quantized Input 36

Adaptive Neural Output Feedback Control for Nonstrict-Feedba...

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第36届中国控制会议

作者： Yan Dong Zhaoxu Yu Fangfei Li Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Department of Mathematics East China University of Science and Technology

ISBN: (纸本)9781538629185

This paper focuses on the problem of adaptive output feedback tracking control for a class of nonstrict-feedback nonlinear systems with unknown control coefficients and quantized input. The difficulty from the unknown control direction is solved by using the linear state transformation and the Nussbaum gain function（NGF） approach. Based on the combination of input-driven observer, backstepping technique, neural network（NN） parametrization and variable separation method, a novel adaptive output feedback quantized control scheme involving only one adaptive parameter is developed for such systems. The designed quantized controller ensures that all signals of closed-loop systems are semi-globally uniformly ultimately bounded（SGUUB）, and the tracking error converges to an adjustable neighborhood of the origin. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed control design.

关键词： Nonstrict-feedback systems quantized input signal output feedback adaptive control unknown control direction

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Estimation of spatially distributed processes using mobile sensor networks with missing measurements

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Chinese Physics B 2015年第2期24卷 109-115页

作者：江正仙崔宝同 Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan University School of IoT Engineering Jiangnan University School of Science Jiangnan University

This paper investigates the estimation problem for a spatially distributed process described by a partial differential equation with missing *** randomly missing measurements are introduced in order to better reflect the reality in the sensor *** improve the estimation performance for the spatially distributed process,a network of sensors which are allowed to move within the spatial domain is *** aim to design an estimator which is used to approximate the distributed process and the mobile trajectories for sensors such that,for all possible missing measurements,the estimation error system is globally asymptotically stable in the mean square *** constructing Lyapunov functionals and using inequality analysis,the guidance scheme of every sensor and the convergence of the estimation error system are ***,a numerical example is given to verify the effectiveness of the proposed estimator utilizing the proposed guidance scheme for sensors.

关键词： estimation spatially distributed process mobile sensor network missing measurements

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Blind source separation of underdetermined mixtures based on local mean decomposition and conjugate gradient algorithm

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Journal of Communications 2014年第5期9卷 425-432页

作者： Li, Wei Yang, Huizhong Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi 214122 Jiangsu China

Most of the existing underdetermined blind source separation (BSS) approaches assume that the source signals are strictly or partially sparse. This paper, however, presents a new BSS method in underdetermined mixing situation for non-sparse signals. The proposed method first introduces the local mean decomposition algorithm into the BSS problem to rebuild some extra mixing signals. Such signals are then combined with the initial mixtures such that the underdetermined BSS problem is transformed to a determined one and the difficulty of the deficiency of the mixtures is overcome. For the rebuilt mixtures and the newly formed determined BSS problem, the minimum mutual information principle is employed as the BSS cost function. A conjugate gradient learning algorithm is then derived for training the separating matrix. In each update step of the algorithm, the term of score function is estimated by a kernel function estimation algorithm. The simulation results have demonstrated the efficacy of the proposed underdetermined BSS method. © 2014 Engineering and Technology Publishing.

关键词： Blind source separation

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In-field automatic detection of maize tassels using computer vision

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Information processing in Agriculture 2021年第1期8卷 87-95页

作者： Mingqiang Ji Yu Yang Yang Zheng Qibing Zhu Min Huang Ya Guo Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan University214122 WuxiJiangsuChina

The heading stage of maize is an important period during its growth and development and indicates the beginning of its *** this regard,an automated method for maize tassel detection is highly important to monitor maize ***,the recognition of maize heading stage mainly relies on visual *** method presents some limitations,such as expensive and *** work proposed a novel method for automatic tassel *** the proposed algorithm,a color attenuation prior model was used to model the scene depth of saturation graph to remove image *** Itti visual attention detection algorithm was used to detect the area of *** features and vegetation indices were used to develop a classification model to eliminate false *** were captured using a commercial camera for two years to verify the stability of the proposed *** indices were calculated to quantitatively assess and rate the *** results show that the proposed method outperforms other existing methods,and its recall,precision,and F1 measure values are 86.30%,91.44%,and 88.36%,*** indicate that the proposed method can effectively detect maize tassels in field images and remain stable with time.

关键词： Maize tassel detection Texture feature Vegetation index Saliency based

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An Interpretable Regression Method for Upper Limb Motion Trajectories Detection with EEG Signals

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Tian, Miao Li, Shurui Xu, Ren Cichocki, Andrzej Jin, Jing East China University of Science and Technology School of Mathematics Shanghai200237 China Gtec medical engineering GmbH Schiedlberg4521 Austria Polish Academy of Science Systems Research Institute Warsaw01-447 Poland RIKEN Advanced Intelligence Project Tokyo103-0027 Japan Tokyo University of Agriculture and Technology Tokyo184-8588 Japan East China University of Science and Technology School of Mathematics and Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China

Objective: The motion trajectory prediction (MTP) based brain-computer interface (BCI) leverages electroencephalography (EEG) signals to reconstruct the three-dimensional trajectory of upper limb motion, which is pivotal for the advancement of prosthetic devices that can assist motor-disabled individuals. Most research focused on improving the performance of regression models while neglecting the correlation between the implicit information extracted from EEG features across various frequency bands with limb kinematics. Current work aims to identify key channels that capture information related to various motion execution movements from different frequency bands and reconstruct three-dimensional motion trajectories based on EEG features. Methods: We propose an interpretable motion trajectory regression framework that extracts bandpower features from different frequency bands and concatenates them into multi-band fusion features. The extreme gradient boosting regression model with Bayesian optimization and Shapley additive explanation methods are introduced to provide further explanation. Results: The experimental results demonstrate that the proposed method achieves a mean Pearson correlation coefficient (PCC) value of 0.452, outperforming traditional regression models. Conclusion: Our findings reveal that the contralateral side contributes the most to motion trajectory regression than the ipsilateral side which improves the clarity and interpretability of the motion trajectory regression model. Specifically, the feature from channel C5 in the Mu band is crucial for the movement of the right hand, while the feature from channel C3 in the Beta band plays a vital role. Significance: This work provides a novel perspective on the comprehensive study of movement disorders. © 1964-2012 IEEE.

关键词： Brain computer interface

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