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SSRN 2023年

作者： Yu, Yuanyuan Huang, Jiahui Jin, Ling Yu, Miao Yu, Xiaolong Zhu, Xifen Sun, Jianteng Zhu, Lizhong Guangdong Provincial Key Laboratory of Petrochemical Pollution Processes and Control School of Environmental Science and Engineering Guangdong University of Petrochemical Technology Guangdong Maoming525000 China Department of Civil and Environmental Engineering Department of Health Technology and Informatics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong State Key Laboratory of Marine Pollution City University of Hong Kong Kowloon Hong Kong The Jackson Laboratory For Genomic Medicine 10 Discovery Dr FarmingtonCT06032 United States Department of Environmental Science Zhejiang University Zhejiang Hangzhou310058 China

Tricresyl phosphate (TCP) is extensively used organophosphorus flame retardants and plasticizers that posed risks to organisms and human beings. In this study, the translocation and biotransformation behavior of isomers tri-p-cresyl phosphate (TpCP), tri-m-cresyl phosphate (TmCP), and tri-o-cresyl phosphate (ToCP) in rice and rhizosphere microbiome was explored by hydroponic exposure. TpCP and TmCP were found more liable to be translocated acropetally, compared with ToCP, although they have same molecular weight and similar Kow. Rhizosphere microbiome named microbial consortium GY could reduce the uptake of TpCP, TmCP, and ToCP in rice tissues, and promote rice growth. New metabolites were successfully identified in rice and microbiome, including hydrolysis, hydroxylated, methylated, demethylated, methoxylated, and glucuronide- products. The methylation, demethylation, methoxylation, and glycosylation pathways of TCP isomers were observed for the first time in organisms. What is more important is that the demethylation of TCPs could be an important and overlooked source of triphenyl phosphate (TPHP), which broke the traditional understanding of the only manmade source of toxic TPHP in the environment. Active members of the microbial consortium GY during degradation were revealed and metagenomic analysis indicated that most of active populations contained TCP-degrading genes. It is noteworthy that the strains and function genes in microbial consortium GY that responsible for TCP isomers’ transformation were different. These results can improve our understanding of the translocation and transformation of organic pollutant isomers in plants and rhizosphere microbiome. © 2023, The Authors. All rights reserved.

关键词： Isomers

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Towards living manufacturing systems

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Procedia CIRP 2020年 93卷 323-328页

作者： László Monostori József Váncza Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

The impact of the forth industrial revolution on the social and natural environment is considered significant and far-reaching, even though the interactions of the human, natural and manufactured assets are less understood, extremely complex and unpredictable. The paper discusses how present days’ – so-called cyber-physical – manufacturing systems operate in the fabrics of society and the natural environment. It is underlined that they show and – to an increasing extent – will show features and capabilities reminiscent of living beings and organizations. This view helps to understand how manufacturing interacts with the ecosystem and suggests resolutions for the dilemma of competitive and sustainable manufacturing.

关键词： Manufacturing sustainability life

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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Integrating backstepping control of outdoor quadrotor UAVs

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2019年第2期63卷 122-132页

作者： Bodó, Zsófia Lantos, Béla Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar Tudósok krt. 2 BudapestH-1117 Hungary

In this paper an improved approach is presented for integrating backstepping control of outdoor quadrotor UAVs. The controller uses the approximated nonlinear dynamic model, while for simulation or test purposes the quadrotor can be modeled either with the precise or the simplified model. A hierarchical integrating backstepping control algorithm was constructed that has the capability of handling every effect in the dynamic model and in the meantime successfully ignores the realistic measurement noises. The hierarchical control structure consists of position, attitude and rotor control, extended with path design with continuous acceleration and/or continuous jerk. The state estimation is based on sensor fusion. control parameters can be easily tuned. Adaptive laws are elaborated for mass and vertical disturbance force estimation. The tracking algorithm is able to follow the prescribed path with small error. The sensory system and the state estimation are prepared for outdoor applications. The embedded control system contains a HIL extension to test the control algorithms before the first flight under real time conditions. © 2019 Budapest University of Technology and Economics. All rights reserved.

关键词： State estimation

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Multi-Objective Squirrel Search Algorithm for EEG Feature Selection

SSRN

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SSRN 2022年

作者： Wang, Chao Li, Songjie Shi, Miao Zhao, Jie Acharya, U. Rajendra Xie, Neng-Gang Cheong, Kang Hao College of Civil Engineering and Architecture Anhui Polytechnic University Anhui Wuhu241000 China Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems Anhui Ma’anshan 243002 China Department of Management science and Engineering Anhui University of Technology Anhui Maanshan243002 China 8 Somapah Road Singapore487372 Singapore Department of Mechanical Engineering Anhui University of Technology Maanshan243002 China Department of Electronics and Computer Engineering Ngee Ann Polytechnic S599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Feature selection plays a critical role in the application of Brain Computer Interface (BCI) systems. Many methods have been used to solve the feature selection problem, but they model it as a single-objective problem, considering only classification accuracy or number of features. To close this critical gap, we improve the squirrel search algorithm by combining it with the grid method, and propose a Multi-Objective Squirrel Search Algorithm (MOSSA) to solve the feature selection problem in BCI. We conduct experiments on three publicly available motion imagery datasets, and the experimental results reveal the best classification results of the method on dataset 1. The average classification accuracy of dataset 2 is 96.71%, with the number of selected features reduced to 18 on average. The highest classification accuracy of dataset 3 is 83.57% on the training set and 82.86% on the test set. In addition, we compare MOSSA with other algorithms and the results show the superiority of our proposed method in solving the feature selection problem. Finally, we combine MOSSA with an online application of BCI, where subjects visualise controlling the robot to perform the corresponding actions by the left and right hand movements. The average recognition rate of the three subjects is approximately 70%. In summary, the MOSSA is an effective method for solving the feature selection problem and is useful for the development of online © 2022, The Authors. All rights reserved.

关键词： Brain computer interface

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TimeGPT-based multi-step-ahead key quality indicator forecasting for industrial processes

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control engineering Practice 2025年 164卷

作者： Zhang, Xinmin Chen, Yuwei He, Bocun Song, Zhihuan Kano, Manabu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University 38 Zheda Rd. Zhejiang Hangzhou310027 China Huzhou Institute of Industrial Control Technology Zhejiang Huzhou313098 China Department of Informatics Kyoto University Kyoto606-8501 Japan

Multi-step prediction is one of the most challenging problems in the field of industrial soft sensing. Recently, large language models have been widely used in various fields. Inspired by TimeGPT, a popular large-scale model for time series forecasting, this paper proposes a novel multi-step key quality indicator forecasting method for industrial processes, namely the TimeGPT-based Multi-step-ahead Forecasting (TiMF) model. The proposed TiMF model is designed based on pre-trained TimeGPT, and historical process variable information is integrated into the prediction model as an auxiliary guide to improve the utilization of industrial data information. To evaluate the effectiveness of the proposed method, it was applied to the debutanizer industrial process and the sintering industrial process. The application results show that the proposed TiMF can achieve better prediction accuracy than other existing methods. This work provides a new attempt for the industrial soft sensing application of the large-scale time series prediction model. © 2025

关键词： Soft sensing Multi-step-ahead forecasting TimeGPT Fine-tune Time-series

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Design and Implementation of Adaptive NMPC in industrial applications: case study

Design and Implementation of Adaptive NMPC in industrial app...

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International Carpathian control Conference (ICCC)

作者： Stepan Ozana Filip Krupa Jakub Nemcik Gergely Takacs Jakub Mozaryn Petr Beremlijski Abhaya Singh Aleksandra Kawala-Sterniuk Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava Czech Republic Institute of Automation Measurement and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovak Republic Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland Department of Applied Mathematics VSB-Technical University of Ostrava Ostrava Czech Republic Department of engineering University of Sannio Sannio Italy Department of Electrical Power and Renewable Energy Opole University of Technology Opole Poland

This paper deals with the design and implementation of adaptive nonlinear predictive control (NMPC). The resulting control system is deployed on a softPLC embedded target in real-time via the REXYGEN control system. The paper describes functional prototype using softPLC hosting the NMPC algorithm applied for a nonlinear system consisting of three coupled tanks. The NMPC algorithm is solved via the receding horizon control method (RHC), formulated as a nonlinear programming problem (NLP) and numerically computed using the SOLNP solver. The adaptivity is provided by online identification via moving horizon estimation (MHE), keeping the model updated to avoid a model-plant mismatch. The methodology described in this paper can be easily transferred to a wider group of industrial nonlinear processes. Such created control unit can run in a standalone mode or can be connected to some exiting control system providing a necessary computational power and thus upgrading the quality of control significantly.

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Optimization of Methodology for Identifying Partial Discharges in Insulating Liquids through Optical Radiation Analysis Using Machine Learning

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Koziol, Michal Boczar, Tomasz Jancarczyk, Daniel Bernas, Marcin Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole45-758 Poland University of Bielsko-Biala Department of Computer Science and Automatics Bielsko-Biala43-309 Poland

The emission of optical radiation is associated with electrical discharges in high-voltage power systems. Detecting, measuring, and analyzing this radiation can enable the identification of the type of electrical discharge and the determination of its source. The article presents the results of measurements and analysis of optical spectra in the context of their application for recognizing different forms of electrical discharges in selected insulating liquids. Optical radiation in the ultraviolet, visible light, and near-infrared range, emitted by electrical discharges in insulating liquids commonly used in electrical engineering, such as mineral oil, natural ester, and synthetic ester, was analyzed. As part of the research, an innovative methodology for measuring and analyzing optical radiation emitted by electrical discharges was proposed, utilizing machine learning tools for precise analysis and interpretation of measurement data. The research results enrich current knowledge with new parameters and measurement indicators, representing another stage in the development of diagnostic methods that can be used to build expert systems and new diagnostic techniques for high-voltage insulation systems. © 1963-2012 IEEE.

关键词： Electric insulation

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Covariance estimation from compressive data partitions using a projected gradient-based algorithm

arXiv

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arXiv 2021年

作者： Monsalve, Jonathan Ramirez, Juan Esnaola, Iñaki Arguello, Henry Department of Electrical Engineering Universidad Industrial de Santander Bucaramanga680002 Colombia Department of Computer Science Universidad Rey Juan Carlos Madrid28933 Spain Department of Automatic Control and Systems Engineering The University of Sheffield Western Bank Sheffield United Kingdom Department Systems Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia

Covariance matrix estimation techniques require high acquisition costs that challenge the sampling systems’ storing and transmission capabilities. For this reason, various acquisition approaches have been developed to simultaneously sense and compress the relevant information of the signal using random projections. However, estimating the covariance matrix from the random projections is an ill-posed problem that requires further information about the data, such as sparsity, low rank, or stationary behavior. Furthermore, this approach fails using high compression ratios. Therefore, this paper proposes an algorithm based on the projected gradient method to recover a low-rank or Toeplitz approximation of the covariance matrix. The proposed algorithm divides the data into subsets projected onto different subspaces, assuming that each subset contains an approximation of the signal statistics, improving the inverse problem’s condition. The error induced by this assumption is analytically derived along with the convergence guarantees of the proposed method. Extensive simulations show that the proposed algorithm can effectively recover the covariance matrix of hyperspectral images with high compression ratios (8 − 15% approx) in noisy scenarios. Additionally, simulations and theoretical results show that filtering the gradient reduces the estimator’s error recovering up to twice the number of eigenvectors. © 2021, CC BY-NC-ND.

关键词： Covariance matrix

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Cardiac arrhythmia prediction by adaptive analysis via bluetooth

Mendel

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Mendel 2020年第2期26卷 29-38页

作者： Rodríguez-Jorge, Ricardo Bila, Jiri Department of Informatics Jan Evangelista Purkyně University Czech Republic Department of Instrumentation and Control Engineering Czech Technical University in Prague Czech Republic

In this work, the development of a data acquisition system for adaptive monitoring based on a dynamic quadratic neural unit is presented. Acquisition of the continuous signal is achieved with the BITalino biomedical data acquisition card. The system is trained sample-by-sample with a real time recurrent learning method. Then, possible cardiac arrhythmia is predicted by implementing the adaptive monitoring in real time to recognize patterns that predict cardiac arrhythmia up to 1 second in advance. For the evaluation of the interface, tests are performed using the obtained signal in real time. © 2020, Brno University of Technology. All rights reserved.

关键词： Data acquisition

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