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18th IFAC Workshop on Time Delay systems, TDS 2024

作者： Duro, João A. Ward, Rob Rooke, Jack Pickford, Emily Bonnell, Rich Brown, Adam Purshouse, Robin C. Curtis, David Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom University of Sheffield Advanced Manufacturing Research Centre University of Sheffield RotherhamS60 5TZ United Kingdom

Metallic subtractive process planning is challenging. It requires a blend of expert knowledge, experience, and established manufacturing methods. Decision-making in this domain is multifaceted and complex, and this includes the need to satisfy multiple conflicting demands faced by the manufacturing sector today. However, with the evolving landscape of manufacturing, combined with increased adoptions of digital technologies, the traditional rules of thumb are becoming obsolete. In response, this research investigated these decision-making processes within a subset of the digital framework for subtractive process planning. In particular, it has demonstrated that an advanced open-source optimisation tool (Liger) could be integrated with commercial industrial software and applied to a machining case study. The integrated software intelligently executed and automated simulations using advanced optimisation techniques. The outputs are a range of optimised solutions that support manufacturing engineers' decision-making. One of the major benefits of the open-source software is that it provides an intuitive to use interface suitable for the non-expert in optimisation, offers a varied range of state-of-the-art multi-objective optimisation algorithms, and is capable of incorporating many different third-party types of software, models and simulations. In this paper, the Liger software has been applied to a simple case study to find the best feedrate and depths of cut that simultaneously minimise the cutting force and the time it takes to complete the machining process. The case study demonstrates proof-of-principle results that the optimisation software can be implemented in a wider process planning context with additional digital manufacturing simulation models. © 2024 The Authors.

关键词： Process planning

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Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning

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Microsystems & Nanoengineering 2023年第5期9卷 281-294页

作者： Yijie Liu Xuexuan Li Ben Pei Lin Ge Zhuo Xiong Zhen Zhang State Key Laboratory of Tribology in Advanced Equipment Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China Beijing Key Laboratory of Precision/Ultra-precision Manufacturing Equipments and Control Tsinghua UniversityBeijing 100084China Biomanufacturing Center Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing Beijing 100084China ‘Biomanufacturing and Engineering Living Systems’Innovation International Talents Base(111 Base) Beijing 100084China NT-MDT Spectrum Instruments China office Beijing 100053China

Scanning probe lithography(SPL)is a promising technology to fabricate high-resolution,customized and costeffective features at the ***,the quality of nano-fabrication,particularly the critical dimension,is significantly influenced by various SPL fabrication techniques and their corresponding process ***,the identification and measurement of nano-fabrication features are very time-consuming and *** tackle these challenges,we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning(ML).Different from traditional SPL techniques that rely on manual labeling-based experimental methods,the proposed framework intelligently extracts reliable and global information for statistical analysis to finetune and optimize process *** on the proposed framework,we realized the processing of smaller critical dimensions through the optimization of process parameters,and performed direct-write nano-lithography on a large ***,data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.

关键词： lithography process characterization

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Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments 13

Exchange Strategies for Multi-Colony Ant Algorithms in Dynam...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Mavrovouniotis, Michalis Li, Changhe Yazdani, Danial Handjimitsis, Diofantos Eratosthenes Centre of Excellence Limassol Cyprus Cyprus University of Technology Department of Civil Engineering and Geomatics Limassol Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China University of Technology Sydney Faculty of Engineering & Information Technology Sydney Australia

ISBN: (纸本)9798350308365

In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonies, which is a critical factor influencing algorithm performance, are investigated. Using the dynamic traveling salesman problem as a foundation, we generate test cases to reflect real-world complexities. Our results on a set of problem instances reveal that the choice of communication strategy between colonies significantly impacts the adaptability and efficiency of multi-colony ACO algorithms in tracking moving optimum. © 2024 IEEE.

关键词： Ant colony optimization

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Improved material descriptors for bulk modulus in intermetallic compounds via machine learning

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Rare Metals 2023年第7期42卷 2396-2405页

作者： De-Xin Zhu Kun-Ming Pan Yuan Wu Xiao-Ye Zhou Xiang-Yue Li Yong-Peng Ren Sai-Ru Shi Hua Yu Shi-Zhong Wei Hong-Hui Wu Xu-Sheng Yang National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials Henan Key Laboratory of High-Temperature Structural and Functional MaterialsHenan University of Science and TechnologyLuoyang471003China Longmen Laboratory Luoyang471003China State Key Laboratory for Advanced Metals and Materials University of Science and Technology BeijingBeijing100083China Guangdong Province Key Laboratory of Durability for Marine Civil Engineering School of Civil EngineeringShenzhen UniversityShenzhen518060China Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong999077China

Bulk modulus is an important mechanical property in the optimal design and selection of intermetallic *** this study,bulk modulus datasets of intermetallic compounds were collected,and the features affecting the bulk modulus of intermetallics were screened via feature *** features B_(cal),dB_(avg),and TIE(corresponding to calculated bulk modulus,mean bulk modulus,and third ionization energy,respectively)were found to be the dominant factors influencing bulk modulus and can be extended to other multi-component ***,we predicted the bulk modulus with an accuracy of 95%using surrogate machine learning models with the selected features,and these features were also demonstrated to be effective for high-entropy ***,symbolic regression provided an expression for the relationship between bulk modulus and the screened *** machine learning models provide a new approach for optimizing and predicting the bulk moduli of intermetallic compounds.

关键词： Bulk modulus Intermetallic compounds Machine learning Symbolic regression

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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Analysis and State Feedback control Bifurcation of Fractional-Order Cyclic Gene Regulatory Networks with Time Delay 41

Analysis and State Feedback Control Bifurcation of Fractiona...

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第41届中国控制会议

作者： Feng Liu Shujiang Sun Juan Zhao Feiyue Zhuo Hua O Wang School of Automation China University of Geosciences Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Mechanical Engineering Boston University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a fractional-order cyclic gene regulatory network model with time delay is ***,the total time delay of the system is selected as the bifurcation parameter,and the condition for the existence of Hopf bifurcation is derived by analyzing its characteristic *** is found that the time delay affects the stability of the system,and the order affects the position of the bifurcation *** the time delay is greater than the critical time delay,the system loses ***,a state feedback controller is designed for the unstable *** is proved that the control method has good control effect for the system instability caused by Hopf ***,the correctness of the theoretical derivation is verified by simulation.

关键词： Fractional-order cyclic gene regulatory network Hopf bifurcation feedback control time delay

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Detection of Termite Nests in Dams Using Ground Penetrating Radar Based on Deep Learning 20

Detection of Termite Nests in Dams Using Ground Penetrating ...

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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Peng, Jiao Xue, Wei Wang, Wenkai Song, Junlei Zhou, Feng Huang, Pengchun Zhang, Zhixuan Zhang, Feng Zhang, Xu Chen, Longjia Sun, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China Dam Safety Monitoring and Termite Control Center of Hubei Provincial Department of Water Resources China Key Laboratory of Termite Control Ministry of Water Resources China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS). © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Energy management in smart homes with adversary detection and noise mitigation using a moving prediction window scheme

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Sustainable Energy, Grids and Networks 2025年 43卷

作者： Ghazvini, Hamid Reza Babaei Adelipour, Saeed Haeri, Mohammad Advanced Control Systems Lab. Electrical Engineering Department Sharif University of Technology Tehran11155-4363 Iran

This paper presents an energy management algorithm for scheduling a large number of residential households, utilizing the moving prediction window to make it resilient against false data injection attacks and communication noise. We model multiple communities of smart homes, each managed by a local controller, where self-interested residential households engage in a global non-cooperative game. The cost functions of the households are influenced by a constrained aggregated power consumption term across all households from all communities. The interactions among households are modeled through a multi-community aggregative game. To reach a Nash equilibrium, we propose an iterative algorithm wherein local controllers estimate the coupling aggregate term and corresponding Lagrange multiplier for their respective households and collaborate with other controllers via an unreliable communication network to refine the aggregate estimations. Given the vulnerability of the communication network to external intrusions and the potential for internal controllers to behave maliciously, we explore a moving horizon window technique to detect false data injection attacks and mitigate communication noise. In this regard, first, a moving horizon estimator predicts the community's current behavior based on historical data;second, a residual-based detection mechanism flags an attack when predicted residuals exceed a dynamic threshold;and third, corrupted measurements are discarded, and the average of the predictions is used in the Krasnoselskii-Mann update to reduce the noise impact. Numerical simulations show the effectiveness of the proposed algorithm in increasing the speed of reaching consensus by about 30 percent while managing the energy consumption of households. © 2025 Elsevier Ltd

关键词： Cost functions

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Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition 16

Geometric Parameterized Macromodel of Uniaxial Capacitance A...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Gridchin, Aleksandr V. Koleda, Aleksei N. Barbin, Evgenii S. Nesterenko, Tamara G. Kolchuzhin, Vladimir A. Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

ISBN: (纸本)9798350330885

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer's dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters. © 2023 IEEE.

关键词： Capacitance

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Recent advances on distributed online optimization

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control Theory and Technology 2021年第1期19卷 153-156页

作者： Xiuxian Li Department of Control Science and Engineering College of Electronics and Information Engineeringthe Institute for Advanced Studyand the Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 201210China

Online optimization has received numerous attention in recent two decades,mostly inspired by its potential applica-tions to auctions,smart grids,portfolio management,dic-tionary learning,neural networks,and so on[1-4].Gener-ally,online optimization is a sequence of decision making processes,where a sequence of time-varying loss functions are gradually revealed in a dynamic environment which may be *** each time instant.

关键词： networks optimization neural

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