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IEEE Transactions on Automatic control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

Modelling and Experimental Characteristics of Photovoltaic Modules in Typical Days at an Actual Photovoltaic Power Station 4

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Modelling and Experimental Characteristics of Photovoltaic M...

4th IEEE International Conference on automation, Electronics and Electrical Engineering, AUTEEE 2021

作者： Lodhi, Ehtisham Yang, Pu Wang, Lina Lodhi, Zeeshan Khan, M. Adil Muhammad, Sijjad Tamir, Tariku Sinshaw Beihang University School of Automation Science and Electrical Engineering Beijing100191 China COMSATS University Islamabad Department of Electrical and Computer Engineering Abbottabad Campus 22060 Pakistan Chang'An University Department of Computer and Technology Xi'an710062 China Real Engineering Services Deans Trade Center Peshawar25000 Pakistan Institute of Automation Chinese Academy of Sciences The SKL For Management and Control of Complex Systems Beijing100190 China

ISBN: (纸本)9781665428392

Solar energy among the renewable energy sources (RES) plays an important role in fulfilling any country's energy demands. When the sunlight irradiates the photovoltaic (PV) modules, part of the light energy is directly converted into electric energy. Another part is converted into heat energy, which increases the working temperature and affects the output performance characteristics of the PV module. This paper proposes a dynamic mathematical model of PV modules working on solar irradiance and ambient temperature by calculating the PV module model parameters under different environmental conditions. The dynamic model of the PV module is suggested to simulate PV modules' output characteristics (voltage and current) under any environmental conditions. By employing MATLAB/Simulink, a PV output characteristics program is compiled. The whole day output characteristics of PV strings of a real PV power station are tested and compared with simulation results. The results depict that the established model can effectively reflect PV modules' output characteristics in actual operation and can be used for dynamic simulation of PV power generation. © 2021 IEEE.

关键词： Solar energy

A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-Based Feature Fusion

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A Wavelet Guided Attention Module for Skin Cancer Classifica...

IEEE International Symposium on Biomedical Imaging

作者： Ayush Roy Sujan Sarkar Sohom Ghosal Dmitrii Kaplun Asya Lyanova Ram Sarkar Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering International Institute of Information Technology Naya Raipur Chhattisgarh India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be very helpful. In this paper, we propose a novel model, which uses a novel attention mechanism to pinpoint the differences in features across the spatial dimensions and symmetry of the lesion, thereby focusing on the dissimilarities of various classes based on symmetry, uniformity in texture and color, etc. Additionally, to take into account the variations in the boundaries of the lesions for different classes, we employ a gradient-based fusion of wavelet and soft attention-aided features to extract boundary information of skin lesions. We have tested our model on the multi-class and highly class-imbalanced dataset, called HAM10000, and achieved promising results, with a 91.17% F1-score and 90.75% accuracy. The code is made available at: https://***/AyushRoy2001/WAGF-Fusion.

关键词： Solid modeling Image color analysis Biological system modeling Melanoma Medical services Feature extraction Skin

A WAVELET GUIDED ATTENTION MODULE FOR SKIN CANCER CLASSIFICATION WITH GRADIENT-BASED FEATURE FUSION

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arXiv 2024年

作者： Roy, Ayush Sarkar, Sujan Ghosal, Sohom Kaplun, Dmitrii Lyanova, Asya Sarkar, Ram Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering International Institute of Information Technology Chhattisgarh Naya Raipur India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

关键词： Wavelet transforms

Guarding Force: Safety-Critical Compliant control for Robot-Environment Interaction

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arXiv 2024年

作者： Wang, Xinming Yang, Jun Mao, Jianliang Liang, Jinzhuo Li, Shihua Yan, Yunda The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China The Department of Aeronautical and Automotive Engineering Loughborough University LoughboroughLE11 3TU United Kingdom The College of Automation Engineering Shanghai University of Electric Power Shanghai201306 China The Department of Computer Science University College London LondonWC1E 6BT United Kingdom

In this study, we propose a safety-critical compliant control strategy designed to strictly enforce interaction force constraints during the physical interaction of robots with unknown environments. The interaction force constraint is interpreted as a new force-constrained control barrier function (FC-CBF) by exploiting the generalized contact model and the prior information of the environment, i.e., the prior stiffness and rest position, for robot kinematics. The difference between the real environment and the generalized contact model is approximated by constructing a tracking differentiator, and its estimation error is quantified based on Lyapunov theory. By interpreting strict interaction safety specification as a dynamic constraint, restricting the desired joint angular rates in kinematics, the proposed approach modifies nominal compliant controllers using quadratic programming, ensuring adherence to interaction force constraints in unknown environments. The strict force constraint and the stability of the closed-loop system are rigorously analyzed. Experimental tests using a UR3e industrial robot with different environments verify the effectiveness of the proposed method in achieving the force constraints in unknown environments. Note to Practitioners—Robotic manipulators are increasingly involved in complex interaction missions with humans and various environments. As these interactions become more prevalent, ensuring the safety of both robots and their human and environmental counterparts has become imperative. Although advanced controllers with safety constraints have been explored, ensuring strict interaction force guarantees remains challenging, particularly in unknown environments. To address this challenge, this paper proposes a new safety-critical compliant control scheme using the control barrier function (CBF) technique. By formulating the approach as quadratic programming and dynamically adjusting the nominal compliant controller online, the propose

关键词： controllers

Designing a Secure Smart Remote Patient Monitoring and Warning System using Big Data and Internet of Things Ecosystems

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Designing a Secure Smart Remote Patient Monitoring and Warni...

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： S.P. Ramesh Ali Hadi Abdulwahid Afsana Anjum N. Venkatesh Rajesh Singh M. Kalyan Chakravarthi Department of Computer Science and Engineering AMET University Kanathur Tamil Nadu Department of Control &Automation Southern Technical University Engineering Technical College Basra Iraq Department of Information Technology & Security Jazan University Jazan KSA School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi India

The demand on healthcare is growing as a result of the expanding global population, rising demands for successful treatment, and an underlying improvement in life quality. Consequently, healthcare remains one of the most significant social and economic problems in the world, necessitating the development of new and more sophisticated scientific and technological treatments.E-health services are provided via personalised healthcare systems to meet the medical and support needs of the ageing population. An important development in the Big Data age is the Internet of Things, which facilitates several real-time engineering solutions through improved services. Healthcare systems are now using analytics over IoT data streams as a stream of user data to find new information, forecast rapid recognition, and make decisions on life-threatening situations. In this study, researchers have carried out a thorough review of the hottest recent revolutionary technologies in bespoke healthcare systems, with a focus on hypervisor, cloud infrastructure, big data analytic, the Web of Things, and smartphone apps. In order to make early disease detection and diagnosis possible, it is important to develop a better healthcare system. We have examined these problems and suggested potential solutions while offering secure e-health services. The purpose of this study is to design a smart and secure remote monitoring and warning system using the latest emerging technologies in 21 st century such as big data analytics and Internet of Things (IoT) ecosystems. In this study the researchers discussed a detailed research methodology to meet the outcomes of the reerach. All the relevant findings and analysis are explained in descriptive manner. Moreover, this study highlights the constantly expanding requirements for better medical systems in the present and offers potential directions for future research.

关键词： Industries Patient monitoring Ecosystems Sociology Medical services Big Data Alarm systems

Collaborative learning technologies for project work 1

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Collaborative learning technologies for project work

1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021

作者： Galkin, Alexander Blyumin, Semen Saraev, Pavel Pimenov, Vladimir Lipetsk State Technical University Faculty of Automation and Computer Science Lipetsk Russia Lipetsk State Technical University Department of Applied Mathematics Lipetsk Russia Lipetsk State Technical University Department of Automated Control Systems Lipetsk Russia Novolipetsk Steel Corporation Lipetsk Russia

ISBN: (纸本)9781665443968

The paper presents an approach to learning based on the implementation of projects under the joint supervision and control of the educational organization and industrial partner. When students carry out projects, collaborative learning technologies are implemented. Periodic joint consultations of students by specialists of different profiles of educational organization and industrial partner help to bring the training closer to the real production process, and upon the completion of the project to get a ready-to-use product. Working in this mode allows students using modern technologies to work on the project, in particular, flexible methodologies of agile development. It helps not to formulate a completely ready technical task for the project, but to adjust it in the process of implementation. Tasks are formulated for a limited time interval of 1-2 weeks, after which the results are evaluated and tasks for the next stage are formed. The paper provides examples of works by students of Lipetsk State Technical University, performed in this format. © 2021 IEEE.

关键词： Students

Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

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arXiv 2023年

作者： Li, Shilei Li, Lijing Shi, Dawei Lou, Yunjiang Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Information and Control Engineering China University of Mining and Technology China The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the multi-kernel correntropy-based gradient descent (CGD) and the multi-kernel correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the least square (LS) criterion in algorithm derivation, making them vulnerable to external acceleration and magnetic interference. To address this issue, we first demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function under the maximum likelihood estimation (MLE) framework when the noise follows a specific type of heavy-tailed distribution. Then, we provide some important properties of the MKCL as a cost function. By replacing the LS cost with the MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

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arXiv 2023年

作者： Li, Shilei Chen, Yihan Lou, Yunjiang Shi, Dawei Li, Lijing Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Automation Beijing Institute of Technology China The School of Information and Control Engineering China University of Mining and Technology China

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and state that the kernel bandwidth plays an important role in minimizing the lowest upper error bound. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution but its tail shape is heavy-tailed, and the extent of heavy-tail is controlled by the kernel bandwidth. It becomes a Gaussian distribution when the bandwidth is infinite, which allows one to tackle both Gaussian and non-Gaussian problems without explicitly investigating the noise distributions. To explore the optimal underlying distribution parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the distribution parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and it significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method. Note to Practitioners-The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian assumptions. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both th

关键词： Linear regression

Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

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Selection of Cancer Biomarkers from Microarray Gene Expressi...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Jianqing Lin Huachao Zhu Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy.

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