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Norm Optimal Iterative Learning Control with Auxiliary Optimization: A Switching Approach

IFAC Proceedings Volumes 2013年第11期46卷 140-145页

作者： David H. Owens Chris T. Freeman Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield S1 3JD UK Electronics and Computer Science University of Southampton Highfield Southampton SO17 1BJ UK (Department of) Advanced Robotics Instituto Italiano di Tecnologia via Morego 30 16163 Genova

The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously minimizing a quadratic cost function. Motivated by practical problems in automation and control, this enables point-to-point motion tasks to be performed whilst reducing effects such as payload spillage, vibration tendencies and actuator wear. Solutions combine feedforward and feedback actions, and are experimentally tested using a robotic arm.

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Norm Optimal Iterative Learning Control for Planar Tracking Tasks

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IFAC Proceedings Volumes 2013年第11期46卷 683-688页

The Iterative Learning Control (ILC) problem in which tracking is only required at a subset of isolated time points along the trial duration has recently gained significant attention since it addresses the practical needs of many *** paper extends the framework by embedding simultaneous iterative convergence of subsets of outputs to reference trajectories on subintervals. This enables it to tackle tasks which mix ‘point to point’ movements with linear tracking requirements, which substantially broadens the application domain (e.g. to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments). A solution to the problem is presented in the framework of Norm Optimal ILC (NOILC), providing well-defined convergence properties, design guidelines and supporting experimental results.

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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VHS to HDTV video translation using multi-task adversarial learning

arXiv

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arXiv 2021年

作者： Luo, Hongming Liao, Guangsen Hou, Xianxu Liu, Bozhi Zhou, Fei Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham Nottingham United Kingdom

There are large amount of valuable video archives in Video Home System (VHS) format. However, due to the analog nature, their quality is often poor. Compared to High-definition television (HDTV), VHS video not only has a dull color appearance but also has a lower resolution and often appears blurry. In this paper, we focus on the problem of translating VHS video to HDTV video and have developed a solution based on a novel unsupervised multi-task adversarial learning model. Inspired by the success of generative adversarial network (GAN) and CycleGAN, we employ cycle consistency loss, adversarial loss and perceptual loss together to learn a translation model. An important innovation of our work is the incorporation of super-resolution model and color transfer model that can solve unsupervised multi-task problem. To our knowledge, this is the first work that dedicated to the study of the relation between VHS and HDTV and the first computational solution to translate VHS to HDTV. We present experimental results to demonstrate the effectiveness of our solution qualitatively and quantitatively. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Powered Upper-Limb Control using Passivity-Based Nonlinear Disturbance Observer for Unknown Payload Carrying Applications

Powered Upper-Limb Control using Passivity-Based Nonlinear D...

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IEEE International Conference on robotics and Automation

作者： Min Jun Kim Woongyong Lee Jae Yeon Choi Yong Sik Park Sung Ho Park Goobong Chung Kyung-Lyong Han I1 Seop Choi I1 Hong Suh Youngjin Choi Wan Kyun Chung the Robotics Laboratory School of Mechanical Engineering Pohang University of Science and Technology (POSTECH) Pohang 790-784 Gyung-buk Korea the Korea Institute of Robot and Convergence (KIRO) Pohang 790-784 Gyung-buk Korea the POSCO Technical Research Laboratories Pohang 790-300 Gyung-buk Korea the Department of Electronics and Computer Engineering Hanyang University Seoul Korea the Department of Electronic Systems Engineering Hanyang University Ansan Korea

ISBN: (纸本)9781467380270

This paper proposes a passivity-based nonlinear disturbance observer (DOB) design for a powered upper-limb robot control. The proposed DOB allows for the nonlinearities of the robot dynamics, whereas the typical DOB designs cannot. Moreover, by virtue of the passivity property, human operator and environmental interactions can be embedded in the control loop. As a DOB, the proposed approach has a disturbance observation property that makes the actual robot behave like a nominal model selected by the user. Performance analysis proposes a gain tuning rule. In experimental validation, actual powered upper-limb robot is used to perform unknown payload carrying applications.

关键词： Observer Unknown DOB

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Drone-Based Predictive Structure Analysis: A Review

Drone-Based Predictive Structure Analysis: A Review

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Trushant Patil Durgesh Nandan Kanhaiya Sharma Deepak Parashar Department of Robotics and Automation SIT Symbiosis International (Deemed University) Pune India Department of Electronics and Telecommunication Engg. SIT Symbiosis International (Deemed University) Pune India Department of Computer Science & Engineering SIT Symbiosis International (Deemed University) Pune India Faculty of Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research.

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Alzheimer’s Disease Prediction using SVM Kernal’s with PCA Architecture

Alzheimer’s Disease Prediction using SVM Kernal’s with PCA...

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Information Technology (OCIT), OITS International Conference on

作者： Ruchi Patel Ashok Kumar Verma Ravindra V. Kshirsagar Neelesh Kumar Sahu Department of Computer Science & Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur MP India Department of Electronics & Communication Gyan Ganga Institute of Technology and Sciences Jabalpur MP India Department of Artificial Intelligence and Robotics Gyan Ganga Institute of Technology and Sciences Jabalpur MP India

Memory and other mental processes are both severely disrupted by Alzheimer's disease (AD), a neurodegenerative condition. It interferes with almost every cognitive process that the brain is capable of. This ultimately results in a smaller and less volumetric brain as time passes. There is currently no treatment that can reverse the effects of Alzheimer's disease. The only way for early detection and therapy to be possible is for the symptoms to be recognized promptly. Throughout the years, there has been a significant amount of research conducted on the application of machine learning to the diagnosis and classification of Alzheimer's disease. The performance of linear, polynomial, and RBF (Radial Basis Function) classification kernels in the context of AD classification using the Support Vector Machine (SVM) with the Principal Component Architecture (PCA) is investigated in this study. As a result, a framework is provided for the categorization of AD, which is comprised of the following steps: data input, model building, hyperparameter tuning, prediction on test data, performance evaluation, and selection of the most effective SVM kernel model and PCA for dimension reduction. ADNI and the Alzheimer's MRI Preprocessed dataset that is available on Kaggle are used to conduct an evaluation of the proposed framework. The performance of the model is evaluated using accuracy measure for both the datasets. Across both datasets, the SVM linear kernel with PCA achieved the maximum accuracy, which was 99.99 percent.

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Record Framework Utilization for the Collection of Wireless Device Static Users

Record Framework Utilization for the Collection of Wireless ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Maganti Syamala Annaji Kuthe V. Selvakumar Alankrita Joshi K. Baranidharan Joshuva Arockia Dhanraj Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering K. D. K. College of Engineering Nagpur Maharashtra India Department of Master of Business Administration Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Electronics & Communication Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Master of Business Administration Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

The cell of today is a typical instrument. It has become a significant "social item," as opposed to only a minor "specialized object," in the regular daily existences of its users. In this article, we look at a few ongoing improvements in the examination of mobile phone data. With the developing openness of enormous, anonymised datasets, this field of concentrate previously seemed decade prior and has since formed into an independent subject. The estimating investigation of record framework utilization for a gathering of mobile gadget users in different spots is introduced in this work. The purpose of this research is to refute the notion that mobile system behaviour can be modelled as being uniform across different environments. The research demonstrates that when participants travel between places, they obtain statistically substantially diverse sets of data. These findings call into question how mobile computing systems are normally evaluated, which often models a user's behaviour as being comparable to her behaviour at a single place over a longer length of time.

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Geometric collision detection and potential field based time delay planning for dual arm systems

Geometric collision detection and potential field based time...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sukhan Lee H. Moradi G. Kardaras Sung-Kwon Kim Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Computer Science-EE Systems Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA Production Engineering Center Samsung Electronics Company Limited Suwan South Korea

In this paper a method for collision detection and collision avoidance for dual arm robots is proposed. The collision detection, a fast geometric approach based on link intersection, detects the collision and constructs the collision region for given trajectories. The time delay planning determines the minimum necessary time delay to avoid collisions using a potential field representation for collision regions. The planning is based on a flexible, massively parallel approach. It effectively avoids any local minima problems due to the potential field representation and can be used regardless of the complexity of the collision region (e.g., concave or complicated regions).

关键词： Delay effects Robotic assembly Manipulators Orbital robotics Intelligent robots Parallel robots Parallel processing Assembly systems robotics and automation Path planning

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UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

arXiv

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arXiv 2021年

作者： Abdar, Moloud Salari, Soorena Qahremani, Sina Lam, Hak-Keung Karray, Fakhri Hussain, Sadiq Khosravi, Abbas Acharya, U. Rajendra Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Electrical Engineering Sharif University of Technology Tehran Iran The Centre for Robotics Research Department of Engineering King’s College London London United Kingdom The Centre for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada The Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The System Administrator Dibrugarh University Dibrugarh India The Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore The Department of Computer Science University of Quebec in Montreal MontrealQC Canada

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority Such automatic systems are usually based on traditional machine learning or deep learning methods Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images We argue that the uncertainty of the model’s predictions should be taken into account in the learning process, even though most of existing studies have overlooked it We quantify the prediction uncertainty in our feature fusion model using effective Ensemble MC Dropout (EMCD) technique A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively Moreover, our UncertaintyFuseNet model was generally robust to noise and performed well with previously unseen data The source code of our implementation is freely available at: https://***/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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