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arXiv 2022年

作者： Hajieghrary, Hadi Deisenroth, Marc Peter Bekiroglu, Yasemin Chalmers University of Technology Department of Electrical Engineering Automatic Control Research Group Sweden University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom

In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbances on the joint controllers of the manipulator. In designing a conventional PID controller, one should make a trade-off between the performance and agility of the closed-loop system and its stability margins. The proposed nonlinear adaptive PID controller provides a mechanism to relax the need for such a compromise by adapting the gains according to the magnitude of the error without expert tuning. Therefore, we can achieve agile performance for the system while seeing damped overshoot in the output and track the reference as close as possible, even in the presence of external disturbances and uncertainties in the modeling of the system. We have employed a Bayesian optimization approach to choose the parameters of a nonlinear adaptive PID controller to achieve the best performance in tracking the reference input and rejecting disturbances. The results demonstrate that a well-designed nonlinear adaptive PID controller can effectively regulate a mobile manipulator's joint variables while carrying an unspecified heavy load and an abrupt base movement occurs. © 2022, CC BY-NC-ND.

关键词： Closed loop systems

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Policy and Value Deep RL for Temporal Language-Agnostic Street Image Captioning 3

Policy and Value Deep RL for Temporal Language-Agnostic Stre...

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3rd IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2021

作者： Chan, Christopher Chun Ki Chen, Chih-Cheng Delaney, Steven Yang, Cheng-Fu Department of Information Management Chaoyang University of Technology Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung Taiwan Department of Computer Science Ryerson University Canada Department of Chemical and Materials Engineering National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9781728193045

Dashcams and street cameras capture a huge amount of street and traffic video data. Temporal image captioning for such video data has been approached with an encoder-decoder framework and achieved substantial success in captioning accuracy. Most recently, policy and value deep reinforcement learning (PVRL) emerged as an outperformer over other decision-making frameworks for image captioning. In this paper, we design a framework that utilizes PVRL on an in-house dataset containing temporal images of East Asia streets as a step towards designing a language-agnostic street image captioning framework that is capable of captioning temporal images of any street regardless of location. For language-invariance, the framework includes cross-modal retrieval at the character level so that similar words in different languages but in the same word-embedding space are grouped together. Our results show that PVRL can be applied successfully to temporal video captured the streets and achieve natural semantic captions;preliminary studies on our dataset suggest that the framework is capable of use in multi-language scenarios. © 2021 ECBIOS 2021. All rights reserved.

关键词： Deep learning

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Domain Migration Representation Learning for Blind MR Image Super-Resolution

SSRN

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SSRN 2023年

作者： Liu, Xiaodong Guo, Haipeng Liu, Huanyu Li, Junbao Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin150080 China

Recently, magnetic resonance imaging has been widely used in clinical treatment to assist doctors in diagnosis because of its high resolution of soft tissue and no radiation. However, limited by imaging equipment and imaging time, the resolution of MR imaging is low. The previous super-resolution algorithms used for MR images use paired datasets to generate down-sampled images by artificially assuming the image degradation process, and forming a sample pair to monitor and train the super-resolution network. However, the super-resolution network obtained by this way will cause serious degradation of the network performance in practical application. In this paper, we propose a blind super-resolution framework DMN based on domain migration, which decouples the blind super-resolution network with the idea of domain migration, and avoids the false detail generation problem of the blind super-resolution network in the MR images super-resolution task through independent training. At the same time, we propose a multi-level parallel transformer feature enhancement module MPTB, which takes noise as the original input and embeds noise features step by step to enhance the fitting ability of the network while reducing the complexity of the network structure design. Besides, we also use the k-space loss function to form a constraint function, so that the domain migration network can fully consider the k-space characteristics of MR images. The experimental results on the MR images of the knee joint and brain in the fastMRI dataset show that the super-resolution performance of our proposed DMN network is better than the previous super-resolution networks. © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Critical Analysis and Evaluation of Current Digital Healthcare Solutions 23

Critical Analysis and Evaluation of Current Digital Healthca...

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23rd International Conference on control Systems and computer Science Technologies, CSCS 2021

作者： Ianculescu, Marilena Alexandru, Adriana Pop, Florin Systems and Applications for Society Department National Institute for RandD in Informatics Bucharest Romania Doctoral School of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania Department of Computer Science and Engineering University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665439398

Digitalization of health, healthcare and healthcare delivery unequivocal have positive consequences contributing to health system goals, but they also might have some negative ones which have to be avoided or mitigated. The scope of this paper is to introduce the necessity of the assessment of Health Technology Assessment (HTA) and to identify the most appropriate and important criteria needed for the evaluation and comparison of functional digital healthcare solutions. It aims to outline consistent criteria against which digital health technologies and services can be evaluated during the prototyping phase, but also during long-term operation. © 2021 IEEE.

关键词： Health care

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on control and Automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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A Quantitative Research to Determine User's Requirements for the Mind-controlled Prosthesis Arm Intelligent System 13

A Quantitative Research to Determine User's Requirements for...

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13th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2021

作者： Ali, Haider Abdullah Goga, Nicolae Vasilateanu, Andrei Ali, Liwa Abdullah Abd-Almuhsen, Ghufran Saady Naji, Hasan K. University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania Al-Emamain Al Kadhimain Medical City Department of Neurosurgery Baghdad Iraq Madenat Alelem University College Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9781665425346

Brain-computer Interface (BCI) identifies user thinking through different electrophysiological signals. These signals are detected through electrodes placed on the scalp, and the neuronal activity of the cortical is collected by electrodes implanted inside the brain. Regardless of reasons, limbs losses affect a large percentage of the world's people yearly. The necessity for solutions for those having these conditions can be related to health issues and research. Myoelectric and other types of prostheses are currently in use, but it has some drawbacks like the high-price, heaviness, and the dependency on the intact nerves. These drawbacks can be overcome using electroencephalography (EEG) mind-controlled prosthesis arm. The mind-controlled prosthesis arm uses EEG non-invasive technique which provides powerful help for disabled persons in their daily activities, particularly to help arm amputees move their prosthesis arm easily. In this paper, we present a quantitative questionnaire to identify and validate the user's functional and non-functional requirements and to know how users want to interact with the system. We aim to build inexpensive, lightweight, and smart functional prosthesis arm according to functional and non-functional requirements to meet users' goals and requirements. The participants' interests and recommendations are considered while designing and building a smart functional prosthesis arm system. This is one of the very few surveys that handle the user's requirements of a mind-controlled prosthesis arm according to the amputees and specialists' opinions. © 2021 IEEE.

关键词： Electroencephalography

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Segmenting Alzheimer's Disease MRI Slices for Hippocampal Volume Calculation

Segmenting Alzheimer's Disease MRI Slices for Hippocampal Vo...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Hsiao-Chuan Tan Gilang Titah Ramadhan Wei-Cheng Shen Wen-Thong Chang Chih-Yang Lin Ben-Yi Liau Yih-Kuen Jan Yori Pusparani Chi-Wen Lung Department of Artificial Intelligence PhD Program Asia University Taichung Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Creative Product Design Asia University Taichung Taiwan Department of Mechanical Engineering National Central University Taoyuan Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana America Department of Digital Media Design Asia University Taichung Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Alzheimer's is a neurodegenerative disease that quietly steals human memory. This study analyzed hippocampal volume in Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal Cognition (NC) using MRI image slices from the ADNI database and YOLOv8 instance segmentation. We used 300 images to segment the left and right hippocampal. This study focused on volume calculation from five MRI image slices. The results showed 0.98 (AD), 0.92 (MCI), and 0.90 (NC) and revealed significant volumetric differences showed in the NC class (P = 0.045). This suggests early neuroanatomical changes can occur without cognitive symptoms, highlighting the potential of deep learning in the early detection of neurodegenerative diseases.

关键词： Instance segmentation Deep learning Accuracy Databases Magnetic resonance imaging Cognition Alzheimer's disease

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Distributed Finite-Time k-means Clustering with Quantized Communucation and Transmission Stopping

Distributed Finite-Time k-means Clustering with Quantized Co...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Gabriele Oliva Christoforos N. Hadjicostis Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Engineering Unit of Automatic Control Università Campus Bio-Medico di Roma Rome Italy Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we present a distributed version of the k-means algorithm for multi-agent systems with directed communication links. The goal of k-means is to partition the network’s agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. Our distributed algorithm allows each node to transmit quantized values in an event-driven fashion, and exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck, whereas distributed stopping preserves available resources. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents in mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.

关键词： Clustering algorithms Bandwidth Partitioning algorithms Distributed algorithms Multi-agent systems

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A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation

arXiv

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arXiv 2023年

作者： Cao, Yuxue Zhao, Wenbo Wang, Shengjie Zheng, Xiang Ma, Wenke Wang, Zhaolei Zhang, Tao Beijing Institute of Control Engineering China Department of Automation Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science City University of Hong Kong Hong Kong Qian Xuesen Laboratory of Space Technology China Beijing Aerospace Automatic Control Institute China

Symmetric bi-manual manipulation is an essential skill in on-orbit operationsdue to its potent load capacity. Previous works have applied compliant controlto maintain the stability of manipulations. However, traditional methods haveviewed motion planning and compliant control as two separate modules, which canlead to conflicts with the simultaneous change of the desired trajectory andimpedance parameters in the presence of external forces and ***, the joint usage of these two modules requires experts to manuallyadjust parameters. To achieve high efficiency while enhancing adaptability, wepropose a novel Learning-based Adaptive Compliance algorithm (LAC) thatimproves the efficiency and robustness of symmetric bi-manual ***, the algorithm framework integrates desired trajectory generationand impedance-parameter adjustment under a unified framework to mitigatecontradictions and improve efficiency. Second, we introduce a centralizedActor-Critic framework with LSTM networks preprocessing the force states,enhancing the synchronization of bi-manual manipulation. When evaluated indual-arm peg-in-hole assembly experiments, our method outperforms baselinealgorithms in terms of optimality and robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Dynamic Optimization of Electric Vehicle Charging for Sustainable Power Grids using Sine Hunter-Prey Optimization with GRUs

Dynamic Optimization of Electric Vehicle Charging for Sustai...

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Mohd Khizar Syed Prakash Chandra Behera P Joel Josephson Shamim Ahmad Khan Timur Kholmukhamedov Ponni Valavan M Department of Physics Kohinoor college Maharashtra India Department of Science St. Claret College Bangalore India Department of ECE Malla Reddy Engineering College Medchal Malkajgiri Telangana India Glocal School of Science & Technology Glocal University Uttar Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University Tashkent Uzbekistan Department of AI & DS K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

The integration of electric vehicles (EVs) into power grids presents a significant challenge for sustainability due to their dynamic charging demands and the need to accommodate renewable energy sources. Existing optimization methods often fail to adequately address the complexity of this problem. In this study, propose a novel approach that combines Sine Hunter-Prey Optimization (SHPO) with Gated Recurrent Units (GRUs) to dynamically optimize EV charging for sustainable power grid operation. Traditional optimization methods struggle to capture the nonlinear and time-varying nature of EV charging demands and renewable energy availability. To overcome this limitation, SHPO's ability to efficiently explore complex solution spaces and GRUs' capability to model temporal dependencies in sequential data. This proposed method optimizes EV charging schedules by considering grid stability, renewable energy utilization, and cost minimization. Validating the approach using historical data on EV charging patterns, renewable energy generation, and grid conditions. Performance metrics include grid stability indices, renewable energy integration rates, and cost savings compared to baseline strategies. Experimental results demonstrate that the method outperforms traditional optimization techniques, leading to more sustainable and resilient power grid operation with 92% stability. Overall, this study contributes to advancing the modern methods in dynamic optimization of EV charging, paving the way for more sustainable transportation and energy systems.

关键词： Renewable energy sources Costs Stability criteria Power system dynamics Optimization methods Power system stability Electric vehicle charging

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