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27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Marciniak, Tomasz Stankiewicz, Agnieszka Poznan University of Technology Institue of Automatic Control and Robotics Department of Automatic Control Robotics and Electrical Engineering Poznan Poland

ISBN: (纸本)9788362065486

The aim of this article is to analyze the impact of adaptive histogram equalization of the human eye OCT B-scans on the effectiveness of the classification of pathological changes. Tests were performed on two datasets. The first dataset contains 4 class B-scans (CNV, DME, DRUSEN, and NORMAL) taken with the use of Spectralis Heidelberg device, while the second dataset contains images from two devices (Spectralis Heidelberg OCT and Avanti Optovue OCT) and additionally contains the VMT (i.e., vitreomacular traction) class. The images were subjected to standard histogram equalization and contrast limited adaptive histogram equalization (CLAHE) with various parameters. The classification process was carried out using VGG16 artificial neural network. It can be observed that histogram equalization has no significant impact on improving classification performance. © 2024 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Optical coherence tomography

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A Study on Optimizing Indoor Air Quality Through the Integration of Multi-Point Monitoring and the Design of a Wet Scrubber 2

A Study on Optimizing Indoor Air Quality Through the Integra...

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2nd International Conference on Intelligent control and Computing, IC-C 2024

作者： Hung, San-Shan Yang, Chun-Yun Shih, Tian-Ming Chen, Po-Ju Tsai, Yi-Jui Feng-Chia University Department of Automatic Control Engineering Taichung Taiwan

ISBN: (纸本)9798350351873

Indoor pollutants such as particulate matter (PM), contain fine particles that stay in the air for a long time and can be harmful to human health. This paper studies an indoor air quality system by integrating a multi-point monitoring system and wet scrubber to reduce PM. The study utilizes the atomization of small water droplets to capture fine airborne particles. The captured particles undergo a cycle of condensation and heating before being released as purified and comfortable air. Indoor air quality serves as the standard for safety warnings and whether to start the scrubber in this research. In addition, this system is expected for future applications in residential environments, aiming to reduce harm from indoor particulate matter and enhance overall indoor air quality. To validate the effectiveness of the wet scrubber, this study conducted experiments indoors to cleanse the air. The results revealed that when incense was burned indoors, activating the wet scrubber led to a faster decrease in PM concentration compared to natural settling. Furthermore, increasing the liquid-to-gas ratio further enhanced the cleansing efficiency of the system. © 2024 IEEE.

关键词： Indoor air pollution

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Path Planning for Multi-agent Systems Using Deep Q-Networks Reinforcement Learning 25th

Path Planning for Multi-agent Systems Using Deep Q-Networks...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Alispahić, Ibrahim Tahirović, Adnan Faculty of Electrical Engineering Department of Automatic Control and Electronics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9783031720611

This work presents a comprehensive study of the application of multi-agent reinforcement learning (MARL) based on deep Q-networks (DQN), aiming to enhance the cooperation and coordination of multiple agents in complex environments. The core problem addressed is the multi-agent traveling salesman problem, i.e. the effective collaboration and target-reaching by multiple agents. This challenge is inherently present in a variety of applications, including inspection and maintenance tasks based on autonomous aerial robotic systems. The research delves into the intricacies of multi-agent systems, emphasizing the dynamic interaction between agents and the environment. Although the proposed algorithm is developed and trained in a planar space, its principles and methodologies are easily extendable to any operational environment. The proposed approach is capable of training on a number of different scenarios, using various numbers of agents and targets, regardless of their initial positions, making it highly adaptable to scenarios like aerial inspections, where the number of agents, the number of inspection sites and their locations are unknown a priori. Furthermore, the work outlines how the enhanced DQN algorithm can be tailored for scenarios with numerous autonomous agents and targets, ensuring efficient path planning and target acquisition in constrained and unstructured spaces. This work also provides a solid foundation for future research in integrating advanced reinforcement learning techniques into the design and operational strategies for solving tasks, which can be described by the multi-agent traveling salesman problem, including inspection and maintenance with an autonomous multi-agent system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Traveling salesman problem

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Based on Embedded Technology to Real-Time control for Analogous Active Suspension System 2nd

Based on Embedded Technology to Real-Time Control for Analog...

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2nd International Conference on Intelligent Systems Design and engineering Applications, ISDEA 2023

作者： Hung, San-Shan Tsai, Yi-Jui Lin, Kuo-Wei Department of Automatic Control Engineering Feng-Chia University Taichung Taiwan

ISBN: (纸本)9789819963027

Conventional vehicle suspension system is mainly composed of coil springs and hydraulic dampers, and the elasticity coefficient K ad damping coefficient C are fixed values, so the traditional suspension system cannot adjust the suspension system according to different road conditions. Due to the varying road quality, many potholes, patches and manhole covers, and rainy climate, the comfort and maneuverability of driving are reduced. Therefore, this study proposes the application of embedded technology combined with road image to determine and control the class of Analogous Active Suspension System. In this study, the Analogous Active Suspension System is composed of air springs, proportional valve hydraulic dampers instead of traditional suspension system, and various sensors. For different types of uneven road surfaces such as potholes, speed bump, manhole covers, etc., the controller Area Network (CAN bus) communication is used to transmit the road surface information to the vehicle embedded system, and then the control decision is calculated to adjust the corresponding air spring coefficients according to the corresponding conditions. At the same time, the optimal damping coefficients are calculated by using neural-like algorithms, so that the system can adjust the suspension parameters immediately according to the current road conditions to achieve better driving comfort and handling. Finally, the dynamic characteristics of the suspension system and the vehicle are investigated in a real vehicle dynamic test to optimize the control decision parameters to achieve the best driving comfort and to give the best driving safety. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embedded systems

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The Unscented Kalman Filter for Nonlinear Parameter Identification of Adaptive Cruise control Systems

arXiv

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arXiv 2023年第8期8卷 4094页

作者： Ampountolas, Konstantinos The Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

This paper develops and investigates a dual unscented Kalman filter (DUKF) for the joint nonlinear state and parameter identification of commercial adaptive cruise control (ACC) systems. Although the core functionality of stock ACC systems, including their proprietary control logic and parameters, is not publicly available, this work considers a car-following scenario with a human-driven vehicle (leader) and an ACC engaged ego vehicle (follower) that employs a constant timeheadway policy (CTHP). The objective of the DUKF is to determine the CTHP parameters of the ACC by using real-time observations of space-gap and relative velocity from the vehicle's onboard sensors. Real-time parameter identification of stock ACC systems is essential for assessing their string stability, large-scale deployment on motorways, and impact on traffic flow and throughput. In this regard, L2and L∞string stability conditions are considered. The observability rank condition for nonlinear systems is adopted to evaluate the ability of the proposed estimation scheme to estimate stock ACC system parameters using empirical data. The proposed filter is evaluated using empirical data collected from the onboard sensors of two 2019 SUV vehicles, namely Hyundai Nexo and SsangYong Rexton, equipped with stock ACC systems;and is compared with batch and recursive least-squares optimization. The set of ACC model parameters obtained from the proposed filter revealed that the commercially implemented ACC system of the considered vehicle (Hyundai Nexo) is neither L2nor L∞string stable. Copyright © 2023, The Authors. All rights reserved.

关键词： Adaptive cruise control

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A Power-Communication Co-Simulation Platform for Real-Time Microgrid Cyber Security Analysis

A Power-Communication Co-Simulation Platform for Real-Time M...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Qiu, Dongmeng Liu, Mengxiang Zhang, Xin University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798331507886

The rapid digitalisation process in power system has greatly benefited the large-scale integration of distributed energy resources (DERs), essentially accelerating the progress towards net zero. However, numerous cyber attacks such as false data injection (FDI) attacks are impeding the normal control and operation activities. This paper aims to establish a real-Time microgrid co-simulation platform to fully study the impacts of realistic attack events on micorgrid's control performance. Although there exist a large number of power-communication co-simulation platforms for the power system, they are designed to mainly provide validation services for control and operation algorithms and little attention has been paid to the cyber security. In the established microgrid platform, power and communication parts are simulated in Typhoon Hardware-in-The-loop(HIL) 602+ and EXata Network Simulator, respectively, and a dedicated synchronisation scheme is developed and embedded in Raspberry Pis to achieve master-slave data exchange between them. Based on the advanced cyber libraries provided by EXata, realistic FDI and packet drop attacks are mounted against the mapped communication links within EXata. The results indicate that these cyber attacks can severely influence the microgrid control performance including destabilising system states and specific state deviations, further demonstrating the necessity of deploying effective cyber-resilience enhancement mechanisms. © 2024 IEEE.

关键词： Cyber attacks

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Learning Input Driven Dynamic Bayesian Networks with Measurement Noise 23

Learning Input Driven Dynamic Bayesian Networks with Measure...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Veres, David Li, Ping Kadirkamanathan, Visakan The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350374889

Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum likelihood Doubly-Iterative Expectation Maximization (DI-EM) Algorithm is developed for the identification of grey-box ARMAX state-space model representations of DBNs involving known, noisy measurement processes. The grey-box model incorporates network dependencies among time series variables and exploits time series data of low longitudinal and high cross-sectional dimensions. A network learning procedure is developed using a score-based structure-selection method to find the underlying network of an input-driven dynamical system. By computing a finite data version of the Bayesian Information Criterion (BIC) for small sample sizes, the proposed method's performance is investigated on simulated and real-world data. The algorithm recovers the underlying ground-truth networks of simulated systems under finite data criteria with Jaccard Coefficient values of up to 0.84, and selects structures with improved weighted mean-squared error loss over a baseline black-box model fit on real-world data. © 2024 IEEE.

关键词： Expectation maximization algorithm

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Optimized Spatial Filter Selection for Transfer Learning in Brain-Computer Interface 3

Optimized Spatial Filter Selection for Transfer Learning in ...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： A A Mohamed, Mohamed Giles, Joshua Arvaneh, Mahnaz University of Sheffield Automatic control and Systems Engineering Department Sheffield United Kingdom

ISBN: (纸本)9798350378009

The Common Spatial Patterns (CSP) algorithm has shown great efficacy in extracting features for Brain-Computer Interfaces (BCIs), particularly in motor imagery BCIs. However, CSP performs poorly when dealing with limited labeled data, which leads to long calibration time at the beginning of each session. To overcome this challenge, we leverage source data, i.e., labeled data from other subjects, to transfer knowledge across subjects. This work proposes a novel approach termed Scaled and Warped CSP-based Transfer Learning (SW-CSP-TL). This method aligns source data with the temporal and amplitude structure of target data before computing CSP filters, ensuring optimization for capturing discriminative features relevant to the target domain. We evaluate the proposed SW-CSP-TL algorithm using the publicly available dataset 2a from BCI Competition IV and compare its performance with that of the classical CSP algorithm and the transfer learning CSP algorithm (CSP-TL) without amplitude scaling and temporal warping. Our algorithm exhibits superior performance particularly when the training data size is relatively small. Results demonstrate that for 5 and 15 training trials per class from the target session, SW-CSP-TL outperformed the classical CSP by an average of 10%, and 8%, respectively. © 2024 IEEE.

关键词： Transfer learning

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Research on the Method of Remaining Useful Life Prediction of Lithium-Ion Battery Based on LSTM 2

Research on the Method of Remaining Useful Life Prediction o...

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2nd IEEE International Conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Wang, Shenhang Wang, Junjie Xiang, Gang Lin, Ruishi Peng, Yu Yang, Zhiming Beijing Aerospace Automatic Control Institute Department of System Engineering Beijing China

ISBN: (纸本)9798350368208

Lithium-ion batteries are prone to capacity degradation during use, leading to a decline in their reliability and potentially causing catastrophic failures. This necessitates the study of advanced methods for estimating the lifespan of lithium-ion batteries. To address this need, this paper conducts research in three areas: the extraction of degradation features, the construction of health factors, and the development of predictive models, proposing a comprehensive framework for estimating the remaining useful life of lithium-ion batteries. Firstly, the general trends of external feature parameters as a function of cycling are observed to identify the features that indicate battery degradation. Next, the relationship between these features and battery capacity is determined using Grey Relation Analysis, allowing for the construction of an indirect health factor. Finally, a data-driven prediction method is employed to construct a predictive network model using Long Short-Term Memory networks, with the health factor used for training to accurately predict the health state of the lithium-ion battery. The effectiveness of the proposed remaining life prediction method is validated using the NASA Battery Set dataset from NASA Ames Research Center. © 2024 IEEE.

关键词： NASA

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An Evaluation of the Dual Vectors Based Solutions for Robot-World/Hand-Eye Calibration 1

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25th International Symposium on Measurement and control in Robotics, ISMCR 2023

作者： Ganciu, Aura Burlacu, Adrian Department of Automatic Control and Applied Informatics Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iași Iași Romania

ISBN: (数字)9783031510854

ISBN: (纸本)9783031510847

The hand-eye calibration problem represents a major challenge in robotics, arising from the widespread usage of robotic systems along with robot-mounted sensors. Briefly, consisting of estimating the position and orientation of a visual sensor (usually a camera attached to the robot’s end-effector) with respect to the robot itself, calibration is critical in tracking rigid bodies—a common task within mixed reality applications. Hand-eye calibration implies solving the problem formulated as AX = XB or AX = YB equations. The solution lies in computing the unknown homogeneous transformation matrices X and Y, given the A and B parameterizations of rigid body motions. We present the dual vectors based approaches for AX = XB and AX = YB calibration problems, making use of the isomorphism between the special Euclidean group and the orthogonal dual tensor group. Comparisons with state-of-the-art algorithms are used to assess our obtained results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Mixed reality

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