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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Anil Kadu Rajnandini Dharashive Rachit Nimje Arya Rajvaidya Sanket Palkar Vijay Gaikwad Department of Instrumentation and Control Vishwakarma Institute of Technology Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electronics and Telecommunication Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

In recent years, the importance of ensuring road safety has intensified, prompting the development of advanced driver monitoring systems. This paper presents a real-time bus driver monitoring system utilizing a Raspberry Pi, OpenCV, a RaspiCam and machine learning techniques. The system aims to enhance passenger safety by detecting driver fatigue and distraction, which are major contributors to road accidents. The proposed solution leverages the Raspberry Pi's processing capabilities and the RaspiCam's imaging features to continuously capture and analyze the driver's facial expressions and eye movements. OpenCV's robust image processing algorithms are employed to identify signs of drowsiness and inattentiveness. The innovative aspects of the proposed solution include the use of Eye Aspect Ratio (EAR) for drowsiness detection and Mouth Aspect Ratio (MAR) for detecting yawning. Both the EAR and MAR are calculated through real-time facial landmark analysis. The system issues alerts when unsafe driving behavior is detected, thereby allowing for timely intervention. The proposed monitoring system is cost-effective and scalable. It offers a promising approach to improving public transportation safety by mitigating the risks associated with driver fatigue and distraction. Experimental results showed an accuracy of 92% for fatigue detection and 88% for distraction detection. This demonstrates the system's effectiveness in real-time detection and its potential for deployment in commercial bus fleets.

关键词： Accuracy Image processing Machine learning Fatigue Real-time systems Road safety Safety Monitoring Public transportation Vehicles

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Performance Assessment for Stochastic Anomaly Detectors in Industrial Alarm Systems

Performance Assessment for Stochastic Anomaly Detectors in I...

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International Conference on control and Automation (ICCA)

作者： Jing Zhou Jun Shang Tongwen Chen Department of Electrical & Computer Engineering University of Alberta Edmonton Canada Department of Control Science & Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In recent years, stochastic detectors have gained prominence in networked systems for anomaly detection. These detectors have demonstrated advantages over their traditional counterparts, particularly in safe-guarding against data integrity attacks targeting state estimation. Despite these advancements, the impact of the detector on alarm performance-such as alarm-triggering rates at normal conditions-remains under-explored, especially in scenarios where delay timers are applied to the raw alarm sequence. This study delves into the monitoring of a correlated Gaussian process variable using stochastic detectors. An explicit formula for the alarm performance is given, highlighting how it is influenced by the duration of delay timers. The efficacy of the proposed approach is validated through numerical examples and a simplified process model.

关键词： Automation Data integrity Detectors Gaussian processes Delays Numerical models State estimation

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Computationally Efficient Implementation of the Weighted Kalman Filter for Quadratic Systems

Computationally Efficient Implementation of the Weighted Kal...

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American control Conference (ACC)

作者： Damiano Rotondo Marcin Witczak Lothar Seybold Department of Electrical and Computer Engineering (IDE) University of Stavanger Norway Institute of Control and Computation Engineering University of Zielona Gora Poland RAFI GmbH & Co. KG Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The weighted Kalman filter (WKF) can be per- ceived as a variant of the extended Kalman filter (EKF), which incorporates the so-called weighted linearisation. The price behind such an extension is expressed by an increased computational complexity, which aims at improving the overall convergence of the filter. Owing to the increased complexity, the WKF is in general not suitable for high-order nonlinear systems. To overcome this difficulty, the objective of this paper is to show that a closed-form expression of the WKF can be obtained for a specific class of nonlinear systems, i.e., those characterised by a quadratic state equation. This closed-form of the WKF scales well with the order of the system, enabling the application of the WKF to high-order nonlinear quadratic systems, as exemplified in the final part of the paper using a comprehensive Monte Carlo analysis applied to a large number of random systems.

关键词： Closed-form solutions Monte Carlo methods Simulation Integral equations control systems Mathematical models Computational efficiency

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Hierarchical Clustering-Based State Grouping Reinforcement Learning for Switching Decision of Autonomous Vehicles

Hierarchical Clustering-Based State Grouping Reinforcement L...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Ouyang, Wenjie Jiao, Yiwen Liu, Yang Li, Yang Hu, Manjiang Qin, Hongmao College of Mechanical and Vehicle Engineering Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Mechanical and Computer Science Delft University of Technology Delft Netherlands College of Mechanical and Vehicle Engineering Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China

ISBN: (纸本)9798350316308

Reinforcement learning (RL) has gained wide attention, but its implementation in autonomous vehicles is still limited by insufficient sample efficiency and heavy training costs. The training efficiency of RL agents is influenced by the dimension of the state space, which can be partitioned to reduce the complexity of sampling and computation. This study proposes a hierarchical clustering-based state grouping reinforcement learning (HCSG-RL) method for the switching decision of autonomous vehicles. First, we partition the base state space into groups and generate a hierarchical tree of state space groups. Then, we train multiple sub-agents for each node in the hierarchical tree. Finally, we add these trained-well sub-model into master policy. This method allows us to fully explore all state spaces and improve the training efficiency of individual agents, which handles the 'long-tail' issue and the curse of dimensionality issue. We conduct experiments in a simulation environment and results show that the proposed method has 16-72% reward improvement compared to the tree model in different road length. © 2023 IEEE.

关键词： Reinforcement learning

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System

Prediction of Radiated Electromagnetic Interference Based on...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Yixin Shi Wenjie Chen Fengjiao Li Jinpeng Kan Yongqing Meng Guang Yang School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Electronic Systems Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems.

关键词： Motor drives Accuracy Silicon carbide Electromagnetic interference Layout Predictive models Motors Topology Gallium nitride Cables

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A Very-Short-Term Online PV Power Prediction Model Based on RAN With Secondary Dynamic Adjustment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1214-1224页

作者： Zhang, Tengfei Ma, Fumin Peng, Chen Yu, Yang Yue, Dong Dou, Chunxia O'Hare, Gregory M. P. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence The Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing210023 China Nanjing University of Finance and Economics College of Information Engineering Nanjing210023 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Trinity College Dublin School of Computer Science and Statistics Dublin 2 Ireland

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations that are fundamentally dynamic. To address this failing, this article proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors that exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrate the validity of the secondary dynamic adjustment strategy for online learning while also providing higher accuracy levels from the prediction model. © 2020 IEEE.

关键词： Forecasting

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Feature selection and prediction of small-for-gestational-age infants

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1881-1895页

作者： Li, Jianqiang Liu, Lu Zhou, MengChu Yang, Ji-Jiang Chen, Shi Liu, HuiTing Wang, Qing Pan, Hui Sun, ZhiHua Tan, Feng School of Software Engineering Beijing University of Technology Beijing China Department of Electronical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Tsinghua University Beijing China Department of Endocrinology Peking Union Medical College Hospital Beijing China Department of Internal Medicine Peking Union Medical College Hospital Beijing China Beijing Chaoyang District Maternal and Child Healthcare Hospital Beijing China Chinese Center For Disease Control And Prevention Beijing China

The small-for-gestational-age (SGA) condition often causes serious problems. Therefore, identifying the risk factors for SGA is important. Traditional statistical methods such as stepwise logistic regression (LR) have been widely utilized to discover possible risk factors. However, other feature selection methods from machine learning field have rarely been employed for the task. In this paper, a comparison of five feature selection methods from both fields for SGA risk factors analysis is conducted for the first time. To evaluate their performance, four classification algorithms are used to construct SGA prediction models. The evaluation criteria are precision and the area under the receiver operator characteristic curve. Stepwise LR achieves the best performance among the five feature selection methods, because it conducts both a univariate significance test and a model significance test, which make it more suitable for handling the complex relations among features. The top 20 features selected by each feature selection method and the 27 features selected by four or five of them could assist physicians to revise traditional SGA evaluation models. Ensemble method is also exploited to build effective SGA prediction models based on the feature subsets, which is indeed superior compared with the individual ones shown in the results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Feature Selection

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Unpredictable Switching for Cyber-Physical Security Against Worst-case Attackers

Unpredictable Switching for Cyber-Physical Security Against ...

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IEEE Conference on Decision and control

作者： Aris Kanellopoulos Filippos Fotiadis Kyriakos G. Vamvoudakis Henrik Sandberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method.

关键词： Robust control Actuators Simulation Switches control systems Stability analysis Security Optimization

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MoFiM: A morphable fish modeling method for underwater binocular vision system

MoFiM: A morphable fish modeling method for underwater binoc...

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作者： Yin, Jingfang Zhu, Dengming Shi, Min Li, Zhaoxin Duan, Ming Mi, Xiangyuan Wang, Zhaoqi Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China Institute of Hydrobiology Chinese Academy of Sciences Hubei Wuhan China

Fish morphology is an essential basis for fishery management, as it can reflect the growth status of fishes. Noncontact 3D reconstruction of underwater fish is a new way to obtain fish morphology. While it is difficult to reconstruct fish on account of the inadequate information caused by fish swimming and poor underwater imaging. This article introduces a morphable fish modeling method for the underwater binocular vision system. First, we define a fish representation based on selected landmarks. Then, we propose a chirality-supervision incorporated hourglass network to estimate fish orientation and fish 2D landmarks simultaneously, and calculate fish 3D landmarks by triangulation. Next, we propose a fish modeling method which is based on 3D landmarks and introduce the optimization procedure of fish modeling. Finally, we obtain the complete 3D fish model corresponding to the input images. To train our network and build a parametric model, we constructed an underwater vision dataset and fish instance dataset respectively. We conducted experiments with grass carp as an example, and the experimental results show that our method can achieve effective fish modeling and is useful for noncontact measurement of underwater fish. © 2022 John Wiley & Sons Ltd.

关键词： Morphology

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A Linear Parameter-Varying Approach to Data Predictive control

arXiv

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arXiv 2023年

作者： Verhoek, Chris Berberich, Julian Haesaert, Sofie Tóth, Roland Abbas, Hossam S. The Control Systems Group Eindhoven University of Technology Netherlands The University of Stuttgart Institute for Systems Theory and Automatic Control Germany The Institute for Electrical Engineering in Medicine Universität zu Lübeck Germany The Systems and Control Lab Institute for Computer Science and Control Hungary

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system. © 2023, CC BY-NC-SA.

关键词： Predictive control systems

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