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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Muhammad Shafly Hamzah Endra Joelianto Herman Yoseph Sutarto Adiyana Putri Instrumentation and Control Master Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi Bandung Indonesia Doctoral Program of Engineering Physics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems.

关键词： Training Adaptive systems Uncertainty Scalability Urban areas Traffic control Robustness Standards Traffic congestion Testing

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Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

arXiv

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arXiv 2023年

作者： Qu, Jiaqi Wei, Lu Sun, Qiang Zareipour, Hamidreza Qian, Zheng School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability. Copyright © 2023, The Authors. All rights reserved.

关键词： Failure analysis

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Mathematical Modeling of the Impact of a 5 Day Interim Relaxation at 21 Days in 42 days Lockdown Strategy on COVID-19 Transmission in India

Mathematical Modeling of the Impact of a 5 Day Interim Relax...

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International Conference on Biosignals, Images and instrumentation (ICBSII)

作者： Flavia Immaculyne BakiyaAmbikapathy Kamalanand Krishnamurthy Lourduraj De Britto Schlumberger- Reda Production System Benoi Cresent Singapore Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Instrumentation Engineering MIT Campus Anna University Chennai India Indian Council of Medical Research Vector Control Research Centre Puducherry India

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

As of the first week of May 2020, the COVID-19 pandemic is present in 212 nations globally. Many countries have enforced lockdown measures strategically to slow down the epidemic's spread, with the objective of relieving pressure on healthcare systems. Mathematical modelling serves as a useful tool for the prediction of the future numbers of the infected population and for analyzing the effects of various intervention strategies. In this work, a mathematical model based on ordinary differential equations has been developed for analyzing the impact of an interim relaxation of 5 days at 21 days in a 42 days lockdown period. Results of the simulation demonstrate that the relaxation of 5 days during the 42 days lockdown does not result in any adverse effects if the transmission rate remains the same. However, the model-based analysis suggests that an increase in the transmission rate by a factor of 1.2 times during the relaxation period due to the possible aggregation of the susceptible with the infected, it could shift the growth pattern of infected cases from sub-exponential to exponential.

关键词： COVID-19 Analytical models Biological system modeling Sociology Predictive models Ordinary differential equations Routing

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Human Following Robot Based on Object Detection for Aerospace Application

Human Following Robot Based on Object Detection for Aerospac...

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Aerospace Electrical engineering and Informatics (ICAAEEI), International Conference of Adisutjipto on

作者： Arwin Datumava Wahvudi Sumari Ahnaf Bagus Aryandika Ndaru Atmi Purnami Denny Dermawan Ika Noer Syamsiana Department of Electrical Engineering Cognitive Artificial Intelligence Research Group State Polytechnic of Malang Malang Indonesia Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Digital System Microprocessor Microcontroller and Programmable Logic Devices Research Group Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

ISBN: (数字)9798331507930

ISBN: (纸本)9798331507947

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications.

关键词： YOLO Tensors Accuracy Prototypes Wheels Cameras Hardware Maintenance Aircraft Aerospace industry

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Fault Tolerant Localization Based on K-means Clustering in Wireless Sensor Networks 6

Fault Tolerant Localization Based on K-means Clustering in W...

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6th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2020

作者： Bhat, Soumya J Santhosh, K.V. Department of Instrumentation and Control Engineering Centre for Cyber Physical System Manipal Institute of Technology Manipal India

ISBN: (纸本)9781728168289

Wireless Sensor Network (WSN) has been considered as an essential tool for many applications. Most of the applications of WSNs require the location of sensor nodes to be known. But, because of high power consumption and cost, it is not suitable to attach a GPS receiver with every node. For estimating the location of sensor nodes, localization algorithms make use of few location-aware nodes as reference nodes. But, sometimes, these reference nodes can turn faulty. They may estimate their locations wrongly due to software or hardware malfunctions. This affects the localization of the entire network. To overcome this problem, we have reported a fault tolerant localization algorithm called clustering based DV-Hop. This algorithm filters out faulty nodes using K-means clustering and majority voting methods. The performance of the algorithm is then compared with other localization algorithms. The reported algorithm shows better localization accuracies and more stable performance under faulty conditions. © 2020 IEEE.

关键词： K-means clustering

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High-Performance Nonlinear control for Three-Phase Two-Level Boost PFC Rectifier

High-Performance Nonlinear Control for Three-Phase Two-Level...

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2024 International Conference on Materials and Energy: Energy in Electrical engineering, ICOME-EE 2024

作者： Sriprang, Songklod Nahid-Mobarakeh, Babak Takorabet, Noureddine Pierfederici, Serge Siangsanoh, Apinya Yodmanee, Kanapot Mungporn, Pongsiri Thounthong, Phatiphat Poonnoy, Nitchamon Bizon, Nicu Suyata, Thong-In Noosawang, Sakrit Université de Lorraine GREEN-ENSEM Laboratory 54505 France Rajamangala University of Technology Rattanakosin Wang Klai Kangwon Campus Petchkasem RD Prachuap Khiri Khan77110 Thailand Université de Lorraine LEMTA Laboratory 54505 France Thailand Thai-French Innovation Institute King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Faculty of Technical Education Dept. Teacher Training in Electrical Engineering Thailand University of Pitesti Faculty of Electronics Communication and Computers Pitesti110040 Romania University of Technology Faculty of Rajamangala Department of Electrical Engineering Thanyaburi 39 Moo 1 Rangsit-Nakhonnayok Road Pathum Thani 12110 Thailand King Mongkut's University of Technology Thonburi Faculty of Engineering Dept. of Control System and Instrumentation Engineering Bangkok10140 Thailand

ISBN: (纸本)9798331531577

This paper presents a high-performance nonlinear control strategy for a three-phase, two-level boost Power Factor Correction (PFC) rectifier. The proposed control method aims to achieve superior performance in terms of power factor correction, output voltage regulation, and dynamic response under various operating conditions. Utilizing advanced nonlinear control techniques, the system ensures optimal tracking of the reference signals, minimizing total harmonic distortion (THD) and improving overall efficiency. The effectiveness of the control strategy is validated through extensive simulations and experimental results, demonstrating significant improvements in the transient and steady-state performance of the rectifier. This approach provides a robust and efficient solution for PFC applications in modern power electronics systems. © 2024 IEEE.

关键词： Nonlinear control systems

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Special Aspects of the Kinetics of Reactive Ion Etching of SiO2 in Fluorine-, Chlorine-, and Bromine-Containing Plasma

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Russian Microelectronics 2020年第2期49卷 94-102页

作者： Efremov, A.M. Murin, D.B. Betelin, V.B. Kwon, K.-H. Department of Technology of Instruments and Materials of Electronic Engineering Ivanovo State University of Chemistry and Technology Ivanovo153000 Russia Scientific Research Institute of System Analysis Moscow117218 Russia Department of Control and Instrumentation Engineering Korea University Sejong339-700 Korea Republic of

Abstract: The influence of the type of active gas and the ratio of the concentration of the components of the CF4 + Ar, Cl2 + Ar, and HBr + Ar mixtures on the parameters of the plasma, steady-state concentrations of the active particles, and kinetics of reactive-ion etching of SiO2 is investigated. It is demonstrated that the rate of etching SiO2 decreases in the CF4–Cl2–HBr series and is characterized by nonmonotonic (with the maximum at 20–25% Ar) behaviour under the variation of the initial composition of the mixtures. It is found that the reasons for the nonmonotonicity for each of the mixtures are the increased effective probability of the interaction and decreased yield of etching due to the change in the physical parameters of the plasma and densities of the fluxes of the active particles. © 2020, Pleiades Publishing, Ltd.

关键词： Silica

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Drowsy Driver Detection system For Poor Light Conditions

Drowsy Driver Detection System For Poor Light Conditions

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2023 IEEE International Conference on Mechatronics (ICM)

作者： Ahmed Ibnouf Ayman Fadlallah Muaiz Ali Abdelmalek Zidouri Control & Instrumentation Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Information & Computer Science Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department Interdisciplinary Research Center for Intelligent Secure System King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

In recent years, the number of car accidents has been gradually increasing. A major factor in this increase is the drowsiness of drivers. There are many image recognition research studies on drowsiness detection but tend to neglect the case of low-light conditions. This paper focuses on detecting drowsy drivers in low-light conditions. In this paper, a behavioralbased drowsy driver detection system (DDDS) was proposed. The proposed DDDS applies a preprocessing algorithm that improves illumination and then focuses on detecting the driver’s face and eyes to calculate eye blink duration. A deep neural network model was used for facial recognition, and Haar Cascade classifiers were used for eyes detection. Every single eye was based to a Convolutional Neural Network (CNN) model to predict its state as either ‘‘Open’’ or ‘‘Closed’’. The proposed CNN was developed using a dataset of 1452 samples and gave an accuracy of 97.92% on the testing dataset, 15% of the 1452 samples. Different case scenarios in poor light conditions were implemented to evaluate the proposed DDDS. The system was able to identify the state of the driver even in harsh and severe poor light conditions.

关键词： Deep learning Face recognition Transportation industry Lighting Safety Convolutional neural networks Standards

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Biosignal-Based Attention Monitoring for Evaluating Train Driver Safety-Relevant Tasks

SSRN

引用

SSRN 2024年

作者： Kim, Jung Hwan Kim, Yeongjun Cho, Younggeol Kim, Tae Kyun Jang, Tongil Park, Chanwoo Kang, Seongkeun Advanced Instrumentation & Control Research Section Korea Atomic Energy Research Institute Daejeon Korea Republic of Department of Nuclear & Quantum Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Railroad Electrical & Electronic Engineering Korea National University of Transportation Uiwang Korea Republic of System Safety Research Department Korea Railroad Research Institute Uiwang Korea Republic of

This study explores the impact of phone usage on train driver performance, a critical aspect influencing the safety of railroad operations. The persistent problem of phone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. Leveraging advancements in technology, this study introduces a novel approach combining electroencephalogram (EEG)-based analysis with deep learning algorithms to assess the effects of phone usage on the attention states of train drivers. Utilizing a full-type train simulator to replicate real-life scenarios, this study conducts an experimental design involving scenarios with and without phone engagement, aiming to provide a comprehensive assessment of phone usage on performance in critical situations. The deep learning model employed achieved an impressive classification accuracy of approximately 85.6% in distinguishing attention states among 25 participants, with high precision and recall values indicating its efficacy in detecting phone interaction. Moreover, findings reveal that phone usage leads to a 1.4 times increase in response time to situational awareness, significantly affecting driver performance. These findings highlight the significant impact of phone usage on train driver attention and underscore the potential of EEG-based analysis and deep learning in improving railroad safety measures. This study contributes to the growing body of safety literature by focusing on train drivers, offering fresh insights and practical implications for enhancing the safety and efficiency of railroad operations. © 2024, The Authors. All rights reserved.

关键词： Railroads

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Analysis of stiction fault in pneumatic control valves 16th

Analysis of stiction fault in pneumatic control valves

引用

16th control instrumentation system Conference, CISCON 2019

作者： Navada, Bhagya R. Santhosh, K.V. Department of Instrumentation and Control Engineering Centre for Cyber Physical System Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

ISBN: (纸本)9789811546754

To cater to the ever-increasing needs of customers, industries have to give more importance toward maintenance of health of industrial process equipments through better monitoring system. Thus, fault detection and diagnosis techniques have a vital role in maintaining industrial process health. Stiction is one of the commonly occurring control valve faults in most of the flow process industries. In this paper, an attempt is made to synthesis the Choudhury’s stiction model to analyze the behavior of the control valve with stiction fault. Behavior of control valve is analyzed under different stages like linear, deadband, undershoot, overshoot and no offset conditions. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Fault detection

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