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23rd International Conference on control systems and Computer Science Technologies, CSCS 2021

作者： Balaban, Beatrix-May Sacala, Ioan Stefan Petrescu-Nita, Alina Claudia Pop, Florin University Politehnica of Bucharest Department of System Engineering Faculty of Automatic Control and Computers Bucharest Romania

ISBN: (纸本)9781665439398

Nowadays, intelligent vehicles are becoming more and more important in the transportation field. What makes such a vehicle vulnerable is the number of interconnected components that individually have a strong security but can cause weakness points when they are connected. The challenge is to design and build a powerful system that uses both cyber-physical components. The proposed model is a system with integrated modules for fingerprint and face recognition to certify that a driver has the right to access the vehicle. The aim is to develop a security system for intelligent vehicles that can be also a secure keyless entry system using the power of biometrics. When a driver approaches the car, a sensor will activate the recognition modules. Only after the total completion of the recognition process the driver is authenticated and has the right to use the vehicle. We simulated real-time face detection and eye tracking using Matlab 2020a. Face detection is performed using Viola-Jones algorithms and the tracking is made with bbox-es. Besides normal conditions, other tests were made to challenge the algorithm, for example: angle rotations, the use of glasses, face mask, glasses and face mask together. © 2021 IEEE.

关键词： Cyber Physical system

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Preventing Frequency Instability Using Large-Scale Photovoltaic Resources

Preventing Frequency Instability Using Large-Scale Photovolt...

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Technology and Energy Management (ICTEM), International Conference on

作者： Siavash Yari Hamid Khoshkhoo Kumars Rouzbehi S. M. Ale Emran Faculty of Electrical Engineering Sahand University of Technology Sahand Iran Department of System Engineering and Automatic Control University of Seville Seville Spain Faculty of Engineering University of Quebec at Chicoutimi (UQAC)

Various control methods have been presented to prevent the occurrence of instability in power systems, among which, it seems that methods based on the special protection system (SPS) are more effective and efficient than other ones. In this article, a SPS using the capabilities of large-scale photovoltaic power plants (LSPVPPs) is presented to prevent frequency increase and instability in power systems. According to the presented algorithm, when frequency increases, the proposed SPS uses the ability of photovoltaic power plants to quickly change their generation powers based on the operating point and frequency changes to prevent frequency instability. To confirm the proposal, LSPVPPs along with their controllers are modeled in the DSL environment of DIgSILENT PowerFactory software. The proposed SPS is implemented and tested on the IEEE 39-bus test system and the results of dynamic simulations show that this SPS prevents frequency instability in different conditions by taking timely measures.

关键词： Photovoltaic systems Time-frequency analysis Power system dynamics Software algorithms Power system stability Stability analysis Software

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Failure Detection from the Knocking Sounds Using Convolutional Neural Network

Failure Detection from the Knocking Sounds Using Convolution...

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IEEE Region 10 International Conference TENCON

作者： Punyapat Areerob Rithea Sum Chanon Khongprasongsiri Sudchai Boonto Department of Control System and Instrumentation Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand National Astronomical Research Institute Thailand

Toilet quality assurance is a crucial process in ensuring lavatories meet rigorous performance, durability, and hygiene standards. The current standard of Maximum Performance (MaP) testing faces challenges, leading researchers to explore innovative approaches such as sound source classification for quality assurance. This approach involves collecting a diverse dataset of lavatory sounds and extracting relevant acoustic features. Deep learning models, particularly convolutional neural networks (CNNs), are trained on these features to accurately classify sound sources. The trained models were evaluated and compared by considering metrics such as classification accuracy, computational complexity, and model parameters. This paper performs these tests and chooses the most effective model to enhance the quality assurance process for toilets. Incorporating sound source classification techniques has several benefits, including the optimization of testing processes, non-intrusive performance assessment, and efficient resource utilization through targeted testing and troubleshooting. By improving the standards of lavatory quality, this approach ensures enhanced performance, durability, and hygiene of lavatories.

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Energy harvesting and speed sensing with a hybrid rotary generator for self-powered wireless monitoring

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Acta Mechanica Sinica 2024年第9期40卷 116-130页

作者： Zhixia Wang Hongyun Qiu Xuanbo Jiao Wei Wang Qichang Zhang Ruilan Tian Dongxing Cao School of Mechanical Engineering Tianjin UniversityTianjin 300350China Tianjin Key Laboratory of Nonlinear Dynamics and Control Tianjin 300350China State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China Department of Mechanics Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China Faculty of Materials and Manufacturing Beijing University of TechnologyBeijing 100124China

Real-time onboard health monitoring systems are critical for the railway industry to maintain high service quality and operational ***,the issue with power supplies for monitoring sensors persists,especially for freight trains that lack onboard ***,we propose a hybrid piezoelectric-triboelectric rotary generator(HPT-RG)for energy harvesting and vehicle speed *** HPT-RG incorporates a rotational self-adaptive technique that softens the equivalent stiffness,enabling the piezoelectric non-resonant beam to surpass resonance limitations in a low-frequency *** experiments demonstrate the feasibility of using the HPT-RG as an energy harvesting module to collect the rotational energy of the freight rail transport and power the wireless temperature *** allow multiple monitoring in confined spaces on trains,a triboelectric sensing module is added to the HPT-RG to sense the operation speed and mileage of ***,the generator exhibits favorable mechanical durability under more than 600 h of official testing on the train bogie *** proposed HPT-RG is essential for creating a truly self-powered,maintenance-free,and zero-carbon onboard wireless monitoring system on freight railways.

关键词： Energy harvesting Speed sensing Rotational self-adaptive technique Low-frequency Self-powered wireless monitoring

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Designing of Delta Manipulator as Human-Robot Interaction for Collaborative Mobile Robot

Designing of Delta Manipulator as Human-Robot Interaction fo...

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SICE Annual Conference

作者： Siritakorn Sutthi Adul Phaiyakarn Suwapich Prueksakunnatam Issarapong Khuankrue Chettapong Janya-Anurak Department of production Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Control System and Instrumentation Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

This research aims to develop an adaptable and advanced human-robot interaction system for collaborative mobile robots. A delta manipulator with a series elastic actuator is designed as a collaborative medium between humans and mobile robots. The design of the delta manipulator as a human-robot interaction interface and its kinematics are presented. Three control strategies for translational motion and two control strategies for rotational motion are studied and discussed based on their advantages and disadvantages, depending on specific application requirements. This design approach enables the creation of a comprehensive controller system that can effectively command the mobile robot to perform a wide range of movements and maneuvers, enhancing its overall performance and versatility. The resulting system efficiently accomplishes complex tasks in various scenarios, making it a valuable tool for a range of applications.

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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Computer systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Cooperation of Autonomous Mobile Robots with Different Maneuverability in Transportation

Cooperation of Autonomous Mobile Robots with Different Maneu...

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SICE Annual Conference

作者： Pete Rangsichamras Natchanon Wittayaarereekul Satjaban Tantinarasak Issarapong Khuankrue Chettapong Janya-Anurak Department of production Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Control System and Instrumentation Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

This study proposes a cooperative transportation system, which uses multiple autonomous mobile robots with different maneuverability cooperating together to transport a rigid object. The system uses a leader-follower formation control strategy, by using the non-holonomic leading the way while the holonomic AMRs follow. The object is assumed as a constraint between the robots, and the cooperation is tested using a designed destination point, in which each follower robot receives commands from the leader sent through the laptop. To walk straight, all robots receive the same command, but for rotating, leader robot must evaluate the command for the follower robots by the designed method. The Robot Operating system (ROS) is used for the development of the robots and for standard communication. The proposed method is validated through simulation tasks using autonomous mobile robots with different wheel types. The results of the experiments have resulted and supported that holonomic and non-holonomic robots being able to cooperate with each other’s in transportation.

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Principle Component Analysis Based Naïve Bayes and K-Nearest Neighbors Classifiers for the Identification of Oral Mucosal Lesions

Principle Component Analysis Based Naïve Bayes and K-Neares...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Pavan Kumar Arsala Zamir Khan Vikrant Yashwant Ganvir Shashikant Rathod Dipti V Jamkar Hemlata Vikrant Ganvir Department of BSH Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DMIHER) Wardha India Department of Applied Physics Yeshwantrao Chavan College of Engineering (YCCC) Nagpur India Department of Instrumentation and Control Engineering COEP Technological University Pune India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Identification of the oral mucosal lesions has been executed by the machine learning methods in the present study. In the machine learning methods, principle component analysis, naïve Bayes and k-nearest neighbor’s classifiers, and receiver operating characteristic analysis are utilized. PCA is dimension reduction method and employed to reduce the dimension of the fluorescence data. Naïve Bayes is a probabilistic model which is based on the Bayes theorem. KNN is based on distance metric such as Euclidean distances. These methods are adopted for classification among the diseased (cancerous) and non-diseased groups. However, ROC is implemented for the finding of diagnostic parameters like sensitivity, specificity, and accuracy. Data collection was done by the use of fluorescence based portable device. A total of 38 patients (20 OSCC & 18 dysplastic), and 36 healthy volunteers were included in this study. Fluorescence spectral data were recorded from the lateral boarder of tongue (LBT) of the oral cavity. In the spectra, presence of two fluorophores namely FAD and porphyrin were seen. A significant enhancement in the porphyrin fluorescence was noticed on OSCC patients than the volunteers. Data analysis exhibited by the naïve Bayes and KNN classifiers on the testing data differentiated among Normal to OSCC, Normal to Dysplasia, and Dysplasia to OSCC with the accuracy values of 89%, 90%, 88% and 94%, 85%, and 100% respectively. Results indicate that KNN classifier can differentiate the three groups with slightly higher values of accuracy than the naïve Bayes classifier.

关键词： Accuracy Tongue Sensitivity Nearest neighbor methods Fluorescence Bayes methods Lesions Object recognition Principal component analysis Testing

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Improved Artificial Neural Networks Based Whale Optimization Algorithm

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for Computer Science and Mathematics 2023年第3期4卷 195-202页

作者： Ibrahem Alhayali, Royida A. Aladamey, Mostafa Khaled Abd Alrahman Subhi, Mohammed Rashid Mohammed, Mostafa Abdulghafoor Amir, Amiza Abdalkareem, Zahraa A. Department of Architecture Engineering College of Engineering University of Diyala Diyala Iraq Alimam Aladham University College Baghdad Iraq Department of Petroleum System Control Engineering College of Petroleum Processes Engineering Tikrit University Iraq Faculty of Engineering & Electronic Engineering Technology University Malaysia Malaysia

Owing to the increasing interest in artificial neural networks (ANNs) across various fields of study, many studies have focused on enhancing their performance through the utilisation of different learning algorithms. This study examines the use of the Whale Optimization Algorithm (WOA) as a training algorithm to improve the classification accuracy of ANNs. To achieve a high level of classification accuracy with ANN models, it is imperative to ensure that the model is appropriately designed in terms of the employed structure, training algorithm and activation function. In this work, WOA was adopted to train ANN models using 10 well-known datasets sourced from the UCI machine learning repository. The classification accuracy of a WOA-trained ANN was compared with that of a backpropagation-trained ANN, and the results showed that the WOA-trained ANN exhibited superior performance. © 2023 College of Education, Al-Iraqia University. All rights reserved

关键词： Neural networks

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Design and Flight Test of a Compact and Rapidly-Deployable Air-Launched Inflatable-Winged Aircraft

Design and Flight Test of a Compact and Rapidly-Deployable A...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Ge, Hang Xu, Yixiang Sun, Donglei Chen, Xinmin Chen, Boyang School of Aerospace Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China Department of Mechanical Materials and Manufacturing Engineering UNNC-NFTZ Blockchain Laboratory University of Nottingham Ningbo China Zhejiang Ningbo315100 China Advanced Intelligent Manufacturing Research Group Control System Lab University of Nottingham Ningbo China Zhejiang Ningbo315100 China Zhejiang Provincial Engineering Research Center of Special Aircraft Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Zhejiang Ningbo315100 China

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In this research, a high-altitude, compact and rapidly deployable, air-launched, inflatablewinged aircraft is designed, developed, and flight-tested to investigate the flight characteristics of inflatable-winged aircraft as well as the in-flight inflation and dynamic deployment properties when launched from various carriers. To start with, two types of inflatable wings employing different materials have been designed, fabricated, and tested, the tests including structural characteristics tests, inflation properties tests, and low-Reynolds-number wind-tunnel tests. A full-scale 30-kilogram prototype unmanned aerial vehicle with inflatable wings was then designed, assembled and tested with some basic low-altitude flights to verify the feasibility of the aerodynamic layout and the trim and stability and control characteristics. Finally, low- and high-altitude in-flight launches will be conducted to study the dynamic deployment properties and high-altitude flight characteristics of this design. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Wind tunnels

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