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2024 International Conference on Information and Communication technology for Development for Africa, ICT4DA 2024

作者： Hailu, Gashaye L. Yetayew, Tefera T. Bahir Dar University Electrical and Computer Engineering Faculty Bahir Dar Institute of Technology Bahir Dar Ethiopia Adama Science and Technology University Department of Power and Control Engineering Adama Ethiopia

ISBN: (纸本)9798331527792

In this research, nature inspired metaheuristic optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Techniques are formulated to tune optimal combinations of PID controller parameters for a quadruple tank level control application. The degree of relation of the best paring for both interacted control loops are justified by using Relative Gain Array (RGA) calculation. The overall system has been modeled and implemented in MATLAB/Simulink and performance evaluations has been done for time domain performance specifications of settling time, rise time, percentage overshoot and steady state error as comparison criterions of controllers. Accordingly, for the minimum phase case, GA tuned PID has settling time of 0.36sec and 0.31second for the tank 1 and tank 2 respectively. And PSO tuned PID controller has settling time of 0.96sec and 0.35sec for tank 1and tank 2 respectively. The quantitative and qualitative analysis of results reveals that there is fast dynamic response using GA PID relative to PSO PID even though the difference is not significant that may be due to stochastic feature of the metahueristic optimization algorithms. In summary, the optimal combination of PID controller algorithms' parameters can give robust performance compared to PID controller designed using the classical approaches or manual tuning techniques. © 2024 IEEE.

关键词： Genetic algorithms

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Trajectory Tracking Control of Three-Degree-of-Freedom Articulated Robot Manipulator Using Neural Network Controller 11th

Trajectory Tracking Control of Three-Degree-of-Freedom Artic...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Tegegne, Tsehaynesh Mulusew Hailu, Wubie Engdew Lemma, Lebsework Negash Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Addis Ababa University Addis Ababa Ethiopia

ISBN: (纸本)9783031641503

Articulated robot manipulators are commonly used in industrial settings due to their ability to execute complex movements and handle heavy loads. To ensure successful task completion, it is crucial to have precise trajectory tracking control for these manipulators. However, the manipulator’s dynamics are highly complex and nonlinear. Additionally, the manipulator’s performance is significantly affected by unknown disturbances. To overcome these, a neural network controller (NNC) is utilized to achieve the desired trajectory. The NNC possesses the ability to learn and adapt to complex and nonlinear systems. To train the NN, data from a PID controller is collected under two conditions: normal condition and with external torque disturbance. The NN is trained using the nntool Matlab toolbox. The proposed NNC’s robustness is analyzed by introducing an external torque disturbance. The controller’s performance is evaluated using metrics root mean square error (RMSE) and integral absolute error (IAE). The results demonstrate that the proposed controller is effective in external disturbance rejection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Disturbance rejection

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Wide-Angle Scanning Antennas for Millimeter-Wave 5G Applications

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engineering 2022年第4期8卷 60-71页

作者： Raj Mittra Abdelkhalek Nasri Ravi Kumar Arya Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816USA Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electronics and Communication Engineering National Institute of Technology DelhiDelhi 110040India

The fifth generation(5G)network communication systems operate in the millimeter waves and are expected to provide a much higher data rate in the multi-gigabit range,which is impossible to achieve using current wireless services,including the sub-6 GHz *** this work,we briefly review several existing designs of millimeter-wave phased arrays for 5G applications,beginning with the low-profile antenna array designs that either are fixed beam or scan the beam only in one *** then move on to array systems that offer two-dimensional(2D)scan capability,which is highly desirable for a majority of 5G ***,in the main body of the paper,we discuss two different strategies for designing scanning arrays,both of which circumvent the use of conventional phase shifters to achieve beam *** note that it is highly desirable to search for alternatives to conventional phase shifters in the millimeter-wave range because legacy phase shifters are both lossy and costly;furthermore,alternatives such as active phase shifters,which include radio frequency amplifiers,are both expensive and *** this backdrop,we propose two different antenna systems with potential for the desired 2D scan performance in the millimeter-wave *** first of these is a Luneburg lens,which is excited either by a 2D waveguide array or by a microstrip patch antenna array to realize 2D scan ***,for second design,we turn to phased-array designs in which the conventional phase shifter is replaced by switchable PIN diodes or varactor diodes,inserted between radiating slots in a waveguide to provide the desired phase shifts for ***,we discuss several approaches to enhance the gain of the array by modifying the conventional array *** describe novel techniques for realizing both one-dimensional(1D)and 2D scans by using a reconfigurable metasurface type of panels.

关键词： Luneburg lens Scanning arrays Millimeter-wave antennas Switchable phase shifters Reconfigurable metasurfaces

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Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

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Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

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Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1331-1341页

作者： Zhiming Zhang Shangce Gao MengChu Zhou Mengtao Yan Shuyang Cao Faculty of Engineering University of ToyamaToyama 930-8555Japan Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Gaoling School of Artificial Intelligence Renmin University of ChinaBeijing 100872China College of Civil Engineering Tongji UniversityShanghai 200092China IEEE

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering ***,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow *** this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity ***-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored *** our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the *** method significantly reduces the workload of data measure-ment in practical engineering *** experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual ***,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field *** with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence *** confirmation underscores the considerable potential of MSU for practical applications in real engineering *** code is available at https://***/zhangzm0128/MSU.

关键词： Convolutional neural network deep learning recurrent neural network turbulence prediction wind load predic-tion.

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Forwarding Strategy Performance Using NFD Pipeline Measurements on Vehicular Named Data Networking (V-NDN) 9

Forwarding Strategy Performance Using NFD Pipeline Measureme...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ahdan, Syaiful Syambas, Nana Rachmana Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

The Named Data Networking (NDN) paradigm has been used as a promising vehicle communication model, namely the Vehicular Named Data Networking (V-NDN) model. In NDN, delivery of interest in the NDN network is done by selecting an existing interface. The problem is as to which interface is right in terms of fulfilling the request that suits the user. In this study, we compare the effectiveness of each forwarding strategy by creating new scenarios, such as variations in the number of interests and simulation time. We also measure the effectiveness of the forwarding strategy using the parameters specified in the NFD Forwarding Pipeline. The conclusion from the experiment is that superior forwarding strategy recommendations are based on content store (CS) management and data compatibility obtained based on consumer demand for producers. © 2023 IEEE.

关键词： Pipelines

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Advances in random topology

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Journal of Applied and Computational Topology 2024年第6期8卷 1445-1448页

作者： Bobrowski, Omer Yogeshwaran, D. School of Mathematical Sciences Queen Mary University of London Viterbi Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Theoretical Statistics and Mathematics Unit Indian Statistical Institute

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Physical Layer Authentication Using Ensemble Learning Technique in Wireless Communications

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computers, Materials & Continua 2022年第12期73卷 4489-4499页

作者： Muhammad Waqas Shehr Bano Fatima Hassan Shanshan Tu Ghulam Abbas Ziaul Haq Abbas Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan School of Engineering Edith Cowan UniversityJoondalup Perth6027WA Australia Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan

Cyber-physical wireless systems have surfaced as an important data communication and networking research *** is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking *** to their high reliability,sensitivity and connectivity,their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping,spoofing,botnets,man-in-the-middle attack,denial of service(DoS)and distributed denial of service(DDoS)and *** methods use physical layer authentication(PLA),themost promising solution to detect ***,the cyber-physical systems(CPS)have relatively large computational requirements and require more communication resources,thus making it impossible to achieve a low latency *** methods perform well but only in stationary *** have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile *** features are fed to ensemble learning algorithms,such as AdaBoost,LogitBoost and Gentle Boost,to classify *** authentication of the received signal is considered a binary classification *** transmitted data is labeled as legitimate information,and spoofing data is illegitimate ***,this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing *** detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are *** proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time.

关键词： Physical layer authentication machine learning cyber-physical systems security

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Ontology-Driven Semantic Web Framework with Sentiment Analysis for Risk Assessment and Diagnosis of Diabetes Mellitus 4

Ontology-Driven Semantic Web Framework with Sentiment Analys...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Velmurugan, R. Kumar, R. Rajan, M. Sundar Sivaraj, V. Karnataka Bengaluru India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798331523923

This paper uses semantic web and ontology techniques to predict the risk analysis of patients with diabetes mellitus. The data is collected from patients through personal interaction and by accessing their previous medical history. However, during personal interactions, medical staff often fail to gather all the necessary details for an accurate diagnosis, and patients may lose their previous medical records, which hinders doctors' ability to gain a full understanding of the problem. To address this issue, ontology techniques are employed, and additional details are collected from patients, such as their food habits, smoking history, alcohol consumption, sexual activity, and cardiovascular health. Using the collected data, ontology and semantic web technologies are applied to predict the risk analysis of diabetic patients. The results are visualized in a graph, providing a more comprehensive and accurate assessment of the patients' health risks. © 2025 IEEE.

关键词： Electronic health record

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Dynamic Gesture Recognition for Data Glove Based on Multi-stream One-dimensional Convolution 7th

Dynamic Gesture Recognition for Data Glove Based on Multi-st...

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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Hu, Zhenyu Shang, Jie Wang, Xun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Ningbo Institute of Materials Technology and Engineering CAS Ningbo China

ISBN: (纸本)9789819927296

In order to meet the demand for gesture recognition in the field of human–computer interaction, a new method for gesture recognition based on wearable data gloves is proposed. This method utilizes a data glove to collect specific 10 dynamic gesture data, which includes five finger bending data and six axis inertia data collected by MEMS-IMU on the back of the glove. After filtering and other data preprocessing operations are carried out on the collected data, a multi-feature fusion one-dimensional convolutional neural network (MS-1DCNN) is used to conduct deep learning model training on the gesture data. This allows for the classification and visualization of dynamic gestures under a custom gesture dataset. In real-time gesture classification experiments, a fixed-length window data interception algorithm is used for data sampling, and classification recognition is carried out by the model. The experimental results demonstrate that the one-dimensional convolutional neural network with multi-feature fusion proposed in this paper can accurately classify 10 kinds of dynamic gestures, with a recognition rate of 97.88%. This method can be used for gesture interaction in specific scenes, improving the convenience of human–computer interaction and expanding the usage scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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