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Trajectory-based fast ball detection and tracking for an autonomous industrial robot system

International Journal of Intelligent systems Technologies and Applications 2021年第2期20卷 126-145页

作者： AbdElKhalek, Youssef M. Awad, Mohammed Ibrahim Abd El Munim, Hossam E. Maged, Shady A. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Mechatronics Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Engineering Galala University Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt

Autonomising industrial robots is the main goal in this paper;imagine humanoid robots that have several degrees of freedom (DOF) mechanisms as their arms. What if the humanoid's arms could be programmed to be responsive to their surrounding environment, without any hard-coding assigned? This paper presents the idea of an autonomous system, where the system observes the surrounding environment and takes action on its observation. The application here is that of rebuffing an object that is thrown towards a robotic arm's workspace. This application mimics the idea of high dynamic responsiveness of a robot's arm. This paper will present a trajectory generation framework for rebuffing incoming flying objects. The framework bases its assumptions on inputs acquired through image processing and object detection. After extensive testing, it can be said that the proposed framework managed to fulfil the real-time system requirements for this application, with an 80% successful rebuffing rate. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Object detection

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A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FOR HAND-REHABILITATION: A CASE STUDY 24

A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FO...

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24th International Conference on engineering Design, ICED 2023

作者： Bos, Mikaël Ivar Karahanoǧlu, Armaǧan Haarman, Juliet Nizamis, Kostas Chair of Systems Engineering and Multidisciplinary Design Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Interaction Design Research Group Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Human Media Interaction Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of at-home rehabilitation. The materials of these products are essential for their success, however, selecting materials for DIHP has not been investigated yet. Previous research on material selection showed that it is done strictly on material properties or based on a human-centered approach. Hence, in this study, we propose a hybrid model for choosing materials for DIHP. To achieve this, we first combined the findings of previous material selection processes into a comprehensive material selection model. We applied this model in a case study, in which we first selected three materials based on their properties. Following, we 3d printed a DIHP out of the chosen materials and tested the feeling of the materials with multiple expert groups. Our findings suggest that the proposed material selection method is promising and highlights that our comprehensive model provides more insights when compared to a strict material property-based selection. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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Analysis and Assessment of Wind Energy Potential of Almukalla in Yemen

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computers, Materials & Continua 2022年第8期72卷 3113-3129页

作者： Murad A.A.Almekhlafi Fahd N.Al-Wesabi Majdy M.Eltahir Anwer Mustafa Hilal Amin M.El-Kustaban Abdelwahed Motwakel Ishfaq Yaseen Manar Ahmed Hamza Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayelAseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayelAseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Electronic Department Faculty of EngineeringUniversity of Science and TechnologySana’aYemen

Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the *** Republic of Yemen has very good potential to use renewable ***,we find few studies on renewable wind energy in *** the lack of a similar analysis for the coastal city,this research newly investigates wind energy’s potential near the Almukalla area by analyzing wind ***,evaluation,model identification,determination of available energy density,computing the capacity factors for several wind turbines and calculation of wind energy were extracted at three heights of 15,30,and *** wind speeds were obtained only for the currently available data of five recent years,2005–*** study involves a preliminary assessment of Almukalla’s wind energy potential to provide a primary base and useful insights for wind engineers and *** research aims to provide useful assessment of the potential of wind energy in Almukalla for developing wind energy and an efficient wind *** Weibull distribution shows a perfect approximation for estimating the intensity of Yemen’s wind *** on both theWeibullmodel and the results of the annual wind speed data analysis for the study site in Mukalla,the capacity factor for many turbines was also calculated,and the best suitable turbine was *** to the International Wind Energy Rating criteria,Almukalla falls under Category 7,which is,rated“Superb”most of the year.

关键词： Almukalla energy potential Rayleigh distribution Weibull distribution wind power density wind speed

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Various parameters influence on field distribution in eccentric disc-type permanent magnet machine based on analytical method

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Australian Journal of electrical and Electronics engineering 2021年第1期18卷 21-30页

作者： Rajabi-Sebdani, Mohammad Darabi, Ahmad Faiz, Jawad Faculty of Electrical and Robotic Engineering Shahrood University of Technology Shahrood Iran Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Eccentricity fault is a common fault in axial flux permanent magnet (PM) machines that can be destructive. The finite element method (FEM) has been already used for modelling eccentricity fault in disc type PM machine. Applying three-dimensional (3D) FEM to this motor is essential. However, 3D-FEM is a very time-consuming technique. Therefore, it is more appropriate to use an analytical technique to investigate the impact of different parameters upon the motor’s performance. The existing analytical methods usually represent a qualitative overview of the faulted machine. In this paper, a precise and quick analytic method is introduced, reflecting the impact of various important parameters, including rotor angle and fault position on the motor’s performance. This method enables the calculation of the induced voltage in a search coil. Comparing the results obtained from the proposed method and FEM, in both the no-load and full-load conditions, confirms the method’s accuracy. Finally, the impact of the search coil’s various parameters on dynamic and static eccentricity faults is examined. In addition, choosing the proper search coil pitch for eccentricity faults detection is discussed. ©, Engineers Australia.

关键词： Fault detection

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Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

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24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology Research Academy 2024.

关键词： Generative adversarial networks

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Analysis of Magnetic Field Simulations of Magnetoelastic Sensors Quasi-Statically using COMSOL Multiphysics and MATLAB Software 5

Analysis of Magnetic Field Simulations of Magnetoelastic Sen...

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5th IEEE International Conference on Modern electrical and Energy System, MEES 2023

作者： Gans, Simon Molnar, Jan Naida, Vitalii Maliakova, Mariia Technical University of Košice Department of Theoretical and Industrial Electrical Engineering Košice Slovakia Technical University of Košice Department of Theoretical and Electrical Engineering Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer Systems Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Electrical Engineering Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This work describes a procedure in which a transient simulation of electromagnetic fields can be done when the electrical conductivity of the ferromagnetic medium can be effectively neglected transforming it into a quasi-static problem. A pressductor-type magnetoelastic sensor simulation model was created. The sensor's output consists of a change of the induced voltage in the secondary coil due to applied compressive stress. Induced voltage is proportional to the time-derivative of the magnetic flux flowing through the coil and therefore is transient in nature. However, transient analyses tend to be computationally expensive and can yield noisy outputs when the model is not sufficiently densely discretized and appropriate tolerances are chosen. To overcome this, a quasi-stationary analysis of the same system is introduced and computed using the COMSOL Multiphysics FEM simulation software with further processing in the MATLAB environment, and the results are compared. © 2023 IEEE.

关键词： Multiphysics

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A 1D Residual CNN with SMOTE-Tomek and Cosine Annealing for Enhanced ECG Arrhythmia Classification

A 1D Residual CNN with SMOTE-Tomek and Cosine Annealing for ...

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IEEE Latin American Symposium on Circuits and systems (LASCAS)

作者： Feng Jiang Yang Wei Lim Siti Anom Ahmad Faisul Arif Ahmad Luthffi Idzhar Ismail Ahmed Faeq Hussein Fakhrul Zaman Rokhani Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Department of Electrical and Electronics Engineering Faculty of Engineering Universiti Putra Malaysia Department of Biomedical Engineering Faculty of Engineering Al-Nahrain University Baghdad Iraq

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper proposes a 1D residual convolutional neural network (CNN) for classifying arrhythmias based on electrocardiogram (ECG) signals. The additional residual blocks and skip connections effectively alleviate the gradient problem in deep neural networks by providing a direct path for gradient propagation and enhancing the feature information propagation. In addition, the SMOTE-Tomek technique was demonstrated in this work to mitigate the effects of class imbalance caused by a large amount of data during model training and effectively increase the SNR of the training set. The robustness of the model is increased by adding the cosine annealing technique and L 2 regularization. The proposed model was evaluated with various performance metrics, resulting in an accuracy of $\mathbf{9 8. 7 4 \%}$ , sensitivity of $\mathbf{9 8. 3 5 \%}$ , specificity of $\mathbf{9 1. 8 6 \%}$ , precision of $\mathbf{9 7. 9 5 \%}$ , and F1 score of $\mathbf{9 8. 1 4 \%}$ on the MIT-BIH Arrhythmia Database. The result shows better performance for classifying previously unseen data for the proposed approach as compared to the state-of-the-art works.

关键词： Training Annealing Sensitivity Arrhythmia Electrocardiography Robustness Convolutional neural networks Signal to noise ratio Residual neural networks Overfitting

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Q-switched triple-wavelength erbium-doped fiber laser with black phosphorus absorber

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Optik 2024年 311卷

作者： Alghamdi, Turki Ali Adwan, Somaya Arof, Hamzah Harun, Sulaiman Wadi Department of Computer Sciences Faculty of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

We achieved a notable advance in laser technology by generating a triple-wavelength output in a Q-switched Erbium-doped fiber laser (EDFL) using a new black phosphorous saturable absorber (BPSA). The BPSA was made by exfoliating multilayer BP material from a bulk crystal and transferring it onto a fiber ferrule tip. Upon integration into an EDFL ring cavity, this BPSA facilitated the production of Q-switched pulses at wavelengths of 1530.4, 1531.5, and 1532.6 nm. This distinctive triple-wavelength lasing phenomenon, characterized by uniform spacing, arises from standing wave interference within the ring cavity, leveraging the highly nonlinear active gain medium. With an increase in pumping strength from 147 mW to 223 mW, the repetition rate escalated from 41.15 to 60.83 kHz, accompanied by a reduction in pulse width from 8.45 µs to 6.06 µs. Notably, the highest recorded pulse energy peaked at 11.8 nJ. These findings highlight the efficacy of BP as a Q-switcher for the generation of multi-wavelength pulses, marking a significant advancement in laser technology. © 2024 Elsevier GmbH

关键词： Fiber lasers

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Analysis of Harmonic and Noise Influence on Temporal Envelope Estimation for Detecting Broken Rotor Bars in Asynchronous Machines

Analysis of Harmonic and Noise Influence on Temporal Envelop...

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electrical engineering and Automatic Control (ICEEAC), International Conference on

作者： Hamid Khelfi Samir Hamdani Salim Hamouda Kamel K Baghdouche Zineb Ammari Abdelkader Attab Department of Electrical Engineering (DGE) Faculty of Electrical Engineering (FGE) Electrical and Industrial Systems Laboratory (EISL). University of Science and Technology Houari Boumediene (USTHB) Algiers Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

This research investigates the impact of harmonics generated by inverters and different level of Signal-to-Noise Ratio (SNR) measurement, on diagnostic methods used to identify damaged rotor bars in induction machines. The purpose of this study is to assess the efficiency of envelope estimation techniques, namely peak detection, in power supply networks that are affected by inverter harmonics. The investigation integrates both time domain envelope estimation and frequency domain analyses of signals obtained through stator current measurements. Initially, the methodology and theoretical background of the proposed technique are explained. Subsequently, the study validates spectral analysis of the modulated signal through practical testing under two power supply conditions: signals sourced from inverters and network sources. Furthermore, the research examines the impact of different SNR levels on stator current measurements and their implications for the sensitivity and precision of envelope extraction in the diagnostic method. Spectrum analysis of the stator current envelope demonstrates successful defect detection across various SNR levels, even amidst the presence of inverter-induced harmonics, provided an appropriate filter is employed.

关键词： Power supplies Estimation Rotors Stators Harmonic analysis Inverters Power harmonic filters

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Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches 5

Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis ...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Sultan Bin Habib, Al Zadid Bin Syed, Md Asif Islam, Md. Ekramul Tasnim, Tanpia West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States Stamford University Bangladesh Department of Computer Science & Engineering Dhaka1217 Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Narayanganj Dhaka1461 Bangladesh

ISBN: (纸本)9798350394290

Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate;they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy. © 2023 IEEE.

关键词： Adaptive boosting

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