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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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A New Pulse Skipping Control for Series Resonant Converter 3

A New Pulse Skipping Control for Series Resonant Converter

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Bhowmick, Supratik Chakraborty, Soham Maiti, Suman Indian Institute of Technology Kharagpur Department of Electrical Engineering Kharagpur721302 India Calcutta University UG Program of Electrical Engineering Department of Applied Physics Kolkata700009 India

ISBN: (纸本)9798331540319

This paper proposes a pulse skipping based combined frequency and duty ratio control for a series resonant converter which is targeted for voltage regulator applications. The proposed pulse skipping control combines duty ratio based phase shifted control for maintaining output voltage at its reference value, along with frequency based control which assists in development of the pulse skipping algorithm. This algorithm is structured so that pulses are skipped in generated gate pulses for upper switching devices present in the converter whenever the output voltage tries to be higher than its specified maximum value or be lower than its specified minimum value. These maximum and minimum voltages are determined based on its reference voltage and allowable voltage ripple within which the oscillations in output voltage are desired to be in. The converter system with the proposed control method is simulated in MATLAB/Simulink environment to validate its performance under transient conditions e.g., variation in load resistance and output voltage reference. The system performance with proposed method is also compared with conventional method where the proposed control demonstrates its superiority by significantly minimizing the output voltage ripple. © 2024 IEEE.

关键词： Resonant converters

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Performance Optimization of Series Resonant Converter by Combined Frequency and Duty Ratio Control 3

Performance Optimization of Series Resonant Converter by Com...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Chakraborty, Soham Bhowmick, Supratik Maiti, Suman Calcutta University UG Program of Electrical Engineering Department of Applied Physics Kolkata700009 India Indian Institute of Technology Kharagpur Department of Electrical Engineering Kharagpur721302 India

ISBN: (纸本)9798331540319

Resonant converters are widely used for voltage regulator applications. The basic converters i.e., Series resonant Converter (SRC) and Parallel Resonant Converter (PRC) are having some merits and limitations. These are addressed in multi element topologies at the cost of adding extra elements. This work takes up the SRC and reports a combined frequency and duty ratio control for performance optimization of the SRC. The key issue of SRC is that the voltage cannot be controlled at a low Q (i.e. Quality factor) operation. For example, at no load Q becomes zero making the converter inert to frequency variation. This however can be eliminated by pulse width modulation technique. Duty ratio can be properly controlled to achieve the output voltage at an extended load range. The work further takes up challenge to make efficiency optimized. For this a combined control of duty ratio and frequency are proposed. Efficiency Optimization Control (EOC) requires two voltage sensors (one at the input side and another at the output) and a current sensor. Following this, a new minimum rms current algorithm is developed that only requires a current and a voltage sensor. All the proposed techniques are simulated and verified using MATLAB/Simulink. © 2024 IEEE.

关键词： Resonant converters

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Wearable Vibration Device to Assist with Ambulation for the Visually Impaired 14th

Wearable Vibration Device to Assist with Ambulation for the ...

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14th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2023

作者： Dow, Douglas E. Robbins, Jared J. Roberts, Kelley C. Bannish, Seth G. Cote, Bailey J. Program in Electrical Engineering Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9783031431340

People with visual impairment have increased difficulty in performing activities of daily living, such as walking without bumping into obstacles. Many assistive technologies are used to help with ambulation as one walks forward, such as a white walking cane or a service dog. These have proven to be of tremendous help, but the cane may miss suspended objects not touching the ground, and service dogs are not available to all who need them. Further assistive technologies continue to be developed and tested. In nature, those without visual acuity tend to obtain much information from their environment through the other senses, such as hearing or tactile touch. This study is exploring the mapping of obstacle detection to tactile vibration motors on the skin. Ultrasonic sensors were used to detect obstacles in the forward direction where the user would be walking, and calculate the distance. The distance was mapped to a vibration pattern, with the pattern being more intense for closer obstacles. A prototype was developed and had several tests run. Obstacle detection and distance were useful up to 3 m. The functional field of view was 10° to 30° from centerline, but became more narrow as the distance increased and for harder to detect obstacles. The distance was mapped to 3 different vibration patterns, and human subjects were able to distinguish the patterns in a consistent manner. The prototype shows promise, but more testing and development would be required toward widespread application. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Ultrasonic applications

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Coordinated-axis Weight for Real-Time Trace Transform

Coordinated-axis Weight for Real-Time Trace Transform

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2023 International electrical engineering Congress, iEECON 2023

作者： Jundang, Nattapong Sitjongsataporn, Suchada Mahanakorn University of Technology The Electrical Engineering Graduate Program Faculty of Engineering and Technology Bangkok Thailand Mahanakorn University of Technology Faculty of Engineering and Technology Department of Electronic Engineering Bangkok Thailand

ISBN: (纸本)9798350332360

This paper presents a method for building Coordinated-axis Weight (CW) that has the advantage can be used to increase the speed for converting image data into the domain of real-time trace transforms. The main idea for this method is to use a feature reduction method for image data that must be used in cooperation with supervised machine learning outcome predictions. But the obtained result still retains the distinctive features of the data image in the form of a trace transform. Start by reducing the number of features from the image data, which will be used to reduce the size of the image to the appropriate image size, which in this article will reduce the image size to 45x45 pixels. The next step is to train the data using a Decision Tree (DT) method by aligning the reduced image data in a 1D model. In addition to the image data, two more features are added, i.e. the φ and ρ positions, to inform the prediction that each position has a different value. And the last step is to bring the data to create a model for predicting the answer. The weights obtained from the model are called Coordinated-axis Weights (CW). The experiments within this paper were experimented using the Caltech-256 database, which was able to measure the MSE, RMSE, and PNSR of the images compared to the original trace transform response images at 4.1, 2.02, and 42.00, respectively, including the highest predictive speed of 0.2 ms per image. © 2023 IEEE.

关键词： Decision trees

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Variable Length Multi-Objective Whale Optimization for Trust Aware Data Gathering in Wireless Sensor Network

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IEEE Access 2025年 13卷 32100-32121页

作者： Saad, Mohammed Ayad Jaafar, Rosmina Chellappan, Kalaivani Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University Department of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Universitas Airlangga Biomedical Engineering Study Program Faculty of Science and Technology Surabaya60115 Indonesia

Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in enhancing data-gathering capabilities. However, existing methods often overlook the importance of trust, leaving the network susceptible to malicious or faulty nodes. To address this issue, we propose a novel trust-aware data-gathering algorithm, Variable Length Multi-Objective Whale Optimization Data Gathering (VLMOWO-DG). Our algorithm simultaneously optimizes energy consumption, delay, and trust while employing mobile sinks to collect data from sensor nodes. By considering trust as a key performance metric, we improve data reliability and security. The VLMOWO-DG algorithm introduces flexibility through variable-length solution representation and application-oriented operators, allowing for efficient exploration of the solution space. Simulation results demonstrate that our proposed algorithm significantly outperforms established benchmarks, NSGA-II and NSGA-III, in terms of domination and hypervolume. This leads to a remarkable improvement of 200% in data gathering performance. © 2015 The Authors.

关键词： Data collection

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Reinforced Deep Learning By Discriminant Feature Trace Transform

Reinforced Deep Learning By Discriminant Feature Trace Trans...

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2023 International electrical engineering Congress, iEECON 2023

ISBN: (纸本)9798350332360

Classification of objects using YOLO deep learning is currently very popular. When the discovered object is rotated, however, the pre-trained YOLO model frequently generates incorrect answer predictions. In order to improve the accuracy of the YOLO deep learning pre-trained model, we present in this paper a method for rotating an object's image back to its initial gain using the DFTF algorithm. The DFTF algorithm is designed to take the results of the trace transform and accumulate the values for each column in order to predict the object's degree of rotation. The results of this calculation are then used to rotate the object back to its normal axis. The experiment is therefore a collaboration between YOLO and DFTF with the aim of more accurately predicting the object's responses. Five groups of data will be chosen for experimentation, each of which can be used to determine the precision of the experimental image with rotational issues. The average of accuracy is 98.56%. © 2023 IEEE.

关键词： Forecasting

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Security and Privacy Relate Future Aspects of Artificial Intelligence-Powered Electric Vehicle technology 11

Security and Privacy Relate Future Aspects of Artificial Int...

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11th International Conference on electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Sutikno, Tole Pamungkas, Anggit Santosa, Budi Puriyanto, Riky Dwi Cahya Baswara, Ahmad Raditya Universitas Ahmad Dahlan Master Program of Electrical Engineering Yogyakarta Indonesia Yogyakarta Indonesia Universitas Ahmad Dahlan Automotive Technology of Vocational Education Yogyakarta Indonesia Universitas Ahmad Dahlan Undergraduate Program of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350355314

Artificial Intelligence (AI) and electric vehicles EVs are revolutionizing the future of transportation, automation, and connectivity. The integration of AI in EV technology heralds a transformative era in transportation, characterized by enhanced efficiency, autonomy, and user experience. However, as AI-powered EVs become increasingly prevalent, critical security and privacy challenges emerge that necessitate comprehensive scrutiny. This paper explores the future aspects of security and privacy in AI-powered EVs, highlighting potential vulnerabilities and the implications of data exploitation. Key areas of focus include the cybersecurity threats posed by interconnected vehicle networks, the safeguarding of personal data from unauthorized access, and the development of robust encryption methods to protect vehicular communication systems. Additionally, the paper examines regulatory frameworks and industry standards essential for ensuring the secure deployment of AI in EVs. By addressing these concerns, the research aims to provide a holistic understanding of the measures required to fortify the security and privacy of AI-driven electric vehicles, ultimately contributing to developing a safe and trustworthy transportation ecosystem. © 2024 IEEE.

关键词： Differential privacy

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Feature Extraction of Internal Fault Types for the Solar PV System using the Novel NIFM Techniques 9

Feature Extraction of Internal Fault Types for the Solar PV ...

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9th Asia Conference on Power and electrical engineering, ACPEE 2024

作者： Chang, Hsueh-Hsien Guo, Ren-Rong Lin, Ching-Lung Minghsin University of Science and Technology Bachelor Program of Wind Power Hsinchu30401 Taiwan Yuan Ze University Dept. Electrical Engineering Program B Taoyuan32003 Taiwan Minghsin University of Science and Technology Dept. Electrical Engineering Hsinchu30401 Taiwan

ISBN: (纸本)9798350309638

In low-voltage solar grids, traditional fault analysis and monitoring systems are used to detect power faults. These include wide-area protection technology with GPS and phasor measurement units (PMUs) and protective relays at each transmission line end. However, these designs increase data complexity and system cost. This paper uses the non-intrusive load identification technology, which involves installing a measuring device at the entrance of the extra high-voltage (EHV) transmission network. The measurement signal is processed by a proposed feature extraction method to form power energy features of various fault types in the frequency domain of the internal line of the low-voltage solar photovoltaic power grid. This approach is not only easier to install, but it is also more cost-effective. The results of the proposed method show the apparent differences for the different fault types. © 2024 IEEE.

关键词： Extraction

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