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

作者： Tahim, Ekamjot Singh Kaushik, Abhay Rahman, Shadman Saqlain Ramesh, Rahul Raman Jatskevich, Juri University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798331540319

Interior permanent magnet synchronous machines (IPMSMs) are widely used in industrial applications, electric propulsion and precision applications. Accurate and efficient simulations of IPMSM drives are essential for the design and tuning of such systems. Field-oriented control (FOC) is commonly used to achieve precise control by decoupling the stator currents into flux- and torque-controlling components. The FOC-based controllers, however, require addressing the nonlinear flux-current relationships in IPMSMs caused by saliency, magnetic saturation, and cross-coupling effects. Different methods, such as parameter estimation and look-up tables (LUTs) techniques, are used to model these nonlinearities, with LUTs offering accuracy but demanding substantial memory allocation. Alternatively, memory-compact models such as polynomial-based models are used when systems impose constraints on memory allocation. This paper presents the efficient implementation of both the LUT-based and polynomial-based IPMSM drive system models for offline and real-time simulators. The accuracy of the models is verified through offline studies performed in MATLAB/Simulink and OPAL-RT's OP5700 real-time simulator. The obtained results demonstrate the difference in system resource utilization, such as memory requirement and computational efficiency, when using LUT-based and polynomial-based IPMSM models. © 2024 IEEE.

关键词： MATLAB

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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The Optimum Switching Bilateral of Various Mix Noise Filters to reduce Medical X-Ray Images 12

The Optimum Switching Bilateral of Various Mix Noise Filters...

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12th International electrical engineering Congress, iEECON 2024

作者： Langampol, Kriengkri Prayote, Akara Lee, Wilaiporn Srisomboon, Kanabadee Rajamangala University Of Technology Phra Nakhon Faculty Of Engineering Department Of Computer Engineering Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Computer And Information Science Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Thailand

ISBN: (纸本)9798350383591

In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the symptom diagnosis applications. However, the medical image quality may drop due to the imperfection of devices and the environment. Noise is one of the most critical factors that reduces the image quality where the noise may form by combining two types of noises. In this paper, we considered four well-known mixed noise filters including TF, BM3D, NCSR and OSBF filters. Since the OSBF filter is the alternative version of SBF filter that requires to determine the optimal weights, therefore we first determine the optimal weights for the medical image under the combination of Gaussian noise and salt-and-pepper noise. Then, we evaluate the filtering performance under three metrics including PSNR, SSIM and filtering time. As a result, OSBF filter achieves the best filtering performance since it can reduce the noise effect, preserve image details and consumes the short filtering time. On the other hand, the filtering performance of the TF filter suffers from the combining of salt-and-pepper noise. © 2024 IEEE.

关键词： Salt and pepper noise

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DualViT: A Hierarchical Vision Transformer for Broad and Fine Class Embeddings 27th

DualViT: A Hierarchical Vision Transformer for Broad and F...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chatterjee, Ankita Dutta, Sandip Mukhopadhyay, Jayanta Das, Partha Pratim Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science Ashoka University Haryana Sonipat India

ISBN: (纸本)9783031781650

Human brains are natural learning systems which inherently recognise image objects in a hierarchical pattern. Similar association exists among different categories of images which interact while training a deep learning model, leading to misclassification and indistinct features. As semantically related classes form clusters in the embedding space, we can list these classes under a single aggregate class, and make the learning faster by leveraging this knowledge while training a model. In this paper, we introduce a hierarchical architecture that simultaneously learns both broad-class and fine-class embeddings using transformer-based encoders. These encoders are trained alternately, and the hierarchical knowledge is encoded through tensor product between the coarse and fine class representations. Our approach has been able to achieve state-of-the-art performance on benchmark datasets with very less epochs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Ophthalmic Diseases Detection using CNN: A Comparative Analysis of ResNet and VGG Architectures 16

Ophthalmic Diseases Detection using CNN: A Comparative Analy...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Al Razi, Alif Khan, Zarif Ul Sami, Zawwad Khan, Mohammad Monirujjaman North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798331505264

Ocular illnesses present a considerable risk to worldwide public health, frequently resulting in visual impairment or blindness if not identified and addressed appropriately. This study focuses on the essential task of automated identification of eye diseases via convolutional neural network (CNN) models. With the increasing availability of digital retinal imaging data, there is a critical need for precise and efficient diagnostic tools to aid healthcare personnel in identifying common eye diseases, including diabetic retinopathy, cataracts, glaucoma, and normal eye conditions. The majority of eye disease diagnoses now occur through manual examination by ophthalmologists, which can be laborious and prone to inter-observer variability. By leveraging CNN architectures like VGG19, VGG16, ResNet18, and ResNet50, we propose a robust framework for automated eye disease detection. Our methods involve preprocessing retinal images, training CNN models on labeled datasets, and evaluating their performance on unseen data. Our results showcase high classification accuracy, sensitivity, and specificity across all disease categories. Specifically, our models achieved an accuracy of 92% for VGG19, 91% for VGG16, 87% for ResNet18, and 85% for ResNet50. The findings offer a promising solution to streamline the diagnostic process and improve access to timely eye care services. Integrating this CNN-based system into clinical workflows can expedite diagnosis, enable early intervention, and prevent vision loss, contributing to advancements in computer-aided diagnosis systems and enhancing efficiency in medical imaging analysis. © 2024 IEEE.

关键词： Ophthalmology

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Technological advancements in the detection and diagnosis of migraine: A review 4th

Technological advancements in the detection and diagnosis of...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Kaur, Shivneet Kaur, Parneet Gosain, Monika Kaur, Roopkamal Department of Computer Science & Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Headaches are the most prevalent medical condition in the globe. The migraine is one of the various types of headaches. Along with their excruciating headaches, migraineurs often have nausea, vomiting fits, and light and sound sensitivities. Individuals have unique symptoms, some of which may be more intense than others. A migraine episode is about to begin when an aura, a symptom, appears. Based on the nature of symptoms, migraines can be divided into two basic categories: aura-producing and aura-less. Every individual has different triggers for migraine. This study provides an overview of the state-of-the-art deep learning and machine learning techniques for migraine identification. The primary subjects of this study include migraine kinds, phases, causes, and symptoms. © 2025 the Author(s).

关键词： Diseases

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A Robust Malware Classification Approach Leveraging Explainable AI

A Robust Malware Classification Approach Leveraging Explaina...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Hafiz, Md. Ferdous Bin Khan, Niaz Ashraf Kamal, Zahid Hossain, Sohrab Barman, Shohag University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh East Delta University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798350375237

Malware detection is a critical aspect of cybersecurity, with the constant evolution of malicious software posing significant challenges to computer systems and networks. Traditional security measures often struggle to detect obfuscated malware, which employs sophisticated techniques to evade detection. This research explores the utilization of memory analysis techniques coupled with machine learning and deep learning algorithms, augmented by Explainable AI (XAI), to enhance malware detection and classification. Leveraging a well-balanced dataset specifically tailored for memory analysis, various algorithms including Random Forest, XGBoost, Logistic Regression, LSTM, GRU, ANN, and hybrid models were employed for multiclass classification. Ensemble methods were also implemented to improve classification accuracy. The results demonstrate the efficacy of ensemble methods like Random Forest and XGBoost in accurately detecting obfuscated malware, with Random Forest achieving an impressive accuracy of 87.30%. Furthermore, the integration of GRU and LSTM architectures in hybrid models showed promising results, highlighting the effectiveness of combining different neural network architectures for improved performance in multiclass classification tasks. Through Explainable AI techniques such as LIME, insights into the underlying features and decision criteria influencing classification outcomes were provided, enhancing the interpretability and transparency of the models. This research contributes to advancing the field of malware detection and combating evolving cyber threats, with practical implications for real-world cybersecurity applications. © 2024 IEEE.

关键词： Cybersecurity

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Multi-wavelength nanowire micro-LEDs for future high speed optical communication

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Opto-Electronic Advances 2024年第3期7卷 1-3页

作者： Ayush Pandey Zetian Mi Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA

The future of optoelectronics is directed towards small-area light sources,foremost being ***,their use has been inhibited so far primarily due to fabrication and integration challenges,which impair efficiency and ***,bottom-up nanostructures grown using selective area epitaxy have garnered attention as a solution to the aforementioned *** Fu *** used this technique to demonstrate uniform p-i-n core-shell InGaAs/InP nanowire array light emitting *** devices are capable of voltage and geometry-controlled multi-wavelength and high-speed *** publication accentuates the wide capabilities of bottom-up nanostructures to resolve the difficulties of nanoscale optoelectronics.

关键词： communication high operations

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Investigation of Discrepancies Between Induction Motor Test Results and Design Simulation

Investigation of Discrepancies Between Induction Motor Test ...

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2024 International Conference on electrical Machines, ICEM 2024

作者： Falekas, Georgios Kolidakis, Zafeirios Karlis, Athanasios Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi Greece

ISBN: (纸本)9798350370607

This work investigates discrepancies in parameters and performance between an induction machine design simulation and the produced unit. The squirrel cage motor is explored analytically and in finite element space based on the complete design blueprints and material properties. The built motor is subjected to the DC, no-load, locked-rotor, and load tests. The simulated model is created parametric, according to the manufacturing process and tolerances. Solution space search results indicate that performance and parameters can deviate with minor structural changes. Reliable simulation prior to production can greatly reduce production time and cost, while improving performance and allowing for optimization. The resulting approach will be used as a basis for a manufacturing digital twin in further collaboration. © 2024 IEEE.

关键词： Squirrel cage motors

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Operation Analysis and Verification of H-Bridge Modular Cascaded Linear Amplifier to Improve Efficiency by Increasing the Number of Cells 50

Operation Analysis and Verification of H-Bridge Modular Casc...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ishida, Shunsuke Obara, Hidemine Department of Electrical and Computer Engineering Yokohama National University Yokohama Japan

ISBN: (纸本)9781665464543

The harmonic and electromagnetic noise issues by switching operations are becoming more serious due to the enhancement of the fast-switching capability of power devices. A modular cascaded linear amplifier (MCLA) has been proposed as a power conversion circuit that does not generate electromagnetic interference without any output filter by utilizing partial linear operation. An H-bridge MCLA can improve efficiency by increasing the number of cascaded H-bridge cells and applying unequal DC capacitor voltages. In this study, the operating modes of the MCLA are analyzed to increase the number of H-bridge cells and enhance the efficiency. Based on the analysis, an extended control scheme including capacitor voltage balancing for the MCLA with four H-bridge cells is developed utilizing a regularity of the operation, and an experiment was conducted using a laboratory prototype. It is verified that the prototype MCLA realizes sinusoidal voltage output with balanced capacitor voltages, and achieves the efficiencies of 88.8% and 92.1% under equal and unequal DC capacitor voltages, respectively, even in a linear amplifier. © 2024 IEEE.

关键词： Capacitor bank

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