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2022 International Conference on Emerging Technologies in electronics, Computing and Communication, ICETECC 2022

作者： Hussain, Shouaib Yuedong, Zhan Raza, Asif Kunming University of Science and Technology Department of Information Engineering and Automation Kunming650504 China University of Electronic Science & Technology of China China

ISBN: (纸本)9781665490870

When it comes to the transport sector, fossil fuel consumption is one of the major pressing concerns in today's globe. Due to the usage of the huge amount of fuel consumption in recent years that creates pollution. To reduce pollution, various technologies are introduced in vehicles to reduce fuel consumption like fuel cells, photovoltaic cell, regenerative braking, dynamo, super-capacitor, batteries, etc. Therefore, environmentally friendly and new energy technologies are needed to overcome fossil fuel consumption in the transport sector. The aim of this research to design an energy system is to charge the vehicle battery when the internal oil of the shock absorber is heated during vehicle bumping on roads. This research contains the thermopile installed in the shock absorber for the vehicle suspension system, to generate and save power in the battery with the help of power electronics (Ultra Low Power DC-DC Converter) using Proteous Software. The simulation results suggested that thermopile with Ultra Low Power DC-DC Converter has the potential to harvest low power as 0-20mV and boosted up to 13.7-13.9V and 4-6mA current is generated. This is further regulated to 12V for storing in the battery and enhances the vehicle battery efficiency. © 2022 IEEE.

关键词： Fuel cells

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis 2

Complex Permeability Determination for EE Core Based on Flex...

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2nd China Power Supply Society Electromagnetic Compatibility Conference, CPEMC 2024

作者： Jie, Huamin Zhao, Zhenyu Chang, Yongqi Wang, Changdong Jiang, Shiqi Gao, Richard Xian-Ke See, Kye Yak School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Institute of High Performance Computing A*STAR Singapore Singapore

ISBN: (纸本)9798331509552

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz. ©2024 IEEE.

关键词： Magnetic flux

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Research on 256QAM with Large Instantaneous Bandwidth 16

Research on 256QAM with Large Instantaneous Bandwidth

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16th IEEE International Conference on electronic Measurement and Instruments, ICEMI 2023

作者： Guo, Lianping Yu, Xiaoyu Zeng, Hao Tian, Yu Ye, Peng Wang, Meng Liu, Xiao School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study UESTC Shenzhen China School of Automation Engineering UESTC Chengdu China Co. Ltd Department of Research and Development Dongguan China

ISBN: (纸本)9798350327144

As an efficient modulation technique for joint carrier amplitude and phase, Quadrature Amplitude Modulation (QAM) is a widely used modulation method in modern communications. This article designs a system of 256QAM modulation signal synthesis technology, which supports scrambling, serial data to parallel data, differential coding, channel coding, constellation mapping, shaping filtering, and digital modulation. This article also provides verification tests for the key blocks of the 256QAM algorithm, including the functional test of the scrambling block, the functional test of the shaping filter module, and the functional test of the overall modulated signal. The test results show that the designed module meets the design requirements and meets the expected indicators. © 2023 IEEE.

关键词： Quadrature amplitude modulation

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Segmentation of Fish in the Natural Habitat Using Deep Learning for Fish Size Measurement Application

Segmentation of Fish in the Natural Habitat Using Deep Learn...

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International Conference on Green technology and Sustainable Development (GTSD)

作者： Quang-Huy Nguyen Hoang-Minh-Quang Le Thi-Thao Tran Tuan Ninh Nguyen Van-Truong Pham Department of Automation Engineering School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

Assessing the size of fish in their natural habitat could provide crucial information for managing and monitoring these resources, thereby enabling the development of sustainable fishing and resource conservation strategies. However, manual methods are time-consuming, labor-intensive, and can have adverse effects on the health of the fish. Therefore, this study proposes a novel automated monitoring method by utilizing fish image segmentation, which allows for easier estimation of their size. Our proposed model namely AquaAttSeg has Ushape architecture with an encoder and a decoder. AquaAttSeg utilizes the PVTv2-B3 model as the backbone of encoder to enhance the ability to extract semantic information from input. In contrast to conventional U-shape models that directly connect low-level features from the encoder to high-level features in the decoder, leading to information imbalance, we proposed the HFIM module, which includes two our proposed modules SAM and CAM to replace conventional skip connections in order to enhance important information from the encoder to the decoder and enhance the processing of low-level features to extract more semantic information before combine with high-level features to reduce the information imbalance. In the decoder stage, we also proposed the Convmix Transpose Custom block, which can effectively synthesize rich and multidimentional information between the encoder and decoder to efficiently recover lost information and enhance the performance of the model. The performance of our proposed model has been tested on two datasets including Deepfish dataset and SUIM dataset. Additionally, we have compared the results of our model with other state-of-the-art models, demonstrating that our model produces highly competitive and promising outcomes.

关键词： Image segmentation Computational modeling Habitats Semantics Fish Feature extraction Decoding

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MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation

arXiv

引用

arXiv 2024年

作者： Nguyen, Thi-Nhu-Quynh Ho, Quang-Huy Nguyen, Duy-Thai Le, Hoang-Minh-Quang Pham, Van-Truong Tran, Thi-Thao Department of Automation Engineering School of Electrical and Electronic Engineering Hanoi University of Science and Technology Viet Nam

Early detection of skin abnormalities plays a crucial role in diagnosing and treating skin cancer. Segmentation of affected skin regions using AI-powered devices is relatively common and supports the diagnostic process. However, achieving high performance remains a significant challenge due to the need for high-resolution images and the often unclear boundaries of individual lesions. At the same time, medical devices require segmentation models to have a small memory footprint and low computational cost. Based on these requirements, we introduce a novel lightweight model called MambaU-Lite, which combines the strengths of Mamba and CNN architectures, featuring just over 400K parameters and a computational cost of more than 1G flops. To enhance both global context and local feature extraction, we propose the P-Mamba block, a novel component that incorporates VSS blocks alongside multiple pooling layers, enabling the model to effectively learn multi-scale features and enhance segmentation performance. We evaluate the model’s performance on two skin datasets, ISIC2018 and PH2, yielding promising results. Our source code is publicly available at: https://***/nqnguyen812/MambaU-Lite. © 2024, CC BY.

关键词： Biomedical equipment

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Reducing Overfitting Risk in Small-Sample Learning with ANN: A Case of Predicting Graduate Admission Probability 1st

Reducing Overfitting Risk in Small-Sample Learning with ANN:...

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Han, Mengjie Cai, Daomeng Huo, Zhilin Shen, Zhao Tang, Lianghu Yang, Shan Wang, Cong School of Marxism University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Chengdu China School of Mechanical Engineering and Automation Beihang University Beijing China CSCC System Engineering Research Institute Beijing China The Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China Department of Chemistry Physics and Atmospheric Sciences Jackson State University JacksonMS United States

ISBN: (纸本)9789819712762

AI-assisted personal educational career planning holds immense promise, especially with artificial neural network algorithms demonstrating significant potential in predicting graduate admission probabilities for specific schools. However, issues like graduate admission probability prediction are typical instances of small-sample learning problems. Overcoming neural network overfitting in small-sample learning is a major challenge. To address this, we first introduce a method that cyclically runs shallow neural networks to avoid the traditional use of a high number of epochs called Cyclic-LightNet(CLN). Secondly, in previous research, a validation set is typically employed to promptly detect overfitting. However, this approach further depletes the actual training data in the dataset. Consequently, we employ a dynamic training method during the training process called Dynamic Training Set Augmentation(DTSA), supplementing the training set with the validation set. This method effectively mitigates the risk of overfitting. Finally, we put CLN and DTSA into practice on publicly available small-sample datasets, achieving precise predictions of graduate admission probabilities. Experimental results demonstrate a significant improvement in prediction accuracy and effective resistance against overfitting risk. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural networks

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LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation

arXiv

引用

arXiv 2024年

作者： Tran, Ngoc-Du Tran, Thi-Thao Nguyen, Quang-Huy Vu, Manh-Hung Pham, Van-Truong Department of Automation Engineering School of Electrical and Electronic Engineering Hanoi University of Science and Technology Viet Nam

The emergence of deep learning techniques has advanced the image segmentation task, especially for medical images. Many neural network models have been introduced in the last decade bringing the automated segmentation accuracy close to manual segmentation. However, cutting-edge models like Transformer-based architectures rely on large scale annotated training data, and are generally designed with densely consecutive layers in the encoder, decoder, and skip connections resulting in large number of parameters. Additionally, for better performance, they often be pretrained on a larger data, thus requiring large memory size and increasing resource expenses. In this study, we propose a new lightweight but efficient model, namely LiteNeXt, based on convolutions and mixing modules with simplified decoder, for medical image segmentation. The model is trained from scratch with small amount of parameters (0.71M) and Giga Floating Point Operations Per Second (0.42). To handle boundary fuzzy as well as occlusion or clutter in objects especially in medical image regions, we propose the Marginal Weight Loss that can help effectively determine the marginal boundary between object and background. Additionally, the Self-embedding Representation Parallel technique is proposed as an innovative data augmentation strategy that utilizes the network architecture itself for self-learning augmentation, enhancing feature extraction robustness without external data. Experiments on public datasets including Data Science Bowls, GlaS, ISIC2018, PH2, Sunnybrook, and Lung X-ray data show promising results compared to other state-of-the-art CNN-based and Transformer-based architectures. Our code is released at: https://***/tranngocduvnvp/LiteNeXt. © 2024, CC BY.

关键词： Medical imaging

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Control and Stability of Large-scale Power system with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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Design and Control of Magnetic-driving Fiber-coupled system

Design and Control of Magnetic-driving Fiber-coupled System

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Qi Xu, Han Zhang, Xiwei Li, Yuming Liu, Li School of Electronic Engineering and Automation Guilin University of Electronic Technology The Key Laboratory of Intelligent Integrated Automation in Guangxi Universities Guilin541004 China The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

The magnetic-driving fiber-coupled systems are one of the hot medical device research topics for the treatment of laryngeal cancer. However, its narrow steering scope limits its practical application. To address this issue, this paper proposes a novel magnetic-driving fiber-coupled system, which has a larger steering scope than its counterpart. And the current estimation equation of our system is derived so as to provide the corresponding current to the coils according to the desired magnetic field excitation. Based on the obtained current estimation equation, the PID controller is applied to the system laser scanning control. The effectiveness of the PID controller for the proposed system and the influence of the expansion of the steering scope on the PID control performance will also be given in this paper. © 2022 IEEE.

关键词： Three term control systems

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