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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Darwich, Mahmoud Khalil, Kasem Ismail, Yasser Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering MS38677 United States Southern University A&m College Electrical Engineering Department Baton RougeLA70807 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798350302103

The exponential increase in video streaming has overburdened data centers, making efficient video streaming in the cloud essential. One potential solution is the tradeoff between transcoding and storing a video stream, as storing operations are cheaper than transcoding. This research explores the use of machine learning ARIMA algorithms to predict the number of views for each video in the repository for the next period of time. This enables informed decisions about which videos to keep or delete from the cloud, ultimately reducing cloud service costs. The study evaluates the accuracy of the ARIMA model in predicting future views for three datasets of varying sizes and levels of variability. Our results show 82% of predicting accurate views, indicating that the ARIMA model can effectively capture the overall trend of video views, providing valuable insights for service providers looking to optimize their storage management practices and reduce costs. This research contributes to the development of more cost-efficient video streaming in the cloud, which is essential for meeting the increasing volume of video stream traffic. By leveraging the ARIMA model, cloud-based platforms can make informed decisions on storage management, enhancing their overall service quality and user experience. © 2023 IEEE.

关键词： ARIMA cloud machine learning video streaming

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Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Jingyao Yuan, Haitao Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios. © 2024 IEEE.

关键词： Mobile edge computing

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A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

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Nano Research 2024年第10期17卷 9181-9192页

作者： Negar Hosseinzadeh Kouchehbaghi Maryam Yousefzadeh Aliakbar Gharehaghaji Safoora Khosravi Danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu Terasaki Institute for Biomedical Innovation Los AngelesCA 91367USA Department of Textile Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez Avenue*** TehranIran Department of Electrical and Computer Engineering University of British ColumbiaVancouverBC V6T1Z4Canada Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90007USA

Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.

关键词： wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber

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Underwater Image Enhancement Based on Improved U-Net Convolutional Neural Network 18

Underwater Image Enhancement Based on Improved U-Net Convolu...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Wang, Zhengcai Zhang, Ke Yang, Zhihao Da, Zikai Huang, Sanao Wang, Peizhen Anhui University of Technology School of Electrical and Information Engineering Ma'Anshan China Northeastern University College of Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350312201

Aiming at the problems of low contrast, dim and color distortion of underwater images caused by the attenuation and scattering of light in water propagation, an underwater image enhancement algorithm, which is based on a U-Net convolutional Neural Network is proposed. The algorithm consists of an image synthesis module and an image enhancement module. Firstly, a style transfer network based on transfer learning is built and employed to synthesize underwater images from clear images, image pairs are composed of these synthesized and original images for data expansion. Secondly, an underwater image enhancement network with a U-shaped convolutional variational autoencoder is constructed, and the image pairs serve as input to the second module for training. The qualitative and quantitative analysis results show that the proposed algorithm has good performs favorably in the color fidelity and detailed reservation of the target object. © 2023 IEEE.

关键词： Convolutional neural networks

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Speech Enhancement Algorithm using Deep Learning and Hahn Polynomials 17

Speech Enhancement Algorithm using Deep Learning and Hahn Po...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Al-Zubaidi, Ammar S. Abduljabbar, Riyadh Bassil Mahmmod, Basheera M. Abdulhussain, Sadiq H. Naser, Marwah A. Alsabah, Muntadher Hussain, Abir Al-Jumeily, Dhiya Computer Center University of Baghdad Baghdad Iraq Department of Computer Engineering University of Baghdad Baghdad Iraq Medical Technical College Al Farahidi University Baghdad Iraq Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates School of Computer Science and Mathematics Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9798350368697

Speech enhancement algorithms and machine learning can play a fundamental role in signal processing to improve speech quality. These techniques can be used to reduce noise and distortions in speech signals, hence ensuring clearer and more intelligible speech. By leveraging advanced machine learning, speech enhancement algorithms not only improve the listener’s auditory system, but also increase the efficacy of speech recognition systems. In particular, deep learning is a class of machine learning techniques, which have recently been used in speech enhancement. This paper proposes the use of Discrete Hahn polynomials (DHPs) o extract spectral features from noisy signals using fully connected neural networks and convolutional neural network. Deep learning can efficiently capture the contextual information of speech signals, resulting in superior improvements in speech quality and intelligibility properties. The results are evaluated based on the well-known TIMIT database. The results show that the presented model is able to enhance the speech signal for different conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Auto Image Captioning Using LSTM and Densenet Network 6

Smart Auto Image Captioning Using LSTM and Densenet Network

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arumugam, M. Vijayakumar, P. Siddharrth, G.M. Rohini, J. Karthikeyan, A. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350304480

The integration of semantic attention increase photo captioning ability. The premise underlying semantic attention-based techniques is that the model should prioritize words or qualities with high semantic value. Prior studies focused on the attribute detector and the captioning network as distinct entities. This hampers the best use of semantic data. Image captioning is the computational process of producing accurate written descriptions of an image's or video's visual content. Captioning images makes it easier to construct contextualized interpretations, allowing people to situate photographs within a larger context. Using photo captions can be useful in a variety of contexts, such as conducting research with an unlabeled image collection or attempting to identify. previously unknown patterns. Subtitles added to the photos using a Deep Learning Model. Recent advances in the fields of deep learning and natural language processing have made it much easier to generate captions for input photographs. This study will use neural networks to generate image captions on their own. The Long Short-Term Memory (LSTM) model, a type of recurrent neural network, used as an encoder, together with a pre-trained lexicon and visual attributes. This enables the extraction of image data and the generation of captions. © 2023 IEEE.

关键词： Deep Learning Model Densenet Network Long Short-Term Memory Remote Sensing

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Research on An Interleaved Phase-Shift Half-Bridge LLC Resonant Converter 11

Research on An Interleaved Phase-Shift Half-Bridge LLC Reson...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Wang, Shu-Po Hong, Min-Rui Wu, Ping-Tsang Chuang, Ching-Chun Lu, Shiue-Der National Formosa University Department of Computer Science & Information Engineering Taiwan National Chin-Yi University of Technology Department of Electrical Engineering Taiwan

ISBN: (纸本)9788957083505

This paper presents the analysis and simulation for a novel high-power density electric vehicle dc-dc converter. In this paper, a novel interleaved phase-shift half-bridge converter that mainly comprises two modules of a half-bridge converter and a double full-bridge rectifier is proposed. In the case of interfacing the high-voltage battery module, the isolated DC/DC converter is used to control the voltage at the input of the battery according to the battery terminal voltage and optimize the efficiency of the dc-dc converter in a wider range of operating voltage. It operates as a voltage doubler to achieve an output voltage of 450V for the charging of a high-voltage battery. The isolated dc-dc converter is operated as an LLC resonant converter owing to its advantages of simple control and soft switching operation. This converter not only has a wide range of zero-voltage-switching (ZVS) turn-on of power switches on the primary side of the transformer but also has a zero-current-switching (ZCS) turn-off of rectifier diode on the secondary side of the transformer. The proposed converter can achieve the output voltage range of 250-450V by adapting the phase shift between the two square waves. The two LLC half-bridge resonant converters are connected in parallel, and it's operated at a fixed frequency and constant duty cycle. The simplified control strategy in the proposed converter can be achieved since the switching frequency of the converter operates at the resonant point. Finally, a 3.4kW design prototype with 400V input voltage and the 250-450V output voltage is simulated to verify the effectiveness of the phase-shift mode control method. © 2023 The Korean Institute of Power Electronics.

关键词： Resonant circuits

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Numerical Study of Liquid Ammonia Flow Cooling of Power Electronics for Electric Aviation

Numerical Study of Liquid Ammonia Flow Cooling of Power Elec...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Guo, Y. Kwon, J. Sumption, M.D. Collings, E.W. Cao, D. Meng, H. Marzang, V. Liu, X. Robert, R. Layhew, G. Schafer, D. The Ohio State University College of Engineering Department of Material Science and Engineering ColumbusOH43210 United States University of Dayton School of Engineering Department of Electrical and Computer Engineering DaytonOH45469 United States Tennessee Tech University School of Engineering Department of Electrical and Computer Engineering CookevilleTN38505 United States Tennessee Tech University College of Engineering Department of Mechanical Engineering CookevilleTN38505 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The rapid development of electric aircraft technology drives the need for highly effective cooling systems to manage the significant heat generated by high-power electronic components. In this work, liquid ammonia, with its excellent thermophysical properties including a high specific heat, and volumetric energy density, is evaluated as a good coolant (especially relevant when it will also serve aviation fuel at the same time). This work focuses on designing fuel storage and cooling strategies to address thermal management challenges both globally and locally for the electric aircraft. Aluminum was chosen as the material for the heat exchanger/heat sink due to its excellent resistance to liquid ammonia corrosion, high thermal conductivity, and low density. Using computational fluid dynamics (CFD) simulations, we analyzed the heat transfer performance and liquid ammonia flow characteristics within the aluminum heat sink. Temperature distribution of aluminum heat sink and flowing liquid are simulated to determine the viability and effectiveness of this cooling strategy. In addition, various design configurations, including fluid storage methods (cold-temperature or high-pressure) and flow channel topology, are explored to optimize the cooling efficiency. The results shows that liquid ammonia cooling is feasible to maintain the power electronics within safe operating temperatures under the given flow rates and operating conditions, especially for the cold-temperature storage strategy. This research provides valuable insights into the design and implementation of advanced cooling systems for next generation electric aircraft, indicating the potential of liquid ammonia as an effective and innovative cooling media good cooling media, despite challenges related to environmental, safety, and corrosion concerns. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Aluminum corrosion

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Improved YOLOv5 Algorithm for Power Insulator Defect Detection 13

Improved YOLOv5 Algorithm for Power Insulator Defect Detecti...

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13th International Conference on Power, Energy and electrical engineering, CPEEE 2023

作者： Chen, Hefan Zhang, Zhaoyun Dongguan University of Technology School of Electrical Engineering & Intelligentization School of Computer Science and Technology Dongguan China Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan China

ISBN: (纸本)9781665490146

Intelligent inspection of transmission lines by UAVs has become the mainstream of the industry, and insulator defect detection is a key part of the intelligent inspection operation. To address the problem of low accuracy of insulator defect detection in complex environments, this paper proposes an improved YOLOv5s-based insulator defect detection algorithm. First, the K-means algorithm is used to cluster the data set to obtain the best anchor frame size, which effectively improves the generalization ability and localization accuracy of the model;second, the Backbone part of YOLOv5s is embedded with the Coordinate Attention module to improve the feature extraction ability of the network to solve the influence of invalid features on the recognition accuracy;finally, the EIOU-Loss is used to improve the accuracy of insulator defect detection. Finally, the performance of the model is optimized using the EIOU-Loss function, and ablation experiments are set up to validate the proposed method. The experimental results show that the Precious and mAP of the improved YOLOv5s model are improved by2.S% and 1.6%, respectively, compared with the original YOLOv5s network. © 2023 IEEE.

关键词： K-means clustering

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