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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Matury, Sai Sathwik Sam Stewart, X. Ra, Nawin Kumba, Krishna School of Computer Science & Engineering Vellore Institute of Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350388664

The proposed work delves into the comprehensive design, development, and evaluation process of a 3D printed prosthetic arm, employing servo motors controlled by an Arduino Uno microcontroller to enable gesture mimicry through flex sensors integrated into a wearable glove. The primary objective is to provide an affordable and accessible solution for individuals with upper limb disabilities. The paper outlines the intricacies of the design process, system architecture, programming nuances, and rigorous testing procedures, showcasing promising results in terms of gesture accuracy, adaptability, and user-friendliness. The prosthetic arm demonstrates high precision in replicating various hand gestures. Future work will focus on enhancing the durability and responsiveness of the system to further improve its practical application. User feedback has been overwhelmingly positive, indicating significant potential for real-world adoption. © 2024 IEEE.

关键词： Servomotors

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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

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Journal of Energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi’an University of TechnologyXi’an 710048ShaanxiChina School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjing 211167JiangsuChina Department of Energy Aalborg UniversityAalborg 9220Denmark

To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles'degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM)network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Battery degradation trajectory Early prediction Autoencoder Synthetic data generation

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CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第10期42卷 3289-3300页

作者： Nguyen, Thai-Hoang Imran, Muhammad Choi, Jaehyuk Yang, Joon-Sung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical Engineering School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad44000 Pakistan Department of Semiconductor Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electrical and Electronic Engineering Department of System Semiconductor Engineering Yonsei University Seoul03722 Korea Republic of

Deep neural networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging nonvolatile memories (NVMs), with their better scalability, nonvolatility, and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues, such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This article proposes CRAFT, i.e., criticality-aware fault-tolerance enhancement techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of noncritical bits when more errors (due to stuck-at faults) are present in the critical bits. Experiments of CRAFT architecture with various DNN models indicate that the robustness of a DNN against stuck-at faults can be enhanced by up to 105 times on the CIFAR-10 dataset and up to 29 times on ImageNet dataset with only a minimal amount of storage overhead, i.e., 1.17%. Being orthogonal, CRAFT can be integrated with existing fault-tolerance schemes to further enhance the robustness of DNNs aga

关键词： Fault tolerance

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Signal Reconstruction Method Based CNN for Atrial Fibrillation Detection 17

Signal Reconstruction Method Based CNN for Atrial Fibrillati...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Gao, Jiahui Li, Yongjian Chen, Meng Zhang, Xiuxin Sun, Yiheng Jiang, Xinge Wei, Shoushui School of Control Science and Engineering Shandong University Jinan250100 China School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China

ISBN: (纸本)9798331507398

Wearable devices can monitor patients' heart activity in real-time, supporting early detection and treatment. However, ensuring high performance while maintaining low computational requirements is crucial for wearable devices. To enhance disease identification by providing more dimensions and features, and to improve accuracy and interpretability of data analysis, this study explores an innovative signal reconstruction method. By converting one-dimensional (I-D) time series into two-dimensional (2-D) signal matrices, this method better captures important information in the time series. Subsequently, by employing multi-scale convolutional kernels, the network structure is deepened, enabling the capture of spatial information across different ranges. This approach helps learn more complex features while effectively reducing the number of parameters. On the MIT-BIH Atrial Fibrillation Database, this method achieved an accuracy of 99.30%. Furthermore, validation on a clinical dataset yielded an accuracy of 99.04%. Compared to one-dimensional time series, this method improved accuracy by 1.17% in atrial fibrillation identification tasks. Additionally, compared to other common types of 2-D images, the new input format significantly reduces the computational burden during model training, making it more suitable for practical applications in wearable devices. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting the Power Spectrum of Amplified OFDM Signals Using Higher-Order Intercept Points

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Chinese Journal of Electronics 2022年第2期31卷 213-219页

作者： YAN Siyuan YANG Xianzhen WANG Xiaoru LI Fu Department of Electrical and Computer Engineering Portland State University School of Computer Science and Technology Beijing University of Posts and Telecommunications

Orthogonal frequency-division multiplexing(OFDM) has been developed into a popular modulation scheme for wireless communication systems, used in applications such as LTE and 5 G. In wireless communication systems, nonlinearity caused by radio frequency(RF)amplifiers will generate distortions to both passband and adjacent channels such that the transmission quality is degraded. The study of this article aims to predict the power spectrum for OFDM based signals at the output of an RF amplifier due to the nonlinearity. In this article,based on Taylor polynomial coefficients, a power spectrum expression for amplified OFDM signals in terms of intercept points(up to nth-order) is derived. This model is useful to RF engineers in choosing and testing RF amplifiers with appropriate specifications, such as intercept points and gain, to meet the requirements of wireless standards. Measurements are carried out to confirm the results of the proposed model.

关键词： adjacent channels modulation scheme radio frequency amplifiers power spectrum expression Taylor polynomial coefficients RF amplifier higher-order intercept points passband channels amplified OFDM signals OFDM modulation wireless communication systems radiofrequency amplifiers wireless standards radio networks polynomials orthogonal frequency-division multiplexing OFDM based signals

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Optimal Trajectory Planning of an Omni-directional Mobile Manipulator for Obstacle Avoidance

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Transactions of the Korean Institute of electrical Engineers 2023年第8期72卷 952-958页

作者： Kim, Minsu Sung, Young Whee School of Electrronic Engineering Kumoh National Institute of Technolgy Korea Republic of Dept. of Electrical Engineering and Computer Science DGIST Korea Republic of

The ultimate purpose of a mobile robot is to do some tasks in addition to moving to a desired point, so it is very natural for a mobile robot to be equipped with a manipulator. Among many types of mobile robots, omni-directional mobile robot has been very popular because it has advantages in trajectory planning and control. In this paper, we deal with an omni-directional mobile manipulator that consists of a four Mecanum wheeled mobile robot platform and a manipulator mounted on it. In general, it is very difficult to solve inverse kinematics for a mobile manipulator because it has infinite solutions. Omni-directional mobile robot platform can move in any direction and can rotate on a pivot in a plane, so we propose it to be modeled as a manipulator with two prismatic joints and one revolute joint. Then the overall omni-directional mobile manipulator can be considered as a manipulator with an (n+3) DOF manipulator if the mounted manipulator has n DOF. We propose a weighted pseudo-inverse based method to solve the inverse kinematics problem at velocity level for the omni-directional mobile manipulator. In our method, in addition to a primary task of following end-effector trajectory, a secondary task of obstacle avoidance is accomplished by maximizing a performance index that represents a distance between a link of the manipulator and an obstacle. Simulation results showed the feasibility of the proposed method. © 2023 Korean Institute of electrical Engineers. All rights reserved.

关键词： Manipulators

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An Improved Multi-objective Artificial Hummingbird Algorithm for Capacity Allocation of Supercapacitor Energy Storage Systems in Urban Rail Transit

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Journal of Bionic engineering 2025年第2期22卷 866-883页

作者： Xin Wang Jian Feng Yuxin Qin College of Electrical and Information Engineering Hunan University of TechnologyZhuzhou412007HunanChina Hunan Engineering Research Centre of Electrical Drive and Regenerative Energy Storage and Utilization Zhuzhou412007HunanChina School of Computer Science University of GlasgowGlasgowG128QQScotlandUK

To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.

关键词： Multi-objective artificial hummingbird algorithm Tent mapping based on random variables Urban rail transit Supercapacitor energy storage systems Capacity allocation

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Combustion-assisted low-temperature ZrO_(2)/SnO_(2) films for high-performance flexible thin film transistors

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npj Flexible Electronics 2024年第1期8卷 132-142页

作者： Bongho Jang Junil Kim Jieun Lee Geuntae Park Gyuwon Yang Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaeguSouth Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaeguSouth Korea School of Electronics Engineering Kyungpook National UniversityDaeguSouth Korea Convergence Research Advanced Centre for Olfaction DGISTDaeguSouth Korea

We developed high-performance flexible oxide thin-film transistors(TFTs)using SnO_(2) semiconductor and high-k ZrO_(2) dielectric,both formed through combustion-assisted sol-gel *** method involves the exothermic reaction of fuels and oxidizers to produce high-quality oxide films without extensive external *** combustion ZrO_(2) films were revealed to have an amorphous structure with a higher proportion of oxygen corresponding to the oxide network,which contributes to the low leakage current and frequency-independent dielectric *** ZrO_(2)/SnO_(2) TFTs fabricated on flexible substrates using combustion synthesis exhibited excellent electrical characteristics,including a field-effectmobility of 26.16 cm^(2)/Vs,a subthreshold swing of 0.125 V/dec,and an on/off current ratio of 1.13×10^(6) at a low operating voltage of 3 ***,we demonstrated flexible ZrO_(2)/SnO_(2) TFTs with robust mechanical stability,capable of withstanding 5000 cycles of bending tests at a bending radius of 2.5 mm,achieved by scaling down the device dimensions.

关键词： film high dielectric

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AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

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computers, Materials & Continua 2024年第9期80卷 4085-4110页

作者： Syed Kamran Haider Abbas Ahmed Noman Mujeeb Khan Ali Nauman Sung Won Kim College of Internet of Things(IoT)Engineering Hohai UniversityChangzhou213001China Department of Electrical and Electronics Engineering Beaconhouse International CollegeIslamabad44000Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongsan38428Republic of Korea

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network *** research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in *** proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path *** a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication *** comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced ***,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

关键词： UAV trajectory clustering next-generation wireless sensor network(NGWSN) energy efficiency mobile sink

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Decoding Tomorrow's Gold Prices: A Comparative Study of GRU and CNN-LSTM in the Iranian Market 10

Decoding Tomorrow's Gold Prices: A Comparative Study of GRU ...

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10th International Conference on Web Research, ICWR 2024

作者： Baradaran, Amir Hossein Bohlouli, Masoumeh Motlagh, Mohammad Reza Jahed Iran University of Science and Technology School of Electrical Engineering Tehran Iran Alzahra University Department of Computer Engineering Tehran Iran Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

With the rising prominence of gold as a lucrative investment avenue in Iran, this research delves into predicting the future price of 18-carat gold. In pursuit of this objective, a comprehensive comparison is conducted between two neural network architectures: the Gated Recurrent Unit (GRU) as a single structure and a hybrid model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The evaluation criteria employed focus on error metrics to gauge the accuracy of price predictions. Results reveal that the CNN-LSTM hybrid neural network exhibits superior performance, showcasing lower error values in predicting the price of 18-carat gold in Iran. Consequently, the chosen model, CNN-LSTM, is employed to forecast the following day's gold prices, providing valuable insights for investors navigating the Iranian market. This research contributes to the ongoing discourse on gold investment strategies by highlighting the effectiveness of advanced neural network models in enhancing predictive accuracy. © 2024 IEEE.

关键词： Long short-term memory

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