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arXiv 2022年

作者： Xing, Jiarui Wang, Shuo Bilchick, Kenneth C. Epstein, Frederick H. Patel, Amit R. Zhang, Miaomiao Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States Department of Biomedical Engineering University of Virginia Health System United States School of Medicine University of Virginia Health System United States

The selection of an optimal pacing site, which is ideally scar-free and late activated, is critical to the response of cardiac resynchronization therapy (CRT). Despite the success of current approaches formulating the detection of such late mechanical activation (LMA) regions as a problem of activation time regression, their accuracy remains unsatisfactory, particularly in cases where myocardial scar exists. To address this issue, this paper introduces a multi-task deep learning framework that simultaneously estimates LMA amount and classify the scar-free LMA regions based on cine displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI). With a newly introduced auxiliary LMA region classification sub-network, our proposed model shows more robustness to the complex pattern cause by myocardial scar, significantly eliminates their negative effects in LMA detection, and in turn improves the performance of scar classification. To evaluate the effectiveness of our method, we tests our model on real cardiac MR images and compare the predicted LMA with the state-of-the-art approaches. It shows that our approach achieves substantially increased accuracy. In addition, we employ the gradient-weighted class activation mapping (Grad-CAM) to visualize the feature maps learned by all methods. Experimental results suggest that our proposed model better recognizes the LMA region pattern. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Convolutional Neural Networks and YOLOv5 for the Detection of License Plates in Digital Photographic Images

Convolutional Neural Networks and YOLOv5 for the Detection o...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Guillermo Enrique Medina Zegarra Ciro Rodriguez Rodriguez Edgar Lobaton Roger Chipa Sierra Department of Systems and Informatics Engineering Graduate School Universidad Nacional Mayor de San Marcos Lima Peru School of System Engineering and Informatic Universidad Nacional Mayor de San Marcos Lima Peru School of Electrical and Computer Engineering North Caroline State University North Caroline USA School of Economic Engineering and Statistics Universidad Nacional de Ingenierías Lima Peru

In this research, a dataset of two thousand images was obtained, which were taken through a speed camera to vehicles in the city of Lima, Peru, with the purpose of detecting license plates. Therefore, a distribution of this dataset was made: 70% for training (1400 images), 20% for validation (400 images), and 10% for testing (200 images). To visualize and analyze the behavior and convergence of the loss function, a distribution was made using the cross-validation method with the following three folds: 1400, 840, and 280, with two optimization methods (stochastic gradient descent with momentum and Adam), both optimization techniques were used for training from scratch and transfer learning. The results obtained demonstrated that the loss function converges better with the first optimization method. We used precision metric, too.

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Designing of a Solar MPPT Controller with the Technique of Differentiation Between Uniform Irradiance and Partial Shading Conditions 9

Designing of a Solar MPPT Controller with the Technique of D...

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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

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Two-Layer SVM, Towards Deep Statistical Learning 1

Two-Layer SVM, Towards Deep Statistical Learning

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1st International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Kazemi, Alireza Boostani, Reza Odeh, Mahmoud Al-Mousa, Mohammad Rasmi Shiraz University Shiraz Faculty of Science Mathematics Department Fars Iran Electrical and Computer Engineering School Shiraz University Shiraz CSEandIT Dept. Fars Iran Zarqa University Department of Management Information System Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350332742

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs). © 2022 IEEE.

关键词： Support vector machines

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Enhancing wide-band trajectory tracking and finite-time control of Dielectric Elastomer Actuators using Nonsingular terminal sliding mode control scheme

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Mechatronics 2025年 109卷

作者： Kassim, Shakiru Olajide Zou, Jiang Behnamgol, Vahid Vaziri, Vahid Aphale, Sumeet S. Group School of Engineering University of Aberdeen ScotlandAB24 3UE United Kingdom The state Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China The Robotics Institute School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Renewable Energy Research Centre Damavand Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering The Federal Polytechnic Damaturu Yobe State 620221 Nigeria

The utilization of Dielectric Elastomer Actuators (DEAs) in soft robotics is becoming increasingly popular due to their distinctive properties. However, controlling these actuators is challenging due to their nonlinear nature. This study addresses precise control of DEAs while minimizing nonlinear effects over a wide frequency range using a Sliding Mode Control (SMC) scheme. Traditional SMC approaches face two main issues: they do not ensure finite-time convergence during the sliding phase and compromise tracking accuracy due to approximations used to reduce chattering. This work presents a non-singular terminal sliding mode control combined with an enhanced boundary layer switching function to improve precision and finite-time stability. Using stability analysis, extensive MATLAB/SIMULINK simulations, and experimental validation on a conical DEA across diverse reference trajectories and its resilience against uncertainties, the proposed scheme demonstrates superior performance in trajectory tracking compared to PID-based and conventional SMC schemes. The results highlight the scheme's effectiveness in high-frequency trajectory tracking and its robustness to possible uncertainties and disturbances, offering a robust theoretical framework for dielectric elastomer actuator control and a promising approach for advancing soft robotic technologies. © 2025 The Authors

关键词： Intelligent robots

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Microcomb-driven photonic convolutional engine

Microcomb-driven photonic convolutional engine

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Bai, Bowen Yang, Qipeng Shu, Haowen Chang, Lin Wu, Yichen Yang, Fenghe Shen, Bitao Tao, Zihan Xie, Weiqiang Hu, Weiwei Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Zhangjiang Laboratory Shanghai201210 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We demonstrate a microcomb-driven chip-based photonic convolutional engine utilizing time-wavelength plane stretching approach. A preeminent photonic-core compute density of 1.04 TOPS/mm2 and a record high weight cont... 详细信息

ISBN: (纸本)9781957171258

关键词： Convolution

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception

Advswap: Covert Adversarial Perturbation with High Frequency...

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International Conference on Intelligent Transportation

作者： Yuanhao Huang Qinfan Zhang Jiandong Xing Mengyue Cheng Haiyang Yu Yilong Ren Xiao Xiong School of Transportation Science and Engineering Beihang University Beijing P.R.China State Key Lab of Intelligent Transportation System Beijing P.R.China Zhongguancun Laboratory Beijing P.R.China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability.

关键词： Wavelet transforms Training Neural networks Noise Visual systems Robustness Classification algorithms Data mining Information exchange Autonomous vehicles

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A Composite Decomposition Method for Large-Scale Global Optimization

arXiv

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arXiv 2024年

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China The Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Engineering Westlake University Hangzhou310030 China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74048 United States

Cooperative co-evolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of non-additively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups non-separable variables by recursively considering the interactions between each non-separable variable and the formed non-separable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group non-separable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG series designs. © 2024, CC BY.

关键词： Efficiency

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JOINT DEEP LEARNING FOR IMPROVED MYOCARDIAL SCAR DETECTION FROM CARDIAC MRI

arXiv

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arXiv 2022年

作者： Xing, Jiarui Wang, Shuo Bilchick, Kenneth C. Patel, Amit R. Zhang, Miaomiao Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States School of Medicine University of Virginia Health System United States

Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGECMR) is limited by image noise and artifacts such as those related to motion and partial volume effect. This paper presents a novel joint deep learning (JDL) framework that improves such tasks by utilizing simultaneously learned myocardium segmentations to eliminate negative effects from non-region-of-interest areas. In contrast to previous approaches treating scar detection and myocardium segmentation as separate or parallel tasks, our proposed method introduces a message passing module where the information of myocardium segmentation is directly passed to guide scar detectors. This newly designed network will efficiently exploit joint information from the two related tasks and use all available sources of myocardium segmentation to benefit scar identification. We demonstrate the effectiveness of JDL on LGE-CMR images for automated left ventricular (LV) scar detection, with great potential to improve risk prediction in patients with both ischemic and non-ischemic heart disease and to improve response rates to cardiac resynchronization therapy (CRT) for heart failure patients. Experimental results show that our proposed approach outperforms multiple state-of-the-art methods, including commonly used two-step segmentation-classification networks, and multitask learning schemes where subtasks are indirectly interacted. © 2022, CC BY.

关键词： Message passing

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