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Deterministic forecasts and Prediction Intervals for Wind Speed using Enhanced Multi-Quantile Loss based Dilated Causal Convolutions

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IEEE Transactions on Sustainable Energy 2025年第3期16卷 2002-2014页

作者： Saeed, Adnan Li, Chaoshun Zhu, Qiannan Ahmad, Belal Huazhong University of Science and Technology School of Civil and Hydraulic Engineering Institute of Water Resources and Hydropower Science Hubei Key Laboratory of Digital River Basin Science and Technology Wuhan430074 China Huazhong University of Science and Technology school of Computer Science and Technology Wuhan430074 China

With rising wind power penetration into power systems obtaining wind speed forecasts with associated uncertainty becomes crucial for better planning and dispatch. This study proposes an enhanced multi quantile regression-based loss function specially tailored to train models to generate both deterministic forecast and the corresponding prediction intervals. Though the regression architecture of the model plays an important role in extracting precise forecasts, however, its efficiency is often ignored which may be a downside for short term forecasting scenarios where model training time may also be a significant factor. The present study therefore designed a multi-scale dilated convolution-based architecture for enhanced efficiency. The architecture generates predictions at different scales which are combined using particle swarm optimization to obtain optimal forecasts. The model is trained using the proposed loss function on datasets from both NREL simulations and operational Chinese state grid measurements across three different locations. The proposed model exhibits excellent forecasting performance in comparative experiments with both simulated and real-world operational datasets. © 2025 IEEE.

关键词： Wind forecasting

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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Universal and Efficient Adversarial Training Framework with Membership Inference Resistance

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IEEE Internet of Things Journal 2025年第12期12卷 18665-18677页

作者： Yan, Ran Du, Ruiying He, Kun Chen, Jing Li, Qiao Wu, Cong The Collaborative Innovation Center of Geospatial Technology School of Cyber Science and Engineering Wuhan University Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Shenzhen Institute of Wuhan University China Nanyang Technological University College of Computing and Data Science Singapore Singapore

Adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this paper, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailor-made example called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement our framework using four adversarial training methods on the FMNIST and CIFAR10 datasets and compare its performance with deep learning differential privacy. Our experimental findings demonstrate that our framework mitigates model overfitting and outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime. © 2014 IEEE.

关键词： Adversarial machine learning

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Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4470-4478页

作者： Dhanaraj, Rachel Selva Sridevi, M. Goa College of Engineering Computer Engineering Department Goa 403401 India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets - CelebA, CIFAR-10, and MNIST - were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss. © 2020 IEEE.

关键词： Deep neural networks

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Efficient multiverse electro search optimization for multi-cloud task scheduling and resource allocation

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Multimedia Tools and Applications 2024年 1-26页

作者： Ravi, Rupesh Pillai, Manu J. Department of Computer Science and Engineering TKM Institute of Technology Karuvelil Kerala Kollam691505 India Department of Computer Science and Engineering TKM College of Engineering Karicode Kollam India

Effective task scheduling and resource allocation have become major problems as a result of the fast development of cloud computing as well as the rise of multi-cloud systems. To successfully handle these issues, we provide a novel strategy in this study termed Efficient Multiverse Electro Search Optimization (EMESO). This study presents an Efficient Multiverse Electro Search optimization to improve work scheduling behaviour, with significance on electron transport near the nucleus and cosmological principles. The method uses exploration, exploitation, and local search to discover the optimal solution from a huge amount of potential alternatives. EMESO examines a broad search space to uncover optimal solutions for work scheduling and resource allocation by exploiting the concept of a multiverse, which represents a collection of parallel universes. While related to modern work, the proposed approach performs much better in big and extra-big workflow jobs than in smaller and midrange workflow jobs. It may decrease costs by 6%, shorten project timelines by 8%, and increase resource efficiency by 55% while still enabling all activities to be completed by the deadlines. The proposed approach significantly improves efficiency, resource utilization, and overall performance, making it one of the best options for managing complex cloud systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cloud computing

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Face Recognition Using LBPH and CNN

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Recent Advances in computer science and Communications 2024年第5期17卷 48-58页

作者： Shukla, Ratnesh Kumar Tiwari, Arvind Kumar Mishra, Ashish Ranjan Department of Computer Science & Engineering Shambhunath Institute of Engineering & Technology Uttar Pradesh Prayagraj211012 India Department of Computer Science & Engineering Kamla Nehru Institute of Engineering & Technology Uttar Pradesh Sultanpur228118 India Department of Computer Science & Engineering Rajkiya Engineering College Uttar Pradesh Sonbhadra231206 India

Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security;however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result. © 2024 Bentham science Publishe.

关键词： Face recognition

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Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1530-1543页

作者： Guijun Ma Zidong Wang Weibo Liu Jingzhong Fang Yong Zhang Han Ding Ye Yuan The School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUnited Kingdom the School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized *** this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution *** standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based *** proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge *** results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based *** Py Torch implementation of the proposed PSO-DDA method is available at https://***/mxt0607/PSO-DDA.

关键词： Deep transfer learning domain adaptation hyperparameter selection lithium-ion batteries(LIBs) particle swarm optimization state of health estimation(SOH)

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(Information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao Junjun Jiang Yixuan Yuan Electronic Information School Wuhan UniversityWuhan 430072China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

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Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

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Journal of Communications and Information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

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