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Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

computers, Materials & Continua 2024年第1期78卷 1363-1379页

作者： Kwok Tai Chui Brij B.Gupta Varsha Arya Miguel Torres-Ruiz School of Science and Technology Hong Kong Metropolitan UniversityHong Kong999077China Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Center for Advanced Information Technology Kyung Hee UniversitySeoul02447Korea Symbiosis Centre for Information Technology Symbiosis International UniversityPuneMaharashtra411042India Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon School of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Business Administration Asia UniversityTaichung41354Taiwan Center for Interdisciplinary Research University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science&Engineering Chandigarh UniversityChandigarh160036India Centro de Investigación en Computación Instituto Politécnico NacionalUPALM-ZacatencoMexico City07320Mexico

The visions of Industry 4.0 and 5.0 have reinforced the industrial *** have also made artificial intelligence incorporated as a major *** machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe *** learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two *** response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning *** is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative *** algorithm also adaptively adjusts the portion of training data to balance the learning rate and training *** proposed algorithm is evaluated and analyzed using ten benchmark *** with other algorithms from existing works,SA-ITL improves the accuracy of all *** studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

关键词： Deep learning incremental learning machine fault diagnosis negative transfer transfer learning

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Fruit Image Classification Using Deep Learning

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computers, Materials & Continua 2022年第6期71卷 5135-5150页

作者： Harmandeep Singh Gill Osamah Ibrahim Khalaf Youseef Alotaibi Saleh Alghamdi Fawaz Alassery Department of Computer Science Guru Arjan Dev Khalsa College Chohla Sahib143408PunjabIndia Al-Nahrain University Al-Nahrain Nano-Renewable Energy Research CenterBaghdad964Iraq Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Fruit classification is found to be one of the rising fields in computer and machine *** deep learning-based procedures worked out so far to classify images may have some ill-posed *** performance of the classification scheme depends on the range of captured images,the volume of features,types of characters,choice of features from extracted features,and type of classifiers *** paper aims to propose a novel deep learning approach consisting of Convolution Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-TermMemory(LSTM)application to classify the fruit *** accuracy depends on the extracted and selected optimal *** learning applications CNN,RNN,and LSTM were collectively involved to classify the *** is used to extract the image *** is used to select the extracted optimal features and LSTM is used to classify the fruits based on extracted and selected images features by CNN and *** study shows the supremacy of proposed over existing Support Vector Machine(SVM),Feed-forwardNeural Network(FFNN),and Adaptive Neuro-Fuzzy Inference System(ANFIS)competitive techniques for fruit images *** accuracy rate of the proposed approach is quite better than the SVM,FFNN,and ANFIS *** has been concluded that the proposed technique outperforms existing schemes.

关键词： Image classification feature extraction type-II fuzzy logic integrator generator deep learning

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Machine learning assisted snort and zeek in detecting DDoS attacks in software-defined networking

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International Journal of information Technology (Singapore) 2024年第3期16卷 1627-1643页

作者： AbdulRaheem, Muyideen Oladipo, Idowu Dauda Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Lee, Cheng-Chi Balogun, Ghaniyyat Bolanle Adeoti, Joshua Oluwatobi Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin 240003 Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum 44801 Germany Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science Fu Jen Catholic University New Taipei City 24205 Taiwan Department of Computer Science and Information Engineering Asia University Taichung City 41354 Taiwan

A new network architecture called the Software-Defined Network (SDN) gives next-generation networks a more flexible and efficiently controlled network architecture. Using the programmable central controller design, network supervisors may easily supervise and manage the entire infrastructure. However, due to its centralized structure, SDN has been a target of various attack vectors. The most successful attack method against the SDN among these has been Distributed Denial of Service (DDoS). Therefore, this study proposes a snort and Zeek enabled with machine learning (ML) based model to classify the benign traffic from DDoS attack traffic. This study main contribution is the discovery of new features for DDoS attack detection, which made it difficult to distinguish authorized traffic from attack traffic when spread across so many points of origin. Using the ML-based enabled RYU controller with SNORT and ZEEK created fewer false positives and a smaller variety of true positives per attack than the existing methods. The processing time of ML-based enabled with SNORT and ZEEK on the real-time testbed is better contrasted to the existing methods. Using the open resource technologies offered a far better understanding of cyber safety and its benefits from the readily available programs to construct a solid network keeping an eye on the traffic. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cybersecurity Distributed denial of service Intrusion detection systems Snort Software-defined networks Virtual machine Zeek

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Physics-informed tracking of qubit fluctuations

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Physical Review Applied 2024年第1期22卷 014033-014033页

作者： Fabrizio Berritta Jan A. Krzywda Jacob Benestad Joost van der Heijden Federico Fedele Saeed Fallahi Geoffrey C. Gardner Michael J. Manfra Evert van Nieuwenburg Jeroen Danon Anasua Chatterjee Ferdinand Kuemmeth Center for Quantum Devices Lorentz Institute and Leiden Institute of Advanced Computer Science Center for Quantum Spintronics Department of Physics QDevil Quantum Machines 2750 Ballerup Denmark Department of Engineering Science Department of Physics and Astronomy Birck Nanotechnology Center Elmore Family School of Electrical and Computer Engineering School of Materials Engineering

Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to timescales set by the estimation efficiency. We implement a physics-informed and an adaptive Bayesian estimation strategy and apply them in real time to a semiconductor spin qubit. The physics-informed strategy propagates a probability distribution inside the quantum controller according to the Fokker-Planck equation, appropriate for describing the effects of nuclear spin diffusion in gallium arsenide. Evaluating and narrowing the anticipated distribution by a predetermined qubit probe sequence enables improved dynamical tracking of the uncontrolled magnetic field gradient within the singlet-triplet qubit. The adaptive strategy replaces the probe sequence by a small number of qubit probe cycles, with each probe time conditioned on the previous measurement outcomes, thereby further increasing the estimation efficiency. The combined real-time estimation strategy efficiently tracks low-frequency nuclear spin fluctuations in solid-state qubits, and can be applied to other qubit platforms by tailoring the appropriate update equation to capture their distinct noise sources.

关键词： quantum control quantum fluctuations & noise quantum information with solid state qubits Double quantum dots Bayesian methods Fokker–Planck equation Machine learning Radio frequency techniques

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Provably Overwhelming Transformer Models with Designed Inputs

arXiv

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arXiv 2025年

作者： Stambler, Lev Nezhadi, Seyed Sajjad Coudron, Matthew Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Computer Science University of Maryland United States Neon Tetra LLC iluvatar Technologies National Institute of Standards and Technology United States

We develop an algorithm which, given a trained transformer model M as input, as well as a string of tokens s of length nfix and an integer nfree, can generate a mathematical proof that M is "overwhelmed" by s, in time and space Oe(n2fix + n3free). We say that M is "overwhelmed" by s when the output of the model evaluated on this string plus any additional string t, M(s + t), is completely insensitive to the value of the string t whenever length(t) ≤ nfree. Along the way, we prove a particularly strong worst-case form of "over-squashing" (Alon & Yahav, 2021;Barbero et al., 2024), which we use to bound the model’s behavior. Our technique uses computer-aided proofs to establish this type of operationally relevant guarantee about transformer models. We empirically test our algorithm on a single layer transformer complete with an attention head, layer-norm, MLP/ReLU layers, and RoPE positional encoding. We believe that this work is a stepping stone towards the difficult task of obtaining useful guarantees for trained transformer models. © 2025, CC BY.

关键词： Electric transformer testing

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Social-Aware Group Display Configuration in VR Conference 38

Social-Aware Group Display Configuration in VR Conference

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Hsu, Bay-Yuan Shen, Chih-Ya Yuan, Hao Shan Lee, Wang-Chien Yang, De-Nian Department of Industrial Engineering and Engineering Management National Tsing Hua University China Department of Computer Science National Tsing Hua University China Institute of Information Systems and Applications National Tsing Hua University China Department of Computer Science and Engineering Pennsylvania State University United States Institute of Information Science Academia Sinica Taiwan Research Center of Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)1577358872

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature - displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Virtual reality

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Classification of WBC based on Deep Learning using Microscopic Images 1

Classification of WBC based on Deep Learning using Microscop...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Rajalakshmi, T. Senthilkumar, C. Department of Computer and Information Science Annamalai University Chidambaram India Department of Computer Science Annamalai University PG Extension Center Villupuram India

ISBN: (纸本)9781665460842

The diagnosis of White Blood cells (WBC) also named as Leukocytes is to classify and detect the total number of WBCs in the human blood cells in order to find the infections, allergies, and diseases. The WBCs are analyzed beneath the microscope, wherein variations in structure and shape reveal the presence of certain diseases. While the examination of WBC images by physicians clinically, a variety of issues may occur due to individual misinterpretations. The Deep Learning method is the best method for accurately and rapidly obtaining white blood cell types. The samples are preprocessed with pixel enhancement, augmentation, normalization, and resizing steps. The Convolutional Neural Network (CNN) method-based results are obtained for WBC classifications. At that point, a Transfer Learning model is used for fine-tuning. As a result, the new model produces classification using the Softmax classifier and finds a higher accuracy equated to other classification approaches. © 2022 IEEE

关键词： Convolutional neural networks

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ATM-NeRF: Accelerating Training for NeRF Rendering on Mobile Devices via Geometric Regularization

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IEEE Transactions on Multimedia 2025年 27卷 3279-3293页

作者： Chen, Yang Zhang, Lin Zhao, Shengjie Zhou, Yicong Tongji University School of Software Engineering Shanghai201804 China Ministry of Education Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Shanghai201804 China University of Macau Department of Computer and Information Science 999078 China

Recently, an increasing number of researchers have been dedicated to transferring the impressive novel view synthesis capability of Neural Radiance Fields (NeRF) to resource-constrained mobile devices. One common solution is to pre-train NeRF and bake it into textured meshes which are well supported by mobile graphics hardware. However, the training process of existing methods often requires several hours even with multiple high-end NVIDIA V100 GPUs. The underlying reason is that these schemes mainly rely on photometric rendering loss, neglecting the geometric relationship between the pre-trained NeRF and the baked results. Standing on this point, we present ATM-NeRF (Accelerating Training for Mobile rendering based on NeRF), which is the first to apply effective geometric regularization constraints during both the pre-training and the baking training stages for faster convergence. Specifically, in the initial NeRF pre-training stage, we enforce consistency of the multi-resolution density grids representing the scene geometry to mitigate the shape-radiance ambiguity problem to some extent, achieving a coarse mesh with smoothness. In the second stage, we utilize the positions and geometric features of 3D points projected from the pre-trained posed depths to provide geometric supervision for joint refinement of geometry and appearance of the coarse mesh. As a result, our ATM-NeRF achieves comparable rendering quality to MobileNeRF with a training speed that is about 30× ∼ 70× faster while maintaining finer structure details of the exported mesh. Our source code and the demo video have been released at https://***/ATM-NeRF/. © 1999-2012 IEEE.

关键词： Mesh generation

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