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Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

Friction 2022年第4期10卷 583-596页

作者： Vigneashwara PANDIYAN Josef PROST Georg VORLAUFER Markus VARGA Kilian WASMER Laboratory for Advanced Materials Processing(LAMP) Swiss Federal Laboratories for Materials Science and Technology(Empa)Thun CH-3602Switzerland AC2T research GmbH Viktor-Kaplan-Straße 2/CWiener Neustadt 2700Austria

Functional surfaces in relative contact and motion are prone to wear and tear,resulting in loss of efficiency and performance of the workpieces/*** occurs in the form of adhesion,abrasion,scuffing,galling,and scoring between ***,the rate of the wear phenomenon depends primarily on the physical properties and the surrounding *** the integrity of surfaces by offline inspections leads to significant wasted machine time.A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon,followed by in situ classification using a state-of-the-art machine learning(ML)*** this technique is better than offline inspection,it possesses inherent disadvantages for training the ML ***,supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid *** collection of such a dataset is very cumbersome and expensive in practice,as in real industrial applications,the malfunction period is minimal compared to normal ***,classification models would not classify new wear phenomena from the normal regime if they are *** a promising alternative,in this work,we propose a methodology able to differentiate the abnormal regimes,i.e.,wear phenomenon regimes,from the normal *** is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission(AE)signals captured using a microphone related to the normal *** a result,the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new,unseen signal *** achieve this goal,a generative convolutional neural network(CNN)architecture based on variational auto encoder(VAE)is built and *** the validation procedure of the proposed CNN architectures,we were

关键词： surface integrity acoustic emission auto encoders condition monitoring wear prediction

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Identification of Anomaly Scenes in Videos Using Graph Neural Networks

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Computers, Materials & Continua 2023年第3期74卷 5417-5430页

作者： Khalid Masood Mahmoud M.Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam Mohammad Hamza Kashif Department of Software Engineering Lahore Garrison UniversityLahore54000Pakistan School of Business Skyline University CollegeSharjahUAE Al-Madinah International University–Faculty of Computer and Information Technology Kuala LumpurMalaysia College of Computer and Information Systems Islamic University of MadinahMadinahKingdom of Saudi Arabia School of IT Skyline University CollegeSharjahUAE Faculty of Engineering Computing and Science(FECS)-Swinburne University of TechnologySarawak Department of General Education Skyline University CollegeUniversity City of SharjahUAE Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan

Generally,conventional methods for anomaly detection rely on clustering,proximity,or *** themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques *** research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured *** node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph *** to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and *** detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of *** Graph-based deep learning networks are designed to predict unknown objects and *** our case,they detect unusual objects in the form of malicious *** edges between nodes represent a relationship of nodes among each *** case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an *** encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible *** show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance *** autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public *** suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniq

关键词： Graph neural network deep learning anomaly detection auto encoders

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Deep-fake video detection approaches using convolutional–recurrent neural networks

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Journal of Control and Decision 2023年第2期10卷 198-214页

作者： Shraddha Suratkar Sayali Bhiungade Jui Pitale Komal Soni Tushar Badgujar Faruk Kazi Department of Electrical Engineering Veermata Jijabai Technological InstituteAn autonomous Instituteaffiliated to Mumbai UniversityMumbaiIndia

Deep-Fake is an emerging technology used in synthetic media which manipulates individuals in existing images and videos with someone else’s *** paper presents the comparative study of different deep neural networks employed for Deep-Fake video *** the model,the features from the training data are extracted with the intended Convolution Neural Network model to form feature vectors which are further analysed using a dense layer,a Long Short-Term Memoryand Gated Recurrent by adopting transfer learning with fine tuning for training the *** model is evaluated to detect Artificial Intelligence based Deep fakes images and videos using benchmark *** analysis shows that the detections are majorly biased towards domain of the dataset but there is a noteworthy improvement in the model performance parameters by using Transfer Learning whereas Convolutional-Recurrent Neural Network has benefits in sequence detection.

关键词： Deep-FAKES Convolution Neural Network(CNN) Generator Adversarial Network(GAN) auto encoders Recurrent Neural Network(RNN) Long Short-Term Memory(LSTM)

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