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Novel Approach for Intelligent Fault Diagnosis in Electromechanical Drive Systems Using an Attention-Based Residual 1D Convolutional Neural Network 4

Novel Approach for Intelligent Fault Diagnosis in Electromec...

引用

4th international conference on artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Okonda, Jonathan Xu, Danping Kilundu, Bovic Shanghai University School of Mechatronic Engineering and Automation Shanghai China Department of Mechanical Engineering Brussels Belgium

ISBN: (纸本)9798331531225

Numerous existing deep learning-based fault diagnosis models, have been implemented for intelligent fault diagnosis in electromechanical drive systems (EMDS). However, these models often are designed to detect and diagnose single fault types at specific operating conditions. these models are frequently susceptible to overfitting, suffer from the vanishing gradient problem, and struggle with long-range feature dependencies. Such limitations significantly hinder their accuracy and reliability in diagnosing complex fault scenarios for EMDS under different operating conditions using noisy data. To address these limitations, we propose a novel deep learning-based fault diagnosis model named Attention-based Residual 1D Convolutional Neural Network (AR1D-CNN). this model is designed to effectively diagnose complex faults in EMDS across various load conditions using noisy and imbalanced vibration data. Experimental results demonstrate exceptional classification accuracies of 99.98% and 100.00% on the KAIST dataset for 0 and 2 Nm load conditions, respectively, and 99.31% on the AETWPSU broken rotor bar dataset. Notably, the AR1D-CNN outperforms existing state-of-the-art methods by approximately 4% on the AETWPSU dataset, highlighting its robustness and reliability. this research significantly enhances predictive maintenance strategies by enabling early and precise fault detection, ultimately improving the operational efficiency of EMDS. © 2024 IEEE.

关键词： Convolutional neural networks

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Advances in Deep Learning for Remote Sensing: Automatic Target Detection and High-Resolution Image Classification 4

Advances in Deep Learning for Remote Sensing: Automatic Targ...

引用

4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Jagendra Sahu, Dinesh Prasad Reddy, Premkumar Mohan, L. Madan Alsalami, Zaid Vani, M. Sree Bennett University School of Computer Science Engineering & Technology Greater Noida India Sr Softwarer Engineer FriscoTX United States Vishwa Vishwani School of Business Management Osmania University Department of Mba Hyderabad India The Islamic University Najaf Iraq Bvrit Hyderabad College of Engineering for Women Department of Cse Hyderabad India

ISBN: (纸本)9798350364729

the research has been conducted by introducing deep learning models for automatic target detection and high resolution image classification on remote sensing, especially based upon a selected region. the research further evaluate its performance using four of the state-of-the-art deep learning architectures - Convolutional Neural Network (CNN), Residual Network (ResNet), Densely Connected Convolutional Networks (DenseNet) and Visual Geometry Group Network. For each model, evaluation is using several performance metrics that quantitatively assess our approach's ability to detect reports of human and vehicle presence in satellite and camera images. the results show that the ResNet obtained a better performance metric where one can observe it has accuracy of 0.94, 0.92 precision of 0.92, recall of 0.93 and F1 Score of 0.92. the second best algorithm was DenseNet with the highest accuracy of 0.93, the precision of about 0.91 and recall also around at 0.92, F1-score similar to that of ResNet. CNN highly performed with accuracy of 0.92, precision of 0.89, and recall of 0.91 and F1-score of 0.90. VGG had competitive performance as it achieved an accuracy of 0.91, precision of 0.88, recall of 0.90 and F1-score of 0.89. these findings summaries our understanding of the potential for technological intrusion in ecology, yielding exciting new techniques and methods that can be used to monitor environmental change, perform security surveillance or coordinate disaster response over extremely large spatial scales across remote regions. © 2024 IEEE.

关键词： Convolutional neural networks

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A New art Design Method Based on AIGC: Analysis from the Perspective of Creation Efficiency 4

A New Art Design Method Based on AIGC: Analysis from the Per...

引用

4th international conference on Intelligent Design, ICID 2023

作者： Rao, Jian Xiong, Mengzhen Hubei University of Technology Digital Industry Art Institute Wuhan430068 China Hubei University of Technology School of Art and Design Wuhan430068 China

ISBN: (纸本)9798350342574

With the development of "AI-generated content(AI GC)"technology, the field of art and design is witnessing a new transformation. Along with applying AI technologies such as generative adversarial networks, diffusion models, and transformer architecture in the visual text filed, art design has transformed from the traditional personal inspiration-centered creation model in to an AI large model-driven creation model. Starting from the efficiency of art design creation, this paper proposes a new "text-image"iterative art creation method based on prompt word engineering. It introduces the specific process and steps of art creation through AIGC tools. At the same time, questionnaires, semi-structured interviews, and other methods to quantitatively analyze the creative methods of teachers and design students. However, the results of the AIGC tools in deep cultural semantic understanding are poor and must be further supplemented manually. Given the above research, this paper further elaborates on the subsequent development direction of AIGC tools, which should take dynamic real-time interaction as the core. Multi-modal domain information should be integrated and embedded into the creation process, which will realize the dynamic creation and improve the quality of works. © 2023 IEEE.

关键词： Efficiency

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Enhanced Shape Recognition through Multi-Scale Morphological Image Processing and Preprocessing 4

Enhanced Shape Recognition Through Multi-Scale Morphological...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Vigenesh, M. Sohal, Jagmeet Aravind, Ar Gupta, Shikhar Dasarathy, A.K. Borade, Monali Ravindra Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331518981

Important for surgical planning and therapy, hemorrhage stroke identification using shape priors is a key component of medical image processing. the complex architecture and high dimensionality of medical imaging data make this task difficult. the lack of a large number of labelled training samples and the low intensity contrast between the blood circulation and the complex shape topology are two of the obstacles. Many methods, such as deep learning models and low-level image analysis, have been proposed to deal with these problems. the usage of a DL-based Multiscale-MMNN for accurate outline appreciation of hemorrhage stroke is the primary emphasis of this research thesis. From the vantage points of PSNR, SSIM, and MSE, the suggested approach is contrasted with preexisting form recognition algorithms like CNN, Residual CNN, and MCDNN. When associated to up-to-date methods, the suggested methodology achieves 99.5% of its measured outcomes. the following phase employs morphological procedures to semantically segment hemorrhage stroke using Multiresolution U-Net. At a 97.9% improvement over state-of-the-art segmentation methods like U-net, SegNet, ResNet, and 3D-ResU-Net, this method accurately and rapidly identifies hemorrhagic stroke. Superior sensitivity, specificity, accuracy, and precision were attained by the suggested methodology's performance rate when contrasted with older conventional approaches. © 2024 IEEE.

关键词： Medical image processing

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A Novel System to Recognize Human Gaits by Using Intelligent Learning Assisted Feature Analysis Scheme 4

A Novel System to Recognize Human Gaits by Using Intelligent...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Kiran, Ajmeera Kumar, Dammati Pavan Edpuganti, Sai Krishna Babu, P. Venkaiah Sane, Amruta Ramdas Deepak, V. Mlr Institute of Technology Department of Cse Hyderabad India Tirumala Engineering College Department of Cse Narsaraopet India Vignan's Lara Institute of Technology & Science Department of Information Technology Vadlamudi India Acharya Institute of Graduate Studies Department of Mba Bengaluru India Deemed to be University Bharati Vidyapeeth Pune India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India

ISBN: (纸本)9798331508845

In the recent decade, human gait detection has evolved as a subfield of biometric authentication that uses a person's motion, time, and clothes to determine who they are. It also works well as a video surveillance system. the fundamental problem with these methods is that conventional methods of extracting and categorizing features waste too much time and produce inaccurate results. Our proposed ensemble deep learning technique for human gait detection takes use of recent developments in deep learning for a wide range of applications, including video monitoring and biometrics. We build a model using the Inception framework and the LightGBM method. the CASIA B dataset is used to fine-tune and train Inception models utilizing transfer learning. the final optimal features are then categorized with a LightGBM classifier. Experiments were run, and the results showed that the suggested model had an average accuracy of 97.25 percent, making it more accurate than recently developed state-of-the-art procedures. © 2024 IEEE.

关键词： Biometrics

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DALR: A Dynamically Adjustable Linear Regression Model for Handling Stream Data 4

DALR: A Dynamically Adjustable Linear Regression Model for H...

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4th international conference on Computer, Communication, Control and Information Technology, C3IT 2024

作者： Mondal, Rishita Mandal, Sourab Dutta, Paramartha Pal, Arindrajit Visva-Bharati Department of Computer & System Sciences WB Santiniketan India Academy of Technology Department of Computer Science and Engineering Adisaptagram WB India

ISBN: (纸本)9798350389128

In response to the escalating demand for machine learning techniques capable of handling real-time data streams, particularly in applications like stock markets, this research dives deep into the domain of stream regression. the objective is to adeptly navigate challenges posed by evolving data over time, differentiating it from conventional static regression. Existing models, such as Fast and Incremental Model Trees with Drift Detection (FIMT-DD), k-Nearest Neighbours (KNN), and Adaptive Linear Regression (ALR) and some other studies, though competent, exhibit certain limitations like learning based on certain portions of data, using a significantly large memory and not capable of handling each data in every timestamp it arrives. We propose the Dynamically Adjustable Linear Regression Model (DALR) to address these shortcomings. DALR introduces an adaptive regression model that dynamically evolves based on historical knowledge and newly acquired data, providing an innovative solution to the intricacies of dynamic data streams. this endeavor has also highlighted that the DALR algorithm consistently demonstrates strong performance across various datasets according to the RMSE metric, surpassing other state-of -the-art algorithms in most instances. © 2024 IEEE.

关键词： Financial markets

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A Comprehensive Analysis of Secure Attack Detection on Virtual Machines in Cloud Computing 4

A Comprehensive Analysis of Secure Attack Detection on Virtu...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Nemirajaiah, Naveen Kumar Adalagere Raju, Channa Krishna Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Computer Science and Engineering Tumakuru India Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Artificial Intelligence and Machine Learning Tumakuru India

ISBN: (纸本)9798350352931

Cloud computing provides various computing services including processing power, storage, networking, software, analytics, and databases. A Virtual Machine (VM) is a software-based simulation of a physical computer that runs an operating system and applications. the VM can run multiple functions on a single physical machine. Secure attack detection on VMs utilizes implementation techniques to detect and monitor malicious activities in the cloud environment. this detection involves Intrusion Detection Systems (IDS), anomaly detection methods, and real-time monitoring tools that analyse network traffic, system logs, and user behaviour. Optimization techniques are used to enhance the efficiency of the detection models. Swarm-based algorithms such as Intelligent Water Drop (IWD), Enhanced Shark Smell Optimization Algorithm (ESSOA), Brownian movement-centered gravitation search algorithm (BMGSA), Dual Conditional Moth Flame Algorithm (DC-MFA), Bat Optimization Algorithm (BOA), and Ant Colony Algorithm (ACO) are employed for this purpose. Additionally, Evolution-based algorithms such as Multi-objective Whale Optimization algorithm-based Differential Evolution (M-WODE), Gene Expression programming (GEP), Genetic Algorithm (GA), and DragonFly-based Genetic Deep Belief Network (DGDBN) have also been used for anomaly detection. the performance metrics of makespan, energy consumption, Degree of Imbalance (DOI), and execution cost are used to evaluate these approaches. © 2024 IEEE.

关键词： Genetic algorithms

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EmoBlend Fusion: Leveraging Handcrafted and Deep Features for Emotion Detection 4

EmoBlend Fusion: Leveraging Handcrafted and Deep Features fo...

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4th IEEE international conference on Human-Machine Systems, ICHMS 2024

作者： Anzum, Fahim Gavrilova, Marina L. University Of Calgary Dept. Of Computer Science CalgaryAB Canada

ISBN: (纸本)9798350315790

Correct detection of human emotions plays a vital role in human-machine communication and decision-making. Recent advancements in large language models (LLMs) have sparked research in opinion mining, emotion detection, and sentiment analysis. While features extracted from texts using pre-trained LLMs can capture patterns effectively, integrating domain-specific handcrafted features remains crucial for constructing predictive models using user-generated microblogs. this paper proposes EmoBlend Fusion, a hybrid emotion detection model incorporating handcrafted features and latent deep features using pre-trained embeddings extracted from tweets. While the handcrafted features are trained using an ensemble of random forest, XGBoost, and linear SVM classifiers, a new bidirectional convolutional neural network-based model is trained using the deep features. Model predictions are then fused through a weighted ensemble mechanism for classifying emotions into one of the six distinct classes: sadness, joy, love, anger, fear, and surprise. the experimental results demonstrate that the proposed model outperforms the state-of-the-art techniques, achieving the highest recall (0.94), precision (0.95), accuracy (0.94), and F1-score (0.94) on a benchmark Twitter emotion detection dataset. © 2024 IEEE.

关键词： Sentiment analysis

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A Moving Object Detection Method Based on Graph Neural Network 4

A Moving Object Detection Method Based on Graph Neural Netwo...

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4th international conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Zeng, Can Qiao, Yulong Institute of Information and Communication Harbin Engineering University Harbin China

ISBN: (纸本)9798350326444

Moving object detection means segmenting objects that exhibit motion in image sequences. Several deep neural network-based methods have been proposed and have almost perfect performance. However, when these algorithms are used on unseen videos, their performance drops dramatically. In this paper, we propose a moving object detection method based on Graph Neural Network(GNN) for unseen videos called IGMOD. Specifically, we use Mask-RCNN for segmenting instances, use the temporal median filter to initialize the background and extract optical flow, texture, and intensity features for representing each instance. then, we use k-Nearest Neighbors(K-NN) to construct a graph for all instances. Finally, we use the improved GraphSAGE to solve a problem of node classification, to predict whether the instance is foreground or background. We conducted experiments on the CDNet2014 dataset to evaluate our proposed algorithm , the results indicate that our method surpasses state-of-the-art algorithms. © 2023 IEEE.

关键词： Median filters

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Data-driven Analysis and Evaluation of Pedagogical Innovation 4

Data-driven Analysis and Evaluation of Pedagogical Innovatio...

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4th international conference on Educational Technology, ICET 2024

作者： Cui, Mengjie Li, Yangyang He, Xiuling Zhou, Ruijie Central China Normal University National Engineering Research Center of Educational Big Data Wuhan China

ISBN: (纸本)9798350376944

the Digital Competence Framework for Teachers released by the European Union aims to improve teachers' digital teaching competence, with the focus shifting from enhancing teachers' information technology application competence to supporting technology-supported pedagogical innovations. the evaluation of teachers' teaching should shift from the application competence of information technology to analyzing and evaluating teachers' pedagogical innovation and cultivating higher-order competence of students. this study investigates the analysis and evaluation method of teaching innovation supported by technology based on accompanying collected teachers' behaviors. 102 courses were selected as subjects. Firstly, the coding rules were explored on the basis of the PICRAT (Passive, Interactive, Creative-Replacement, Amplification, Transformation) framework, and the teachers' behaviors were automatically coded. Secondly, multiple analyses of teaching behaviors were conducted to identify three technologically supported levels of pedagogical innovation, i.e., the basic single type, the integrated progressive type, and the high-level integration type. this study will provide practical guidance for the development of teachers' digital competence. © 2024 IEEE.

关键词： Teaching

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