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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Epilepsy Detection Using Supervised Learning Algorithms 2

Epilepsy Detection Using Supervised Learning Algorithms

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Krishnasamy, Lalitha Sriwastav, Yash Kumar Bharat, Suchak Pratik Ganachar, Sanjana R Nandha Engineering College Department of Artificial Intelligence and Data Science Erode India School of Engineering and Technology Christ University Department of CSE Bengaluru India School of Engineering and Technology Christ University Department of CSE AIML Bengaluru India

ISBN: (纸本)9798350383652

In the current scenario, people are suffering and isolated by themselves by seizure detection and prediction in epilepsy. Also, it is highly essential that it needs to be identified through wearable devices. Researchers discussed this issue and outlined future developments in this field, suggesting that Machine Learning (ML) techniques could radically change how we diagnose and manage patients with epilepsy. However, as data availability has increased, Deep Learning (DL) techniques have become the most cutting-edge approach to adopt and use with wearable devices. On the other hand, large amounts of data are needed to train DL models, making overfitting problematic. DL models are created with open-source toolboxes and Python, allowing researchers to create automated systems and broaden computational accessibility. This work thoroughly overviews deep learning (DL) methods and neuroimaging modalities for automated epileptic seizure identification. It covers several MRI and EEG techniques for epileptic seizure diagnosis and treatment programmes designed to treat these seizures. The study also covers the difficulties in precise detection, the benefits and drawbacks of DL-based strategies, potential DL models and upcoming research in this area. © 2024 IEEE.

关键词： Electroencephalography

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NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative database

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IEEE Transactions on Emerging Topics in Computing 2025年第2期13卷 283-298页

作者： Zhao, Dongdong Liu, Zhihui Zhang, Fengji Liu, Lei Keung, Jacky Wai Yu, Xiao Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China City University of Hong Kong Department of Computer Science Hong Kong Xi'an Jiaotong University School of Electronic Science and Engineering Xi'an China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

The emergence of Aging-Related Bugs (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resourceintensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, CrossProject Aging-Related Bug Prediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called Cross-Project AgingRelated Bug Prediction based on Negative database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Secondly, the corresponding Negative dataBase (NDB) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARB-prone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature vector from the NDB to represent the original feature vector, facilitating data sharing and ARB prediction objectives. Experimental results on Linux, MySQL, and NetBSD datasets demonstrate that NegCPARBP achieves a high defense against attacks (privacy protection performance reaching 0.97) and better data utility compared to existing privacy protection methods. © 2025 IEEE.

关键词： Differential privacy

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Supervised Satellite Image Retrieval using Defined Patterned with ASMC 2

Supervised Satellite Image Retrieval using Defined Patterned...

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2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Gaikwad, Rohini Siddiqui, Rizwana Sies Graduate School of Technology Artificial Intelligence & Data Science Navi Mumbai India Sies Graduate School of Technology Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350322064

It is suggested to use remotely sensed image retrieval with query-by-example approach. This often entails using query techniques that permit descriptive semantics, queries that incorporate user feedback, machine learning-capable systems, and systems that can gauge user satisfaction. This study uses the Advanced Split and Merge Clustering approach to extract features from the textures of similar grayscale images and the spectral distribution using two-dimensional Gabor functions. The resulting pattern will be the concatenation of texture features and Spectral Distribution (SD). Modified Earth Mover's Distance (EMD) is used to limit how much of each vector can be predefined in pieces. EMD technique for establishing center correspondence between the patterns uses interactive search. © 2023 IEEE.

关键词： Textures

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Automated Image Captioning and Speech Synthesis 2

Automated Image Captioning and Speech Synthesis

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Mankawade, Amruta Pavitha, N. Vpatil, Amruta Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science India Vishwakarma University Department of Computer Engineering India Sjjtu Rajasthan India

ISBN: (纸本)9798350308846

An innovative system that creates natural language captions for supplied photos and transforms them to voice for increased accessibility is Automated Image Captioning and voice Synthesis. The suggested system extracts important information from the image and generates a relevant and coherent caption using cutting-edge technologies of deep learning like Convolutional Neural Networks & Recurrent Neural Networks. Text-to-speech synthesis techniques are then used to translate the generated captions to speech, allowing visually challenged users to access the material. The suggested approach was tested using typical benchmark datasets and yielded encouraging results. Because the system can create natural language captions and convert them to voice, it May be used in diverse applications, consisting of media accessibility & assistive technology. © 2023 IEEE.

关键词： Deep learning Image captioning LSTM RNN

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Necessity of Information Security in Safeguarding Smart Grid Infrastructure 5

Necessity of Information Security in Safeguarding Smart Grid...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Joshi, Namra Khandelwal, Achala Joshi, Shubham SVKM's Institute of Technology Department of Electrical Engineering Maharashtra Dhule India Thakur College of Engineering & Technology Kandivali Mumbai India Vishwakarma Institute of Information Technology Dept. of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798331504403

The Indian economy is continuously growing, so the power demand is increasing abruptly. The power extraction from non-conventional energy sources has also increased in the past 10 years. Somehow generation and demand are matched. However, a competent infrastructure is needed to handle power generated from multiple sources. Smart Grid Systems has excellent properties to handle such situations in power systems. The highlights of the fundamentals of smart grid, recent developments in Smart Grid as well as Importance of Information Security in Smart Grid is discussed. © 2024 IEEE.

关键词： Power grids

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A Study on Integrating Machine Learning Techniques for Waste Management 7

A Study on Integrating Machine Learning Techniques for Waste...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Vaidya, Ruchika Dattangire, Rahul Biradar, Divya Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Data Engineering Publicis Sapient HoustonTX77002 United States University of Texas Department of Computer Science ArlingtonTX76019 United States

ISBN: (纸本)9798350372816

The current study focuses on how various Machine Learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. The system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. The accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. The discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management. © 2024 IEEE.

关键词： Adversarial machine learning

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Realizing the Potential of Metaverse to Revolutionize the Future of Healthcare 2

Realizing the Potential of Metaverse to Revolutionize the Fu...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ishtaiwi, Abdelraouf Hadi, Wael Al-Qerem, Ahmad University Of Petra Department Of Data Science And Artificial Intelligence Amman Jordan University Of Petra Department Of Information Security Amman Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350394962

The emergence of metaverse technology, underpinned by virtual reality, augmented reality, and artificial intelligence, holds profound implications for transforming healthcare. This paper reviews the current applications of metaverse in medical education and training, mental healthcare, remote patient monitoring, surgical procedures, and biomedical research. It analyzes the challenges around adopting this technology, spanning technical limitations, ethical dilemmas, threats to privacy, and regulatory gaps. Finally, it proposes strategies and future directions to harness the metaverse's potential to increase healthcare access, efficiency, personalization and democratization. This encompasses research on innovative use cases, developing ethical governance frameworks, and policy reforms to incentivize equitable metaverse adoption across the healthcare ecosystem. A nuanced, evidence-based approach can enable the metaverse to usher a new paradigm of patient-centric healthcare delivery. © 2024 IEEE.

关键词： Augmented reality

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Brain-Powered Vehicles for Individuals with Disabilities Leveraging AI 4

Brain-Powered Vehicles for Individuals with Disabilities Lev...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Jaison, T. Jisna Thambi, Priyanka E. Pinheiro, Nikita Scms School of Engineering and Technology Dept. of Artificial Intelligence & Data Science Karukutty India Kalamasery India St. Paul's College Dept. of Computer Science Kalamasery India

ISBN: (纸本)9798350387490

This paper provides a novel method for enhancing mobility and independence for people with disabilities via integrating Brain-pc interface (BCI) generation with artificial intelligence (AI) for vehicle control. The proposed device permits customers to control important vehicle features which includes steerage, braking, and acceleration the usage of their Brain indicators, captured through non-invasive EEG sensors. Preliminary checking out with diverse person agencies established an outstanding accuracy price of over ninety% in deciphering person intentions and efficaciously controlling the automobile. The device adapts to person neural styles, ensuring personalized and dynamic control, and extensively improves person autonomy and self-assurance. However, demanding situations which include sign noise, user variability, and long-time period device stability stay. This studies demonstrates the capacity for BCIs and AI to revolutionize transportation for individuals with disabilities, supplying an exceptional level of independence. Future trends will focus on improving signal clarity, improving machine stability, and addressing regulatory issues to make sure safe, real-world application. © 2024 IEEE.

关键词： Neurons

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