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Long-term structural health monitoring of long-span suspension bridges and anomaly detection using statistical indicators 11

Long-term structural health monitoring of long-span suspensi...

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11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Çetindemir, Oğuzhan Tepe, Esra Zülfikar, Abdullah Can Yesilyurt, Ali Apaydin, Nurdan Memişoğlu Department of Civil Engineering Gebze Technical University Kocaeli Gebze41400 Turkey Department of Computer Engineering Istanbul University-Cerrahpaşa Istanbul34320 Turkey Department of Civil Engineering Istanbul University-Cerrahpaşa Avcilar Istanbul34320 Turkey Ministry of Transport and Infrastructure Uskudar Istanbul34668 Turkey

Some research has been conducted on structural health monitoring (SHM) utilizing raw data to discover structural behavior changes, anomalies, or damage assessment. However, few of them employed statistical indicators represented as input data. Long-term monitoring has several advantages (before/after event comparison, detecting/tracking abrupt changes or aging effects), while the data collection process may have disadvantages (data storing issues, impractical computationally expensive analyses). thus, the current work proposes two unsupervised machine learning techniques for detecting anomalies using statistical indicators extracted from long-term raw dynamic measurements recorded from a three-dimensional accelerometer installed on the midspan of the Osman Gazi Bridge built in Turkey. For this purpose, one-class Support Vector Machine (SVM) and Local Outlier Factor (LOF) are used as machine learning algorithms to detect anomalous instances. Comparison between these two machine learning revealed similar anomalies. the acquired results support the development of computational methods for assessing structural anomalies based on statistical indicators of acceleration measurements. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Suspension bridges

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Using Grasshopper Optimization in Big data 11th

Using Grasshopper Optimization in Big Data

引用

11th and 12th EAI international conference on Big data Technologies and Applications, BDTA 2021 and BDTA 2022

作者： Khalf, Asmaa G. Ghany, Kareem Kamal A. Faculty of Computers and Information Beni-Seuf University Beni Suef Egypt Faculty of Computers and Information Sadat Academy Cairo Egypt

ISBN: (纸本)9783031336133

From algorithms that have been popular and presently used in meta-heuristic is the grasshopper optimization method, which has made many theoretical breakthroughs and is widely applied in numerous optimization issues across different fields such as image processing, machine learning, engineering design, control over wireless networking, power systems, and other things. In this study, we review the literature that is currently accessible on the grasshopper optimization technique and its extensions to the chaotic, binary, multi-objective scenarios and hybrid. Finally, the grasshopper optimization algorithm has proven superior to other optimization algorithms in most literature. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： data mining

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A Machine learning (ML)-Inspired Method for Intrusion Detection in IoT Devices Networks 5th

A Machine Learning (ML)-Inspired Method for Intrusion Detect...

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5th international conference on Computing Science, Communication and Security

作者： Venkatesh, Veeramuthu Raj, Pethuru Nedium, Roshitha Edara, Jahnavi Reddy, Kalluru Amarnath Anushiadevi, R. SASTRA Deemed Univ Sch Comp Thanjavur 613401 Tamilnadu India Reliance Jio Cloud Serv JCS Bangalore 560025 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

the Internet of things (IoT) and Machine learning (ML) represent dynamic research fields with significant growth. IoT applications have gained popularity among technology researchers and developers, and the increasing deployment of IoT devices in critical infrastructures improves efficiency and reliability and raises concerns about cyber-attacks. Enterprises have effectively implemented IoT services, enabling automated production through remote and intelligent control. However, this adoption has concurrently introduced novel security vulnerabilities. Addressing the security and privacy challenges in IoT, especially considering energy limitations and scalability issues, remains a critical focus in computer security. this paper aims to prevent multiple attacks targeting sensor nodes' data manipulation. It encompasses a range of threats, such as sensing layer attacks, malfunctions, tamper attacks, false data injection, base node and clone attacks. the proposed approach involves a threat model and a pairing algorithm that utilizes machine learning to associate each sensor node with its corresponding node. To accomplish the goal, the performance of the proposed solution is compared with various machine learning models, including Decision Tree, Linear Regression, k-nearest neighbors (KNN), Random Forest, and AdaBoost. the evaluation utilized two openly accessible real-world datasets, with metrics such as accuracy in attack detection, training time, and testing time being considered. By incorporating machine learning algorithms to improve attack detection in IoT environments, the proposed approach represents a significant advancement in enhancing security and privacy. the results highlight the importance of adopting advanced techniques to safeguard IoT systems from potential threats. the proposed method achieved an impressive 96.75% accuracy rate in detecting attacks, surpassing existing solutions with nearly twice the speed in training and testing times. As the IoT landsc

关键词： Sensor nodes Security attacks Intrusion Detection System Critical infrastructure Machine learning Internet of things (IoT)

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Deep learning-based Anomaly Detection in Structural Health Monitoring 11

Deep Learning-based Anomaly Detection in Structural Health M...

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11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Zhao, Mengchen Sadhu, Ayan Capretz, Miriam Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada Department of Civil and Environmental Engineering Western University LondonONN6A 3K7 Canada

A structural health monitoring (SHM) system involves the collection of large amounts of data and data transmission. However, sensors installed on civil infrastructures can get malfunctioned due to normal aging, exposure to outdoor weather conditions, and delayed replacements, resulting in the collection of multiclass anomalies in measured data. It is often combined with imbalance issues where most of the collected data remained biased to a certain type of anomaly. this paper proposes a convolutional neural network (CNN) with hyperparameter tuning using random search to optimize the performance. the proposed model is examined with a multiclass time-series of anomaly data obtained from a real-life cable-stayed bridge. the study concludes that balancing the database with a time shift window to increase the database has generated the optimum results, with an overall accuracy of 97.74%. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Convolutional neural networks

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An intelligent IoT Solution with Real-Time Monitoring for Road Safety 11

An Intelligent IoT Solution with Real-Time Monitoring for Ro...

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11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Azeez, Jasna K. Mary, Bella Manoj, G. Sivasankari, G. Karunya Institute of Technology and Science Department of Electronics And Communication Tamil Nadu India

ISBN: (纸本)9798350379136

Driver drowsiness is the major reason for accidents, as per the research conducted all over India in October 2023. Paper introduces an innovative IoT solution aimed at combating accidents resulting from drowsy driving. Leveraging Raspberry Pi, alcohol sensor, GPS, ultrasonic sensor, ADC, WiFi, and IoT connectivity enables real-time monitoring. Central to its functionality is the integration of Convolution Neural Network (CNN) algorithms for accurate drowsiness detection. the Raspberry Pi serves as the central processing unit, coordinating data collection and analysis from multiple sensors. the alcohol sensor detects alcohol levels, while the ultrasonic sensor identifies obstacles on the road. GPS technology tracks the vehicle's location, aiding navigation and emergency response. ADC ensures precise sensor measurements. WiFi and IoT connectivity enable seamless data transfer to remote servers for analysis. IoT provides timely alerts to prevent accidents caused by drowsy driving. Behavioral features are non intrusive and is used for monitoring the driver. Analysis of data dumped in the server aims to refine algorithms, improve accuracy, and enhance effectiveness in preventing accidents by using the dataset for training of deep learning algorithm, ensuring continuous optimization of the system. For accident prevention, once the drowsiness is detected in the server, speed control of the vehicle and informing relatives via IoT can be considered as future work. © 2024 IEEE.

关键词： Wi-Fi

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High-Dimensional Fault Tolerance Testing of Highly automated Vehicles Based on Low-Rank Models 27

High-Dimensional Fault Tolerance Testing of Highly Automated...

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27th IEEE international conference on intelligent Transportation Systems, ITSC 2024

作者： Mei, Yuewen Nie, Tong Sun, Jian Tian, Ye Ministry of Education Tongji University Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering Shanghai201804 China

ISBN: (纸本)9798331505929

Ensuring fault tolerance of Highly automated Vehicles (HAVs) is crucial for their safety due to the presence of potentially severe faults. Hence, Fault Injection (FI) testing is conducted by practitioners to evaluate the safety level of HAVs. To fully cover test cases, various driving scenarios and fault settings should be considered. However, due to numerous combinations of test scenarios and fault settings, the testing space can be complex and high-dimensional. In addition, evaluating performance in all newly added scenarios is resourceconsuming. the rarity of critical faults that can cause security problems further strengthens the challenge. To address these challenges, we propose to accelerate FI testing under the lowrank Smoothness Regularized Matrix Factorization (SRMF) framework. We first organize the sparse evaluated data into a structured matrix based on its safety values. then the untested values are estimated by the correlation captured by the matrix structure. To address high dimensionality, a low-rank constraint is imposed on the testing space. To exploit the relationships between existing scenarios and new scenarios and capture the local regularity of critical faults, three types of smoothness regularization are further designed as a complement. We conduct experiments on car following and cut in scenarios. the results indicate that SRMF has the lowest prediction error in various scenarios and is capable of predicting rare critical faults compared to other machine learning models. In addition, SRMF can achieve 1171 acceleration rate, 9 9. 3 % precision and 9 1. 1 % F1 score in identifying critical faults. To the best of our knowledge, this is the first work to introduce low-rank models to FI testing of HAVs. © 2024 IEEE.

关键词： Matrix factorization

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intelligent Algorithm Big data Analysis in Personalized Film and Television Recommendation Algorithms 11th

Intelligent Algorithm Big Data Analysis in Personalized Film...

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11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Lyu, Lin Ma, Chuanming School of Media&communication Changchun Humanities and Sciences College Jilin Changchun130117 China School of Creative Industry Jilin Animation Institute Jilin Changchun130012 China Department of Integrative Arts Silla University Busan46958 Korea Republic of

Many people can't receive accurate push according to their personal preferences when watching movies and TV (television) plays like Tiktok, and the recommendation accuracy of the existing movie and TV recommendation systems in the market is not high. this article comprehensively analyzed the relevant theories and methods of intelligent algorithms and big data analysis, and explored their specific applications in personalized film and television recommendations. Emphasis was placed on recommendation algorithms based on Generative Adversarial Networks (GANs), and big data analysis techniques such as user behavior mining and association analysis were introduced. this research found that the algorithm achieved the highest accuracy of 99% in personalized film and television recommendation, and could provide more accurate and personalized recommendation results by learning user behavior and film and television content characteristics. Big data analysis technology can explore user interests and preferences, discover associations and similarities between users, and build more accurate and personalized recommendation models. © 2024 the Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Machine learning Model for Medical data Classification for Accurate Brain Tumor Cell Detection 4th

Machine Learning Model for Medical Data Classification for A...

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4th international conference on Innovative Product Design and intelligent Manufacturing System, IPDIMS 2022

作者： Navabothu, Gnana Sri Sai Sujith Sakode, Himanshu Gottipati, Jagathi Rama Devi, Polagani Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada520007 India

ISBN: (纸本)9789819916641

the health industry has grown into a large organization, and a vast number of medical data can be produced every day by the healthcare industry to collect information for the prediction of future diseases that can be produced by a patient using treatment history and health information. In a variety of medical diagnostic applications, automated defect identification in medical imaging has become an arising sector. automated brain tumor identification and categorization are the focus of our work. In most cases, CT or MRI scans are used to examine the anatomy of the brain. the main objective of brain tumor detection is to help in clinical diagnosis. the goal is to design a machine learning method that ensures the existence of a tumor and to provide a reliable technique of tumor diagnosis that aids medical staff to provide proper medication to the patient. We get deep convolutional neural network (CNN) models that have already been trained using the transfer learning method to extract features from brain MRI scans. the Visual Geometry Group (VGG 16) is the most notable model when it comes to image recognition architectures as it surpasses and outperforms other traditional convolutional neural network models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Measuring Acceptance of Adoption Metaverse in elearning by Using TAM Model 11

Measuring Acceptance of Adoption Metaverse in eLearning by U...

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11th international conference on Information Technology, ICIT 2023

作者： Alhalaybeh, Awf Althunibat, Ahmad Software Engineering Al-Zaytoonah University of Jordan Madaba Jordan Software Engineering Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9798350320060

this paper examines the Acceptance of Adoption Metaverse (AAM) in elearning by utilizing the Technology Acceptance Model (TAM) framework. the TAM model is a widely recognized theoretical framework that is used to understand the factors that influence users' acceptance of technology. In this study, we explore the relationship between AAM and elearning adoption by investigating the key factors that influence users' behavioral intention towards AAM. the Metaverse is a virtual world that simulates the real-world environment and offers learners an interactive, immersive, and collaborative learning experience. It posits that perceived usefulness and perceived ease of use are the key factors that influence users' behavioral intention to use technology. the study employs a quantitative research approach to collect data from students through a survey questionnaire. the data collected will be analyzed using both multiple linear regression model and simple linear regression model to examine the relationship between Model variables for the acceptance of AAM in elearning. © 2023 IEEE.

关键词： Linear regression

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intelligent Bearing Fault Diagnosis With a Lightweight Neural Network 11

Intelligent Bearing Fault Diagnosis With a Lightweight Neura...

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11th international conference on Control, Automation and Information Sciences (ICCAIS)

作者： Nguyen Duc thuan Nguyen thi Hue Pham Quang Vuong Hoang Si Hong Hanoi Univ Sci & Technol Sch Elect & Elect Engn Hanoi Vietnam Phenikaa Univ Fac Elect & Elect Engn Hanoi Vietnam

ISBN: (数字)9781665452489

ISBN: (纸本)9781665452489

Bearing fault diagnosis plays a vital role in machine health monitoring. With the support of deep learning methods, bearing fault diagnosis based on neural networks using vibration signals has achieved excellent results. Compact models with high accuracy that can be embedded in handheld devices are in high demand. this research proposes a novel method for bearing fault diagnosis based on vibration signals using a low-computation-cost deep learning model. Generally, the vibration signal collected from the bearing of the electrical motor is transformed into the spectrogram images by Constant-Q nonstationary Gabor transform, in which these images are used as the training data. Subsequently, the proposed method uses a compact convolutional neural network, called MobileNetV3, combined with AutoCompress pruning method to decrease the number of required parameters and multiply-accumulate operations. Experimental results manifest that the nominated method gains an accuracy of up to 99.34%. Compared with other deep learning models with similar accuracy, the proposed model has a 2-times less number of parameters and approximately 8-times less number of multiply-accumulate operations.

关键词： fault diagnosis bearing fault machine health monitoring vibration signals convolutional neural network compression model

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