检索结果-内蒙古大学图书馆

Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

来源：评论

学校读者我要写书评

暂无评论

Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning

Monochromatic Image Dehazing Using Enhanced Feature Extracti...

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350334357

Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Application of Artificial Intelligence in Healthcare systems: A Scientometric Analysis

Application of Artificial Intelligence in Healthcare Systems...

引用

2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Samuel-Soma M. Lee, Angela Siew Hoong Jasser, Muhammed Basheer Akinola, Tomiwa Faith Sunway University Dept of Computing and Information Systems Selangor Malaysia Istanbul Ticaret Univ Dept of Comp. Engineering Turkey Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Malaysia University of Ibadan Faculty of Public Health Department of Health Promotion Nigeria

ISBN: (纸本)9798350358155

This bibliometric study examines the worldwide progression of machine learning applications in medical and healthcare research between 2010 and 2023. A large dataset of published articles pertaining to machine learning applications in the medical and healthcare sectors is mined for useful information in this study. The data extraction procedure involves retrieving pertinent information from primary sources such as journals, books, and conference proceedings. Subsequently, the retrieved data is subjected to analysis to discern the patterns and tendencies in the use of machine learning in medical and healthcare research. A total of 1,220 publications were found in the Scopus database over the past 14 years. In addition, the study demonstrated that most AIHS research has concentrated on artificial intelligence applications to address a wide range of problems, including patient data security and chronic medical difficulties (such as cardiovascular disorders). Policymakers, healthcare practitioners, and researchers around the world may find this study's conclusions helpful. Emerging ethical concerns, integration, and real-world uses in smart healthcare systems, and the Internet of Things (IoT), could be the focus of future research. © 2024 IEEE.

关键词： artificial intelligence bibliometric analysis healthcare systems machine learning medical research

来源：评论

学校读者我要写书评

暂无评论

Distributed intelligence for IoT-based smart cities: a survey

引用

Neural computing and Applications 2024年第27期36卷 16621-16656页

作者： Hashem, Ibrahim Abaker Siddiqa, Aisha Alaba, Fadele Ayotunde Bilal, Muhammad Alhashmi, Saadat Mehmood Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science Federal College of Education Zaria Kaduna Tudun Wada810282 Nigeria Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32610 United States Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Development of Kansei-Based Visualization Pattern for E-Learning Website 4th

Development of Kansei-Based Visualization Pattern for E-Lear...

引用

4th International Conference on Advances in Computational Science and engineering, ICACSE 2023

作者： Aning, Alberto Mohd Naim, Nur Faraha Baharum, Aslina Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88450 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819729760

Through their websites, a lot of businesses and people directly offer their services. The way in which these services are offered may affect users emotionally. A website that elicits pleasant emotions in its users can make them feel happy, engaged, trustworthy, or satisfied. Well-known for its ability to analyze customer emotional demands and define the connection between emotions and product design aspects, Kansei engineering (KE) is a user-centered technology. The development of e-learning software, e-learning authoring tools, learning management systems, and other related technologies is currently a major investment made by universities, training centers, and government bodies. Even though there are many advantages for educators using e-learning technology, getting students to use these services can be difficult, especially for the e-learning websites of Malaysia’s higher education institutions (HEIs). Few standards exist for user interface (UI) design that encourages emotional connection on the e-learning platforms of Malaysian HEIs. Students were disengaging from e-learning platforms in substantial numbers because of this issue. Hence, this paper aims to explore users’ needs and emotions while using an HEI website and produce a new guideline for HEIs e-learning website. Using the card sorting technique, a visualization pattern has been presented as a design guideline for an e-learning graphical user interface (GUI) website for HEIs in Malaysia. Positive outcomes have been obtained by implementing the suggested guideline on the GUI of the prototype website. The purpose of this guideline is to help HEIs as well as website developers and designers create e-learning websites that will keep students interested in e-learning over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

引用

China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Driven Worker Activity Recognition with Wearable IoT and Smartphone Sensors 2nd

Machine Learning-Driven Worker Activity Recognition with Wea...

引用

2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hafizh, Hadyan Yunus, Muhammad Razi Bin Majeed, Anwar P. P. Abdul Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Sarawak Metro Sarawak Kuching93100 Malaysia

ISBN: (纸本)9789819639489

Worker activity recognition is a crucial area of research with significant applications in manufacturing, healthcare, and security. Traditional methods relying on computer vision-based algorithms often demand extensive infrastructure, such as video cameras. This study presents a more efficient approach by utilizing data from embedded sensors in smartphones and microcontrollers. These wearable devices capture worker activities, including walking, sitting, standing, laying, and climbing stairs, through accelerometer sensors. The data is transmitted and monitored via an IoT platform, where machine learning techniques are employed for activity recognition and classification. The k-NN and Naïve Bayes classifiers achieved the highest accuracy rates, with 77.8% for smartphones and 76.1% for microcontrollers. This system provides a practical solution for recognizing and monitoring worker activities, with potential applications in factories for enhancing health, safety, and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy 5

Optimized Deep Learning Mechanism for Intrusion Detection: L...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajkumar, M. Vs, Lakshi Karthik, R. Pavithra, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Smart Computing Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331530013

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

引用

IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Simulation Models for Evolving Robot Collective Motion in Industrial Coverage and Inspection Tasks 51

Comparative Analysis of Simulation Models for Evolving Robot...

引用

51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Ghanem, Reda Ali, Ismail M. Kasmarik, Kathryn Garratt, Matthew School of Systems and Computing University of New South Wales CanberraACT2600 Australia School of Engineering and Technology University of New South Wales CanberraACT2600 Australia

This study presents a comprehensive comparative analysis of simulation approaches for optimizing the collective motion behavior of robot swarms, with a focus on industrial applications. We investigate the feasibility of bridging the simulation-reality gap by utilizing fast, low-fidelity simulators for parameter evolution, thereby enhancing the efficiency of real-world deployments. Our analysis examines transferability of optimized swarm parameters derived from two low-fidelity simulators to a high-fidelity robot simulation environment. These low-fidelity simulators are employed to fine-tune motion parameters for both ground and aerial vehicle swarms addressing complex coverage problems. The results indicate that while a differential drive simulator requires significantly more time to generate parameters compared to a point-mass simulator, it yields superior swarming behavior and coverage performance across both robot types in high-fidelity simulations. This work highlights the potential for effective parameter transfer to real-world scenarios, paving the way for advanced applications in areas such as autonomous inspection, environmental monitoring, and search-and-rescue operations. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：