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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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GPU-Based Acceleration of the Rao Optimization Algorithms: Application to the Solution of Large Systems of Nonlinear Equations 24th

GPU-Based Acceleration of the Rao Optimization Algorithms:...

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24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Silva, Bruno Lopes, Luiz Guerreiro Doctoral Program in Informatics Engineering University of Madeira Madeira Is. Funchal Portugal Regional Secretariat for Education Science and Technology Regional Government of Madeira Funchal Portugal Faculty of Exact Sciences and Engineering University of Madeira Madeira Is. Funchal9020-105 Portugal

ISBN: (纸本)9783031482311

In this paper, parallel GPU-based versions of the three Rao metaphor-less optimization algorithms are proposed and used to solve large-scale nonlinear equation systems, which are hard to solve with traditional numerical methods, particularly as the size of the systems get bigger. the parallel implementations of the Rao algorithms were performed in Julia and tested on a high-performance GeForce RTX 3090 GPU with 10 496 CUDA cores and 24 GB of GDDR6X VRAM using a set of challenging scalable systems of nonlinear equations. the computational experiments carried out demonstrated the efficiency of the proposed GPU-accelerated versions of the Rao optimization algorithms, with average speedups ranging from 57.10 × to 315.12 × for the set of test problems considered in this study. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Graphics processing unit

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A novel ropeway-based system for bridge apparent scanning 11th

A novel ropeway-based system for bridge apparent scanning

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11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Pan, Y. Zhuang, X.L. Wang, D.L. Tian, H. College of Civil Engineering Tongji University Shanghai China College of electronic and information Engineering Tongji University Shanghai China Tongji University Shanghai China Key Laboratory of Road and Bridge Detection and Maintenance Technology of Zhejiang Province Zhejiang China

ISBN: (纸本)9781032345314

With the increasing of the operation times of a bridge, cracks and other defects are unavoidable produced under the actions of vehicles and environments. To make a scientific decision for structure safety, the inspection of a structure is forcedly required in each country over the world. However, due to the characteristics of bridge structure, the efficiency and quality of inspection are usually limited by the working equipment. Commonly, an inspection vehicle for human-carrying with bracket and hand-tools are assembled for these works. To improve the quality of data and make a fasten bridge inspection, in this paper, a novel ropeway-based image acquisition system is introduced. With a statistical analysis of the crossing shape of usual concrete bridge built in urban area, a novel form of inspection by using ropeway-based image acquisition system is proposed firstly. then, the real-time deep learning-based defects identification algorithms are compared and embedded in the whole system. By comprehensive application of a few advanced image processing methods, a fasten bridge inspection system is produced. With the proposed system, some experiments are executed. the results indicate this system can save 50% time-costs at least than the traditional working mode. And the inspection data is standardized and digitalized for the further analysis. Based on the digitalized data, the evaluation of the healthy status of a bridge girder on its bottom is carried-out conveniently with new statistical indexes. Above all, this research is much innovative and meaningful for the further engineering application and research under intelligence era. © 2022 the Author(s).

关键词： Inspection

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Fall Detection in the Wild: An intelligent Emergency Assistance System 1

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11th international conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Li, Yang Guo, Ge Li, Jixin Sun, Yu Ren, Gaoming He, Lei Su, Yong Shaanxi Institute of Technology Shaanxi China Tianjin Normal University Tianjin China

ISBN: (数字)9789819926534

ISBN: (纸本)9789819926527

Recent years have witnessed increasing attention to fall detection, powered by the strong demands of intelligent EmergencyAssistance Systems (IEAS), as it is crucial to reduce incidents of serious injuries caused by falls. In this paper, we propose an IEAS, a framework that integrates deep learning-based fall detection and edge computing-based local processing. First, we introduce a novel challenging benchmark dataset, called WildFall, to better evaluate fall detection models in real-world environments. the WildFall consists of 2,000 various types of video clips, involving movies, TV series, and surveillance. then, the conceptual design, modules, and practical implementation of IEAS are described in detail. Finally, we discuss and analyze the extensibility of IEAS and future work. We believe that both the proposed IEAS and the public availability of WildFall will attract more research attention, to provide timely emergency assistance for the injured. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fall detection

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Improving RBF data Fusion Algorithm for Underground Safety Monitoring of Gob-Side Entry Retaining in Coal Mines 11th

Improving RBF Data Fusion Algorithm for Underground Safety M...

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

作者： Gao, Yuzhu Liu, Yude Guo, Chaoyi CHN Energy Wuhai Energy Huangbaici Mining Co. Ltd. Wuhai016000 China School of Mining Safety North China Institute of Science and Technology Langfang065201 China Ganzhou Urban Investment Holding Group Co Ltd Ganzhou341000 China

With the increasingly stringent safety requirements of coal mine production, the traditional underground safety monitoring system faces many challenges in data processing and risk prediction. In this study, a data fusion algorithm based on an improved Radial Basis Function (RBF) network is proposed, aiming to improve the monitoring effect and warning accuracy along the open stay. the specific method includes the use of improved learning mechanisms and optimized kernel functions for effective fusion processing of multi-source data from downhole, and the combination of machine learning techniques for intelligent identification and prediction of abnormal states. this new algorithm not only improves the real-time and accuracy of data processing, but also dynamically adjusts the learning parameters according to historical and real-time data to enhance the model's generalization ability and adaptability. the predicted values of the improved RBF model are closer to the actual values at several time points, such as 0.9% of gas concentration at 16:00 and 0.4% at 4:00, which shows that the model performs better when dealing with extreme values. the study shows that the warning response time and accuracy of the underground safety monitoring system can be significantly improved by this improved RBF data fusion algorithm, which has an important practical application value for ensuring the production safety of coal mines. this study not only optimizes the safety monitoring framework along the air-retaining alleys, but also provides a technical path that can be used for safety management in other high-risk industrial areas. © 2024 the Authors. Published by Elsevier B.V.

关键词： Coal mines

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Performance Evaluation of Machine learning Techniques for Detecting Cross-Site Scripting Attacks 11

Performance Evaluation of Machine Learning Techniques for De...

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11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Kumar, Atul Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350348422

the entire world shares the digital form of information through the Internet by accessing multiple websites. the link to these websites can be shared by authenticated or unauthenticated sources. Cross-site scripting attacks are the category of cyberattack in which malicious code can be inserted into the webpage code. these attacks can be so severe that they can steal the entire data of the machine or server and can harm the network infrastructure. Ransomware attackers also utilize cross-scripting attacks as the medium for breaking the security policies of the victim's network. Machine learning techniques are widely employed to detect different types of cyberattacks in the industry. In this research paper, machine learning techniques, Naïve Bayes, Logistic Regression, AdaBoost, XGBoost, and decision tree are used to detect the cross-scripting attacks from the information about scripts. the results prove that the AdaBoost technique gains 97.29% accuracy for detecting cross-scripting attacks and the Naïve Bayes methodology shows minimum accuracy among the employed machine learning methodology. these achieved results form the future directions for building smart extensions for Internet browsers to detect such attacks in advance. © 2023 IEEE.

关键词： Decision trees

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Hybrid Predictive Modeling for Automotive After-Sales Pricing: Integrating BiLSTM-Attention and Fuzzy Logic 11th

Hybrid Predictive Modeling for Automotive After-Sales Pricin...

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11th international conference on Industrial engineering and Applications - Europe (ICIEA - Europe)

作者： Amellal, Asmae Amellal, Issam Ech-charrat, Mohammed Rida Abdelmalek Essaadi Univ ENSA Tetouan Tetouan Morocco Hassan 1st Univ ENSA Berrechid Settat Morocco

ISBN: (纸本)9783031581120;9783031581137

In the automotive service and spare parts distribution sector, effective supply chain management is paramount as it directly impacts customer satisfaction, profits, and overall competitiveness. To optimize these crucial aspects, a robust fuzzy logic framework has been developed, taking into account the intricate connections between customer demand, customer sentiment, product availability, and pricing strategy. this study focuses on after-sales services provided by Moroccan automobile companies, utilizing workshop entry data from Enterprise Resource Planning (ERP) systems across multiple cities in the country. To accurately capture customer sentiment, a Bidirectional Long Short-Term Memory (BiLSTM) with attention model, a deep learning strategy, is employed to complete any missing customer sentiment data. Leveraging the power of neural networks, this approach effectively analyzes and predicts sentiment patterns. Once the sentiment data is completed, a Fuzzy Logic model is utilized to determine the most optimal pricing strategy. By considering various factors through fuzzy sets and rules, this model allows informed decision-making to strike the right balance between profitability and customer satisfaction. the employed models demonstrated strong performance, as evidenced by metrics such as MSE and R2. Specifically, the BiLSTM-attention model achieved an R2 of 0.87, while the fuzzy logic model registered an impressive R2 of 0.91. Furthermore, when juxtaposed with alternative models, our framework's superior efficacy became evident.

关键词： Supply chain management price prediction Deep learning BiLSTM-attention Fuzzy method

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Research on the Optimization of Sample Point Placement for Ground Substrate Survey based on Interpretable Machine learning 11

Research on the Optimization of Sample Point Placement for G...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Tang, YuLei Zhang, Baowei Wang, Minhua Tian, Qin Lu, Yichen China Geological Survey Center for Geophysical Survey Langfang China China Geological Survey Langfang Center for General Survey of Natural Resources Langfang China University College London London United Kingdom

ISBN: (纸本)9798350303513

To obtain comprehensive and accurate data, it is necessary to conduct field surveys of ground substrate, arable land, soil quality, and other natural resource classes. this is in difficult and key areas. Manual sweeping can improve survey accuracy, but the investment in human and material resources, and the long survey cycle can complicate taking into account the impact of geological, hydrological, soil, and other factors on the deployment of sample points. In addition, technical experts can be relied on for expertise, professionalism, and a scientific approach. However, generalizability is weak due to its subjective nature. Machine learning can form an efficient and scientific scheme to deploy sample points. A large amount of data, such as soil, geology, and meteorology, can be simulated using machine learning models to generate effective multidimensional data features for engineering, identify potential patterns, and optimize sample point locations and quantities based on nonlinear and higher-order interactions. In contrast to traditional black box models that use prediction accuracy as a single metric, interpretable machine learning (IML) provides insight into how the model learns and makes predictions. As a result, we will be able to identify potential biases and errors more easily and build more reliable sample point placement models. In this study, we proposed the RF-GA algorithm, which integrates environmental factor data often relied on by experts' judgments, such as topography, soil type, elevation, slope, surface cover, and historical soil texture. By using genetic algorithms to filter variables, the feature space can be optimized. A plan is developed for surface matrix survey points in northeast Tieling using intermediate scale factor modeling. there are a variety of ecosystems and topography types in the Tieling area, which lies between the Liaohe Plain and Liaodong mountainous hilly area. An optimized plan for regional ground substrate survey point plac

关键词： Machine learning

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Urban Road Underground Defects Detection Using the Resistivity Method with an Irregular Array 11

Urban Road Underground Defects Detection Using the Resistivi...

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11th international conference on Environmental and engineering Geophysics, ICEEG 2024

作者： Jiulong, Cheng Zhongzhong, Xu Yuqi, Zhang Honpeng, Zhao Yangchun, Han College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing China State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources China University of Mining and Technology Beijing China

While the resistivity method offers clear advantages for detecting underground defects beneath urban roads, the complex surface conditions of these roads preclude the deployment of regular arrays. this complexity significantly challenges conventional resistivity detection methods for underground defects. this study designed a high-density resistivity detection observation system adaptable to the deployment of irregular arrays along urban roads. this method obtains high-dimensional apparent resistivity data and employs Principal Component Analysis (PCA) for its dimensionality reduction. It introduces an improved position update method within the Chimpanzee Optimization Algorithm (ChOA), combined with Extreme learning Machine (ELM), to propose a PCA-ChOA-ELM combination algorithm for inversion. this is then compared with BP (Back Propagation Neural Network), GABP (Genetic Algorithm optimized Back Propagation Neural Network), and ELM. To further test the PCAChOA-ELM resistivity inversion's effectiveness in detecting underground defects beneath urban roads with an irregular array, a geological-geophysical model of underground defects was established. Numerical and physical experiments were combined to perform inversion imaging on models of underground defects, both with and without water. the research results show that the high-density resistivity method based on PCA-ChOA-ELM with an irregular array can adapt to the detection of underground defects under urban roads, can better reflect the electrical characteristics, location, and distribution of underground defects under urban roads, and achieve efficient and precise positioning of underground defects under urban roads. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Dimensionality reduction

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IncidentNet: Traffic Incident Detection, Localization and Severity Estimation with Sparse Sensing 27

IncidentNet: Traffic Incident Detection, Localization and Se...

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

作者： Peddiraju, Sai Shashank Harapanahalli, Kaustubh Andert, Edward Shrivastava, Aviral School for Engineering of Matter Transport and Energy United States Arizona State University Tempe United States School of Computing and Augmented Intelligence United States

ISBN: (纸本)9798331505929

Prior art in traffic incident detection relies on high sensor coverage and is primarily based on decision-tree and random forest models that have limited representation capacity and, as a result, cannot detect incidents with high accuracy. this paper presents IncidentNet - a novel approach for classifying, localizing, and estimating the severity of traffic incidents using deep learning models trained on data captured from sparsely placed sensors in urban environments. Our model works on microscopic traffic data that can be collected using cameras installed at traffic intersections. Due to the unavailability of datasets that provide microscopic traffic details and traffic incident details simultaneously, we also present a methodology to generate a synthetic microscopic traffic dataset that matches given macroscopic traffic data. IncidentNet11https://***/MPSLab-ASU/IncidentNet achieves a traffic incident detection rate of 98%, with false alarm rates of less than 7% in 197 seconds on average in urban environments with cameras on less than 20% of the traffic intersections. © 2024 IEEE.

关键词： Decision trees

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Optimization of Tiny YOLOv7 Lightweight Model through Network Structure Re-parameterization for Coral Reef Fish Identification 11

Optimization of Tiny YOLOv7 Lightweight Model Through Networ...

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11th international conference on ICT for Smart Society, ICISS 2024

作者： Wibowo, Sandi Utama, Andria Ansri School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Research Center for Conservation of Marine and Inland Water National Reasearch and Innovation Agency Jakarta Indonesia

ISBN: (纸本)9798350389654

Coral reefs face significant threats, leading to rapid declines in reef fish populations due to destructive fishing activities and climate change. Conventional methods used to estimate coral reef fish biomass abundance are underwater visual census (UVC) and diver-operated video (DOV). Limitations of underwater visual census (UVS) and diver-operated video (DOV) methods include potential bias from the presence of the diver, reliance on diver skill and video quality, time-consuming analysis, and high-quality equipment, all of which can lead to inaccuracies in identification. this study addresses these challenges by developing a cost-effective reef fish monitoring system using an IoT platform equipped with stereo cameras. this system is designed as edge computing, object detection is carried out on the IoT platform to overcome time-consuming analysis. the system detects fish species and measures the length of the fish using depth information from the stereo camera. To improve detection accuracy and speed, we use a reparametrized YOLO Tiny single-stage CNN architecture. By optimizing the model with CBL and CBM blocks, the Tiny YOLOv7 with adjusting the number of CNN layer achieved an F1 score of 86.64 and a mean average precision (mAP) of 90.50%. the scaled-down model efficiently uses computing resources, operating at only 3 % of the embedded system capacity, equivalent to 472 GFLOPs. the results of the embedded transfer learning model obtained a detection speed performance of 11 FPS from a 24 FPS camera stream. © 2024 IEEE.

关键词： Reefs

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