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Three-dimensional Reconstruction of Wire-structured Optical Welds and Point Cloud Filtering Methods

Three-dimensional Reconstruction of Wire-structured Optical ...

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2024 ieee international conference on Cognitive computing and Complex data

作者： Shen, Yili Zeng, Chuixi Wuzhou Univ Sch Mech & Resource Engn Wuzhou Peoples R China Guangxi Aiibbot Intelligent Technol Co Tech Dept Nanning Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Three-dimensional reconstruction of weld surfaces is essential for evaluating weld quality. However, the fixed positions of the camera and laser light source prevent direct reconstruction within the camera's coordinate system using line structured light scanning. To address this, we mounted the camera and laser light source on a six-axis robot and calibrated the transformation matrix between the base and camera coordinate systems. This setup allows us to generate a point cloud representing the weld surface within the base coordinate system. Considering the specific characteristics of 3D reconstruction and the topographic features of weld surfaces, we developed a reduced-dimensional point cloud filtering method. Comparative analysis with traditional filtering algorithms shows that this method effectively eliminates isolated and cluster noise, ensuring accurate topographic reconstruction of weld surfaces.

关键词： line structured light weld seam 3D reconstruction point cloud filtering

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Research on Transmission line alternatives selection based on cross-system data fusion 8

Research on Transmission line alternatives selection based o...

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8th international conference on intelligent computing and Signal Processing, ICSP 2023

作者： He, Bing Xu, Xiaoming Mao, Jiaying State Grid Shanghai Extra High Voltage Company Shanghai China

ISBN: (纸本)9798350302455

The safety of transmission lines is measured from several different perspectives. The data of different video surveillance, UAV monitoring, Blockchain social inspection, and internal and external network manual inspection of the transmission line are all connected to the display screen of the transmission line monitoring center. Then the health level of the transmission line is evaluated based on the different perspectives of data in real time. The evaluation needs a series of standard systems to evaluate comprehensively, the data of a single system is not accurate enough. In this paper, the fuzzy multi-attribute decision-making method is used to evaluate the optimal selection of data for each subsystem, and the optimal alternative is selected during the transmission line comprehensive, The method of mathematics can evaluate the health level of transmission lines more accurately, the results hope to provide some proposals for the operation of transmission lines to develop transmission line maintenance policies. © 2023 ieee.

关键词： Electric lines

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Deep Reinforcement-Based Anomaly Detection: An Enhanced Unsupervised Approach for Medical Time Series data 4

Deep Reinforcement-Based Anomaly Detection: An Enhanced Unsu...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Briskilla, M. Raikumar, T. Dhiliphan Kalasalingam Academy of Research and Education Dept. of Cse Tamilnadu India

ISBN: (纸本)9798331529635

In the era of big data, the ability to identify anomalies in medical time series data has become increasingly vital for a variety of healthcare applications. Timely identification of anomalies in critical vital signs, such as heart rate and blood pressure, is essential for ensuring patient safety and guiding clinical decision-making. Traditional anomaly detection methods, however, often struggle with the inherent complexity of time series data, including patterns of periodicity, trends, and seasonality. This study introduces Deep Reinforcement-Based Anomaly Detection, a revolutionary unsupervised deep learning approach that effectively detects several forms of anomalies in time series data, including point anomalies, contextual anomalies, and discords. By leveraging Convolutional Neural Networks (CNNs), the method predicts future values based on time series windows, and compares these predictions with actual data points to identify anomalies in real time. This approach has the advantage of being able to operate without labeled data and to manage scenarios where anomalies make up less than 5% of the dataset, making it suitable for healthcare applications where anomalies are rare. Empirical evaluations on publicly available benchmark datasets show that the approach we employ outperforms cutting-edge methods across diverse datasets, including medical time series data. Furthermore, its versatility in supporting both univariate and multivariate time series enables wide applicability, making it a powerful tool for detecting time series anomalies. © 2024 ieee.

关键词： Deep reinforcement learning

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Detection of False data Injection Attacks in Smart Power Grid Using data Balancing-Based Machine Learning Algorithms 3

Detection of False Data Injection Attacks in Smart Power Gri...

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3rd international conference on advanced Electrical Engineering, ICAEE 2024

作者： Gasmi, Younes Bali, Noureddine Karim, Atif Belagoune, Soufiane Usthb Department of Electrical Engineering Algiers Algeria

ISBN: (纸本)9798350349351

The state estimation technique in intelligent power systems is critical for estimating state variables based on appropriate measurement data. Nevertheless, this technique is exposed to cyber-attacks, the best known of which is the false data injection attack (FDIA), which poses a danger to advanced energy systems. This work describes a method for detecting fraudulent attacks using machine learning techniques that include Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). Different oversampling approaches have been used to tackle the imbalanced dataset issue including the Random Oversampling Technique (ROT), Synthetic Minority Oversampling Technique (SMOTE) and Borderline SMOTE. The approach was applied to well-known Oak Ridge National Laboratory datasets. Various measures were used to analyze the efficiency of this study, including accuracy, F1-score, precision and recall. © 2024 ieee.

关键词： Phasor measurement units

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Diagnosis of Alzheimer Disease from Multi-Modal data: Role of Artificial Intelligence

Diagnosis of Alzheimer Disease from Multi-Modal Data: Role o...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Julaiha, A.G. Noorul Vasudevan, B. Kannan, S. Rajesh Rajalakshmi Institute of Technology Anna University Department of Computer Science & Engineering Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331543617

Alzheimer's disease (AD) is another area of medical research concern mainly due to the current prognosis of the disease that is further impacting the patient and health care organizations. This paper aims at investigating the performance of the predictive model that employs artificial intelligence (AI) to forecast Alzheimer's disease based on clinical histories, neuro-images, and genomics. The CNN integrated with LSTM can allow for the analysis of such multifaceted data sources since it has outstanding performance in processing and interpreting the data. To understand Alzheimer's disease, we used a combination of CNNs and LSTMs. CNNs helped us identify patterns in brain scans, while LSTMs analyzed how clinical and genetic factors changed over time. The following model defines an enhanced decision-making approach that could prove useful in early diagnosis and treatment regimen planning compared to standard approaches. Based on the findings, multi-modal data contributes significantly to the identification of Alzheimer's disease prediction with high accuracy and less variability so that fruitful intervention trials and research would be well pioneered strategies. © 2024 ieee.

关键词： Electronic health record

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5G based Edge computing for Internet of Medical Things in Fast Healthcare data Processing and Management 2

5G based Edge Computing for Internet of Medical Things in Fa...

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2nd ieee international conference on advanced Technologies in intelligent Control, Environment, computing and Communication Engineering, ICATIECE 2022

作者： Rao, Sunita Kumari, Neeraj Jaipur National University Department of Engineering and Technology Jaipur India Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India

ISBN: (纸本)9781665493963

Internet of Medical Things is one of the fastest growing fields in technology, and it is predicted to bring about the largest technological delivery ever. Medical systems have been pushed to improve online services across the board by the COVID-19 pandemic, as the edge-enabled Online learning system in the healthcare system offers incredible opportunities. With the introduction of cutting-edge digital health technologies, patients will be able to avoid unneeded, preventative hospitalizations. Furthermore, the utilization of adaptable and readily available media by both patients and medical professionals would allow telehealth-based medical platforms to make healthcare more efficient and economical. © 2022 ieee.

关键词： Online systems

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Proceedings - ieee Congress on Cybermatics: 2023 ieee international conferences on Internet of Things, iThings 2023, ieee Green computing and Communications, GreenCom 2023, ieee Cyber, Physical and Social computing, CPSCom 2023 and ieee Smart data, Smartdata 2023

Proceedings - IEEE Congress on Cybermatics: 2023 IEEE Intern...

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2023 ieee Congress on Cybermatics: 16th ieee international conferences on Internet of Things, iThings 2023, 19th ieee international conference on Green computing and Communications, GreenCom 2023, 16th ieee international conference on Cyber, Physical and Social computing, CPSCom 2023 and 9th ieee international conference on Smart data, Smartdata 2023

ISBN: (纸本)9798350309461

The proceedings contain 117 papers. The topics discussed include: inter-satellite resource balancing based on genetic algorithm in terrestrial-satellite networks;collaborative multi-task learning across internet edges with device-to-device communications;deep reinforcement learning for UAV-assisted spectrum sharing: a minority game approach;TIDE: a timing-deterministic and efficient executor for micro-ROS;outage probability analysis of the symbiotic backscatter-NOMA systems;MCMSys: multimodal data closed-loop management system for autonomous driving;design and implementation of security enhancement mechanism for smart distribution transformer combine terminal based on WAPI;and change back meter carrier module intelligent sorting and management techniques based on ARM.

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Modeling Method of Flight Operation Information Flow Based on TIMED PETRI NET 5

Modeling Method of Flight Operation Information Flow Based o...

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5th international conference on intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Liu, Yujie Li, Naixin Zhang, Xia Li, Chen China Electronic Product Reliability and Environmental Testing Research Institute Key Laboratory of Industrial Equipment Quality Big Data MIIT GuangZhou China China Electronic Product Reliability and Environmental Testing Research Institute GuangZhou China China Aeronautical Radio Electronics Research Institute National Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Shanghai China

ISBN: (纸本)9798350368284

Time performance is an important indicator of flight operation program design and evaluation, which affects the time urgency and workload of pilots to perform tasks. The modeling method of flight operation information flow based on the timed Petri net is proposed, and the upper and lower bounds of time constraints for different flight operation types such as serial operation, parallel operation and selection operation are defined. A static time performance analysis method based on network structure characteristics is proposed, and a dynamic time performance analysis method affected by pilot behavior is proposed. Combined with the rejected take-off operation procedure of a certain aircraft, an example of information flow modeling and time performance analysis based on the timed Petri net is given, which demonstrates the feasibility and effectiveness of the method. © 2024 ieee.

关键词： Software design

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A Novel Machine Learning-Enhanced Swarm Intelligence Algorithm for Cost-Effective Cloud Load Balancing

A Novel Machine Learning-Enhanced Swarm Intelligence Algorit...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Satheesh, Nimmati Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Cloud computing refers to the instant accessibility of shared computer resources over the internet on-demand, thereby revolutionizing the way organizations deploy and manage their IT infrastructure. Efficient load balancing in a cloud environment scales up, utilizing resources with optimized cost-effectiveness. This paper introduces a new Machine Learning-guided Improved Swarm Intelligence Algorithm for reliable load balancing in cloud computing. Methodology It spans on three main elements. The first is the data center controller for the management of tasks. VM allocation uses an Improved Beetle Swarm Optimization Algorithm and one-dimensional convolutional probabilistic neural network is used for host load prediction. To propose an approach that looks to optimize the key metrics such as make span, average cost of VM, resource utilization, and value response time through iterative optimization process. The results of the experiments reflect the success of the proposed algorithm, because an improved performance of load balancing is obtained in comparison with those achieved using PSO, Grey Wolf Optimization, and Genetic Algorithm. The convergence diagram displays the iterative optimization process achieved by the proposed algorithm in obtaining a balance among various performance metrics. Overall, the methodology proposed in this work will provide promising facilitation in the increased efficiency, scalability, and cost-effectiveness of environments based on cloud computing with a reliable load balancing. © 2024 ieee.

关键词： Genetic algorithms

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data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes 6

Data processing, analysis and Machine Learning of Electrical...

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6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Germanicus, Rosine Coq De Wolf, Peter Klapetek, Petr Normandie Univ Unicaen Ensicaen CNRS CRISMAT UMR6508 Caen14000 France Bruker Nano Surfaces Santa BarbaraCA93117 United States Czech Metrology Institute Brno638 00 Czech Republic

ISBN: (纸本)9798331506520

With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing performances. This paper details data processing techniques and machine learning methodologies for the analysis and prediction of advanced electrical Atomic Force Microscopy datasets. The datasets are acquired using a hyperspectral data Cube approach, which combines both mapping and spectroscopy acquisitions. In the context of decarbonization and electrification, current challenges are related to energy storage and transport. A cathode material used in Lithium-ion battery electrodes is investigated for its electrical transport properties. To comprehensively characterize the electrical properties, the conductive mode of Atomic Force Microscopy is employed using the data Cube method, which enables current-voltage spectroscopy at each pixel of the acquisition. The analysis of experimental datasets reveals localized electrical behavior of the material's components as a function of applied voltage. Furthermore, machine learning algorithms are employed to predict current maps and cluster distinct regions based on their electrical characteristics. © 2025 ieee.

关键词： Network security

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