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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 53955 条记录，以下是181-190 订阅

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Research on the recognition system of visual arc welding

Research on the recognition system of visual arc welding

引用

ieee information Technology and Mechatronics Engineering conference (ITOEC)

作者： Mingsheng Zhang Jianyu Gu Qiming Li Shiqi Sun Yu Liu Jiangsu Automation Research Institute Lianyungang China College of Materials Engineering North China Institute of Aerospace Engineering Langfang China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

At present, many domestic scholars and research institutions have studied the laser vision welding seam tracking system. By improving the control strategy of the robot and welding seam tracking system, the processing time is optimized and the real-time response of the welding seam tracking system is promoted. However, the practical application of the research results is not yet satisfactory. This paper carries out an experimental study of vision-based arc weld tracking. Its main content contains welding robot kine matics and control systems, weld image processing technology, right angle weld tracking technology, and so on. The research shows that the feature extraction of arc weld based on vision can be completed and arc weld recognition can be realized. It provides some reference for the application of laser vision welding seam tracking systems in industry.

关键词： Productivity visualization Image recognition Welding Neural networks Process control Feature extraction Real-time systems Data mining Robots

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Joint Source and Channel Coding for Multi-Modal Satellite-to-Ground Semantic Communications

Joint Source and Channel Coding for Multi-Modal Satellite-to...

引用

ieee conference on Wireless Communications and Networking

作者： Yanbo Yin Shu Liu Dingzhu Wen Youlong Wu Yuanming Shi School of Information Science and Technology Shanghaitech University

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper presents a novel Joint Source and Channel Coding (JSCC) method for semantic communication to enhance the communication efficiency for transmitting high-resolution multi-modal data from Low Earth Orbit (LEO) satellites to ground stations. On the satellite, a JSCC encoder consisting of neural networks (NNs) is utilized to map the input multi-modal data into a common signal, while an NN-based JSCC decoder at the ground station reconstructs the original input from the received signal. Throughout this process, the common semantic information among each modality is learned to optimize coding space, and a robust coding scheme specific to the satellite downlink channel is developed between the encoder and decoder. Experiments have demonstrated that the proposed method outperforms existing signal modality JSCC approaches, achieving multi-modal transmission with significantly smaller coding space and thereby reducing communication overhead.

关键词： Space vehicles visualization Satellites Simulation Low earth orbit satellites Artificial neural networks Semantic communication Downlink Decoding Channel coding

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Triplet Attention Mechanism-Based Framework for Tower Hard Hat Detection

Triplet Attention Mechanism-Based Framework for Tower Hard H...

引用

Power Electronics, Computer Applications (ICPECA), ieee International conference on

作者： Ming Wu Guangxian Xu Fei Ma Ordos Research Institute Liaoning Technical University Ordos China School of Electronic and Information Engineering Liaoning Technical University Huludao China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Given the poor performance of traditional approaches in identifying small hard hat objects against intricate backgrounds and over long distances, this work proposes a refined TBI-DETR algorithm. The algorithm enhances the model's recognition capability for hard hats by integrating Triplet Attention Mechanisms within the network structure and utilizing a multi-branch architecture to capture cross-dimensional interactive features of the input data. Additionally, a Bi_FPN is constructed to optimize the feature extraction process, promoting effective integration of feature information. Furthermore, a novel Inner-GIoU loss function is developed to refine training efficiency, leading to faster convergence of the model. Evaluation on the SHWD dataset reveals that the proposed approach exceeds state-of-the-art methods by at least 2% in accuracy and 1.4% in mAP@0.5. Its superior detection efficacy offers robust technical solutions for industrial safety management.

关键词： Training visualization Attention mechanisms Computational modeling Surveillance Prevention and mitigation Safety management Feature extraction Helmets Power electronics

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Health Guard: Patient Records and Wellness Detection

Health Guard: Patient Records and Wellness Detection

引用

2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Celin, J. Jeya A. Nakirikanti, Abhinaya Pavani, Vadlamudi Namburu, Sravani Anamika Information Technology Kalasalingam Academy of Research and Education Virudhunagar Krishnankovil India

ISBN: (纸本)9798331521394

The 'Health Guard: Wellness Detection and Patient Records' initiative involves developing a smart, data-driven solution for the better detection of diseases in proper interpretation of submitted symptoms by an individual. This uses complex machine learning algorithms in the analysis of a vast range of symptoms to match them with possible causes and suggest accurate, dependable predictions. In a patient records system, medical history is continuously tracked;therefore, it recognizes some form of pattern in recurring symptoms to provide health advice over time. The purpose of such a system is to deliver the best possible doctor in order to treat the maximum number of patients with proper medical care, be it a diagnostic purpose or an uninterrupted continuity, even if there is no doctor initially for diagnostics. It can indeed make a huge contribution by diagnosing patients early when an early medical check is appropriate. It is intended to provide timely and effective health care for the patient through a very interface that is intended to benefit both patients and healthcare providers when it is effectively integrated. This means that the system will always have security measures that safeguard sensitive health information, so personal information would never fall into wrong hands. The innovative system might remake the healthcare because it makes it more responsive to the needs of every patient and therefore leads to improved health outcomes across populations. Since the system evolves perpetually with the advancement of medical knowledge, the users can at best receive only the latest and most relevant health information available. © 2025 ieee.

关键词： Diseases

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Skin-Inspired Electronics: An Emerging Sensing and Computing Platform

Skin-Inspired Electronics: An Emerging Sensing and Computing...

引用

ieee International conference on Solid-State Circuits (ISSCC)

作者： Margherita Ronchini Weichen Wang Yuya Nishio Yating Yao Zhenan Bao Stanford University Stanford CA Aarhus University Aarhus Denmark

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

Skin is the interface between our body and the environment. Its unique properties and sensing capabilities allow us to perceive the world around us. information about shape, texture, stiffness, temperature, and pain conveyed by touch facilitates our ability to perform tasks and manipulate objects dexterously [1]. Adding sensory feedback in prosthetic limbs will enable users to interact with the environment naturally and effectively, instead of relying solely on visual cues. Tactile sensing technologies, in the form of highly dense sensor arrays and neuromorphic encoding circuits coupled to a neuromodulator, are poised to revolutionize prosthetic devices. On the other hand, such a sensing and signal computing system architecture can provide a general platform for seamless and minimally invasive wearable and implantable devices (Fig. 12.4.1).

关键词： Temperature sensors visualization Temperature Shape Systems architecture Skin Sensors Solid state circuits Wearable devices Sensor arrays

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XGridDS: An Explainable Data Analytics Toolkit for Grid Operations

XGridDS: An Explainable Data Analytics Toolkit for Grid Oper...

引用

Grid Edge Technologies conference & Exposition (Grid Edge), ieee PES

作者： Mary Ngo Alexander Boone Thao Pham Indra Chakraborty Pacific Science & Engineering Group Inc San Diego CA Lawrence Livermore National Laboratory Livermore CA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

Synchrophasors and other smart meters that are a part of the advanced metering infrastructure (AMI) provide vast amounts of data that have the potential to be used by power grid operators to detect anomalies and other stresses on the grid before they become problematic. Maximizing the use of this data has been an ongoing challenge considering the overwhelming amount of data requiring processing, lack of trust and rejection of predictive models produced from the data, lack of actionable information to guide users in their decisions, and lack of intuitive visualizations to effectively inform decisions using the data. This paper describes a proposed data-analytics and advanced processing toolkit that would employ artificial intelligence/machine learning models in an intuitive user interface to support decision-making for grid operators.

关键词： Data analysis Decision making Data visualization Learning (artificial intelligence) Predictive models Smart meters Power grids Phasor measurement units Stress

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RFID based Automatic Pathway for Emergency Vehicles

RFID based Automatic Pathway for Emergency Vehicles

引用

ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Anjali Dani Durva Rane Sanchi Bansal Avinash More Dept. Electronics and Telecommunication MPSTME NMIMS Mumbai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This project introduces an automated traffic control system aimed at facilitating the swift movement of emergency vehicles through urban intersections. Leveraging the integration of RFID and IoT technologies, the system utilizes Node MCU and Arduino Mega microcontrollers for seamless communication and traffic management. RFID tags affixed to emergency vehicles trigger dynamic control over traffic signals, ensuring unobstructed passage. Audible alerts through a buzzer and real-time information displayed on an LCD enhance safety and situational awareness. Coordinated LED signals optimize traffic flow, while IoT integration with Adafruit enables remote operation capabilities. Future enhancements, including GPS tracking with the SIM800L module, would support real-time, location-specific traffic adjustments, significantly reducing response times and enhancing the efficiency of city traffic management.

关键词： visualization Urban areas Traffic control Real-time systems Time factors Internet of Things Security Vehicle dynamics Global Positioning System Vehicles

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Neural Network Interpretability with Layer-Wise Relevance Propagation: Novel Techniques for Neuron Selection and visualization

Neural Network Interpretability with Layer-Wise Relevance Pr...

引用

ieee Annual Computing and Communication Workshop and conference (CCWC)

作者： Deepshikha Bhati Fnu Neha Md Amiruzzaman Angela Guercio Deepak Kumar Shukla Ben Ward Kent State University Kent Ohio USA West Chester University West Chester PA USA Rutgers University Newark NJ USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Interpreting complex neural networks is crucial for understanding their decision-making processes, particularly in applications where transparency and accountability are essential. This proposed method addresses this need by focusing on Layer-wise Relevance Propagation (LRP), a technique used in explain-able artificial intelligence (XAI) to attribute neural network outputs to input features through backpropagated relevance scores. Existing LRP methods often struggle with precision in evaluating individual neuron contributions. To overcome this limitation, we present a novel approach that improves the parsing of selected neurons during LRP backward propagation, using the Visual Geometry Group 16 (VGG16) architecture as a case study. Our method creates neural network graphs to highlight critical paths and visualizes these paths with heatmaps, optimizing neuron selection through accuracy metrics like Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE). Additionally, we utilize a deconvolutional visualization technique to reconstruct feature maps, offering a comprehensive view of the network's inner workings. Extensive experiments demonstrate that our approach enhances interpretability and supports the development of more transparent artificial in-telligence (AI) systems for computer vision applications. This advancement has the potential to improve the trustworthiness of AI models in real-world machine vision applications, thereby increasing their reliability and effectiveness.

关键词： Measurement Heating systems Geometry visualization Explainable AI Machine vision Neurons Focusing Reliability Biological neural networks

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A Study on Enhancing Object Detection of Bell Pepper Pests and Diseases Through Image Segmentation

A Study on Enhancing Object Detection of Bell Pepper Pests a...

引用

ieee International conference on Consumer Electronics (ICCE)

作者： Chih-Hua Lin Tu-Liang Lin Department of Management Information Systems National Chiayi University Chiayi City Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

For farmers cultivating bell peppers, timely identification of areas affected by pests and diseases can effectively reduce the risk of infection to surrounding crops. Before deep learning-based image classification tasks were deeply researched in agriculture, the identification of pests and diseases was usually carried out by experts. However, relying on experts not only consumes a lot of time and money but also requires specialized knowledge. Object detection has proven to be effective in classifying crop pests and diseases. However, the learning process of object detection is often a “black box,” meaning that users do not know which regions the model focuses on when learning pest and disease images. Therefore, this study aims to use a visualization method to observe the regions that object detection focuses on during learning. If the object detection model tends to focus on other regions during learning, this study will use deep learning-based image segmentation to segment the target object and the background, retaining only the target area. The segmented images will then be re-learned using the object detection model. Results indicate that the segmented images improve the confidence of the object detection model for this category. This study investigates four different types of pests and diseases, and the results show that the object detection model's mean Average Precision (mAP) for these four categories can reach over 70% after image segmentation, which is an improvement of about 2-5% compared to the object detection model using unsegmented images.

关键词： Deep learning Training Image segmentation visualization Plant diseases Pathology Crops Object detection Manuals Diseases

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Guiding Autonomous Vehicles in Challenging Weather Using Machine Learning Techniques

Guiding Autonomous Vehicles in Challenging Weather Using Mac...

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2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Pavithran, S. Praveenraj, R. Vishnupriyan, M.K. Arulmurugan, S. K.S.Rangasamy College of Technology Department of Information Technology Tamil Nadu Tiruchengode India K.S.Rangasamy College of Technology Department of Information Technology Tiruchengode India

ISBN: (纸本)9798331521394

The emergence of autonomous cars has transformed transport, providing enhanced safety, efficiency, and convenience. Yet, it is still difficult to incorporate these cars into regular traffic, especially under unfavorable weather conditions like rain, snow, fog, and storms, which greatly affect the performance of their perception and sensing systems. This paper surveys the current state of machine learning, sensor fusion, and intelligent transportation systems (ITS) to improve autonomous vehicle operation in adverse environments. Different methodologies such as Support Vector Machines (SVM), Deep Neural Networks (DNN), and Convolutional Neural Networks (CNN) are analyzed for their efficiency in traffic congestion forecasting and vehicular control. In addition to this, RSUs' use in traffic observation, smart cloud-based transport infrastructure, and automobile location tracking are examined. Outcomes show that sophisticated machine learning algorithms and sensor fusion are both effective in the reduction of negative impacts of unfavorable weather on the navigation of self-driving cars. This review highlights the importance of additional research in the integration of strong machine learning models and smart transportation solutions to make autonomous vehicle deployment safer and more reliable under varying environmental conditions. © 2025 ieee.

关键词： Traffic congestion

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