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Power Science and Technology (ICPST), ieee International Conference on

作者： Dun Qian Zhi Yang Yuzhe Feng Electric Power Research Institute State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Oil-immersed inverted current transformers are widely used in power systems with voltage levels of 110kV and above. The insulation breakdown of oil-immersed inverted current transformer leads to a large explosion hazard. In order to solve this problem, deployed a wide frequency device for partial discharges based on the pulse current method. The article presents a preliminary exploration of partial discharge identification in oil-immersed inverted current transformers using a wide frequency device. The device employs the pulse current method to capture partial discharge data, which is then analyzed through cluster analysis, algorithmic judgment, and data visualization with manual judgment. The approach offers a promising tool for enhancing the safety and reliability of these critical electrical equipment.

关键词： Partial discharges Current transformers Electric breakdown Clustering algorithms data visualization Manuals Oil insulation

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data+Shift: Supporting visual investigation of data distribution shifts by data scientists

Data+Shift: Supporting visual investigation of data distribu...

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24th Eurographics Conference on visualization, EuroVis 2022

作者： Palmeiro, João Malveiro, Beatriz Costa, Rita Polido, David Moreira, Ricardo Bizarro, Pedro Feedzai

ISBN: (纸本)9783038681847

Machine learning on data streams is increasingly more present in multiple domains. However, there is often data distribution shift that can lead machine learning models to make incorrect decisions. While there are automatic methods to detect when drift is happening, human analysis, often by data scientists, is essential to diagnose the causes of the problem and adjust the system. We propose data+Shift, a visual analytics tool to support data scientists in the task of investigating the underlying factors of shift in data features in the context of fraud detection. Design requirements were derived from interviews with data scientists. data+Shift is integrated with JupyterLab and can be used alongside other data science tools. We validated our approach with a think-aloud experiment where a data scientist used the tool for a fraud detection use case. © 2022 The Author(s) © 2022 The Eurographics Association.

关键词： Machine learning

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Deep Learning-Based Semantic Segmentation in Autonomous Driving 23

Deep Learning-Based Semantic Segmentation in Autonomous Driv...

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23rd ieee International Conference on High Performance Computing and Communications, 7th ieee International Conference on data Science and Systems, 19th ieee International Conference on Smart City and 7th ieee International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Jebamikyous, Hrag-Harout Kashef, Rasha Ryerson University Dept of Electrical and Computer Engineering Toronto Canada

ISBN: (纸本)9781665494571

Perception is the first and most important task of any autonomous driving system. It extracts visual information about the surrounding environment of the vehicle. The perception data is then fed to a decision-making system to provide the optimum decision given a specific scenario to avoid potential collisions. In this paper, we have developed variants of the U-Net model to perform semantic segmentation on urban scene images to understand the surroundings of an autonomous vehicle. The U-N et model and its variants are adopted for semantic segmentation in this project to account for the power of the UNet in handling large and small datasets. We have also compared the best-performing variant with other commonly used semantic segmentation models. The comparative analysis was performed using three well-known models, including FCN-16, FCN-8, and SegNet. After conducting sensitivity and comparative analysis, it is concluded that the U-Net variants performed the best in terms of the Intersection over Union (IoU) evaluation metric and other quality metrics. © 2021 ieee.

关键词： Image segmentation visualization Analytical models Sensitivity Smart cities Semantics Decision making

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Deep Learning based Brain Tumor Classification for MR Images using ResNet50 12

Deep Learning based Brain Tumor Classification for MR Images...

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12th International symposium on Digital Forensics and Security, ISDFS 2024

作者： Kokcam, Omer Mirac Boyaci, Aytug Colak, Muhammed Emre Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey National Defence University Department of Computer Engineering Air Force Academy İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, 'Early Diagnosis Saves Lives'. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis. © 2024 ieee.

关键词： Medical imaging

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G-PCGRL: Procedural Graph data Generation via Reinforcement Learning

G-PCGRL: Procedural Graph Data Generation via Reinforcement ...

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ieee symposium on Computational Intelligence and Games, CIG

作者： Florian Rupp Kai Eckert Department of Computer Science University of Applied Sciences Mannheim Mannheim Germany

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

Graph data structures offer a versatile and powerful means to model relationships and interconnections in various domains, promising substantial advantages in data representation, analysis, and visualization. In games, graph-based data structures are omnipresent and represent, for example, game economies, skill trees or complex, branching quest lines. With this paper, we propose G-PCGRL, a novel and controllable method for the procedural generation of graph data using reinforcement learning. Therefore, we frame this problem as manipulating a graph’s adjacency matrix to fulfill a given set of constraints. Our method adapts and extends the Procedural Content Generation via Reinforcement Learning (PCGRL) framework and introduces new representations to frame the problem of graph data generation as a Markov decision process. We compare the performance of our method with the original PCGRL, the run time with a random search and evolutionary algorithm, and evaluate GPCGRL on two graph data domains in games: game economies and skill trees. The results show that our method is capable of generating graph-based content quickly and reliably to support and inspire designers in the game creation process. In addition, trained models are controllable in terms of the type and number of nodes to be generated.

关键词： Analytical models Procedural generation Markov decision processes data visualization Reinforcement learning Evolutionary computation data structures

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Cinema Darkroom: A Deferred Rendering Framework for large-Scale datasets 10

Cinema Darkroom: A Deferred Rendering Framework for Large-Sc...

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Lukasczyk, Jonas Garth, Christoph Larsen, Matthew Engelke, Wito Hotz, Ingrid Rogers, David Ahrens, James Maciejewski, Ross Arizona State Univ Tempe AZ 85287 USA TU Kaiserslautern Kaiserslautern Germany Lawrence Livermore Natl Lab Livermore CA 94550 USA Linkoping Univ Linkoping Sweden Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781728184685

This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once-which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters-such as the applied color map or the strength of ambient occlusion-where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CD's ability to effectively decouple the complexity and size of the dataset from its visualization.

关键词： Deferred Rendering Image databases In Situ visualization Post Hoc analysis Image-Based Shading

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Event-based Bipartite Synchronization of Nonlinear Dynamical Networks With Sampled data

Event-based Bipartite Synchronization of Nonlinear Dynamical...

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2022 ieee International symposium on Product Compliance Engineering - Asia, ISPCE-ASIA 2022

作者： Ayepah, Kwaku Sun, Mei Jia, Qiang School of Mathematical Sciences Jiangsu University Zhenjiang21213 China Institute of Applied System Analysis Jiangsu University Zhenjiang212013 China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

This research examines the bipartite synchronization of dynamical network with switching signed topologies, where the nodes are governed by certain nonlinear dynamics, and an event-triggered control strategy is applied by under periodic sampling communications. It is shown that when the topologies switch within a finite set of signed graph, all nodes are guaranteed to achieve bipartite synchronization if the time average of the algebraic connectivity of the corresponding unsigned graph over certain length of time is large enough, and the main theorem details the impacts of nodal dynamics, network structure on synchronization, and gives a criterion for selecting the involved control parameters. Finally, some numerical simulations are presented to show the validity of our theoretical results and the efficiency of the proposed controller. © 2022 ieee.

关键词： Synchronization

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Automated Optimization of Electrode Placement in Transcutaneous Electrical Nerve Stimulation Using Numerical Simulation

Automated Optimization of Electrode Placement in Transcutane...

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ieee International symposium on Biomedical Imaging

作者： Takashi Ota Hideaki Kuzuoka Tomohiro Amemiya Kazuma Aoyama The University of Tokyo Japan Gunma University Japan

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Electrode placement is a crucial parameter in transcutaneous electrical nerve stimulation. However, it is challenging to identify the optimal electrode placement that effectively stimulates a specific tissue because of the complex structure of the human body, which comprises multiple tissues. In this study, we developed a system that automatically identifies the optimal electrode placement from a large number of potential electrode positions. The system uses the finite element method to calculate the current density distribution in the target tissue for all conditions and outputs the optimal placement that stimulates the target tissue most efficiently. To evaluate the effectiveness of this system, we conducted experiments to optimize the electrode placement for electrical stimulation of the lower limbs and head. We confirm that the optimal elec-trode placement determined by the proposed system is reasonable since it was similar to that in a previous study. Fur-thermore, the automation enables verification of several conditions that would be impractical to verify using conventional methods.

关键词： Electrodes Wrist visualization Limbs Force Electrical stimulation Numerical simulation Finite element analysis Optimization Tendons

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Novel Framework for Sentiment analysis of Movie Reviews based on large Language Models and Deep Learning Methods

Novel Framework for Sentiment Analysis of Movie Reviews base...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), ieee International Conference on

作者： Suhag Pandya

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Background: Sentiment analysis has become a crucial research area, enabling businesses to gauge public opinion on products or movies without manually reviewing content. Methods: Using IMDb data that was heavily preprocessed to improve model training, this research investigates sentiment analysis of movie reviews using the ELECTRA model. A multimodal approach incorporating textual, audio, and visual data was explored to improve emotional context understanding. Results: ELECTRA outperformed other models, including BERT, SVM, and CNN, achieving an impressive 95.6% accuracy based on F1score, precision, recall, and accuracy metrics. While CNN also performed well with 90% accuracy, BERT and SVM achieved 84.01% and 86.18%, respectively, demonstrating comparatively lower effectiveness. Conclusion: The findings highlight that deep learning models, particularly ELECTRA, excel in extracting subjective sentiments from movie reviews. As large language models continue to evolve, sentiment analysis methodologies will likely advance further, offering even greater accuracy and contextual understanding in the future.

关键词： Support vector machines Deep learning Sentiment analysis Analytical models visualization Accuracy Reviews large language models Motion pictures data models

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Learning From large-Scale Noisy Web data With Ubiquitous Reweighting for Image Classification

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2021年第5期43卷 1808-1814页

作者： Li, Jia Song, Yafei Zhu, Jianfeng Cheng, Lele Su, Ying Ye, Lin Yuan, Pengcheng Han, Shumin Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Natl Engn Lab Video Technol Beijing 100871 Peoples R China Comp Vis Technol Dept Baidu Beijing 100871 Peoples R China

Many important advances of deep learning techniques have originated from the efforts of addressing the image classification task on large-scale datasets. However, the construction of clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that can learn an image classification model from noisy web data. By observing the web data, we find that there are five key challenges, i.e., imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. With these challenges in mind, we assume every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags, and the labels. In this manner, the influence of bias and noise in the data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the WebVision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks first place in the image classification task.

关键词： Noise measurement Deep learning Task analysis Training Annotations Solid modeling visualization Image classification noisy web data CNNs ubiquitous reweighting deep learning

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