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ieee Student Conference on Electric Machines and Systems

作者： Yulin Song Han Yan Chenxi Sun Jianwei Huang The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

Due to privacy and security concerns surrounding actual power system structure data, many studies have focused on generating simulated power grids and providing corresponding datasets. However, existing methods and datasets often struggle with effective visualization and lack diverse power grid scenarios. To address these challenges, we propose a large language model-assisted workflow for small-scale synthetic power grid generation, verification, and visualization. Moreover, by taking into account the variety of node types and connection relationships inherent in power networks, our approach aims to enhance understanding of structural characteristics through detailed feature representation. We leverage the large language model to enrich the generated power grid structure data, uncovering potential internal structures. Furthermore, we employ advanced visualization techniques to generate power structure images, facilitating analysis by human experts and supporting potential learning tasks. Our results demonstrate that the generated power grid structures align well with the properties of real-world networks, and the visual outputs effectively highlight the intricacies of these structures. The code and examples are available at https://***/SYLLABLE604/PG-generator.

关键词： Knowledge engineering Adaptation models visualization large language models Power system dynamics data visualization Power grids data models Real-time systems Security

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Efficient Raycasting of Volumetric Depth Images for Remote visualization of large Volumes at High Frame Rates

Efficient Raycasting of Volumetric Depth Images for Remote V...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Aryaman Gupta Ulrik Günther Pietro Incardona Guido Reina Steffen Frey Stefan Gumhold Ivo F. Sbalzarini Technische Universität Dresden Center for Systems Biology Dresden MPI-CBG Dresden CASUS Görlitz Center for Systems Biology Dresden MPI-CBG Dresden Visualization Research Center University of Stuttgart University of Groningen Technische Universität Dresden

We present an efficient raycasting algorithm for rendering Volumetric Depth Images (VDIs), and we show how it can be used in a remote visualization setting with VDIs generated and streamed from a remote server. VDIs are compact view-dependent volume representations that enable interactive visualization of large volumes at high frame rates by decoupling viewpoint changes from expensive rendering calculations. However, current rendering approaches for VDIs struggle with achieving interactive frame rates at high image resolutions. Here, we exploit the properties of perspective projection to simplify intersections of rays with the view-dependent frustums in a VDI and leverage spatial smoothness in the volume data to minimize memory accesses. Benchmarks show that responsive frame rates can be achieved close to the viewpoint of generation for HD display resolutions, providing high-fidelity approximate renderings of Gigabyte-sized volumes. We also propose a method to subsample the VDI for preview rendering, maintaining high frame rates even for large viewpoint deviations. We provide our implementation as an extension of an established open-source visualization library.

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A Scalable Pipeline for Gigapixel Whole Slide Imaging analysis on Leadership Class HPC Systems 36

A Scalable Pipeline for Gigapixel Whole Slide Imaging Analys...

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36th ieee International Parallel and Distributed Processing symposium (ieee IPDPS)

作者： Dash, Sajal Hernandez, Benjamin Tsaris, Aristeidis Alamudun, Folami T. Yoon, Hong-Jun Wang, Feivi Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9781665497473

Whole Slide Imaging (WSI) captures microscopic details of a patient's histopathological features at multiple resolutions organized across different levels. Images produced by WSI are gigapixel-sized, and saving a single image in memory requires a few gigabytes which is scarce since a complicated model occupies tens of gigabytes. Performing a simple metric operation on these large images is also expensive. High-performance computing (HPC) can help us quickly analyze such large images using distributed training of complex deep learning models. One popular approach in analyzing these images is to divide a WSI image into smaller tiles (patches) and then train a simpler model with these reduced-sized but large numbers of patches. However, we need to solve three pre-processing challenges efficiently for pursuing this patch-based approach. 1) Creating small patches from a high-resolution image can result in a high number (hundreds of thousands per image) of patches. Storing and processing these images can be challenging due to a large number of I/O and arithmetic operations. To reduce I/O and memory accesses, an optimal balance between the size and number of patches must exist to reduce I/O and memory accesses. 2) WSI images may have tiny annotated regions for cancer tissue and a significant portion with normal and fatty tissues;correct patch sampling should avoid dataset imbalance. 3) storing and retrieving many patches to and from disk storage might incur I/O latency while training a deep learning model. An efficient distributed data loader should reduce I/O latency during the training and inference steps. This paper explores these three challenges and provides empirical and algorithmic solutions deployed on the Summit supercomputer hosted at the Oak Ridge Leadership Computing Facility.

关键词： Training Deep learning Leadership Analytical models Codes Computational modeling Pipelines

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Validating Simulation-Based Evaluation of Redirected Walking Systems

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第5期28卷 2288-2298页

作者： Azmandian, Mahdi Yahata, Rhys Grechkin, Timofey Thomas, Jerald Rosenberg, Evan Suma Univ Southern Calif Inst Creat Technol Los Angeles CA 90089 USA Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

Developing effective strategies for redirected walking requires extensive evaluations across a variety of factors that influence performance. Because these large-scale experiments are often not practical with user studies, researchers have instead utilized simulations to systematically test different algorithm parameters, physical space configurations, and virtual walking paths. Although simulation offers an efficient way to evaluate redirected walking algorithms, it remains an open question whether this evaluation methodology is ecologically valid. In this paper, we investigate the interaction between locomotion behavior and redirection gains at a micro-level (across small path segments) and macro-level (across an entire experience). This examination involves analyzing data from real users and comparing algorithm performance metrics with a simulated user model. The results identify specific properties of user locomotion behavior that influence the application of redirected walking gains and resets. Overall, we found that the simulation provided a conservative estimate of the average performance with real users and observed that performance trends when comparing two redirected walking algorithms were preserved. In general, these results indicate that simulation is an empirically valid evaluation methodology for redirected walking algorithms.

关键词： Legged locomotion Solid modeling Prediction algorithms Biological system modeling Task analysis Virtual environments Heuristic algorithms Virtual reality redirected walking locomotion simulation

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Review on Integrating Geospatial Big datasets and Open Research Issues

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ieee ACCESS 2021年 9卷 10604-10620页

作者： Al-Yadumi, Sohaib Xion, Tan Ee Wei, Sharon Goh Wei Boursier, Patrice Taylors Univ Sch Comp Sci & Engn Subang Jaya 47500 Malaysia Int Med Univ Sch Pharm Life Sci Bukit Jalil 57000 Malaysia

Big data and geographic information systems (GIS) are two technologies that have increasingly influenced many areas in the last 10 years and will continue to improve and help solve serious global problems, such as consequences of climate change or global pandemics. A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. Geospatial big data integration is designed to accomplish the compatibility of distinct geospatial datasets regardless of their spatial coverage. The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. For instance, risk management datasets related to healthcare and the environment are heterogeneous and disparate. Obtaining a unified view of such geospatial big datasets is complicated and challenging, especially if we consider problems related to healthcare pandemics and environmental disasters. Hence, before we can attempt to predict and mitigate processes occurring in these domains, we must realize that geospatial big data integration is crucial in consolidating datasets. We explore and discuss issues involved in integrating geospatial big datasets in this study. We then classify big data integration processes into three categories, namely, data warehousing, data transformation and integration methods. Furthermore, several research challenges focused on geospatial big data, big earth data, data warehousing, data transformation and linked data are presented. Lastly, open research issues and emerging trends that require in-depth investigations in the near future are highlighted in this study.

关键词： Geospatial analysis Big data data integration Warehousing data models Spatial databases data visualization Big data integration geographic information system (GIS) geospatial big data

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Leveraging large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 ieee.

关键词： Electronic health record

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MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 4335-4348页

作者： Wang, Xiao Shu, Xiujun Zhang, Shiliang Jiang, Bo Wang, Yaowei Tian, Yonghong Wu, Feng Peng Cheng Lab Shenzhen Peoples R China Peking Univ Peng Cheng Lab Beijing 100871 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230093 Peoples R China Peking Univ Beijing 100871 Peoples R China Univ Sci & Technol China Sch Informat Sci Hefei 230026 Peoples R China

Many RGB-T trackers attempt to attain robust feature representation by utilizing an adaptive weighting scheme (or attention mechanism). Different from these works, we propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data by adaptively adjusting the convolutional kernels for various input images in practical tracking. Given the image pairs as input, we first encode their features with the backbone network. Then, we concatenate these feature maps and generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. Inspired by residual connection, both the generated visible and thermal feature maps will be summarized with input feature maps. The augmented feature maps will be fed into the RoI align module to generate instance-level features for subsequent classification. To address issues caused by heavy occlusion, fast motion and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target driven attention mechanism. The spatial and temporal recurrent neural network is used to capture the direction-aware context for accurate global attention prediction. Extensive experiments on three large-scale RGB-T tracking benchmark datasets validated the effectiveness of our proposed algorithm.

关键词： Target tracking Tracking Heuristic algorithms visualization Task analysis Kernel Vehicle dynamics RGB-T tracking dynamic filter generation multi-modal fusion local and global search deep learning

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PRIMARY analysis OF MARS GEOLOGICAL STRUCTURE BASED ON HIGH FREQUENCY RADAR data OF CHINA'S ZHURONG MARS ROVER

PRIMARY ANALYSIS OF MARS GEOLOGICAL STRUCTURE BASED ON HIGH ...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Geng, Jianrong He, Juan Ye, Hongxia Li, Yuxi Fudan Univ Key Lab Informat Sci Electromagnet Waves Shanghai 200433 Peoples R China Macau Univ Sci & Technol State Key Lab Lunar & Planetary Sci Taipa Macao Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

On 15 May 2021, Zhu Rong Rover successful landed on the southern area of the flat Utopia Plain. The subsurface penetrating radar is used for the exploration of Mars regolith thickness and subsurface shallow geological structures. In past eight months, the rover traveled about 950 meters, and the subsurface penetrating radar has collected a large amount of echo data. This paper presents a set of process methods to analyze the high-frequency channel data. First, the predictive deconvolution algorithm is used to eliminate multiple reflections tailing of radar wavelet. Then the wavelet transform is used to remove the random noise, and the two-state SVD decomposition and reconstruction are used to remove the direct wave of the surface and suppress the fusion noise. After these processing, the signal-to-noise ratio of echo signal and data resolution is improved. The processed B-scan images in some areas show abnormal signal within 40ns. Suppose the relative permittivity of Mars' soil is 3.5, it is corresponding to the abnormal structure within 3m depth. Combined with the optical image of the navigation and terrain cameras, we infer that these abnormal structures may be the buried stones or cavities.

关键词： Mars Zhu Rong GPR

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Efficient Human-in-the-Loop Pancreatic Tumor Annotation via large-Scale Pre-Trained Model with Adaptive Post-Processing 22

Efficient Human-in-the-Loop Pancreatic Tumor Annotation via ...

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22nd ieee International symposium on Biomedical Imaging, ISBI 2025

作者： Zhou, Xinze Zhao, Yuxuan Zhuang, Chuntung Yu, Dexin Yuille, Alan Zhou, Zongwei Johns Hopkins University United States Qilu Hospital of Shandong University China

ISBN: (纸本)9798331520526

Deep learning has significantly impacted fields like medical image analysis. However, the effectiveness of supervised learning models depends on large volumes of annotated data. Annotating medical images, especially for tumor regions, is costly and time-intensive. To overcome this limitation, this paper propose a novel framework to accelerate tumor annotation, producing a unique dataset with pancreatic lesions labeled in 300 CT scans from multiple centers. Traditional annotation methods would require an experienced radiologist around 216 hours for this task, whereas our framework completed it in 57 hours, achieving similar or even improved annotation quality. This success stems from two core elements: (1) pre-training the model on a large-scale multi-organ dataset, then fine-tuning it on the target domain before using the fine-tuned model to generate preliminary labels, and (2) adaptive post-processing based on statistical insights from an extensive in-house JHH dataset and clinical evidence. More importantly, our framework could help both the AI model and annotation processes, substantially reducing the annotation costs required to produce large-scale datasets for tumor detection and segmentation tasks for other organs, such as liver and kidney. With our framework, we introduce AbdomenAtlas2.0-Mini, a dataset of 300 CT scans with high-quality voxel-level pancreatic lesions annotations. © 2025 ieee.

关键词： Supervised learning

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visualization Challenges of Variant Interpretation in Multiscale NGS data

Visualization Challenges of Variant Interpretation in Multis...

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24th Eurographics Conference on visualization, EuroVis 2022

作者： Ståhlbom, E. Molin, J. Lundström, C. Ynnerman, A. Division of Media and Information Technology Linköping University Sweden Sectra AB Sweden Center for Medical Image Science and Visualization Linköping University Sweden

ISBN: (纸本)9783038681854

There is currently a movement in health care towards precision medicine, where genomics often is the central diagnostic component for tailoring the treatment to the individual patient. We here present results from a domain characterization effort to pinpoint problems and possibilities for visualization of genomics data in the clinical workflow, with analysis of copy number variants as an example task. Five distinct characteristics have been identified. Clinical genomics data is inherently multiscale, riddled with artifacts and uncertainty, and many findings have unknown significance, so it is a challenging visual analytics domain. Moreover, as in other clinical domains, high efficiency is key. This characterization will form the basis for follow-on visualization prototyping. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： visualization

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