检索结果-内蒙古大学图书馆

2nd ieee International Power Electronics and Application symposium, PEAS 2023

作者： Yang, Xiaofei Li, Ziyang Hang, Lijun He, Yuanbin Tang, Sai He, Zhen Lin, Huipin College of Automation Hangzhou Dianzi University Hangzhou China College of Electronic Information Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798350313765

Due to the digital control delay and difference of sampling point position, the average model cannot accurately describe the nonlinear characteristics and frequency response of digital controlled Boost converters with Leading Edge Modulation (LEM) and Trailing Edge Modulation (TEM), while the sampling data modeling method can accurately predict due to the lack of simplified high-frequency information analysis. However, the sampled data model has no intuitive physical meaning. In this paper, the improved approximation method of discrete Iterative method is used to model the digitally controlled Boost converter. By sampling the state variables with discrete time, both large signal models of the Boost converter with LEM and TEM were derived, and the second-order global equivalent circuits were obtained. Based on the proposed large signal model, the z-domain small-signal model of Boost converter can be derived, and the frequency response of the system under different modulation modes can be analyzed and compared. Finally, the accuracy of the model and theoretical analysis are verified through experiment. © 2023 ieee.

关键词： digital controlled Boost converter LEM minimum phase response sampling-data model second-order global equivalent circuits small-signal model TEM

来源：评论

学校读者我要写书评

暂无评论

Vision-Language Models in Remote Sensing: Current progress and future trends

引用

ieee GEOSCIENCE AND REMOTE SENSING MAGAZINE 2024年第2期12卷 32-66页

作者： Li, Xiang Wen, Congcong Hu, Yuan Yuan, Zhenghang Zhu, Xiao Xiang King Abdullah Univ Sci & Technol Thuwal 23955 Saudi Arabia New York Univ Abu Dhabi Dept Elect & Comp Engn Abu Dhabi 129188 U Arab Emirates Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China Tech Univ Munich Chair Data Sci Earth Observat D-80333 Munich Germany Munich Ctr Machine Learning D-80333 Munich Germany

The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-4) have sparked a wave of interest and research in the field of large language models (LLMs) for artificial general intelligence (AGI). These models provide intelligent solutions that are closer to human thinking, enabling us to use general artificial intelligence (AI) to solve problems in various applications. However, in the field of remote sensing (RS), the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research in RS focuses primarily on visual-understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-LMs (VLMs) excel as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. VLMs can go beyond visual recognition of RS images and can model semantic relationships as well as generate natural language descriptions of the image. This makes them better suited for tasks that require both visual and textual understanding, such as image captioning and visual question answering (VQA). This article provides a comprehensive review of the research on VLMs in RS, summarizing the latest progress, highlighting current challenges, and identifying potential research opportunities. Specifically, we review the application of VLMs in mainstream RS tasks, including image captioning, text-based image generation, text-based image retrieval (TBIR), VQA, scene classification, semantic segmentation, and object detection. For each task, we analyze representative works and discuss research progress. Finally, we summarize the limitations of existing works and provide possible directions for future development. This review aims to provide a comprehensive overview of the current research progress of VLMs in RS (see Figure 1), and to inspire further research in this exciting and promising field.

关键词： Task analysis visualization Transformers Computational modeling Feature extraction Chatbots Semantics Visual analytics Communication systems

来源：评论

学校读者我要写书评

暂无评论

A Clustering Method Unifying Cell-Type Recognition and Subtype Identification for Tumor Heterogeneity analysis

引用

ieee-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第2期20卷 822-832页

作者： Li, Xiong Lin, Yangkai Xie, Chengwang Li, Zejun Chen, Min Wang, Peng Zhou, Juan East China Jiaotong Univ Sch Software Nanchang 330013 Peoples R China South China Normal Univ Sch Data Sci & Engn Guangzhou 510000 Peoples R China Hunan Inst Technol Hengyang 421010 Peoples R China Hunan Inst Technol Sch Comp Sci & Technol Hengyang 421010 Peoples R China

The rapid development of single-cell technology has opened up a whole new perspective for identifying cell types in multicellular organisms and understanding the relationships between them. Distinguishing different cell types and subtypes can identify the components of different immune cells and different tumor clones in the tumor microenvironment, which is the basic work of tumor heterogeneity analysis and can help researchers understand the mechanism of tumor immune escape. Existing algorithms treat both cell types and subtypes as populations of cells with specific gene expression patterns, which is not conducive to accurate cell typing. For that, we proposed a cell similarity metric that unifies cell type recognition and subtype identification (UCRSI), with the assumption that selectively expressed genes represent differences in underlying cell type with on/off manner, while differences in expression level represent different cell subtype with more/less manner. Our method calculates these two kinds of differences separately, and then combines them using a consensus adjacency matrix, and finally cell typing is completed using spectral clustering algorithm. The results show that UCRSI can reconstruct expert annotation of single-cell RNA sequencing datasets more robustly than existing methods. And, UCRSI is useful for analyzing tumor heterogeneity and improving visualization of large-scale cell clustering.

关键词： Cell type assignment subtype identification tumor heterogeneity spectral clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

RSMoDM: Multimodal Momentum Distillation Model for Remote Sensing Visual Question Answering

引用

ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 16799-16814页

作者： Li, Pengfei Liu, Gang He, Jinlong Meng, Xiangxu Zhong, Shenjun Chen, Xun Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China Monash Univ Monash Biomed Imaging Clayton Vic 3800 Australia Natl Imaging Facil Woolloongabba Qld 4102 Australia Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Dataspace Hefei 230088 Peoples R China

Remote sensing (RS) visual question answering (VQA) is a task that answers questions about a given RS image by utilizing both image and textual information. However, existing methods in RS VQA overlook the fact that the ground truths in RS VQA benchmark datasets, which are algorithmically generated rather than manually annotated, may not always represent the most reasonable answers to the questions. In this article, we propose a multimodal momentum distillation model (RSMoDM) for RS VQA tasks. Specifically, we maintain the momentum distillation model during the training stage that generates stable and reliable pseudolabels for additional supervision, effectively preventing the model from being penalized for producing other reasonable outputs that differ from ground truth. Additionally, to address domain shift in RS, we employ the Vision Transformer (ViT) trained on a large-scale RS dataset for enhanced image feature extraction. Moreover, we introduce the multimodal fusion module with cross-attention for improved cross-modal representation learning. Our extensive experiments across three different RS VQA datasets demonstrate that RSMoDM achieves state-of-the-art performance, particularly excelling in scenarios with limited training data. The strong interpretability of our method is further evidenced by visualized attention maps.

关键词： Feature extraction visualization Task analysis Transformers Question answering (information retrieval) Computational modeling Computer architecture Cross-modal representation learning domain shift momentum distillation remote sensing visual question answering (RS VQA)

来源：评论

学校读者我要写书评

暂无评论

visualization of Big Spatial data Using Coresets for Kernel Density Estimates

引用

ieee TRANSACTIONS ON BIG data 2021年第3期7卷 524-534页

作者： Zheng, Yan Ou, Yi Lex, Alexander Phillips, Jeff M. Visa Res Palo Alto CA 94306 USA Expedia Inc Bellevue WA 98004 USA Univ Utah Salt Lake City UT 84112 USA

The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for subsampling of spatial data suitable for creating kernel density estimates from very large data and demonstrate that it results in less error than random sampling. We also introduce a method to ensure that thresholding of low values based on sampled data does not omit any regions above the desired threshold when working with sampled data. We demonstrate the effectiveness of our approach using both, artificial and real-world large geospatial datasets.

关键词： data visualization Kernel Spatial databases visualization Big data Geospatial analysis Sociology Spatial data visualization sampling big data coresets

来源：评论

学校读者我要写书评

暂无评论

Enhancing Change Detection and Super-Resolution with Nimbo data

Enhancing Change Detection and Super-Resolution with Nimbo D...

引用

ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Thomas Corpetti Thomas Cusson Antoine Lefebvre CNRS UMR 6554 LETG Univ Rennes Univ Brest Nantes Université Rennes France Kermap Cesson-Sévigné France

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The Sentinel-2 (S2) satellite constellation offers new opportunities for monitoring the Earth. However, the raw data is difficult to use due to cloud cover and other limitations (10m resolution limited for analysis at fine scale, 16-bits representation that prevents from large scale visualization for example). As a consequence, several platforms have emerged to synthesize the data and offer easy-to-use products over the past *** them, Nimbo is a free, cloud-based platform that provides users with access to monthly images of the entire Earth. The platform uses deep neural networks to remove clouds from satellite images, colorizing images and providing users with a clear view of the Earth’s *** homogeneous images makes it easier to develop large-scale tasks, in particular by creating reliable and robust datasets for various applications. This is illustrated in this paper, through two important applications: change detection and super-resolution.

关键词： Earth Location awareness Satellite constellations Superresolution Geoscience and remote sensing data visualization Satellite images

来源：评论

学校读者我要写书评

暂无评论

Efficient analysis and visualization of High-Resolution Computed Tomography data for the Exploration of Enclosed Cuneiform Tablets

Efficient Analysis and Visualization of High-Resolution Comp...

引用

ieee symposium on large data analysis and visualization (LDAV)

作者： Stephan Olbrich Andreas Beckert Cécile Michel Christian Schroer Samaneh Ehteram Andreas Schropp Philipp Paetzold Universität Hamburg Centre National de la Recherche Scientifique (CNRS - ArScAn) Nanterre and Universität Hamburg Deutsches Elektronen-Synchrotron (DESY) Hamburg and Universität Hamburg

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

Cuneiform is the earliest known system of writing, first developed for the Sumerian language of southern Mesopotamia in the second half of the 4th millennium BC. Cuneiform signs are obtained by impressing a stylus on fresh clay tablets. For certain purposes, e.g. authentication by seal imprint, some cuneiform tablets were enclosed in clay envelopes, which cannot be opened without destroying them. The aim of our interdisciplinary project is the non-invasive study of clay tablets. A portable X-ray micro-CT scanner is developed to acquire density data of such artifacts on a high-resolution, regular 3D grid at collection sites. The resulting volume data is processed through feature-preserving denoising, extraction of high-accuracy surfaces using a manifold dual marching cubes algorithm and extraction of local features by enhanced curvature rendering and ambient occlusion. For the non-invasive study of cuneiform inscriptions, the tablet is virtually separated from its envelope by curvature-based segmentation. The computational- and data-in-tensive algorithms are optimized for near-real-time offline usage with limited resources at collection sites. To visualize the complexity-reduced and octree-based compressed representation of surfaces, we develop and implement an interactive application. To facilitate the analysis of such clay tablets, we implement shape-based feature extraction algorithms to enhance cuneiform recognition. Our workflow supports innovative 3D display and interaction techniques such as autostereoscopic displays and gesture control.

关键词： Three-dimensional displays Noise reduction data visualization Seals Writing Feature extraction Rendering (computer graphics) Workstations data mining Surface treatment

来源：评论

学校读者我要写书评

暂无评论

Range Restriction to Harden CNNs Against Hardware Faults: A Broad Empirical analysis 37

Range Restriction to Harden CNNs Against Hardware Faults: A ...

引用

37th ieee International symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2024

作者： Bolchini, Cristiana Cassano, Luca Miele, Antonio Passarella, Daria Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Italy

ISBN: (纸本)9798350366884

Due to the increasing use of Deep Learning in mission/safety-critical application contexts, in the recent past several techniques have been designed to harden the system to guarantee its correct behavior even in presence of faults affecting the hardware. Often, such new techniques are evaluated on a reduced set of Convolutional Neural Network (CNN) models and/or data sets, such that their generality and robustness could actually be limited. This paper presents a broad and systematic experimental evaluation of a state-of-the-art range restriction technique presented in literature, i) by applying it to a large set of CNNs, implementing different functional tasks, and ii) by using multiple datasets. The obtained results demonstrate that the effectiveness of the technique highly depends on the complexity of the considered task;in particular, classification CNNs benefit the most, while regression, image segmentation, and object detection are subject to different levels of benefits. © 2024 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Fiducial markers detection trained exclusively on synthetic data for image-to-patient alignment in HMD-based surgical navigation

Fiducial markers detection trained exclusively on synthetic ...

引用

2023 ieee International symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Benmahdjoub, Mohamed Thabit, Abdullah Niessen, Wiro J. Wolvius, Eppo B. Van Walsum, Theo Erasmus Mc Biomedical Imaging Group Rotterdam Department of Oral and Maxillofacial Surgery Netherlands Erasmus Mc Biomedical Imaging Group Rotterdam Netherlands Erasmus Mc Department of Oral and Maxillofacial Surgery Netherlands

ISBN: (纸本)9798350328912

Surgical navigation guides surgeons during interventions. It provides them with spatial insights on where the anatomy and surgical instruments are in the patient space, and with respect to preoperative images. Image-to-patient alignment in this case is an important step which enables the visualization of preoperative images directly overlayed on the patient. Conventionally, image-to-patient alignment can be done with surface or point-based registration using anatomical or artificial landmarks. In case of point-based registration, surgeons use a trackable pointer to pinpoint some landmarks on the patient (fiducial markers placed preoperatively) and match them with their counterparts in the preoperative image. This method although accurate can be cumbersome and time-consuming. Direct detection of these landmarks in video may speed up the registration process, making it a first step towards AR navigation using head-mounted displays. Detection of objects, including such landmarks, is a task that can be performed with deep learning networks;however, the training of such networks requires large sets of annotated data, which are normally not available in clinical practice. In this study, we investigate the feasibility of using a deep learning model trained on synthetic images in detecting medical fiducial markers in real images, therefore bypassing the need for large sets of annotated patient data. To this end, we generate photorealistic synthetic images of subjects with landmarks using Unreal Engine and MetaHuman, train the detection model using these generated images and assess the model's capability of detecting the registration markers on real 2D images. Our experimental results demonstrate that the object detection model, although trained exclusively on synthetic data, is capable of detecting the markers on the HoloLens 2 video feed with a F1 score of 81%, which can be used for image-to-patient alignment. © 2023 ieee.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

The visualization, Exploration, and data analysis (VEDA) Platform: A Modular, Open Platform Lowering the Barrier to entry to Cloud Computing

The Visualization, Exploration, and Data Analysis (VEDA) Pla...

引用

ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Brian M. Freitag Manil Maskey Aimee Barciauskas Jonas Solvsteen James Colliander James Munroe NASA Marshall Space Flight Center Huntsville AL USA Development Seed Washington DC USA 2i2c / Code for Science and Society Portland OR USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Increasing data volumes and migration of data to the cloud introduces challenges related to scalability and complexity to users of National Aeronautics and Space Administration (NASA) Earth science data. The visualization, Exploration, and data analysis (VEDA) platform is an open-source modular cyberinfrastructure that leverages community standards and builds upon existing open-source capabilities for data services and geospatial data visualization. Four major components make up the VEDA platform – a cloud-optimized data store, backend data services, a web-based visualization and data-driven storytelling dashboard, and an analysis hub. The modular design of the VEDA platform enables the tailored use of the platform components or the platform in whole as has been demonstrated by other applications leveraging VEDA within NASA and the U.S. government.

关键词： Cloud computing data analysis Scalability NASA Government data visualization Geoscience

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：