检索结果-内蒙古大学图书馆

Research on Starlink Ephemeris Published by SpaceX

Chinese Astronomy and Astrophysics 2025年第1期49卷 193-218页

作者： Ai-rong, Liu Yong-qing, Xiong Jian-jiang, Hui Xiao-li, Xu Jun, Gong Purple Mountain Observatory Chinese Academy of Sciences Nanjing 210023 School of Astronomy and Space Sciences University of Science Technology of China Hefei 230026 China Key Laboratory of Space Object and Debris Observation Chinese Academy of Sciences Nanjing 210023 Beijing Institute of Tracking and Telecommunication Technology Beijing 100094

Faced with the significant disruption of normal space activities caused by Starlink satellites, the Starlink ephemeris published by SpaceX provides an opportunity for relevant research on Starlink satellites. However, due to the lack of clear instructions, it is challenging to utilize the data further. Analysis of the ephemeris file reveals that it primarily represents the predicted orbit for the next 3 d. Research on mean orbital elements indicates that the front part of the ephemeris is an extrapolated orbit with perturbations up to at least the 20th order of Earth's gravitational field, while the latter part is a design orbit with J₂ perturbation. The ephemeris includes covariance information, providing accuracy details. The covariance indicates that the accuracy of the ephemeris for satellites located in parking and working orbits is better than 2 km in the first day, while the accuracy of satellites in raising orbit increases rapidly to thousands of meters in less than half a day. Utilizing the designed orbit of the ephemeris enables a more accurate analysis of constellation configuration, especially the designated position of each satellite, thus facilitating the identification and tracking of Starlink satellites. The study provides valuable insights for the extensive application of Starlink ephemeris, serving as a beneficial reference for maneuver detection and collision warnings of Starlink satellites. © 2024

关键词： celestial mechanics—space vehicles data analysis mean element—methods Starlink—ephemerides, methods

来源：评论

学校读者我要写书评

暂无评论

TC-GS: Tri-plane based Compression for 3D Gaussian Splatting

arXiv

引用

arXiv 2025年

作者： Wang, Taorui Yu, Zitong Xu, Yong Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China School of Computing and Information Technology Great Bay University China Dongguan Key Laboratory for Intelligence and Information Technology China

Recently, 3D Gaussian Splatting (3DGS) has emerged as a prominent framework for novel view synthesis, providing high fidelity and rapid rendering speed. However, the substantial data volume of 3DGS and its attributes impede its practical utility, requiring compression techniques for reducing memory cost. Nevertheless, the unorganized shape of 3DGS leads to difficulties in compression. To formulate unstructured attributes into normative distribution, we propose a well-structured tri-plane to encode Gaussian attributes, leveraging the distribution of attributes for compression. To exploit the correlations among adjacent Gaussians, K-Nearest Neighbors (KNN) is used when decoding Gaussian distribution from the Triplane. We also introduce Gaussian position information as a prior of the position-sensitive decoder. Additionally, we incorporate an adaptive wavelet loss, aiming to focus on the high-frequency details as iterations increase. Our approach has achieved results that are comparable to or surpass that of SOTA 3D Gaussians Splatting compression work in extensive experiments across multiple datasets. The codes is available at TC-GS. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

A Category-Driven Contrastive Recovery Network for Double Incomplete Multi-view Multi-label Classification

引用

IEEE Transactions on Multimedia 2025年

作者： Wang, Yiming Li, Qun Chang, Dongxia Wen, Jie Xiao, Fu Zhao, Yao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China

In the field of multi-view multi-label learning, the challenges of incomplete views and missing labels are prevalent due to the complexity of manual labeling and data acquisition errors. These challenges significantly reduce the quality of latent representations and hinder prediction by multi-label classification. To address this issue, we propose a novel Category-driven Semi-supervised Contrastive Recovery (CSCR) framework in this study. Our framework aims to fully integrate existing label information into incomplete representation learning and classification. Specifically, to address the limitations posed by incomplete views and labels, we construct a label coincidence matrix based on existing labels, which serves as a similarity matrix in subsequent semi-supervised contrastive learning and multi-view classification. By leveraging this matrix, we design a semi-supervised multi-view contrastive learning module, which constructs sample pairs on the basis of inter-view correspondences and label similarity. It learns discriminative latent representations without the need for data augmentation. A weighted multi-label classification module is subsequently employed to integrate the predictions from each view to obtain the final classification result. Experimental evaluations on five challenging datasets demonstrate the superiority of our model over existing state-of-the-art methods. © 1999-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

arXiv

引用

arXiv 2025年

作者： Hu, Yongting Lin, Yuxin Liu, Chengliang Luo, Xiaoling Dou, Xiaoyan Xu, Qihao Xu, Yong School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Ophthalmology Department Shenzhen Second People’s Hospital Shenzhen China

Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still challenging due to the variable sizes and scattered locations of lesions. Furthermore, existing multi-view DR methods typically merge multiple views without considering the correlations and redundancies of lesion information across them. Therefore, we propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion. Specifically, we introduce a two-branch network to obtain both local lesion features and their global dependencies. The high-frequency component of the wavelet transform is used to exploit lesion edge information, which is then enhanced by global semantic to facilitate difficult lesion learning. Additionally, we present a cross-view fusion module to improve multi-view fusion and reduce redundancy. Experimental results on large public datasets demonstrate the effectiveness of our method. The code is open sourced on https://***/HuYongting/WGLIN. Copyright © 2025, The Authors. All rights reserved.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Structure-guided Deep Multi-View Clustering

arXiv

引用

arXiv 2025年

作者： Cui, Jinrong Wu, Xiaohuang Zhang, Haitao Dong, Chongjie Wen, Jie College of Mathematics and Informatics South China Agricultural University Guangzhou510620 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Dongguan Polytechnic Dongguan China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China

Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. However, most of the existing clustering methods often neglect to fully mine multi-view structural information and fail to explore the distribution of multi-view data, limiting clustering performance. To address these limitations, we propose a structure-guided deep multi-view clustering model. Specifically, we introduce a positive sample selection strategy based on neighborhood relationships, coupled with a corresponding loss function. This strategy constructs multi-view nearest neighbor graphs to dynamically redefine positive sample pairs, enabling the mining of local structural information within multi-view data and enhancing the reliability of positive sample selection. Additionally, we introduce a Gaussian distribution model to uncover latent structural information and introduce a loss function to reduce discrepancies between view embeddings. These two strategies explore multi-view structural information and data distribution from different perspectives, enhancing consistency across views and increasing intra-cluster compactness. Experimental evaluations demonstrate the efficacy of our method, showing significant improvements in clustering performance on multiple benchmark datasets compared to state-of-the-art multiview clustering approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Multimodal Evidential Learning for Open-World Weakly-Supervised Video Anomaly Detection

引用

IEEE Transactions on Multimedia 2025年

作者： Huang, Chao Huang, Weiliang Jiang, Qiuping Wang, Wei Wen, Jie Zhang, Bob Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518000 China University of Macau PAMI Research Group Department of Computer and Information Science China Ningbo University Faculty of Information Science and Engineering Ningbo China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen518055 China

Efforts in weakly-supervised video anomaly detection center on detecting abnormal events within videos by coarse-grained labels, which has been successfully applied to many real-world applications. However, a significant limitation of most existing methods is that they are only effective for specific objects in specific scenarios, which makes them prone to misclassification or omission when confronted with previously unseen anomalies. Relative to conventional anomaly detection tasks, Open-world Weakly-supervised Video Anomaly Detection (OWVAD) poses greater challenges due to the absence of labels and fine-grained annotations for unknown anomalies. To address the above problem, we propose a multi-scale evidential vision-language model to achieve open-world video anomaly detection. Specifically, we leverage generalized visual-language associations derived from CLIP to harness the full potential of large pre-trained models in addressing the OWVAD task. Subsequently, we integrate a multi-scale temporal modeling module with a multimodal evidence collector to achieve precise frame-level detection of both seen and unseen anomalies. Extensive experiments on two widely-utilized benchmarks have conclusively validated the effectiveness of our method. The code will be made publicly available. © 1999-2012 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and Grounding

引用

IEEE Transactions on Multimedia 2025年

作者： Wang, Mengzhao Li, Huafeng Zhang, Yafei Li, Jinxing Xie, Minghong Tao, Dapeng Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Harbin Institute of Technology at Shenzhen Shenzhen518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen518055 China Yunnan University FIST LAB School of Information Science and Engineering Kunming650091 China

Video Paragraph Grounding (VPG) aims to precisely locate the most appropriate moments within a video that are relevant to a given textual paragraph query. However, existing methods typically rely on large-scale annotated temporal labels and assume that the correspondence between videos and paragraphs is known. This is impractical in real-world applications, as constructing temporal labels requires significant labor costs, and the correspondence is often unknown. To address this issue, we propose a Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and Grounding method (DMR-JRG). In this method, retrieval and grounding tasks are mutually reinforced rather than being treated as separate issues. DMR-JRG mainly consists of two branches: a retrieval branch and a grounding branch. The retrieval branch uses inter-video contrastive learning to roughly align the global features of paragraphs and videos, reducing modality differences and constructing a coarse-grained feature space to break free from the need for correspondence between paragraphs and videos. Additionally, this coarse-grained feature space further facilitates the grounding branch in extracting fine-grained contextual representations. In the grounding branch, we achieve precise cross-modal matching and grounding by exploring the consistency between local, global, and temporal dimensions of video segments and textual paragraphs. By synergizing these dimensions, we construct a fine-grained feature space for video and textual features, greatly reducing the need for large-scale annotated temporal labels. Meanwhile, we design a grounding reinforcement retrieval module (GRRM) that brings the coarse-grained feature space of the retrieval branch closer to the fine-grained feature space of the grounding branch, thereby reinforcing retrieval branch through grounding branch, and finally achieving mutual reinforcement between tasks. Extensive experiments on three challenging datasets demonstrate the effec

关键词： Electric grounding

来源：评论

学校读者我要写书评

暂无评论

Investigation of Geiger-mode detector in multi-hit model for laser ranging

引用

Science China(Technological Sciences) 2015年第5期58卷 943-950页

作者： ZHAO Peng ZHANG Yan QIAN WeiPing XUAN Ying Beijing Institute of Tracking and Telecommunications Technology Key Laboratory of Space Object Measurement China Defense Science and Technology Information Center

The performance of detector limits the overall performance of laser ranging system. And the design of multi-hit detector is one of the feasible ways to promote the performance of detector. Currently, the segmentation method or the recursive method is commonly used to analyze the multi-hit detector model. To the best of our knowledge, this paper is the first to propose a combinatorial method to solve the multi-hit detector model from the perspective of discrete time. Then, universal formulas of total signal detection probability and the average count are deduced based on the Poisson distribution signal. Furthermore, analysis is made to figure out how the average count changes with different parameters, such as the dead time, gating time, rate intensity. As a result, for GM-APD, the multi-hit detector model is verified advantageously compared to the single-hit detector model in improving the average count theoretically. Meanwhile, a discrete step feature is presented when average count changes with dead time or the gating time, which is of great significance in gating time optimization.

关键词： segmentation gating verified recursive combinatorial deduced figure trigger feasible universal

来源：评论

学校读者我要写书评

暂无评论

Applying object technology to software analysis and maintenance system development

引用

JOURNAL OF SYSTEMS AND SOFTWARE 1996年第1期32卷 41-56页

作者： Ketabchi, MA Sadeghi, KJ Object Technology Laboratory School of Engineering Santa Clara University Santa Clara California USA

object-oriented database management system (DBMS) technology promises to meet the requirements of software systems in which large amount of complex stored and computed data with rich interconnections are shared by multiple interactive tools. Software analysis and maintenance is such a software system. Analyzing and maintaining a large amount of code is an essential but very expensive and time-consuming activity in large corporations. A software analysis and maintenance system called SAMS is being developed to apply object-oriented DBMS technology to provide a solution to the problem of analysis and maintenance of a large amount of code. SAMS is a system with an object-oriented model-based architecture. SAMS tools semiautomatically construct an object-oriented model of software from the specifications of programming languages and software domain models. This object-model is realized by a commercial object-DBMS and is automatically populated by objects that compose the software systems under maintenance. Various analysis and maintenance tools are implemented as the methods of the persistent objects in the database and therefore are fully integrated. The approach, architecture, and an overview of the functionality of SAMS are presented. Experiences in using object-oriented DBMS technology to develop large sophisticated systems such as SAMS are discussed.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

A SYSTEMATIC-APPROACH TO CORRECTIVE MAINTENANCE

引用

COMPUTER JOURNAL 1994年第9期37卷 764-778页

作者： JAMBORSADEGHI, K KETABCHI, MA CHUE, JJ GHIASSI, M Object Technology Laboratory School of Engineering Santa Clara University Santa Clara CA 95053 USA

A process-driven, model-based solution to corrective maintenance is described. The solution approach starts by identifying the set of ordered steps that should be performed to complete a corrective maintenance task. Once the steps in the process are clearly defined, the information needed at each step is organized into maintenance information models. A set of tools that operate on the models and provide the capabilities needed for the process of corrective maintenance are then identified. Realizing the models and providing the tools through a uniform interface lead to a software maintenance system that supports effective and reliable corrective maintenance. An overview of SAMS, a Software Analysis and Maintenance System developed based on this approach, is presented. SAMS integrates various tools that are needed to support maintenance processes Including the corrective maintenance process. SAMS tools are developed on top of an object model of maintenance information realized using an object-oriented database management system.

关键词：

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：