检索结果-内蒙古大学图书馆

Motion Planning for Minimizing Base Disturbance of Space Manipulators

Journal of Donghua University(English Edition) 2015年第2期32卷 252-256页

作者：纪路周东生张强 Key Laboratory of Advanced Design and Intelligent Computing(Dalian University) Ministry of Education

A simulated annealing particle swarm optimization(PSO)algorithm is utilized in this paper to plan the motion of space manipulators with minimized base disturbance,not only its attitude but also its *** space manipulators must meet the Law of Momentum Conservation,any motion of manipulators will disturb the spacecraft which is free-float in the space *** paper tries to limit this effect to the ***,this paper establishes the mathematical model for space manipulators by generalized Jacobian matrix(GJM) and analyzes its inverse kinematics by Theory of ***,a polynomial function of seventh degree is used to parameterize the joint motion and quaternion representation is also used to represent attitude of ***,this paper designs a proper objective function and depicts this algorithm detailedly and ***,the results of numerical simulation are verified by the proposed algorithm.

关键词： utilized kinematics verified disturbance inverse annealing attitude polynomial effector spacecraft

来源：评论

学校读者我要写书评

暂无评论

Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

引用

IEEE Transactions on Affective computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Stfi: Structure-Aware and Time-Frequency Domain Interaction Network for Deformable Medical Image Registration

SSRN

引用

SSRN 2025年

作者： Hu, Rui Wang, Nian Zhang, Yuxin Li, Teng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Advanced Imaging Research Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas75390 United States School of Cyber Science and Engineering Southeast University No.2 Southeast University Road Nanjing211189 China

Deformable image registration is crucial in medical image analysis. Recently, unsupervised methods using intensity matching have shown promising results without labeled data. However, these methods still face challenges: (1) They often encounter local optima when handling anatomical structures with significant variations in intensity distribution;(2) Time domain convolution in decoders leads to loss of high-frequency fine features, limiting the effectiveness of generating fine deformation vector fields. To address these issues, we propose a novel structure-aware and time-frequency domain interaction (STFI) network. STFI integrates a structure-aware frequency domain loss and a time-frequency domain interaction module. The former leverages principal structural information from the weighted phase spectrum to guide the registration process, thereby reducing the risk of local optima. The latter processes and amalgamates information from both the time and frequency domains, effectively preserving high-frequency details. The efficacy and adaptability of our approach have been demonstrated on two types of data, offering a new perspective on exploiting time-frequency domain information for medical image registration. © 2025, The Authors. All rights reserved.

关键词： Image registration

来源：评论

学校读者我要写书评

暂无评论

On the simulation of expressional animation based on facial MoCap

引用

Science China(Information Sciences) 2013年第10期56卷 265-276页

作者： FANG XiaoYong WEI XiaoPeng ZHANG Qiang ZHOU ChangJun Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education School of Management Dalian University of Technology

MoCap(motion capture)-based animation is a hot issue in computer animation research *** on the optical MoCap system,this paper proposes a novel cross-mapping based facial expression simulating *** overcome the problem of the false upper and lower jaw correlation derived from the facial global RBFbased cross-mapping method,we construct a functional partition based RBF cross-mapping *** model animating,enhanced markers are added and animated by our proposed skin motion *** addition,based on the enhanced markers,an improved RBF-based animating approach is raised to derive realistic facial *** more,a pre-computing algorithm is presented to reduce computational cost for real-time *** experiments proved that the method can not only map the MoCap data of one subject to diferent personalized faces but generate realistic facial animation.

关键词： facial animation cross-mapping functional partition motion mechanism RBF

来源：评论

学校读者我要写书评

暂无评论

Comparison study of Gauss, MQ and TPS for interpolation application

引用

International Journal of Industrial and Systems Engineering 2014年第2期18卷 185-198页

作者： Zhang, Qiang Zheng, Guangguo Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 China

There are mainly two parts in this paper. In the first part, we briefly introduce the basic theory of the radial basis function (RBF) and its application in scattered data interpolation, and investigate the difference among three kernel functions: multi-quadric, Gauss and thin plate spline, in detail. According to the multiple examples, we make a lot of experiments, and develop a system in the C++ programme language and the OpenGL open graphics library to make simulation experiments. Finally, we analyse and summarise the different characteristics of the each kernel function in the process of interpolation. In addition, on the basis of the well-known interpolation theory and the analysis of the experimental data, we list several examples for interpolation comparisons. In the second part, the distance of the current marked points down to its most neighbouring marked points is used as shape parameter. Combining with other shape parameter for analysis and comparisons, the results represent the different influences of RBF shape parameter on the curve and surface construction. Our experiment results obtained in this article have theoretical guide values in graphic interpolation modelling. Copyright © 2014 Inderscience Enterprises Ltd.

关键词： Interpolation

来源：评论

学校读者我要写书评

暂无评论

Scattered data processing approach based on optical facial motion capture

引用

Applied Bionics and Biomechanics 2013年第2-3期10卷 75-87页

作者： Zhang, Qiang Liang, Xiaoying Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

In recent years, animation reconstruction of facial expressions has become a popular research field in computer science and motion capture-based facial expression reconstruction is now emerging in this field. Based on the facial motion data obtained using a passive optical motion capture system, we propose a scattered data processing approach, which aims to solve the common problems of missing data and noise. To recover missing data, given the nonlinear relationships among neighbors with the current missing marker, we propose an improved version of a previous method, where we use the motion of three muscles rather than one to recover the missing data. To reduce the noise, we initially apply preprocessing to eliminate impulsive noise, before our proposed three-order quasi-uniform B-spline-based fitting method is used to reduce the remaining noise. Our experiments showed that the principles that underlie this method are simple and straightforward, and it delivered acceptable precision during reconstruction. © 2013 - IOS Press and the authors. All rights reserved.

关键词： Noise abatement

来源：评论

学校读者我要写书评

暂无评论

Time-varying stiffness analysis on rotating shaft with elliptical-front crack

引用

International Journal of Industrial and Systems Engineering 2014年第3期17卷 302-314页

作者： Wei, Xiaopeng Liu, Dajun Zhao, Lasheng Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

The aim of this paper is to investigate the difference of time-varying stiffness characteristics between a semi-elliptical front crack and a straight front crack of shaft-element in order to obtain a more accurate and realistic cracked-rotor model for rotating machinery system. By calculating the correct time-varying area moments of inertia for the cross-section of cracked element and taking account the neutral axis position changes, the switching function is obtained, meanwhile the stiffness of shaft in a steady rotating cycle is determined. The result shows that a significant difference of stiffness appears as crack depth deepens. Especially, the difference between elliptical-front (EF) and straight-front (SF) cracked element is more obvious in the early crack propagation. © 2014 Inderscience Enterprises Ltd.

关键词： Cracks

来源：评论

学校读者我要写书评

暂无评论

LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

来源：评论

学校读者我要写书评

暂无评论

MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

引用

IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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：