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Skin panel design for a morphing mechanism

ICIC Express Letters, Part B: Applications 2016年第10期7卷 2161-2167页

作者： Zhao, Qiang Guo, Xliuan Xi, Fengfeng Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province College of Information Science and Engineering Yanshan University No. 438 Hebei Avenue Qinhuangdao066004 China Department of Aerospace Engineering Ryerson University TorontoONM5B 2K3 Canada

A segmentation method is proposed to aid rigid skin panel design for an existing modular morphing wing mechanism. The method divides the surface of the modular morphing mechanism into panels through geometry discretization, and then the skin which is made up of these rigid panels will have better rigidity and it will morph more free without any gap. The method comprises three basic morphing types based on the motion of upper platform. Wherein, the mechanism side is firstly divided into a plurality of panels and each of them is approximated to flat, and then shape changes of one panel before and after morphing are analyzed. Therefore, panels can be designed as moveable and fixed patches to achieve seamless morphing. At last, computer simulation is set up to further verify the correctness of the method. © 2016 ICIC International.

关键词： Morphing mechanism Seamless morphing Skin panel Surface segmentation

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Mining critical nodes in software execution network based on complex network

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ICIC Express Letters, Part B: Applications 2016年第9期7卷 1879-1884页

作者： Wang, Lei Dong, Jun Ren, Jiadong College of Information Science and Engineering Yanshan University No. 360 Hebei Ave Qinhuangdao066004 China E & A College Hebei Normal University of Science and Technology No. 360 Hebei Ave Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave Qinhuangdao066004 China

It is significant for measuring the importance of nodes accurately to improve software stability and robustness in software network. A software execution directed network takes function as a node and relationship of function as an edge in this paper. To nd the critical nodes in the network, a novel method is proposed to measure the nodes' importance by means of depth search mining in software execution. According to the principle of cascading failure, a novel critical nodes metrics FID is dened for nodes measuring and sorting. Critical nodes mining (CNM) algorithm is put forward for calculating FID value of each node and sorting. We choose top-ranking nodes as critical nodes which play an important role in software execution process. Experimental results show that CNM algorithm can measure the critical nodes accurately in software network. © 2016 ICIC International.

关键词： Complex networks

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Layered iterative load balancing algorithm for distributed virtual environment

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Ruan Jian Xue Bao/Journal of Software 2008年第9期19卷 2471-2482页

作者： Wang, Shao-Feng Zhou, Zhong Wu, Wei State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China School of Computer Science and Technology Beihang University Beijing 100191 China

To support large-scale users to share a virtual environment, multi-server architecture is applied to the virtual environment system and each server handles a partition of the virtual environment. The unpredictable movements and interactions of avatars may cause some servers to be overloaded. Existing load balancing algorithms try to redistribute load among servers and incur too much overhead, which may reduce the interactivity of system. In this paper, a layered iterative dynamic load balancing algorithm is proposed. Setting the overloaded region as the center, the algorithm chooses a limited number of neighboring regions as the load adjusting goals and iteratively spreads the overload outward from the center. The load balancing state can be achieved through iterations. Based on the virtual environment wherein avatars are scattered under skewed and clustered distribution, the algorithm is built and comparison with the existing load balancing algorithms are made. The results show that this algorithm can adjust load efficiently and incur less overhead.

关键词： virtual reality

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Minkowski sum modeling of polyhedra based on VRML

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ICIC Express Letters 2013年第2期7卷 455-460页

作者： Zhang, Jianfei Guo, Xijuan Geng, Qingjia Information Science and Engineering College Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province No. 438 Hebei Ave. Qinhuangdao 066004 China Computer and Control Engineering College Qiqihar University No. 42 Wenhua Street Qiqihar 161006 China

On the basis of the introduction to basic methods of the geometry modeling based on virtual reality modeling language (VRML) and the concepts of Minkowski sum computation of polyhedra, we present a method of Minkowski sum computation and three dimensional modeling based on VRML. The method can simplify Minkowski sum computation of general polyhedra, and experimental results indicate that this method can calculate Minkowski sum of general polyhedra much faster and realize three-dimensional representation more accurately. This method can provide important technical references for Minkowski sum visualization of polyhedra. © 2013 ISSN 1881-803X.

关键词： Geometry

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MS-net: Multi-site network for improving prostate segmentation with heterogeneous MRI data

arXiv

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arXiv 2020年

作者： Liu, Quande Dou, Qi Yu, Lequan Heng, Pheng Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Radiation Oncology Stanford University StanfordCA94305 United States Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training. In this paper, we propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations, leveraging multiple sources of data. To compensate for the inter-site heterogeneity of different MRI datasets, we develop Domain-Specific Batch Normalization layers in the network backbone, enabling the network to estimate statistics and perform feature normalization for each site separately. Considering the difficulty of capturing the shared knowledge from multiple datasets, a novel learning paradigm, i.e., Multi-site-guided Knowledge Transfer, is proposed to enhance the kernels to extract more generic representations from multi-site data. Extensive experiments on three heterogeneous prostate MRI datasets demonstrate that our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Urology

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Frequent closed itemsets mining using ITBitree

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International Journal of Advancements in Computing technology 2012年第17期4卷 271-279页

作者： Ren, Jiadong Song, Wei Yu, Shiying College of Information Science and Engineering Yanshan University Qinhuangdao Hebei 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China Hebei Provincial S and T Management Information Center 159 Dongfeng Road Shijiazhuang Hebei 050021 China

The set of frequent closed itemsets is much smaller than the one of all frequent itemsets. Meanwhile it retains the completeness. In this paper, we propose an ITBitree-based algorithm, called ITBitreeFCIM (ITBitree Frequent Closed Itemsets Miner), for mining the frequent closed itemsets directly. A novel structure termed ITBitree (Itemset Tidset Binary Tree) is defined to store the transaction and itemset information. Frequent closed itemsets are generated directly by searching the right nodes in each layer and the right nodes in their respective search space deeply from this tree using top-down strategy. For the items with same support in the adjacent layers or the items with the same Tids, we use item merging technique to prune the search space. The searched space is used to avoid the generation of duplicates and enormous closure checks. In the process we do not need to check whether a candidate itemset is closed, so we save a lot of time. The experiment proofs the efficiency of the proposed approach.

关键词： Data mining

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Design of Gesture Recognition System Based on Multi-Channel Myoelectricity Correlation

Design of Gesture Recognition System Based on Multi-Channel ...

引用

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Di Wu Huiyong Li Xuefeng Liu Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Gesture recognition systems based on myoelectric signal have raised more and more attention from researchers. Traditional gesture recognition methods are susceptible to multiple types of noise and require a large number of features, which increase overhead and decrease recognition efficiency. Fully utilizing the characteristics of the signal to recognize gestures is a big challenge. This paper proposes an improved empirical mode decomposition method (XB-EMD) based on autocorrelation function to denoise myoelectric signal. In addition, a novel deep neural network (CRNet) which combines the CNN and RNN layers together is trained for classifying the gestures based on denoised myoelectric signal. Experimental results show that the proposed gesture recognition system can improve recognition effectiveness at 97.4\% with 10 typical gestures, and 72.55\% with 16 complicated gestures.

关键词： Correlation Gesture recognition Feature extraction Empirical mode decomposition Recurrent neural networks Sensors

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mvEchoSeg: One-shot In-context Learning for Multi-view Echocardiography Segmentation

mvEchoSeg: One-shot In-context Learning for Multi-view Echoc...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuting Guo Ya Duan Wenfeng Song Nannan Li Aili Li Shuai Li State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Computer Science Beijing Information Science and Technology University Beijing China School of Information Science and Technology Dalian Maritime University Dalian China China-Japan Friendship Hospital Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Echocardiography is the clinical standard for evaluation of cardiac morphology, and function, and providing hemodynamic parameters in patients with known or suspected heart disease. Due to the diversity and complexity of diagnostic tasks, comprehensive interpretation of echocardiography often requires multi-view imaging for integrated metric analysis. Deep learning methods have become the mainstream approach for echocardiography segmentation. However, achieving segmentation of multi-view echocardiography still requires a substantial amount of annotation. To remedy this, we propose a one-shot in-context learning network mvEchoSeg for multi-view echocardiography segmentation. This network requires only one annotated image for each view. Specifically, we propose a Task Prompt Identifier (TPI) module to identify the task type of the image and allocate the most precise task prompt for it, with minimal adaptation of CLIP and few-shot strategies. Additionally, we leverage a unified In-Context Model (ICM) capable of performing a diverse set of echocardiography segmentation tasks automatically. Using fine-tuning and low-rank adapters improved the performance of the pre-trained model, achieving significant results with minimal training cost. Furthermore, we collect a multi-view echocardiography dataset (MVECD) with 8 views to evaluate our method. The results show an improvement of more than 10% in the DICE score compared to the SOTA foundational medical image segmentation models. To our knowledge, this is the first exploration of a one-shot model for multi-view echocardiography segmentation. Our codes and models are available at https://***/stellating/mvEchoSeg.

关键词： Training Measurement Image segmentation Adaptation models Echocardiography Biological system modeling Morphology Hemodynamics Standards Medical diagnostic imaging

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Complementary Segmentation of Primary Video Objects with Reversible Flows

arXiv

引用

arXiv 2018年

作者： Li, Jia Wu, Junjie Zheng, Anlin Song, Yafei Zhang, Yu Chen, Xiaowu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China National Engineering Laboratory for Video Technology School of EE&CS Peking University Beijing100871 China A.I. Labs Alibaba Group Beijing100102 China SenseTime Group Limited 100084 China

—Segmenting primary objects in a video is an important yet challenging problem in computer vision, as it exhibits various levels of foreground/background ambiguities. To reduce such ambiguities, we propose a novel formulation via exploiting foreground and background context as well as their complementary constraint. Under this formulation, a unified objective function is further defined to encode each cue. For implementation, we design a Complementary Segmentation Network (CSNet) with two separate branches, which can simultaneously encode the foreground and background information along with joint spatial constraints. The CSNet is trained on massive images with manually annotated salient objects in an end-to-end manner. By applying CSNet on each video frame, the spatial foreground and background maps can be initialized. To enforce temporal consistency effectively and efficiently, we divide each frame into superpixels and construct neighborhood reversible flow that reflects the most reliable temporal correspondences between superpixels in far-away frames. With such flow, the initialized foregroundness and backgroundness can be propagated along the temporal dimension so that primary video objects gradually pop-out and distractors are well suppressed. Extensive experimental results on three video datasets show that the proposed approach achieves impressive performance in comparisons with 18 state-of-the-art models. Copyright © 2018, The Authors. All rights reserved.

关键词： Motion compensation

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Dynamic spectrum allocation scheme with real-time traffic and its performance optimization

引用

ICIC Express Letters, Part B: Applications 2015年第8期6卷 2243-2250页

作者： Shi, Guoying Jin, Shunfu Li, Yongqiang School of Information Science and Engineering Yanshan University No. 438 Hebei Avenue Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Avenue Qinhuangdao China The First Hospital of Qinhuangdao No. 258 Wenhua Road Qinhuangdao China

Aiming at relieving the shortage problem of wireless frequency resource, sec- ondary users in cognitive radio networks occupy the spectrum hole opportunistically. In cognitive radio networks, primary users have preemptive priority for the licensed spectrum, and the data transmission of a secondary user is possible to be interrupted by a primary user at any time. For the applications with real-time traffic, the interrupted data is supposed to be discarded by the system. A continuous time queueing model with potential transmission interruption is established accordingly. By using the Laplace transform, the performance measures, such as the average latency, the normalized throughput and the spectrum switching ratio, are presented. The individually and socially optimal strategies are investigated with a non-cooperative game theory, and then a pricing policy for the secondary users is proposed. Experimental results show that the dynamic spectrum allocation scheme with real-time traffic can be optimized socially. © 2015, ICIC International.

关键词： Laplace transforms

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