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Multi tool path managing and NC programming aided software based on UG

IOP Conference Series: Materials Science and Engineering 2020年第1期740卷

作者： Zihan Gao Yingduo Lan Yidu Zhang Qiong Wu Sentang Zhang State key Laboratory of Virtual Reality Technology and System School of Mechanical Engineering & Automation Beihang University Beijing 100191 China Shenyang Liming Aero-Engine (Group) Ltd. Shenyang 110043 China

UG is a universal CAD and CAM software which lacks of modules for specific applications. For some specific types of workpieces, a professional software can be developed to improve programming efficiency and quality using UG application program interface. A NC programming aided software is developed through UG secondary development method with the function of reorganizing application menus, creating programs using group technology, translating and managing tool paths created by hypermill. All these functions of the aided software can increase the rang of UG applications and make it easier and more efficient for specific problems.

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MULTI-ATTRIBUTE DRIVEN VEHICLE RE-IDENTIFICATION WITH SPATIAL-TEMPORAL RE-RANKING

MULTI-ATTRIBUTE DRIVEN VEHICLE RE-IDENTIFICATION WITH SPATIA...

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IEEE International Conference on Image Processing

作者： Na Jiang Yue Xu Zhong Zhou Wei Wu State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China

Vehicle re-identification (re-id) is a promising topic, which focuses on retrieving the same vehicles across different cameras. It is challenging due to the variations of illumination and camera viewpoints. To solve these problems, we present a multi-attribute driven vehicle re-id approach to learn discriminative representations. The proposed approach consists of a multi-branch architecture and a re-ranking strategy. The multi-branch architecture extracts color, model, and appearance features, which explicitly leverages the vehicle attribute cues to enhance the generalization ability, especially for the different vehicles with similar appearance and the same vehicles with different orientations. The re-ranking strategy introduces the spatial-temporal relationship among vehicles from multiple cameras to construct the similar appearance sets and utilizes Jaccard distance between these similar appearance sets to re-rank. Extensive experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art re-id methods on the popular VeRi-776 dataset and VehicleID dataset.

关键词： Feature extraction Image color analysis Computer architecture Machine learning Probes Lighting Cameras

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SAR real-time guidance system based on NLM despeckling and gradient binary description

SAR real-time guidance system based on NLM despeckling and g...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Mengjie Zhou Guofeng Zhang Xiaoguang Hu Jin Xiao The State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China

In recent years, Synthetic Aperture Radar (SAR) has been widely used in guidance systems due to its high imaging quality and other superior functions. As the basis of system, SAR image registration has a direct impact on guidance precision. However, due to some inherent characteristics, especially the polarization and speckle noise, we need to adopt more effective approach to achieve the high precision and low computation of SAR guidance system than other optical image based systems. Therefore, in this paper, first, we implement NLM (Non-local Means) filter to improve the image quality damaged by speckle. Then, at feature detection and description stage, we adopt well-known FAST (Features from Accelerated Segment Test) and gradient-based binary descriptors, LDB (Local Difference Binary), to overcome strict real-time requirement and polarization phenomenon. Finally, at matching stage, in order to increase the number of correct pairs, the FSC (Fast Sample Consensus) is used to discard outliers matched by Hamming distance. And the transformation parameters could be estimated with affine model. To evaluate the performance of algorithms, three comparative experiments have been accomplished. The results demonstrate a good performance of our proposed method.

关键词： Synthetic aperture radar Image registration Real-time systems Feature extraction Speckle Robustness Radar imaging

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Image semantic segmentation based on fully convolutional neural network and CRF 4th

Image semantic segmentation based on fully convolutional neu...

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4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Li, Huiyun Qian, Xin Li, Wei State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China

ISBN: (纸本)9789811039652

Image semantic segmentation is a popular research direction in the computer vision field. Semantic segmentation algorithms based on deep learning outperforms the traditional methods. Fully convolutional neural network (FCN) whose fully connected layers are transformed into convolution layers is a kind of convolutional neural network (CNN). In this paper, FCN is used to operate the image semantic segmentation, which could take input of arbitrary size image and implement end-to-end segmentation task. Due to the limited number of training images, some layers are fine-tuned from AlexNet and the dataset is enlarged by mirroring. The hierarchical feature maps from FCN are combined to improve the segmentation effect. Conditional random fields (CRF) is used on the segmentation result of FCN, which takes into account the positional relationship and color features between any two pixels. Experiments show that our method could refine the segmentation result of FCN, especially using CRF as post-processing. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Semantic Segmentation

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Deep Multi-Model Fusion for Single-Image Dehazing

Deep Multi-Model Fusion for Single-Image Dehazing

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International Conference on Computer Vision (ICCV)

作者： Zijun Deng Lei Zhu Xiaowei Hu Chi-Wing Fu Xuemiao Xu Qing Zhang Jing Qin Pheng-Ann Heng South China University of Technology Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology CAS The Chinese University of Hong Kong State Key Laboratory of Subtropical Building Science Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Sun Yat-sen University The Hong Kong Polytechnic University CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology CAS

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

This paper presents a deep multi-model fusion network to attentively integrate multiple models to separate layers and boost the performance in single-image dehazing. To do so, we first formulate the attentional feature integration module to maximize the integration of the convolutional neural network (CNN) features at different CNN layers and generate the attentional multi-level integrated features (AMLIF). Then, from the AMLIF, we further predict a haze-free result for an atmospheric scattering model, as well as for four haze-layer separation models, and then fuse the results together to produce the final haze-free image. To evaluate the effectiveness of our method, we compare our network with several state-of-the-art methods on two widely-used dehazing benchmark datasets, as well as on two sets of real-world hazy images. Experimental results demonstrate clear quantitative and qualitative improvements of our method over the state-of-the-arts.

关键词： Atmospheric modeling Predictive models Scattering Computational modeling Neural networks Computer vision Solid modeling

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Frontoparietal connectivity neurofeedback training for promotion of working memory: An fNIRS study in healthy male participants

arXiv

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arXiv 2020年

作者： Xia, Meiyun Xu, Pengfei Yang, Yuanbin Jiang, Wenyu Wang, Zehua Gu, Xiaolei Yang, Mingxi Li, Deyu Li, Shuyu Dong, Guijun Wang, Ling Wang, Daifa School of Biological Science and Medical Engineering Beihang University Beijing100083 China College of Computer Science Sichuan Normal University Chengdu610101 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100083 China State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100083 China Department of Rehabilitation Medicine Wangjing Hospital China Academy of Chinese Medical Sciences Beijing100102 China Department of Neurological Rehabilitation Guangxi Jiangbin Hospital Nanning530021 China School of Sports and Health Shandong Sport University Jinan250102 China

Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently. In this study, we investigated a novel functional near-infrared spectroscopy (fNIRS)based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory, involving healthy adults. Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation, as neurofeedback, yielding a strong targeting effect. In the experiment, 10 participants (test group) received three cognitive training sessions of 15 min using fNIRS-based frontoparietal FC as neurofeedback, and another 10 participants served as the control group. Frontoparietal FC was significantly increased in the test group (p = 0.03), and the cognitive functions (memory and attention) were significantly promoted compared with the control group (accuracy of 3-back test: p = 0.0005, reaction time of 3-back test: p = 0.0009). After additional validations on long-term training effect and on different patient populations, the proposed method exhibited considerable potential to be developed as a fast, effective, and widespread training approach for cognitive function enhancement. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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Modeling detailed cloud scene from multi-source images 26

Modeling detailed cloud scene from multi-source images

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26th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2018

作者： Cen, Yunchi Liang, Xiaohui Chen, Junping Yang, Bailin Li, Frederick W.B. State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China School of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou310018 China Department of Computer Science University of Durham DurhamDH1 3LE United Kingdom

ISBN: (纸本)9783038680734

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For example, natural images are usually used to model small-scale cloud with details, and satellite images and WRF data are used to model large scale cloud without details. To construct large-scale cloud scene with details, we propose a novel method to extract relevant cloud information from both satellite and natural images. Experiments show our method can produce more detailed cloud scene comparing with existing methods. © 2018 The Author(s).

关键词： Flight simulators

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Research on measurement method of on-orbit maintenance time 27th

Research on measurement method of on-orbit maintenance time

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27th European Safety and Reliability Conference, ESREL 2017

作者： Xue, Long Zhou, Dong Qiu, Biao Zhou, Wenqiang State Key Laboratory of Virtual Reality Technology and System Beijing China School of Reliability and System Engineering Beihang University Beijing China

ISBN: (纸本)9781138629370

With the construction and development of China’s space station, on-orbit maintenance to ensure the safe and reliable operation of the space station is very important. Maintenance time is a key quantitative factors. Due to the lack of logistical support, complex space environments, and the limited time available for astronauts, so accurately predicting maintenance time are very significant for space station. Comparing to the terrestrial, one of the most difficulty is the maintenance activities under microgravity. By analyzing the data of International Space Station (ISS) maintenance time, and comparing with the time of maintenance activities on the earth, a motion-time method under microgravity is proposed. In order to facilitate the calculation of maintenance time, we break down the maintenance tasks into many simple motions. A maintenance time measurement method has been proposed under microgravity. A water filter’s demolition in ISS is used to illustrate the effectiveness of the proposed method. © 2017 Taylor & Francis Group, London.

关键词： Space stations

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virtual reality Oriented Modeling and Simulation of Water-dropping From Helicopter

Virtual Reality Oriented Modeling and Simulation of Water-dr...

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作者： Yangnanbing Han Hu Liu Yongliang Tian Zhengyuan Chen Zhengxin Nie School of Aeronautical Science & Engineering Beihang University State Key Laboratory of Virtual Reality Technology and System Aircraft Advanced Design Technology Key Laboratory of Ministry of Industry and Information Technology College Of Aerospace Engineering Nanjing University of Aeronautics and Astronautics

Fire-fight helicopter is the core of forest aviation *** virtual reality technology to train the crew of helicopter can improve their skill to put out fire efficiently and reduce property ***,current methods of visualizing water-dropping of helicopter in virtual environment cannot simultaneously ensure real-time performance and *** the one hand,the existing visualization methods,which have been widely used,are often just visual imitations and cannot reflect the physical laws of *** the other hand,complex computations are required for the general fluid modeling method,which cannot meet the requirement of real-time response in a VR *** is of great significance to establish a simulation-oriented model of water-dropping from helicopter for achieving realistic water visualization and further increasing the fidelity and immersion of the virtual *** this paper,we demonstrate an effective approach to establish a water-dropping model of helicopter and visualize it in a VR system,simulating physical laws and virtual effects at the same time.

关键词： Aerial firefighting Helicopter Visualization Water-dropping model

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Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

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Science China(Information Sciences) 2017年第11期60卷 43-64页

作者： Jibing GONG Xiaoxia GAO Hong CHENG Jihui LIU Yanqing SONG Mantang ZHANG Yi ZHAO School of Information Science and Engineering Yanshan University The Key Lab for Computer Virtual Technology and System Integration Yanshan University State Key Lab of Mathematical Engineering and Advanced Computing Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

Friend recommendation is a fundamental service in both social networks and practical applications,and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend++, a hybrid multi-individual recommendation model that integrates a weighted average method(WAM) into the random walk(RW) framework by seamlessly employing social ties, behavior context, and personal information. In Friend++, the first plus signifies recommending a new friend through network features, while the second plus stands for using node features. To verify our method, we conduct experiments on three social datasets crawled from the Sina microblog system(Weibo). Experimental results show that the proposed method significantly outperforms six baseline methods in terms of recall, precision, F1-measure, and MAP. As a final step, we describe a case study that demonstrates the scalability and universality of our method. Through discussion, we reach a meaningful conclusion: although common interests are more important than user activities in making recommendations, user interactions may be the most important factor in finding the most appropriate potential friends.

关键词： multi-individual friend recommendation architecture behavior context analysis Intimacy degree random walk framework social networks

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