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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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Q-value path decomposition for deep multiagent reinforcement learning 37

Q-value path decomposition for deep multiagent reinforcement...

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37th International Conference on Machine Learning, ICML 2020

作者： Yang, Yaodong Hao, Jianye Chen, Guangyong Tang, Hongyao Chen, Yingfeng Hu, Yujing Fan, Changjie Wei, Zhongyu College of Intelligence and Computing Tianjin University China Huawei Noah‘s Ark Lab Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Tencent Quantum Lab NetEase Fuxi AI Lab Fudan University China

ISBN: (纸本)9781713821120

Recently, deep multiagent reinforcement learning (MARL) has become a highly active research area as many real-world problems can be inherently viewed as multiagent systems. A particularly interesting and widely applicable class of problems is the partially observable cooperative multiagent setting, in which a team of agents learns to coordinate their behaviors conditioning on their private observations and commonly shared global reward signals. One natural solution is to resort to the centralized training and decentralized execution paradigm and during centralized training, one key challenge is the multiagent credit assignment: how to allocate the global rewards for individual agent policies for better coordination towards maximizing system-level’s benefits. In this paper, we propose a new method called Q-value Path Decomposition (QPD) to decompose the system’s global Q-values into individual agents’ Q-values. Unlike previous works which restrict the representation relation of the individual Q-values and the global one, we leverage the integrated gradient attribution technique into deep MARL to directly decompose global Q-values along trajectory paths to assign credits for agents. We evaluate QPD on the challenging StarCraft II micromanagement tasks and show that QPD achieves the state-of-the-art performance in both homogeneous and heterogeneous multiagent scenarios compared with existing cooperative MARL algorithms. Copyright 2020 by the author(s).

关键词： Multi agent systems

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Object Pose Estimation for Robotic Grasping based on Multi-view keypoint Detection

Object Pose Estimation for Robotic Grasping based on Multi-v...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zheyuan Hu Renluan Hou Jianwei Niu Xiaolong Yu Tao Ren Qingfeng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Industrial robots can replace human labour to perform a variety of tasks. Among these tasks, robotic grasping is the most primary industrial robot operation. However, conventional robotic grasping methods could become inapplicable for cluttered and occluded objects. To address the issue, we adopt object pose estimation (OPE) to facilitate robotic grasping of cluttered and occluded objects and propose an object detection model based on 2D-RGB multi-view features. The proposed model is built by adding four transpose convolution layers into the Resnet backbone to obtain desirable 2D feature maps of object keypoints in each image. In addition, we design a feature-fusion model to produce 3D coordinates of keypoints from 2D multi-view features based on the volumetric aggregation method, along with a keypoint-detection confidence of each view to assist the optimality judgment of the robotic grasping. Extensive experiments are conducted to verify the accuracy of OPE, and the experimental results indicate the substantial performance improvements of the proposed approach over conventional methods in various scenarios.

关键词： Solid modeling Three-dimensional displays Service robots Image color analysis Robot kinematics Pose estimation Robot vision systems

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RAR-U-NET: A residual encoder to attention decoder by residual connections framework for spine segmentation under noisy labels

arXiv

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arXiv 2020年

作者： Wang, Ziyang Zhang, Zhengdong Voiculescu, Irina Department of Computer Science University of Oxford United Kingdom State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

Segmentation algorithms for medical images are widely studied for various clinical and research purposes. In this paper, we propose a new and efficient method for medical image segmentation under noisy labels. The method operates under a deep learning paradigm, incorporating four novel contributions. Firstly, a residual interconnection is explored in different scale encoders to transfer gradient information efficiently. Secondly, four copy-and-crop connections are replaced by residual-block-based concatenation to alleviate the disparity between encoders and decoders. Thirdly, convolutional attention modules for feature refinement are studied on all scale decoders. Finally, an adaptive denoising learning strategy (ADL) is introduced into the training process to avoid too much influence from the noisy labels. Experimental results are illustrated on a publicly available benchmark database of spine CTs. Our proposed method achieves competitive performance against other state-of-the-art methods over a variety of different evaluation measures. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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EmgAuth: An EMG-based Smartphone Unlocking System Using Siamese Network

EmgAuth: An EMG-based Smartphone Unlocking System Using Siam...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Boyu Fan Xuefeng Liu Xiang Su Pan Hui Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science University of Helsinki Finland

ISBN: (数字)9781728146577

ISBN: (纸本)9781728146584

Screen lock is a critical security feature for smart-phones to prevent unauthorized access. Although various screen unlocking technologies including fingerprint and facial recognition have been widely adopted, they still have some limitations. For example, fingerprints can be stolen by special material stickers and facial recognition systems can be cheated by 3D-printed head models. In this paper, we propose EmgAuth, a novel electromyography(EMG)-based smartphone unlocking system based on the Siamese network. EmgAuth leverages the Myo armband to collect the EMG data of smartphone users and enables users to unlock their smartphones when picking up and watching their smartphones. In particular, when training the Siamese network, we design a special data augmentation technique to make the system resilient to the rotation of the armband. We conduct experiments including 40 participants and the evaluation results show that EmgAuth can effectively authenticate users with an average true acceptance rate of 91.81% while keeping the average false acceptance rate of 7.43%. In addition, we also demonstrate that EmgAuth can work well for smartphones with different sizes and at different locations, and is applicable for users with different postures. EmgAuth bears great promise to serve as a good supplement for existing screen unlocking systems to improve the safety of smartphones.

关键词： Electromyography authentication Siamese Net-work unlocking smartphone Smartphone unlocking Electromyogram Consumers Authentication Facial Recognition siamese unauthorized access Fingerprints

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A Novel Shape-based Robotic Sorting Approach based on computer Vision

A Novel Shape-based Robotic Sorting Approach based on Comput...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Tanhong Chen Tao Ren Jianwei Niu Qingfeng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

In recent years, intelligent robotic sorting has be-come a popular application of industrial robots, whose performance is to a large extent affected by object estimation. Many researchers have been devoted to design efficient pose estimation methods based on deep learning. However, there remains two challenges. One is that the annotation of the dataset is labor-intensive and time-consuming, which makes it difficult to build large pose estimation datasets. The other is that, for objects of interest, the final pose estimation depends on an accurate 3D model of the object, which makes most existing methods of object pose estimation stay at the instance level, i.e., only objects known to the method can be identified. To address the above challenges, this paper employs the game engine to build a virtual dataset that can be automatically annotated, and proposes a shape-based robot vision sorting approach that can efficiently classify and grasp objects with regular shapes. Experimental results indicate that the proposed approach can achieve category-level object pose estimation and thus make robot grasping more applicable.

关键词： Solid modeling Three-dimensional displays Service robots Shape Pose estimation Object segmentation Grasping

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Modeling yarn-level geometry from a single micro-image

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Frontiers of Information technology & Electronic Engineering 2019年第9期20卷 1165-1174页

作者： Hong-yu WU Xiao-wu CHEN Chen-xu ZHANG Bin ZHOU Qin-ping ZHAO State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Different types of cloth show distinctive appearances owing to their unique yarn-level geometrical *** its importance in applications such as cloth rendering and simulation,capturing yarn-level geometry is nontrivial and requires special hardware,e.g.,computed tomography scanners,for conventional *** this paper,we propose a novel method that can produce the yarn-level geometry of real cloth using a single micro-image,captured by a consumer digital camera with a macro *** a single input image,our method estimates the large-scale yarn geometry by image shading,and the fine-scale fiber details can be recovered via the proposed fiber tracing and generation *** results indicate that our method can capture the detailed yarn-level geometry of a wide range of cloth and reproduce plausible cloth appearances.

关键词： Single micro-images Yarn geometry Cloth appearance

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Research on the Influence of Improved K-shell Algorithm on Commodity Profit

Research on the Influence of Improved K-shell Algorithm on C...

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作者： Jing Chen Jincheng Huang College of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and system Integration of Hebei Province Software Engineering Laboratory in Hebei Province

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN(Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.

关键词： Influence threshold K-shell influence maximization profit

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Research on a Novel Influence Maximization Algorithm Based on Community Structure

Research on a Novel Influence Maximization Algorithm Based o...

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作者： Jing Chen Jiangchuan Liu College of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Software Engineering Laboratory in Hebei Province

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model.

关键词： Seed node influence maximization community division overlapping influence community structure independent cascade model

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Correction: Automatic Generation of 3D Scene Animation Based on Dynamic Knowledge Graphs and Contextual Encoding

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International Journal of computer Vision 2023年第1期132卷 285-285页

作者： Song, Wenfeng Zhang, Xinyu Guo, Yuting Li, Shuai Hao, Aimin Qin, Hong Computer Science Beijing Information Science and Technology University Beijing People’s Republic of China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing People’s Republic of China Zhongguancun Laboratory Beijing People’s Republic of China Research Unit of Virtual Body and Virtual Surgery Technologies Chinese Academy of Medical Sciences Beijing People’s Republic of China Department of Computer Science Stony Brook University Stony Brook New York USA

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