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arXiv 2018年

作者： Xie, Jiyang Song, Zeyu Li, Yupeng Ma, Zhanyu Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications Beijing China State Key Lab. of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently adopted methods of data analysis are reviewed. Three typical applications of MBD analysis, namely wireless channel modeling, human online and offline behavior analysis, and speech recognition in the internet of vehicles, are introduced respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis. Copyright © 2018, The Authors. All rights reserved.

关键词： Big data

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Reinforcement learning for relation classification from noisy data

arXiv

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arXiv 2018年

作者： Feng, Jun Huang, Minlie Zhao, Li Yang, Yang Zhu, Xiaoyan State Key Lab. of Intelligent Technology and Systems National Lab. for Information Science and Technology Dept. of Computer Science and Technology Tsinghua University Beijing100084 China Microsoft Research Asia College of Computer Science and Technology Zhejiang University

Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag level, cannot identify the mapping between a relation and a sentence, and largely suffers from the noisy lab.ling problem. In this paper, we propose a novel model for relation classification at the sentence level from noisy data. The model has two modules: an instance selector and a relation classifier. The instance selector chooses high-quality sentences with reinforcement learning and feeds the selected sentences into the relation classifier, and the relation classifier makes sentencelevel prediction and provides rewards to the instance selector. The two modules are trained jointly to optimize the instance selection and relation classification processes. Experiment results show that our model can deal with the noise of data effectively and obtains better performance for relation classification at the sentence level. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Atomic force microscope tip localization and tracking through deep learning based vision inside an electron microscope

Atomic force microscope tip localization and tracking throug...

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2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Shuai Liang Mokrane Boudaoud Catherine Achard Weibin Rong Stéphane Régnier Stéphane Régnier are with the Sorbonne Université campus Pierre et Marie Curie/ ISIR CNRS UMR 72224 Place Jussieu CC 173 Pyramide - T55/65 Paris State Key Lab. of Robotics and System Harbin Institute of Technology 92 West Dazhi Street Nan Gang District Harbin China

Scanning Electron Microscopy (SEM) is an ideal observation tool for small scales robotics. It has the potential to achieve automated nano-robotic tasks such as nano-handling and nano-assembly. Path following control of nano-robot end effectors using SEM vision feedback is a key for an intuitive programming of elementary robotic tasks sequences. It requires the ability to track end effectors under various SEM scan speeds. SEM suffers however from tricky issues that limits robotic tracking capabilities. This paper focuses on one specific issue related to the compromise between the scan speed and the image quality. This restriction seriously limits the performance of conventional vision tracking algorithms when used with electron images. At high scan speed, the image quality is very noisy making very difficult to differentiate the robot end effector from the background, hence limiting the tracking capabilities. The work related in this paper explores for the first time the potential value of Convolutional Neural Networks (ConvNet) in the context of nano-robotic vision tracking inside SEM. The aim is to localize an end-effector, AFM cantilever in the case of the study, from SEM images for any scan speed configuration and despite of low images quality. For that purpose, a data set of AFM tip images is build up from SEM images for the learning algorithm. Network performances are estimated under different SEM scan speeds. Thanks to the learning algorithm, experimental results show robust AFM tip tracking capabilities inside the SEM under various scan speed conditions.

关键词：

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Convolution Neural Network with Sample Stretch for SAR Target Recognition

Convolution Neural Network with Sample Stretch for SAR Targe...

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2018 China International SAR Symposium, CISS 2018

作者： Zhang, Linbin Xiong, Boli Kuang, Gangyao State Key Lab. of Complex Electromagnetic Environment Effects On Electronics and Information System School of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9781538671931

Convolution neural networks doing well in the field of image classification obeys the precondition that test images are similar to the train images. In SAR vehicle image classification field, the recognition rate drops rapidly as the depression angle increasing. The difference of SAR imaging in depression angle obviously affects the classification accuracy of SAR images. In order to overcome the low accuracy in the classification of three categories of SAR vehicles, experiments with a train set composed of depression angle 17° images and a test set composed of depression angle 45° from MSTAR database are explored. To make the test and train set become more similar, the test images are stretched in both horizontal and vertical directions. The stretched method is verified through the cross-correlation coefficient experiments. Meanwhile, an improved structure of convolutional neural network is adopted in the paper to act as the classifier. The recognition accuracy is compared with other traditional machine learning methods and the A-ConvNets method. The results show that the stretching method and the proposed CNN exceed most of existing classifiers in SAR vehicle recognition. © 2018 IEEE.

关键词： Convolution

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Sparse Least Square Support Vector Machines based on Random Entropy 36

Sparse Least Square Support Vector Machines based on Random ...

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第36届中国控制会议

作者： Wenlu Ma Han Liu Faculty of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Lab. of Complex System Control and Intelligent Information Processing Faculty of Automation and Information EngineeringXi’an University of Technology

ISBN: (纸本)9781538629185

Least squares support vector machines(LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.

关键词： Sparsity LSSVM Large-scale data Random Entropy

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Predict Robot Grasp Outcomes based on Multi-Modal Information

Predict Robot Grasp Outcomes based on Multi-Modal Informatio...

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IEEE International Conference on Robotics and Biomimetics

作者： Chao Yang Peng Du Fuchun Sun Bin Fang Jie Zhou Department of Computer Science and Technology State Key Lab. of Intelligent Technology and Systems Beijing China School of Automation University of Electronic Science and Technology of China Chengdu China Fuzhou University Fuzhou China

ISBN: (纸本)9781728103785;9781728103778

In the service robot application scenario, the stable grasp requires careful balancing the contact forces and the property of the manipulation objects, such as shape, weight. Deducing whether a particular grasp would be stable from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through tactile sensor provides an appealing avenue toward more successful and consistent robotic grasping. Other than this, an object's shape and weight would also decide whether to grasping stabilize or not. In this work, we investigate the question of whether tactile information and object intrinsic property aid in predicting grasp outcomes within a multi-modal sensing framework that combines vision, tactile and object intrinsic property. To that end, we collected more than 2550 grasping trials using a 3-finger robot hand which mounted with multiple tactile sensors. We evaluated our multi-modal deep neural network models to directly predict grasp stability from either modality individually or multimodal modalities. Our experimental results indicate the visual combination of tactile readings and intrinsic properties of the object significantly improve grasping prediction performance.

关键词： Grasping Visualization Feature extraction Videos Tactile sensors

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Zero-resource neural machine translation with multi-agent communication game

arXiv

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arXiv 2018年

作者： Chen, Yun Liu, Yang Li, Victor O.K. Department of Electrical and Electronic Engineering University of Hong Kong State Key Lab. of Intelligent Technol. and Syst. Tsinghua Natl. Lab. for Info. Sci. and Technology Department of Computer Science and Technology Tsinghua University Beijing China

While end-to-end neural machine translation (NMT) has achieved notable success in the past years in translating a handful of resource-rich language pairs, it still suffers from the data scarcity problem for low-resource language pairs and domains. To tackle this problem, we propose an interactive multimodal framework for zero-resource neural machine translation. Instead of being passively exposed to large amounts of parallel corpora, our learners (implemented as encoder-decoder architecture) engage in cooperative image description games, and thus develop their own image captioning or neural machine translation model from the need to communicate in order to succeed at the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that the proposed learning mechanism significantly improves over the state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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Standard Analysis for Transfer Delay in CTCS-3

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Chinese Journal of Electronics 2017年第5期26卷 1057-1063页

作者： CAO Yuan MA Lianchuan XIAO Shuo ZHANG Xia XU Wei National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Computer Sciences and Technology China University of Mining & Technology National Mobile Communications Research Lab. Southeast University

According to the standard for the GSM for railway(GSM-R) wireless systems in China train control system level 3(CTCS-3),the control data transfer delay should be no larger than 500 ms with greater than99% *** of both non-redundant networks and intercross redundant networks and cases of single Mobile terminals(MTs) and redundant MTs on one train are considered,and the corresponding vehicle-ground communication models,delay models,and fault models are *** simulation results confirm that the transfer delay can meet the standard requirements under all *** particular,the probability is greater than 99.996% for redundant MTs and networks,and the standard of transfer delay in CTCS-3 will be improved inevitably.

关键词： China train control system level 3(CTCS-3) GSM for railway(GSM-R) Data transfer delay Deterministic and stochastic petri net(DSPN)

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Structure Design and Analysis of Reconfigurable Fixture Robot Based on the Auto-body Panels

Structure Design and Analysis of Reconfigurable Fixture Robo...

引用

IEEE International Conference on Mechatronics and Automation

作者： Jilong Li Jun Liu Xiaofeng Wang Weimin Ge School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Xiqing District Tianjin China Tianjin Key Lab. of the Design & Intell. Control Adv. Mechatronical Syst. Tianjin China

The fixture is an important equipment, and is widely used in the auto-body panels to be assembled and welded. However, most fixtures, which installed on the production lines, are special to make the production cost higher and the production efficiency lower. Therefore, the reconfigurability of the fixture becomes a key factor that effects the production efficiency of automobiles. To handle the above problems, the new reconfigurable fixture robot is proposed in this paper, which can meet the handing, assembling and welding requirements of different automobile's sheet parts on the automobile production lines. First, the three-dimensional model and the kinematic equations of the proposed reconfigurable fixture robot are established. Then, the working space of the reconfigurable fixture robot is also obtained. Finally, the modal analysis of the reconfigurable fixture robot is performed to determine the cause of resonances. Reconfigurable fixture robots are capable of gripping the auto-body panels with the different radii of curvature, which can also meet the work scopes on the production lines.

关键词： Fixtures Manipulators Kinematics Modal analysis Clamps

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