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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Chi, Xiaokai Wang, Shaoping Zhang, Yixin Wang, Xingjian Guo, Qing School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781785617911

This paper presents a tailless butterfly-type flapping wing aircraft, which is inspired from natural morpho insects. Based on the butterfly shape, the proposed butterfly-type flapping wing aircraft focuses on low aspect ratio. In order to reduce the weight, the aircraft carries out optimal design and determines the structure with thin membrane, ultra-high modulus carbon rods and custom connections. Through a large number of biological observation to mimic body movement and wing trajectory, this paper proposes a new kinematic model and analyzes its performance of prototype with computational fluid dynamics software. Flight test indicates that the proposed tailless butterfly-type flapping wing aircraft can realize one minutes cruising in specific condition. © 2018 IET Conference Publications. All rights reserved.

关键词： Wings

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s-LWSR: Super lightweight super-resolution network

arXiv

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arXiv 2019年

作者： Li, Biao Liu, Jiabin Wang, Bo Qi, Zhiquan Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Information Technology and Management University of International Business and Economics Beijing100029 China Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States College of Information Science and Technology University of Nebraska OmahaNE68182 United States

Deep learning (DL) architectures for super-resolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devices. To address this problem, in this paper, we propose a super lightweight SR network: s-LWSR. There are mainly three contributions in our work. Firstly, in order to efficiently abstract features from the low resolution image, we build an information pool to mix multi-level information from the first half part of the pipeline. Accordingly, the information pool feeds the second half part with the combination of hierarchical features from the previous layers. Secondly, we employ a compression module to further decrease the size of parameters. Intensive analysis confirms its capacity of trade-off between model complexity and accuracy. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information for performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance to other cumbersome DL-SR methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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Active ankle prosthesis powered by electrohydrostatic actuation technology: Design and implementation

Active ankle prosthesis powered by electrohydrostatic actuat...

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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Fang, Jing Wang, Xingjian Li, Rufei Wang, Shaoping Wang, Weiqun School of Automation Science and Electrical Engineering Beihang University Beijing100191 China State Key Laboratory of Fluid Power and Mechatromc Systems Zhejiang University Hangzhou310027 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China

ISBN: (纸本)9781785617911

Currently, the majority of published ankle prostheses employ electrical drives or hydraulic drives. In this paper, an active ankle prosthesis powered by electrohydrostatic actuation (EHA) is proposed. EHA is a new actuator that integrates the actuation system energy source and the output actuator. It has been successfully applied to aircrafts such as the A380, A400M, B787 and F35. The use of EHA in the prosthesis provided high power density, and the ability to switch quickly and smoothly between passive and active modes. The prototype consists of more than ten components, such as the motor, doubleplunger pump, valves, double outlet hydraulic cylinder, pressure sensors, accumulators, carbon fiber foot plate, etc. After rigorous verification via simulation and evaluation, this study proved that its structure was reliable and durable. A simulation model was then developed to analyze the performance of EHA, results showed that this hydraulic system could provide enough torque to satisfy energy needs of human bodies. The innovation of this study was reflected in the creative use of combining the hydraulic cylinder and the plunger pump to be a closed hydraulic circuit, thereby forming proactive assistance prosthesis. Moreover, another novelty is that micro-accumulators also used for energy storage, released when in necessity. With laboratory experiments, the overall control of the prosthesis and structural improvement are also in progress at present. © 2018 IET Conference Publications. All rights reserved.

关键词： Cylinders (shapes)

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Remaining useful life prediction based on particle filtering for high-speed pump in load sensing electrohydrostatic actuator

Remaining useful life prediction based on particle filtering...

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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Shi, Cun Wang, Shaoping Wang, Xingjian Wang, Jun School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781785617911

This paper focuses on the remaining useful life (RUL) prediction of the electro-hydrostatic actuator (EHA) highspeed pump, for which a mathematical model describing the slipper wear caused degradation is proposed. In order to improve the long-term prediction accuracy, the developed method involves the particle filtering algorithm with the state dynamic model and a measurement model to estimate the posterior probability density function for the degrading state at present time and future time. According to the failure threshold set in advance, the RUL can be obtained. The proposed model avoids making simplifying assumptions of linearity and Gaussian noise system, but provides a robust long-term prediction framework for the RUL estimation with the consideration of uncertainties. Simulation experiments and analysis have been performed to verify the effectiven ess and accu ra cy of the prop osed life p rediction mod el. © 2018 IET Conference Publications. All rights reserved.

关键词： Wear of materials

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Load balancing for ultra-dense networks: A deep reinforcement learning based approach

arXiv

引用

arXiv 2019年

作者： Xu, Yue Xu, Wenjun Wang, Zhi Lin, Jiaru Cui, Shuguang Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

—In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Fault diagnosis of electromechanical actuator based on principal component analysis and support vector machine

Fault diagnosis of electromechanical actuator based on princ...

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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Wang, Xingjian Zhao, Jian Wang, Shaoping Ma, Zhonghai School of Automation Science and Electrical Engineering Beihang University Beijing100191 China State Key Laboratory of Fluid Power and Mechatromc Systems Zhejiang University Hangzhou310027 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China

ISBN: (纸本)9781785617911

The communication proposes a method to classify the fault mode of EMAs. Traditional fault diagnosis just purposes on single components (BLDCM, ball screw, LVDT) signal analysis of EMA. Under actual circumstance, the signal detection for closed-loop electromechanical actuator system including controller and mechanical device cannot reflect single component fault. data adopted by sensor is multidimensional and difficult to deal with. Principal component analysis(PCA) is capable to change the projection surface which decreases the amplitude of low correlation thereby reduce the data dimension. Support vector machine(SVM) is applied to data classification of less sample. This paper employs PCA to process the data adopted from mathematical model in Simulink, integrates the result and classifies the fault mode by SVM at the end. © 2018 IET Conference Publications. All rights reserved.

关键词： Principal component analysis

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Correction to “Frequency Separation Network for Image Super-Resolution”

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IEEE Access 2020年 8卷 54698-54698页

作者： Shanshan Li Qiang Cai Haisheng Li Jian Cao Lei Wang Zhuangzi Li Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China National Engineering Laboratory for Agri-Product Quality Traceability Beijing Technology and Business University Beijing China Academy of Broadcasting Science National Radio and Television Administration Beijing China

In the above-named article, the funding information was incorrect. In addition, there was an error in the author affiliation that is corrected here.

关键词： Business big data Safety Education Object recognition Broadcasting

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Thrust allocation modes design and analysis of propulsion controlled aircraft

Thrust allocation modes design and analysis of propulsion co...

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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Jin, Fengyu Wang, Shaoping Wang, Xingjian School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781785617911

Propulsion Controlled Aircraft (PCA) is an effective fault-tolerant control strategy when aircraft no longer controllable by normal aerodynamic means. PCA generates yaw torque by adjusting engine thrust to control the lateral/directional movement of aircraft. For large commercial aircraft, there are four engines. Differential thrust not only can be generated by external engine but can also be co-generated by interna l and extern al engines. Th erefore, this p ap er d iscusses t he influence of different thrust allocation mode on PCA system in term of system response and economy. Through simulation and analysis, the optimal thrust allocation mode is obtained. © 2018 IET Conference Publications. All rights reserved.

关键词： Aircraft

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SVM-based deep stacking networks

arXiv

引用

arXiv 2019年

作者： Wang, Jingyuan Feng, Kai Wu, Junjie MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science Engineering Beihang University Beijing100191 China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations School of Economics and Management Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number

Word2Cluster: A New Multi-Label Text Clustering Algorithm wi...

引用

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Kaili Mao Jianwei Niu Xuefeng Liu Shui Yu Longbo Zhao 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 School of Computer Science and Cyber Engineering Guangzhou University Guangdong China China aerospace science and industry corporation China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.

关键词： Clustering algorithms Biological system modeling Merging Task analysis Computer science Technological innovation Natural language processing

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