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

作者： Yongmou Li Yijie Wang Hongtao Guan Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Most of the existing outlier detection methods in the literature often adopt the assumption that the outliers would locate in the low-density area of the input data space. However, such low-density assumption may not hold in the presence of clustered outliers, which usually demonstrate relatively high density. For this reason, the clustered and cluster-based outlier detection methods are proposed; however, these methods either make strong data distribution assumptions or rely on the high-quality clustering results which are often not available. To address this issue, we propose a graph-based outlier detection approach which is capable of significantly enhancing the performance of existing outlier detection methods, in the presence of clustered outliers. Experiments are conducted on both synthesized and real-world datasets to demonstrate the superiority of our proposed method. In particular, our proposed method outperforms the state-of-the-art clustered outlier detection methods by a large margin.

关键词： Anomaly detection Support vector machines Kernel distributed databases Forestry Vegetation Transforms

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A control system architecture with cloud platform for multi-UAV surveillance 4

A control system architecture with cloud platform for multi-...

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4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018

作者： Hong, Chen Shi, Dianxi Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781538693803

Unmanned aerial vehicle is considered one of the most promising technologies. In the multi-UAV systems, a great challenge is to control multiple UAVs remotely by one person without being limited by distance. In this paper, to address this problem, we propose a control system architecture based on cloud platform which has been developed in order to allow multiple users to control and monitor multiple UAVs simultaneously irrespective of the location. Besides, the cloud platform acts as a remote brain for UAVs by providing extra storage and computing resources. This system architecture not only simplifies the development of control station, but also enhances the capabilities of unmanned system. © 2018 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A distributed Computing Framework Based on Variance Reduction Method to Accelerate Training Machine Learning Models

A Distributed Computing Framework Based on Variance Reductio...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Zhen Huang Mingxing Tang Feng Liu Jinyan Qiu Hangjun Zhou Yuan Yuan Changjian Wang Dongsheng Li Yuxing Peng Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China H.R. Support Center PLA Changsha China Hunan University of Finance and Economics Changsha China

ISBN: (数字)9781728169811

ISBN: (纸本)9781728169828

To support large-scale intelligent applications, distributed machine learning based on JointCloud is an intuitive solution scheme. However, the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly, which highly demand on computing and memory resources. To overcome the challenges, we propose a computing framework for L-BFGS optimization algorithm based on variance reduction method, which can utilize a fixed big learning rate to linearly accelerate the convergence speed. To validate our claims, we have conducted several experiments on multiple classical datasets. Experimental results show that the computing framework accelerate the training process of solver and obtain accurate results for machine learning algorithms.

关键词： Acceleration Stochastic processes Machine learning algorithms Approximation algorithms Symmetric matrices Machine learning Computational modeling

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Highly stretchable strain sensors using conductive MoS 2 composited PVA-PAA-Li + hydrogels for information interaction

Transactions of Materials Research

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Transactions of Materials Research 2025年第3期1卷

作者： Zhixiang Zhang Jianhua Zhou Xiaoling Ke Lili Zhao Xiaojiang Mu Yulian Chen Lida Jia Fengmei He Jie Gao Jing Liu Jun-Liang Chen Xiaoyang Wang Lei Miao Libo Yuan Guangxi Key Laboratory of Information Materials Guangxi Collaborative Innovation Center of Structure and Property for New Energy Materials School of Material Science and Engineering Guilin University of Electronic Technology Guilin 541004 China College of Optoelectronic Engineering Guilin University of Electronic Technology Guilin 541004 China Guangdong Provincial Key Laboratory of Distributed Energy Systems Dongguan University of Technology Dongguan 523808 China Guangxi Novel Battery Materials Research Center of Engineering Technology State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures School of Physical Science and Technology Guangxi University Nanning 530004 China Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin 541004 China

Wearable sensors using conductive hydrogels have been widely used in advanced technologies such as artificial intelligence, information interaction, and medical monitoring. However, it remains a major challenge to strike a balance between mechanical properties and electrical conductivity. In this work, we propose and demonstrate a conductivity-enhanced strain sensor with molybdenum disulfide (MoS 2 ) compounded as a conductive filler in the hydrogel matrix. MoS 2 not only facilitates the molding of the dual-networked poly(vinyl alcohol) and poly(acrylic acid) but also provides a physical cross-linking point for the hydrogel matrix, which improves the mechanical properties of the hydrogel with tensile strains up to 780%. Upon further immersion in lithium chloride (LiCl) solution, the conductivity of the hydrogel sensors was significantly improved up to 3.88 S m −1 because the intercalation of Li + into MoS 2 induces the synergistic effect of ionic and electronic conductivity. The inherent -OH and -COOH groups in the network made the hydrogels also self-adhesive. This study provides a new approach for the development of advanced information-interaction devices, with potential applications extending to next-generation wearable electronics and responsive sensor technologies.

关键词： Strain sensors Wearable sensors Information interaction MoS 2 Conductive hydrogel

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An efficient SIMD compression format for sparse matrix-vector multiplication

An efficient SIMD compression format for sparse matrix-vecto...

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作者： Chen, Xinhai Xie, Peizhen Chi, Lihua Liu, Jie Gong, Chunye Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Institute of Advanced Science and Technology Hunan Institute of Traffic Engineering Hengyang China

Sparse matrix-vector multiplication (SpMV) is an essential kernel in sparse linear algebra and has been studied extensively on all modern processor and accelerator architectures. Compressed Sparse Row (CSR) is a frequently used format for sparse matrices storage. However, CSR-based SpMV has poor performance on processors with vector units. In order to take full advantage of SIMD acceleration technology in SpMV, we proposed a new matrix storage format called CSR-SIMD. The new storage format compresses the non-zero elements into many variable-length data fragments with consecutive memory access addresses. Thus, the data locality of sparse matrix A and dense vector x expands and the floating-point operations for each fragment can be completely calculated by vectorized implementation on wide SIMD units. Our experimental results indicate that CSR-SIMD has better storage efficiency and low-overhead for format conversion. Besides, the new format achieves high scalability on wide SIMD units. In comparison with the CSR-based and BCSR-based SpMV, CSR-SIMD obtains better performance on FT1500A, Intel Xeon, and Intel Xeon Phi. © 2018 John Wiley & Sons, Ltd.

关键词： Storage efficiency

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Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Geng, Mingyang Li, Yiying Ding, Bo Wang, And Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

ISBN: (纸本)9781509060146

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learningbased approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot visionbased trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the singlerobot system. © 2018 IEEE.

关键词： Multipurpose robots

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Optimization and parallelization Single Particle Cryo-EM Software RELION with GPU

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2018年第2期55卷 409-417页

作者： Su, Huayou Wen, Wen Li, Dongsheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

Single particle cryo-electron microscopy (cryo-EM) is one of the most important methods of macromolecular structure. RELION (regularized likelihood optimization) is an open-source computer program for the refinement of macromolecular structures by single-particle analysis of cryo-EM data. Due to its easy usage and high quality results, RELION has attracted a lot of attentions from researchers. However, the computation requirement of this program is too huge to solve some large molecular structures with CPU, which harpers the popularization of RELION. In this paper, we characterize the algorithm of RELION and parallelize it with GPU. Firstly, the mathematical theory, computer patterns and performance bottlenecks of RELION are analyzed comprehensively. Then, we optimize the program targeting at fine-grained many-core architecture processor, such as GPU. We propose an efficient multi-level parallel model to utilize the powerful computation capacity of many-core processor. In order to achieve high performance, we reconstruct the data structure for GPU continues memory access. To avoid the limitation of GPU memory size, we implement an adaptive framework. The experimental results show that the proposed GPU based algorithm can achieve good performance. When compared with the CPU implementation, the speedup ratio of the application is more than 36 times, while the speedup ratio of compute-intensive algorithm is about 75X. Moreover, the testing results on multi GPUs show that the GPU based implementation has good scalability. © 2018, science Press. All right reserved.

关键词： Graphics processing unit

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Stochastic extra-gradient based alternating direction methods for graph-guided regularized minimization

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Frontiers of Information technology & Electronic Engineering 2018年第6期19卷 755-762页

作者： Qiang LAN Lin-bo QIAO Yi-jie WANG College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

In this study, we propose and compare stochastic variants of the extra-gradient alternating direction method, named the stochastic extra-gradient alternating direction method with Lagrangian function（SEGL） and the stochastic extra-gradient alternating direction method with augmented Lagrangian function（SEGAL）, to minimize the graph-guided optimization problems, which are composited with two convex objective functions in large scale.A number of important applications in machine learning follow the graph-guided optimization formulation, such as linear regression, logistic regression, Lasso, structured extensions of Lasso, and structured regularized logistic regression. We conduct experiments on fused logistic regression and graph-guided regularized regression. Experimental results on several genres of datasets demonstrate that the proposed algorithm outperforms other competing algorithms, and SEGAL has better performance than SEGL in practical use.

关键词： Stochastic optimization Graph-guided minimization Extra-gradient method Fused logistic regression Graph-guided regularized logistic regression

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Modeling complex relationship paths for knowledge graph completion

Modeling complex relationship paths for knowledge graph comp...

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作者： Zeng, Ping Tan, Qingping Meng, Xiankai Zhang, Haoyu Xu, Jianjun College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing Changsha China

Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translationmodels obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task. © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Knowledge graph

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DCIGAN: A distributed Class-Incremental Learning Method Based on Generative Adversarial Networks

DCIGAN: A Distributed Class-Incremental Learning Method Base...

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

作者： Hongtao Guan Yijie Wang Xingkong Ma Yongmou Li Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R.China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Class incremental learning has received wide attention due to its better adaptability to the changing characteristics of online learning. However, data cannot be shared between organizations in the data isolated islands scenario. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIGAN. It uses GAN generators to store the information of past data and continuously update GAN parameters with new data. In particular, we proposed CIGAN to ensure that the distribution of generated pseudo data is as close as possible to the real data on a single node, which guarantees the accuracy of class-incremental learning. Furthermore, we propose GF, a generators fusion method to integrate local generators of multi-nodes into a new global generator. To evaluate the performance of DCIGAN, we conduct experiments on six datasets under various parameter settings on both two and multi nodes distributed scenarios. Extensive experiments confirm that DCIGAN outperform the general baselines and achieve classification accuracy closes to the method of data aggregating.

关键词： Generators Generative adversarial networks distributed databases Diseases Servers Hospitals Training

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