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Chinese Control Conference (CCC)

作者： Guoxian Zheng Lei Zhang Huaxi Yulin Zhang Bo Ding State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China MIS Universite de Picardie Jules Verne Amiens France National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

In this paper, we propose an indoor robot autonomous navigation system. The robot firstly explores in an unknown environment, and then navigates autonomously by using the explored map. The robot is equipped a 2D laser scanner as the main sensor. The laser scanner is used for path planning and frontier-based exploration. A 2D global occupancy map is built for path planning, frontier-based exploration and multi-objective autonomous navigation. Laser scans are transmitted into Simultaneous Localization and Mapping (SLAM) process in the exploration phase. In indoor environment, the exploration efficiency is improved by merging a heuristic algorithm. By using multi-threading technology and a 3D perception approach proposed in this paper, the robot equipped with a low-cost RGBD sensor can detect all kinds of obstacles to achieve highly reliable navigation in complicated 3D environment. Meanwhile, we develop a multi-objective navigation application to make human-robot interaction more convenient and satisfy multi-task deployment. Our approaches are demonstrated by experimental results.

关键词： Navigation Three-dimensional displays Robot sensing systems Lasers Path planning Two dimensional displays

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A systematic review of structured sparse learning

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Frontiers of Information Technology & Electronic Engineering 2017年第4期18卷 445-463页

作者： Lin-bo QIAO Bo-feng ZHANG Jin-shu SU Xi-cheng LU College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

High-dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

关键词： Sparse learning Structured sparse learning Structured regularization

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Exploiting contention and congestion aware switch allocation in network-on-chips 17

Exploiting contention and congestion aware switch allocation...

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50th ACM Turing Conference - China, ACM TUR-C 2017

作者： Li, Cunlu Dongy, Dezun Liao, Xiangke National Laboratory for Parallel and Distributed Processing Collaborative Innovation Center of High Performance Computing College of Computer National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781450348737

Network-on-chip system plays an important role to improve the performance of chip multiprocessor systems. As the complexity of the network increases, congestion problem has become the major performance bottleneck and seriously influence the performance of NoCs. Prior works have focused on designing effective routing algorithm based on collecting congestion and contention information to load balance the traffic. However, most prior works do not consider balancing the traffic load during switch allocation. Due to the lack of congestion information in switch allocation stage, switch allocator performs allocation only based on packet requests and thus aggravates the congestion in the ports of switch. In this paper, we propose to add the congestion and contention information into the switch allocation process and design an efficient on-chip switching strategy which utilizes the contention and congestion information to load balance the traffic and achieve efficient switch allocation. To further enhance the performance of our design, we carefully design our switch allocation strategy to balance the trade-off between traffic load balance and the matching efficiency in switch allocation. We evaluate our design under synthetic traffic and trace of parsec benchmarks. Our evaluations show this mechanism achieves optimal latency compared to best previous switch allocation strategies. © 2017 ACM.

关键词： Network-on-chip

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General audio tagging with ensembling convolutional neural networks and statistical features

arXiv

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arXiv 2018年

作者： Xu, Kele Zhu, Boqing Kong, Qiuqiang Mi, Haibo Ding, Bo Wang, Dezhi Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China University of Surrey United Kingdom College of Meteorology and Oceanography National Univ. of Defense Tech Changsha China

Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convolutional neural network architectures to solve the audio tagging task. Statistical features are applied to capture statistical patterns of audio features to improve the classification performance. Ensemble learning is applied to ensemble the outputs from the deep classifiers to utilize complementary information. a sample reweight strategy is employed for ensemble training to address the noisy label problem. Our system achieves a mean average precision (mAP@3) of 0.958, outperforming the baseline system of 0.704. Our system ranked the 1st and 4th out of 558 submissions in the public and private leaderboard of DCASE 2018 Task 2 challenge. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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CloudDPI: Cloud-based privacy-preserving deep packet inspection via reversible sketch 1

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Li, Jie Su, Jinshu Wang, Xiaofeng Sun, Hao Chen, Shuhui School of Computer National University of Defense Technology Changsha Hunan410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan410073 China

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

Hardware-based middleboxes are ubiquitous in computer networks, which usually incur high deployment and management expenses. A recently arsing trend aims to address those problems by outsourcing the functions of traditional hardware-based middleboxes to high volume servers in a cloud. This technology is promising but still faces a few challenges. First, the widely adopted data encryption techniques contradict with payload inspection needs of some middleboxes such as DPI and IDS devices. Second, the inspection rules of middleboxes may be commercial properties, thus the middlebox providers want to keep their rules confidential under third-party cloud environments, and this creates hindrances for the cloud to perform outsourced middlebox functions. Third, performance of the outsourced middlebox is an inevitable issue that needs deliberate consideration. In this paper, we propose a cloud-based DPI middlebox implementation which performs payload inspection over encrypted traffic while preserving the privacy of both communication data and inspection rules. Our design employs a modified reversible sketch structure which is used for efficient error-free membership testing, and we utilize unkeyed one-way hash functions instead of complex cryptographic protocols to achieve the privacy preservation requirements. CloudDPI supports a wide range of real-world inspection rules, we conduct evaluations on ClamAV rule set and the experiment results demonstrate the effectiveness of our proposal. © 2017, Springer International Publishing AG.

关键词： Network function virtualization

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Growing construction and adaptive evolution of complex software systems

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Science China(Information Sciences) 2016年第5期59卷 5-7页

作者： Huaimin WANG Bo DING National Key Laboratory of Parallel and Distributed Processing College of ComputerNational University of Defense Technology

distributed software systems are becoming more and more complex *** is easy to find a huge amount of computing nodes in a nationwide or global information *** example,We Chat(Wei Xin),a well-known mobile application in China,has reached a record of 650 million monthly active users in the third quarter of *** the same time,researchers are starting to talk about software systems which have billions of lines of codes[1]or can last one hundred years.

关键词： Growing construction and adaptive evolution of complex software systems

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parallel Algorithm Design for Audio Feature Extraction

Parallel Algorithm Design for Audio Feature Extraction

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2017 5th International Conference on Machinery,Materials and Computing Technology(ICMMCT 2017)

作者： Xiaocheng Luo Jingfei Jiang Junjie Zhu Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Audio feature extraction is a very important technique in the field of sound processing. It extremely impacts the effectiveness and correctness of sound recognition, sound verification, etc. It is a computation intensive stage in the whole sound recognition process, which is a challenging for acceleration. In this paper, a coarse-grained parallel feature extraction algorithm for high throughput of audio slices is proposed to improve the efficiency of audio feature extraction. Three typical audio feature extraction algorithms, Mel Frequency Cepstrum Coefficients(MFCC), Spectrogram image features(SIF), Octave-Based Spectral Contrast, are chosen to parallelize. Experiments results on different platforms show that the speedup of accelerated audio feature extraction is up to 17.23 on the platform with 16 cores 32 threads.

关键词： Feature Extraction Sound Recognition parallel Algorithm

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Zero Pronoun Identification in Chinese Language with Deep Neural Networks

Zero Pronoun Identification in Chinese Language with Deep Ne...

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2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Tao Chang Shaohe Lv Xiaodong Wang Dong Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (纸本)9781510845541

Zero pronoun resolution is very important in natural language processing. Identification of zero pronoun is the fundamental task of its resolution. Existing feature engineering based identification approaches are unsuitable for big data applications due to labor-intensive work. Furthermore, as extracted from parse trees which are not unique for a certain sentence, features may be improper for zero pronoun identification. In this paper, we constructed a two-layer stacked bidirectional LSTM model to tackle identification of zero pronoun. To extract semantic knowledge, the first layer obtains the structure information of the sentence, and the second layer combines the part-of-speech information with obtained structure information. The results in two different kinds of experimental environment show that, our approach significantly outperforms the state-of-the-art method with an absolute improvement of 4.3% and 20.3% F-score in Onto Notes 5.0 corpus respectively.

关键词： zero pronoun Identification LSTM

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A Research on Minisat Using Coarse-grained parallel Technique Based on Multi-core and Multi-platform

A Research on Minisat Using Coarse-grained Parallel Techniqu...

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2017 5th International Conference on Machinery,Materials and Computing Technology(ICMMCT 2017)

作者： Junjie Zhu Jingfei Jiang Xiaocheng Luo Yong Dou National Laboratory of Parallel and Distributed Processing National University of Defence Technology

In this paper, we make a research on a widely-used SAT solver, Minisat, aiming to improve its performance using coarse-grained parallel method on multi-core and multi-platform. Firstly, we parallel the Minisat by mean of Open MP and test its performance with different threads by running a test set consisting of 2000 SAT problems on an X86 computer. Besides, a scheduling strategy with time sequence is added to the process and achieves a better speed-up ratio. Then, we move the algorithm to an ARM computer and repeat the same process, finding that the performance of Minisat on X86 is better than that on ARM, but ARM platform has a better scale effect than X86 platform when running at full load and is able to perform better than X86 when they have the same hardware configuration.

关键词： Minisat solver coarse-grained multi-core multi-platform

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Single event upset induced by single event double transient and its well-structure dependency in 65-nm bulk CMOS technology

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Science China(Information Sciences) 2016年第4期59卷 152-159页

作者： Pengcheng HUANG Shuming CHEN Jianjun CHEN College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Single event upset (SEU) is one of the most important origins of soft errors in aerospace *** technology scales down persistently, charge sharing is playing a more and more significant effect on SEU of flip-flop. Charge sharing can often bring about multi-node charge collection in storage nodes and non-storage nodes in a flip-flop. In this paper, multi-node charge collection in flip-flop data input and flip-flop clock signal is investigated by 3D TCAD mixed-mode simulations, and the simulate results indicate that single event double transient (SEDT) in flip-flop data input and flip-flop clock signal can also cause a SEU in flip-flop. This novel mechanism is called the SEDT-induced SEU, and it is also verified by heavy-ion experiment in 65 nm twin-well process. The simulation results also indicate that this mechanism is closely related with the well-structure,and the triple-well structure is more effective to increase the SEU threshold of this mechanism than twin-well structure.

关键词： single event upset (SEU) single event double transient (SEDT) SEDT-induced SEU parasitic bipolar effect (PBE) charge sharing

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