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2015 Annual Conference of the Prognostics and Health Management Society, PHM 2015

作者： Yan, Wuzhao Dou, Wanchun Liu, Datong Peng, Yu Zhang, Bin Department of Electrical Engineering University of South Carolina ColumbiaSC29208 United States Department of Computer Science and Technology State Key Lab. for Novel Software Technology Nanjing University NanjingJiangsu210093 China Department of Automatic Test and Control Harbin Institute of Technology HarbinHeilongjiang150080 China

ISBN: (纸本)9781936263202

Traditional fault diagnosis and prognosis (FDP) approaches are based on Riemann sampling (RS), in which samples are taken and algorithms are executed in a periodic time interval. With the increase of system complexity, the real-time implementation of this Riemann sampling-based FDP (RS-FDP) becomes a bottleneck, especially for distributed applications. To overcome this problem, a Lebesgue sampling-based FDP (LS-FDP) is proposed. LS-FDP takes samples on the fault dimension axis and provides a need-based diagnostic philosophy in which the algorithm is executed only when necessary. In previous Lebesgue sampling-based FDP, the Lebesgue length is a constant. To accommodate the change of fault dynamics, it is desirable to execute FDP algorithm more frequently when the fault growth is fast while less frequently when fault growth is slow. This requires to change the Lebesgue length adaptively. The goal of this paper is to deliver an improved LS-FDP method with varying Lebesgue length, which enables the FDP to be executed according to needs. The design and implementation of varying Lebesgue length LS-FDP based on a particle filtering algorithm are illustrated with experimental results on Li-ion batteries to verify the performances of the proposed approach. The experimental results show that the new varying LS-FDP is accurate and time-efficient on long term prognosis and also keeps a closer monitoring on the fast increase of fault size. © 2015, Prognostics and Health Management Society. All rights reserved.

关键词： Real time control

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ALETHEIA: Robust large-scale spectrum auctions against false-name bids 15

ALETHEIA: Robust large-scale spectrum auctions against false...

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16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2015

作者： Wang, Qinhui Ye, Baoliu Tang, Bin Xu, Tianyin Guo, Song Lu, Sanglu Zhuang, Weihua National Key Laboratory for Novel Software Technology Nanjing University China Department of Military Management Army Command College China Department of Computer Science and Engineering U.C. San Diego United States School of Computer Science and Engineering University of Aizu Japan Department of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (纸本)9781450334891

Auction is a promising approach for dynamic spectrum access in Cognitive Radio Networks. Existing auction mechanisms are mainly proposed to be strategy-proof to stimulate bidders to reveal their valuations of spectrum truthfully. However, they would suffer significantly from a new cheating pattern named false-name bids, where a bidder can manipulate the auction by submitting bids under multiple fictitious names. We show such false-name bid cheating is easy to make but hard to be detected in dynamic spectrum auctions. To resolve this issue, we propose ALETHEIA, a novel flexible, false-name-proof auction framework for large-scale dynamic spectrum access. ALETHEIA has the following important features: (1) it not only guarantees strategy-proofness but also resists false-name bids, (2) it enables spectrum reuse across a large number of bidders, (3) it provides the bidders the flexibility of diverse demand formats, and (4) it incurs low computational overhead. Simulation results show that ALETHEIA achieves both high spectrum redistribution efficiency and auction efficiency. Copyright © 2015 ACM.

关键词： Efficiency

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A Community Evolution Model Based on Visibility Graph

A Community Evolution Model Based on Visibility Graph

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第三十四届中国控制会议

作者： WANG Hong SUN Yan-shen School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Department of Earth Sciences Zhejiang University

ISBN: (纸本)9781467374439

One of the most important work to analyze networks is community detection. We present a dynamic community discovery method based on Visibility Graph. Firstly, we put forward related definitions of Visibility Graph for multi-dimensional time series. Then, we present algorithms to describe how to use Visibility Graph in finding communities in complex networks. Finally, we present algorithms to find dynamic multi-relational communities. We apply our method in some real data sets. Experimental results show that our methods do better for finding dynamic communities in complex networks.

关键词： community evolution,visibility graph,community discovery,complex network

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An explicit sampling dependent spectral error bound for column subset selection 32

An explicit sampling dependent spectral error bound for colu...

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32nd International Conference on Machine Learning, ICML 2015

作者： Yang, Tianbao Zhang, Lijun Jin, Rong Zhu, Shenghuo Department of Computer Science University of Iowa Iowa City United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Engineering Michigan State University East Lansing United States Institute of Data Science and Technologies Alibaba Group Seattle United States

ISBN: (纸本)9781510810587

In this paper, we consider the problem of column subset selection. We present a novel analysis of the spectral norm reconstruction for a simple randomized algorithm and establish a new bound that depends explicitly on the sampling probabilities. The sampling dependent error bound (i) allows us to better understand the tradeoff in the reconstruction error due to sampling probabilities, (ii) exhibits more insights than existing error bounds that exploit specific probability distributions, and (iii) implies better sampling distributions. In particular, we show that a sampling distribution with probabilities proportional to the square root of the statistical leverage scores is better than uniform sampling, and is better than leverage-based sampling when the statistical leverage scores are very nonuniform. And by solving a constrained optimization problem related to the error bound with an efficient bisection search we are able to achieve better performance than using either the leverage-based distribution or that proportional to the square root of the statistical leverage scores. Numerical simulations demonstrate the benefits of the new sampling distributions for low-rank matrix approximation and least square approximation compared to state-of-the art algorithms.

关键词： Sampling

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Let's work together: Fast tag identification by interference elimination for multiple RFID readers

Let's work together: Fast tag identification by interference...

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International Conference on Network Protocols

作者： Xuan Liu Bin Xiao Feng Zhu Shigeng Zhang College of Computer Science and Electronic Engineering Hunan University P. R. China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory for Novel Software Technology Nanjing University P.R. China School of Information Science and Engineering Central South University P. R. China

ISBN: (纸本)9781509032822

Fast tag identification is a fundamental challenging problem in multi-reader RFID systems. The challenge is how to effectively handle Reader-Tag (RT) collisions and Reader-Reader (RR) collisions among adjacent readers. These collisions are caused by interfering signals simultaneously transmitted by the readers, and may disallow adjacent readers to work together. Prior works tackle this problem by scheduling adjacent readers to work in different time slots. The readers that are selected to simultaneously work, however, are usually only a small proportion of the total readers. This greatly restricts the identification throughput. Our insightful investigation on the current tag identification protocol reveals that RT collisions are caused by asynchronous actions of readers. i.e., a reader transmits signal while its adjacent readers receive. We thus develop Slot Splitting, a technique that can completely eliminate RT collisions by synchronizing actions of readers. We also propose a reader selection algorithm that minimizes RR collisions by selecting a reader set with maximum reading efficiency. The combination of these new techniques inspires Federal, a Fast and efficient tag identification protocol with interference-elimination-based reader scheduling. To our knowledge, Federal is the first identification protocol that completely eliminates RT collisions and minimizes RR collisions in both regularly and randomly deployed multi-reader systems. We validate the feasibility of Slot Splitting with experiments on the USRP platform and evaluate the performance of Federal through extensive simulations. The results show that Federal can increase tag identification throughput by up to 218% compared with the state-of-the-art work.

关键词： Protocols Throughput Radiofrequency identification Interference Radiation detectors Conferences Monitoring

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Never Ignore The Significance Of Different Anomalies: A Cost-sensitive Algorithm Based On Loss Function For Anomaly Detection

Never Ignore The Significance Of Different Anomalies: A Cost...

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International Conference on Tools with Artificial Intelligence

作者： Heng-yang LU Fei-yu CHEN Ming XU Chong-jun WANG Jun-yuan XIE National Key Laboratory for Novel Software Technology at Nanjing University Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9781509001644

In our daily life, anomalies are everywhere in various application domains. Most anomalies may cause huge losses if we fail to detect them in advance. A lot of researches on this field have been carried out for years so as to detect anomalies as soon as possible. Among them, machine learning is one of the most used techniques. Previous work tries to improve detection by choosing various classifiers, which has achieved some success. But few has considered the different losses each anomaly might cause. As we know, anomalies with higher significance will cause higher losses. In this paper, we aim to minimize the losses by proposing an improved cost-sensitive GBDT algorithm named LF-GBDT. LF-GBDT is designed to optimize a self-defined loss function. Experiments with both traditional classification algorithms such as CART, Adaboost etc. and cost-sensitive algorithms such as MetaCost, CSC show that our method can both improve the detection of important anomalies and reduce the total losses.

关键词： anomaly detection cost-sensitive LF-GBDT loss function significance of anomalies

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Topic modeling in semantic space with keywords 15

Topic modeling in semantic space with keywords

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Pu, Xiaojia Jin, Rong Wu, Gangshan Han, Dingyi Xue, Gui-Rong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Engineering Michigan State University East Lansing United States Alibaba Group Hangzhou China

ISBN: (纸本)9781450337946

A common and convenient approach for user to describe his information need is to provide a set of keywords. Therefore, the technique to understand the need becomes crucial. In this paper, for the information need about a topic or category, we propose a novel method called TDCS(Topic Distilling with Compressive Sensing) for explicit and accurate modeling the topic implied by several keywords. The task is transformed as a topic reconstruction problem in the semantic space with a reasonable intuition that the topic is sparse in the semantic space. The latent semantic space could be mined from documents via unsupervised methods, e.g. LSI. Compressive sensing is leveraged to obtain a sparse representation from only a few keywords. In order to make the distilled topic more robust, an iterative learning approach is adopted. The experiment results show the effectiveness of our method. Moreover, with only a few semantic concepts remained for the topic, our method is efficient for subsequent text mining tasks. © 2015 ACM.

关键词： Compressed sensing

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Radiation constrained wireless charger placement

Radiation constrained wireless charger placement

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Haipeng Dai Yunhuai Liu Alex X. Liu Lingtao Kong Guihai Chen Tian He State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu CHINA Third Research Institute of Ministry of Public Security Shanghai CHINA Shanghai key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China Computer Science and Engineering University of Minnesota Minneapolis MN USA Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781467399548

Wireless Power Transfer has become a commercially viable technology to charge devices because of the convenience of no power wiring and the reliability of continuous power supply. This paper concerns the fundamental issue of wireless charger placement with electromagnetic radiation (EMR) safety. Although there are a few wireless charging schemes consider EMR safety, none of them addresses the charger placement issue. In this paper, we propose PESA, a wireless charger Placement scheme that guarantees EMR SAfety for every location on the plane. First, we discretize the whole charging area and formulate the problem into the Multidimensional 0/1 Knapsack (MDK) problem. Second, we propose a fast approximation algorithm to the MDK problem. Third, we optimize our scheme to improve speed by double partitioning the area. We prove that the output of our algorithm is better than (1 - ϵ) of the optimal solution to PESA with a smaller EMR threshold (1 - ϵ/2)Rt and a larger EMR coverage radius (1 + ϵ/2)D. We conducted both simulations and field experiments to evaluate the performance of our scheme. Our experimental results show that in terms of charging utility, our algorithm outperforms the prior art by up to 45.7%.

关键词： Safety Wireless communication Wireless sensor networks Approximation algorithms Partitioning algorithms Inductive charging Aggregates

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Topic2Vec: Learning Distributed Representations of Topics

Topic2Vec: Learning Distributed Representations of Topics

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The International Conference on Asian Language Processing (IALP)

作者： Liqiang Niu Xinyu Dai Jianbing Zhang Jiajun Chen Natural Language Processing Research Group Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University

Latent Dirichlet Allocation(LDA) mining thematic structure of documents plays an important role in nature language processing and machine learning areas. However, the probability distribution from LDA only describes the statistical relationship of occurrences in the corpus and usually in practice, probability is not the best choice for feature representations. Recently, embedding methods have been proposed to represent words and documents by learning essential concepts and representations, such as Word2 Vec and Doc2 Vec. The embedded representations have shown more effectiveness than LDA-style representations in many tasks. In this paper, we propose the Topic2 Vec approach which can learn topic representations in the same semantic vector space with words, as an alternative to probability distribution. The experimental results show that Topic2 Vec achieves interesting and meaningful results.

关键词： learning topic embedding

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Diagnosing energy efficiency and performance for Mobile Internetware applications: Challenges and opportunities

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IEEE software 2015年第99期PP卷

作者： Liu, Yepang Xu, Chang Cheung, S.C. Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong State Key Lab for Novel Software Technology Department of Computer Science and Technology Nanjing University China

Mobile Internetware applications sense physical and cyber environments and connect a tremendous world of users and things. Smartphone applications are one typical example. However, the smart services of many real-world smartphone applications are realized in an energy-inefficient or performance-ill way, seriously affecting user experience. What is even worse, developers lack powerful tools to combat such problems. This curbs the continuous growth of Internet-based mobile computing. There thus exists a strong call from research communities and industries for effective techniques to diagnose energy and performance bugs in smartphone applications. In this article, we study the characteristics of these bugs and discuss challenges in diagnosing them. We then review state-of-the-art techniques in this field and explore future research directions. Finally, we study the use of one representative tool in analyzing commercial Android applications and Samsung Mobile SDK. We show how the tool can provide useful diagnostic information to developers and discuss effective ways to support Internet-based mobile computing and beyond. © 2015 IEEE.

关键词： Energy efficiency

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