检索结果-内蒙古大学图书馆

作者： Xie, Kun Wang, Lele Wang, Xin Xie, Gaogang Wen, Jigang Zhang, Guangxing Cao, Jiannong Zhang, Dafang College of Computer Science and Electronics Engineering Hunan University Changsha410006 China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony BrookNY11794 United States Network Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as capacity planning and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a 2-D matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel sequential tensor completion algorithm, which can efficiently exploit the tensor decomposition result based on the previous traffic data to derive the tensor decomposition upon arriving of new data. Furthermore, to better capture the changes of data correlation over time, we propose a dynamic sequential tensor completion algorithm. To the best of our knowledge, we are the first to propose sequential tensor completion algorithms to significantly speed up the traffic data recovery process. This facilitates the modeling of Internet traffic with the tensor to well exploit the hidden structures of traffic data for more accurate missing data inference. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithms can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high. © 1993-2012 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

IoT data analytics using deep learning

arXiv

引用

arXiv 2017年

作者： Xie, Xiaofeng Wu, Di Liu, Siping Li, Renfa Key Laboratory for Embedded Networking Computing of Hunan Province Hunan University

Deep learning is a popular machine learning approach which has achieved a lot of progress in all traditional machine learning areas. Internet of thing (IoT) and Smart City deployments are generating large amounts of time-series sensor data in need of analysis. Applying deep learning to these domains has been an important topic of research. The Long-Short Term Memory (LSTM) network has been proven to be well suited for dealing with and predicting important events with long intervals and delays in the time series. LTSM networks have the ability to maintain long-term memory. In an LTSM network, a stacked LSTM hidden layer also makes it possible to learn a high level temporal feature without the need of any fine tuning and preprocessing which would be required by other techniques. In this paper, we construct a long-short term memory (LSTM) recurrent neural network structure, use the normal time series training set to build the prediction model. And then we use the predicted error from the prediction model to construct a Gaussian naive Bayes model to detect whether the original sample is abnormal. This method is called LSTM-Gauss-NBayes for short. We use three real-world data sets, each of which involve long-term time-dependence or short-term time-dependence, even very weak time dependence. The experimental results show that LSTM-Gauss-NBayes is an effective and robust model. Copyright © 2017, The Authors. All rights reserved.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

The Impact of Word Segmentation on CCG-based Arabic-English SMT

The Impact of Word Segmentation on CCG-based Arabic-English ...

引用

2017 2nd International Conference on Artificial Intelligence: Techniques and Applications (AITA 2017)

作者： Hamdi Ahmed Rajeh Zhi-yong LI Al-Ghaili Mohammed College of Computer Science and Electronic Engineering Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province

This paper presents a comparative study of two approaches to statistical machine translation(SMT).We present a study on Factored Machine Translation for the Arabic–English pair of *** illustrate pre-processing step for the Arabic source language and the new factors which added to the English target *** experiments that injected English side by part-ofspeech(POS) tags,Combinatory Categorical Grammar(CCG) supertags and segmented Arabic sentences displayed a considerable progress in terms of the BLEU *** experiments gained the premier examine of the Baseline phrase-based models during two approaches,Segmented(#S1) and non-segmented(#S2).In both approaches,CCG models acquired the greatest BLEU *** results show that the segmented approach which consists of CCG produce the highest accuracy at 30.91%(1.84% superior than baseline).As shown,the results presented considerable improvement in translating the segmented Arabic rather than the non-segmented into English language.

关键词： Phrase-based translation model Combinatory Categorial Grammar Part-of-speech Factored translation model Word segmentation

来源：评论

学校读者我要写书评

暂无评论

A Recommendation Method of Educational Resources Based on Knowledge Structure

A Recommendation Method of Educational Resources Based on Kn...

引用

International Conference on Computer and Communications (ICCC)

作者： Ronghua Shi Lei Mao Chao Hu Sixiang Li Information and Network Center Central South University Changsha China School of Information Science and Engineering Central South University Changsha China Key Laboratory of Medical Information Research of Hunan Province Central South University Changsha China School of Software Central South University Changsha China

ISBN: (纸本)9781538683408

With the development of online learning platform, network education resources become more and more rich. It makes online learners easier to choose the time, place and content of learning. Because of the huge number of educational re-sources in learning platform, it is difficult for learners to find learning re-sources quickly and efficiently. The user-based collaborative filtering recommendation algorithm is improved by knowledge structure. A new method of education resource recommendation is brought up in this paper. In this method, not only the recommendation result accords with the learner's interest, but also accords with the existing knowledge system of the learner. It better meets the learner's individualized learning needs and improves the learning efficiency. In this paper, the experiment was carried out by the method of controlled experiment. The experimental results are evaluated through two aspects of correlation score and learning process score, and good experimental results have been obtained.

关键词： Education Collaboration Recommender systems Knowledge based systems Internet Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

A long lifetime,low error rate RRAM design with self-repair module

引用

Journal of Semiconductors 2016年第11期37卷 94-98页

作者：尤志强胡飞黄黎明刘鹏邝继顺李实英 Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic EngineeringHunan University

Resistive random access memory （RRAM） is one of the promising candidates for future universal memory. However, it suffers from serious error rate and endurance problems. Therefore, exploring a technical solution is greatly demanded to enhance endurance and reduce error rate. In this paper, we propose a reliable RRAM architecture that includes two reliability modules： error correction code （ECC） and self-repair modules. The ECC module is used to detect errors and decrease error rate. The self-repair module, which is proposed for the first time for RRAM, can get the information of error bits and repair wear-out cells by a repair voltage. Simulation results show that the proposed architecture can achieve lowest error rate and longest lifetime compared to previous reliable designs.

关键词： self-repair ECC RRAM memristor

来源：评论

学校读者我要写书评

暂无评论

EBCRO: Hybrid chemical reaction with employed bee operator

EBCRO: Hybrid chemical reaction with employed bee operator

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhiyong Li Tingkun Yuan Bo Yang Shilong Jiang Yanmei Xie College of Computer Science and Electronic Engineering of Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China PKU-HKUST Shenzhen-Hongkong Institution Shenzhen China Hunan Vocational College of Modern Logistics Changsha China

His article proposes a new hybrid algorithm EBCRO, which combines the local search operator of chemical reaction optimization algorithm (CRO) with modified employed bee operator (EB) main part of artificial bee colony algorithm (ABC), and the introduction of probability-based selection of molecular method. This algorithm creates new solutions not only by local search operation of CRO but also mechanisms of EB. CRO is a newly, effectively swarm intelligence optimization algorithm. However, owing to random characteristics of CRO and not make use of other solutions information, its search ability and convergence speed are inefficient. EB as a part of ABC has simplicity and efficiency global search ability, but the solution is updated only by other solutions information which leading to lack of local search ability. Therefore, this paper presents EBCRO, which can balance the exploitation and exploration of algorithm of numerical function optimization. In this study, EBCRO is tested on 23 benchmark numerical functions, and the simulation results of EBCRO, CRO, orthogonal chemical reaction optimization (OCRO), hybrid particle swarm and chemical reaction optimization (HP-CRO2), and ABC showed that EBCRO outperforms other algorithms in convergence performance.

关键词： Optimization Chemicals Convergence Particle swarm optimization Linear programming Kinetic energy

来源：评论

学校读者我要写书评

暂无评论

Fairness-aware resource allocation in OFDM-based cognitive radio networks for energy efficiency

Fairness-aware resource allocation in OFDM-based cognitive r...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Fanzi Zeng Chen Liu Jisheng Xu Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China

ISBN: (纸本)9781509040940

In this paper, we presented a fairness-aware scheduling scheme in OFDM-based cognitive radio networks. Our objective is to maximize the systems energy efficiency while maintain the fairness among secondary users. In addition, many factors will be considered such as the channel quality, the interferences taken to the primary system and the total transmission power of the cognitive radio system. The resource allocation process consists of two steps. First, we use an improved heuristic algorithm to assign subcarriers to secondary users and then use an iterative algorithm to distribute power to each subcarrier. Simulation results demonstrate that our scheme is able to gain a higher level for energy efficiency and guarantee the minimum transmission requirements of secondary users.

关键词： Resource management Heuristic algorithms Interference OFDM Quality of service Cognitive radio Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Sequential channel selection for decentralized cognitive radio sensor network based on modified Q-Learning algorithm

Sequential channel selection for decentralized cognitive rad...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Fanzi Zeng Hanshan Liu Jisheng Xu Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China

Cognitive radio sensor network (CRSN) is a novel wireless sensor network, which can effectively alleviate the scarcity of spectrum resource by equipping the cognitive technology. Usually, CRSN deploys numerous sensor nodes around the monitoring area. But the energy and storage of those nodes are both limited. Compared with the centralized CRSN, we focus more on a decentralized situation without the control center or cluster in this paper, especially on a circumstance with few information exchanges among sensor nodes. In the case of that, our goal is to help each sensor node to find an optimal strategy of channel selection in the decentralized CRSN quickly, and then, we apply a Multi-Agent Best-Response Q-Learning algorithm based on Timesharing-Tracking Framework to sequential channel selection for the first time. The algorithm not only has a low storage cost, but also can overcome the poor convergence of strategy caused by Multi-Agent simultaneous learning. For comparison, the optimal channel assignment in centralized network, the Multi-Agent Independent Q-Learning and the Win-or-Learn-Fast Policy Hill Climbing these three algorithms are taken into account, and experimental data validates that our proposed algorithm is feasible in decentralized network.

关键词： Sensors Throughput Wireless sensor networks Convergence Algorithm design and analysis Cognitive radio Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Schedulability analysis for messages of FlexRay/CAN networks interconnected by a gateway

引用

Journal of Computational Information Systems 2015年第15期11卷 5531-5543页

作者： Li, Rui Du, Changjie Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China

Schedulability analysis and scheduling strategies are very important for the in-vehicle network system consisting of CAN bus and FlexRay bus interconnected via a gateway. The aim of this study is to investigate the time behavior of such network and generate scheduling solutions to guarantee real-time constrains. First, a schedulability analysis approach based on end-to-end response time analysis is presented. In order to calculate the worse response time of the messages effectively, a new simple MAX-MIN algorithm are proposed to guarantee real-time constrains and make the arrival of messages more predictable. Experimental results show that MAX-MIN algorithm can effectively calculate end-to-end response time in the large-scale set of messages. Copyright © 2015 Binary Information Press.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Efficient differential fault analysis attacks to AES decryption for low cost sensors in IoTs

Efficient differential fault analysis attacks to AES decrypt...

引用

International Symposium on Circuits and Systems

作者： Limin Zhu Yi Wang Renfa Li Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China Institute for Infocomm Research A?STAR Singapore

ISBN: (纸本)9781479953424

Robust sensor system plays an important role in Internet of Things (IoTs). These intelligent sensors are required to be low cost and reliable, which provides confidentiality for private sensitive data. However, this protected system is still under the risk of Differential Fault Analysis (DFA) attacks. In this paper, we focus on DFA attacks to AES decryption as decryption receives the equalling importance as encryption. First, we induce a fault at the input of the third round in the procedure of AES decryption, in which w e successfully break it using one pair of fault-free and faulty plaintexts within 232 searching space. Then, we improve this attack by use of S-Box distribution table, which reduces the computational time from 853 ms to 70 ms on a dual Intel(R) Pentium(R) E6700 core (3.20 GHz). Compared to the existing work, our proposed attack reduces 79.5% computational time when both methods employ two pairs of fault-free and faulty ciphertexts/plaintexts.

关键词： Ciphers Mathematical model Encryption Internet of things Intelligent sensors Sensor systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：