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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

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An Improved Convolutional Neural network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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

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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

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EBCRO: Hybrid chemical reaction with employed bee operator

EBCRO: Hybrid chemical reaction with employed bee operator

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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

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

Sequential channel selection for decentralized cognitive rad...

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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

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

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

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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

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

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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

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arXiv

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arXiv 2017年

作者： Lang, Guangming School of Mathematics and Statistics Changsha University of Science and Technology Changsha Hunan410114 China Department of Computer Science and Technology Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Mathematics and Econometrics Hunan University Changsha Hunan410004 China

In practice, there are many dynamic covering decision information systems, and knowledge reduction of dynamic covering decision information systems is a significant challenge of covering-based rough sets. In this paper, we first study mechanisms of constructing attribute reducts for consistent covering decision information systems when adding objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when adding objects. Then we investigate mechanisms of constructing attribute reducts for consistent covering decision information systems when deleting objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when deleting objects. Finally, the experimental results illustrates that the related family-based methods are effective to perform attribute reduction of dynamic covering decision information systems when object sets are varying with time. Copyright © 2017, The Authors. All rights reserved.

关键词： Rough set theory

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arXiv

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arXiv 2017年

作者： Lang, Guangming School of Mathematics and Statistics Changsha University of Science and Technology Changsha Hunan410114 China Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Mathematics and Econometrics Hunan University Changsha Hunan410004 China

In practical situations, there are many dynamic covering information systems with variations of attributes, but there are few studies on related family-based attribute reduction of dynamic covering information systems. In this paper, we first investigate updated mechanisms of constructing attribute reducts for consistent and inconsistent covering information systems when varying attribute sets by using related families. Then we employ examples to illustrate how to compute attribute reducts of dynamic covering information systems with variations of attribute sets. Finally, the experimental results illustrates that the related family-based methods are effective to perform attribute reduction of dynamic covering information systems when attribute sets are varying with time. Copyright © 2017, The Authors. All rights reserved.

关键词： Information use

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

Efficient differential fault analysis attacks to AES decrypt...

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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

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