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A Decode-and-Forward Scheme for LDPC Coded Three-Way Relay Fading Channels

China Communications 2015年第8期12卷 46-54页

作者： YE Xia GAO Feifei Department of Computer Science & Information Management Wenzhou Medical University Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList)

In this paper a low-density pairwise check(LDPC) coded three-way relay system is considered, where three user nodes desire to exchange messages with the help of one relay node. Since physical-layer network coding is applied, two time slots are sufficient for one round information exchange. In this paper, we present a decode-and-forward(DF) scheme based on joint LDPC decoding for three-way relay channels, where relay decoder partially decodes the network code rather than fully decodes all the user messages. Simulation results show that the new DF scheme considerably outperforms other common schemes in three-way relay fading channels.

关键词： three-way relay channel physicallayer network coding LDPC codes decode-and forward

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Trainable back-propagated functional transfer matrices

arXiv

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arXiv 2017年

作者： Cai, Cheng-Hao Xu, Yanyan Ke, Dengfeng Su, Kaile Sun, Jing Department of Computer Science University of Auckland 38 Princes Street Auckland1142 New Zealand School of Information Science and Technology Beijing Forestry University 35 Qing-Hua East Road Beijing100083 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhong-Guan-Cun East Road Beijing100190 China Institute for Integrated and Intelligent Systems Griffith University 170 Kessels Road NathanQLD4111 Australia

Connections between nodes of fully connected neural networks are usually represented by weight matrices. In this article, functional transfer matrices are introduced as alternatives to the weight matrices: Instead of using real weights, a functional transfer matrix uses real functions with trainable parameters to represent connections between nodes. Multiple functional transfer matrices are then stacked together with bias vectors and activations to form deep functional transfer neural networks. These neural networks can be trained within the framework of back-propagation, based on a revision of the delta rules and the error transmission rule for functional connections. In experiments, it is demonstrated that the revised rules can be used to train a range of functional connections: 20 different functions are applied to neural networks with up to 10 hidden layers, and most of them gain high test accuracies on the MNIST database. It is also demonstrated that a functional transfer matrix with a memory function can roughly memorise a non-cyclical sequence of 400 digits. Copyright © 2017, The Authors. All rights reserved.

关键词： Transfer matrix method

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Social role clustering with topic model

Social role clustering with topic model

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Jie Bai Linjing Li Daniel Zeng Junjie Lin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation China School of Computer and Control Engineering University of Chinese Academy of Sciences China Department of Management of Information Systems The University of Arizona USA

ISBN: (纸本)9781509038664

In this paper, we propose a new role analyzing paradigm for social networks enlightened by topic modeling, which can be adopted as a primitive building block in various security related tasks, such as hidden community finding, important person recognizing and so on. We first present the social network under analyzing as a heterogeneous network constructed by both the users and the subjects discussed among them. We then view this network in a Bag-of-Users schema, which mimics its classical Bag-of-Words counterpart. In this schema, the subjects discussed are treated as “documents” while the users are treated as “words” which construct the “documents”. Based on this novel presentation, we finally apply topic modeling technology to perform the social role clustering. Experiments on a practical security-related social network dataset prove the effectiveness of our approach.

关键词： Social network services Heterogeneous networks Analytical models Computational modeling Finance Security Data models

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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Control of Three-Phase Voltage Source Converters with the Derivative-Free Nonlinear Kalman Filter

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Intelligent Industrial systems 2016年第1期2卷 21-33页

作者： G. Rigatos P. Siano N. Zervos C. Cecati Unit of Industrial Automation Industrial Systems Institute Patras Greece Dept. of Industrial Eng. University of Salerno Fisciano Italy Department of Information Engineering Computer Science and Mathematics University of L’ Aquila Via Vetoio L’Aquila Italy

The paper is concerned with proving differential flatness of the three-phase voltage source converter (VSC) model and its resulting description in the Brunovsky (canonical) form. For the linearized canonical model of the converter a feedback controller is designed. At a second stage, a novel Kalman Filtering method (Derivative-free nonlinear Kalman Filtering) is introduced. The proposed Kalman Filter is redesigned as disturbance observer for estimating additive input disturbances to the VSC model. These estimated disturbance terms are finally used by a feedback controller that enables the DC output voltage track desirable setpoints. The efficiency of the proposed state estimation-based control scheme is tested through simulation experiments.

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Data science applications to improve accuracy of thermocouples

Data science applications to improve accuracy of thermocoupl...

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International IEEE Conference on Intelligent systems, IS

作者： Hu Zhengbing Vladimir Jotsov Su Jun Orest Kochan Mykola Mykyichuk Roman Kochan Taras Sasiuk School of Educational Information Technology Central China Normal University Wuhan China School of Computer Science Hubei University of Technology Wuhan China Department of Intelligent Systems Institute of information technologies Sofia Bulgaria Institute of Computer Technologies and Automation Lviv Polytechnic National University Lviv Ukraine

This paper considers the usage of artificial intelligence, in particular, neural networks, to correct and compensate thermocouple errors. There are the correction of the thermocouple tolerance, the error due to conversion characteristic drift under the influence of high operating temperatures as well as the compensation of the error due to acquired thermoelectric inhomogeneity of thermocouple legs proposed in this paper. The correction is carried out using individual mathematical models based on neural networks. It is proposed the neural network method for controlling a temperature field to compensate the error due to acquired thermoelectric inhomogeneity.

关键词： Temperature measurement Artificial neural networks Calibration Legged locomotion Nonhomogeneous media Temperature Training

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Attitude synchronization of multiple rigid bodies in SE(3) over proximity networks

Attitude synchronization of multiple rigid bodies in SE(3) o...

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第35届中国控制会议

作者： Juan Deng Zhixin Liu Lin Wang John S.Baras Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceCAS Department of Automation Shanghai Jiao Tong Universityand the Key Laboratory of System Control and Information ProcessingMinistry of Education Department of Electrical and Computer Engineering and the Institute for Systems Research University of MarylandCollege Park

ISBN: (纸本)9781509009107

This paper investigates the attitude synchronization of a group of moving rigid bodies whose states including positions and rotation matrices evolve in the Lie group SE(3).The commonly used circular neighborhood is adopted,and the neighbor graphs are described by proximity *** order to avoid introducing discussions on the abrupt changes of neighbor graphs,a dwell time is *** distributed sampled-data control law for the angular velocity is *** the assumptions on the initial states,we provide sufficient conditions for the attitude synchronization of multiple rigid body *** with the existing results,our condition depends only on the neighborhood radius,the moving velocity and the dwell time.

关键词： rigid body SE(3) attitude synchronization sampled-data control proximity network

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New words enlightened sentiment analysis in social media

New words enlightened sentiment analysis in social media

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Chiyu Cai Linjing Li Daniel Zeng Chinese Academy of Sciences Institute of Automation Beijing China School of Computer and Control Engineerina University of Chinese Academy of Sciences China Department of Management Information Systems University of Arizona Tucson AZ USA

ISBN: (纸本)9781509038664

Public sentiment permeated through social media is usually regarded as an important measure for hot event detecting, policy making and so forth, hence many governments and intelligence agencies have been launching various initiatives to facilitate theories, technologies and systems toward monitoring its fluctuation. Recently, massive new words are created and widely spread in social media, and they pose a great influence on sentiment analysis. Facing this situation, most previous work still just add those new words into sentiment lexicon, none of the existed researches focuses on the role and influence of new words in emotional expression. In this paper, we pay more attention to the influence of new words and propose two novel new words based sentiment analysis methods, named NWLb and NWSA, the former only with the help of lexicon and the latter further incorporates machine learning, which utilize the distinctive role of new words to improve the effectiveness of sentiment analysis in social media. Experiments on real social media dataset demonstrate the effectiveness and performance of our methods.

关键词： Sentiment analysis Learning systems Twitter Computational modeling Analytical models Support vector machines

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GPU-Accelerated Texture Analysis Using Steerable Riesz Wavelets

GPU-Accelerated Texture Analysis Using Steerable Riesz Wavel...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Anamaria Vizitiu Lucian Mihai Itu Ranveer Joyseeree Adrien Depeursinge Henning Müller Constantin Suciu Imaging and Computer Vision Corporate Technology Romania Automation and Information Technology Transilvania University of Brasov Romania HES-SO Sierre Institute of Information Systems Switzerland ETHZ Zürich Switzerland Medical Informatics University University Hospitals of Geneva Switzerland

Visual pattern recognition is a key research topic in the field of image processing and computer vision. Texture analysis based on steerable Riesz wavelets is powerful, but requires computing pixel-wise operations resulting in a run time in the order of days when large volumes of data are processed. To overcome this limitation we propose a Graphics Processing Unit (GPU) based solution. A standard CPU version is used as starting point for the development of baseline GPU versions. To further increase the performance, and to overcome compute and memory limitations we apply a series of optimization techniques, leading to five versions in total. The best performing GPU solution ensures a speed-up of 93× for the parallelized section of the application and of 29.6× for the entire application. Furthermore, we show that a higher Riesz order and/or a higher image resolution further increases the speed-up.

关键词： Graphics processing units Instruction sets Registers Support vector machines Memory management Kernel Training

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Index Modulated OFDM with ICI Self-Cancellation for V2X Communications

Index Modulated OFDM with ICI Self-Cancellation for V2X Comm...

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International Conference on Computing, Networking and Communications

作者： Yuke Li Miaowen Wen Xiang Cheng Liu-Qing Yang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Electronic and Information Engineering South China University of Technology School of Electronics Engineering and Computer Science Peking University

ISBN: (纸本)9781467385800

Recent years have witnessed the rapid development of intelligent transportation systems (ITS), which urgently call for highly reliable and effective communication technologies for vehicle-to-vehicle and vehicle-to-infrastructure (V2X) applications. Orthogonal frequency division multiplexing (OFDM) is widely believed to be the most promising candidate among these applications. However, the orthogonality of subcarriers in OFDM can be easily destroyed by the Doppler-induced inter-carrier interference (ICI), which is very common in V2X channels. In this paper, we propose a novel scheme which integrates the ICI self-cancellation technique into the index modulated (IM-) OFDM framework. Via careful designs, the proposed scheme can not only inherit the advantages of IM-OFDM but also suppress the ICI from active subcarriers. Simulations validate that in V2X channels, the proposed scheme significantly outperforms existing IM-OFDM and more importantly shows better performance than the conventional OFDM with ICI self-cancellation without sacrificing the spectral efficiency.

关键词： OFDM Vehicles Indexes Detectors Modulation Computational complexity Doppler shift

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