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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Y. Attia A. Abdelrahman M. Hamouda M. Youssef Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Department of Electronic and Information Engineering Beijing Electronic Science and Technology Institute Beijing China

ISBN: (纸本)9781509012732

This paper presents the technical and economical merits of using low losses fast switching Silicon Carbide (SiC) accumulated gate field effect transistor (ACCUFET) switches in comparison with a Hybrid module of Silicon (Si) IGBT with anti-parallel schottky barrier diode (SBD) in DC/DC converter applications of the Nissan Leaf electric vehicle (EV) motor drive. The performance of the car, at steady state conditions, is studied at different temperatures. The power losses, current, voltage, torque and speed results are given. The results showed lower total switching power losses with the SiC ACCUFET. This reduction in losses was recorded at different switching frequencies in comparison to the hybrid switch. The fast switching of SiC Trench ACCUFET reduces current-voltage cross-over losses and enables high frequency operation thus achieving high record efficiency. The high frequency operation will result in a smaller foot print of the converter together with a weight reduction; thus making the car lighter. It is expected that a lighter car will result in a longer range Nissan Leaf with the same battery. Increasing the car mileage of the battery and lowering the cost of magnetic components in the EV will surpass the higher cost of the SiC ACCUFET from an economical point of view.

关键词： Switches Silicon carbide Silicon Insulated gate bipolar transistors Voltage control

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Sparse Tensor Discriminative Locality Alignment for Gait Recognition

Sparse Tensor Discriminative Locality Alignment for Gait Rec...

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International Joint Conference on Neural Networks

作者： Nengwen Zhao Lefei Zhang Bo Du Liangpei Zhang Dacheng Tao Jane You State Key Laboratory of Software Engineering School of Computer Wuhan University Department of Computing The Hong Kong Polytechnic University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781509006212

Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed for gait data, including the conventional vector representation based methods principal components analysis (PCA) and, locality preserving projection (LPP), and the recently proposed multi-linear subspace learning based approaches such as multilinear principal component analysis (MPCA). In this paper, inspired by the advantages of the tensor representation and manifold learning, we propose a novel sparse tensor discriminative locality alignment for human gait feature representation and dimensionality reduction algorithm, and sub-sequently apply the refined feature for gait recognition by a lazy classifier of the KNN. The proposed method adopts sparse multi-way projection based on the high-order version of discriminative locality alignment, by which the class separability is enhanced and the potential model overfitting is simultaneously avoided. Extensive experiments on the University of South Florida (USF) HumanID Gait Database show that the proposed method achieves better recognition rate compared with some existing classical dimensionality reduction algorithms.

关键词： Gait recognition dimensionality reduction images computer vision Tensor Locomotion Gait principal components analysis vector representation Alignment

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A separability-entanglement classifier via machine learning

arXiv

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arXiv 2017年

作者： Lu, Sirui Huang, Shilin Li, Keren Li, Jun Chen, Jianxin Lu, Dawei Ji, Zhengfeng Shen, Yi Zhou, Duanlu Zeng, Bei Department of Physics Tsinghua University Beijing100084 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD United States Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Computation & Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics Chinese Academy of Sciences Beijing100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint

Regularizing Deep Convolutional Neural Networks with a Struc...

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IEEE International Conference on Data Mining (ICDM)

作者： Wei Xiong Bo Du Lefei Zhang Ruimin Hu Dacheng Tao Computer School of Wuhan University National Engineering Research Center for Multimedia Software Wuhan China Collaborative Innovation Center of Geospatial Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong China National Engineering Research Center for Multimedia Software Computer School of Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW Australia

ISBN: (纸本)9781509054749

Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer, named Structured Decorrelation Constraint (SDC), which is applied to the activations of the hidden layers to prevent overfitting and achieve better generalization. SDC impels the network to learn structured representations by grouping the hidden units and encouraging the units within the same group to have strong connections during the training procedure. Meanwhile, it forces the units in different groups to learn non-redundant representations by minimizing the cross-covariance between them. Compared with Dropout, SDC reduces the co-adaptions between the hidden units in an explicit way. Besides, we propose a novel approach called Reg-Conv that can help SDC to regularize the complex convolutional layers. Experiments on extensive datasets show that SDC significantly reduces overfitting and yields very meaningful improvements on classification performance (on CIFAR-10 6.22% accuracy promotion and on CIFAR-100 9.63% promotion).

关键词： Neurons Correlation Decorrelation Training Data models Electronic mail Biological neural networks

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VIDEO SUPER-RESOLUTION BASED ON NONLINEAR MAPPING AND PATCH SIMILARITY

VIDEO SUPER-RESOLUTION BASED ON NONLINEAR MAPPING AND PATCH ...

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2016 4th IEEE International Conference on Cloud Computing and Intelligence Systems（IEEE CCIS2016）

作者： Linghui Li Junping Du Meiyu Liang JangM yung Lee Luoming Meng Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Department of Electronics Engineering Pusan National University State Key Laboratory Network & Switching Technology Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509012572

Aiming at recovering lost detailed information in low-resolution videos, a novel video super-resolution algorithm based on nonlinear mapping and patch similarity was proposed in this paper. Taking advantage of the strong fitting ability of the convolutional neural network for the nonlinear relationship, this algorithm utilizes the learned reconstruction parameters to pad missing structures and make the low-resolution video approach to the ground truth. However, it is unavoidable to introduce irrelevant information and amplify noises in this process. The patch similarity is imported to improve the spatial-temporal consistency and anti-noise ability of this algorithm. Moreover, instead of considering the nonlocal self-similarity only, this algorithm combines the nonlocal external sparsity and spatial-temporal similarity to enrich the prior information. All these patch similarities are used to optimize the intermediate video achieved from the mapping process. Experimental results demonstrated that the proposed algorithm achieves a competitive super-resolution quality on both the subjective and the objective evaluations, when compared to other state-of-the-art algorithms.

关键词： video super-resolution reconstruction convolutional neural network nonlocal similarity

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An ant colony optimization algorithm for learning brain effective connectivity network from fMRI data

An ant colony optimization algorithm for learning brain effe...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinduo Liu Junzhong Ji Aidong Zhang Peipeng Liang Beijing Municipal Key Laboratory of Multimedia and Intelligent Software the College of Computer Science and Technology Beijing University of Technology Beijing China Department of Computer Science and Engineering University at Buffalo The State University of New York Buffalo America Beijing Key Lab of MRI and Brain Informatics Department of Radiology Capital Medical University Beijing China

ISBN: (纸本)9781509016129

Identifying brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is an important advanced subject in neuroinformatics in recent years, where the learning method based on bayesian networks (BN) has become a new hot topic in the field. This paper proposes a new method to learn the brain effective connectivity network structure by combining ant colony optimization (ACO) with BN method, named as ACOEC. In the proposed algorithm, a brain effective connectivity network is first mapped onto an ant, and then the ant colony optimization by simulating real ants looking for food is employed to construct network structures and finally an ant with the highest score is obtained as the optimal solution. The experimental results on simulated and real fMRI data sets show that the new method can not only accurately identify the connections and directions of the brain networks, but also quantitatively describe the connection strength of the brain networks, which has a good clinical application prospects.

关键词： Ant colony optimization Bayes methods Measurement Mathematical model Brain modeling Optimization Electronic mail

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Quantum State Tomography via Reduced Density Matrices

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Physical Review Letters 2017年第2期118卷 020401-020401页

作者： Tao Xin Dawei Lu Joel Klassen Nengkun Yu Zhengfeng Ji Jianxin Chen Xian Ma Guilu Long Bei Zeng Raymond Laflamme State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics University of Guelph Guelph N1G 2W1 Ontario Canada Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph N1G 2W1 Ontario Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences 100190 Beijing China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland USA Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Canadian Institute for Advanced Research Toronto M5G 1Z8 Ontario Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Quantum state tomography via local measurements is an efficient tool for characterizing quantum states. However, it requires that the original global state be uniquely determined (UD) by its local reduced density matrices (RDMs). In this work, we demonstrate for the first time a class of states that are UD by their RDMs under the assumption that the global state is pure, but fail to be UD in the absence of that assumption. This discovery allows us to classify quantum states according to their UD properties, with the requirement that each class be treated distinctly in the practice of simplifying quantum state tomography. Additionally, we experimentally test the feasibility and stability of performing quantum state tomography via the measurement of local RDMs for each class. These theoretical and experimental results demonstrate the advantages and possible pitfalls of quantum state tomography with local measurements.

关键词： Quantum entanglement Quantum information processing Quantum tomography Random matrix theory

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-degree coverage algorithm based on optimization nodes deployment in wireless sensor networks

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International Journal of Distributed Sensor Networks 2017年第2期13卷

作者： Zeyu Sun Chuanfeng Li Xiaofei Xing Huihui Wang Ben Yan Xuelun Li Department of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Power and Intelligent Control Research Cluster Queen’s University Belfast Belfast UK School of Computer Science & Educational Software Guangzhou University Guangzhou China Department of Engineering Jacksonville University Jacksonville FL USA School of Electronic Information Engineering Tianjin University Tianjin China

In the process of the wireless sensor network research, the issue on the energy consumption and coverage is an essential and critical one. According to the characteristic of the sensor nodes, it is homogeneous, and we proposed the k-degree coverage algorithm based on optimization nodes deployment. First, the algorithm gives the solving procedure of the maximum seamless coverage ratio, when the three-node joint coverage has been provided. Second, when the sensor nodes are covering the monitoring area, the algorithm gives the solving procedure of the expected coverage quality and the judgment methods of the coverage ratio, when the nodes are compared with the nearby ones. And when there is redundancy coverage in the given monitoring area, we have given the solving procedure of any sensor nodes that exist in the redundant nodes coverage. Finally, using the simulation experiment, the results of the coverage algorithm based on optimization nodes deployment are compared with other algorithms in terms of the coverage quality and the network lifetime, the performance indexes have enhanced to 13.36% and 12.92% on average. Thus, the effectiveness and viability of the coverage algorithm based on optimization nodes deployment have been proved.

关键词： Wireless sensor networks sensor nodes networks lifetime coverage ratio optimization deployment

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On Secrecy Performance of MISO SWIPT Systems with TAS and Imperfect CSI

On Secrecy Performance of MISO SWIPT Systems with TAS and Im...

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作者： Pan, Gaofeng Lei, Hongjiang Deng, Yansha Fan, Lisheng Yang, Jing Chen, Yunfei Ding, Zhiguo Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing400715 China School of Computing and Communications Lancaster University LancasterLA14YW United Kingdom Chongqing Key Laboratory of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Centre for Telecommunications King's College London LondonWC2R2LS United Kingdom School of Computer Science and Educational Software Guangzhou University Guangzhou510000 China Department of Electronic Engineering Shantou University Guangdong515063 China School of Information Engineering Yangzhou University Yangzhou225009 China School of Engineering University of Warwick CoventryCV47AL United Kingdom

In this paper, a multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, including one base station (BS) equipped with multiple antennas, one desired single-antenna information receiver (IR), and N (N > 1) single-antenna energy-harvesting receivers (ERs) is considered. Assuming that the information signal to the desired IR may be eavesdropped by ERs if ERs are malicious, we investigate the secrecy performance of the target MISO SWIPT system when imperfect channel state information (CSI) is available and adopted for transmit antenna selection at the BS. Considering that each eavesdropping link experiences independent but not necessarily identically distributed Rayleigh fading, the closed-form expressions for the exact and the asymptotic secrecy outage probability, and the average secrecy capacity are derived and verified by simulations. Furthermore, the optimal power splitting factor is derived for each ER to realize the tradeoff between the energy harvesting and the information eavesdropping. Our results reveal the impact of the imperfect CSI on the secrecy performance of MISO SWIPT systems in the presence of multiple wiretap channels. © 2016 IEEE.

关键词： Energy harvesting

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Denoising Auto-Encoders toward Robust Unsupervised Feature Representation

Denoising Auto-Encoders toward Robust Unsupervised Feature R...

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International Joint Conference on Neural Networks

作者： Wei Xiong Bo Du Lefei Zhang Liangpei Zhang Dacheng Tao State Key Laboratory of Software Engineering School of Computer Science Wuhan University China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW 2007 Australia

ISBN: (纸本)9781509006212

Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked convolutional denoising auto-encoders (SCDAE), which can map raw images to hierarchical representations in an unsupervised manner. Our network is elaborately designed to fit for the visual recognition tasks. It is established by stacking the denoising auto-encoders. Unlike the prior works, in the training phase, the auto-encoders are trained patch-wisely so that the latent features can be applied to powerful regularizers for better representation;in the inference phase, the denoising auto-encoders are stacked convolutionally, hence the generated feature maps in the higher layers can preserve the coherent structures within the features in the lower layers. To achieve better performance, we apply whitening to each layer to sphere the input features. Our network is evaluated on the challenging image datasets MNIST, CIFAR-10 and STL-10 and demonstrates superior performance to the state-of-the-art unsupervised networks.

关键词： Noise reduction Convolutional codes Training Feature extraction Kernel Optimized production technology Robustness

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