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International Symposium on Computational Intelligence and Design, ISCID

作者： Meiyin Wu Li Chen College of Computer Science and Technology Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781509035595

This paper proposed a novel blind image quality assessment method that is created by training a convolutional neural network to learn discriminant features of image quality and fitting the features with a support vector regression to get an evaluation score. The pooling procedure is help to reduce the feature dimension and improve computation efficiency. The proposed method does not need any hand-crafted features contrast with most previous BIQA methods. It achieves better performance than previous BIQA methods on LIVE database. The experimental results show that the proposed method has good consistency, robustness and efficiency.

关键词： Feature extraction Image quality Databases Distortion Training Kernel Robustness

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A spatial-constrained multi-target regression model for human brain activity prediction

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Applied Informatics 2016年第1期3卷 1-9页

作者： Zhenfu Wen Yuanqing Li Center for Brain Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Guangzhou Key Laboratory of Brain Computer Interaction and Applications South China University of Technology Guangzhou China

Analyzing functional magnetic resonance imaging (fMRI) data from the encoding perspective provides a powerful tool to explore human vision. Using voxel-wise encoding models, previous studies predicted the brain activity evoked by external stimuli successfully. However, these models constructed a regularized regression model for each single voxel separately, which overlooked the intrinsic spatial property of fMRI data. In this work, we proposed a multi-target regression model that predicts the activities of adjacent voxels simultaneously. Different from the previous models, the spatial constraint is considered in our model. The effectiveness of the proposed model is demonstrated by comparing it with two state-of-the-art voxel-wise models on a publicly available dataset. Results indicate that the proposed method can predict voxel responses more accurately than the competing methods.

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Enhanced automatic sleep spindle detection: a sliding window-based wavelet analysis and comparison using a proposal assessment method

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Applied Informatics 2016年第1期3卷 1-9页

作者： Zhuang, Xiaobin Li, Yuanqing Peng, Nengneng Center for Brain Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Guangzhou Key Laboratory of Brain Computer Interaction and Applications South China University of Technology Guangzhou China

Sleep spindles are thought to be related to some sleep diseases and play an important role in memory consolidation. They were traditionally identified by physiology experts based on rules and recently detected by automatic algorithms. However, many automatic approaches were validated on the different electroencephalogram (EEG) using various assessment methods, making it difficult to appraised a method objectively and fairly. In this paper, we proposed a sliding window-based probability estimation (SWPE) method for sleep spindle detection. We performed a continuous wavelet transform with Mexican hat wavelet function, following by a sliding window to find out the candidate spindle points corresponding to the large wavelet coefficients at the frequencies of spindles and estimated their probabilities. To enhance the results, we used the envelope of the rectified signal to reject some false sleep spindle candidates. This was an enhanced method and we called it SWPE-E in this paper. Finally, we compared our approaches with four approaches on the same public available EEG database, and the result showed the significative improvement of our proposed approaches.

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Roadmap on Quantum Thermodynamics

arXiv

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arXiv 2025年

作者： Campbell, Steve D’Amico, Irene Ciampini, Mario A. Anders, Janet Ares, Natalia Artini, Simone Auffèves, Alexia Oftelie, Lindsay Bassman Bettmann, Laetitia P. Bonança, Marcus V.S. Busch, Thomas Campisi, Michele Cavalcante, Moallison F. Correa, Luis A. Cuestas, Eloisa Dag, Ceren B. Dago, Salambô Deffner, Sebastian Del Campo, Adolfo Deutschmann-Olek, Andreas Donadi, Sandro Doucet, Emery Elouard, Cyril Ensslin, Klaus Erker, Paul Fabbri, Nicole Fedele, Federico Fiusa, Guilherme Fogarty, Thomás Folk, Joshua Guarnieri, Giacomo Hegde, Abhaya S. Hernández-Gómez, Santiago Hu, Chang-Kang Iemini, Fernando Karimi, Bayan Kiesel, Nikolai Landi, Gabriel T. Lasek, Aleksander Lemziakov, Sergei Monaco, Gabriele Lo Lutz, Eric Lvov, Dmitrii Maillet, Olivier Mehboudi, Mohammad Mendonça, Taysa M. Miller, Harry J.D. Mitchell, Andrew K. Mitchison, Mark T. Mukherjee, Victor Paternostro, Mauro Pekola, Jukka Perarnau-Llobet, Martí Poschinger, Ulrich Rolandi, Alberto Rosa, Dario Sánchez, Rafael Santos, Alan C. Sarthour, Roberto S. Sela, Eran Solfanelli, Andrea Souza, Alexandre M. Splettstoesser, Janine Tan, Dian Tesser, Ludovico Van Vu, Tan Widera, Artur Halpern, Nicole Yunger Zawadzki, Krissia School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland School of Physics Engineering and Technology University of York York United Kingdom York Centre for Quantum Technologies University of York York United Kingdom Boltzmanngasse 5 ViennaA-1090 Austria Institute of Physics and Astronomy University of Potsdam Potsdam14476 Germany Department of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom Università degli Studi di Palermo Dipartimento di Fisica e Chimica - Emilio Segrè via Archirafi 36 PalermoI-90123 Italy MajuLab CNRS-UCA-SU-NUS-NTU International Joint Research Laboratory France Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore NEST Istituto Nanoscienze-CNR and Scuola Normale Superiore Pisa56127 Italy School of Physics Trinity College Dublin College Green Dublin 2 Ireland Gleb Wataghin Physics Institute The University of Campinas Campinas São Paulo13083-859 Brazil Quantum Systems Unit OIST Graduate University Okinawa Onna904-0495 Japan Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States Quantum Science Institute University of Maryland Baltimore County BaltimoreMD21250 United States Universidad de La Laguna La Laguna38203 Spain Departamento de Física Universidad de La Laguna La Laguna38203 Spain Jülich54245 Germany Department of Physics Indiana University BloomingtonIN47405 United States Department of Physics Harvard University 17 Oxford Street CambridgeMA02138 United States National Quantum Laboratory College ParkMD20740 United States Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg Donostia International Physics Center San SebastianE-20018 Spain Au

The last two decades has seen quantum thermodynamics become a well established field of research in its own right. In that time, it has demonstrated a remarkably broad applicability, ranging from providing foundational advances in the understanding of how thermodynamic principles apply at the nano-scale and in the presence of quantum coherence, to providing a guiding framework for the development of efficient quantum devices. Exquisite levels of control have allowed state-of-the-art experimental platforms to explore energetics and thermodynamics at the smallest scales which has in turn helped to drive theoretical advances. This Roadmap provides an overview of the recent developments across many of the field’s sub-disciplines, assessing the key challenges and future prospects, providing a guide for its near term progress. Copyright © 2025, The Authors. All rights reserved.

关键词： Temperature

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XGRouter： high-quality global router in X-architecture with particle swarm optimization

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Frontiers of computer Science 2015年第4期9卷 576-594页

作者： Genggeng LIU Wenzhong GUO Rongrong LI Yuzhen NIU Guolong CHEN College of Mathematics and Computer Science Fuzhou University Fuzhou 350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou 350116 China

This paper presents a high-quality very large scale integration （VLSI） global router in X-architecture, called XGRouter, that heavily relies on integer linear pro- gramming （ILP） techniques, partition strategy and particle swarm optimization （PSO）. A new ILP formulation, which can achieve more uniform routing solution than other formu- lations and can be effectively solved by the proposed PSO is proposed. To effectively use the new ILP formulation, a partition strategy that decomposes a large-sized problem into some small-sized sub-problems is adopted and the routing re- gion is extended progressively from the most congested re- git）n. In the post-processing stage of XGRouter, maze rout- ing based on new routing edge cost is designed to further optimize the total wire length and mantain the congestion uniformity. To our best knowledge, XGRouter is the first work to use a concurrent algorithm to solve the global rout- ing problem in X-architecture. Experimental results show that XGRouter can produce solutions of higher quality than other global routers. And, like several state-of-the-art global touters, XGRouter has no overflow.

关键词： global routing overflow total wire length con-gestion uniformity X-architecture particle swarm optimiza-tion integer linear programming

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Cloud Data Life Cycle Security Issues and Research of Encryption technology

Cloud Data Life Cycle Security Issues and Research of Encryp...

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2016 International Conference on Communications, information Management and Network Security (CIMNS2016)

作者： Nengneng Li Yongsheng Zhang Yueqin Fan Liang Chang School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology

ISBN: (纸本)9781510830981

With the further development of the cloud computing, the security of the cloud computing is becoming increasingly prominent. Based on analyzing the development status of cloud computing, this article will focus on studying the security of cloud computing in data, and discuss the data security problems in the process of data life cycle in cloud environment. Then the paper will put forward the corresponding measures and data encryption technology, in order to protect the data security of cloud computing.

关键词： cloud computing cloud security data security encryption erase technology

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Tolerance Granulation Based Community Detection Algorithm

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Tsinghua Science and technology 2015年第6期20卷 620-626页

作者： Shu Zhao Wang Ke Jie Chen Feng Liu Menghan Huang Yanping Zhang Jie Tang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Center of Information Support & Assurance TechnologySchool of Computer Science and TechnologyAnhui University School of Computer Science and Technology Tsinghua University

Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting networks. Tolerance Granulation based Community Detection Algorithm(TGCDA) is proposed in this paper, which uses tolerance relation(namely tolerance granulation) to granulate a network hierarchically. Firstly, TGCDA relies on the tolerance relation among vertices to form an initial granule set. Then granules in this set which satisfied granulation coefficient are hierarchically merged by tolerance granulation operation. The process is finished till the granule set includes one granule. Finally, select a granule set with maximum granulation criterion to handle overlapping vertices among some granules. The overlapping vertices are merged into corresponding granules based on their degrees of affiliation to realize the community partition of complex networks. The final granules are regarded as communities so that the granulation for a network is actually the community partition of the *** on several datasets show our algorithm is effective and it can identify the community structure more accurately. On real world networks, TGCDA achieves Normalized Mutual information(NMI) accuracy 17.55% higher than NFA averagely and on synthetic random networks, the NMI accuracy is also improved. For some networks which have a clear community structure, TGCDA is more effective and can detect more accurate community structure than other algorithms.

关键词： tolerance relation community tolerance granulation

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Adaptive Feature Extraction Algorithms and SVM with Optimal Parameters on Fault Diagnosis of bearing

Adaptive Feature Extraction Algorithms and SVM with Optimal ...

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第35届中国控制会议

作者： Qinxue LI Qinghua Zhang Xiaowen Liao College of Computer and Electronic Information Guangdong University of Petrochemical Technology Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis

ISBN: (纸本)9781509009107

Combining adaptive feature extraction algorithms with multi scale entropy(MSE),a synthetic method for fault diagnosis of bearing based on support vector machine(SVM) is *** performances of two new adaptive feature extraction algorithms and Empirical Mode Decomposition(EMD) on the fault feature extraction of vibration signals are first time compared *** is shown that multi-scale entropy(MSE) of the optimal mode is introduced for fault classification,and the effect is better when the scale factor is selected *** addition,as the feature vectors of SVM,the MSE of optimal mode complete fault diagnosis of bearing,under the optimal penalty parameter C and kernel parameter y of RBF kernel function have been searched by Cross-validation and Grid-search Finally,data in engineering are used to prove that not only the performance of new adaptive feature extraction algorithm but also the diagnostic unit on SVM are further improved,and the accuracy of diagnosis is raised to 97% over EMD+SVM only.

关键词： Adaptive feature extraction fault diagnosis bearing support vector machine multi-scale entropy optimal parameter

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A term frequency based weighting scheme using naïve bayes for text classification

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Journal of Computational and Theoretical Nanoscience 2016年第1期13卷 319-326页

作者： Feng, Guozhong Wang, Han Sun, Tieli Zhang, Libiao Key Laboratory of Intelligent Information Processing Jilin Universities School of Computer Science and Information Technology Northeast Normal University Changchun130117 China Key Laboratory for Applied Statistics of MOE Northeast Normal University Changchun130024 China

Term weighting is a strategy to assign weights to terms to improve the performance of many classi-fiers, such as kNN and SVM in text classification. Supervised term weighting methods have received increasing attention, in which information on the membership of training documents to classes is used. Most existing methods follow the local weight multiplies the global weight framework, but the contribution of term frequency for term weighting has not been fully investigated. In this paper, we propose a weighting scheme named term frequency-relevance term frequency based on a probabilistic model. After investigating two kinds of widely used naïve Bayes (NB) models, we employ the term event Multinomial NB model to capture the term frequency information. The matching score function based on the prediction probability ratio can then be factorized. Finally, we get the weight for each term by replacing the parameter by an estimator, term frequency is used in formulating not only the local weight factor but also the global weight factor. Numerical experiment results on two benchmark text datasets (Reuters-21578 and 20 Newsgroups) demonstrate that our proposed method outperforms the representative term weighting methods. © 2016 American Scientific Publishers All rights reserved.

关键词： Numerical methods

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A Human Identity Recognition System Based on Kinect Skeletal Tracking and ANN Classifier

A Human Identity Recognition System Based on Kinect Skeletal...

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2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Xianhui Zeng Jinwen Deng Mujun Liu Yue Wang Xuejun Zhang School of Computer Electronics and InformationGuangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology & Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing Guangxi University

ISBN: (纸本)9781510835368

We proposed a novel method of people recognition based on the structure of bones,which is detected by Kinect sensor for skeletal *** using 3D depth image achieved by Kinect,body's skeleton information is extracted as 20 joints with joint coordinates,from which body's skeletal features are calculated into different length ratio between each *** these features are processed by a BP artificial neural network(ANN) classifier to differentiate several *** results show a high accuracy up to 98% test samples,which indicate that the body's skeletal features can provide the effective judgment in people recognition.

关键词： human identity recognition kinect ANN classifier skeletal identification

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