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Improved SDA-ss algorithm for nonsymmetric algebraic Riccati equations

Journal of Physics: Conference Series 2020年第1期1592卷 012049-012049页

作者： Runsheng Wu Bo Tang College of Mathematics and Computing Science Guilin University of Electronic Technology Guilin P. R. China Guangxi Key Laboratory of Cryptography and Information Security P. R. China

It is well known that nonsymmetric algebraic Riccati equation can be efficiently solved by using the structure-preserving doubling algorithm (SDA) with the shift-and-shrink transformation and the corresponding doubling algorithm is called the SDA-ss. In this paper, we propose an improved SDA-ss algorithm. Preliminary numerical experiments show that the algorithm is efficient to derive the minimal nonnegative solution of nonsymmetric algebraic Riccati equation with M-matrix.

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Robust classification with sparse representation fusion on diverse data subsets

arXiv

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arXiv 2019年

作者： Feng, Chun-Mei Xu, Yong Li, Zuoyong Yang, Jian Bio-Computing Research Center Harbin Institute of Technology Shenzhen Guangdong518055 China Key Laboratory of Network Oriented Intelligent Computation Shenzhen Guangdong518055 China Fuzhou350121 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu210094 China

Sparse Representation (SR) techniques encode the test samples into a sparse linear combination of all training samples and then classify the test samples into the class with the minimum residual. The classification of SR techniques depends on the representation capability on the test samples. However, most of these models view the representation problem of the test samples as a deterministic problem, ignoring the uncertainty of the representation. The uncertainty is caused by two factors, random noise in the samples and the intrinsic randomness of the sample set, which means that if we capture a group of samples, the obtained set of samples will be different in different conditions. In this paper, we propose a novel method based upon Collaborative Representation that is a special instance of SR and has closed-form solution. It performs Sparse Representation Fusion based on the Diverse Subset of training samples (SRFDS), which reduces the impact of randomness of the sample set and enhances the robustness of classification results. The proposed method is suitable for multiple types of data and has no requirement on the pattern type of the tasks. In addition, SRFDS not only preserves a closed-form solution but also greatly improves the classification performance. Promising results on various datasets serve as the evidence of better performance of SRFDS than other SR-based methods. The Matlab code of SRFDS will be accessible at http://***/***. Copyright © 2019, The Authors. All rights reserved.

关键词： Sampling

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Relative influence maximization in competitive social networks

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Science China(Information Sciences) 2017年第10期60卷 252-254页

作者： Dingda YANG Xiangwen LIAO Huawei SHEN Xueqi CHENG Guolong CHEN College of Physics and Information Engineering Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Institute of Computing Technology Chinese Academy of Sciences

In many realistic scenarios,such as political election and viral marketing,two opposite opinions,i.e.,positive opinion and negative opinion,spread simultaneously in the same social networks[1,2].Consequently,to achiev... 详细信息

关键词： net RIM Relative influence maximization in competitive social networks Figure

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A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural network

arXiv

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arXiv 2020年

作者： Hua, Xuan Han, Jungang Zhao, Chen Tang, Haipeng He, Zhuo Tang, Jinshan Chen, Qing-Hui Tang, Shaojie Zhou, Weihua College of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing in Shaanxi Xi’an710121 China College of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China College of Computing Michigan Technological University HoughtonMI49931 United States School of Computing University of Southern Mississippi Long Beach39560 United States Deprtment of Kinesiology and Integrative Physiology Michigan Technological University HoughtonMI49931 United States School of Automation Xi’an University of Posts and Telecommunications Xi’an710121 China

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural networks (CNN) to aid the classification of ECG signals. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) for segmenting the original ECG data into segments in order to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments. The performance of the classification models for 5-class and 6-class are verified with ECG signals from 48 records of the MIT-BIH arrhythmia database. As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile, according to the recommended practice from the Association for Advancement of Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes (i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unclassifiable beats), the beat classification accuracy, the sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The experimental results show that the proposed method achieves better performance than the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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An application of insurance mechanism in risk management for energy internet

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High technology Letters 2018年第2期24卷 196-202页

作者： Yang Yunxue Zhang Qin Zhang Libo CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 P. R. China College of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China Business College Yantai Nanshan University Yantai 265713 P. R. China

As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the *** paper proposes an electricity pricing model based on insurance from the perspective of maximizing the benefits of Energy Internet service providers by using the principal-agent theory. The consumer prepays the provider insurance premiums and signs a contract. The provider sets electricity price according to the premiums and therefore provides differentiated electric services for the consumer. Loss suffered by the consumer due to the power failure is compensated by the provider according to the contract. The equivalent model is presented and a necessary condition of the optimal strategy is obtained on the basis of Pontryagin's maximum principle. At last,a numerical example is presented,which illustrates the effectiveness of the proposed model.

关键词： energy internet insurance risk management optimal control fuzzy theory

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Author Correction: Orbital-selective effect of spin reorientation on the Dirac fermions in a non-charge-ordered kagome ferromagnet Fe3Ge

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Nature communications 2024年第1期15卷 10655页

作者： Rui Lou Liqin Zhou Wenhua Song Alexander Fedorov Zhijun Tu Bei Jiang Qi Wang Man Li Zhonghao Liu Xuezhi Chen Oliver Rader Bernd Büchner Yujie Sun Hongming Weng Hechang Lei Shancai Wang Leibniz Institute for Solid State and Materials Research IFW Dresden 01069 Dresden Sachsen Germany. lourui09@***. Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 12489 Berlin Germany. lourui09@***. Joint Laboratory "Functional Quantum Materials" at BESSY II 12489 Berlin Germany. lourui09@***. Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China. University of Chinese Academy of Sciences Beijing 100049 China. Department of Physics Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) and Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing 100872 China. Leibniz Institute for Solid State and Materials Research IFW Dresden 01069 Dresden Sachsen Germany. Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 12489 Berlin Germany. Joint Laboratory "Functional Quantum Materials" at BESSY II 12489 Berlin Germany. School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China. ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai 201210 China. School of Information Network Security People's Public Security University of China Beijing 100038 China. Institute of High-Pressure Physics and School of Physical Science and Technology Ningbo University Ningbo 315211 China. Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China. Institute of Solid State and Materials Physics TU Dresden 01062 Dresden Sachsen Germany. Department of Physics and Guangdong Basic Research Center of Excellence for Quantum Science Southern University of Science and Technology (SUSTech) Shenzhen 518055 China. Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China. Institute of Advanced Science Facilities Shenzhen Guangdong 5181

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Traffic sign classification base on latent dirichlet allocation 1

引用

6th International Conference on Frontier computing, FC 2017

作者： Song, Lei Liu, Zheyuan Zhang, Xiaoteng Duan, Huixian Liu, Na Dai, Jie The Third Research Institute of the Ministry of Public Security Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai International Technology and Trade United Co. Ltd Shanghai China

ISBN: (数字)9789811073984

ISBN: (纸本)9789811073977

Traffic sign classification is a significant issue in the intelligent vehicle domain, which helps vehicles to follow the traffic rules and ensure the safety. Feature selection and description are very important and difficult for classification. In this paper, a novel traffic sign classification method is proposed which is based on the Latent Dirichlet Allocation (LDA) model. Feature topics are modeled based on various traffic signs by the LDA automatically. And traffic signs captured onboard are classified according to the modeled features. The experiment results show the efficiency of our work. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Traffic signs

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Abnormal event detection based on in vehicle monitoring system

Abnormal event detection based on in vehicle monitoring syst...

引用

International Conference on Applications and Techniques in Cyber security and Intelligence, ATCSI 2017

作者： Song, Lei Dai, Jie Duan, Huixian Liu, Zheyuan Liu, Na The Third Research Institute of the Ministry of Public Security Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai International Technology & Trade United Co. Ltd Shanghai China

ISBN: (纸本)9783319670706

Nowadays, the cameras of traffic monitoring systems are mounted toward roads or interactions. The views are fixed and limited. To extend the monitoring area, a novel onboard abnormal event detection system is proposed based on the In Vehicle Monitoring System (IVMS). Videos are captured by the camera mounted in the front of a vehicle. Traffic information extracted by the system is combined with the GPS and electronic map to discover abnormal events. The system can be installed on police vehicles, buses, even private cars. Therefore, the anomaly monitoring can be processed anywhere, instead of on the certain interactions or roads. © 2018, Springer International Publishing AG.

关键词： Cameras

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Local unitary classification for sets of generalized Bell states

引用

Physical Review A 2018年第2期98卷 022304-022304页

作者： Bujiao Wu Jiaqing Jiang Jialin Zhang Guojing Tian Xiaoming Sun Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

In this paper, we study the local unitary (LU) classification for pairs (triples) of generalized Bell states, based on the local unitary equivalence of two sets. In detail, we first introduce some general unitary operators which give us more local unitary equivalent sets besides Clifford operators. And then we present two necessary conditions for local unitary equivalent sets which can be used to examine the local inequivalence. Following this approach, we completely classify all pairs in the d⊗d quantum system into ∏j=1n(kj+1)−1 LU-inequivalent pairs when the prime factorization of d=∏j=1npjkj. Moreover, all triples in the pα⊗pα quantum system for prime p can be partitioned into (α+3)6pα+O(αpα−1) LU-inequivalent triples; especially, when α=2 and p>2, there are exactly ⌊56p2⌋+⌊p−26+(−1)⌊p3⌋p3⌋+3 LU-inequivalent triples.

关键词： Nonlocality Quantum entanglement

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Phishing detection research based on LSTM recurrent neural network 4th

Phishing detection research based on LSTM recurrent neural n...

引用

4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Chen, Wenwu Zhang, Wei Su, Yang Key Laboratory for Network and Information Security of Chinese Armed Police Force Engineering University of Chinese Armed Police Force Xi’anShaanxi China Department of Electronic Technology Engineering University of the Chinese Armed Police Force Xi’anShaanxi China

ISBN: (纸本)9789811322020

In order to effectively detect phishing attacks, this paper designed a new detection system for phishing websites using LSTM Recurrent neural networks. LSTM has the advantage of capturing data timing and long-term dependencies. LSTM has strong learning ability, has strong potential in the face of complex high-dimensional massive data. Experimental results show that this model approach the accuracy of 99.1%, is higher than that of other neural network algorithms. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Long short-term memory

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