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Field-assisted spin-polarized electron transport through a single quantum well with spin-orbit coupling

Chinese Physics B 2012年第3期21卷 389-394页

作者：丁秀欢张存喜王瑞周运清孔令民 Department of Mathematics Zhejiang Ocean University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Department of Physics Zhejiang Ocean University

We have investigated theoretically the field-driven electron transport through a single-quantum-well semiconductor heterostructure with spin-orbit coupling. The splitting of the asymmetric Fano-type resonance peaks due to the Dresselhaus spin-orbit coupling is found to be highly sensitive to the direction of the incident electron. The splitting of the Fano-type resonance induces the spin-polarization dependent electron current. The location and the line shape of the Fano-type resonance can be controlled by adjusting the energy and the direction of the incident electron, the oscillation frequency, and the amplitude of the external field. These interesting features may be used to devise tunable spin filters and realize pure spin transmission currents.

关键词： Fano resonance Floquet channel spin-orbit coupling

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Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

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Journal of Computer Science & Technology 2006年第3期21卷 393-400页

作者：杨博刘大有 College of Computer Science and Technology & Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P.R. China

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.

关键词： incremental algorithm community structure dynamic network

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Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Liu, Bei Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Kim, Dong In The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

arXiv

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng D., Niyato D.I., Kim College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

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Fano resonance and wave transmission through a chain structure with an isolated ring composed of defects

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Chinese Physics B 2012年第3期21卷 212-217页

作者：张存喜丁秀欢王瑞周运清孔令民 Department of Physics Zhejiang Ocean University Department of Mathematics Zhejiang Ocean University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

We consider a discrete model that describes a linear chain of particles coupled to an isolated ring composed of N defects. This simple system can be regarded as a generalization of the familiar Fano Anderson model. It can be used to model discrete networks of coupled defect modes in photonic crystals and simple waveguide arrays in two-dimensional lattices. The analytical result of the transmission coefficient is obtained, along with the conditions for perfect reflections and transmissions due to either destructive or constructive interferences. Using a simple example, we further investigate the relationship between the resonant frequencies and the number of defects N, and study how to affect the numbers of perfect reflections and transmissions. In addition, we demonstrate how these resonance transmissions and refections can be tuned by one nonlinear defect of the network that possesses a nonlinear Kerr-like response.

关键词： wave transmission Fano resonance defect bistability

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Classification of evaluation measurement taken predicted scores into account

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Journal of Information and computational Science 2015年第5期12卷 1723-1730页

作者： Ding, Tienan Li, Xiongfei Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

Classifying performance evaluation is one of open problems in data mining and machine learning fields. We note that nearly all the existing evaluation measures ignore the predicted probabilities which are greatly significant in the process of classifiers' evaluation. In this paper, we construct a weighted confusion matrix to reflect the information on predicted probabilities. In addition, based on the weighted confusion matrix, traditional evaluation measures, such as accuracy, precision, recall, F-measure, are redefined to taking predicted probabilities into account. Finally, properties of the re-written evaluation measures are investigated. Experimental results show that the re-defined evaluation measures are superior to traditional ones in terms of discrimination. Copyright © 2015 Binary Information Press.

关键词： Probability

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Robust and efficient annotation based on ontology evolution for deep web data

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Journal of Computers 2011年第10期6卷 2029-2036页

作者： Chen, Kerui Zuo, Wanli Zhang, Fan He, Fengling Chen, Yongheng Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China College of Information Engineering Zhongzhou University Zhengzhou 450044 China

Among those researches in Deep Web, compared to research of data extraction which is more mature, the research of data annotation is still at its preliminary stage. Currently, although the approach of applying ontology in data annotating has been approved by most researchers, there are many weaknesses existed, such as the complexity of the ontology, as well as the limitation on static ontology's ability to annotate new pages. Respond to those problems, this paper proposes a robust, highly efficient data annotation method based on ontology evolution. It needs to be noticed that this paper defines a simpler ontology which can improve annotating efficiency significantly. Experiments indicate that this method could improve the accuracy and efficiency of data annotation. © 2011 ACADEMY PUBLISHER.

关键词： Ontology

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Canonical Huffman code based full-text index

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Progress in Natural Science:Materials International 2008年第3期18卷 325-330页

作者： Yi Zhang a,b, Zhili Pei a, Jinhui Yanga, Yanchun Liang a, a College of Computer Science and Technology, Jilin University, key laboratory of symbol computation and knowledge engineering of the ministry of education, Changchun 130012, China b Department of Computer Science, Jilin Technology and Business College, Changchun 130062, China College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Department of Computer Science Jilin Technology and Business College Changchun 130062 China

Full-text indices are data structures that can be used to find any substring of a given string. Many full-text indices require space larger than the original string. In this paper, we introduce the canonical Huffman code to the wavelet tree of a string T[1. . .n]. Compared with Huffman code based wavelet tree, the memory space used to represent the shape of wavelet tree is not needed. In case of large alphabet, this part of memory is not negligible. The operations of wavelet tree are also simpler and more efficient due to the canonical Huffman code. Based on the resulting structure, the multi-key rank and select functions can be performed using at most nH0 + jRj(lglgn + lgn lgjRj)+O(nH0) bits and in O(H0) time for average cases, where H0 is the zeroth order empirical entropy of T. In the end, we present an efficient construction algorithm for this index, which is on-line and linear.

关键词： Full-text index Su?x automaton Canonical Hu?man code

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Optimization strategy of top-down join enumeration on modern multi-core CPUs

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Journal of Computers 2011年第10期6卷 2004-2012页

作者： Zuo, Wanli Chen, Yongheng He, Fenglin Chen, Kerui Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

Most contemporary database systems query optimizers exploit System-R's bottom-up dynamic programming method (DP) to find the optimal query execution plan (QEP) without evaluating redundant subplans. The distinguished exceptions are Volcano/Cascades using transforms to generate new plans according to a topdown approach. As recent research has revealed, bottom-up dynamic programming can improve performance with respect to the shape of the join graph and parallelism. However top-down join enumeration dynamic programming method can derive upper bounds for the costs of the plans it generates which is not available to typical bottom-up DP method. In this paper, we propose a comprehensive and practical framework for parallelizing top-down dynamic programming query optimization with complex non-inner join in the multi-core processor architecture, referred as PTDhyp. We have implemented such a search strategy and experimental results show that can improve optimization time effective compared to known existing algorithms. © 2011 ACADEMY PUBLISHER.

关键词： Dynamic programming

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Improved filter feature selection methods by samples localization

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Journal of Bionanoscience 2013年第4期7卷 371-377页

作者： Sun, Ying Wang, Yan Chen, Liang Du, Wei Cao, Zhongbo Zhou, You College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China College of Chemistry Jilin University Changchun 130012 China

With the development of high-throughput microarray technology, a large number of microarray data has been obtained by tens of thousands of simulation experiments on gene expression. However, due to the high cost, gene expression data of each experiment has a small number of samples for tens of thousands of genes. Furthermore, most of the genes, such as the features, are unrelated to classification. Therefore, developing an effective and robust method to extract informative genes subset from high dimensional microarray data is a challenging and important issue for microarray data analysis. In this article, we propose an improved feature selection method by samples localization. We only use localized samples to select genes subset, in order to effectively overcome the influence on the results of feature selection by outlier samples and the distribution of samples. Experimental results obtained by SVM classifiers on Leukemia dataset, Prostate dataset, Colon dataset, Breast dataset, DLBCL dataset and Lung dataset demonstrate that the proposed method is superior to those original filter feature selection methods both in best and average accuracies. Copyright © 2013 American Scientific Publishers. Website © 2013 Publishing Technology.

关键词： Feature Selection

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