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Ordered proposition fusion based on consistency and uncertainty measurements

Science China(Information Sciences) 2017年第8期60卷 117-135页

作者： Dayou LIU Yungang ZHU Ni NI Jie LIU College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Beijing Entry-Exit Inspection and Quarantine Bureau

The fusion of ordered propositions is an important and widespread problem in artificial intelligence,but existing fusion methods have difficulty handling the fusion of ordered propositions. In this paper, we propose a solution based on consistency and uncertainty measurements. The main contributions of this paper are as follows. First, we propose the concept of convexity degree, mean, and center of basic support function to comprehensively describe the basic support function of ordered propositions. Second, we introduce entropy as a measure of uncertainty in the basic support function of ordered propositions. Third, we generalize the indeterminacy of the basic support function and propose a novel method to measure the consistency between two basic support functions. Finally, based on the above researches, we propose a novel algorithm for fusing ordered propositions. Theoretical analysis and experimental results demonstrate that the proposed method outperforms state-of-the-art methods.

关键词： uncertainty processing ordered proposition evidence theory dempster rule of combination information fusion

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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

arXiv

引用

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 Niyato, Dusit Kim, Dong In 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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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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

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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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A fast rigid registration algorithm for medical images

引用

Journal of Fiber Bioengineering and Informatics 2014年第3期7卷 409-418页

作者： Feng, Yuncong Li, Xiongfei Zhang, Xiaoli Ding, Tienan Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Department of Computer Science and Technology Changchun China

Image registration is a vital research branch in medical image processing and analysis. In this paper, we proposed a new framework for rigid medical image registration. It can also be regarded as a pre-processing of non-rigid image registration algorithms. The interest of the algorithm lies in its simplicity and high e±ciency. In the registration algorithm, we firstly segmented the reference image and °oat image into two parts: tissue parts and background parts. Then the centers of the two images were located through performing distance transform on the two segmented tissue images. Finally, we detected the longest radius of the two tissue regions, by which we determined the rotating angle. We tested the registration algorithm on dozens of medical images, and the experimental results show us that the algorithm is competent for medical image registration.

关键词： Image registration

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Error Formulas for Lagrange Projectors Determined by Cartesian Sets

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Journal of Systems Science & Complexity 2018年第4期31卷 1090-1102页

作者： LI Zhe ZHANG Shugong DONG Tian GONG Yihe School of Science Changchun University of Science and Technology Changchun 130022 China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun 130012 China School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering (Ministry of Education) Jilin University Changchun 130012 China Northeast Electric Power University Jilin 132012 China School of Mathematics Jilin University Changchun 130012 China

This paper studies error formulas for Lagrange projectors determined by Cartesian sets. Cartesian sets are properly subgrids of tensor product grids. Given interpolated functions with all order continuous partial derivatives, the authors directly construct the good error formulas for Lagrange projectors determined by Cartesian sets. Owing to the special algebraic structure, such a good error formula is useful for error estimate.

关键词： Cartesian sets error formulas ideal interpolation multivariate polynomial interpolation

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AutoFE-Pointer: Auto-weighted feature extractor based on pointer network for DNA methylation prediction

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International Journal of Biological Macromolecules 2025年 311卷 143668页

作者： Feng, Xin Xin, Ruihao Wu, Jiezhang Zheng, Jiaxin Wang, Chenyan Yu, Cuinan School of Science Jilin Institute of Chemical Technology Jilin130000 China State Key Laboratory of Inorganic Synthesis and Preparative Chemistry College of Chemistry Jilin University Changchun130012 China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin130000 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Civil Aviation General Hospital Beijing100123 China School of Engineering Westlake University Zhejiang Hangzhou310030 China Hunan Bojii Life Technology Co. Ltd. Hunan Province Changsha410000 China

DNA methylation is a critical epigenetic modification that plays a central role in gene regulation, cellular differentiation, and the development of various diseases, including cancers. Aberrant methylation patterns have emerged as both biomarkers and mechanistic drivers in pathogenesis, underscoring the urgent need for precise and efficient predictive tools. Although some deep learning techniques have advanced methylation prediction, most existing models are trained independently on single-species datasets. This species-specific approach limits efficiency because each model can only handle one dataset at a time, often at the expense of predictive performance. Additionally, the state-of-the-art deep learning models tend to have enormous parameter counts and computational overhead, making them impractical for integration into local offline software applications. To overcome these challenges, we propose AutoFE-Pointer, a lightweight and novel framework that harnesses an improved softened pointer network to dynamically extract and weight features from diverse DNA sequences. AutoFE-Pointer is designed to simultaneously process 17 different benchmark datasets spanning multiple species, achieving superior performance compared to models that are trained individually on single-species data. In doing so, it not only offers state-of-the-art predictive accuracy and robust cross-species generalization but also significantly reduces computational demands, facilitating its deployment in local offline environments. This breakthrough represents a significant advancement in the field of epigenetic modeling and computational biology. © 2025

关键词： Enterprise software

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