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TOA positioning algorithm of LBL system for underwater target based on PSO

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系统工程与电子技术:英文版 2023年第5期34卷 1319-1332页

作者： XING Yao WANG Jiongqi HE Zhangming ZHOU Xuanying CHEN Yuyun PAN Xiaogang College of liberal arts and Sciences National University of Defense TechnologyChangsha 410073China school of Mathematics and Big Data Foshan UniversityFoshan 528000China College of Systems Engineering National University of Defense TechnologyChangsha 410073China

For the underwater long baseline(LBL)positioning systems,the traditional distance intersection algorithm simplifies the sound speed to a constant,and calculates the underwa-ter target position parameters with a nonlinear ***,due to the complex underwater environment,the sound speed changes with time and space,and then the acoustic propagation path is actually a curve,which inevitably causes some errors to the traditional distance intersection positioning *** reduce the position error caused by the uncertain underwater sound speed,a new time of arrival(TOA)intersection underwater positioning algorithm of LBL system is ***,combined with the vertical layered model of the underwater sound speed,an implicit positioning model of TOA intersection is constructed through the constant gradient acoustic ray *** then an optimization function based on the overall TOA residual square sum is advanced to solve the position parameters for the underwater ***,the particle swarm optimization(PSO)algorithm is replaced with the tra-ditional nonlinear least square method to optimize the implicit positioning model of TOA *** with the traditional distance intersection positioning model,the TOA intersec-tion positioning model is more suitable for the engineering practice and the optimization algorithm is more *** results show that the proposed methods in this paper can effectively improve the positioning accuracy for the underwater target.

关键词： long baseline(LBL)positioning system sound speed profile constant gradient acoustic ray tracing time of arrival(TOA)intersection model particle swarm optimization(PSO)

Polarity Reversal of Terahertz Electric Field from Heavily p-Doped Silicon Surfaces

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Chinese Physics Letters 2021年第7期38卷 34-39页

作者： Hai-Zhong Wu Quan Guo Yan-Yun Tu Zhi-Hui Lyu Xiao-Wei Wang Yong-Qiang Li Zhao-Yan Zhou Dong-Wen Zhang Zeng-Xiu Zhao Jian-Min Yuan College of liberal arts and Sciences National University of Defense TechnologyChangsha 410073China Northwest Institute of Nuclear Technology Xi'an 710024China Graduate school of China Academic of Engineering Physics Beijing 100193China

Above-band-gap optical excitation of electron-hole pairs screens the doping-induced surface electric field and generates terahertz(THz)pulses via free-carrier *** emission from a heavily doped silicon surface is much weaker than that of lightly doped samples.A polarity reversal of the THz electric field is observed in heavily doped p-type silicon,indicating that the doping related and carrier induced surface electric fields oppose each *** comparing the penetration depth of the excitation laser with the thickness of the depletion layer for the doped silicon,it is shown that competition between diffusion and drift current causes the polarity reversal.

关键词： excitation doping polarity

Enhanced KOCED Routing Protocol with K-means Algorithm

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Computers, Materials & Continua 2021年第6期67卷 4019-4037页

作者： SeaYoung Park Jong-Yong Lee Daesung Lee Department of Plasma Bioscience and Display KwangWoon University Graduate SchoolSeoul01897Korea Ingenium College of liberal arts KwangWoon UniversitySeoulKorea Department of Computer Engineering Catholic University of PusanBusanKorea

Replacing or recharging batteries in the sensor nodes of a wireless sensor network(WSN)is a significant ***,efficient power utilization by sensors is a critical requirement,and it is closely related to the life span of the *** a sensor node consumes all its energy,it will no longer function ***,various protocols have been proposed to minimize the energy consumption of sensors and thus prolong the network ***,clustering algorithms combined with artificial intelligence have been proposed for this *** particular,various protocols employ the K-means clustering algorithm,which is a machine learning *** number of clustering configurations required by the K-means clustering algorithm is greater than that required by the hierarchical ***,the selection of the cluster heads considers only the residual energy of the nodes without accounting for the transmission distance to the base *** terms of energy consumption,the residual energy of each node,the transmission distance,the cluster head location,and the central relative position within the cluster should be considered *** this paper,we propose the KOCED(K-means with Optimal clustering for WSN considering Centrality,Energy,and Distance)protocol,which considers the residual energy of nodes as well as the distances to the central point of the cluster and the base station.A performance comparison shows that the KOCED protocol outperforms the LEACH protocol by 259%(223 rounds)for first node dead(FND)and 164%(280 rounds)with 80%alive nodes.

关键词： WSN routing protocol K-means K-optimal LEACH KCE KOCED

Electron localization enhanced photon absorption for the missing opacity in solar interior

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Science China(Physics,Mechanics & Astronomy) 2022年第3期65卷 49-61页

作者： JiaoLong Zeng Cheng Gao PengFei Liu YongJun Li CongSen Meng Yong Hou DongDong Kang JianMin Yuan College of Science Zhejiang University of TechnologyHangzhou 310023China Graduate school of China Academy of Engineering Physics Beijing 100193China Department of Physics College of Liberal Arts and SciencesNational University of Defense TechnologyChangsha 410073China

The internal solar structure predicted by the standard solar model disagrees with the helioseismic observations even by utilizing the most updated physical inputs, such as the opacity and element abundances. By increasing the Rosseland mean, the decade-old open problem of the missing opacity can be resolved. Herein, we propose that the continuum electrons in the radiative processes lose phases and coherence as matter waves, giving rise to a phenomenon of transient spatial localization. It not only enhances the continuum opacity but also increases the line widths of the bound-bound transitions. We demonstrate our theoretical formulation by investigating the opacity of solar mixtures in the interior. The Rosseland mean demonstrates an increase of 10%-26% in the range of 0.3 R⊙-0.75 R⊙. The results are compared with the recent experimental data and the existing theoretical models. Our findings provide novel clues to the open problem of the missing opacity in the solar interior and new insight on the radiative opacity in the hot dense-plasma regime.

关键词： opacity dense plasmas solar interior transient spatial localization solar structure and evolution

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

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Chinese Journal of Aeronautics 2020年第11期33卷 2930-2945页

作者： Chang WANG Lizhen WU Chao YAN Zhichao WANG Han LONG Chao YU College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China College of liberal arts National University of Defense TechnologyChangsha 410073China school of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510275China

Unmanned Aerial Vehicles(UAVs)are useful in dangerous and dynamic tasks such as search-and-rescue,forest surveillance,and anti-terrorist *** tasks can be solved better through the collaboration of multiple UAVs under human ***,it is still difficult for human to monitor,understand,predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being *** this paper,the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent ***,an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources,execution time,social rules and ***,a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and ***,a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s *** results demonstrate the effectiveness of the proposed methods.

关键词： Coactive design Deep reinforcement learning Human-robot teamwork Mixed-initiative Multi-agent system Task planning UAV

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

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Computers, Materials & Continua 2020年第10期65卷 387-404页

作者： Xinyue Lin Jin Liu Jianming Zhang Se-Jung Lim College of Information Engineering Shanghai Maritime UniversityShanghai201306China school of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China liberal arts&Convergence Studies Honam UniversityGwangju62399Korea

Neural Machine Translation(NMT)is an end-to-end learning approach for automated translation,overcoming the weaknesses of conventional phrase-based translation *** NMT based systems have gained their popularity in commercial translation applications,there is still plenty of room for *** the most popular search algorithm in NMT,beam search is vital to the translation ***,traditional beam search can produce duplicate or missing translation due to its target sequence selection *** to alleviate this problem,this paper proposed neural machine translation improvements based on a novel beam search evaluation *** we use reinforcement learning to train a translation evaluation system to select better candidate words for generating *** the experiments,we conducted extensive experiments to evaluate our *** corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our *** experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.

关键词： Neural machine translation beam search reinforcement learning

Low-intensity treadmill exercise and/or bright light promote neurogenesis in adult rat brain

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Neural Regeneration Research 2013年第10期8卷 922-929页

作者： Sung Jin Kwon Jeongsook Park So Yun Park Kwang Seop Song Sun Tae Jung So Bong Jung Ik Ryeul Park Wan Sung Choi Sun Ok Kwon Department of Physical Education Gyeongsang National University Department of Anatomy and Neurobiology Medical Research Center for Neural DysfunctionInstitute of Health ScienceSchool of MedicineGyeongsang National University Department of liberal arts Gyeongnam National University of Science and Technology

The hippocampus is a brain region responsible for learning and memory functions. The purpose of this study was to investigate the effects of low-intensity exercise and bright light exposure on neurogenesis and brain-derived neurotrophic factor expression in adult rat hippocampus. Male Sprague-Dawley rats were randomly assigned to control, exercise, light, or exercise ＋ light groups （n = 9 per group）. The rats in the exercise group were subjected to treadmill exercise （5 days per week, 30 minutes per day, over a 4-week period）, the light group rats were irradiated （5 days per week, 30 minutes per day, 10 000 Ix, over a 4-week period）, the exercise ＋ light group rats were subjected to treadmill exercise in combination with bright light exposure, and the control group rats remained sedentary over a 4-week period. Compared with the control group, there was a significant increase in neurogenesis in the hippocampal dentate gyrus of rats in the exercise, light, and exercise ＋ light groups. Moreover, the expression level of brain-derived neurotrophic factor in the rat hippocampal dentate gyrus was significantly higher in the exercise group and light group than that in the control group. Interestingly, there was no significant difference in brain-derived neurotrophic factor expression between the control group and exercise ＋ light group. These results indicate that low-intensity treadmill exercise （first 5 minutes at a speed of 2 m/min, second 5 minutes at a speed of 5 m/min, and the last 20 minutes at a speed of 8 m/min） or bright-light exposure therapy induces positive biochemical changes in the brain. In view of these findings, we propose that moderate exercise or exposure to sunlight during childhood can be beneficial for neural development.

关键词： neural regeneration neurogenesis neurorehabilitation exercise bright light brain-derivedneurotrophic factor hippocampus childhood grants-supported paper neuroregeneration

Mechanism of Digital Business Model Innovation Driven by Big Data in Manufacturing Enterprises

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Frontiers of Business Research in China 2024年第2期18卷 95-120页

作者： SUN Xinbo WANG Haochong ZHANG Yuan HE Jiandu school of Business Administration Northeastern UniversityShenyang 110167China

Based on a data-driven theoretical approach,this study utilizes Qingdao Internet of Food Co.,Ltd.(loF)as a study case to explore the mechanism of digital business model innovation within manufacturing *** a dynamic logic framework of"data-driven processes-dynamic capability enhancement-digital business model innovation,"this paper elaborates the process of digital business model innovation using the Grounded *** findings indicate that realizing digital business model innovation is contingent upon the effective utilization of organizational data ***-driven processes are crucial driving factors for digital business model innovation,unfolding through three critical stages:demand source identification,scenario chain linkage,and network sharing and *** data-driven approaches significantly influence and enhance organizational dynamic capabilities,supporting the intermediary mechanism of data-driven processes as the antecedents to optimize the components of an enterprise's digital business *** a holistic generative perspective,digital business model innovation is realized through a dynamic process encompassing three dimensions:value proposition alignment,scenario experience optimization,and value network *** study aims to offer theoretical insights and practical suggestions for business model innovation in the digital economy era.

关键词： data-driven dynamic capability business model innovation digital business model case study

Parameters Compressing in Deep Learning

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Computers, Materials & Continua 2020年第1期62卷 321-336页

作者： Shiming He Zhuozhou Li Yangning Tang Zhuofan Liao Feng Li Se-Jung Lim school of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on TransportationChangsha University of Science and TechnologyChangsha410114China liberal arts&Convergence Studies Honam UniversityGwangju62399Korea

With the popularity of deep learning tools in image decomposition and natural language processing,how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be *** parameters are huge and can be as many as *** present,a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage *** methods include matrix decomposition and tensor *** let vector take advance of the compressing performance of matrix decomposition and tensor decomposition,we use reshaping and unfolding to let vector be the input and output of Tensor-Factorized Neural *** analyze how reshaping can get the best compress *** to the relationship between the shape of tensor and the number of parameters,we get a lower bound of the number of *** take some data sets to verify the lower bound.

关键词： Deep neural network parameters compressing matrix decomposition tensor decomposition