Exploration of TCM Masters Knowledge Mining

Traditional Chinese medicine （TCM） has a rich knowledge about human health and disease by its special way evolved along a very long history. As modern medicine is achieving much progress, arguments and disputes toward TCM never end. To avoid losing precious knowledge of living TCM masters, endeavors have been engaged to systematic collection of those knowledge of TCM masters, such as their growth experiences, effective practical cases toward diseases and typical therapeutic principles and methods. Knowledge mining methods have been expected to explore some useful or hidden patterns to unveil some mysteries of the TCM system. In the paper, some computerized methods are applied toward those collected materials about some living TCM masters in China mainland to show a different way of exposing essential ideas of those TCM masters by correspondence visualization which aims to help people understand TCM holistic views toward disease and body, and facilitate tacit knowledge transfer and sense-making of the essence of TCM. The work is one kind of qualitative meta-synthesis of TCM masters＇ knowledge.     

Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network

The manner and conditions of running the decision-making system with self-defense electronic jamming are given.After proposing the scenario of applying discrete dynamic Bayesian network to the decision making with self-defense electronic jamming,a decision-making model with self-defense electronic jamming based on the discrete dynamic Bayesian network is established.Then jamming decision inferences by the aid of the algorithm of discrete dynamic Bayesian network are carried on.The simulating result shows that this method is able to synthesize different targets which are not predominant.In this way,various features at the same time,as well as the same feature appearing at different time complement mutually;in addition,the accuracy and reliability of electronic jamming decision making are enhanced significantly.     

Krylov subspace method based on data preprocessing technology

The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest （SOI） component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom （DOF） of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.     

New model for load-sharing k-out-of-n: G system with different components

On the basis of capacity flow model, a new model is developed for a load-sharing k-out-of-n： G system consisting of different components, to describe the increase of the same component＇s failure rates under different loads. All components have exponential life distributions and are nonrepairable in the system. Reliability of load- sharing 2-out- of-3： G system is calculated and some special cases for the system are discussed. The calculation and discussions show that the model is right and practical.     

Coordinated Bayesian optimal approach for the integrated decision between electronic countermeasure and firepower attack

The coordinated Bayesian optimization algorithm (CBOA) is proposed according to the characteristics of the function independence, conformity and supplementary between the electronic countermeasure (ECM) and the firepower attack systems. The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome. Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations, but also maintain the population multiplicity, and enhance the algorithm performance. The simulation result confirms the algorithm validity.     

雷达大地回波反射和散射系数的测量

介绍了雷达大地回波特性的一种测量方法 ,包括测量系统框图和基于回波波形的数据处理方法。利用该方法得出了一种地面的反射系数和散射系数 ,该结果对雷达高度表的设计和其它地面反射和散射系数测量均具有参考价值     

一种复杂系统研究方法——基于多智能体的整体建模仿真方法

本文在对一般复杂系统进行研究分析的基础上,针对目前国内外的研究现状,提出了一种研究复杂系统的新方法－基于多智能体的整体建模仿真方法,对这种方法的操作流程和步骤进行了详细的说明,并介绍了支撑这种方法的ＳＷＡＲＭ仿真平台,最后指出了这种建模仿真方法在军事和经济系统中的应用前景。     

Margin optimization algorithm for digital subscriber lines based on particle swarm optimization*

The margin maximization problem in digital subscriber line (DSL) systems is investigated. The particle swarm optimization (PSO) theory is applied to the nonconvex margin optimization problem with the target power and rate constraints. PSO is a new evolution algorithm based on the social behavior of swarms, which can solve discontinuous, nonconvex and nonlinear problems efficiently. The proposed algorithm can converge to the global optimal solution, and numerical example demonstrates that the proposed algorithm can guarantee the fast convergence within a few iterations.     

The distributed representation for societal risk classification toward BBS posts

The risk classification of BBS posts is important to the evaluation of societal risk level within a period. Using the posts collected from Tianya forum as the data source, the authors adopted the societal risk indicators from socio psychology, and conduct document-level multiple societal risk classification of BBS posts. To effectively capture the semantics and word order of documents, a shallow neural network as Paragraph Vector is applied to realize the distributed vector representations of the posts in the vector space. Based on the document vectors, the authors apply one classification method KNN to identify the societal risk category of the posts. The experimental results reveal that paragraph vector in document-level societal risk classification achieves much faster training speed and at least 10% improvements of F-measures than Bag-of-Words. Furthermore, the performance of paragraph vector is also superior to edit distance and Lucene-based search method. The present work is the first attempt of combining document embedding method with socio psychology research results to public opinions area.     

System deployment optimization in architecture design

Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.