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.     

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.     

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.     

Synthesis of N-acetamide C60 nitride and its performance of optical limiting

N-acetamide C₆₀ nitride (N-acetamide-1,2-dihydro-1,2-aza-fullerene [60]) has been synthesized by the reaction of C₆₀ and azidoacetamide and initially characterized. Its mechanism was that the objective product is formed through intermediate triazoline. The computational study shows that N-nitride structure is thermodynamically more stable by using the semi-empirical AM1 SCF-MO method. The properties of optical limiting were tested by using double frequency Nd: YAG pulse laser system and showed that the optical limiting mechanism is reverse saturation absorption. At wavelength of 532 nm, compared with optical limiting for ns pulse, this new material has good optical limiting character for ps pulse. At wavelength of 707 nm, the capacity of optical limiting of the derivative is prior to C₆₀.     

水泥窑协同处置生活垃圾的燃烧特性分析优化

针对传统方法难以分析掺烧生活垃圾后的水泥窑复杂燃烧特性的问题,引入数据挖掘技术,以国内某水泥厂为对象,采集相关参数数据,使用稳定性选择算法分析各参数对煤耗与NO_x排量的影响系数,通过随机森林算法建立煤耗与NO_x排量的数学模型,结合K-means聚类算法得出关键优化参数及其最优值。结果表明,该方法能够建立精确的煤耗与NO_x排量模型,挖掘出节能减排的关键优化参数及其最优目标值。通过改善关键优化参数至最优值,能够大大降低煤耗与NO_x排量,可指导水泥厂优化窑内燃烧特性。     

基于参照点收益价值函数化的资产选择模型

诺贝尔经济学奖得主丹尼尔.卡尼曼及其合作者阿莫斯.特沃斯基的展望理论指出决策行为会受到价值函数形式的影响。本文首先介绍展望理论,给出若干具体的价值函数形式,在考虑投资者决策受价值函数影响的基础上建立基于对参照点收益价值函数转化的若干资产选择模型,从直觉上能够判断出这些模型更加符合投资决策心理和行为,而实证结果也表明这些资产选择模型能够有效地降低负离差风险。     

ISAR target recognition based on non-negative sparse coding

Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar (ISAR) target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding (NNSC) and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis (PCA) and non-negative matrix factorization (NMF) via simulations.