基于超图理论的中医方剂网络药对挖掘方法

随着复杂网络研究的深入发展,利用复杂网络理论对中药药方数据进行建模分析已成为研究中药配伍规律的重要方法之一。然而,传统的“药-药”网络结构只基于“点-边”关系,无法体现方剂和中药之间的高阶交互关系,从而影响药对挖掘的准确率和效果。针对该问题,提出了一种基于超图的方药超网络建模方法来刻画方剂与中药之间的高阶关联关系,以方剂为超边、方剂中的中药为节点构建方药超网络,对比传统“药-药”网络和方药超网络的统计特性发现,方药超网络节点超度分布更加符合幂律分布特征,具有小世界和无标度特性,证明了方药超网络建模方法具有合理性和可行性;基于方药超网络提出一种融合多因素的双权重度中心性指标(double weight degree centrality,DWDC)挖掘方剂中的常见中药,引入超边属性对节点特性的影响,以网络的脆弱性和鲁棒性为依据,通过各算法之间的对比实验,验证所提出方法能够更有效筛选出方剂超网络中的常见中药;再将DWDC指标推广到二阶常见药对挖掘,提出高阶指标H-DWDC,基于真实数据集的实验结果表明,该方法能够有效地从海量数据中挖掘出常见药对。

基于集体离群点挖掘的城市交通异常检测研究

针对时空数据环境下的城市交通异常检测问题,文章提出一种基于集体离群点挖掘的“线下拟合-线上检测”一体化检测模型。该模型采用以距离-密度-权重为度量的改进聚类(distance-density-weight k-medoids,DDWK-medoids)算法,根据城市交通态势自适应确定交通枢纽点的数量和位置,通过对数据进行不同分辨率拟合,将交通流量异常检测与交通轨迹异常检测相结合。在该检测模型中,数据的时间属性与空间属性均未以数值的形式直接参与计算,有效降低了运算复杂度。实验结果表明,该模型算法对于不同规模数据集的处理速度均显著优于对比算法,尤其是对于样本充足的大规模数据集,检测的准确率更具有明显优势。

火工品装配工房危险源评估的初步探讨

本文着重讨论了适合火工品装配工房危险源评估的方法,并引入了权重度系数,提出了多品种火炸药同时操作工序定量评估可行方法,增强了火工品装配工房危险源评估的可行性和可靠性。

从自然语言向SPARQL语言映射的歧义消解算法

针对计算机各语言间的无岐义映射问题,提出一种从自然语言向SPARQL语言映射过程中的歧义消解算法.该算法基于自然语言的特征,拟合知识丰富程度和文本相似度消解实体映射过程中的歧义性,拟合语义权重度和文本相似度消解关系映射过程中的歧义性.实验结果表明,该算法效果较好.

3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.

Comprehensive evaluation method for superstructures of prestressed concrete girder bridges

A multi-level evaluation model for the superstructure of a damaged prestressed concrete girder or beam bridge is established, and the evaluation indices of the model as well as the rating standards are defined. A normal relative function about the evaluation indices of each element is developed to calculate the relative degree, and for each element there are no sub-level elements. When evaluating the elements in the sub-item level or the index level of the model, the weights of elements pertain to one adopted element, taking into account their degrees of deterioration. Since the relative degrees and structure evaluation scales on the damage conditions are applied to characterize the superstructure of damaged prestressed concrete girder bridges, this method can evaluate the prestressed structure in detail, and the evaluation results agree with the Code for Maintenance of Highway Bridges and Culvers （JTG Hll--2004 ）. Finally, a bridge in Jilin province is taken as an example, using the method developed to evaluate its damage conditions, which gives an effective way for bridge engineering.

APPROACH FOR FUZZY MULTI-ATTRIBUTE DECISION-MAKING WITH FUZZY COMPLEMENTARY PREFERENCE RELATION ON ALTERNATIVES

In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives （FPISA） and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment.

Scales,methods and technical analysis of China's residential environment evaluation

To improve China＇s residential environment evaluation system and enhance its guiding role, current research results are analyzed and summarized from three aspects including research scales, evaluation methods and applied technology by means of comparison, induction and empirical application. The guiding role of the current macro-scale evaluation system of urban planning and construction is generally not obvious, whereas the guiding role of medium and micro-scale systems to the improvement of residential environments is improving. There are diversified methods for determining the threshold values and the weights of indices in China＇s evaluation system. For instance, the analytic hierarchy process（AHP） method is adopted to determine the weights of indices. The advantages and disadvantages of the method are analyzed on the basis of empirical calculation. In the course of comprehensive analyses, a nonlinear model can reflect interactions among indices more than a linear model; the evaluation model under the ARCGIS platform prevails since it combines space and attribute, and it has intuitive results. So far, the methodological system of China＇s residential environment evaluation has not been established; its subject coverage and research category should be expanded, and its guiding role should be enhanced.

响水矿井煤与瓦斯突出的多层次模糊综合评价

针对响水矿井煤与瓦斯突出各因素的控制程度进行综合评价,结合影响煤与瓦斯突出各因素众多的特点,运用模糊数学理论,建立了响水矿井煤与瓦斯突出多层次模糊综合评价模型。采用事故致因理论从人、机、环境、管理的角度系统地分析了响水矿井煤与瓦斯突出事故的致因因素。考虑各类因素以及同类因素间的模糊性,计算出煤与瓦斯突出因素类及因素间权重度和隶属度,针对控制较为薄弱的因素采取行之有效的措施,防止煤与瓦斯突出事故的发生。

Some OWA operator weights determination methods with RIM quantifier

Based on the quantifier guided method,an ordered weighted averaging(OWA)weights generating method under given orness level with regular increasing monotone(RIM)quantifiers is proposed.Then the RIM quantifier based OWA weights generating method is modified to make the generated weights be monotonic,which can be used to express the decision maker's consistent preference information.Finally,both of these weights generating methods are extended to their generic forms,so that they can generate the OWA weights for any ordinary elements set with any given aggregated value.

Multi-attribute Decision Making Method Based on the Attribute Difference Degree and Its Application in Rice Breeding

[Objective]The aim was to establish a multi-attribute decision making method and introduce its application in rice breeding.[Method]Based on the defined closeness degree among attributes,the difference degrees among attributes were discussed.Furthermore,the weights of attributes were determined based on the difference degrees among the attributes.[Result]A multi-attribute decision making method based on difference degrees among attributes was established,the feasibility of applying it in rice breeding was also analyzed.[Conclusion]This study enriched the methods to determine attribute weights in multi-attribute decision making and provided the necessary theoretical support for selecting rice varieties scientifically and rationally.

Improved random noise attenuation using f-x empirical mode decomposition and local similarity

Conventional f-x empirical mode decomposition（EMD） is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events.However,when a seismic event is not horizontal,the use of f-x EMD is harmful to most useful signals.Based on the framework of f-x EMD,this study proposes an improved denoising approach that retrieves lost useful signals by detecting effective signal points in a noise section using local similarity and then designing a weighting operator for retrieving signals.Compared with conventional f-x EMD,f-x predictive filtering,and f-x empirical mode decomposition predictive filtering,the new approach can preserve more useful signals and obtain a relatively cleaner denoised image.Synthetic and field data examples are shown as test performances of the proposed approach,thereby verifying the effectiveness of this method.

Study on the safety production evaluation of the coal mine based on entropy-TOPSIS

In order to evaluate the safety production situation of the coal mine effectively,selected the indicators about mine production safety, applied entropy method to determinethe objective weight of each index, calculated the distance and close degree between themines and the ideal point and negative ideal point, and then evaluated the safety productionsituation of the mines according to the close degree.The results show that the methodprovides a reasonable, concise and more objective idea to evaluate the safety productionsituation of the mines.

Seismic velocity inversion based on CNN-LSTM fusion deep neural network

Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.

Evaluating gravity gradient components based on a reweighted inversion method

In gravity gradient inversion,to choose an appropriate component combination is very important,that needs to understand the function of each component of gravity gradient in the inversion.In this paper,based on the previous research on the characteristics of gravity gradient components,we propose a reweighted inversion method to evaluate the influence of single gravity gradient component on the inversion resolution The proposed method only adopts the misfit function of the regularized equation and introduce a depth weighting function to overcome skin effect produced in gravity gradient inversion.A comparison between different inversion results was undertaken to verify the influence of the depth weighting function on the inversion result resolution.To avoid the premise of introducing prior information,we select the depth weighting function based on the sensitivity matrix.The inversion results using the single-prism model and the complex model show that the influence of different components on the resolution of inversion results is different in different directions,however,the inversion results based on two kind of models with adding different levels of random noise are basically consistent with the results of inversion without noises.Finally,the method was applied to real data from the Vinton salt dome,Louisiana,USA.

Risk assessment model of tunnel water inrush based on improved attribute mathematical theory

Tunnel water inrush is one of the common geological disasters in the underground engineering construction.In order to effectively evaluate and control the occurrence of water inrush,the risk assessment model of tunnel water inrush was proposed based on improved attribute mathematical theory.The trigonometric functions were adopted to optimize the attribute mathematical theory,avoiding the influence of mutation points and linear variation zones in traditional linear measurement functions on the accuracy of the model.Based on comprehensive analysis of various factors,five parameters were selected as the evaluation indicators for the model,including tunnel head pressure,permeability coefficient of surrounding rock,crushing degree of surrounding rock,relative angle of joint plane and tunnel section size,under the principle of dimension rationality,independence,directness and quantification.The indicator classifications were determined.The links among measured data were analyzed in detail,and the objective weight of each indicator was determined by using similar weight method.Thereby the tunnel water inrush risk assessment model is established and applied in four target segments of two different tunnels in engineering.The evaluation results and the actual excavation data agree well,which indicates that the model is of high credibility and feasibility.

Gray weighted algorithm for variable voltage CT reconstruction

In conventional computed tomography （CT） reconstruction based on fixed voltage, the projective data often ap- pear overexposed or underexposed, as a result, the reconstructive results are poor. To solve this problem, variable voltage CT reconstruction has been proposed. The effective projective sequences of a structural component are obtained through the variable voltage. The total variation is adjusted and minimized to optimize the reconstructive results on the basis of iterative image using algebraic reconstruction technique （ART）. In the process of reconstruction, the reconstructive image of low voltage is used as an initial value of the effective proiective reconstruction of the adjacent high voltage, and so on until to the highest voltage according to the gray weighted algorithm. Thereby the complete structural information is reconstructed. Simulation results show that the proposed algorithm can completely reflect the information of a complicated structural com- ponent, and the pixel values are more stable than those of the conventional.

Fuzzy Ontology Construction Based on Incomplete Knowledge

There exists widely incomplete knowledge all over the world, but incomplete knowledge still cannot be dealt with in the process of ontology construction. Hence, a method for fuzzy ontology construction based on incomplete knowledge is proposed. First, the calculation principle of the attribute weight of the ontology concept is presented, and the calculation function of the attribute weight is derived through experiments. Then, the membership degree of the incomplete individual to the concept is computed. Finally, the incomplete individual is classified according to the principle of the variable precision rough set model. The experimental results show that the average precision of the classification of the incomplete individuals is 81.7% when the common attributes are omitted and that it is difficult to classify the incomplete individuals correctly when the private attributes are omitted. This method is significant for handling incomplete knowledge in the process of ontology construction.

The Degree of Association between Concepts Focusing on the Viewpoint

For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this association function, it is necessary to quantify the degree of association between two concepts. In the present paper, we propose a method for quantifying degree of association focusing on the viewpoint that uses a concept base （a knowledge base that expresses concepts as a collection of pairs, each pair consisting of an attribute word used to describe the concept and a weighting that expresses the word＇s importance）. Here, ＂Viewpoint＂ is the perspective from which a concept is viewed; for example, consider the degree of association between ＂airplane＂ and ＂automobile＂, and the degree of association between ＂airplane＂ and ＂bird＂. From the viewpoint of ＂vehicle＂, ＂airplane＂ and ＂automobile＂ are highly related, while from the viewpoint of ＂flight＂, ＂airplane＂ and ＂bird＂ are highly related. We present herein a comparison of two methods for calculating degree of association focusing on the viewpoint, and demonstrate that the method involving modulation of attribute weightings based on viewpoint results in degree of association calculations that are closer to human senses.

The Equivalence Theorem of Appoximation for Two-dimensional Baskakov Operators

In this paper, we study the convergence rate of two-dimensional Baakakov operators with Jacobi-weights and the approximation equivalence theorem is obtained, making use of multivariate decompose skills and results of one-dimensional Baskakov operators.