地震属性对云质岩储层识别的探讨

由于油气预探阶段钻井相对较少,地震属性分析技术能弥补此阶段钻、测井资料不足的劣势,其在储集性能评价和含油气性评价中起了重要作用。本文应用地震属性分析技术对新疆某地区风城组云质岩储层进行识别,并通过已钻井进行对比分析,从而确定研究工作的可靠性,对该地区储层预测工作具有指导意义和应用价值。     

可信网络中基于动态信息增益的多维属性决策*

综合考虑多重信任关系,将分类思想应用于可信网络多维决策属性下的服务授权问题,提出一种基于动态信息增益的多维属性信任决策模型。采用信息熵描述交易样本及各决策属性对服务授权级别的不确定性程度,采用信息增益描述决策属性的信息量。基于信息增益设置各决策属性权值。使用滑动窗口机制对各属性信息增益和权值动态调节。算法分析表明,所提出的总体信任度计算模型既符合人类信任决策对各种因素依赖程度的差异性,又能够随网络节点行为的动态变化自适应地进行调节。     

一种针对C程序缓冲区溢出的检测方法*

为了增强对程序缓冲区溢出漏洞的检测,提出一种利用CCured和BLAST对C程序进行分析的检测方法。首先利用CCured对C语言源程序进行运行时检测的代码插桩;然后用BLAST提供的自定义安全属性语言对这些插桩代码进行相关约束描述;最后让BLAST根据约束描述文件对代码插桩后的程序进行模型检测,就可以尽可能地找出C语言程序中潜在的缓冲区溢出漏洞。     

产品总体方案评价与优选的证据推理决策

产品总体方案评价与优选决策是产品设计过程中的重要步骤,它是决定总体设计之后进行详细设计的关键环节。考虑产品总体方案评价与选择决策过程中不确定、不完全主观判断的混合多属性决策问题,提出基于证据推理的决策模型;对决策属性中定量属性的证据处理以及不同定性属性不同辨识框下的情况进行了讨论。最后给出了实例。     

Learning from examples with unspecified attribute values

A challenging problem within machine learning is how to make good inferences from data sets in which pieces of information are missing. While it is valuable to have algorithms that perform well for specific domains, to gain a fundamental understanding of the problem, one needs a “theory” about how to learn with incomplete data. The important contribution of such a theory is not so much the specific algorithmic results, but rather that it provides good ways of thinking about the problem formally. In this paper we introduce the unspecified attribute value (UAV) learning model as a first step towards a theoretical framework for studying the problem of learning from incomplete data in the exact learning framework.In the UAV learning model, an example x is classified positive (resp., negative) if all possible assignments for the unspecified attributes result in a positive (resp., negative) classification. Otherwise the classification given to x is “?” (for unknown). Given an example x in which some attributes are unspecified, the oracle UAV-MQ responds with the classification of x. Given a hypothesis h, the oracle UAV-EQ returns an example x (that could have unspecified attributes) for which h(x) is incorrect.We show that any class of functions learnable in Angluin’s exact model using the MQ and EQ oracles is also learnable in the UAV model using the MQ and UAV-EQ oracles as long as the counterexamples provided by the UAV-EQ oracle have a logarithmic number of unspecified attributes. We also show that any class learnable in the exact model using the MQ and EQ oracles is also learnable in the UAV model using the UAV-MQ and UAV-EQ oracles as well as an oracle to evaluate a given boolean formula on an example with unspecified attributes. (For some hypothesis classes such as decision trees and unate formulas the evaluation can be done in polynomial time without an oracle.) We also study the learnability of a universal class of decision trees under the UAV model and of DNF formulas under a representation-dependent variation of the UAV model.     

基于VB技术的SolidWorks二次开发方法

介绍在VisualBasic环境中进行SolidWorks软件的二次开发。重点介绍SolidWorks对象的属性和使用方法，并给出了实例。     

Energy Optimization Oriented Three-Way Clustering Algorithm for Cloud Tasks

Cloud computing has developed as an important information technology paradigm which can provide on-demand services.Meanwhile,its energy consumption problem has attracted a growing attention both from academic and industrial communities.In this paper,from the perspective of cloud tasks,the relationship between cloud tasks and cloud platform energy consumption is established and analyzed on the basis of the multidimensional attributes of cloud tasks.Furthermore,a three-way clustering algorithm of cloud tasks is proposed for saving energy.In the algorithm,first,the cloud tasks are classified into three categories according to the content properties of the cloud tasks and resources respectively.Next,cloud tasks and cloud resources are clustered according to their computation characteristics (e.g.computation-intensive,data-intensive).Subsequently,greedy scheduling is performed.The simulation results show that the proposed algorithm can significantly reduce the energy cost and improve resources utilization,compared with the general greedy scheduling algorithm.     

K-Harmonic means type clustering algorithm for mixed datasets

K-means type clustering algorithms for mixed data that consists of numeric and categorical attributes suffer from cluster center initialization problem. The final clustering results depend upon the initial cluster centers. Random cluster center initialization is a popular initialization technique. However, clustering results are not consistent with different cluster center initializations. K-Harmonic means clustering algorithm tries to overcome this problem for pure numeric data. In this paper, we extend the K-Harmonic means clustering algorithm for mixed datasets. We propose a definition for a cluster center and a distance measure. These cluster centers and the distance measure are used with the cost function of K-Harmonic means clustering algorithm in the proposed algorithm. Experiments were carried out with pure categorical datasets and mixed datasets. Results suggest that the proposed clustering algorithm is quite insensitive to the cluster center initialization problem. Comparative studies with other clustering algorithms show that the proposed algorithm produce better clustering results.     

Physicochemical and sensory characteristics of soya protein isolate hydrolysates with added substrate‐like amino acids

Soya protein isolate (SPI) with or without added substrate‐like amino acid was subject to enzymatic hydrolysis catalysed by commercial proteases (Alcalase 2.4 L, flavourzyme and pancreatin). Addition of a small amount of amino acids (amino acid: SPI = 1: 2500, mol g^?1) during hydrolysis would cause a significantly (P < 0.05) reduced protein recovery, increased degree of hydrolysis, and altered amino acid composition and antioxidant activities of SPI hydrolysates. The SPI hydrolysates prepared with added Asp, Arg or Lys exhibited a higher antioxidant activity than the control. The bitterness of SPI hydrolysates was largely reduced upon addition of Met, Asp or Glu during hydrolysis, whilst the umami taste and mouthfeel‐liking were remarkably increased. Therefore, adding amino acid during hydrolysis is a feasible and beneficial approach to improve both the functional and sensory properties of SPI hydrolysate.     

Microwave-Assisted Pulse-Spouted Vacuum Drying of Apple Cubes

Microwave-assisted pulse-spouted vacuum drying (MPSVD) of apple cubes was examined in a laboratory-scale apparatus. Aside from the drying time, structural and textural properties of the dried cube were measured. Results are compared with alternative drying techniques developed earlier in our laboratory. These include microwave-spouted bed drying (MSBD), microwave vacuum drying (MVD), and conventional vacuum drying (VD). Comparison is made in terms of the key quality parameters, viz. color, texture, apparent density, rehydration property, and sensory evaluation. Over the range of operating conditions tested, MPSVD apple cubes had the best color and significantly highest sensory evaluation score.