Distributed component object model

A component-based prototype for intrusion detection and response has been realized. It is based on Microsoft s component architecture, the Component Object Model (COM) and its extension, the Distributed Component Object Model (DCOM). 

Microsoft DCOM distributed component object model (2006),... 

COM has been further enhanced to operate across multiple platforms in a network environment and in a distributed manner. This is known a D-COM (distributed component object model), but even D-COM fails to address... 

DCOM (distributed component object model) is the ORB that Microsoft promotes. A DCOM is implemented on the Windows operating system, and the development of applications can be practiced by using a Microsoft development environment such as Visual Basic. 

DBMSs, see Database management systems DBR (drum-buffer-rope) scheduling, 558 DCOM (distributed component object model), 721... 

Discrimination, price, 681-682 Diseases, occupational. See also Occupational safety and health definition of, 1168-1170 descriptions of, 1167, 1169-1170 statistics related to, 1157, 1173-1174 Dispatching, 1723-1725 basic rules, 1723-1724 composite rules, 1724-1725 first-come-first-served (FCFS), 1511-1513 major activities of, 1770 notation used in modeling of, 1719-1722 rules, dispatching, 1511, 1513 Dispensing (solder paste), 425, 426 Display systems, virtual environment, 2502 Distance, computer screen viewing, 1197 Distance learning, 940 Distributed commerce model, 271-272 Distributed component object model (DCOM), 721... 

DCOM distributed component object model EDI electronic data interchange HTTP hypertext transfer protocol HOP internet inter-orb protocol JVM java virtual machine ORB object request broker SET secure electronic transaction TCP/IP transmission control protocol/in-ternet protocol... 

What is OPC This is another tool for system integration. OPC is open connectivity in industrial automation for interoperability supported by the creation and maintenance of open standards and specifications. OPC is a standardized interface for accessing process data. Object linking and embedding (OLE), component object model (COM)/distributed component object model (DCOM), was developed by Microsoft. When this is applied to the process control, OPC (OLE for process control) is developed. OPC is based on the Microsoft COM/DCOM standard and has been expanded according to the manufacturer s requirements. 

Microsoft Standard, which is Distributed Component Object Model (DCOM)... 

On a larger scale, more-complex models can be used to represent the types of whole systems or components and are usually shown pictorially. In an abstract model, the attributes and their types are chosen to help specify the operations on the component as a whole and, according to good object-oriented analysis practice, are based on a model of the domain. However, anyone who has been involved in practical OOD is aware that the design phase introduces all sorts of extra classes as patterns are applied to help generalize the design, make it more efficient, distribute the design, provide persistence or a GUI, and so on. But we can still retrieve the abstract model from any tme implementation in the same way as for the simpler models. 

Consideration of the expected value of profit alone as the objective function, which is characteristic of the classical stochastic linear programs introduced by Dantzig (1955) and Beale (1955), is obviously inappropriate for moderate and high-risk decisions under uncertainty since most decision makers are risk averse in facing important decisions. The expected value objective ignores both the risk attribute of the decision maker and the distribution of the objective values. Hence, variance of each of the random price coefficients can be adopted as a viable risk measure of the objective function, which is the second major component of the MV approach adopted in Risk Model I. 

In SIMCA the distribution of the object in the inner model space is not considered, so the probability density in the inner space is constant and the overall PD appears as shown in Figs. 29, 30 for the enlarged and reduced SIMCA models. In CLASSY, Kernel estimation is used to compute the PD in the inner model space, whereas the errors in the outer space are considered, as in SIMCA, uncorrelated and with normal multivariate distribution, so that the overall distribution, in the inner and outer space of a one-dimensional model, looks like that reported in Fig. 31. Figures 32, 33 show the PD of the bivariate normal distribution and Kernel distribution (ALLOC) for the same data matrix as used for Fig. 31. Although in the data set of French wines no really important differences have been detected between SIMCA (enlarged model), ALLOC and CLASSY, it seems that CLASSY should be chosen when the number of objects is large and the distribution on the components of the inner model space is very different from a rectangular distribution. 

PLS is related to principal components analysis (PCA) (20), This is a method used to project the matrix of the X-block, with the aim of obtaining a general survey of the distribution of the objects in the molecular space. PCA is recommended as an initial step to other multivariate analyses techniques, to help identify outliers and delineate classes. The data are randomly divided into a training set and a test set. Once the principal components model has been calculated on the training set, the test set may be applied to check the validity of the model. PCA differs most obviously from PLS in that it is optimized with respect to the variance of the descriptors. 

In this chapter focus has been given to the derivation and application of the so-called Langmuir-Hinshel-wood-Hougen-Waston models to describe catalyzed reactions. In spite of objections that can be made against their underlying assumptions these expressions have been proved to be quite successful. An important reason is probably the fact that it contains intrinsically a capacity, a limited number of catalytically active centers where the reaction takes place and which are distributed among the different adsorbing components. 

Exploratory data analysis is a collection of techniques that search for structure in a data set before calculating any statistic model [Krzanowski, 1988]. Its purpose is to obtain information about the data distribution, about the presence of outliers and clusters, and to disclose relationships and correlations between objects and/or variables. Principal component analysis and cluster analysis are the most well-known techniques for data exploration [Jolliffe, 1986 Jackson, 1991 Basilevsky, 1994]. 

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