Organizational maintenance manual : automatic data processor equipment maintenance, guided missile air defense system AN/TSQ-73.

Includes index.

Compliance with standards, abatement of violations and effectiveness of OSHA safety inspections : results of an analysis of OSHA management information system data /

Bibliography: p. 52.

Arizona mineral-resource data : information available through the U.S. Geological Survey Mineral Resource Data System /

Mode of access: Internet.

Popland : a family planning client record data system /

Mode of access: Internet.

National automotive sampling system : crashworthiness data system, 1992-1994.

"DOT HS 808 538"--P. [4] of cover.

Phase-equilibrium data and liquidus for the system "MnO"-CaO-Al2O3-SiO2 at Al2O3/SiO2 of 0.55 and 0.65

Phase-equilibrium data and liquidus isotherms for the system MnO-CaO-(Al2O3 + SiO2) at silicomanganese alloy saturation have been determined in the temperature range of 1373 to 1723 K. The results are presented in the form of the pseudoternary sections MnO-CaO-(Al2O3 + SiO2) with Al2O3/SiO2 weight ratios of 0.55 and 0.65. The primary-phase fields have been identified in this range of conditions.

Development of a quasi-chemical viscosity model for fully liquid slags in the Al2O3-CaO-'FeO'-MgO-SiO2 system. Part 2. A review of the experimental data and the model predictions for the Al2O3-CaO-MgO, CaO-MgO-SiO2 and Al2O3-MgO-SiO2 systems

A structurally-based quasi-chemical viscosity model has been developed for the Al2O3 CaO-'FeO'-MgO-SiO2 system. The model links the slag viscosity to the internal structure of melts through the concentrations of various anion/cation Si0.5O, Me2/nn+O and Me1/nn+Si0.25O viscous flow structural units. The concentrations of structural units are derived from the quasi-chemical thermodynamic model. The focus of the work described in the present paper is the analysis of experimental data and the viscosity models for fully liquid slags in the Al2O3-CaO-MgO, Al2O3 MgO-SiO2 and CaO-MgO-SiO2 systems.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

DVMS 1.5: A user manual (the data visualization and modeling system)

The data available during the drug discovery process is vast in amount and diverse in nature. To gain useful information from such data, an effective visualisation tool is required. To provide better visualisation facilities to the domain experts (screening scientist, biologist, chemist, etc.),we developed a software which is based on recently developed principled visualisation algorithms such as Generative Topographic Mapping (GTM) and Hierarchical Generative Topographic Mapping (HGTM). The software also supports conventional visualisation techniques such as Principal Component Analysis, NeuroScale, PhiVis, and Locally Linear Embedding (LLE). The software also provides global and local regression facilities . It supports regression algorithms such as Multilayer Perceptron (MLP), Radial Basis Functions network (RBF), Generalised Linear Models (GLM), Mixture of Experts (MoE), and newly developed Guided Mixture of Experts (GME). This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install & use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.