Generation of the additional fluorescence radiation in the elastomeric shields used in computer tomography (CT)

Two commercially available (EP, Z) and eight new elastomeric composites (M1–M4, G1–G4, of thickness ≈1 mm) containing mixtures of differing proportions of heavy metal additives (Bi, W, Gd and Sb) have been synthesised and examined as protective shields. The intensity of the X-ray fluorescence radiation generated in the typical elastomeric shields for CT, containing Bi and other heavy metal additives influence on the practical shielding properties. A method for assessing the radiation shielding properties of elastomeric composites used in CT examination procedures via X-ray spectrometry has been proposed. To measure the radiation reduction ability of the protective shields, the dose reduction factor (DRF) has been determined. The lead equivalents for the examined composites were within the ranges of 0.046–0.128 and 0.048–0.130 mm for 122.1 and 136.5 keV photons, respectively. The proposed method, unlike to the common approach, includes a dose contribution from the induced X-ray fluorescence radiation of the heavy metal elements in the protective shields. The results clearly indicate that among the examined compositions, the highest values DRF have been achieved with preparations containing Bi+W, Bi+W+Gd and Bi+W+Sb mixtures with gradually decreasing content of heavy metal additives in the following order: Bi, W, Gd and Sb. The respective values of DRF obtained for the investigated composites were 21, 28 and 27 % dose reduction for a 1 mm thick shield and 39 and ~50 % for a 2 mm thick layer (M1–M4).     

Scheduling electric power production at a wind farm

We present a model for scheduling power generation at a wind farm, and introduce a particle swarm optimization algorithm with a small world network structure to solve the model. The solution generated by the algorithm defines the operational status of wind turbines for a scheduling horizon selected by a decision maker. Different operational scenarios are constructed based on time series data of electricity price, grid demand, and wind speed. The computational results provide insights into management of a wind farm.     

The scattering support

We discuss inverse problems for the Helmholtz equation at fixed energy, specifically the inverse source problem and the inverse scattering problem from a medium or an obstacle. We introduce the convex scattering support of a far field, a set which will be a subset of the convex hull of the support of any source which can produce it. We give several theorems which explain how to compute the convex scattering support and how to relate it to the actual support of a source, medium, or obstacle. © 2003 Wiley Periodicals, Inc.     

Mining Pareto-optimal modules for delayed product differentiation

This paper presents a framework for finding optimal modules in a delayed product differentiation scenario. Historical product sales data is utilized to estimate demand probability and customer preferences. Then this information is used by a multiple-objective optimization model to form modules. An evolutionary computation approach is applied to solve the optimization model and find the Pareto-optimal solutions. An industrial case study illustrates the ideas presented in the paper. The mean number of assembly operations and expected pre-assembly costs are the two competing objectives that are optimized in the case study. The mean number of assembly operations can be significantly reduced while incurring relatively small increases in the expected pre-assembly cost.     

Analysis of process models

Process modeling tools, such as the integrated definition (IDEF) methodology, allow for a systematic representation of processes in manufacturing, product development, and service applications. Most of the process modeling methodologies are based on informal notation, lack mathematical rigor, and are static and qualitative, and thus can be difficult to use for analysis. In this paper, a new analysis approach for process models based on signed directed graphs (SDGs) and fuzzy sets is presented. A membership function of fuzzy sets quantifies and transforms incomplete and ambiguous information of process variables into an SDG qualitative model. The effectiveness of the approach is illustrated with an industrial example. The architecture of an intelligent system for qualitative/quantitative analysis of process models is presented     

Feature transformation methods in data mining

The quality of knowledge extracted from a data set can be enhanced by its transformation. Discretization and filling missing data are the most common forms of data transformation. A new transformation method named feature bundling is introduced. A feature bundle involves a set of features in its pure or transformed form. The computational results reported in this paper show that the classification accuracy of decision rules generated from data sets with feature bundles is enhanced. The proposed concept of feature bundling is applied to a data set from the semiconductor industry     

Rough set theory: a data mining tool for semiconductormanufacturing

The growing volume of information poses interesting challenges and calls for tools that discover properties of data. Data mining has emerged as a discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decisionmaking. In this paper, the basic concepts of rough set theory and other aspects of data mining are introduced. The rough set theory offers a viable approach for extraction of decision rules from data sets. The extracted rules can be used for making predictions in the semiconductor industry and other applications. This contrasts other approaches such as regression analysis and neural networks where a single model is built. One of the goals of data mining is to extract meaningful knowledge. The power, generality, accuracy, and longevity of decision rules can be increased by the application of concepts from systems engineering and evolutionary computation introduced in this paper. A new rule-structuring algorithm is proposed. The concepts presented in the paper are illustrated with examples     

Computer integrated manufacturing: a structural perspective

The main components and functional areas of a typical computer-integrated manufacturing (CIM) system are examined. The database structures needed to support integrated industrial applications are discussed. Key issues in applying manufacturing communications are addressed. The impact of machine layout on manufacturing communication is considered     

Synthesis of modular mechatronic products: a testabilityperspective

Producing modular products that combine modules with the consideration of product performance, e.g., testability of electronic systems, is frequently stated as a design goal. However, most of mechatronic frameworks (models) discussed in the literature do not consider testability of electronic subsystems of mechatronic products. This paper assumes that the product modules have been established, and aims at the development of modular mechatronic products with the consideration of testability of electronic subsystems as a performance criterion. The generation of modular products and module testability issues are discussed. Testability points, testability values, and access paths for a module/system are crucial to the generation of modular mechatronic products. A generalized label-correcting algorithm is developed to determine the points of focus, testability values, and access paths in modules     

The part families problem in flexible manufacturing systems

Parts grouping into families can be performed in flexible manufacturing systems (FMSs) to simplify two classes of problems: long horizon planning and short horizon planning. In this paper the emphasis is on the part families problem applicable to the short horizon planning. Traditionally, parts grouping was based on classification and coding systems, some of which are reviewed in this paper. To overcome the drawbacks of the classical approach to parts grouping, two new methodologies are developed. The methodologies presented are very easy to implement because they take advantage of the information already stored in the CAD system. One of the basic elements of this system is the algorithm for solving the part families problem. Some of the existing clustering algorithms for solving this problem are discussed. A new clustering algorithm has been developed. The computational complexity and some of the computational results of solving the part families problem are also discussed.