255 research outputs found
Prime implicants for modularised non-coherent fault trees using binary decision diagrams
This paper presents an extended strategy for the analysis of complex fault trees. The method utilises simplification rules, which are applied to the fault tree to reduce it to a series of smaller subtrees, whose solution is equivalent to the original fault tree. The smaller subtree units are less sensitive to the basic event ordering during BDD conversion. BDDs are constructed for every subtree. Qualitative analysis is performed on the set of BDDs to obtain the prime implicant sets for the original top event. It is shown how to extract the prime implicant sets from complex and modular events in order to obtain the prime implicant sets of the original fault tree in terms of basic events
Birnbaumâs measure of component importance for noncoherent systems
Importance analysis of noncoherent systems is limited,
and is generally inaccurate because all measures of importance
that have been developed are strictly for coherent analysis.
This paper considers the probabilistic measure of component
importance developed by Birnbaum (1969). An extension of this
measure is proposed which enables noncoherent importance analysis.
As a result of the proposed extension the average number
of system failures in a given interval for noncoherent systems
can be calculated more efficiently. Furthermore, because Birnbaumâs
measure of component importance is central to many
other measures of importance; its extension should make the
derivation of other measures possible
Automatic Design of Switching Networks
This thesis develops a method for automatically selecting an optimum set of prime implicants of a Boolean function. The optimization algorithm is based on a minimum cost of mechanization of the simplified function. A FORTRAN IV computer program to implement this approach was written and is included as part of this thesis. This program was developed within the framework of an overall theory for the automation of the design of switching networks. A programing structure as well as the theory for the automation of design is given. Also included is an outline of further areas of study which would be worth exploring as an extension of the present work
Synthesis heuristics for large asynchronous sequential circuits
Many well-known synthesis procedures for asynchronous sequential circuits produce minimal or near-minimal results, but are practical only for very small problems. These algorithms become unwieldy when applied to large circuits with, for example, three or more input variables and twenty or more internal states. New heuristic procedures are described which permit the synthesis of very large machines. Although the resulting designs are generally not minimal, the heuristics are able to produce near-minimal solutions orders of magnitude more rapidly than the minimal algorithms. A method for specifying sequential circuit behavior is presented. Input-output sequences define submachines or modules. When properly interconnected, these modules form the required sequential circuit. It is shown that the waveform and interconnection specifications may easily be translated into flow table form. A large flow table simplification heuristic is developed. The algorithm may be applied to tables having hundreds of rows, and handles both normal and non-normal mode circuit specifications. Nonstandard state assignment procedures for normal, fundamental mode asynchronous sequential circuits are examined. An algorithm for rapidly generating large flow table internal state assignments is proposed. The algorithms described have been programmed in PL/1 and incorporated into an automated design system for asynchronous circuits; the system also includes minimum and near-minimum variable state assignment generators, a code evaluation routine, a design equation generator, and two Boolean equation simplification procedures. Large sequential circuits designed using the system illustrate the utility of the heuristic procedures --Abstract, pages ii-iii
Satisfiability-Based Algorithms for Boolean Optimization
This paper proposes new algorithms for the Binate Covering Problem (BCP), a well-known restriction of Boolean Optimization. Binate Covering finds application in many areas of Computer Science and Engineering. In Artificial Intelligence, BCP can be used for computing minimum-size prime implicants of Boolean functions, of interest in Automated Reasoning and Non-Monotonic Reasoning. Moreover, Binate Covering is an essential modeling tool in Electronic Design Automation. The objectives of the paper are to briefly review branch-and-bound algorithms for BCP, to describe how to apply backtrack search pruning techniques from the Boolean Satisfiability (SAT) domain to BCP, and to illustrate how to strengthen those pruning techniques by exploiting the actual formulation of BCP. Experimental results, obtained on representative instances indicate that the proposed techniques provide significant performance gains for a large number of problem instances
Analysis of minimization algorithms for multiple-valued programmable logic arrays
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.Proceedings of the 18th International Symposium on Multiple-Valued Logic, May 1988, pp. 226-236We compare the performance of three heuristic algorithms [3,6,13] for the minimization of
sum-of-products expressions realized by the newly
developed multiplevalued programmable logic arrays [9]. Heuristic methods are important because exact minimization is extremely time consuming. We compare the heuristics to the exact solution, showing that heuristic methods are reasonably close to minimal. We use as a basis of comparison the average number of product terms over a set of randomly generated functions. All three heuristics produce nearly the same average number of product terms. Although the averages are close, there is surprisingly little overlap among the set of functions where the best realization is achieved. Thus, there is a benefit to applying different heuristics and then choosing the best realization
Success factors in new land-based industries
Part of the changing structure of New Zealand agriculture and horticulture includes a move from traditional land uses to new land uses. Not all new land uses, however, become established industries. The research objective of this study was to focus on a wide range of new land-based industries and address the question of why some new industries succeed and why others do not. The research also introduces a relatively new method, the Qualitative Comparative Analysis method, which identifies critical factors in industry success in a way that combines the richness of case studies with the rigour of comparative analysis. Results will be of interest to primary producers seeking to learn from recent experience of new industries, and to policy-makers interested in promoting new land-based industries.Funding for this research was provided by the Foundation for Research, Science, and Technology, via Crop and Food Research under Contract No. C02810 and entitled New Crops
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