Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Deriving a dynamic programming algorithm for batch scheduling in the refinement calculus
Download
index.pdf
Date
2003
Author
Aktuğ, İrem
Metadata
Show full item record
Item Usage Stats
194
views
0
downloads
Cite This
Refinement Calculus is a formalization of stepwise program construction.In this approach a program is derived from its specification by applying refinement rules.The Refinement Calculator,developed at TUCS,Finland,provides tool support for the Refinement Calculus.This thesis presents a case study aiming to evaluate the applicability of the theory and the performance of the tool.The Refinement Calculator is used for deriving a dynamic progaramming algorithm for a single-machine batch scheduling problem.A quadratic algoritm is derived by refining a formal specification of this problem into executable code.The need for stronger support for relevant domain theories and abstraction mechanisms in the target language have been noted.
Subject Keywords
Computer programming
,
Computer logic
,
Electronic data processing
URI
http://etd.lib.metu.edu.tr/upload/1128511/index.pdf
https://hdl.handle.net/11511/13670
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Comparison of rough multi layer perceptron and rough radial basis function networks using fuzzy attributes
Vural, Hülya; Alpaslan, Ferda Nur; Department of Computer Engineering (2004)
The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties أlow...
Monte Carlo analysis of ridged waveguides with transformation media
Ozgun, Ozlem; Kuzuoğlu, Mustafa (Wiley, 2013-07-01)
A computational model is presented for Monte Carlo simulation of waveguides with ridges, by combining the principles of transformation electromagnetics and the finite methods (such as finite element or finite difference methods). The principle idea is to place a transformation medium around the ridge structure, so that a single and easy-to-generate mesh can be used for each realization of the Monte Carlo simulation. Hence, this approach leads to less computational resources. The technique is validated by me...
New montgomery modular multıplier architecture
Çiftçibaşı, Mehmet Emre; Yücel, Melek D; Department of Electrical and Electronics Engineering (2005)
This thesis is the real time implementation of the new, unified field, dualا radix Montgomery modular multiplier architecture presented by Savaş et al, for performance comparison with standard Montgomery multiplication algorithms. The unified field architecture operates in both GF(p) and GF(2n). The dual radix capability enables processing of two bits of the multiplier in every clock cycle in GF(2n) mode, while one bit of the multiplier is processed in GF(p) mode. The new architecture is implemented in a Xi...
Bayesian learning under nonnormality
Yılmaz, Yıldız Elif; Alpaslan, Ferda Nur; Department of Computer Engineering (2004)
Naive Bayes classifier and maximum likelihood hypotheses in Bayesian learning are considered when the errors have non-normal distribution. For location and scale parameters, efficient and robust estimators that are obtained by using the modified maximum likelihood estimation (MML) technique are used. In naive Bayes classifier, the error distributions from class to class and from feature to feature are assumed to be non-identical and Generalized Secant Hyperbolic (GSH) and Generalized Logistic (GL) distribut...
Implementation of an 8-bit microcontroller with system c
Kesen, Lokman; Aşkar, Murat; Department of Electrical and Electronics Engineering (2004)
In this thesis, an 8-bit microcontroller, 8051 core, is implemented using SystemC programming language. SystemC is a new generation co-design language which is capable of both programming software and describing hardware parts of a complete system. The benefit of this design environment appears while developing a System-on-Chip (SoC), that is a system consisting both custom hardware parts and embedded software parts. SystemC is not a completely new language, but based on C++ with some additional class libra...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
İ. Aktuğ, “Deriving a dynamic programming algorithm for batch scheduling in the refinement calculus,” M.S. - Master of Science, Middle East Technical University, 2003.
