• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2468-2483
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• 2017

The construction of completely random sensing matrices of Compressive Sensing requires a large number of random numbers while that of deterministic sensing operators often needs complex mathematical operations. Thus both of them have difficulty in acquiring large signals efficiently. This paper focuses on the enhancement of the practicability of the structurally random matrices and proposes a semi-deterministic sensing matrix called Partial Kronecker product of Identity and Hadamard (PKIH) matrix. The proposed matrix can be viewed as a sub matrix of a well-structured, sparse, and orthogonal matrix. Only the row index is selected at random and the positions of the entries of each row are determined by a deterministic sequence. Therefore, the PKIH significantly decreases the requirement of random numbers, which has a complex generating algorithm, in matrix construction and further reduces the complexity of sampling. Besides, in order to process large signals, the corresponding fast sampling algorithm is developed, which can be easily parallelized and realized in hardware. Simulation results illustrate that the proposed sensing matrix maintains almost the same performance but with at least 50% less random numbers comparing with the popular sampling matrices. Meanwhile, it saved roughly 15%-35% processing time in comparison to that of the SRM matrices.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.128-132
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• 2011

Least mean square adaptive algorithms have played important role in radar, sonar, speech processing, and mobile communication. In mobile communication area, the convergence rate of a LMS algorithm is quite important. However, LMS algorithms have slow and non-uniform convergence rate problem For overcoming these shortcomings, various variable step LMS adaptive algorithms have been studied in recent years. Most of these recent LMS algorithms have used complex variable step methods to get a rapid convergence. But complex variable step methods need a high computational complexity. Therefore, the main merits such as the simplicity and the robustness in a LMS algorithm can be eroded. The proposed deterministic variable step LMS algorithm is based upon a simple deterministic function for the step update so that the simplicity of the proposed algorithm is obtained and the fast convergence is still maintainable.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.401-410
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• 2009

In order to derive hidden information (concept subsumption, concept satisfiability and realization) of OWL ontology, a number of OWL reasoners have been introduced. The most of these ontology reasoners were implemented using the tableau algorithm. However most reasoners simply report this information without providing a justification for any arbitrary entailment and unsatisfiable concept derived from OWL ontologies. The purpose of this paper is to investigate an optimized method for non-deterministic rule of the tableau algorithm and finding axioms to cause inconsistency in ontology. In this paper, therefore, we propose an optimized method for non-deterministic rule and finding axiom to cause inconsistency using NATMS. In the first place, we introduce Dependency Directed Backtracking to deal non-deterministic rule, a tableau-based decision procedure to find unsatisfiable axiom Furthermore we propose an improved method adapting NATMS.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.455-462
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• 2001

In this paper, we propose a deterministic algorithm to solve the resource discovery problem, that is, some subset of machines to learn the existence of each other in a large distributed network. Harchol et al. proposed a randomized algorithm solving this problem within O($log^2\;n$) rounds with high probability, which requires O($nlog^2\;n$) connection communication complexity and O($n^2log^2\;n$) pointer communication complexity, where n is the number of machines in the network. His solution is based on randomization method and it is difficult to determine convergence time. We propose an efficient algorithm which improve performance and the non-deterministic characteristics. Our algorithm requires O(log n) rounds which shows O(mlog n) connection communication complexity and O($n^2log\;n$) pointer communication complexity, where m is the number of links in the network.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2946-2953
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• 1999

Finite automation is a mathematical model to represent a system with discrete inputs and outputs. Finite automata are a useful tool for solving problems such as text editor, lexical analyzer, and switching circuit. In this paper, given a deterministic finite automaton of an input string of length n and m states, we propose a constant time parallel algorithm that represents the transition states of finite automata and determines the acceptance of an input string on a reconfigurable mesh of size [nm/2]$\times$2m.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1600-1604
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• 2010

This paper presents the optimum design of the brushless motor to maximize the output power per weight considering the design parameter tolerance. The optimization is proceeded by commercial software that is adopted the scatter-search algorithm and the characteristic analysis is conducted by FEM. The stochastic optimum design results are compared with those of the deterministic optimization method. We can verify that the results of the stochastic optimization is superior than that of deterministic optimization.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.693-703
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• 2001

In this paper we consider non-deterministic finite Rabin-Scott’s automata. We use a special structure to descibe all the possible edges of non-determinstic finite automaton defining the given regular language. Such structure can be used for solving various problems of finite automata theory. One of these problems is edge-minimization of non-deterministic automata. As we have not touched this problem before, we obtain here two versions of the algorithm for solving this problem to continue previous series of articles.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.13-22
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• 2000

To apply the queueing network theory in evaluating performances of flexible manufacturing systems, it is generally assumed that the processing times are distributed exponentially. However, in FMS, processing times are usually deterministic. In this study the performance measures of FMS are approximated under the assumption that processing times are usually deterministic. Multi-classes of parts and single server and multi-server stations are considered in the model. This study also that, in the numerical example, this approach yields better solutions than those obtained by the pure Linearizer algorithm, when the processing times are deterministic.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.7
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• pp.448-453
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• 2006

We extend a parallel depth-first search (DFS) algorithm for planar graphs to deal with (non-planar) solid grid graphs, a subclass of non-planar grid graphs. The proposed algorithm takes time O(log n) with $O(n/sqrt{log\;n})$ processors in Priority PRAM model. In our knowledge, this is the first deterministic NC algorithm for a non-planar graph class.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.301-302
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• 2007

A speech enhancement algorithm based on both a decomposition of speech into deterministic and stochastic components and a psychoacoustic model is proposed. Noisy speech is decomposed into deterministic and stochastic components, and then each component is enhanced preserving its individual characteristics. A psychoacoustic model is taken into account when enhancing the stochastic component. Simulation results show that the proposed algorithm performs better than some of the more popular algorithms.