• Journal of applied mathematics & informatics
• /
• v.35 no.5_6
• /
• pp.623-649
• /
• 2017

Retrial queues have been widely used to model the many practical situations arising from telephone systems, telecommunication networks and call centers. An approximation method for a simple Markovian retrial queue by reducing the two dimensional problem to one dimensional problem was presented by Fredericks and Reisner in 1979. The method seems to be a promising approach to approximate the retrial queues with complex structure, but the method has not been attracted a lot of attention for about thirty years. In this paper, we exposit the method in detail and show the usefulness of the method by presenting the recent results for approximating the retrial queues with complex structure such as multi-server retrial queues with phase type distribution of retrial time, impatient customers with general persistent function and/or multiclass customers, etc.

• International Journal of Contents
• /
• v.10 no.3
• /
• pp.41-46
• /
• 2014

A cause-effect graph considers only the desired external behavior of a system by identifying input-output parameter relationships in the specification. When testing a software system with cause-effect graphs, it is important to derive a moderate number of tests while avoiding loss in fault detection ability. Pairwise testing is known to be effective in determining errors while considering only a small portion of the input space. In this paper, we present a new testing technique that generates pairwise tests from a cause-effect graph. We use a Boolean Satisbiability (SAT) solver to generate pairwise tests from a cause-effect graph. The Alloy language is used for encoding the cause-effect graphs and its SAT solver is applied to generate the pairwise tests. Using a SAT solver allows us to effectively manage constraints over the input parameters and facilitates the generation of pairwise tests, even in the situations where other techniques fail to satisfy full pairwise coverage.

• Proceedings of the KSR Conference
• /
• 2004.06a
• /
• pp.1374-1378
• /
• 2004

Railway conflict detection and resolution problem (RCDRP) involves complicated field constraints that should be considered for practical service. In this paper, we address those constraints in brief. Particularly, following situations are addressed; (1) temporal change of network topology, (2) consideration of diverse conditions of track and train, for example, single/double tracks and passenger/freight service, (3) siding capacity limitation, (4) bidirectional sides used by both inbound and outbound trains, (5) regulation for passenger transfer service, (6) consideration of siding length, (7) Restriction on stopping before the track segment with steep slope.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.26-29
• /
• 2001

In a target tracking problem the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In most nonlinear situations an Extended Robust Kalman Filter(ERKF) can yield acceptable performance as long as the noises are white Gaussian. However, an Extended Robust $H_{\infty}$ Filter (ERHF) can yield acceptable performance when the noises are Laplacian. In this paper, we use the Interacting Multiple Model(IMM) estimator for the problem of target tracking with glint noise. In the IMM method, two filters(ERKF and ERHF) are used in parallel to estimate the state. Computer simulations of a real target tracking shows that hybrid filter used the IMM algorithm has superior performance than a single type filter.

• Asia pacific journal of information systems
• /
• v.2 no.1
• /
• pp.35-56
• /
• 1992

This paper proposes a synergism of neural networks (NN) and knowledge-based decision support system (KBDSS) to effectively design an intelligent strategic planning system. Since conventional KBDSS becomes inoperative partially or totally when problem deviates slightly from the expected problem-domain, a new DSS concept is needed for designing an effective strategic planning system, where strategic planning environment is usually turbulent and consistently changing. In line with this idea, this paper developes a NN-based DSS, named ConDSS, incorporating the generalization property of NN into its knowledge base. The proposed ConDSS was extensively operated in an experimentally designed environment with three models: BCG matrix, Growth/Gain matrix, and GE matrix. The results proved very promising when encountered with unforeseen situations in comparisons with conventional KBDSS.

• Proceedings of the KIEE Conference
• /
• 1997.11a
• /
• pp.109-112
• /
• 1997

A Discrete Event Dynamic System is a system whose states change in response to the occurrence of events from a predefined event set. A major difficulty in developing analytical results for the systems is the lack of appropriate modeling techniques. This paper proposes the use of Real-time Temporal Logic as a modeling tool for the modeling and control of fixed-time traffic control problem which by way of a DEDS. The Real-time Temporal Logic Frameworks is extended with a suitable structure of modeling hard real-time constraints. Modeling rules are developed for several specific situations. It is shown how the graphical model can be translated to a system of linear equations and constraints.

• Journal of Korean Institute of Industrial Engineers
• /
• v.23 no.1
• /
• pp.39-54
• /
• 1997

This paper presents four process models for machining processes : 1) an economical mathematical model of machining process, 2) a prediction model for surface roughness, 3) a decision model for fuzzy cutting conditions, and 4) a judgment model of machinability with automatic selection of cutting conditions. Each model was developed the economic machining, and these models were applied to theories widely studied in industrial engineering which are nonlinear programming, computer simulation, fuzzy theory, and neural networks. The results of this paper emphasize the human oriented domain of a nonlinear programming problem. From a viewpoint of the decision maker, fuzzy nonlinear programming modeling seems to be apparently more flexible, more acceptable, and more reliable for uncertain, ill-defined, and vague problem situations.

• Proceedings of the KIEE Conference
• /
• 1999.11b
• /
• pp.200-202
• /
• 1999

This paper describes the optimization problem of real power rescheduling and present an algorithm based linear programming for studying the load-shedding and generation reallocation problem when a portion of the transmission system is disabled and at power flow solution cannot be obtained for the overload of some lines. And in case initial is infeasible, solution could not be converge. So this paper gives an algorithm being lie infeasible quantities within limit. The paper describes a LP-based algorithm to obtain the solution in power dispatch related to overload situations in power system and it is easily extened under various objective. The optimization procedures is based in linear programming with bounded variables and use the multi-segment curve method for a objective function and the validity of the algorithm is verified with two examples : 10-bus system and 57-bus system.

• Proceedings of the KSR Conference
• /
• 2008.06a
• /
• pp.371-377
• /
• 2008

A design pattern is a general reusable solution to a commonly occurring problem in software design. A design pattern is not a finished design that can be transformed directly into code. It is a description or template for how to solve a problem that can be used in many different situations. Moreover, the name of pattern itself also can be a kind of common language among developers and patterns can be easily imported to various applications demanding similar requirements. In this paper, we present models of CBTC onboard system which follows ERTMS/ETCS specifications and present patterns applied to our system.

• Journal of Information Science Theory and Practice
• /
• v.1 no.1
• /
• pp.10-26
• /
• 2013

Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.