• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.38
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• pp.131-138
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• 1996

In real world, the organization has multiple objects. Inorder to solve the multiple objects, we present the goal programming for solving project selection problem we also developed expert system which is focused on function of analysis. User which doesn't have knowledge of goal programming can solve the project selection problem and get a result of analysis.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.131-145
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• 1993

This paper deals with scheduling problem of the chemical process system where aircraft parts go through a given sequence of tanks filled with chemical solutions. The system has two hoists which move carriers holding the parts between tanks. A mixed integer programming model is developed from which a maximum throughput schedule can be found for the hoist movements. To show the validity of the model, a real world problem is solved and the results are compared with those with an existing approach.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.9-20
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• 2019

In this paper, we study the university timetabling problem, which consists of two subproblems, the university course timetabling problem and the examination timetabling problem. Given a set of classrooms, students, teachers, and lectures, the problem is to assign a number of courses (and examinations) to suitable timeslots and classrooms while satisfying the given set of constraints. We discuss the modeling and solution approaches to construct course and examination timetables for one of the largest Korean university. By using binary integer programming formulations, we describe these two complex real-world problems. Then, we propose a solution method, called NOGOOD, to solve the examination timetabling model. The computation results show that NOGOOD finds the optimal examination schedule for the given instance. Although we consider a specific instance of the university timetabling problem, the methods we use can be applicable to modeling and solving other timetabling problems.

• Journal of Korea Game Society
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• v.20 no.2
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• pp.183-192
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• 2020

Due to the problem of matching between virtual world space and real-world space, the reality of augmented reality content is rather low. There are many reasons for this result, firstly virtual object is rendered without considering relation between real-world space and virtual world space. Secondly, virtual objects rely too much on the marker to reduce reality. We propose two schemes to improve the reality of augmented reality, one is people occlusion, the other is to reduce the dependence of virtual objects on the marker.

• Journal of Educational Research in Mathematics
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• v.15 no.1
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• pp.1-19
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• 2005

There have been raised the question that students have been of no interst in mathematcs and incompetent for solving real world problem because students have been recognized mathematcs as abstract knowldege. We research whether students' modeles developed in modeling activity can mediate between concrete and abstract. The analysis of our case study revealed that students' modeles aren't decontextualized abstraction but is located in situated abstraction that is a network connecting between concrete and abstract. Thus, these modeles are a tool mediating between concrete and abstract. Also, students' modeling activities can provide students with the opportunity of being competent for solving real world problem.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.197-209
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• 2011

The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attacks as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. In addition, we also investigate the impact of the proposed methods on community detection, a very popular application in the graph mining field. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.519-522
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• 1998

The Constructive Battle simulation Model is very important to the recent military training for the substitution of the field training. However, real battlefield systems operate under rea-time conditions, they are inherently distributed, concurrent and dynamic. In order to reflect these properties by the computer-based simulation systems which represent real world processes, we have been developing constructive simulation model for several years. The constructive simulation system is one of the famous real-time system software, nd the one common feature of all real-time systems is defined as the correctness of the system depend not only on the logical result of computation, but also on the time at which the results are produced. Conventionally, scheduling and resource allocation activities which have timing constraints are major problem of real-time computing systems. To overcome these constraints, we elaborated on these issues and developed the simulation system on commercially available hardware and operating system with lock-free resource allocation scheme and rae monotonic scheduling.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.295-311
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• 2016

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

• Korean Management Science Review
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• v.19 no.1
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• pp.55-66
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• 2002

Given a directed network, a designated arc, and lowers and upper bounds for the distance of each arc, the preprocessing shortest path problem Is a decision problem that decides whether there is some choice of distance vector such that the distance of each arc honors the given lower and upper bound restriction, and such that the designated arc is on some shortest path from a source node to a destination notre with respect to the chosen distance vector. The preprocessing shortest path problem has many real world applications such as communication and transportation network management and the problem is known to be NP-complete. In this paper, we develop an algorithm that solves the problem using the structural properties of shortest paths.