• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.49.1-49.1
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• 2012

Recently it is found that the globular clusters are not only bound in their host galaxies, but also are wandering between galaxies in Virgo and Coma clusters. The cluster-wide distribution of these intracluster globular clusters (IGCs) suggests that IGCs are an important probe to understand hierarchical structure formation. We present a survey of IGCs in Virgo cluster using HST archive images for four HST/ACS fields located from about 9 arcmin to 40 acrmin from the cluster center. We find ten new IGCs and confirm four previously known IGCs. The number density of IGCs decreases as the distance from the cluster center increases. We derive integrated photometry of IGCs. We also obtain photometry of resolved stars in the outer region of each cluster. These IGCs are fainter than $M_V{\approx}-9.5$ and mostly blue in (V-I) color. showing that they are mostly metal poor. The locations of red giant branch stars of IGCs in color-magnitude diagrams also show that they are meal-poor. We discuss the implications of these results.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.109-117
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• 2014

This paper revises the Quine-McCluskey Algorithm to circuit minimization problems. Quine-McCluskey method repeatedly finds the prime implicant and employs additional procedures such as trial-and-error, branch-and-bound, and Petrick's method as a means of circuit minimization. The proposed algorithm, on the contrary, produces an implicant chart beforehand to simplify the search for the prime implicant. In addition, it determines a set cover to streamline the search for $1^{st}$ and $2^{nd}$ essential prime implicants. When applied to 3-variable and 4-variable experimental data, the proposed algorithm has indeed proved to obtain the optimal solutions much more simply and accurately than the Quine-McCluskey method.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.501-502
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• 2015

매년 증가되는 전력소비량에 대응하여 스마트그리드 기술을 기반으로 수용가 측의 에너지관리 중요성이 부각되고 있으며, 홈 에너지관리시스템(HEMS, Home Energy Management System)은 전기요금 절감과 효율적인 전력소비의 중요한 체계로써 기대되고 있다. 일반적으로, 가정에서 높은 전력소비를 가진 가전제품은 계절성 부하로 전기온수기, 냉/난방기를 일컫는다. 즉, 계절성 부하에 적절한 에너지관리, '최적부하 스케줄링'은 전기요금 절감과 직결되는 것을 의미한다. 본 논문은 Modified Branch-and-Bound 기법을 사용하여 사용자의 편의성을 고려한 전기온수기(EWH, Electric Water Heater)의 스케줄링을 실시하겠다. 여기서 사용자의 편의성이란, 외부의 온도변화 또는 습관에 따라 그 부하를 사용하는 것을 의미한다. 온수사용량, 수온 설정온도 변화의 편의성 제약조건을 고려하여 온수기를 효과적으로 운영하는 스케줄링을 실시한다. 이러한 편의성 내에서 온수기를 운영하며, 작동모드는 3가지(정지, 일반/급속가열) 모드가 존재하며, 다양한 요금제도에서의 스케줄링 결과를 절감된 전기요금으로 비교하겠다.

• Management Science and Financial Engineering
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• v.19 no.2
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• pp.19-29
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• 2013

Refuse collection network design, one of major decision problems in reverse logistics, is the problem of locating collection points and allocating refuses at demand points to the opened collection points. As an extension of the previous models, we consider capacity and maximum allowable distance constraints at each collection point. In particular, the maximum allowable distance constraint is additionally considered to avoid the impractical solutions in which collection points are located too closely. Also, the additional distance constraint represents the physical distance limit between collection and demand points. The objective is to minimize the sum of fixed costs to open collection points and variable costs to transport refuses from demand to collection points. After formulating the problem as an integer programming model, we suggest an optimal branch and bound algorithm that generates all feasible solutions by a simultaneous location and allocation method and curtails the dominated ones using the lower bounds developed using the relaxation technique. Also, due to the limited applications of the optimal algorithm, we suggest two heuristics. To test the performances of the algorithms, computational experiments were done on a number of test instances, and the results are reported.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.84-92
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• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6009-6027
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• 2019

Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4021-4037
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• 2018

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.91-100
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• 1990

This paper is concerned with the development of optimal algorithms for multi-stage flowshop scheduling problems with sequence dependent setup times. In the previous researches the setup time of a job is considered to be able to begin at the earliest opportunity given a particular sequence at the start of operations. In this paper the setup time of a job is considered to be able to begin only at the completion of that job on the previous machine to reflect the effects of the setup time to the performance measure of sequence dependent setup time flowshop scheduling. The results of the study consist of two areas; first, a general integer programming(IP) model is formulated and a nixed integer linear programming(MILP) model is also formulated by introducing a new binary variable. Second a depth-first branch and bound algorithm is developed. To reduce the computational burdens we use the best heuristic schedule developed by Choi(1989) as the first trial. The experiments for developed algorithm are designed for a 4$\times$3$\times$3 factorial design with 360 observations. The experimental factors are PS(ratio of processing time to setup time), M(number of machines), N(number of jobs).

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.179-184
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• 2002

We consider the heterogeneous fleet vehicle routing problem (HVRP), a variant of the classical vehicle routing problem (VRP). The HVRP differs from the classical VRP in that it deals with a heterogeneous fleet of vehicles having various capacities, fixed costs, and variables costs. Therefore the HVRP is to find the fleet composition and a set of routes with minimum total cost. We give an integer programming formulation of the problem and propose an algorithm to solve it. Although the formulation has exponentially many variables, we can efficiently solve the linear programming relaxation of it by using the column generation technique. To generate profitable columns we solve a shortest path problem with capacity constraints using dynamic programming. After solving the linear programming relaxation, we apply a branch-and-bound procedure. We test the proposed algorithm on a set of benchmark instances. Test results show that the algorithm gives best-known solutions to almost all instances.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.755-760
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• 2010

In general, Lagrangian method for discrete optimization is a kind of technique to easily manage constraints. It is traditionally used for finding upper bounds in the branch-and-bound method. In this paper, we propose a new Lagrangian search method for the 0-1 knapsack problem with multiple constraints. A novel feature of the proposed method different from existing Lagrangian approaches is that it can find high-quality lower bounds, i.e., feasible solutions, efficiently based on a new property of Lagrangian vector. We show the performance improvement of the proposed Lagrangian method over existing ones through experiments on well-known large scale benchmark data.