• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.636-645
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• 2008

Most of routing schemes that protect the source's location from a malicious attacker usually make use of a path of a long length per message for the sake of lengthening the safety period. The biggest problem to such approaches is taking a very long latency in transferring messages to the destination. In this paper we show the problem to find the least-cost single path that is enough to keep the source-location always secure from the attacker, provided that it is used for the delivery of a set of messages given in priori, is NP-complete. Consequently we propose a routing protocol GSLP-w(GPSR-based Source-Location Privacy with crew size co) that is a trade-off between two extreme approaches. The advantage of GSLP-co lies in its enhanced safety period for the source and its lowered delivery latency in messaging. We consider NSP(Normalized Sefety Period) and NDL(Normalized Delivery Latency), measured in terms of the least number of hops to the destination, to achieve tangible interpretation of the results. We ran a simulation to confirm our claim by generating 100 topologies of 50,000 nodes with the average number of neighbors being 8. The results show that GSLP-$\omega$ provides more enhanced NSP compared to other protocols GSLP, an earlier version of GSLP-$\omega$, and PR-SP(Phantom Routing - Single Path), the most notable existing protocol for the source-location privacy, and less NDL than that of GSLP but more than that of PR-SP.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.641-649
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• 2004

The relationship between chemical structures and biological activities is researched briskly in the area of 'Medicinal Chemistry' At the base of these structure-based drug design tries, medicinal chemists search the existing drugs of similar chemical structure to target drug for the development of a new drug. Therefore, it is such necessary that an automatic system selects drug files that have a set of chemical moieties matching a user-defined query moiety. Substructure searching is the process of identifying a set of chemical moieties that match a specific query moiety. Testing for substructure searching was developed in the late 1950s. In graph theoretical terms, this problem corresponds to determining which graphs in a set are subgraph isomorphic to a specified query moiety. Testing for subgraph isomorphism has been proved, in the general case, to be an NP- complete problem. For the purpose of overcoming this difficulty, there were computational approaches. On the 1990s, a US patent has been granted on an atom-centered indexing scheme, used by the RS3 system; this has the virtue that the indexes generated can be searched by direct text comparison. This system is commercially used(http://www.acelrys.com/rs3). We define the RS3 system's drawback and present a new indexing scheme. The RS3 system treats substructure searching with substring matching by means of expressing chemical structure aspredefined strings. However, it has insufficient 'rerall' and 'precision‘ because it is impossible to index structures uniquely for same atom and same bond. To resolve this problem, we make the minimum-cost- spanning tree for one centered atom and describe a structure with paths per levels. Expressing 2D chemical structure into 1D a string has limit. Therefore, we break 2D chemical structure into 1D structure fragments. We present in this paper a new index technique to improve recall and precision surprisingly.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.917-926
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• 2006

Transportation Economies of scale are the essential properties of hub networks. One critical property of the hub network design problem is to quantify cost savings which stem from economies of scale, the costs of operating hub facilities and opportunity costs associated with delays stemming from consolidation of traffic flows. Due to the NP-complete property of the hub location problem, however, most previous researchers have focused on the development of heuristic algorithms for approximate solutions. The purpose of this paper is to develop a hub network design model considering transportation economies of scale from the consolidation of traffic flows. The model is designed to consider the uniqueness of hub networks and to determine several cost components. The heuristic algorithms for the developed model are suggested and the results of the model are compared with recently published studies using real data. Results of the analysis show that the proposed model reflects transportation economies of scale due to consolidation of flows. This study can form not only the theoretical basis of an effective and rational hub network design but contribute to the assessment of existing and planned logistics systems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.25-34
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• 2010

By introducing additional nodes called Steiner points, the problem of Steiner Minimum Tree whose length can be shorter than Minimum Spanning Tree and which connects all input terminal nodes belongs to Non-Polynomial Complete domain. Though diverse heuristic methods can be applied to the problem, most of them may meet serious pains in computing and waiting for a solution of the problem with numerous input nodes. For numerous input nodes, an efficient PTAS approximation method producing candidate unit steiner trees with portals in most bottom layer, merging them hierarchically to construct their parent steiner trees in upper layer and building swiftly final approximation Steiner tree in most top layer is suggested in this paper. The experiment with 16,000 input nodes and designed 16 unit areas in most bottom layer shows 85.4% execution time improvement in serial processing and 98.9% in parallel processing comparing with pure Steiner heuristic method, though 0.24% overhead of tree length. Therefore, the suggested PTAS Steiner tree method can have a wide range applications to build a large scale approximation Steiner tree quickly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2658-2679
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• 2018

This paper proposes a novel Random Scheduling Algorithm based on Subregion Coverage (RSASC), to solve the SET K-cover problem (an NP-complete problem). SET K-cover problem distributes the set of sensors into the maximum number of mutually exclusive subsets (MESSs) in such a way that each of them can be scheduled for lifetime extension of WSN. Sensor coverage divides the target region into different subregions. RSASC first sorts the subregions in the ascending order concerning their sensor coverage. Then, it forms the subregion groups according to their similar sensor coverage. Lastly, RSASC ensures the K-coverage of each subregion from every group by randomly scheduling the sensors. We consider the target-coverage and area-coverage applications of WSN to analyze the usefulness of our proposed RSASC algorithm. The distinct quality of RSASC is that it utilizes less number of deployed sensors (33% less) to form the optimum number of MESSs with the higher computational speed (saves more than 93% of the time) as compared to the existing three algorithms.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.3
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• pp.341-347
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• 2011

The redundancy allocation problem (RAP) is a famous NP.complete problem that has beenstudied in the system reliability area of ships and airplanes. Recently meta-heuristic techniques have been applied in this topic, for example, genetic algorithms, simulated annealing and tabu search. In particular, tabu search (TS) has emerged as an efficient algorithmic approach for the series-parallel RAP. However, the quality of solutions found by TS depends on the initial solution. As a robust and efficient methodology for the series-parallel RAP, the hybrid metaheuristic (TSA) that is a interactive procedure between the TS and SA (simulated annealing) is developed in this paper. In the proposed algorithm, SA is used to find the diversified promising solutions so that TS can re-intensify search for the solutions obtained by the SA. We test the proposed TSA by the existing problems and compare it with the SA and TS algorithm. Computational results show that the TSA algorithm finds the global optimal solutions for all cases and outperforms the existing TS and SA in cases of 42 and 56 subsystems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.506-516
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• 2005

This paper deals with an investment scheduling problem of maximizing net present value of dividend with reinvestment allowed, where each investment has certain capital requirement and generates deterministic profit. Such deterministic profit is calculated at completion of each investment and then allocated into two parts, including dividend and reinvestment, at each predetermined reinvestment time point. The objective is to make optimal scheduling of investments over a fixed planning horizon which maximizes total sum of the net present values of dividends subject to investment precedence relations and capital limit but with reinvestment allowed. In the analysis, the scheduling problem is transformed to a kind of parallel machine scheduling problem and formulated as an integer programming which is proven to be NP-complete. Thereupon, a depth-first branch-and-bound algorithm is derived. To test the effectiveness and efficiency of the derived algorithm, computational experiments are performed with some numerical instances. The experimental results show that the algorithm solves the problem relatively faster than the commercial software package (CPLEX 8.1), and optimally solves the instances with up to 30 investments within a reasonable time limit.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.79-86
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• 2005

The transportation problem (TP) is one of the traditional optimization problems. Unlike the TP, the fixed charge transportation problem (FCTP) cannot be solved using polynomial time algorithms. So, finding solutions for the FCTP is a well-known NP-complete problem involving an importance in a transportation network design. So, it seems to be natural to use evolutionary algorithms for solving FCTP. And many evolutionary algorithms have tackled this problem and shown good performance. This paper introduces an efficient evolutionary algorithm for the FCTP. The proposed algorithm can always generate feasible solutions without any repair process using the random key representation. Especially, it can guide the search toward the basic solution. Finally, we performed comparisons with the previous results known on the benchmark instances and could confirm the superiority of the proposed algorithm.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.51-62
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• 2013

This paper considers a single machine rescheduling problem whose original (efficiency related) objective is minimizing makespan. We assume that disruptions such as order cancelations and newly arrived orders occur after the initial scheduling, and we reschedule this disrupted schedule with the objective of minimizing a disruption related objective while preserving the original objective. The disruption related objective measures the impact of the disruptions as difference of completion times in the remaining (uncanceled) jobs before and after the disruptions. The artificial due dates for the remaining jobs are set to completion times in the original schedule while newly arrived jobs do not have due dates. Then, the objective of the rescheduling is minimizing the maximum earliness without tardiness. In order to preserve the optimality of the original objective, we assume that no-idle time and no tardiness are allowed while rescheduling. We first define this new problem and prove that the general version of the problem is unary NP-complete. Then, we develop three simple but intuitive heuristics. For each of the three heuristics, we find a tight bound on the measure called modified z-approximation ratio. The best theoretical bound is found to be 0.5 - ${\varepsilon}$ for some ${\varepsilon}$ > 0, and it implies that the solution value of the best heuristic is at most around a half of the worst possible solution value. Finally, we empirically evaluate the heuristics and demonstrate that the two best heuristics perform much better than the other one.

• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1589-1596
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• 2005

The imprecise real-time system provides flexibility in scheduling time-critical tasks. Most scheduling problems of satisfying both 0/1 constraint and timing constraints, while the total error is minimized, are NP complete when the optional tasks have arbitrary processing times. Liu suggested a reasonable strategy of scheduling tasks with the 0/1 constraint on uniprocessors for minimizing the total error. Song et al suggested a reasonable strategy of scheduling tasks with the 0/1 constraint on multiprocessors for minimizing the total error. But, these algorithms are all off-line algorithms. On the other hand, in the case of on line scheduling, Shih and Liu proposed the NORA algorithm which can find a schedule with the minimum total error for a task system consisting solely of on-line tasks that are ready upon arrival. But, for the task system with 0/1 constraint, it has not been known whether the NORA algorithm can be optimal or not in the sense that it guarantees all mandatory tasks are completed by their deadlines and the total error is minimized. So, this paper suggests an optimal algorithm to search minimum total error for the imprecise on-line real-time task system with 0/1 constraint. Furthermore, the proposed algorithm has the same complexity, O(N log N), as the NORA algorithm, where N is the number of tasks.