• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5723-5743
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• 2018

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment-RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.451-456
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• 2007

In this paper, we regard computer systems as heterogeneous multi-server systems and propose a cumulative fair scheduling scheme that pursues long-term fairness. GPS(generalized processor sharing)-based scheduling algorithms, which are usually employed in single-server systems, distribute available capacity in an instantaneous manner. However, applying them to heterogeneous multi-server systems may cause unfairness, since they may not prevent the accumulation of scheduling delays and the extra capacities are distributed in an instantaneous manner. In our scheme, long-term fairness is pursued by proper distribution of extra capacities while guaranteeing reserved capacities. A reference capacity model to determine the ideal progresses of applications is derived from long-term observations, and the scheduler makes the applications gradually follow the ideal progresses while guaranteeing their reserved capacities. A heuristic scheduling algorithm is proposed and the scheme is examined by simulation.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.195-208
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• 2009

A conventional single design optimality criterion has been used to select an efficient experimental design. But, since an experimental design is constructed with respect to an optimality criterion pre specified by investigators, an experimental design obtained from one optimality criterion which is superior to other designs may perform poorly when the design is evaluated by another optimality criterion. In other words, none of these is entirely satisfactory and even there is no guarantee that a design which is constructed from using a certain design optimality criterion is also optimal to the other design optimality criteria. Thus, it is necessary to develop certain special types of experimental designs that satisfy multiple design optimality criteria simultaneously because these multi-optimal designs (MODs) reflect the needs of the experimenters more adequately. In this article, we present a heuristic approach to construct second-order response surface designs which are more flexible and potentially very useful than the designs generated from a single design optimality criterion in many real experimental situations when several competing design optimality criteria are of interest. In this paper, over cuboidal design region for $3\;{\leq}\;k\;{\leq}\;5$ variables, we construct multi-optimal designs (MODs) that might moderately satisfy two famous alphabetic design optimality criteria, G- and IV-optimality criteria using a GA which considers a certain amount of randomness. The minimum, average and maximum scaled prediction variances for the generated response surface designs are provided. Based on the average and maximum scaled prediction variances for k = 3, 4 and 5 design variables, the MODs from a genetic algorithm (GA) have better statistical property than does the theoretically optimal designs and the MODs are more flexible and useful than single-criterion optimal designs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.981-987
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• 2009

Recently, the study on wireless body area network for providing ubiquitous healthcare services has been actively done, including the standardization of the IEEE and others. Wireless body area network is usually configured in tree format using multi-hop communication mode due to the power limitation and the characteristics of human body. In this case, differently from existing sensor network, the wireless body area network tends to be disconnected due to the frequent movement of human body. The number of connections which can be supported at each node has some limitations due to the constraint imposed on power consumption. In this paper, we have proposed a heuristic algorithm for optimal selection of parent node with guaranteed QoS for a disconnected node, which considers the priority on packet transmission. Simulation has been performed to evaluate the performance of the proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.258-264
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• 2018

This paper presents application of a differential search (DS) meta-heuristic optimization algorithm for optimal operation of a micro grid system. The DS algorithm simulates the Brownian-like random-walk movement used by an organism to migrate. The micro grid system consists of a wind turbine, a diesel generator, a fuel cell, and a photovoltaic system. The wind turbine generator is modeled by considering the characteristics of variable output. Optimization is aimed at minimizing the cost function of the system, including fuel costs and maximizing fuel efficiency to generate electric power. The simulation was applied to a micro grid system only. This study applies the DS algorithm with excellence and efficiency in terms of coding simplicity, fast convergence speed, and accuracy in the optimal operation of micro grids based on renewable energy resources, and we compared its optimum value to other algorithms to prove its superiority.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.129-138
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• 2006

After Seoul has introduced semi-public bus management system(public management-private operation), the Seoul Metro Government needs a scientific management tool for optimal scheduling for bus routes, to reduce unnecessary operations and provide demand responsive service. As a product of this effort, this paper proposes a heuristic model that could minimize total passenger waiting time under the constraints, such as number of vehicles, working conditions, max load point, minimax headway. etc. For verifying the validity of the proposed model, it is applied to an existing bus route. It results that headways in rush hours become decreased and the passenger waiting time could be decreased. In conclusion. it is thought that the Proposed model contributes to efficiency of bus operation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.82-96
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• 2020

The Demand Responsive Transit (DRT) system is the flexible public transport service that determines the route and schedule of the service vehicles according to users' requests. With increasing importance of public transport systems in urban areas, the development of stable and fast routing algorithms for DRT has become the goal of many researches over the past decades. In this study, a new heuristic method is proposed to generate fast and efficient routes for multiple vehicles using demand clustering and destination demand priority searching method considering the imbalance of users' origin and destination demands. The proposed algorithm is tested in various demand distribution scenarios including random, concentration and directed cases. The result shows that the proposed method reduce the drop of service ratio due to an increase in demand density and save computation time compared to other algorithms. In addition, compared to other clustering-based algorithms, the walking cost of the passengers is significantly reduced, but the detour time and in-vehicle travel time of the passenger is increased due to the detour burden.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.61-68
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• 2019

Currently many companies are interested in reduction of the carbon emissions associated with their supply chain activities such as transportation and operations. Operational decisions, such as modifications in order quantities could an effective way in reducing carbon emissions in the supply chain. Cap-and-trade regulation, sometimes called emissions trading, is a market-based tool to limit greenhouse gas emissions. Under cap-and-trade regulation, emission credits are allocated to the firms and the firms trades emissions under cap-and-trade schemes. In this paper, we propose a single-manufacturer single-buyer two-echelon supply chain problem under the cap-and-trade mechanism incorporating the carbon emissions caused by transportation and warehousing activities where a single manufacturer produces a family of items in order to deliver a family of items to a single buyer at a fixed interval of time for effective implementation of Just-In-Time (JIT) Purchasing. An integrated multi-product lot-splitting model of facilitating multiple shipments in small lots between buyer and manufacturer is developed in a JIT Purchasing environment. Also, an iterative heuristic algorithm is developed to derive the common order interval, the number of intervals for each product and the number of shipments between the buyer and the manufacturer during the common interval. A numerical example is given to illustrate the savings in reduction of total cost and carbon emissions by the inventory model incorporating cap-and-trade mechanism compared to the classical inventory model. The proposed inventory model could be useful for the practical solution of two-echelon supply chain inventory problem under cap-and-trade mechanism.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.9
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• pp.504-511
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• 2003

The property of circuit symmetry has long been applied to the Problem of minimizing the area and timing of multi-level logic circuits. In this paper, we focus on another important design objective, power minimization, utilizing circuit symmetries. First, we analyze and establish the relationship between several types of circuit symmetry and their applicability to reducing power consumption of the circuit, proposing a set of re-synthesis techniques utilizing the symmetries. We derive an algorithm for detecting the symmetries (among the internal signals as well as the primary inputs) on a given circuit implementation. We then propose effective transformation algorithms to minimize power consumption using the symmetry information detected from the circuit. Unlike many other approaches, our transformation algorithm guarantees monotonic improvement in terms of switching activities, which is practically useful in that user can check the intermediate re-synthesized designs in terms of the degree of changes of power, area, timing, and the circuit structure. We have carried out experiments on MCNC benchmark circuits to demonstrate the effectiveness of our algorithm. On average we reduced the power consumption of circuits by 12% with relatively little increase of area and timing.