• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4640-4661
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• 2018

To cope with the explosive growth of Internet services, Service Function Chaining (SFC) based on Software-defined Networking (SDN) is an emerging and promising technology that has been suggested to meet this challenge. Determining the placement of Virtual Network Functions (VNFs) and routing paths that optimize the network utilization and resource consumption is a challenging problem, particularly without violating service level agreements (SLAs). This problem is called the optimal SFC placement problem and an Integer Linear Programming (ILP) formulation is provided. A greedy heuristic solution is also provided based on an improved two-step mapping algorithm. The obtained experimental results show that the proposed algorithm can automatically place VNFs at the optimal locations and find the optimal routing paths for each online request. This algorithm can increase the average request acceptance rate by about 17.6% and provide more than 20-fold reduction of the computational complexity compared to the Greedy algorithm. The feasibility of this approach is demonstrated via NetFPGA-10G prototype implementation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.211-212
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• 2018

머신 러닝 환경에서 많은 양의 데이터를 한꺼번에 학습하게 되면 데이터 트래픽이 증가함에 따라 흐름 정체가 발생하고 학습 품질이 저하되며 학습속도 지연 등의 문제가 발생한다. 본 연구는 머신러닝 환경에서 빅 데이터 학습 데이터 분할을 위한 핵심 목표인 Greedy 알고리즘에 대해 설명하고 간단한 Greedy 알고리즘을 사용하여 각각의 데이터 파티션을 생성하여 학습 속도의 효율성을 향상시키는 방법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3394-3408
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• 2014

Conventional mobile state (MS) and base station (BS) association based on average signal strength often results in imbalance of cell load which may require more powerful processor at BSs and degrades the perceived transmission rate of MSs. To deal with this problem, a Markov decision process (MDP) for load balancing in a multi-cell system with multi-carriers is formulated. To solve the problem, exploiting Sarsa algorithm of on-line learning type [12], ${\alpha}$-controllable load balancing algorithm is proposed. It is designed to control tradeoff between the cell load deviation of BSs and the perceived transmission rates of MSs. We also propose an ${\varepsilon}$-differential soft greedy policy for on-line learning which is proven to be asymptotically convergent to the optimal greedy policy under some condition. Simulation results verify that the ${\alpha}$-controllable load balancing algorithm controls the behavior of the algorithm depending on the choice of ${\alpha}$. It is shown to be very efficient in balancing cell loads of BSs with low ${\alpha}$.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.433-439
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• 2019

Artificial bee colony algorithm is a strong global search algorithm which exhibits excellent exploration ability. The conventional ABC algorithm adopts employed bees, onlooker bees and scouts to cooperate with each other. However, its one dimension and greedy search strategy causes slow convergence speed. To enhance its performance, in this paper, we abandon the greedy selection method and propose an artificial bee colony algorithm with special division and intellective search (ABCIS). For the purpose of higher food source research efficiency, different search strategies are adopted with different employed bees and onlooker bees. Experimental results on a series of benchmarks algorithms demonstrate its effectiveness.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.145-170
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• 2005

This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these jobs need to be determined. To deal with this scheduling problem, three mixed 0-1 integer programming models, Model 1, Model 2 and Model 3 are provided. We present interesting techniques to reformulate the two mixed integer programming models, Model 1 and Model 2, as pure 0-1 integer programming problems with simple constraint sets and present a lower bound for the optimal value of Model 1. Model 3 is a complicated mixed integer programming model because it involves a set of non-smooth constraints, but it can be proved that its solutions may be obtained by the so-called greedy algorithm. We present numerical results showing that Model 3 is the best among these three models and the greedy algorithm is capable of solving large scale problems.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.954-963
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• 2012

As a greedy algorithm reconstructing the sparse signal from underdetermined system, orthogonal matching pursuit (OMP) algorithm has received much attention. In this paper, we multiple candidate matching pursuit (MuCaMP), which builds up candidate support set in every iteration and uses the minimum residual at last iteration. Using the restricted isometry property (RIP), we derive the sufficient condition for MuCaMP to recover the sparse signal exactly. The MuCaMP guarantees to reconstruct the K-sparse signal when the sensing matrix satisfies the RIP constant ${\delta}_{N+K}<\frac{\sqrt{N}}{\sqrt{K}+3\sqrt{N}}$. In addition, we show a recovery performance both noiseless and noisy measurements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3638-3657
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• 2022

In urban vehicular edge computing (VEC) environments, one edge server always serves many task requests in its coverage which results in the resource-constrained problem. To resolve the problem and improve system utilization, we first design a general hierarchical resource management framework based on typical VEC network structures. Following the framework, a specific interacting protocol is also designed for our decision algorithm. Secondly, a greedy bidding-based multi-hop task scheduling decision algorithm is proposed to realize effective task scheduling in resource-constrained VEC environments. In this algorithm, the goal of maximizing system utility is modeled as an optimization problem with the constraints of task deadlines and available computing resources. Then, an auction mechanism named greedy bidding is used to match task requests to edge servers in the case of multiple hops to maximize the system utility. Simulation results show that our proposal can maximize the number of tasks served in resource constrained VEC networks and improve the system utility.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.839-844
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• 2003

This paper presents a method to segment wafer ID marks on poor quality images under uncontrolled lighting conditions of the semiconductor process. The active multiple templates matching method is suggested to search ID areas on wafers and segment them into meaningful regions and it would have been impossible to recognize characters using general OCR algorithms. This active template model is designed by applying a snake model that is used for active contour tracking. Active multiple template model searches character areas and segments them into single characters optimally, tracking each character that can vary in a flexible manner according to string configurations. Applying active multiple templates, the optimization of the snake energy is done using Greedy algorithm, to maximize its efficiency by automatically controlling each template gap. These vary according to the configuration of character string. Experimental results using wafer images from real FA environment are presented.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.236-239
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• 1997

Snakes is an active contour model or representing image contours. To detect chest boundary on thoracic MRI sequences, we proposed a method based on modified greedy algorithm. Because thoracic MRI sequences have a spatial correlation, we added energy term related with spatial correlation to Snakes energy formulation. A measure of shape similarity called the BMD was used to evaluate the accuracy of the algorithm. The average BMD value or the modified algorithm's result is higher than greedy algorithm's.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.69-80
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• 2016

Some studies were conducted for the purpose of minimizing total clearance time for rapid evacuation from the indoor spaces when disaster occurs. Most studies took a long time to calculate the optimal evacuation route that derived minimum evacuation time. For this reason, this study proposes an evacuation route assignment algorithm that can shorten the total clearance time in a short operational time. When lots of exits are in the building, this algorithm can shorten the total clearance time by assigning the appropriate pedestrian traffic volume to each exit and balances each exit-load. The graph theory and greedy algorithm were utilized to assign pedestrian traffic volume to each exit in this study. To verify this algorithm, study used a cellular automata-based evacuation simulator and experimented various occupants distribution in a building structure. As a result, the total clearance time is reduced by using this algorithm, compared to the case of evacuating occupants to the exit within shortest distance. And it was confirmed that the operation takes a short time In a large building structure.