• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.681-688
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• 2008

In this paper, we propose a new price-based call admission control algorithm for multi-class communication networks. When a call arrives at the network, it informs the network of the number of requested circuits and the minimum amount of time that it will require. The network provides the optimal price for the arrived call with which it tries to maximize its expected revenue. The optimal price is dynamically adjusted based on the information of the arrived call, and the present and the estimated future congestion level of the network during the reservation time of the call. If the call accepts the price, it is admitted. Otherwise, it is rejected. We compare the performance of our dynamic pricing algorithm with that of the static pricing algorithm by Courcoubetis and Reiman [1], and Paschalidis and Tsitsiklis [2]. By the comparison, we show that our dynamic pricing algorithm has better performance aspects such as higher call admission ratio and lower price than the static pricing algorithm, although these two algorithms result in almost the same revenue as shown in [2]. This implies that, in the competitive situation, the dynamic pricing algorithm can attract more users than the static pricing algorithm, generating more revenue. Moreover, we show that if a certain fixed connection fee is introduced to the price for a call, our dynamic pricing algorithm yields more revenue.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.5
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• pp.560-566
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• 2003

Recently, many researches on RC(Reconfigurable Computing) with highly complex FPGA's and reconfigurable processors have been reported, and even some attempts for commercialization have been successful. In this paper, we introduce the design methodology for implementing DES crypto algorithm on small-capacity FPGA by using its dynamic reconfigurability and a system-level verification technique. Throughout this design project, we could evaluate the effectiveness of this approach, which is the dynamic reconfigurability of FPGAs makes the efficient trade-off between the performance and the cost robustly viable.

• Environmental and Resource Economics Review
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• v.24 no.4
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• pp.657-677
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• 2015

We employ a 3 regime-threshold vector error correction models (TVECM) to investigate the nonlinear dynamic adjustments of three marker crude oil prices such as WTI (West Texas Intermediate), Brent and Dubai. Especially we deal with 3 combinations of oil prices including WTI-Brent, WTI-Dubai and Brent-Dubai in order to analyze the dynamic adjustments of the prices based on the effects of the price spreads among these crude oil prices. Our daily spot prices data run from 2001.1.3 to 2014.12.31. We found that each combination is cointegrated over the period. WTI had dropped significantly in 2010 which had affected the movements of the spreads. To accomodate this fact, we divide the period into two sub-periods: 2000.1.3-2009.12.31 and 2010.1.1-2014.12.31. It is found that each combination is cointegrated in both sub-periods. Moroever, in the first sub-period, all three oil prices are shown to follow nonlinear dynamic adjustments. In the second sub-period, however, TVECM is better than VECM(vector error correction model) for WTI-Dubai and Brent-Dubai while VECM performs better for WTI-Brent. The transaction costs are estimated to be reduced for the second sub-period for WTI-Dubai and Brent-Dubai compared to the first sub-period.

• Journal of Global Scholars of Marketing Science
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• v.16 no.1
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• pp.1-11
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• 2006

DRM(Digital Right Management), which realize the controllability of intellectual right in the process of transactions with economically feasible cost, may cause critical changes in the distributions of digital contents. This Paper takes notice of the concept of 'Super Distribution' in which every user can be the core of the distribution and the possibility of usingvarious price structures by introducing DRM. We reviews that Super Distribution can increase the demand of digital contents by decreasing the information cost in the user aspect, and it can also diminish the distribution cost dramatically in the provider aspect. Also, DRM may allow various types of pricing which use different criteria such as the number or duration of usage. The variety in price structure can increase not only the profit of provider but the utility of consumer. These characteristics of DRM, as a whole, increase the efficiency of distribution and make the providers differentiate their services.

• Journal of KIISE:Information Networking
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• v.33 no.2
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• pp.183-192
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• 2006

This paper analyzes the price-selection problem under priority-based scheduling for QoS (Quality of Service) network services, i.e., how to determine the price associated with each service level. In particular, we focus on the problems with the pricing mechanism based on equilibrium analysis. We claim that the assumptions needed to produce equilibrium nay not hold in some important environments. Specifically, (a) the individual user's impact on the system is not infinitesimal and (b) users do not always have up-to-date global system-status knowledge crucial for optimal user decisions required for equilibrium. These may make the equilibrium models inaccurate in realistic environments. We examine the accuracy of some existing equilibrium methods by using a dynamic model that we have developed for system behavior analysis. The analysis indicates that equilibrium methods fail to model accurately the system behavior in some realistic environments.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.741-744
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• 2015

최근 소셜 미디어를 이용한 대량의 데이터로부터 사용자의 의사결정을 지원하기위한 맞춤형 데이터 추천 서비스가 관심을 받고 있으며 사용자의 선호도에 근접한 데이터 추천기법으로 스카이라인 질의가 연구되어왔다. 그러나 기존의 스카이라인 질의는 데이터의 정적속성(위도, 경도, 가격 등)만을 기준으로 모든 사용자에게 동일한 데이터를 반환하기 때문에 맞춤형 데이터를 추천하기 어렵다. 본 논문에서는 사용자의 기호에 대한 정밀도를 높이기 위해 정적속성에서 동적속성(계산속성)을 유도하는 분기한정 동적 스카이라인 질의 기법(Branch and Bound Dynamic Skyline, BBDS)을 구현하였다. 시뮬레이션에서는 대규모 데이터 및 다양한 분포에 따른 실험을 수행한 결과 BBDS가 기존 기법에 비해 데이터 탐색과 추천에 있어서 향상된 성능을 나타내는 것으로 평가되었다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.343-345
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• 2001

전자상거래 기술은 현실 세계에 좀더 완전하고 최적화된 상황과 조건을 요구하고 있다. 초기의 쇼핑 에이전트는 사용자의 모든 요구조건과 특성을 만족시키는 대신에 단순히 판매자가 제시하는 가격 기반 비교만을 하는 문제점이 있다. 본 논문은 기존의 쇼핑 에이전트가 갖고 있는 판매자 자격 중심 비교와 지능화와 자동화의 부족이라는 문제점을 극복하기 위해 각 상품에 따른 특성이 반영될 수 있는 동적 거래 알고리즘을 적용한 협상시스템을 제안하고, 이 알고리즘을 이용한 시스템을 개발하였다.

• Journal of Korean Institute of Industrial Engineers
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• v.16 no.1
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• pp.27-36
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• 1990

This study deals with the dynamic transportation-inventory model for a single product from which the optimal procurement quantities and the transportation modes are determined simultaneously over a finite planning horizon. Moreover, it covers the situation where quantity discounts are applied to the transportation cost as well as the purchase cost and disposals of the excess are possible at the end of each period. For a relevant mathematical model formulated, the theorems and properties of an optimal solution are discussed to present the efficient algorithm. A numerical example is solved to illustrate the algorithm developed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2826-2832
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• 2012

A smart grid is an electric-power grid that employs a new information technology.This technology makes it possible to exchange real-time energy information between suppliers and consumers, finally resulting in high energy efficiency. The energy management system in smart grid provides up to date information on electricity consumption as well as dynamic electricity price to consumers of smart grid system. However, the existing energy management systems only focus on pricing system, for example, real-time electricity prices. In this paper, we try to improve the existing energy management system and propose the energy management system that mainly focuses on the efficiency of electricity consumption. In the proposed management system, PMU(Phasor Measurement Units) installed in switchboards gathers electricity data in a real time. We also propose to use data mining method, which is applied to analyzed electricity data for improving energy efficiency. Also, the proposed energy management system is designed to efficiently control the electricity between PMU and management system in case of a shortage of electricity or surplus electricity.