• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5335-5353
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• 2019

The definition of the bandwidth efficiency (BE) of cognitive cooperative network (CCN) is the ratio between a number of the licensed slot(s) or sub-channel(s) used by the unlicensed users to transmit a single data packet from the unlicensed transmitter to unlicensed destination, and from unlicensed relay(s) to unlicensed destination. This paper analyzes and improves the BE in the underlay CCN with a new reactive relay selection under interference and power constraints. In other words, this paper studies how unlicensed cooperative users use the licensed network slot(s) or sub-channel(s) efficiently. To this end, a reactive relay selection method named as Relay Automatic Repeat Request (RARQ) is proposed and utilized with a CCN under interference and power constraints. It is shown that the BE of CCN is higher than that of cooperative transmission (CT) due to the interference and power constraint. Furthermore, the BE of CCN is affected by the distance of the interference links which are between the unlicensed transmitter to the licensed destination and unlicensed relay to the licensed destination. In addition, the BE for multiple relays selection over a CCN under interference and power constraints is also analyzed and studied, and it is shown that the BE of CCN decreases as the number of relays increases.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.266-268
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• 2001

As we move to competition, transmission cost become more and more important. At the wholesale competition market, the process of settlement must include the cost of transport. The aim of this paper is to calculate approximate transmission cost at each power sales district office using KEPCO data. We considered investment cost, transmission constraints and marginal losses as cost elements which must be taken into account in transmission cost calculation.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.52-59
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• 2008

This paper describes the secant method for solving the economic dispatch (ED) problem with generator constraints and transmission losses. The ED problem is an important optimization problem in the economic operation of a power system. The proposed algorithm involves selection of minimum and maximum incremental costs (lambda values) and then the evaluation of optimal lambda at required power demand is done by secant method. The proposed algorithm has been tested on a power system having 6, 15, and 40 generating units. Studies have been made on the proposed method to solve the ED problem by taking 120 and 200 units with generator constraints. Simulation results of the proposed approach were compared in terms of solution quality, convergence characteristics, and computation efficiency with conventional methods such as lambda iterative method, heuristic methods such as genetic algorithm, and meta-heuristic methods like particle swarm optimization. It is observed from different case studies that the proposed method provides qualitative solutions with less computational time compared to various methods available in the literature.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.2
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• pp.16-26
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• 2010

The transmission valuation methodology we propose here captures the interaction between generation and transmission investment decisions recognizing that a transmission expansion can impact the profitability of new resources investment, so that a methodology should consider both the objectives of investors in resources and the transmission planner. In this perspective, this paper purports to develop the mixed-integer programming based transmission expansion planning optimization software, which is well designed to determine the construction time and place of new generators, transmission lines, and substations as well as their capacities to minimize total expenditures related to their investment and operations while meeting technical constraints such as capacity margin, constitution ratio of power resources, spinning reserves, energy and fuel constraints, transmission line outages and losses, pi-type branching, and so on. Finally, Garver's simple system is adopted to validate not simply the accuracy but the efficiency of the proposed model in this paper.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.28-37
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• 2008

In this paper, we investigate the problem of minimizing the average transmission power of users while guaranteeing the average delay constraints in time-varying uplink channels. We design a scheduler that selects a user for transmission and determines the transmission rate of the selected user based on the channel and backlog information of users. Since it requires prohibitively high computation complexity to determine an optimal scheduler for multi-user systems, we propose a low-complexity scheduling scheme that can achieve near-optimal performance. In this scheme, we reduce the complexity by decomposing the multiuser problem into multiple individual user problems. We arrange the probability of selecting each user such that it can be determined only by the information of the corresponding user and then optimize the transmission rate of each user independently. We solve the user problem by using a dynamic programming approach and analyze the upper and lower bounds of average transmission power and average delay, respectively. In addition, we investigate the effects of the user selection algorithm on the performance for different channel models. We show that a channel-adaptive user selection algorithm can improve the energy efficiency under uncorrelated channels but the gain is obtainable only for loose delay requirements in the case of correlated channels. Based on this, we propose a user selection algorithm that adapts itself to both the channel condition and the backlog level, which turns out to be energy-efficient over wide range of delay requirement regardless of the channel model.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.739-741
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• 2005

The congestion cost caused by transmission capacities is an important issue in a competitive electricity market. To allocate the congestion cost equitably, the active constraints in a constrained dispatch and the sequence of these constraints should be considered. A multi-stage method which was proposed by H.S. Jung reflects the effects of both the active constraints and the sequence. In a multi-stage method, the types of congestion are analyzed in order to consider the sequence, and the relationship between congestion and the active constraints is derived in a mathematical way. But in some cases, the inactive constraints can affect the congestion sequence and it is reasonable to allocate the congestion cost to them.

• Journal of Mechanical Science and Technology
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• v.20 no.6
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• pp.770-781
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• 2006

This paper defined design constraints for the compound CVTs (continuously variable trans-missions) by combining power-circulation-mode CVTs and power-split-mode CVTs, which were proposed for connecting 2K-H I-type differential gear to V-belt-type CVU (Continuously Variable Unit). The design constraints are the necessary and sufficient conditions to avoid geometrical interferences among elements in the compound CVTs, and to guarantee smooth assembly between the power-circulation-mode CVT and power-split-mode CVT Two com-pound CVTs were designed and manufactured in accordance with the design constraints. With these compound CVTs, theoretical analysis and performance experiments were conducted. The results showed that the design constraints were valid and effective design method, and that the designed compound CVTs had the improved performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5191-5211
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• 2016

It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation networks. The present study proposes a QoS-driven multicast scheme for wireless networks in which the transmission rate and end-to-end delay are assumed to be bounded during a multiple multicast session. A distributed algorithm was used to identify a cost-efficient sub-graph between the source and destination which can satisfy QoS constraints of a multicast session. The model was then modified as to be applied for wireless networks in which satisfying interference constraints is the main challenge. A discrete power control scheme was also applied for the QoS-aware multicast model to accommodate the effect of transmission power level based on link capacity requirements. We also proposed random power allocation (RPA) and gradient power allocation (GPA) algorithms to efficient resource distribution each of which has different time complexity and optimality levels. Experimental results confirm that the proposed power allocation techniques decrease the number of unavailable links between intermediate nodes in the sub-graph and considerably increase the chance of finding an optimal solution.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2136-2141
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• 2009

Transmission planning is an important part of power system planning to meet an increasing demand for electricity. The objective of transmission expansion is to minimize operational and construction costs subject to system constraints. There is inherent uncertainty in transmission planning due to errors in forecasted demand and fuel costs. Therefore, transmission planning process is not reliable if the uncertainty is not taken into account. The paper presents a systematic method to find the optimal location and amount of transmission expansion using Cross-Entropy (CE) incorporating uncertainties about future power system conditions. Numerical results are presented to demonstrate the performance of the proposed method.