• ETRI Journal
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• v.36 no.3
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• pp.506-509
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• 2014

This paper studies the energy efficiency power allocation for cognitive radio networks based on uplink orthogonal frequency-division multiplexing. The power allocation problem is intended to minimize the maximum energy efficiency measured by "Joule per bit" metric, under total power constraint and robust aggregate mutual interference power constraint. However, the above problem is non-convex. To make it solvable, an equivalent convex optimization problem is derived that can be solved by general fractional programming. Then, a robust energy efficiency power allocation scheme is presented. Simulation results corroborate the effectiveness of the proposed methods.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.592-594
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• 2001

두 문자열의 부분배치(local alignment)를 찾는 대표적인 알고리즘인 Smith-Waterman 알고리즘(SW 알고리즘)은 정규화된 최적부분배치를 찾지 못하는 단점이 있다. 최근에 fractional programming 기법을 이용하여 여러 번의 SW 알고리즘을 수행함으로써 정규화된 최적부분배티를 찾는 알고리즘이 제시되었지만 이는 매우 많은 시간이 걸린다. 본 논문에서는 fractional programming 기법을 이용하여 정규화된 최적부분배치를 찾는 알고리즘에, 완전매치(Exact Match)을 이용한 휴리스틱 기법인 Banded SW 알고리즘을 적용하여, 낮은 오차를 가지면서 실용적으로 매우 빠른 정규화된 최적부분배치를 찾는 알고리즘을 제시하고 이 알고리즘과 제시하고 이 알고리즘과 기존의 알고리즘을 직접 구현하여 실험한 결과를 비교 분석한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.3-3
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• 1991

• ETRI Journal
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• v.37 no.3
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• pp.471-479
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• 2015

Traditional designs of cognitive radio (CR) focus on maximizing system throughput. In this paper, we study the joint overlay and underlay power allocation problem for orthogonal frequency-division multiple access-based CR. Instead of maximizing system throughput, we aim to maximize system energy efficiency (EE), measured by a "bit per Joule" metric, while maintaining the minimal rate requirement of a given CR system, under the total power constraint of a secondary user and interference constraints of primary users. The formulated energy-efficient power allocation (EEPA) problem is nonconvex; to make it solvable, we first transform the original problem into a convex optimization problem via fractional programming, and then the Lagrange dual decomposition method is used to solve the equivalent convex optimization problem. Finally, an optimal EEPA allocation scheme is proposed. Numerical results show that the proposed method can achieve better EE performance.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1389-1399
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• 2015

This research suggests maximum power point tracking (MPPT) for the solar photovoltaic (PV) power scheme using a new constant voltage (CV) fractional order incremental conductance (FOINC) algorithm. The PV panel has low transformation efficiency and power output of PV panel depends on the change in weather conditions. Possible extracting power can be raised to a battery load utilizing a MPPT algorithm. Among all the MPPT strategies, the incremental conductance (INC) algorithm is mostly employed due to easy implementation, less fluctuations and faster tracking, which is not only has the merits of INC, fractional order can deliver a dynamic mathematical modelling to define non-linear physiognomies. CV-FOINC variation as dynamic variable is exploited to regulate the PV power toward the peak operating point. For a lesser scale photovoltaic conversion scheme, the suggested technique is validated by simulation with dissimilar operating conditions. Contributions are made in numerous aspects of the entire system, including new control algorithm design, system simulation, converter design, programming into simulation environment and experimental setup. The results confirm that the small tracking period and practicality in tracking of photovoltaic array.

• Management Science and Financial Engineering
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• v.7 no.2
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• pp.59-71
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• 2001

In the present paper, we present method to solve a Fractional Bulk Transportation Problem(FBTP) in which the numerator is quadratic in nature and the denominator is linear. A related (FBTP) is formed whose feasible solutions are ranked to reach an optimal solution of the given problem. The method to find these feasible solutions makes use of parametric programming wherein a series of Ordinary Bulk Transportation Problems are solved by the usual methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4387-4404
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• 2015

The energy-efficient design of sensing-based spectrum sharing of a multi-input and multi-output (MIMO) cognitive radio (CR) system with imperfect multiple antenna spectrum sensing is investigated in this study. Optimal resource allocation strategies, including sensing time and power allocation schemes, are studied to maximize the energy efficiency (EE) of the secondary base station under the transmit power and interference power constraints. EE problem is formulated as a nonlinear stochastic fractional programming of a nonconvex optimal problem. The EE problem is transformed into its equivalent nonlinear parametric programming and solved by one-dimension search algorithm. To reduce searching complexity, the search range was founded by demonstration. Furthermore, simulation results confirms that an optimal sensing time exists to maximize EE, and shows that EE is affected by the spectrum detection factors and corresponding constraints.

• Genomics & Informatics
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• v.1 no.1
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• pp.25-31
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• 2003

Local alignment is an important task in molecular biology to see if two sequences contain regions that are similar. The most popular approach to local alignment is the use of dynamic programming due to Smith and Waterman, but the alignment reported by the Smith-Waterman algorithm has some undesirable properties. The recent approach to fix these problems is to use the notion of normalized scores for local alignments by Arslan, Egecioglu and Pevzner. In this paper we consider the problem of finding all local alignments whose normalized scores are above a given threshold, and present a fast heuristic algorithm. Our algorithm is 180-330 times faster than Arslan et al.'s for sequences of length about 120 kbp and about 40-50 times faster for sequences of length about 30 kbp.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.288-289
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• 2019

This paper studies the secrecy capacity for a wireless cooperative network with perfect channel state information at the relays, and receiver. A similar assumption is also made for the instance where there exist a direct link between the transmitter and receiver. Physical Layer security techniques are employed in wireless networks to mitigate against the activity of eavesdroppers. It offers a viable alternative to computationally intensive encryption. In this paper the design of a protocol utilizing jamming (via jamming nodes) for better security and relaying (via relay nodes) for the amplify-and-forward (AF) operation, is investigated. A a signal-to-noise variant of secrecy known as secrecy gap is explored because of its use of lesser computational power - preferable for practical systems. Thus we maximize this signal-to-noise approach instead of the conventional secrecy capacity maximization method. With this, an iterative algorithm using geometric programming (GP) and semi-definite programming (SDP) is presented with appreciable benefits. The results show here highlight the benefits of using fractional components of the powers of the relays to offer better secrecy capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1325-1344
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• 2019

Recently, to satisfy mobile users' increasing data transmission requirement, energy efficiency (EE) resource allocation in distributed antenna systems (DASs) has become a hot topic. In this paper, we aim to maximize EE in DASs subject to constraints of the minimum data rate requirement and the maximum transmission power of distributed antenna units (DAUs) with different density distributions. Virtual cell is defined as DAUs selected by the same user equipment (UE) and the size of virtual cells is dependent on the number of subcarriers and the transmission power. Specifically, the selection rule of DAUs is depended on different scenarios. We develop two scenarios based on the density of DAUs, namely, the sparse scenario and the dense scenario. In the sparse scenario, each DAU can only be selected by one UE to avoid co-channel interference. In order to make the original non-convex optimization problem tractable, we transform it into an equivalent fractional programming and solve by the following two sub-problems: optimal subcarrier allocation to find suitable DAUs; optimal power allocation for each subcarrier. Moreover, in the dense scenario, we consider UEs could access the same channel and generate co-channel interference. The optimization problem could be transformed into a convex form based on interference upper bound and fractional programming. In addition, an energy-efficient DAU selection scheme based on the large scale fading is developed to maximize EE. Finally, simulation results demonstrate the effectiveness of the proposed algorithm for both sparse and dense scenarios.