• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.184-201
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• 2013

In this paper, we study the problem of designing the power and number of cooperative node (CN) in the cooperation phase to maximize the average throughput for secondary user (SU), under the constraint of the total cooperation and transmission power. We first investigate the scheme of cooperative spectrum sensing without a separated control channel. Then, we prove that there indeed exist an optimal CN power when the number of CNs is fixed and an optimal CN number when CN power is fixed. The case without the constraints of the power and number of CN is also studied. Finally, numerical results demonstrate the characteristics and existences of optimal CN power and number. Meanwhile, Monte Carlo simulation results match to the theoretical results well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11A
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• pp.878-891
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• 2011

We consider the throughput-maximization problem for both the up- and downlink in a wireless network with interference channels. For this purpose, we design an iterative and distributive uplink algorithm based on Lagrangian relaxation. Using the uplink power prices and network duality, we achieve throughput-maximization in the dual downlink that has a symmetric channel and an equal power budget compared to the uplink. The network duality we prove here is a generalized version of previous research [10], [11]. Computational tests show that the performance of the up- and downlink throughput for our algorithms is close to the optimal value for the channel orthogonality factor, ${\theta}{\in}$(0.5, 1]. On the other hand, when the channels are slightly orthogonal (${\theta}{\in}$(0, 0.5]), we observe some throughput degradation in the downlink. We have extended our analysis to the real downlink that has a nonsymmetric channel and an unequal power budget compared to the uplink. It is shown that the modified duality-based approach is thoroughly applied to the real downlink. Considering the complexity of the algorithms in [6] and [18], we conclude that these results are quite encouraging in terms of both performance and practical applicability of the generalized duality theorem.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.6
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• pp.266-271
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• 2000

The electric power industries are moving from the conventional monopolistic or vertically integrated environments to deregulated and competitive environments, where each participant is concerned with profit maximization rather than system-wide costs minimization. Consequently, the conventional least-cost approaches for the generation resource schedule can not exactly handle real-world situations. This paper presents a game theory application for analyzing power transactions and market design in a deregulated energy marketplace, where the market participants determine the net profits through the optimal bidding strategies. The demand elasticity of the energy price is considered for the realistic modeling of the deregulated marketplace.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1132-1136
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• 2014

In the context of increasing electric energy consumption in a data center, energy efficiency improvement is strongly emphasized. In a data center, electric energy is largely consumed by DC power supply system, which is based on a rectifier composed by multiple parallel converters. Therefore, rectifier efficiency must be improved for minimizing loss of DC power supply system. Rectifier efficiency can be modulated by load allocation to converters because converter efficiency depends on input AC power. In this paper, we propose a new control method to maximize rectifier efficiency. The method can control load allocation to converters by introducing active power converter control scheme and start-and-stop of converters. In order to illustrate optimal load allocations in a rectifier, a maximization problem of rectifier efficiency is formulated as a nonlinear optimization one. The problem is solved by Lagrangian relaxation method and the computation results provide the validity of proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.15-26
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• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5149-5167
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• 2017

Reducing energy consumption in a wireless video sensor network (WVSN) is a crucial problem because of the high video data volume and severe energy constraints of battery-powered WVSN nodes. In this paper, we present an adaptive dynamic resizing approach for a SRAM communication buffer in a WVSN node in order to reduce the energy consumption and thereby, to maximize the lifetime of the WVSN nodes. To reduce the power consumption of the communication part, which is typically the most energy-consuming component in the WVSN nodes, the radio needs to remain turned off during the data buffer-filling period as well as idle period. As the radio ON/OFF transition incurs extra energy consumption, we need to reduce the ON/OFF transition frequency, which requires a large-sized buffer. However, a large-sized SRAM buffer results in more energy consumption because SRAM power consumption is proportional to the memory size. We can dynamically adjust any active buffer memory size by utilizing a power-gating technique to reflect the optimal control on the buffer size. This paper aims at finding the optimal buffer size, based on the trade-off between the respective energy consumption ratios of the communication buffer and the radio part, respectively. We derive a formula showing the relationship between control variables, including active buffer size and total energy consumption, to mathematically determine the optimal buffer size for any given conditions to minimize total energy consumption. Simulation results show that the overall energy reduction, using our approach, is up to 40.48% (26.96% on average) compared to the conventional wireless communication scheme. In addition, the lifetime of the WVSN node has been extended by 22.17% on average, compared to the existing approaches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.247-254
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• 2009

Over the last decade multiple-input and multiple-output (MIMO) systems have been actively researched and started to be deployed in wireless communications owing to the significant increase in channel capacity. In this paper, we propose a energy saving MAC protocol in systems by focusing on energy efficiency instead of capacity maximization. We considers the energy consumption together with the tradeoff between reliability (i.e., diversity) and throughput (i.e., multiplexing gain), and dynamically chooses an appropriate number of antennas for transmission. In computing the total energy consumption, we counts circuit energy as well as transmission energy. Naturally the circuit energy consumption is directly proportional to the number of active antennas. Through numerical analysis, we confirm that our power saving MAC scheme for MIMO considerably saves energy consumption compared to conventional capacity maximization schemes that use a fixed number of MIMO channels, for a given outage constraint. Our finding is that the capacity maximizing communication which possibly can be regarded best in terms of energy efficiency gives a different solution from the energy minimizing communication.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.425-427
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• 2002

In a deregulated power market, a load model considered the characteristics of electric consumers is required and the congestion management of transmission lines should be resolved in a point of economic benefits. It is essential that the optimal power flow algorithm is applied to manage transmission line congestion. Therefore, in this paper, we implement the optimal power flow with object function of social welfare maximization based linear programming and analysis the nodal prices for generations and loads through a numerical study.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.570-571
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• 2008

This paper presents a method for the determination of total transfer capability in interconnected power systems, which is based on sequential quadratic programming (SQP). The objective function is the maximization of the interconnected line flow. To calculate TTC the control variables are the active power of the generating units, the voltage magnitude of the generator, transformer tap settings and SVC setting. The state variables are the bus voltage magnitude, the reactive power of the generating unit, line flows and the tie line flow. The method proposed is applied to the modified IEEE 14 buses model system.

• International Journal of Fluid Machinery and Systems
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• v.10 no.3
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• pp.264-273
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• 2017

In order to be able to predict the maximum Annual Energy Production (AEP) for tidal power plants, an AEP optimization tool based on Evolutionary Algorithms was developed by ANDRITZ HYDRO. This tool can simulate all operating modes of the units (bi-directional turbine, pump and sluicing mode) and provide the optimal plant operation that maximizes the AEP to the control system. For the Swansea Bay Tidal Power Plant, the AEP optimization evaluated all different hydraulic and operating concepts and defined the optimal concept that led to a significant AEP increase. A comparison between the optimal plant operation provided by the AEP optimization and the full load operating strategy is presented in the paper, highlighting the advantage of the method in providing the maximum AEP.