• Journal of Power Electronics
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• 제11권3호
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• pp.360-368
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• 2011

This paper proposes an active frequency with a positive feedback in the d-q frame anti-islanding method suitable for a robust phase-locked loop (PLL) algorithm using the FFT concept. In general, PLL algorithms for grid-connected PV PCS use d-q transformation and controllers to make zero an imaginary part of the transformed voltage vector. In a real grid system, the grid voltage is not ideal. It may be unbalanced, noisy and have many harmonics. For these reasons, the d-q transformed components do not have a pure DC component. The controller tuning of a PLL algorithm is difficult. The proposed PLL algorithm using the FFT concept can use the strong noise cancelation characteristics of a FFT algorithm without a PI controller. Therefore, the proposed PLL algorithm has no gain-tuning of a PI controller, and it is hardly influenced by voltage drops, phase step changes and harmonics. Islanding prediction is a necessary feature of inverter-based photovoltaic (PV) systems in order to meet the stringent standard requirements for interconnection with an electrical grid. Both passive and active anti-islanding methods exist. Typically, active methods modify a given parameter, which also affects the shape and quality of the grid injected current. In this paper, the active anti-islanding algorithm for a grid-connected PV PCS uses positive feedback control in the d-q frame. The proposed PLL and anti-islanding algorithm are implemented for a 250kW PV PCS. This system has four DC/DC converters each with a 25kW power rating. This is only one-third of the total system power. The experimental results show that the proposed PLL, anti-islanding method and topology demonstrate good performance in a 250kW PV PCS.

• 전기의세계
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• 제21권3호
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• pp.41-47
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• 1972

The requirements of a successful design for single phase induction motors with a high efficiency have, in recent years, led to the use of non-quadrature stator windings motors in which a high starting torque is a prime requisite. The capacitor motor is one of above machines in which various possible forms of asymmetry can be occur. These forms of asymmetry in the stator phase windings, encountered in machine designs, are 1) an asymmetrical disposition in space of their magnetic axes, 2) a difference in their effective number of turns, 3) a difference in the distribution of their coil groups per pole and 4) amounts of capacitance of an auxiary winding. In order to apply the effective performance prediction of these form to motors, mading of lower quality-domestic magnetic materials, the analysis and the experimental investigations of its sample motors are described in this paper. The utility of such a motor is demonstrated and it is shown that the effects- a good efficiency, good power factor and high starting torque-of the motor mechanism with non-quadrature stator phase windings can development disadvantages by using the lower quality-domestis magnetic materials.

• Journal of Advanced Marine Engineering and Technology
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• 제31권5호
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• pp.514-521
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• 2007

There has been considerable interest recently in the topic of renewable energy. This is primarily due to concerns about environmental impacts. Moreover, fluctuating and rising oil prices, increases in demand, supply uncertainties and other factors have led to increased calls for alternative energy sources. Small hydropower, especially using water supply system, attracts high attentions because of relatively lower cost and smaller space requirements to construct the plant. Moreover. newly developed positive displacement turbine has high acceptability for the system. Therefore, the purpose of this study is focused on the examination of the performance characteristics and proposition of a optimum design method of the turbine for the improvement of the performance. The results show that newly proposed optimum design method for the turbine has high accuracy of performance prediction and good applicability for the performance improvement of the turbine.

• 대한전자공학회논문지SD
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• 제44권12호
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• pp.93-101
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• 2007

작은 면적과 저전력으로의 구현은 다양한 멀티미디어 하드웨어, 특히 모바일 환경에서 매우 중요한 요구사항이다. 본 논문은 작은 면적과 그에 따른 저전력을 목표로 H.264/AVC 인트라 예측기기 하드웨어 구조를 제안한다. 이미지 프레임을 예측하기 위해 하나의 연산기로 모든 모드 결정과 계산들이 순차적으로 수행기고 그들 중 최적의 값을 선택하는 방식이며, 그 결과로 다른 기존의 논문들 보다 더 작은 면적의 결과를 얻을 수 있었다. 제안된 구조는 Altera Excalibur device를 이용하여 검증되었고, 구현된 하드웨어 구조는 Synopsys Design Compiler와 Samsung STD130 0.18um CMOS Standard Cell Library를 이용하여 합성하였다. 합성결과 크기는 11.9k의 하드웨어 로직 게이트와 1078 byte의 내부 SRAM을 사용하고 최대 동작 주파수는 약 107MHz가 되었다. 제안한 구조는 하나의 QCIF($176\times144$ 화소) 영상 프레임을 처리하는데 879,617클록이 소요되며, 이는 QCIF 영상을 초당 121.5프레임으로 처리가 가능하며, 이는 하드웨어 기반의 실시간 H.264/AVC 부호화 시스템에 적합한 구조임을 보여준다.

• 한국유체기계학회 논문집
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• 제17권2호
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• pp.48-53
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• 2014

A low-noise regenerative blower is developed for fuel cell application by combining the FANDAS-Regen code and design optimization algorithm under several performance constraints for flow capacity, static pressure, efficiency and power consumption. The optimized blower design model is manufactured with some impeller modification based on low noise design concept and tested by using aerodynamic performance chamber facility and narrow-band noise measurement apparatus. The measured results of the optimized blower satisfy the performance requirements and are also compared favorably with the FANDAS-Regen prediction results within a few percent relative error. Furthermore, the present study shows the remarkable noise reduction by 26 dBA can be achieved through design optimization and low noise design concept.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• 설비공학논문집
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• 제30권3호
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• pp.130-142
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• 2018

We use heat pumps with thermal storage system to reduce peak usage of electric power during winters and summers. A heat pump stores thermal energy in a thermal storage tank during the night, to meet load requirements during the day. This system stabilizes the supply and demand of electric power; moreover by utilizing the inexpensive midnight electric power, thus making it cost effective. In this study, we propose a system wherein the thermal storage tank and heat pump are modeled using the TRNSYS, whereas the control simulations are performed by (i) conventional control methods (i.e., thermal storage priority method and heat pump priority method); (ii) region control method, which operates at the optimal part load ratio of the heat pump; (iii) load response control method, which minimizes operating cost responding to load; and (iv) dynamic programming method, which runs the system by following the minimum cost path. We observed that the electricity cost using the region control method, load response control approach, and dynamic programing method was lower compared to using conventional control techniques. According to the annual simulation results, the electricity cost utilizing the load response control method is 43% and 4.4% lower than those obtained by the conventional techniques. We can note that the result related to the power cost was similar to that obtained by the dynamic programming method based on the load prediction. We can, therefore, conclude that the load response control method turned out to be more advantageous when compared to the conventional techniques regarding power consumption and electricity costs.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• 한국전기전자재료학회논문지
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• 제23권11호
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• pp.831-836
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• 2010

In this paper, Plasma Enhanced Chemical Vapor Deposition (PECVD) $SiO_2$ film properties are modeled using statistical analysis and neural networks. For systemic analysis, Box-Behnken's 3 factor design of experiments (DOE) with response surface method are used. For characterization, deposited film thickness and film stress are considered as film properties and three process input factors including plasma RF power, flow rate of $N_2O$ gas, and flow rate of 5% $SiH_4$ gas contained at $N_2$ gas are considered for modeling. For film thickness characterization, regression based model showed only 0.71% of root mean squared (RMS) error. Also, for film stress model case, both regression model and neural prediction model showed acceptable RMS error. For sensitivity analysis, compare to conventional fixed mid point based analysis, proposed sensitivity analysis for entire range of interest support more process information to optimize process recipes to satisfy specific film characteristic requirements.

• 한국정보과학회논문지:정보통신
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• 제30권1호
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• pp.65-74
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• 2003

본 논문에서는 멀티 서비스 WCDMA 이동 시스템을 위한 새로운 서비스 품질 예측 지표를 사용함으로써 사용자들의 QoS 요구치를 고려한 순방향 서비스 수락 기준을 제안한다. 제안된 순방향 서비스 수락 기준은 상대적인 외부 셀의 간섭을 가정하고 각 서비스의 평균 수신 전력을 계산함으로써 얻어진다. 제안한 방법을 이용하여 우리는 서로 다른 서비스의 사용자들이 허용될 수 있는 순방향 서비스 수락 영역을 얻는다. 그리고 제안한 순방향 서비스 수락 기준에 형평성을 고려하여 각각의 서비스를 사용하는 사용자들을 위한 또 다른 순방향 서비스 수락 기준을 제시한다. 수치적 예에서 음성 서비스와 데이타 서비스들의 형평성을 고려한 순방향 서비스 수락 영역과 형평성을 고려하지 않은 순방향 서비스 수락 영역은 제안된 파카의 순방향 서비스 수락 기준들을 이용하여 얻을 수 있다.