• 정보저장시스템학회:학술대회논문집
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• 2005.10a
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• pp.151-152
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• 2005

Blu-ray 디스크에서는 NA=0.85 의 높은 개구수를 채용함으로써 디스크 보호층의 두께 변동에 따른 구면수차 발생량이 매우 커지는 문제가 발생하게 된다. 따라서 디스크 두께 공차 $3{\mu}m$에서 Blu-ray 시스템의 수차 발생 허용수준을 초과한다. 그리고 Multi-layer 디스크의 경우 각 층의 간격이 $25{\mu}m$ 정도의 간격을 두고 배치되어 있어 기록층 사이에서 발생하는 구면수차의 보정이 필요하며, 종래의 기술은 보상범위가 $3{\mu}m$로 한정되어 사용이 불가능하며 또한 대물렌즈 조립공차가 엄격히 관리되어야 하는 문제점을 안고 있었다. 본 연구에서는 액정렌즈에 의한 구면수차 보정으로 이러한 문제점들을 해결하고자 하였다. 액정렌즈는 오목렌즈 액정소자와 볼록렌즈 액정소자로 구성되며 픽업 광학계 내에 조합하여 설계함으로써 Multi-layer 디스크의 두께 변동에 따라 발생하는 구면수차를 보정하고 대물렌즈 조립공차 문제를 해결할 수 있다.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.78-81
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• 2008

현재 전력사업 분야에서는 산업화의 발전과 더불어 전력수요의 갑작스러운 증가와 전 세계적인 지구 온난화 문제를 해결하기 위해서 환경 친화적인 분산전원에 대한 연구가 활발히 진행되고 있다. 본 논문은 분산전원 시스템의 안정성 및 고효율 운전을 위해 계통과 분산전원 상호간의 영향에 대한 연구를 하였다. 계통과 분산전원의 연계시 계통전원의 악영향에 대한 PV 시스템의 응답특성을 과전압 및 부족전압, 순간정전, 전압 써지 등으로 구분하여 각각에 대한 응답특성을 확인하고, 분산전원의 이상 출력이 계통에 미치는 영향을 IEEE 929-2000의 규정을 바탕으로 시뮬레이션 환경을 만들고 각각의 경우에 대한 응답특성을 주파수 허용 변동범위(59.3Hz~60.5Hz)로 확장하여 계통과 PV시스템 상호간의 영향 및 응답특성을 확인 할 수 있었다.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.05a
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• pp.223-228
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• 2004

물질을 구성하는 분자가 빛을 흡수하면 그 빛의 파장에 따른 복사에너지에 의하여 열적 반응이나 광화학적 반응이 일어난다. 특히 적외선 복사에 의한 열적 반응은 물질의 온도상승이나 온도하강 등의 과정을 통하여 물질의 물리적 손상을 초래한다. 따라서 적외선을 포함하는 복사에너지의 조사에 의하여 전시실, 진열장내의 전시물의 온도 변화를 측정하여 그 온도의 변화 범위보다 적은 온도 변화가 전시물에서 일어나도록 조명을 제한하고 알맞은 광원을 선정하여야 할 필요가 있다. 본 연구에서는 광원에 의한 시료의 온도상승과 적외선 복사량을 측정할 수 있는 측정시스템을 구축하고, 전시조명용으로 많이 사용하는 여러 광원을 대상으로 각 광원의 방사조도를 변화시키면서 시료의 표면온도와 적외선 복사량을 각각 측정하였다. 측정의 결과를 토대로 방사조도와 온도 및 적외선 복사량간의 함수관계를 파악하고, 조도의 변동에 따른 시료의 온도 변화 및 적외선 복사량의 변화를 비교, 분석하였다.

• Proceedings of the KIEE Conference
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• 2005.11c
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• pp.83-86
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• 2005

본 논문은 대단위 아파트를 주 대상으로 서울 7개소, 경기도 7개소, 강원도 10개소, 충청도 6개소, 총 30개소에 대한 승강기설비 전원품질을 측정하고 역률 변화, 고조파, 전압변동, 전압강하 등을 분석하였다. 분석결과 승강기설비 제어반 인입구에서의 실제역률(TPF)과 기본역률(DPF)의 차이가 고조파로 인하여 크게 나타나는 것을 확인하였고, 역률의 변화는 전원측(변압기)에 가까워질수록 작아지는 것을 확인하였다. 전압 종합왜형률(VTHD)은 안정적이지만 전류 종합왜형률(CTHD)은 기준치 이상의 매우 높은 수치가 발생되었고, 전원측(변압기)에 가까워질수록 작아지는 것을 확인하였다. 승강기설비 제어반 인입구에서의 전압강하율은 기준치 5%를 상회하고 있으나, 전압변동률은 최대 5.1%로서 기준치 10% 범위에 포함되었다.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.163-164
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• 2015

본 논문에서는 자기유도 방식의 3.3kW급 무선전력전송회로의 설계 및 실험결과를 제시한다. 본 논문에서 고려하는 무선전력전송회로는 전기차 무선충전에 적용하는 것을 염두에 두고 이격거리는 17.5cm를 기준으로 하는 한편, 최근 전기차 무선충전 분야의 표준화 동향을 고려하여 동작주파수 범위는 85kHz 내외로 유지하는 것을 설계사양으로 채택하였다. 또한, 전기차무선충전에 있어서 급전단과 수전단의 이격거리, 정렬오차 등의 변동으로 인한 자기 결합도의 변동이 있을 수 있음을 감안하여 이를 설계에 반영하였다. 설계사양을 검증하기 위해 실험용 회로를 구성하였고 이에 대한 효율시험 결과를 제시하였다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.295-296
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• 2019

본 논문은 넓은 출력전압 범위와 High Power Density를 위한 절연형 HMF PFC(Isolated Harmonic Modulation PFC)를 제안한다. 제안된 PFC는 변압기 leakage inductance를 회로에 적용시켜 switching device의 voltage stress를 효과적으로 줄일 수 있는 voltage-fed형태의 ful-bridge구조를 기반으로 한다. 출력 측 CV(Constant Voltage) control을 통하여 출력 혹은 link 전압을 load 변동에 상관없이 일정 유지시켜준다. 또한 CC(Constant Current) control 방식을 사용하여 출력 측 battery 특성 조건이 변동되어도 일정하게 충전시켜 줄 수 있도록 한다. HMF 제어방식을 적용한 3.3kW Prototype을 통해 이를 입증한다.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.1
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• pp.1-11
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• 2010

In order to understand the biological environmental characteristics with temporal variations of the physico-chemical factors in 2012 Yeosu Expo site of Korea, we investigated at one station, once per week, from April 2006 to December 2007. The surface water temperature ranged from 6.8 to $27.8^{\circ}C$ and the bottom water temperature ranged from 6.3 to 25.9 $25.9^{\circ}C$. The salinity varied from 12.8 to 33.0 psu in the surface water and from 25.2 to 33.6 psu in the bottom water. A strong halocline was observed between the surface and bottom layers in the summer when a rapid decrease of salinity coincided with heavy rainfall. The DIN concentration ranged from 1.36 to $82.7{\mu}M$ in the surface water and from 0.82 to $25.2{\mu}M$ in the bottom water. Phosphate concentration varied from 0.06 to $2.13{\mu}M$ in the surface water and from 0.07 to $1.38{\mu}M$ in the bottom water. Silicate was $1.68-52.0{\mu}M$ in the surface water and $1.37-30.7{\mu}M$ in the bottom water. The nutrient concentrations were generally high during heavy rainfalls and low water temperature periods, and considerably decreased in spring and autumn. The N/P ratio ranged from 4.43 to 325 in the surface water and from 3.8 to 321 in the bottom water. It increased rapidly during the heavy rainfall season and remained at a value of approximately 16 in other periods. The chlorophyll a concentration ranged from 0.46 to $65.0{\mu}g$ $L^{-1}$ in the surface water and from 0.71 to $15.0{\mu}g$ $L^{-1}$ in the bottom water. $Chl-{\alpha}$ concentration remained low in periods of low water temperature, however rapidly increased in periods of high water temperature. From the results of principal component analysis (PCA) and multiple regression analysis (MRA), we conclude that temporal variations of physico-chemical and biological factors were greatly affected by the influx of fresh water, and that nutrients were well controlled by their uptake and assimilation by phytoplankton. Also, during the low water temperature periods, environmental structure in this study site was affected by recycled nutrients through nutrient cycling and mineralization.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.608-613
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• 2017

In this paper, we consider the stability bound for uncertainty of delayed state variables in the linear discrete interval time-varying systems with time-varying delay time. The considered system has an interval time-varying system matrix for non-delayed states and is perturbed by the unstructured time-varying uncertainty in delayed states with time-varying delay time within fixed interval. Compared to the previous results which are derived for time-invariant cases and can not be extended to time-varying cases, the new stability bound in this paper is applicable to time-varying systems in which every factors are considered as time-varying variables. The proposed result has no limitation in applicable systems and is very powerful in the aspects of feasibility compared to the previous. Furthermore. the new bound needs no complex numerical algorithms such as LMI(Linear Matrix Inequality) equation or upper solution bound of Lyapunov equation. By numerical examples, it is shown that the proposed bound is able to include the many existing results in the previous literatures and has better performances in the aspects of expandability and effectiveness.

• Korean Journal of Ecology and Environment
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• v.36 no.3 s.104
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• pp.257-268
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• 2003

Daily monitoring was conducted to elucidate the changes in turbidity and distribution of particles in the turbid water of a river-type reservoir (Paltang Reservoir) from 1999 to 2001. Water turbidity and the particle distribution of turbid water were principally affected by meteorological factors particularly rainfall patterns and hydrological factors such as inflow and outflow. The mean concentration of turbidity was constant each year, with the concentration of less than 10 NTU accounting for 85%. Seasonal characteristics were remarkable, with winter and spring having < 5 NTU, autumn 5 ${\sim}$ 10 NTU, and summer > 20 NTU. Unlike hydrological changes, maximum turbidity was observed from late July to early August and continuously increased from 1999 to 2001. In particular, the maximum turbidity of reservoirs remarkably increased toward the lower part of reservoir in 2001. Discharge and turbidity increased or decreased slowly in 1999; in contrast, turbidity rapidly increased in the early rainfall period of 2000 and 2001 but later decreased as discharge increased. In the particles of turbid water, clay ingredients were more densely distributed and more dominant in all stations. Of the total particles in turbid water, clay constituted 63.9${\sim}$66.6% and silt 33.4${\sim}$36.1% to account for a combined total of 98.9 ${\sim}$ 100%. Sand made up less than 1.1%. The turbidity of river-type reservoir was also found to be mainly affected by the biomass of plankton in a non-rainfall period. During a rainfall period, however, the quantity and relative ratio of inorganic particles depending on the soil components affected turbidity.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.