• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.112.2-112.2
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• 2011

PVT(Photovoltaic Thermal) 모듈은 태양광과 태양열 에너지를 동시 이용이 가능한 모듈로서 태양광전지(PV, Photovoltaic)모듈에 열교환기를 접합한 형태로 전기에너지뿐만 아니라 열에너지를 동시에 생산할 수 있는 시스템이다. 기존 PV 모듈은 일사량이 많으면 전력 생산량이 증가하는 동시에 PV모듈의 온도가 상승함에 따라 발전 효율이 감소하는 문제점이 있으며 일반적으로 $25^{\circ}C$이상 조건에서 모듈 온도가 $10^{\circ}C$ 증가할수록 발전효율의 약 4~5% 정도 감소하는 것으로 보고되고 있다. PVT 모듈은 기존 태양광모듈에 열교환기를 접합하여 냉각함으로써 PV모듈의 온도를 낮추어 발전효율을 증가시키는 동시에 부가적으로 발생하는 온수를 직접이용하거나 다양한 계통의 보조 열원으로 이용할 수 있는 장점이 있다. 본 연구에서는 수치해석기법(CFD)을 활용하여 PV모듈 냉각 및 온수 발생을 위한 열교환기를 설계하였으며 다양한 형상의 열교환기에 대해 유동해석을 수행하여 최적의 열흡수효율을 갖는 열교환기의 형상을 설계하였다. 또한 최적 설계된 PVT 모듈을 제작하여 실제 태양과 유사한 광원을 갖는 인공태양조건에서의 실내 실험을 통해 PVT 모듈의 성능을 검증하였으며 또한 실제 노상에 설치하여 ASHRAE 93-77의 실험기준과 ECN의 PVT 집열기 성능측정 가이드라인에 따라 옥외 시험평가를 하여 PVT 모듈의 성능 검증을 하였다. 최적 설계된 PVT모듈에 대한 성능평가 결과 기존 PV 모듈보다 발전효율이 약 15%(기존 발전효율 대비) 향상된 결과를 확인하였다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.69-76
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• 2006

This paper proposes a self-repair system which is able to self-repair in cell unit by imitating the structure of living beings. Because the data of artificial cells move even diagonally, our system can self-repair faults not in column unit, but in cell unit. It leads to design an efficient self-repair system for multiple faults. Moreover, in artificial cell design, the usage of logic-based design method has smaller system size than that of the previous register-based design method. Our experimental result for 2-bit up/down counter shows 40.3% reduction in hardware overhead, compared to the previous method [6].

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.5 s.335
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• pp.15-22
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• 2005

This paper proposes an efficient structure which is able to perform self-detection and self-repair for faults in a digital system by imitating the structure of living beings. The self-repair system is composed of artificial cells, which have homogeneous structures in the two-dimension, and spare cells. An artificial cell is composed of a logic block based on multiplexers, and a genome block, which controls the logic block. The cell is designed using DCVSL (differential cascode voltage switch logic) structure to self-detect faults. If a fault occurs in an artificial cell, it is self-detected by the DCVSL. Then the artificial cells which belong to the column are disabled and reconfigured using both neighbour cells and spare cells to be repaired. A self-repairable 2-bit up/down counter has been fabricated using Hynix $0.35{\mu}m$ technology with $1.14{\times}0.99mm^2$ core area and verified through the circuit simulation and chip test.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.10
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• pp.865-871
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• 2016

In recent years, traditional scaffold fabrication techniques such as gas foaming, salt leaching, sponge replica, and freeze casting in tissue engineering have significantly limited sufficient mechanical property and cell interaction effect due to only random pores. Fused deposition modeling is the most apposite technology for fabricating the 3D scaffolds using the polymeric materials in tissue engineering application. In this study, 3D slurry mould was fabricated with a blended biphasic calcium phosphate (BCP)/Silica/Alginic acid sodium salt slurry in PCL mould and heated for two hours at $100^{\circ}C$ to harden the blended slurry. 3D dual-pore BCP/Silica scaffold, composed of macro pores interconnected with micro pores, was successfully fabricated by sintering at furnace of $1100^{\circ}C$. Surface morphology and 3D shape of dual-pore BCP/Silica scaffold from scanning electron microscopy were observed. Also, the mechanical properties of 3D BCP/Silica scaffold, according to blending ratio of alginic acid sodium salt, were evaluated through compression test.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.12
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• pp.1079-1088
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• 2015

Parallelism and performance enhancement of calculating view factors in KSDS developed by KARI is introduced in this paper. View factor is an essential parameters of radiation thermal analysis for a spacecraft, and the amount of computation of them is not negligible. Especially, independent integration of view factors at each position of the orbit because the relative displace between solar panel and main body of a satellite varies with the position on the orbit. This paper introduces a range of parallelism of computing view factor and their performance, detection of obstructions by spatial search algorithm based on KD-Tree, and the reduction of the calculation of view factors of a satellite with relative motion between solar panel and main body, called updating fractional view factor matrix, for satellite thermal analysis.

• Smart Media Journal
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• v.13 no.7
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• pp.9-17
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• 2024

Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal Convolution Network(TCN) and UnSupervised Anomaly Detection for multivariate data(USAD). The proposed model using TCN-based autoencoder and the USAD structure, which uses two decoders and adversarial training, to quickly learn temporal features and enable robust anomaly detection. To validate the performance of TCN-USAD, comparative experiments were performed using two building energy datasets. The results showed that the TCN-based autoencoder can perform faster and better reconstruction than RNN-based autoencoder. Furthermore, TCN-USAD achieved 20% improved F1-Score over other anomaly detection models, demonstrating excellent anomaly detection performance.

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1321-1330
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• 2012

In this study, the daily inflow at the basin of Chungju dam is predicted using wavelet-artificial neural network for nonlinear model. Time series generally consists of a linear combination of trend, periodicity and stochastic component. However, when framing time series model through these data, trend and periodicity component have to be removed. Wavelet transform which is denoising technique is applied to remove nonlinear dynamic noise such as trend and periodicity included in hydrometeorological data and simple noise that arises in the measurement process. The wavelet-artificial neural network (WANN) using data applied wavelet transform as input variable and the artificial neural network (ANN) using only raw data are compared. As a results, coefficient of determination and the slope through linear regression show that WANN is higher than ANN by 0.031 and 0.0115 respectively. And RMSE and RRMSE of WANN are smaller than those of ANN by 37.388 and 0.099 respectively. Therefore, WANN model applied in this study shows more accurate results than ANN and application of denoising technique through wavelet transforms is expected that more accurate predictions than the use of raw data with noise.

• Analytical Science and Technology
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• v.26 no.3
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• pp.190-198
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• 2013

Ambers have been used mostly as beads, jewelry and ornaments from ancient times and excavated as a buried artifact. When excavated, they are severely weathered to be cracked, exfoliated and disintegrated. Monitoring of changes in composition of amber according to weathering is very important for diagnosing the condition of amber and applying conservation materials and techniques. In this study, we tried to find the components of amber by analyzing amber with pyrolysis/GC/MS. The changes in the composition of pyrolzates after artificial ageing for 60 days under heat and oxygen were also observed. Abietic acid was detected as a main component of fresh amber and monoterpene, alkene, aromatic hydrocarbon were detected as major pyrolyzates. Changes with artificial ageing was estimated by comparing the peak area ratio of 23 components, and it was found that abietic acid abruptly decreased in the presence of heat and oxygen together, revealing that oxygen is a key factor to the deterioration of amber. It was also tried to understand the weathered surface of original amber gemstone based on the result of this ageing experiment.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• Journal of the Korean Solar Energy Society
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• v.37 no.2
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• pp.13-22
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• 2017

Two-step water splitting thermochemical cycle with $CeO_2/ZrO_2$ foam device was investigated by using a solar simulator composed of 2.5 kW Xe-Arc lamp and mirror reflector. The hydrogen production of $CeO_2/ZrO_2$ foam device depending on heat recovery of Thermal-Reduction step and Water-Decomposition step was analyzed, and the hydrogen production of $CeO_2/ZrO_2$ and $NiFe_2O_4/ZrO_2$ foam devices was compared. Resultantly, the quantity of hydrogen generation increased by 52.02% when the carrier gas of Thermal-Reduction step is preheated to $200^{\circ}C$ and, when the $N_2/steam$ is preheated to $200^{\circ}C$ in the Water-Decomposition step, the quantity of hydrogen generation increased by 35.85%. Therefore, it is important to retrieve the heat from the highly heated gases discharged from each of the reaction spaces in order to increase the reaction temperature of each of the stages and thereby increasing the quantity of hydrogen generated through this.