• Advanced Composite Materials
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• v.16 no.2
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• pp.115-134
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• 2007

This study proposes two new approaches for identifying damage patterns in a holed CFRP cross-ply laminate using an embedded fiber Bragg grating (FBG) sensor. It was experimentally confirmed that the reflection spectrum from the embedded FBG sensor was significantly deformed as the damage near the hole (i.e. splits, transverse cracks and delamination) extended. The damage patterns were predicted using forward analysis (a damage analysis and an optical analysis) with strain estimation and the proposed damage-identification method as well as the forward analysis only. Forward analysis with strain estimation provided the most accurate damage-pattern estimation and the highest computational efficiency. Furthermore, the proposed damage identification significantly reduced computation time with the equivalent accuracy compared to the conventional identification procedure, by using damage analysis as the initial estimation.

• KIEAE Journal
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• v.16 no.6
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• pp.5-12
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• 2016

Purpose: Quasi-steady state simulations have played a pivoting role to expand the user group of simulation to design engineers and architects in Korea. Initially they are introduced in the market as a building energy performance rating tool. In domestic practice, however, quasi-steady state simulations seem to be regarded as a de facto simulation only available for energy retrofit. Selection of ECMs and economic feasibility analysis are being decided through these tools, which implies that running these tools has become a norm step of the Investment-grade Audit. Method: This study aims at identifying issues and problems with the current practice via test cases, analyzing the reasons and opportunities, and then eventually suggesting proper uses of quasi-steady state and dynamic simulations. Result: The functionality of quasi-steady state simulations is more optimized to the rating. If they are to used for energy retrofits, their off-the-shelf functions also need to be expanded for customization and detailed reports. Yet their roles may be limited only to the go/no go decision; because their algorithms are still weak at precisely estimating energy and load savings that are required for making investment decisions compared to detailed simulations.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.453-456
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• 2022

The role of the power grid is to ensure stable power supply. It is necessary to take various measures to prepare for unstable situations without notice. After identifying the relationship between features through exploratory data analysis using weather data, a machine learning based energy production prediction model is modeled. In this study, the prediction reliability was increased by extracting the features that affect energy production prediction using principal component analysis and then applying it to the machine learning model. By using the proposed model to predict the production energy for a specific period and compare it with the actual production value at that time, the performance of the energy production prediction applying the principal component analysis was confirmed.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.39-44
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• 1997

The International Criticality Safety Benchmark Evaluation Project (ICSBEP) is under way for the purpose identifying, evaluating, and compiling benchmark critical experiment data into a standardized format that allows criticality analysts to easily use the data to validate calculational methods and cross sections. As part of this activity, PNL30-35 experiments, which had been adopted as benchmark problems by CSEWG in 1970s, were reevaluated, which results in some additions and modifications: changes in fuel number density, modification to the experimental keff, modifications to the soluble boron concentration for PNL-31, and addition of an uncertainty in the benchmark-model k$_{eff}$./.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2017.05a
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• pp.87.1-87.1
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• 2017

High functional catalyst to efficiently produce clean and earth-abundant renewable fuels plays a key role in securing energy sustainability and environmental protection of our society. Hydrogen has been considered as one of the most promising energy carrier as represented by focused research works on developing catalysts for the hydrogen evolution reaction (HER) from the water hydrolysis over the last several decades. So far, however, the major catalysts are expensive transition metals. Here using first principles density functional theory (DFT) calculations we screen various pyrites for HER by identifying fundamental descriptor governing the catalytic activity. We enable to capture a strong linearity between experimentally measured exchange current density in HER and calculated adsorption energy of hydrogen atom in the pyrites. The correlation implies that there is an underlying design principle tuning the catalytic activity of HER.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.375-378
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• 2008

This paper aims at identifying the factors that have influenced changes in the level of industrial $CO_2$ emissions. By means of complete decomposition method the observed changes are analyzed into five different factors: output level, energy intensity, energy mix and structural change and utility use. The application study refers to the manufacturing sectors in Korea. Moreover, this paper discusses the relationship between Korea's manufacturing $CO_2$ emission and economic growth (as measured by GDP), investigating whether economic growth is decoupling from $CO_2$ emission.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1177-1184
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• 2008

Non-line-of-sight (NLOS) propagation can severely weaken the accuracy of ranging and localization in wireless location systems. NLOS bias mitigation techniques have recently been proposed to relieve the NLOS effects, but positively rely on the capability to accurately distinguish between LOS and NLOS propagation scenarios. This paper proposes an energy-capture-based NLOS identification method for LDR-UWB systems, based on the analysis of the characteristics of the channel impulse response (CIR). With this proposed energy capture method, the probability of successfully identifying NLOS is much improved than the existing methods, such as the kurtosis method, the strongest path compare method, etc. This NLOS identification method can be employed in adaptive modulation scheme to decrease bit error ratio (BER) level for certain signal-to-noise ratio (SNR). The BER performance with the adaptive modulation can be significantly enhanced by selecting proper modulation method with the knowledge of channel information from the proposed NLOS identification method.

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.769-770
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• 2005

This study aims to demonstrate the practical value of radioscopic differentiation of materials. The dual energy method is proposed for identifying materials according to atomic numbers. The differentiation of materials is achieved by comparing the attenuation ratio of low and high energy photons. We used gamma-rays of 0.662 MeV and 1.25 MeV and NaI(Tl) scintillation detector with a Multi-channel Analyzer (MCA). We also carried out the Monte Carlo simulation for the case of bremsstrahlung radiation from dual electron beams of 4 MeV and 9 MeV.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.193-197
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• 2012

This paper focuses on identifying which appliance is currently operating by analyzing electrical load signature for home energy monitoring system. The identification framework is comprised of three steps. Firstly, specific appliance features, or signatures, were chosen, which are DC (Duty Cycle), SO (Slope of On-state), VO (Variance of On-state), and ZC (Zero Crossing) by reviewing observations of appliances from 13 houses for 3 days. Five appliances of electrical rice cooker, kimchi-refrigerator, PC, refrigerator, and TV were chosen for the identification with high penetration rate and total operation-time in Korea. Secondly, K-NN and Naive Bayesian classifiers, which are commonly used in many applications, are employed to estimate from which appliance the signatures are obtained. Lastly, one of candidates is selected as final identification result by majority voting. The proposed identification frame showed identification success rate of 94.23%.