• Journal of the Korean Solar Energy Society
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• v.26 no.3
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• pp.79-87
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• 2006

This study aims to presents the applicability of apartment building for renewable energy systems using method of uncomplicated calculation and computer simulation. According to the weather conditions (NASA Surface meteorology and Solar Energy) analysis, it has been found that photovoltaic and wind power system can be applied to apartment buildings application. In case study considering layout and block plan, adaptation of solar water heating, photovoltaic and wind energy system to apartment buildings was proved to produce a profit. And the application strategies of renewable energy systems can be used not only for the investment decisions for economic analysis but also for the comparative analysis of uncomplicated calculation and computer simulation.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.64-71
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• 2006

A target of this paper is to study on the usefulness of the adaptive piezoelectric energy harvesting device as a wireless electrical power supply when it is driven by mechanical vibrations of low frequency. For this purpose, an adaptive control technique and a step-down converter are used. A THUNDER series a piezoelectric material (TH7-R), which has been developed by a NASA engineer is selected for this study. In order to provide a mechanical energy to the piezoelectric material, a mechanical motion vibrator is designed. The adaptive controller is implemented using a dSPACE DS1104 controller board. The do-dc converter with an adaptive control technique harvests energy at over five times the rate of direct charging without a converter.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.853-856
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• 2002

Rocket propulsion systems generate very high-level noise (acoustic loads), which is due to supersonic jet emitted by rocket engine. In practice, the sound power level of rocket propulsion systems is over 180 dB. This high level noise excites rocket structures and payloads, so that it causes the structural failure and electronic malfunction of payloads. Prediction method of acoustic loads of rocket enables us to determine the safety of payloads. A popular prediction method is based on NASA SP-8072. This method was used to predict the acoustic loads of KSR-III rocket. Measurement of acoustic loads by KSR-III propulsion system was performed in the stage qualification test. The predicted results were compared with the measured ones.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.63.1-63.1
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• 2016

We observed Ultra-Long GRB 111209A using NASA's 3m InfraRed Telescope Facility (IRTF). The observation was started around 40 min later than T0 = 07:12:08 UT of Swift's BAT, lasted for 24 min. The spectrum was extracted using Spextool package. The NIR SEDs show power law distribution indicating afterglow emission from the GRB according to the fireball model with beta ~ 1.2. Also they do not show thermal emission component compared to the SED of "Christmas burst" GRB 101225A. Because there is no other NIR data with this observation epoch, this data can be compared only with TAROT-R band. It seems NIR data has the same flare which exists in R band as an optical flare.

• East Asian Economic Review
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• v.27 no.4
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• pp.273-301
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• 2023

This study investigates the impacts of long-term climate variability on household consumption in Indonesia, a country highly vulnerable to climate change. The analysis combines household survey data from nearly 5,998 families with satellite-derived weather data from NASA POWER spanning 30 years. We use the long-term variability in temperature and precipitation as a proxy for climate change. This study examines the impact of climate change which proceeds over the long term, unlike previous studies concerning one-off or short-term climate events. In addition, using satellite data enhances the accuracy of households' exposure to climate variability. The analysis finds that households in a village with higher temperature and precipitation variability significantly consume less food. This implies that households more exposed to climate change are at higher risk of malnutrition in developing countries. This study has a limitation that it cannot rule out the potential endogeneity of choosing a climate-vulnerable residential location due to economic poorness.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Journal of the Ergonomics Society of Korea
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• v.32 no.1
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• pp.125-137
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• 2013

Objective: The aim of this study was to comprehend major concepts and flows that penetrate international guides or standards for developing a quantitative possibility measure of human errors that can be committed or omitted in nuclear power plants. Background: For a few past decades, lots of researchers have studied the effect of stress and/or fatigue which can result in human errors. Thus, this study was carried out on the assumption that much of them were summarized as an international guidelines or manuals, if any, for human error prevention. Method: A literal survey was conducted with materials and documentation published by international organizations related with safety and standardization, such as ISO, OSHA, NIOSH, NASA, and so on with special reference to human error prevention through management of work stress and fatigue as major Performance Shaping Factors. Results: International guides or management manuals on stress or fatigue management for human error prevention hardly were found, and most researches seemed to concentrate on one of them individually. Conclusion: There was few vestige of research that studied both concurrently. However, it was verified that not a few researches have been tried to develop quantitative measures to estimate probability or job characteristics for human error prevention and/or performance downgrading. Application: The results of this study would help to develop a causal model of human errors due to work stress and fatigue that can result in unexpected accidents in nuclear power plant.

• Journal of the Korean Society of Safety
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• v.29 no.1
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• pp.47-53
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• 2014

The application of advanced Main Control Room(MCR) is accompanied with lots of changes and different forms and features through the virtue of new digital technologies. The characteristics of these digital technologies and devices give many opportunities to the interface management, and can be integrated into a compact single workstation in advanced MCR so that workers can operate the plant with minimum physical burden under any operation conditions. However, these devices may introduce new types of human errors and thus a means to evaluate and prevent such errors is needed, especially those related to characteristics of digital devices. This paper reviewed the new type of human error hazards of tasks based on digital devices and surveyed researches on physiological assessment related to human error. An experiment was performed to verify human error hazards by physiological responses such as EEG which was measured to evaluate the cognitive workload of operators. And also, the performances of four tasks which are representative in human error hazard tasks based on digital devices were compared. Response time, ${\beta}$ power spectrum rate of each task by EEG, and mental workload by NASA-TLX were evaluated. In the results of the experiment, the rate of the ${\beta}$ power was increased in the task 1 and task 4 which are searching and navigating task and memory task of hierarchical information, respectively. In case of the mental workload, in most of evaluation items, task 1 and 4 were highly rated comparatively. In this paper, human error hazards might be identified by highly cognitive workload. Conclusively, it was concluded that the predictive method which is utilized in this paper and an experimental verification can be used to ensure the safety when applying the digital devices in Nuclear Power Plants (NPPs).

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.383-388
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• 2004

This paper presents the aerodynamic coefficient modeling with a new model structure explored by Least Squares using Modulating Function Technique (LS/MFT) for an F-16XL airplane using wind tunnel data supplied by NASA/LRC. A new model structure for aerodynamic coefficient was proposed, one that considered all possible combination terms of angle of attack ${\alpha}$(t) and ${\alpha}$(t) given number of harmonics K, and was compared with Pearson's model, which has the same number of parameters as the new model. Our new model harmonic results show better agreement with the physical data than Pearson's model. The number of harmonics in the model was extended to 6 and its parameters were estimated by LS/MFT. The model output of lift coefficient with K=6 correspond reasonably well with the physical data. In particular, the estimation performances of four aerodynamic coefficients were greatly improved at high frequency by considering all harmonics included in the input${\alpha}$(t), and by using the new model. In addition, the importance of each parameter in the model was analyzed by parameter reduction errors. Moreover, the estimation of three parameters, i.e., amplitude, phase and frequency, for a pure sinusoid and a finite sum of sinusoids- using LS/MFT is investigated.