• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.257-264
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• 2010

The amount and continuity of precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study a linear programming method treated as a data-driven approach for estimating the missing rainfall data is compared with seven other methods widely used and its superiority is certified. The data used in this research are annual precipitation ones during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station are estimated and the areal averages of annual precipitation data for 32 years at the Han River basin are calculated.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.267-267
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• 2006

전력에 큰 손실을 초래하는 고장전류를 차단하기 위한 한류기(FCL) 소재로서 고온 초전도체인 BSCCO 2212가 사용된다. 고온에서 용융된 BSCCO 2212 분말은 원심성형법에 의해 한류기용 튜브로 제조되었다. BSCCO 튜브의 기계적 특성을 높이고 용융온도를 낮추기 위해 $SrSO_4$(10wt-%)를 첨가하였다. 용탕은 $1200^{\circ}C$에서 완전히 용융되어 금속 몰드로 주입되었고 원심성형에 사용되는 금속 몰드는 $550^{\circ}C$ 온도로 2시간 예열 후 1020 ~ 2520 RPM으로 회전시켰다. 원심력에 의해 성형된 BSCCO 튜브는 약 48시간 동안 로에서 서냉 후 금속 몰드로부터 분리하였다. 튜브의 용이한 분리를 위해 이형제로서 BSCCO 2212 powder를 사용하였고 임계전류측정을 고려하여 Ag tape 단자를 튜브 끝단에 부착하였다. BSCCO 제조 공정에 있어서 몰드의 예열온도, 용융온도, 몰드 회전속도 등의 변수를 조절하여 최적의 조건을 확립하였다. 제조한 BSCCO 튜브의 임계전류($I_c$)와 임계전류밀도($J_c$)는 77K에서 536A와 $205A/cm^2$ 이었다. 본 연구에서는 BSCCO 2212 튜브를 제조하는 공정조건 변화와 각 조건에서 제조된 BSCCO 2212 튜브의 전기적 특성 및 그에 따른 분석에 대해 기술하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.675-680
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around the world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance. CMS(Condition Monitoring System) can be used to aid plant operator in achieving these goals. Its aim is to provide operators with information regarding th e health of their machine, which in turn, can help them improve operation efficiency. In this work, wind turbine fault diagnostic algorithm which can diagnose the mass unbalance and aerodynamic asymmetry of the blades is proposed. Proposed diagnostic algorithm utilizes both FFT(Fast Feurier Transform) of the signal from accelerometers installed inside of nacelle and simple diagnostic logic. Furthermore, to verify the applicability of the proposed system, 3W small sized wind turbine system is tested and physical experiments are carried out.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.95-105
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• 2009

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series including bad or missing observation that result from mechanical problems or sensing environmental condition. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) image was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series from 1996 to 2000 for tracking changes on the ground vegetation. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.117-125
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• 2002

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. Using an adaptive reconstruction system, dynamic compositing approach was developed to recover missing/bad observations. The reconstruction method incorporates temporal variation in physical properties of targets and anisotropic spatial optical properties into image processing. The adaptive system performs the dynamic compositing by obtaining a composite image as a weighted sum of the observed value and the value predicted according to local temporal trend. The proposed system was applied to the sequence of NDVI images of AVHRR observed on the Korean Peninsula from 1999 year to 2000 year. The experiment shows that the reconstructed series can be used as an estimated series with complete data for the observations including bad/missing values. Additionally, the gradient image, which represents the amount of temporal change at the corresponding time, was generated by the proposed system. It shows more clearly temporal variation than the data image series.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.625-632
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• 2022

Gas turbine generators in load-following operation in the domestic power system play a major role in maintaining the rated frequency, but often have poor frequency control. Therefore, after examining the control characteristics of the governor, which is a gas turbine speed control device, and analyzing the failure types, countermeasures were suggested for each case. In addition, it was confirmed through the governor response test that the gas turbine helps in frequency recovery depending on the speed of fuel control, but also acts as a factor impeding stable operation, such as rapid fluctuations in combustion chamber temperature and combustion vibration. Therefore, in order to maintain stable power quality, there was a need for thorough facility management as well as research on the governor control method in which the traditional PID control method and the machine learning algorithm, a core field of the 4th industry, were fused.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.571-578
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• 2018

The deteriorated pipline often causes the unexpected leakage and crack. Negligence and late maintenance leads the enormous damage for gas and water resource. This paper proposes early fault detection and diagnosis algorithm for pipeline using acoustic emission (AE) signals. Early fault detection method for pipeline compares the frequency amplitude of the spectrum to that of the spectrum in normal condition. Larger amplitude of the spectrum indicates abnormal condition. Early fault diagnosis algorithm uses support vector machines (SVM), which is trained for normal and abnormal conditions to diagnose the measured AE signal from the target pipeline. In the experiment, a pipeline testbed is constructed similarly to real industrial pipeline. Normal, 5mm cracked, 10mm holed pipelines are installed and tested in this study. The proposed fault detection and diagnosis technique is validated as an efficient approach to detect early faulty condition of pipeline.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• Journal of the Korean Electrochemical Society
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• v.3 no.2
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• pp.100-103
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• 2000

Polymeric gel electrolytes based on polyacrylronitile blended with poly(vinylidene fluoride-co-hexafluoro-propylene)(P(VdF-co-HFP), which were reinforced with glass fiber cloth(GFC) to increase the mechanical strength, were prepared for the practical use in secondary battery. Test cell consisting of $LiCoO_2$ as a cathode and mesophase pich-based ca.bon fiber (MCF) as an anode material showed a capacity of 110 mAh/g based on the cathode weight at 0.2C rate at room temperature. Over $80\%$ of initial capacity was retained after 400cycles, indicating that GFC is suitable for a reinforcing material to increase the mechanical strength of gel based electrolytes.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.4
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• pp.313-322
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• 2013

Batteries, which are being used as energy sources in various applications, tend to degrade, and their capacity declines with repeated charging and discharging cycles. A battery is considered to fail when it reaches 80% of its initial capacity. To predict this, prognosis techniques are attracting attention in recent years in the battery community. In this study, a method is proposed for estimating the battery health and predicting its remaining useful life (RUL) based on the slope of the charge voltage curve. During this process, a Bayesian framework is employed to manage various uncertainties, and a Particle Filter (PF) algorithm is applied to estimate the degradation of the model parameters and to predict the RUL in the form of a probability distribution. Two sets of test data-one from the NASA Ames Research Center and another from our own experiment-for an Li-ion battery are used for illustrating this technique. As a result of the study, it is concluded that the slope can be a good indicator of the battery health and PF is a useful tool for the reliable prediction of RUL.