• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.158-163
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• 2008

Ozone monitor based on UV techniques has been widely used due to their signal stability. The high concentration ozone monitor for real time ozone monitoring from ozone generator was composed of a low pressure mercury lamp as UV source and a photo multiplier tube as UV detector. The structure could be very useful for low price high concentration ozone monitor and showed good linearity to ozone in the concentration range between 0.05 and 2wt%. For accurate ambient ozone monitoring, the system composed of a high power pulsed xenon lamp as UV source, an optical spectrometer with a high sensitivity linear CCD array as UV detector. The optical signal form the CCD array was converted to digital signal, and the digital signal was displayed on screen using PC interface. The developed system showed good linearity and sensitivity in relatively low measuring range between 10ppm and 10,000ppm, and showed some feasibility of hish resolution ozone monitor using CCD array as a photodetecor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1641-1647
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• 2018

In this paper, we describe a portable potentiostat which is capable of cyclic voltammetry(CV) and amperometry for electrochemical dissolved oxygen sensor. In addition, this portable potentiostat can also transmit the measured data wirelessly to android devices such as smart phone, tablet, etc. through Bluetooth. The potentiostat system consists of three parts; a voltage generator circuit which is controlled by Arduino nano and 12-bit DAC(digital to analog converter) to generate necessary electric potential for operating the electrochemical sensor, an oxidation/reduction current measurement circuit, and a Bluetooth module to transmit data wirelessly to an android device. Once measurements are carried out with the android application, the measured data is transmitted to the android device via Bluetooth and displayed using the android app. in real time. In this paper, we report the measured reduction current with a fabricated dissolved oxygen sensor in both saturated-oxygen state and zero-oxygen states. The results of the developed portable potentiostat system are in good agreement with those of the commercial portable potentiostat (${\mu}stat200$, Dropsens inc.). The measured peak reduction currents using the developed potentiostat and the commercial ${\mu}stat200$ potentiostat were $-0.755{\mu}A$ and $-0.724{\mu}A$, respectively. The reduction currents measured at zero-oxygen state were $-0.005{\mu}A$ and $-0.004{\mu}A$. The discrepancy between those two systems seems very small, which implies successful development of a portable and wireless potentionstat.

• Journal of IKEEE
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• v.23 no.1
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• pp.289-294
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• 2019

At present, domestic small islands mainly supply electric power using diesel generators. However, diesel generators can cause instability of the power system due to slow response on power load fluctuation, and cause environmental problems due to the emission of carbon gas by using fossil fuels. In order to overcome this problem, this paper proposes a method to establish an optimal microgrid by introducing solar power, wind power, and energy storage device to Chujado Island, which is supplied with electric power through a diesel generator. The economical optimum capacity of each distributed power source is calculated by using HOMER (Hybrid Optimization Model for Multiple Energy Resources) program and the proposed microgrid is validated by using PSCAD/EMTDC (Power Systems Computer Aided Design/ Electromagnetic Transients including DC) program which can analyze system stability.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.499-509
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• 2020

This study explores how to build object classification learning data based on artificial intelligence. The data has been investigated recently in image classification fields and, in turn, has a great potential to use. In order to recognize and extract relatively accurate objects using artificial intelligence, a large amount of learning data is required to be used in artificial intelligence algorithms. However, currently, there are not enough datasets for object recognition learning to share and utilize. In addition, generating data requires long hours of work, high expenses and labor. Therefore, in the present study, a small amount of initial aerial image learning data was used in the GAN (Generative Adversarial Network)-based generator network in order to establish image learning data. Moreover, the experiment also evaluated its quality in order to utilize additional learning datasets. The method of oversampling learning data using GAN can complement the amount of learning data, which have a crucial influence on deep learning data. As a result, this method is expected to be effective particularly with insufficient initial datasets.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.2
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• pp.136-141
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• 2021

Heating cables, widely used in office buildings, factories, streets and railways, deteriorate in electrical insulation during operation. The insulation deterioration of heating cables leads to electric discharges that can cause electrical fires. With this background, this paper dealt with a condition monitoring technique for heating cables by the analysis of discharge signals to prevent electrical fires. Insulation deterioration was simulated using an arc generator specified in UL1699 under AC operation, and the characteristic and propagation of discharge signals were analyzed on a 100 meter-long heating cable. Discharge signals produced by insulation deterioration were detected as a voltage pulse because they are as small as a few mV and they are attenuated through propagation path. The frequency spectrum of discharge signals mainly existed in the range from 70 kHz to 110 kHz, and the maximum attenuation of the signal was 84.8% at 100 meters away from the discharge point. Based on the experimental results, a monitoring device, which is composed of a high pass filter with the cut-off frequency of 70 kHz, a comparator, a wave shaper and a microprocessor, was designed and fabricated. Also, an algorithm was designed to discriminate the discharge signal in the presence of noise, compared with the pulse repetition period and the number of pulse counts per 100ms. In the experiment, the result showed that the prototype monitoring device could detect and discriminate the discharge signals produced at every discharge point on a heating cable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• Journal of Radiation Industry
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• v.18 no.1
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• pp.95-100
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• 2024

To establish a radioactive waste life cycle history management system, a series of processes including waste generation, classification, packaging, storage, transportation, and disposal were reflected in the information management system. A preliminary review process was introduced to reduce the amount of radioactive waste generated and manage it efficiently. Through this, the amount of radioactive waste generated must be checked from the beginning of the research, and the generated radioactive waste must be thoroughly managed from the stage of generation to final disposal. In particular, in the case of radioactive waste data generated during nuclear facility operation and each experiment, a radioactive waste information management system must be operated to receive information from the waste generator and integrate it with processing information at the management stage. The application process for small-package containers was reflected so that information such as the generation facility of radioactive waste, generation facility, project information, types of radioactive waste, major radionuclides, etc. In the radioactive waste management process, the preceding steps are to receive waste history from the waste generators. This includes an application for a specified container with a QR label, pre-inspection, and management request. Next, the succeeding steps consist of repackaging, treatment, characterization, and evaluating the suitability of disposal, for a process to transparently manage radioactive wastes.

• Journal of Environmental Impact Assessment
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• v.26 no.4
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• pp.217-226
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• 2017

Although the effects of climate change are universal, Small Island Developing States (SIDS) are considered to be most vulnerable. SIDS heavily rely on imported oil and fossil fuels for electricity generation and transportation, which makes them economically vulnerable and exposed to fluctuating oil price. Among the reasons SIDS highly depend on diesel fuel is due to the dispersed population living in remote islands which means, providing electricity through on on-grid system is difficult. Fiji as one of the SIDS, has actively promoted renewable sourced energy through a national plan to mitigate the impacts of climate change. In order to determine how feasible implementing a renewable energy (RE) system will be in Fiji, this study chose a remote island called Mavuva Island to test application of a hybrid RE system using HOMER. A combination of energy storage system (ESS), solar photovoltaic (PV) and diesel generator turns out to be the most cost effective and optimal configuration, resulting in effective greenhouse gas reduction for the given region.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.511-518
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• 1998

In order to develop the aerosol bolus technique which is thought to be a potential tool for probing geometries or abnormalities of small airways, an experimental system of measuring fast time variations of particle concentration in the inhaled and exhaled breathing air was developed. The system generates monodisperse sebacic acrid particles of 1 micron size and 1.2 of geometric standard deviation in high concentration of $10^8$ particles/cc, delivers a short pulse of particles at the controlled instant during inhalation using a solenoid valve, and measures the fast change of particle concentration in using the laser light scattering. Successful operation of the generator and the measuring system was confirmed by smooth concentration profiles in inhalation. It was also confirmed that maintaining a constant breathing rate is essential to stable outputs and any disturbance in flow rate near the mode (maximum concentration) induces a large number of spurious peaks in the exhalation. Experimental data without strict control of breathing flow rate showed a substantial amount of scatter. The measured results showed an improvement in scatter over the existing results. When compared with theoretical predictions from 1-D convective diffusion equation and other experiments, general characteristics of dispersion for several penetration depths showed a good agreement, but there exists some difference in absolute values, which is attributed to the difference in body conditions. Improvements are needed in the theory, especially in relation to correcting for the effect of breathing flow rate.