• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.203-209
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• 2017

Research on condition monitoring and diagnosis of power facilities has been conducted to improve the safety and reliability of electric power supply. Although insulation diagnostic techniques for unit equipment such as gas-insulated switchgears and transformers have been developed rapidly, studies on monitoring of cables have only included aspects such as whether defects exist and partial discharge (PD) detection; other characteristics and features have not been discussed. Therefore, this paper dealt with PD characteristics against void sizes and positions, and with defect localization in XLPE cable. Four types of defects with different sizes and positions were simulated and PD pulses were detected using a high frequency current transformer (HFCT) with a frequency range of 150kHz~30MHz. The results showed that the apparent charge increased when the defect was adjacent to the conductor; the pulse count in the negative half of the applied voltage was about 20% higher than that in the positive half. In addition, the defect location was calculated by time-domain reflectometry (TDR) method, it was revealed that the defect could be localized with an error of less than1m in a 50m cable.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.504-510
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• 2017

This paper presents an AC-DC power module design and evaluates its efficiency and reliability when used for electronics appliances. This power module consists of a PWM control IC, power MOSFETs, a transformer and several passive devices. The module was tested at an input voltage of 220V (RMS) (frequency 60 Hz). A test was conducted in order to evaluate the operation and power efficiency of the module, as well as the reliability of its protection functions, such as its over-current protection (OVP), overvoltage protection (OVP) and electromagnetic interference (EMI) properties. Especially, we evaluated the thermal shut-down protection (TSP) function in order to assure the operation of the module under high temperature conditions. The efficiency and reliability measurement results showed that at an output voltage of 5 V, the module had a ripple voltage of 200 mV, power efficiency of 73 % and maximum temperature of $80^{\circ}C$ and it had the ability to withstand a stimulus of high input voltage of 4.2 kV during 60 seconds.