• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.21-30
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• 1997

In accelerated life tests, the failure time of an item is observed under a high stress level, and based on the time the performances of items are investigated at the normal stress level. In this paper, when the mean of the prior of a failure rate is known in the exponential lifetime distribution with censored accelerated failure time data, we utilize the empirical Bayesian method by using the moment estimators in order to estimate the parameters of the prior distribution and obtain the empirical Bayesian predictive density and predictive intervals for a future observation under the normal stress level.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.403-411
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• 2023

Functional ultrasound imaging, such as elasticity imaging and micro-blood flow Doppler imaging, enhances diagnostic capability by providing useful mechanical and functional information about tissues. However, the implementation of functional ultrasound imaging poses limitations such as the storage of vast amounts of data in Radio Frequency (RF) data acquisition and processing. In this paper, we propose a sub-Nyquist approach that reduces the amount of acquired axial samples for efficient shear-wave elasticity imaging. The proposed method acquires data at a sampling rate one-third lower than the conventional Nyquist sampling rate and tracks shear-wave signals through RF signals reconstructed using band-pass filtering-based interpolation. In this approach, the RF signal is assumed to have a fractional bandwidth of 67 %. To validate the approach, we reconstruct the shear-wave velocity images using shear-wave tracking data obtained by conventional and proposed approaches, and compare the group velocity, contrast-to-noise ratio, and structural similarity index measurement. We qualitatively and quantitatively demonstrate the potential of sub-Nyquist sampling-based shear-wave elasticity imaging, indicating that our approach could be practically useful in three-dimensional shear-wave elasticity imaging, where a massive amount of ultrasound data is required.

• Journal of Korean Society of Steel Construction
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• v.23 no.5
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• pp.629-636
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• 2011

This paper describes the smart structural system that uses smart materials for real-time monitoring and active control of bolted joints in steel structures. The impedance-based structural health monitoring (SHM) techniques, which utilize the electro-mechanical coupling property of piezoelectric materials, was used to detect loose bolts in bolted joints. By monitoring the measured electrical impedance and comparing it with the measured baseline, a bolt loosening damage was detected. The damage was evaluated quantitatively using the damage metrics in conductance signature with respect to the healthy states. When loosening damage was detected in the bolted joint, the external heater actuated the shape memory alloy (SMA) washer. Then the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. An experiment was conducted by integrating the piezoelectric-material-based SHM function and the SMA-based active control function on a bolted joint, after which the performance of thesmart self-healing joint system was investigated.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.164-164
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• 2017

스마트 돈사 환경의 복지 및 생산성 향상을 위하여 정량 분석법을 기반으로 한 모돈 관리의 중요성이 증가하고 있다. 모돈은 교배, 임신, 분만, 포유, 이유를 순환적 반복하여 이루어지는데 모돈의 관리는 돈사 농장의 생산성 및 경제성과 직결된다. 모돈 관리에 필요한 환경 및 계측정보를 획득하고 이 정보로부터 모돈의 개체관리를 극대화시키고 최적의 방안을 찾고자 지속적으로 계측이 가능한 모돈의 돈사 모니터링 시스템이 필요하다. 모돈의 행동특성 계측이 가능한 시스템이 필요한 이유는 모돈의 행동 특성(섭식 및 지제불량 등)에 상응하는 대사 불량, 질병 및 발정 징후 등을 조기에 발견할 수 있기 때문이다. 돈사 내에서 정지 상태로 판별이 되는 모돈의 지제상태(기립상태, 누운 상태, 앉은 상태)와 다르게 연속적인 움직임으로부터 판별되는 모돈의 섭식상태를 분석하기 위해서는 계측 시스템과 이를 분석해주는 시스템간의 시간적 차이를 최소화 할 수 있는 실시간 신호 처리 기술이 필수적이다. 모돈의 섭식을 정량적으로 지수화하기 위한 센서의 최소 SPS(sample per second)는 600 Hz($100Hz{\times}6$개)로서 최소 6개 ADC 채널과 최소 1,200 Hz 이상으로 샘플링 할 수 있는 마이크로 컨트롤러가 필요하다. 또한 16 비트의 분해능으로 1분 동안 연속 계측을 수행할 경우 필요한 정보량은 153,600 KByte ($1,200sample/s{\times}16bit/sample{\times}8Byte/bit$)으로 실시간 처리를 수행하기에 매우 큰 정보량이라 판단할 수 있다. 수행하고자 하는 정보처리 기법에 따라 다소 상이할 수 있으나, 1분을 주기로 모돈의 섭식 분석을 수행하고자 할 경우 최도 150 MByte의 정보량을 처리하기 위한 최소의 클럭수는 단순 대입의 경우 2.5 Mhz (clock/second) ($=1clock/Byte{\times}150MByte/60seconds$) 이며 덧셈(4 clock)의 경우 10 Mhz, 곱셈(16 clock)의 경우 40 Mhz의 클럭이 필요하다. 또한 정보의 저장 및 도시를 위해 필요한 부가적인 회로(LCD, SD메모리) 구동을 위해 필요한 클럭을 고려할 경우 추가적인 클럭이 필요하다. 이를 종합적으로 고려하여 120 Mhz ($= 40Mhz{\times}3$) 이상의 클럭이 필요하다고 판단할 수 있다. 또한 센서 계측 주기의 시간 분해능을 균등하게 유지하기 위해선 계측->도시->저장의 과정을 교차적으로 수행해야 한다. 이러한 과정을 거처 최종적으로 선정한 마이크로 프로세서는 ARM Cortex-M4이며 168 MHz로 연산 수행이 가능하여 목표하고자 하는 신호처리를 수행 할 수 있다. 현장 예비 실험을 통해 기대 성능을 만족하였으며, 시간 복잡도가 높은 연산을 대비하여 최적 시분할 스케쥴링 기법에 대한 보완이 필요하다고 판단되었다.

• Journal of Conservation Science
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• v.34 no.3
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• pp.195-209
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• 2018

In this study, the characteristics of various raw hairs used for traditional Korean brushes were examined; further, the characteristics and deterioration patterns of Korean, Chinese, and Japanese brushes were compared, with a quantitative evaluation to assess the brush performance. The tensile strength was generally found to be higher with a greater fiber thickness. Among the hairs examined, the back and flank hair of goat was more damaged than that in other parts, and the tensile strength was low. Higher elasticity of the brush made with hair of high cysteine content was measured. Owing to deterioration by use of the brushes, artificial drying brushes had a higher yellowness index and lower tensile strength than natural drying brushes. Further, it was confirmed that brushes with good absorbency exhibited good consistency, but not good elasticity. Thus, the performance of the brush can be influenced by the kind of material used and the brush usage pattern. In addition, it is possible to identify the material science characteristics of brushes which have been produced only by experience; therefore, the results of this study could provide basic data for manufacturing brushes employed in conservation treatment, in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.340-348
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• 2021

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.83-91
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• 2023

In this paper, we propose a robustness sensitivity index (RSI) of highway networks to analyze the effect of congestion in a specific section on the entire highway. The newly proposed RSI is defined as the change in the total mileage of the transportation network per extended unit length when the length of a particular section is extended. When the RSI value is large, traffic congestion in the section has a worse effect on the entire network than in other sections. The existing network robustness index (NRI) simply observes changes in transportation networks with and without specific sections, but the RSI proposed in this study is a kind of performance indicator that allows quantitative analysis of the ripple effect of the entire network according to the degree of congestion in a specific section. While changing the degree of congestion in a particular section, it is possible to calculate how the traffic volume increases, decreases, and the size and location of the congestion section change. This analysis proves the superiority of RSI as it cannot be analyzed with NRI. Various properties of RSI are analyzed using data from the domestic highway network. In addition, using the RSI concept, it is shown that the ripple effect on other sections in which a change in the degree of congestion of a specific section occurs can be analyzed.