• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.6
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• pp.171-178
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• 2010

Pipeline structure is one of core underground infrastructure which transports primary sources. Since the almost pipeline structures are placed underground and connected each other complexly, it is difficult to monitor their structural health condition continuously. In order to overcome this limitation of recent monitoring technique, recently, a Ubiquitous Sensor Network (USN) system based on on-line and real-time monitoring system is being developed by the authors' research group. In this study, real-time pipeline health monitoring (PHM) methodology is presented based on electromechanical impedance methods using USN. Two types of damages including loosened bolts and notches are artificially inflicted on the pipeline structures, PZT and MFC sensors that have piezoelectric characteristics are employed to detect these damages. For objective evaluation of pipeline conditions, Damage metric such as Root Mean Square Deviation (RMSD) value was computed from the impedance signals to quantify the level of the damage. Optimal threshold levels for decision making are estimated by generalized extreme value(GEV) based statistical method. Throughout a series of experimental studies, it was reviewed the effectiveness and robustness of proposed PHM system.

• Korean Journal of Food Science and Technology
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• v.33 no.6
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• pp.686-692
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• 2001

The effects of extrusion on solubilization of brown rice, glutinuous rice, barley and job's tear were analyzed by response surface methodology (RSM). Solubilization of whole grains by extrusion was characterized in terms of water solubility index (WSI), concentration of water soluble polysaccharides (C) and intrinsic viscosity $([\eta])$. Considering both concentration and intrinsic viscosity, a dimensionless target parameter $([\eta])$ was also included for analysing the extrusion effects on cereal extrusion. Response surface methodology analysis showed that the moisture content was the most significant contributor among screw speed, temperature and moisture content affecting the solubilizing phenomena of cereals processed with extrusion. Brown rice was not showed the significant relationship on $([\eta])$ because $([\eta])$ was more affected by intrinsic viscosity. The critical point of whole grains extrusion except brown rice was corresponded to screw speed of 300 rpm, moisture content of 20% and temperature of $120^{\circ}C$.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.10-18
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• 2017

Korean Red Pine (Pinus densiflora) has been protected and used as the most ecologically and socio-culturally important tree species in Korea. However, as dieback of Korean red pines has occurred in the protected area of the forest genetic resources. The aims of this study is to identify causes for dieback of pine tree by investigating topographical characteristics of pine tree dieback and its correlation to meteorological factors. We extracted the dead trees from the time series aerial images and analyzed geomorphological characteristics of dead tree concentration area. As a result, 1,956 dead pine trees were extracted in the study region of 2,600 ha. Dieback of pine trees was found mostly in the areas with high altitude, high solar radiation, low topographic wetness index, south and south-west slopes, ridgelines, and high wind exposure compared to other living pine forest area. These areas are classified as high temperature and high drought stress regions due to micro-climatic characteristics affected by topographic factors. As high temperature and drought stress are generally increasing with climate change, we can evaluated that a risk of pine tree dieback is also increasing. Based on these geomorphological characteristics, we developed a pine tree dieback risk map using Maximum Entropy Model (MaxEnt), and it can be useful for establishing Korean red pine protection and management strategies.

• Economic and Environmental Geology
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• v.43 no.6
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• pp.615-623
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• 2010

Seismic refraction survey is a geophysical method that delineates subsurface velocity structure using direct wave and critically refracted wave. The generalized reciprocal method(GRM) is an inversion technique which uses travel-time data from several forward and reverse shots and which can provide the geometry of irregular inclined refractors and structures underlain by hidden layer such as low velocity zone and thin layer. In this study, a simple Excel-GRM routine was tested for fast mapping of the interface between weathering layer and bedrock during the survey, with employing a pair of forward and reverse shots. This routine was proved to control the maximum dip of approximately $30^{\circ}C$ and maximum velocity contrast of 0.6, based on the panel tests in terms of dipping angle and velocity contrast for the two-layer inclined models. In contrast with conventional operation of five to seven shots with sufficient offset distance and indoor data analysis thereafter, this routine was performed in the field shortly after data acquisition. Depth to the bedrock provided by Excel-GRM, during the field survey for Cheongju granite area, correlates well with the elevation of the surface of soft rock from the drill core and SPS logging data. This cost-effective routine developed for quickly delineating the bedrock surface in the field survey will be readily applicable to mapping of weathering zone in narrow zone with small variation of elevation of bedrock.

• Journal of the Korean Society for Railway
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• v.20 no.3
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• pp.311-319
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• 2017

The primary suspension system of a railway vehicle restrains the wheelset and the bogie, which greatly affects the dynamic characteristics of the vehicle depending on the stiffness in each direction. In order to improve the dynamic characteristics, different stiffness in each direction is required. However, designing different stiffness in each direction is difficult in the case of a general suspension device. To address this, in this paper, an optimization technique is applied to design different stiffness in each direction by using a conical rubber spring. The optimization is performed by using target and analysis RMS values. Lastly, the final model is proposed by complementing the shape of the weak part of the model. An actual model is developed and the reliability of the optimization model is proved on the basis of a deviation average of about 7.7% compared to the target stiffness through a static load test. In addition, the stiffness value is applied to a multibody dynamics model to analyze the stability and curve performance. The critical speed of the improved model was 190km/h, which was faster than the maximum speed of 110km/h. In addition, the steering performance is improved by 34% compared with the conventional model.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1545-1557
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• 2021

Crops show sensitive spectral characteristics according to their species and growth conditions and although frequent observation is required especially in summer, it is difficult to utilize optical satellite images due to the rainy season. To solve this problem, Constrained Cloud-Maximum Normalized difference vegetation index Composite (CC-MNC) algorithm was developed to generate periodic composite images with minimal cloud effect. In thisstudy, using this method, monthly Sentinel-2A/B Normalized Difference Vegetation Index (NDVI) composite images were produced for paddies and high-latitude cabbage fields from 2019 to 2021. In August 2020, which received 200mm more precipitation than other periods, the effect of clouds, was also significant in MODIS NDVI 16-day composite product. Except for this period, the CC-MNC method was able to reduce the cloud ratio of 45.4% of the original daily image to 14.9%. In the case of rice paddy, there was no significant difference between Sentinel-2A/B and MODIS NDVI values. In addition, it was possible to monitor the rice growth cycle well even with a revisit cycle 5 days. In the case of high-latitude cabbage fields, Sentinel-2A/B showed the short growth cycle of cabbage well, but MODIS showed limitations in spatial resolution. In addition, the CC-MNC method showed that cloud pixels were used for compositing at the harvest time, suggesting that the View Zenith Angle (VZA) threshold needsto be adjusted according to the domestic region.

• 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.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1519-1529
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• 2018

The volcanic ash can spread out over hundreds of kilometers in case of large volcanic eruption. The deposition of volcanic ash may induce damages in urban area and transportation facilities. In order to respond volcanic hazard, it is necessary to estimate efficiently the diffusion area of volcanic ash. The purpose of this study is to compare in-situ volcanic deposition and satellite images of the volcanic eruption case. In this study, we used Near-Infrared (NIR) channels 7 and 8 of Geostationary Ocean Color Imager (GOCI) images for Mt. Aso eruption in 16:40 (UTC) on October 7, 2016. To estimate deposit area clearly, we applied Principal Component Analysis (PCA) and a series of morphology filtering (Eroded, Opening, Dilation, and Closing), respectively. In addition, we compared the field data from the Japan Meteorological Agency (JMA) report about Aso volcano eruption in 2016. From the results, we could extract volcanic ash deposition area of about $380km^2$. In the traditional method, ash deposition area was estimated by human activity such as direct measurement and hearsay evidence, which are inefficient and time consuming effort. Our results inferred that satellite imagery is one of the powerful tools for surface change mapping in case of large volcanic eruption.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.297-297
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• 2023

최근 친수성, 경관, 생태계 보전 등을 위해 다양한 호안블록의 시공이 이루어지고 있어 호안블록의 수리적 안정성에 관한 관심이 증가하고 있다. 이러한 배경 하에 한국건설기술연구원 안동하천실험센터(이하 센터)에서는 2019년부터 실규모 수로를 이용하여 여러 건의 호안블록 실규모 수리검토 실험을 실시한 바 있다. 본 연구는 그간의 실험 결과를 종합적으로 고찰하고 수리 검토 실험의 현황과 한계점, 그리고 개선 방향을 제언하는데 그 목적이 있다. A1 수로(급경사수로, 하상경사 1/70)에서는 7건(21회), B1 수로(고유속수로, 하상경사 1/7)에서는 2건(6회)의 실험이 수행되었다. A1 수로 실험의 유량-소류력 관계는 1.0 m³/s에서 약 20 N/m²이며, 1.0 m³/s 증가당 약 11 N/m²이 증가하는 관계를 나타낸다. 7건의 실험 결과 30분 이상 지속된 최대 실험 유량은 6~7 m³/s 정도이며, 이는 A1 최대 공급 유량의 75 % 정도로서 안정적인 수준이라고 판단된다. 이 때의 최대 소류력은 75 N/m² 정도로 나타났다. B1 수로는 5 m/s 이상의 고유속 흐름을 발생시킬 수있으며, 2건의 실험 결과 0.5 m³/s에서 약 100 N/m², 최대 4.5 m³/s에서 330 N/m²까지 소류력을 제공하여 실험을 수행한 바 있다. 따라서 A1, B1 수로를 통해 제공할 수 있는 소류력 범위는 10~330 N/m²이지만, 75~100 N/m²는 실험에서 제공된 바 없었다. 한편, 토양유실의 경우 수준측량에 의해 측정되는데, 대부분의 실험에서 Clopper의 토양손실 지수(1.27 cm) 미만의 결과가 발생하였다. 이는 시험체에 따라 여건이 다르기는 하지만, 수리 검토 실험시 3회 실험을 기본으로 하고 있고 호안재료의 침식이 기준 이하로 유지되면서 최대한의 성능을 발휘할 수 있는 소류력 조건을 얻으려는 실험 목적에 부합하도록 조절된 것으로 볼 수 있다. 이러한 실험 결과를 토대로 고려해볼 수 있는 개선 방향은 다음과 같다. 강성 재료가 아닌 연성 또는 친환경적 호안재료의 허용 소류력 범위를 보다 넓게 평가하기 위해 A1 수로가 제공하는 최대 소류력을 높일 필요가 있다. 이를 위해 기본 3회의 실험 외에 추가로 호안블록이 파괴되거나 토양유실 임계치를 초과할 수 있는 실험을 수행함으로써 각 제품의 한계 성능을 평가하는 것이 필요할 것으로 보인다.