• Smart Structures and Systems
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• v.15 no.6
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• pp.1583-1600
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• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.165-173
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• 2004

Since the conventional image segmentation methods using mathematical morphology tend to yield over-segmented results, they normally need postprocess which merges small regions to obtain larger ones. To solve this over-segmentation problem without postprocess had to increase size of structuring element used marker extraction. As size of structuring element is very large, edge of region segments incorrectly. Therefore, this paper selectively applies structuring element of mathematical morphology to improve performance of image segmentation and classifies input image into texture region, edge region and simple region using averaged local variance and image gradient. Proposed image segmentation method removes the cause for over-segmentation of image as selectively applies size of structuring element to each region. Simulation results show that proposed method correctly segment for pixel region of similar luminance value and more correctly search texture region and edge region than conventional methods.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.348-350
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• 1997

Because of internal voids ininsulators give rise to partial discharge(PD), which cause local breakdown and even entire insulation breakdown. Treeing due to PD is one of the main causes of breakdown of the insulating materials and reduction of the insulation life. Therefore the necessity for establishing a method to diagnose the aging of insulation materials and to predict the breakdown of insulation has become important. From this viewpoint, our studies diagnose insulation degradation using the method of computer sensing system, which has the advantages of PD and acoustic emission(AE) sensing system. To use advantages of these two methods can be used effectively to search for treeing location and PD in some materials. In analysis method of degradation. We analyzed the PD pulse and AE pulses by regression analysis, compared to these obtained the correlation coefficient and determination coefficient by T-distribution and saw that PD and AE pulses show a similar pattern on the whole. Finally using statically operator such as the center of gravity(G), the gradient of the discharge distribution(C), we have analyzed for the prediction of life which we can be obtained the time, occurred of many pulse of small discharge amplitude.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.7
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• pp.922-926
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• 2005

The present study was aimed to investigate proteome affected by Panax ginseng extracts in chicken muscles. The whole muscle proteins from chicken fed boiled extracts of 0% (control), 1%, 3%, and 5% Panax ginseng in water were separated by two-dimensional electrophoresis (2-DE) gels using immobilized non-linear gradient (pH 3-10) strips. More than 300 protein spots were detected on silver staining gels. Among them, four protein spots were distinctively up-regulated by Panax ginseng treatments and further investigated by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). The obtained MS data were searched against SwissProt database using the Mascot search engine. The up-regulated proteins were finally identified as $\alpha$-tropomyosin (2 spots), triosephosphate isomerase, and one unknown protein. Based on the known functions of the identified proteins, they are highly related to muscle development and enhanced immunity in chickens. These proteins can give valuable information of biochemical roles for Panax ginseng in chicken meats.

• Korean Journal of Pharmacognosy
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• v.47 no.3
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• pp.211-216
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• 2016

In the course of our continuing search for biologically active components from Korean medicinal plants, we isolated the main compound, luteolin 5-glucoside from aqueous fraction of Ajuga spectabilis. The structure was elucidated by the basis of $^1H$ and $^{13}C$ NMR and TOF ESI-MS data. Quantitative analysis of luteolin 5-glucoside was carried out on a XBridge C18 column ($S-5{\mu}m$, $4.6{\times}250mm$) with gradient elution composed of acetonitrile:water. The results exhibit that the average content of main compound in A. spectabilis were 0.048%. Oxidative stress plays a major role Alzheimer's disease (AD) and other neurodogenerative disease. AD is major health problem and there is currently no clinically accepted treatment to cure or stop its progression. Pretreatment with luteolin 5-glucoside markedly attenuated $H_2O_2$-induced cell viability loss in a dose-dependent manner. Luteolin 5-glucoside also inhibited the formation of intracellular reactive oxygen species in SH-SY5Y. The results suggest that luteolin 5-glucoside from A. spectabilis has protective effects against oxidative stress-induced cytotoxicity, which might be a potential therapeutic compound for treating and/or preventing neurodegenerative disease implicated with oxidative stress.

• International Journal of Highway Engineering
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• v.8 no.4 s.30
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• pp.159-179
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• 2006

By using the current road design method that is based on the regulation about structure and facilities standard of the road and the route plan guide of a national road and the alignment optimization road design method which is studied in the inside and outside of country, this study operate the route plan of the sample study and compare and analysis the route character, consequently the current design method has local optimization that is formed the plan by the stage and the section. Alignment optimization road design has the system optimal route search. But cost function has limite that caused by construction parameter that is not included in cost function. So we design a road route included cost function in main fields. As a result, we obtain a realistic and economically road route. The alignment optimization road design model has to be made up some problems, like the change of vertical gradient in the tunnel section, though this defects it has a lot of merits as a geometric design tool, especially in the feasibility study and the scheme design.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.151-160
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• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

• Korean Journal of Ecology and Environment
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• v.56 no.1
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• pp.36-44
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• 2023

Environmental DNA (eDNA) can exist in both intracellular and extracellular forms in natural ecosystems. When targeting harmful cyanobacteria, extracellular eDNA indicates the presence of traces of cyanobacteria, while intracellular eDNA indicates the potential for cyanobacteria to occur. However, identifying the "actual" potential for harmful cyanobacteria to occur is difficult using the existing sediment eDNA analysis method, which uses silica beads and cannot distinguish between these two forms of eDNA. This study analyzes the applicability of a density gradient centrifugation method (Ludox method) that can selectively analyze intracellular eDNA in sediment to overcome the limitations of conventional sediment eDNA analysis. PCR was used to amplify the extracted eDNA based on the two different methods, and the relative amount of gene amplification was compared using electrophoresis and Image J application. While the conventional bead beating method uses sediment as it is to extract eDNA, it is unknown whether the mic gene amplified from eDNA exists in the cyanobacterial cell or only outside of the cell. However, since the Ludox method concentrates the intracellular eDNA of the sediment through filtration and density gradient, only the mic gene present in the cyanobacteria cells could be amplified. Furthermore, the bead beating method can analyze up to 1 g of sediment at a time, whereas the Ludox method can analyze 5 g to 30 g at a time. This gram of sediments makes it possible to search for even a small amount of mic gene that cannot be searched by conventional bead beating method. In this study, the Ludox method secured sufficient intracellular gene concentration and clearly distinguished intracellular and extracellular eDNA, enabling more accurate and detailed potential analysis. By using the Ludox method for environmental RNA expression and next-generation sequencing (NGS) of harmful cyanobacteria in the sediment, it will be possible to analyze the potential more realistically.

• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.3-22
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• 2000

이용자의 경로선택 형태를 모사하는 통행배정모형 결과의 정확도는 교통계획에 상당한 영향을 미친다. 이용자의 경로선택 결정과정에서 가장 중요한 판단기준은 통행시간과 통행요금이다. 그런데 통행요금은 이용자의 경로거리에 따라 다양한 방식으로 부과되므로, 링크를 분석단위로 하는 기존의 통행배정모형은 현실적인 통행요금 반영이 힘들었고 또한 수요예측 결과를 이용한 다양한 분석에서 제약을 받아 왔다. 본 연구는 이러한 배경에서 경로교통량을 도출할 수 있는 경로기반 통행배정모형을 구축하였고, 또한 경로거리에 따라 결정되는 현실적인 통행요금을 반영할 수 있는 알고리즘을 개발하였다. 경로기반 배정모형에서는 GP(Gradient Projection) 알고리즘을 이용하였고, 계산상의 효율성 제고를 위해 K-최단경로 알고리즘 중 MPS(Minimal Path Search) 알고리즘을 이용하였다. 개발된 배정 모형은 현실적인 통행요금을 반영할 수 있으므로 통행배정 결과의 정밀도를 향상시켰을 뿐만 아니라 기존 배정모형에 비해 최적해로의 수렴속도도 개선되는 것으로 나타났다. 본 논문의 배정모형은 경로교통량이 도출되고 통행요금을 반영할 수 있으므로, 통행요금과 통행 거리 관계에 따른 목적함수의 규명과 그에 따른 효과척도를 계량화할 수 있다. 따라서 본 모형은 통행배정에서 실재상황을 보다 현실여건에 맞도록 규명할 수 있고, 기존의 제한적인 효과분석의 문제점을 해결할 수 있으므로 그 활용범위가 넓다. 또한 본 논문은 개발된 배정모형의 적용사례로서 고속도로 수요관리 요금체계 개선방안을 제시하였다. 기존의 고속도로 통행요금 산정 방법은 이론적 근거가 미약했던 반면, 본 논문에서 개발된 배정모형과 고속도로 수요관리 요금체계 개선방안은 고속도로 통행료 결정에 대한 과학적이고 합리적인 분석방법을 제공하였다.한 민감도 분석을 실시한 결과 대안1의 경우 교통량의 변화 및 화물통행의 시간가치의 증가시 사회적 편익이 오히려 감소하였고, 대안2와 3의 경우 사회적 편익이 증가하는 것을 알 수 있었다. 이는 경부고속도로의 화물차량의 구성비에 따라 대안 1의 경우 오히려 화물차의 통행시간이 증가함에 그 원인이 있다 할 것이다. 이상과 같은 결론을 통하여 경부고속도로상의 화물전용차선의 설치시는 수답렬 교통량의 구성비와 구간 평균교통량에 의하여 그 효과가 다르게 나타남을 알 수 있었다. 따라서 물류비용 절감차원에서의 화물전용차선의 설치는 본 연구에서 나타낸 방법과 같이 수단간의 경제적 편익을 고려한 구간별 시간대별 효과분석을 통하여 정책의 시행여부가 결정되어야 할 것이다. 한편, 화물전용차선의 설치로 인한 물류비용의 절감을 보다 효과적으로 달성하기 위해서는 종합류류 전산망의 시급한 구축과 함께 화물차의 적재율을 높이고 공차율을 낮출 수 있는 운송체계의 수립이 필요한 것으로 판단된다. 그라나 이러한 화물전용차선의 효과는 단기적인 치유책일 수밖에 없기 때문에 물류유통 시설의 확충을 위한 사회간접자본의 구축을 서둘러 시행하여야 할 것이다.으로 처리한 Machine oil, Phenthoate EC 및 Trichlorfon WP는 비교적 약효가 낮았다.>$^{\circ}$E/$\leq$30$^{\circ}$NW 단열군이 연구지역 내에서 지하수 유동성이 가장 높은 단열군으로 추정된다. 이러한 사실은 3개 시추공을 대상으로 실시한 시추공 내 물리검층과 정압주입시험에서도 확인된다.. It was resulted from increase of weight of single cocoon. "Manta"2.5ppm produced 22.2kg of co

• Tunnel and Underground Space
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• v.32 no.6
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• pp.502-517
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• 2022

As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.