• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.2
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• pp.161-170
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• 2010

To reduce the amount of seismic damage, several design codes are being improved considering the earthquake resistant systems, and many researches are being conducted to develop the earthquake damage evaluation techniques. This study develops the Korean seismic fragility function using the modified HAZUS method applicable to PSC-I girder bridges in Korea. The major coefficients are modified considering the difference between the seismic design levels of America and Korea. Seismic fragility function of the PSC-I girder bridge (one of the standard bridge types in Korea) is evaluated using two methods: numerical analysis and modified HAZUS method. The main coefficients are obtained about 70% of the proposed values in HAZUS. It is found that the seismic fragility function obtained using the modified HAZUS method closes to the fragility function obtained by conventional numerical analysis method.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.181-189
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• 2004

This paper describes the method for detecting vehicles in the rear and rear-side at night by using headlight features. A headlight is the outstanding feature that can be used to discriminate a vehicle from a dark background. In the segmentation process, a night image is transformed to a binary image that consists of black background and white regions by gray-level thresholding, and noise in the binary image is eliminated by a morphological operation. In the feature extraction process, the geometric features and moment invariant features of a headlight are defined, and they are measured in each segmented region. Regions that are not appropriate to a headlight are filtered by using geometric feature measurement. In region classification, a pair of headlights is detected by using relational features based on the symmetry of a pair of headlights. Experimental results show that this method is very applicable to an approaching vehicle detection system at nighttime.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.81-97
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• 2003

In this paper, we suggest a method of robust watermarking for protection of multimedia data using the wavelet transform and artificial neural network. for the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except fur the lowest subband LL$_3$, we apply a calculated threshold about chosen cluster as the biggest. We used binary logo watermarks to make sure that it is true or not on behalf of the Gaussian Random Vector. Besides, we tested a method of dual watermark insertion and extraction. For the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except for the lowest subband LL$_3$, we apply a above mentioned watermark insert method. In the experimental results, we found that it has a good quality and robust about many attacks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1153-1158
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• 2006

The classification of the background and cell areas is very important research area because of the ambiguous boundary. In this paper, the region of cell is extracted from an image of uterine cervical cytodiagnosis using the region growing method that increases the region of interest based on similarity between pixels. Segmented image from background and cell areas is binarized using a threshold value. And then 8-directional tracking algorithm for contour lines is applied to extract the cell area. First, the extracted nucleus is transformed to RGB color that is the original image. Second, the K-means clustering algorithm is employed to classify RGB pixels to the R, G, and B channels, respectively. Third, the Hue information of nucleus is extracted from the HSI models that is the transformation of the clustering values in R, G, and B channels. The backpropagation algorithm is employed to classify and identify the normal or abnormal nucleus.

• Journal of Korea Water Resources Association
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• v.49 no.10
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• pp.877-885
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• 2016

In this study, the Standard Precipitation Index(SPI), meteorological drought index, was used to evaluate the temporal and spatial assessment of drought forecasting results for all cross Korea. For the drought forecasting, the Multi Layer Perceptron-Artificial Neural Network (MLP-ANN) was selected and the drought forecasting was performed according to different forecasting lead time for SPI (3) and SPI (6). The precipitation data observed in 59 gaging stations of Korea Meteorological Adminstration (KMA) from 1976~2015. For the performance evaluation of the drought forecasting, the binary classification confusion matrix, such as evaluating the status of drought occurrence based on threshold, was constituted. Then Receiver Operating Characteristics (ROC) score and F score according to conditional probability are computed. As a result of ROC analysis on forecasting performance, drought forecasting performance, of applying the MLP-ANN model, shows satisfactory forecasting results. Consequently, two-month and five-month leading forecasts were possible for SPI (3) and SPI (6), respectively.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.1
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• pp.8-14
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• 2004

Model-based inversion tools for eddy current signals have been developed by combining neural networks and finite element modeling, for quantitative flaw characterization in steam generator tubes. In the present work, interpretation of experimental eddy current signals was carried out in order to validate the developed inversion tools. A database was constructed using the synthetic flaw signals generated by the finite element model. The hybrid neural networks composed of a PNN classifier and BPNN size estimators were trained using the synthetic signals. Experimental eddy current signals were obtained from axisymmetric artificial flaws. Interpretation of flaw signals was conducted by feeding the experimental signals into the neural networks. The interpretation was excellent, which shows that the developed inversion tools would be applicable to the Interpretation of real eddy current signals.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.543-543
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• 2015

국내에는 17,500여개의 농업용 저수지가 전국적으로 분포하고 있다. 이들 저수지의 대부분은 1945년 이전에 설치되어 경과년수가 70년 이상된 저수지가 전체의 약 52%에 달하며, 1962년 이전에 설치되어 50년 이상된 저수지는 전체의 68.3%에 이르고 있다. 국내 약 70%에 해당하는 저수지는 50년이 넘은 노후화된 저수지로 오염물질의 장기간의 퇴적으로 인해 수용적이 많이 줄었고, 퇴적층의 오염물질 재용출로 인해 호소의 수질악화에 상당한 영향을 주고 있을 것으로 추정된다. 따라서 본 연구에서는 호소 설치 경과년수와 수질간의 상관성을 알아보기 위하여 상관분석을 실시하였고, 일원분산분석과 Scheffe Test를 통해 경과년수에 따라 호소 수질에 유의한 차이가 있는 지에 대해서 살펴보았다. 저수지 설치 경과년수와 수질항목 pH, EC, DO, SS, COD, TN, TP, Chl-a와의 상관성을 분석한 결과, DO를 제외한 모든 항목에서 10~40%의 정(+)의 상관성을 보여 저수지 준공년도가 오래된 것일수록 수질오염도가 높아지는 것으로 나타났다. 농업용 저수지의 준공 경과년수 구간에 따른 수질이 통계적으로 유의한 차이가 있는가를 알아보기 위하여 호소의 경과년수를 70년 이상($Y{\geq}70$), 50년 이상 70년 미만($50{\leq}Y$<70), 30년 이상 50년 미만($30{\leq}Y$<50), 10년 이상 30년 미만($10{\leq}Y$<30), 10년 미만(Y<10) 등 5개 구간으로 분류하여 일원분산분석을 실시하였다. 통계분석에 사용된 자료는 2013년도 농업용수 수질측정망 조사자료 3,175개를 이용하였다. 수온, pH, EC, COD, TN, TP, SS, Chl-a 등 거의 모든 수질항목에서 유의수준 p<0.001 에서 통계적으로 유의한 차이가 있는 것으로 나타났다. 사후검정인 Scheffe Test를 실시한 결과, DO를 제외한 모든 항목에서 70년 이상 된 구간($Y{\geq}70$)과 50년이상 70년 미만(의 구간에서 다른 구간에 비해 다소 높은 값을 보였고, 준공 경과년수가 작아질수록 농도가 점차적으로 낮아지는 경향을 보였다. 이러한 결과는 저수지가 설치된 지 오래되어 노후화가 진행될수록 수질오염도 높아지는 것을 알 수 있었다.

• Economic and Environmental Geology
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• v.54 no.5
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• pp.605-613
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• 2021

When crushing rocks to produce aggregates, solid stone dust or sludge is generated as a by-product. These by-products are classified as waste and are not utilized, and most of them are disposed of landfills. This by-product differs in mineral composition, chemical composition, and physical properties depending on the rock type and aggregate production process. Therefore, if a technology that can make good use of the inherent physical or chemical properties of by-products is developed, economic and environmental benefits can be achieved instead of disposal. In this study, stone dust and sludge were collected from domestic aggregate producers and physical and chemical properties were investigated by performing XRD mineral analysis, particle size analysis, and chemical analysis. In addition, the research trend was identified through a domestic and international research case studies on the use of stone powder and sludge.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.308-321
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• 2021

The purpose of this study was to analyze domestic research trends over the past 20 years and future direction of forest ecology using text mining. A total of 1,015 academic papers and keywords data related to forest ecology were collected by the "Research and Information Service Section" and analyzed using big data analysis programs, such as Textom and UCINET. From the results of word frequency and N-gram analyses, we found domestic studies on forest ecology rapidly increased since 2011. The most common research topic was "species diversity" over the past 20 years and "climate change" became a major topic since 2011. Based on CONCOR analysis, study subjects were grouped intoeight categories, such as "species diversity," "environmental policy," "climate change," "management," "plant taxonomy," "habitat suitability index," "vascular plants," and "recreation and welfare." Consequently, species diversity and climate change will remain important topics in the future and diversifying and expanding domestic research topics following global research trendsis necessary.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.