• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.275-275
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• 2015

최근 기후변화에 따른 극한 강우사상의 증가로 인하여 농업용 저수지의 재해 위험도가 증가하고 있는 추세이며, 사고가 발생할 때 마다 파손/붕괴된 시설물을 보수하는 대응형 유지관리체계에서 벗어나 기반시설의 성능과 생애주기 등을 고려하여 재해 발생을 사전에 예보 및 경보를 알릴 수 있는 예방적 관리체계로의 전환이 필요하다. 한국농어촌공사는 전국 1,500개 저수지에서 10분 단위 수위자료를 측정하고 있으며, 이를 분석하여 재해예방에 활용할 수 있는 기반이 조성되어 있으나 이에 대한 관리가 이루어지지 않고 있고 수집된 자료를 활용하여 재해 징후를 분석할 수 있는 재해 예방적 분석기술이 마련되어 있지 않은 실정이다. 본 연구에서는 농업용 저수지 수위자료를 이용한 저수지 이상거동을 판별하기 위하여 전국 34개 한국농어촌공사 관할 저수의 시계열 수위자료의 특성(Feature)을 분석하고자 한다. 시계열 자료의 시계열 특성을 분석하기 위하여 한국농어촌공사 관할의 전국 34개 저수지를 선정하여 분석을 실시하였다. 대상저수지는 지역별, 저수용량, 안정등급, 붕괴발생, 1개 지사관할 저수지로 각각 구분하여 선정하였으며, 각 저수지의 수위 측정기간(최소 5개년)에 대한 자료를 수집하였다. 농업용 저수지의 시계열 수위 자료의 특성을 분석하기 위하여 자료의 전처리를 수행하였다. 자료의 전처리는 시계열 수위자료의 잡음 특성, 기상자료 관련 변동특성 등 분류(Classification)에 영향을 미치는 노이즈 요소를 제거하는 과정이다. 전처리과정을 거친 자료는 특징(Feature) 추출 과정을 거치게 되고, 추출된 특징의 적합성에 따라 분류 알고리듬 성능에 많은 영향을 미친다. 따라서 시계열 자료의 특성을 파악하고 특징을 추출하는 것은 이상치 탐지에 있어 매우 중요한 과정이다. 본 연구에서는 시계열 자료 특징 추출 방법으로 물리적인 한계치, 확률적인 문턱값(Threshold), 시계열 패턴, 주변 저수지와의 시계열 상관분석 등을 적용하였으며, 이를 데이터베이스로 구축하여 이후 분류알고리듬 학습에 적용하여 정상치와 이상치를 판별하는데 이용될 수 있도록 하였다. 따라서 본 연구에서 제시되는 농업용 저수지의 시계열 특성은 다양한 분류알고리듬에 적용할 수 있으며, 이를 통하여 저수지 이상거동 판별을 위한 최적을 분류알고리듬의 선택에 도움이 될 것이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.131-131
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• 2019

최근 기후변화와 여름철 고온 등으로 인한 녹조현상, 각종 사고로 인한 화학물질 및 유류 유출 등 수질오염과 관련된 사회적 관심이 높아지고 있다. 특히, 화학사고로 인한 유해화학물질 유출은 접촉시 인체에 악영향을 끼치며, 대기 수질 토양을 오염시키고 주변 농작물의 변색이나 괴사를 유발하는 등 발생 시 적절한 조치와 대응이 필요하다. 환경부에서는 유해화학물질 유출사고로 인한 국민건강 및 환경상의 위해를 예방하기 위해 화학물질관리법과 화학물질 등록 및 평가에 관한 법률을 제정하여 유해화학물질을 관리하고 사고에 대응하고 있다. 그러나, 화학사고 발생 시 현장인력에 의존해 공장 인근의 먼지, 악취 등을 감시하거나 화학물질의 유출이 우려되는 곳에 제한적으로 검출센서를 설치해 사고를 감시하고 있으나 미설치 지역에 대한 능동적 탐지가 어렵고, 공간적 분포 탐지가 불가능하여 초동 대응에 한계가 있다. 한편 최근 초분광 영상을 활용하여 물질 고유의 특성을 분석함으로써 토지피복, 식생, 수질 등의 식별에 활용되고 있어 화학물질 감지 가능성도 보여주고 있다. 하지만, 초분광 센서를 활용한 하천의 화학물질 감지를 위한 연구는 아직 미비한 실정이다. 이에 본 연구에서는 우선 유해화학물질의 일종인 황산, 염화티오닐, 톨루엔을 대상으로 지점 분광복사계로 촬영하여 각각의 화학물질이 갖는 분광특성을 수집하여 초분광 영상으로 상호 구분이 가능한 지 확인하고자 하였다. 이상치 검출 및 신뢰도 높은 자료를 구축하기 위해 다회 반복촬영하였으며 반사도의 표준화를 위해 백색판을 동시에 측정하고 이를 정규화하여 분광 라이브러리를 구축한 결과, 대상 화학물질 별 식별이 가능하다는 결과를 도출하였다. 이러한 가능성에 기반하여 추가적인 유해화학물질 분광 라이브러리 데이터베이스를 구축하면, 사고물질의 식별 및 농도를 즉각적으로 확인하고 실시간 모니터링에 적용하여 신속하게 화학사고 발생여부 감지 및 대응에 활용될 것으로 기대한다.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.36-49
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• 2019

The objectives of this study were to extract "Field Survey Based Infection Tree of Pine Wilt Disease(FSB_ITPWD)" and "Object Classification Based Infection Tree of Pine Wilt Disease(OCB_ITPWD)" from the Research Forest at Kangwon National University, and evaluate the spatial distribution characteristics and occurrence intensity of wood infested by pine wood nematode. It was found that the OCB optimum weights (OCB) were 11 for Scale, 0.1 for Shape, 0.9 for Color, 0.9 for Compactness, and 0.1 for Smoothness. The overall classification accuracy was approximately 94%, and the Kappa coefficient was 0.85, which was very high. OCB_ITPWD area is approximately 2.4ha, which is approximately 0.05% of the total area. When the stand structure, distribution characteristics, and topographic and geographic factors of OCB_ITPWD and those of FSB_ITPWD were compared, age class IV was the most abundant age class in FSB_ITPWD (approximately 55%) and OCB_ITPWD (approximately 44%) - the latter was 11% lower than the former. The diameter at breast heigh (DBH at 1.2m from the ground) results showed that (below 14cm) and (below 28cm) DBH trees were the majority (approximately 93%) in OCB_ITPWD, while medium and (more then 30cm) DBH trees were the majority (approximately 87%) in FSB_ITPWD, indicating different DBH distribution. On the other hand, the elevation distribution rate of OCB_ITPWD was mostly between 401 and 500m (approximately 30%), while that of FSB_ITPWD was mostly between 301 and 400m (approximately 45%). Additionally, the accessibility from the forest road was the highest at "100m or less" for both OCB_ITPWD (24%) and FSB_ITPWD (31%), indicating that more trees were infected when a stand was closer to a forest road with higher accessibility. OCB_ITPWD hotspots were 31 and 32 compartments, and it was highly distributed in areas with a higher age class and a higher DBH class.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.2
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• pp.79-87
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• 2017

GenesWell$^{TM}$ BCT is a 12-gene test suggesting the prognostic risk score (BCT Score) for distant metastasis within the first 10 years in early breast cancer patients with hormone receptor-positive, HER2-negative, and pN0~1 tumors. In this study, we validated the analytical performance of GenesWell$^{TM}$ BCT. Gene expression values were measured by a one-step, real-time qPCR, using RNA extracted from FFPE specimens of early breast cancer patients. Limit of Blank, Limit of Detection, and dynamic range for each of the 12 genes were assessed by serially diluted RNA pools. The analytical precision and specificity were evaluated by three different RNA samples representing low risk group, high risk group, and near-cutoff group in accordance with their BCT Scores. GenesWell$^{TM}$ BCT could detect gene expression of each of the 12 genes from less than $1ng/{\mu}L$ of RNA. Repeatability and reproducibility across multiple testing sites resulted in 100% and 98.3% consistencies of risk classification, respectively. Moreover, it was confirmed that the potential interference substances does not affect the risk classification of the test. The findings demonstrate that GenesWell$^{TM}$ BCT have high analytical performance with over 95% consistency for risk classification.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Childhood Kidney Diseases
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• v.17 no.2
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• pp.49-56
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• 2013

Purpose: IgA nephropathy (IgAN) is one of the major causes of end-stage renal disease. Mass school urine screening (SUS) has been performed to enable early detection of chronic renal diseases, including IgAN. We wanted to evaluate the patients with IgAN, including those diagnosed through SUS. Methods: Between 1998 and 2010, 64 children were diagnosed with IgAN based on renal biopsy results obtained at the Pediatric Nephrology Department, ${\bigcirc\bigcirc}$ University Hospital. We divided these patients into the SUS group (37 cases), diagnosed through SUS, and the symptomatic (Sx) group (27 cases), diagnosed clinically. The medical records of both groups were analyzed retrospectively. Results: The mean age of the SUS and Sx groups was $10.8{\pm}2.7$ and $9.5{\pm}3.4$ years (P >0.05), respectively. Both groups had a higher proportion of male patients. The time from the notification of an abnormal urinary finding to a hospital visit or renal biopsy was shorter in the Sx group than in the SUS group. Regarding clinical manifestations, there were fewer cases with gross hematuria (P <0.001) and edema (P =0.008) in the SUS group, but there were no differences in terms of the therapeutic regimen and treatment duration. Regarding laboratory parameters, the Sx group had a higher white blood cell count (P =0.007) and lower hemoglobin (P =0.007) and albumin (P =0.000) levels. There were no differences in the renal biopsy findings in both groups, based on the history of gross hematuria or the severity of proteinuria. However, in all 64 patients with IgAN, the light microscopy findings (Hass classification) were related to a history of gross hematuria or the severity of proteinuria. Conclusion: There were no significant clinical and histological differences between the groups, as both had early stage IgAN. Although SUS facilitates the early detection of IgAN, long-term, large-scale prospective controlled studies are needed to assess the benefits of early diagnosis and treatment in chronic renal disease progression.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.51-60
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• 2014

In this study, ICT medical service provider's level of knowledge fire fighting safety and methods on coping with fires in the regions of Gwangju and Jeonam Province of Korea were investigated to determine the elements affecting such levels and provide basic information on the manuals for educating how to cope with the fire fighting safety in medical facilities. The data were analyzed using SPSS Win 14.0. The scores of level of knowledge fire fighting safety of ICT medical service provider's were 7.06(10 point scale), and the scores of level of recognizing how to cope with fire fighting safety were 6.61(11 point scale). level of recognizing how to cope with fire fighting safety were significantly different according to gender(t=4.12, p<.001), age(${\chi}^2$=17.24, p<.001), length of career(${\chi}^2$=22.76, p<.001), experience with fire fighting safety education(t=6.10, p<.001), level of subjective knowledge on fire fighting safety(${\chi}^2$=53.83, p<.001). In order to enhance the level of understanding of fire fighting safety and methods of coping by the ICT medical service providers it is found that: self-directed learning through avoiding the education just conveying knowledge by lecture tailored learning for individuals fire fighting education focused on experiencing actual work by developing various contents emphasizing cooperative learning deploying patients by classification systems using simulations and a study on the implementation of digital anti-fire monitoring system with multipoint communication protocol, a design and development of the smoke detection system using infra-red laser for fire detection in the wide space, video based fire detection algorithm using gaussian mixture mode developing an education manual for coping with fire fighting safety through multi learning approach at the medical facilities are required.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.3
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• pp.402-413
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• 2001

Food safety is a term broadly applied to food quality that may adversely affect human health. These include zoonotic diseases and acute and chronic effects of ingesting natural and human-made xenobiotics. There are two major areas of concern over the presence of residues of antibiotics in animal-derived foodstuffs with regard to human health. The first is allergic reactions. Some antibiotics, such as penicillins can evoke allergic reactions even though small amounts of them are ingested or exposed by parenteral routes. The second is development of antibiotic resistance in gut bacteria of human. Recently multi-resistant pneumococcal, glycopeptide-resistant enterococci and gram negative bacteria with extended-spectrum $\beta$-lactamases have spread all over the world, and are now a serious therapeutic problem in human. Although it is evident that drugs are required in the efficient production of meat, milk and eggs, their indiscriminate use should never be substituted for hygienic management of farm. Drug should be used only when they are required. In addition to veterinary drugs, environmental contaminants that were contaminated in feed, water and air can make residues in animal products. Mycotoxins, heavy metals, pesticides, herbicides and other chemicals derived from industries can be harmful both to animal and human health. Most of organic contaminants, such as dioxin, PCBs and DDT, and metals are persistent in environment and biological organisms and can be accumulated in fat and hard tissues. Some of them are suspected to have endocrine disrupting, carcinogenic, teratogenic, immunodepressive and nervous effects. The governmental agencies concerned make efforts to prevent residue problems; approval of drugs including withdrawal times of each preparation of drugs, establishment of tolerances, guidelines regarding drug use and sanitation enforcement of livestock products. National residue program is conducted to audit the status of the chemical residues in foods. Recently HACCP has been introduced to promote food safety from farm to table by reducing hazardous biological, chemical and physical factors. Animal Production Food Safety Program, Quality Assurance Programs, Food Animal Residue Avoidance Databank are para- or non-governmental activities ensuring food safety. This topic will cover classification and usage or sources of chemical residues, their adverse effects, and chemical residue status of some countries. Issues are expanded to residue detection methodologies, toxicological and pharmacokinetic backgrounds of MRL and withdrawal time establishments, and the importance of non-governmental activities with regard to reducing chemical residues in food.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.26 no.5
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• pp.462-469
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• 2000

Positron Emission Tomography(PET) is a new diagnostic method that can create functional images of the distribution of positron emitting radionuclides, which when administered intravenously in the body, makes possible anatomical and functional analysis by quantity of biochemical and physiological process. After genetic and biochemical changes in initial stage, malignant tumor undergoes functional changes before undergoing anatomical changes. So, early diagnosis of malignant tumors by functional analysis with PET can be achieved, replacing traditional anatomical analysis, such as computed tomography(CT) and magnetic resonance image(MRI), etc. Similarly, PET can identify malignant tumor without confusion with scar and fibrosis in follow up check. In the Korea Cancer Center Hospital(KCCH) from October 1997 to September 1999, clinical study was performed in 79 cases that underwent 89 times PET evaluation with [18F]-Fluorodeoxyglucose for diagnosis of oral and maxillofacial tumors, and the data was analysed by Bayesian $2{\times}2$ Classification Table. The results were as follows : Evaluation for initial diagnosis with FDG-PET (P<0.005) 1. Agreement rate or accuracy rate is 88.9%. 2. Sensitivity is 95.2%, and specificity 66.7%. 3. Positive predictive rate is 90.9%, and negative predictive rate 80.0%. 4. In consideration of tumor stage, diagnostic rate in less than stage II was 90% and in greater than stage III 100%. 5. In consideration of tumor size, diagnostic rate in less than T2 was 92.3% and in greater than T3 100%. After primary treatment, evaluation for follow up check with FDG-PET (P < 0.001) 1. Agreement rate or accuracy rate is 85.4%. 2. Sensitivity is 87.5%, and specificity 82.4%. 3. Positive predictive rate is 87.5%, and negative predictive rate 82.4%. 4. In 24 recurred cases, 6 had distant metastasis, and 5 of them were diagnosed with FDG-PET, resulting in diagnostic rate of FDG-PET of 83.3%. From the above results, Positron Emission Tomography with [18F]- Fluorodeoxyglucose appears to be more sensitive and accurate for detecting the presence of oral and maxillofacial tumors, and has various clinical applications such as early diagnosis of tumor in initial and follow up check and detection of distant metastasis.