• 제목/요약/키워드: predictive distribution

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H. pylori 감염 진단 시 14C-요소호기검사의 임상적 유용성 (Clinical Usefulness of 14C-Urea Breath Test for the Diagnosis of H. pylori Infection)

  • 김윤식
    • 대한임상검사과학회지
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    • 제39권3호
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    • pp.271-276
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    • 2007
  • Helicobacter pylori (H. pylori) infection is common in korea and high incidence at gastric ulcer and duodenal ulcer. $^{14}C-urea$ breath test ($^{14}C-UBT$) is regarded as a highly reliable and non-invasive method for the diagnosis of H. pylori infection. The purpose of this study was to evaluate the diagnositc performance of a new and rapid $^{14}C-UBT$, which was equipped with Geiger-Muller counter and compared the results with those obtained by gastroduodenoscopic biopsies (GBx). One hundred sixty-eight patients (M : F = 118 : 50) underwent $^{14}C-UBT$, rapid urease test (CLO test), and GBx. The results of $^{14}C-UBT$ were classified as positive (>50 cpm), borderline (25$^{14}C-UBT$ or CLO test results with GBx as a glod standard. In the assessment of the presence of H. pylori infection, the $^{14}C-UBT$ global performance yielded positive predictive value, negative predictive value and accuracy of 93.3% and 83.3%, respectively. However, the CLO test had performance yielded positive predictive value, negative predictive value and accuracy of 76.9%, 50.0%, respectively. In this study $^{14}C-UBT$ is a highly accurate, simple and non-invasive method or the diagnosis of follow up H. pylori infection.

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구강암 환자에서 $^{18}F$ FDG-PET/CT의 경부 림프절 전이 평가 유용성 (USEFULNESS OF $^{18}F$-FDG PET/CT IN THE EVALUATION OF CERVICAL LYMPH NODE METASTASIS IN PATIENTS WITH ORAL CANCER)

  • 유민기;유선열
    • Journal of the Korean Association of Oral and Maxillofacial Surgeons
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    • 제35권4호
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    • pp.213-220
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    • 2009
  • Purpose: The present study was aimed to examine the usefulness of 18F-FDG PET/CT in the evaluation of cervical lymph node metastasis in patients with oral cancer. Materials and methods: Twenty-two patients who underwent neck dissection to treat oral cancer were subjected for examination. The cervical node metastasis was evaluated by means of clinical examination, CT scan, PET, and histologic examination. By comparing the results of each examination modality with those of histologic examination, it's sensitivity, specificity, positive predictive value, and negative predictive value were determined. Results: The oral cancer was more frequent in males with a ratio of 2.14:1. The sixth decade showed the highest incidence in age distribution with mean of $56{\pm}16$. Histologic findings showed that squamous cell carcinoma was the most common (15 patients), and mucoepidermoid carcinoma (3), malignant melanoma (2), and adenoid cystic carcinoma and ghost cell odontogenic carcinoma (1 each), in order. In most cases, wide surgical excision of the primary cancer and neck dissection was performed, followed by reconstruction with free flaps when necessary. When comparing the results of each examination modality with those of the histologic examination, clinical examination showed sensitivity, specificity, positive predictive value, and negative predictive value at 11%, 85%, 33%, and 58%, respectively. CT scans showed at 67%, 77%, 67%, and 77%, while $^{18}F$-FDG PET/CT at 78%, 77%, 70%, and 83%, respectively. Conclusions: These results suggest that PET is more useful, compared with clinical examination and CT scans, in the evaluation of cervical lymph node metastasis in patients with oral cancer.

청소년 신체 성장 예측 모델의 성능 향상을 위한 시각적 분석 방법 (Visual Analytics Approach for Performance Improvement of predicting youth physical growth model)

  • 연한별;피민규;서성범;하서호;오병준;장윤
    • 한국컴퓨터그래픽스학회논문지
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    • 제23권4호
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    • pp.21-29
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    • 2017
  • 예측 시각적 분석 연구는 다양한 대화식 데이터 탐색 기법을 사용하여 예측 결과의 불확실성을 줄이는데 중점을 두었다. 대화식 탐색 기법의 목적은 변수간의 관계를 이해하고 알려지지 않은 변수를 예측하기 위한 적합한 모델을 선택함으로서 의사결정권자의 수준에 따른 예측결과의 품질 차이를 줄이는 것이다. 하지만 청소년 신체 성장 데이터와 같이 전체적인 추세가 알려지지 않은 시계열 데이터를 설명할 수 있는 예측 모델을 만드는 것은 어렵다. 본 논문에서는 불확실한 추세를 가지는 시계열 데이터 단편에서 물리적 성장 값을 예측하기 위한 새로운 예측 방법을 제안한다. 새로운 예측 방법은 특정 시점에서의 데이터 분포를 추정하는 방법으로 실험결과 기존 회귀 모델보다 높은 정확도를 갖는다. 또한 우리는 예측 모델링 과정에서 발생 가능한 불확실성을 최소화 할 수 있는 시각적 분석 방법을 제안한다.

퍼지집합과 베이지안 확률 기법을 이용한 암설사면지형 분포지역 추출에 관한 연구 (The Study on the Extraction of the Distribution Potential Area of Debris Landform Using Fuzzy Set and Bayesian Predictive Discriminate Model)

  • 위눈솔;장동호
    • 한국지형학회지
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    • 제24권3호
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    • pp.105-118
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    • 2017
  • The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

배전계획을 고려한 실데이터 및 기계학습 기반의 배전선로 부하예측 기법에 대한 연구 (Prediction of Electric Power on Distribution Line Using Machine Learning and Actual Data Considering Distribution Plan)

  • Kim, Junhyuk;Lee, Byung-Sung
    • KEPCO Journal on Electric Power and Energy
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    • 제7권1호
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    • pp.171-177
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    • 2021
  • In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

Modeling the Spatial Distribution of Black-Necked Cranes in Ladakh Using Maximum Entropy

  • Meenakshi Chauhan;Randeep Singh;Puneet Pandey
    • Proceedings of the National Institute of Ecology of the Republic of Korea
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    • 제4권2호
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    • pp.79-85
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    • 2023
  • The Tibetan Plateau is home to the only alpine crane species, the black-necked crane (Grus nigricollis). Conservation efforts are severely hampered by a lack of knowledge on the spatial distribution and breeding habitats of this species. The ecological niche modeling framework used to predict the spatial distribution of this species, based on the maximum entropy and occurrence record data, allowed us to generate a species-specific spatial distribution map in Ladakh, Trans-Himalaya, India. The model was created by assimilating species occurrence data from 486 geographical sites with 24 topographic and bioclimatic variables. Fourteen variables helped forecast the distribution of black-necked cranes by 96.2%. The area under the curve score for the model training data was high (0.98), indicating the accuracy and predictive performance of the model. Of the total study area, the areas with high and moderate habitat suitability for black-necked cranes were anticipated to be 8,156 km2 and 6,759 km2, respectively. The area with high habitat suitability within the protected areas was 5,335 km2. The spatial distribution predicted using our model showed that the majority of speculated conservation areas bordered the existing protected areas of the Changthang Wildlife Sanctuary. Hence, we believe, that by increasing the current study area, we can account for these gaps in conservation areas, more effectively.

D 정수장 소독부산물 예측모델 선정 (Predictive Model Selection of Disinfection by-products (DBPs) in D Water Treatment Plant)

  • 김성준;이형원;황정석;원찬희
    • 한국물환경학회지
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    • 제26권3호
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    • pp.460-467
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    • 2010
  • For D-WTP's sedimentation basin and distribution reservoir, and water tap the predictive models proposed tentatively herein included the models for estimating TTHM concentration in precipitated water, for treated water and for tap water, and the estimated correlation formula between treated water's TTHM concentration and tap water. As for TTHM-concentration predictive model in sedimentation water, the coefficient of determination is 0.866 for best-fitted short-term $DOC{\times}UV_{254}$ based Model (TTHM). As for $HAA_5$-concentration predictive model in sedimentation water, the coefficient of determination is 0.947 for the suitable $UV_{254}$-based model ($HAA_5$). In case of the predictive model in treated water, the coefficient of determination is 0.980 for best-fitted $DOC{\times}UV_{254}$ based model (TTHM) using coagulated waters, while the coefficient of determination is 0.983 for best-fitted $DOC{\times}UV_{254}$ based model ($HAA_5$) using coagulated waters, which described the $HAA_5$ concentration well. However, the predictive model for tap water could not be compatible with the one for treated water, only except for possibility inducing correlation formula for prediction, [i.e., the correlation formula between TTHM concentration and tap water was verified as TTHM (tap water) = $1.162{\times}TTHM$ (treated water), while $HAA_5$ (tap water) = $0.965{\times}HAA_5$ (treated water).] The correlation analysis between DOC and $KMnO_4$ consumption by process resulted in higher relationship with filtrated water, showing that its regression is $DOC=0.669{\times}KMnO_4$ consumption - 0.166 with 0.689 of determination coefficient. By substituting it to the existing DOC-based model ($HAA_5$) for treated water, the consequential model formula was made as follows; $HAA_5=8.35(KMnO_4\;consumption{\times}0.669-0.166)^{0.701}(Cl_2)^{0.577}t^{0.150}0.9216^{(pH-7.5)}1.022^{(Temp-20^{\circ}C)}$

Predictive Distribution Modelling of Calamus andamanicus Kurz, an Endemic Rattan from Andaman and Nicobar Islands, India

  • Sreekumar, V.B.;Suganthasakthivel, R.;Sreejith, K.A.;Sanil, M.S.
    • Journal of Forest and Environmental Science
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    • 제32권1호
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    • pp.94-98
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    • 2016
  • Calamus andamanicus Kurz is one of the commercially important solitary rattans endemic to Andaman and Nicobar islands. The habitat suitability modeling program, MaxEnt, was used to predict the potential ecological niches of this species, based on bioclimatic variables. The study revealed high potential distribution of C. andamanicus across both Andaman and Nicobar islands. Of the 33 spatially unique points, 21 points were recorded from South and North Andamans and 12 from Great Nicobar Islands. The islands like Little Andaman, North Sentinel, Little Nicobar, Tllangchong, Teressa were also predicted positive even though this rattan is not recorded from these islands. Mean diurnal range, higher precipitation in the wettest month of the year, annual precipitation and precipitation in the driest month are the main predictors of this species distribution.

배전선로 고장예지를 위한 애자의 고장징후 특성에 관한 연구 (A Feasibility Study on the Characterization of Incipient Insulator Failure for Distribution Fault Prediction)

  • 신정훈;김태원;박성택;김창종
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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    • pp.245-249
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    • 1997
  • A feasibility study on the characterization of incipient insulator failure for distribution fault prediction is presented. In this study, real distribution data was collected and analyzed to isolate incipient failure signatures or parameters which were expected to show distinct behaviors before and after failure incident. Several signal analysis methods were applied to isolate the parameters and a new strategy of analysis, the event-date concept, was also applied to find a relationship between non-harmonic and high frequency signal activities and imminent insulator failures.

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라돈 농도 분포도 작성을 위한 단변량 크리깅 기법의 정량적 비교 (Quantitative Comparison of Univariate Kriging Algorithms for Radon Concentration Mapping)

  • 곽근호;김용재;장병욱;박노욱
    • 한국지리정보학회지
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    • 제20권1호
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    • pp.71-84
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    • 2017
  • 토양, 암석, 지하수로부터 실내에 유입되는 라돈은 인간에게 큰 위해를 끼치는 방사능 가스이다. 라돈 가스의 위해성을 확인하기 위해 실내 라돈 농도를 측정해 오고 있는데, 추가적인 분석 수행을 위해서는 신뢰성 높은 분포도 작성이 매우 중요하다. 본 연구에서는 비대칭 분포를 나타내는 라돈 농도의 공간 분포도 작성을 위해 단변량 크리깅 기법들의 비교를 목적으로 정규 크리깅, 비선형 자료 변환 기반의 로그 정규 크리깅, 다중 가우시안 크리깅과 지시자 크리깅의 예측 능력을 비교하였다. 예측 능력을 비교 분석하기 위해 잭나이프 방법을 이용하여 검증을 수행하였으며, 자료 구간별 오차와 샘플링 밀도의 차이에 따른 오차도 추가적으로 분석하였다. 남한 지역을 대상으로 한 사례 연구 결과에서 전반적으로 정규 크리깅에 비해 비선형 자료 변환 기반 크리깅 기법들이 좋은 예측 능력을 보였으며, 비선형 자료 변환 기반 크리깅은 로그 정규 크리깅, 다중 가우시안 크리깅 순으로 좋게 나타났다. 그러나 공간 패턴과 높은 값의 재생산을 고려할 때, 높은 값의 예측 능력은 정규 크리깅이 가장 우수하였다. 본 연구의 결과는 비대칭 분포 자료의 공간 예측을 위한 크리깅 기법의 선정에 유용하게 사용될 것으로 기대된다.