• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.505-510
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• 1996

Many diseases cause other diseases with strength of influences and time intervals. Prognostic and therapeutic assessments are the important part of clinical medicine as well as diagnostic assessments. In cases where a patient already has manufestations of multiple disorders (complications), progress forecasting and therapy decision by physicians without support tools are very dificult: physicians often say that "Once complications set in, the patient may die". Treating complications are difficult tasks for physicians, because they have to consider all of the complexities, possibilities and interactions between the diseases. The prediction of multiple disorders has many bundles that arise from such time-dependent interrelationships between diseases and nonlinear progress. This paper proposes a model based on time-dependent influences, which appropriately describes the progress of mulitple disorders, and gives some modificaitons for applying this model to medical domains: time-dependent influence matrix manifestation vector, therapy efficacy matrix, S-shaped curve approximation, definitions of which are provided. This research proposes an algorithm for forecasting the state of each disease on the time horizon and for evaluation of therapy alternatives with not toy example, but real patient history of multiple disorders.disorders.

• 한국정보컨버전스학회:학술대회논문집
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• 한국정보컨버전스학회 2008년도 International conference on information convergence
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• pp.191-194
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• 2008

SNP(Single Nucleotide Polymorphism) refers to the difference in a base pair existed in DNAs of individuals. Each of it appears per 1,000 bases in human genome and it enables each gene to defer in junctions, interacts with each other to make different shapes of humans, and produces different disease sensitivities. In this paper, we propose a system to forecast lung cancer sensitivities using SNP data related with the lung cancer. A lung cancer sensitivity forecasting model is also constructed through analysis of genetic and non-genetic factors for squamous cell carcinomas, adeno carcinomas, and small cell carcinomas that may frequently appear in Korean. The proposed system with the model gives the probabilities of the onset of lung cancers in the experimental subjects.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 춘계학술발표대회
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• pp.47-50
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• 2004

본 논문은 대량의 데이터를 처리하는 전염병에 관한 역학조사에 대한 과정을 KDD(Knowledge Discovery in Database)와 데이터마이닝 기법을 이용해서 의료 전문인들의 지식을 데이터베이스화하여 데이터 선정, 정제, 보강, 예측과 빠른 데이터 검출을 하도록 하였다. 그리고 각 바이러스의 동향은 데이터마이닝을 활용하므로 일부분만의 데이터를 산출하지 않고 전체적인 동향을 산출, 예측하도록 한다.

• 한국환경농학회지
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• 제33권1호
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• pp.52-56
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• 2014

BACKGROUND: It is known that impacts of climate change on damage occurrence by insect pests and diseases are increasing. The negative effects of climate change on production will threaten our food security. It is needed that on the basis of analysis of the impacts, proper strategies in response to climate change are developed. METHODS AND RESULTS: The objective of this paper is to estimate impacts of climate change on rice damage occurrence by insect pests and diseases, using the panal model which analyzes both cross-section data and time series data. The result of an analysis on impacts of climate change on rice damage occurrence by pest insect and disease showed that the damage occurrence by Rice leaf roller and Rice water weevil increased if temperature increased, and damage occurrence by Stripe, Sheath blight, and Leaf Blast increased if precipitation(or amount of sunshine) increased(or decreased). CONCLUSION: Adaptation strategies, supplying weather forecasting information by region, developing systematical strategies for prevention of damage occurrence by pest insect and disease, analyzing the factors of damage occurrence by unexpected pest insect and disease, enforcing international cooperation for prevention of damage occurrence are needed to minimize the impacts of damage occurrence on rice production.

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 2003년도 정기총회 및 추계학술발표회
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• pp.108.2-109
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• 2003

The Gyeonggi-do Agricultural Research and Extension Services has developed a web-site (www.epilove.com) in collaboration with EPINET to provide information on agricultural weather and rice disease and insect pest management in Gyeonggi-do. Weather information includes near real-time weather data monitored by automated weather stations (AWS) installed at rice paddy fields of 11 Agricultural Technology Centers (ATC) in Gyeonggi-do, and weekly weather forecast by Korea Meteorological Administration (KMA). Map images of hourly air temperature and rainfall are also generated at 309m x 309m resolution using hourly data obtained from AWS installed at 191 locations by KMA. Based on near real-time weather data from 11 ATC, hourly infection risks of rice blast, sheath blight, and bacterial grain rot for individual districts are estimated by disease forecasting models, BLAST, SHBLIGHT, and GRAINROT. Users can diagnose various diseases and insects of rice and find their information in detail by browsing thumbnail images of them. A database on agrochemicals is linked to the system for disease and insect diagnosis to help users search for appropriate agrochemicals to control diseases and insect pests.

• 대한토목학회논문집
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• 제41권3호
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• pp.317-325
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• 2021

1988년 부산에서 처음 발병된 소나무재선충병(Pine Wilt Disease, PWD)은 우리나라 소나무에 막대한 피해를 주고 있는 심각한 질병이다. 정부에서는 2005년 소나무재선충병 방제특별법을 제정하고 피해지역의 소나무 이동 금지와 방제를 시행하고 있다. 하지만, 기존의 예찰 및 방제방법은 산악지형에서 동시다발적이고 급진적으로 발생하는 소나무재선충병을 줄이기에는 물리적, 경제적 어려움이 있다. 따라서 본 연구에서는 소나무재선충병 감염의심목을 효율적으로 탐지하기 위해 무인항공기를 이용한 영상자료를 바탕으로 딥러닝 객체인식 예찰 방법의 활용가능성을 제시하고자 한다. 소나무재선충병 피해목을 관측하기 위해서 항공촬영을 통해 영상 데이터를 획득하고 정사영상을 제작하였다. 그 결과 198개의 피해목이 확인되었으며, 이를 검증하기 위해서 접근이 불가한 급경사지나 절벽과 같은 곳을 제외하고 현장 조사를 진행하여 84개의 피해목을 확인할 수 있었다. 검증된 데이터를 가지고 분할방법인 SegNet과 검출방법인 YOLOv2를 이용하여 분석한 결과 성능은 각각 0.57, 0.77로 나타났다.

• Journal of Preventive Medicine and Public Health
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• 제53권6호
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• pp.387-396
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• 2020

Objectives: The lack of advance planning in a public health emergency can lead to wasted resources and inadvertent loss of lives. This study is aimed at forecasting the needs for healthcare resources following the expansion of the coronavirus disease 2019 (COVID-19) outbreak in the Republic of Kazakhstan, focusing on hospital beds, equipment, and the professional workforce in light of the developing epidemiological situation and the data on resources currently available. Methods: We constructed a forecast model of the epidemiological scenario via the classic susceptible-exposed-infected-removed (SEIR) approach. The World Health Organization's COVID-19 Essential Supplies Forecasting Tool was used to evaluate the healthcare resources needed for the next 12 weeks. Results: Over the forecast period, there will be 104 713.7 hospital admissions due to severe disease and 34 904.5 hospital admissions due to critical disease. This will require 47 247.7 beds for severe disease and 1929.9 beds for critical disease at the peak of the COVID-19 outbreak. There will also be high needs for all categories of healthcare workers and for both diagnostic and treatment equipment. Thus, Republic of Kazakhstan faces the need for a rapid increase in available healthcare resources and/or for finding ways to redistribute resources effectively. Conclusions: Republic of Kazakhstan will be able to reduce the rates of infections and deaths among its population by developing and following a consistent strategy targeting COVID-19 in a number of inter-related directions.

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 2003년도 정기총회 및 추계학술발표회
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• pp.24-24
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• 2003

Since Rice bacterial grain rot (RGBR) was reported at 1986 in Korea, it has been severely occurred in 1994, 1995, 1998, and especially around 16,609 ha in 2000, and became a major disease in rice cultivation field. This study was focused on investigation of ecology of RGBR, weather conditions that affect development of epidemics, and development of an effective RGBR forecast system based on weather conditions during the rice heading period.(중략)

• The Plant Pathology Journal
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• 제22권4호
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• pp.314-317
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• 2006

A phage technique for detection of Xanthomonas axonopodis pv. citri, a causal bacterium of canker on Sastuma mandarin fruits was developed. Phage and ELISA techniques were compared for their sensitivity for detection of Xanthomonas axonopodis pv. citri on orange fruits. Both of techniques revealed a similar efficiency for the bacterial detection; the pathogenic bacteria were observed in pellet from the fruits with over one canker spot with below 2 mm in diameter. In field assays, the increase of phage population(120%) on surface of the fruits related to the disease development one month later indicated that the bacterial pathogens inhabit on the surface. The procedure will be effectively used for detection of only living bacterial pathogen on fruit surfaces of Satsuma mandarin and for the disease forecasting.

• Mycobiology
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• 제50권5호
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• pp.382-388
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• 2022

White mold (or Sclerotinia stem rot), caused by Sclerotinia species, is a major air, soil, or seed-transmitted disease affecting numerous crops and wild plants. Microscopic or culture-based methods currently available for their detection and identification are time-consuming, laborious, and often erroneous. Therefore, we developed a multiplex quantitative PCR (qPCR) assay for the discrimination, detection, and quantification of DNA collected from each of the three economically relevant Sclerotinia species, namely, S. sclerotiorum, S. minor, and S. nivalis. TaqMan primer/probe combinations specific for each Sclerotinia species were designed based on the gene sequences encoding aspartyl protease. High specificity and sensitivity of each probe were confirmed for sclerotium and soil samples, as well as pure cultures, using simplex and multiplex qPCRs. This multiplex assay could be helpful in detecting and quantifying specific species of Sclerotinia, and therefore, may be valuable for disease diagnosis, forecasting, and management.