• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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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.

• Journal of Preventive Medicine and Public Health
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• v.53 no.6
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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.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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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)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.87-87
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• 2003

The Gyeonggi-do Agricultural Research and Extension Services has developed a web-site (http://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).(omitted)

• International Journal of Contents
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• v.17 no.3
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.

• Journal of Physiology & Pathology in Korean Medicine
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• v.21 no.1
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• pp.1-9
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• 2007

${\ulcorner}$Hamsansachon Dongyi Suse Bowon Gabogubon${\lrcorner}$ , discovered in 2000, can give very precious information in order to study the formation and development process of ${\ulcorner}$Dongyi Suse Bowon${\lrcorner}$ ${\ulcorner}$The Dircourse on the Constitutional Symptoms and Disease${\lrcorner}$ . I examined, by comparison, changes in understanding pathology explained in ${\ulcorner}$Dongyi Suse Bowon${\lrcorner}$ ${\ulcorner}$The Discourse on the Constitutional Symptoms and Disease${\lrcorner}$ of Gabobon and Sinchukbon, and consequently tried to define the concept of Dispositional Symptom(Dispositional disease) as below, in a point of view that ‘Dispositional Symptom(Dispositional disease)’ should be the key word in explaining the changes in understanding of pathology. Dispositional Symptom(dispositional disease) is a new concept that was first troduced in the Kyongjabon, not found in the Gabobon, and that played a key role in editing ${\ulcorner}$Dongyi Suse Bowon${\lrcorner}$ ${\ulcorner}$The Discourse on the Constitutional Symptom and Disease${\lrcorner}$ . Dispositional Symptom(dispositional disease) means an innate temperament or a pathological tendency, which is already constructed in the system of an individual, prior to expression of specific diseases and symptoms, and can be a primary basis to tell the susceptibility and developing pattern of a certain disease, to decide how to treat and forecast the prognosis. Sinchukbon inductively categorized symptoms of the dispositional symptom (dispositional disease) into the concept of ‘Eight principles’, or eight standards of diagnosis, such as superficies-interior, cold-heat, and weakness-strength.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.28-34
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• 2010

The spatial distribution characteristics of damaged trees by the pine wilt disease appear scattered spots spreading from single dead trees. That is the reason why it is difficult to early detect damage and to prevent from extensive damage. Thus, it is very important to forecast and analyze the damage occurrences, to establish strategies for prevention, and to supervise them. However, conventional survey which observes around roads or residential areas by naked eyes was impossible to investigate completely, missing target areas and dangerous areas. Therefore, aerial forecasting techniques on the damaged area were developed using GIS, GPS, and helicopters for an accurate observation of systematic and scientific approach in this study. Moreover, advantages of the techniques application were confirmed to survey 972 dead tree samples at 349 position-coordinates in 32 cities (about $28,810km^2$), 2005. This study is expected to apply widely to find dead trees and the causes, particularly by pine wilt disease.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.772-779
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• 2015

Advancing information technology have rapidly changed our service environment of life, culture, and industry. Computer information communication system is applied in medical, health, distribution, and business transaction. Smart is using new information by combining ability of computer and information. Although agriculture is labor intensive industry that requires a lot of hands, agriculture is becoming knowledge-based industry today. In agriculture field, computer communication system is applied on facilities farming and machinery Agricultural. In this paper, we designed and implemented application that provides personalized agriculture related information at the actual farming field. Also, this provides farmer a system that they can directly auction or sell their produced crops. We designed and implemented a system that parsing information of each seasonal, weather condition, market price, region based, crop, and disease and insects through individual setup on ubiquitous environment using location-based sensor network and processing data.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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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.