• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.53-62
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• 2006

Near infrared reflectance spectroscopy (NIRS) has been increasingly used as a rapid, accurate method of evaluating some chemical compositions in forages. Analysis of forage quality by NIRS usually involves dry ground samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations and spectral math treatments on prediction ability of chemical composition for corn silage by NIRS. A population of 112 corn silage representing a wide range in chemical parameters were used in this investigation. Samples of com silage were scanned at 2nm intervals over the wavelength range 400-2500nm and the optical data recorded as log l/Reflectance(log l/R) and scanned in overt-dried grinding(ODG), liquid nitrogen grinding(LNG) or intact fresh(IF) condition. Samples were analysed for neutral detergent fiber(NDF), acid detergent fiber(ADF), acid detergent lignin(ADL), crude protein(CP) and crude ash content were expressed on a dry-matter(DM) basis. The spectral data were regressed against a range of chemical parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with four spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation(SECV). The results of this study show that NIRS predicted the chemical parameters with very high degree of accuracy(the correlation coefficient of cross validation$(R^2cv)$ range from $0.70{\sim}0.95$) in ODG. The optimum equations were selected on the basis of minimizing the standard error of prediction(SEP). The Optimum sample preparation methods and spectral math treatment were for ADF, the ODG method using 2,10,5 math treatment(SEP = 0.99, $R^2v=0.93$), and for CP, the ODG method using 1,4,4 math treatment(SEP = 0.29. $R^2v=0.91$).

• Korean Journal of Food Science and Technology
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• v.30 no.2
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• pp.341-347
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• 1998

To develop simple and accurate analytical method for freezing time prediction of beef and tylose under various freezing conditions, freezing time (Y) was regressed against the reciprocal $(X_3)$ of difference of initial freezing point and freezing medium temperature, reciprocal $(X_4)$ of surface heat transfer coefficient, the initial temperature $(X_1)$ and thickness $(X_2)$ of samples which should cover most situations arising in frozen food industry. As results of the multiple regression analysis, equations were obtained as follows. $Y_{tylose}=3.45X_1+7642.84X_2+4642.67X_3+2946.89X_4-431.33\;(R^2=0.9568)$ and $Y_{beef}=0.68X_1+7568.98X_2+2430.78X_3+3293.26X_4-299.00\;(R^2=0.9897)$. These equations offered better results than Plank, Nagaoka and Pham's models, shown in satisfactory agreement with models of Cleland & Earle and Hung & Thompson when were compared to previous models, and the accuracy of its was very high as average absolute difference of about 10% in the difference between the fitted and experimental results. Also, thermal diffusivities of beef and tylose were measured as $4.43{\times}10^{-4}m^2/hr$ and $4.39{\times}10^{-4}m^2/hr$ at $6{\sim}7^{\circ}C$, $2.42{\times}10^{-3}m^2/hr$ and $3.32{\times}10^{-3}m^2/hr$ at $-10{\sim}-12^{\circ}C$. Initial freezing points of beef and tylose were $-1.2^{\circ}C\;and\;-0.6^{\circ}C$, respectively. Surface heat transfer coefficients were estimated $20.57\;W/m^2^{\circ}C$ with no-packing, $16.11\;W/m^2^{\circ}C$ with wrap packing and $13.07\;W/m^2^{\circ}C$ with Al-foil packing, and the cooling rate of immersion freezing method was about 10 times faster than that of air blast freezing method.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.933-944
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• 2019

This study analyzes the tide deformation of land boundary regions on the east (Region A) and west (Region B) sides of the Ross Ice Shelf in Antarctica using Double-Differential Interferometric Synthetic Aperture Radar (DDInSAR). A total of seven Sentinel-1A SAR images acquired in 2015-2016 were used to estimate the accuracy of tide prediction model and Young's modulus of ice shelf. First, we compared the Ross Sea Height-based Tidal Inverse (Ross_Inv) model, which is a representative tide prediction model for the Antarctic Ross Sea, with the tide deformation of the ice shelf extracted from the DDInSAR image. The accuracy was analyzed as 3.86 cm in the east region of Ross Ice Shelf and it was confirmed that the inverse barometric pressure effect must be corrected in the tide model. However, in the east, it is confirmed that the tide model may be inaccurate because a large error occurs even after correction of the atmospheric effect. In addition, the Young's modulus of the ice was calculated on the basis of the one-dimensional elastic beam model showing the correlation between the width of the hinge zone where the tide strain occurs and the ice thickness. For this purpose, the grounding line is defined as the line where the displacement caused by the tide appears in the DDInSAR image, and the hinge line is defined as the line to have the local maximum/minimum deformation, and the hinge zone as the area between the two lines. According to the one-dimensional elastic beam model assuming a semi-infinite plane, the width of the hinge region is directly proportional to the 0.75 power of the ice thickness. The width of the hinge zone was measured in the area where the ground line and the hinge line were close to the straight line shown in DDInSAR. The linear regression analysis with the 0.75 power of BEDMAP2 ice thickness estimated the Young's modulus of 1.77±0.73 GPa in the east and west of the Ross Ice Shelf. In this way, more accurate Young's modulus can be estimated by accumulating Sentinel-1 images in the future.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.981-992
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• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Chest Surgery
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• v.13 no.3
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• pp.174-185
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• 1980

It was my great nohour that I can be exposed to such plenty materials of the coronary bypass surgery. Here, I am summarizing the xoronary bypass surgery, clinically. The material is serial 101 patients who underwent coronary bypass surgery between July 17, 1979 to November 30, 1979 in Shadyside Hospital, University of Pittsburgh. 1. Incidence of the Atherosclerosis is frequent in white, male, fiftieth who are living in industrialized country. It has been told the etiologic factor of the atherosclerosis is hereditary, hyperlipidemia, hypertension, smoking, drinking, diabetes, obesity, stress, etc. 2. The main and most frequent complication of the coronary atherosclerosis is angina pectoris. Angina pectoris is the chief cause of coronary bypass surgery and the other causes of coronary bypass surgery are obstruction of the left main coronary artery, unstable angina, papillary muscle disruption or malfunction and ventricular aneurysm complicated by coronary artery disease. 3. The preoperative clinical laboratory examination shows abnormal elevation of plasma lipid in 82 patint, plasma glucose in 40 patient, total CPK-MB in 24 patient stotal LDH in 22 patient out of 101 patient. 4. Abnormal ECG findings in preoperative examine were 29.1% myocardial infarction, 25.8% ischemia and injury, 14.6T conduction defect. 5. Also we had done Echocardiography, Tread Mill Test, Myocardial Scanning, Vectorcardiography and Lung function test to get adjunctive benefit in prediction of prognosis and accurate diagnosis. 6. The frequency of coronary atherosclerosis in main coronary arteries were LAD, RCA and Circumflex in that order. 7. The patients' main complaints which were became as etiologic factor undergoing coronary bypass surgery were angina, dyspnea, diaphoresis, dizziness, nausea and etc. 8. For the coronary bypass surgery, we used cardiopulmonary bypass machine, non-blood, diluting prime, cold cardioplegic solution and moderate cooling for the myocardial protection. 9. We got the grafted veins from Saphenous and Cephalic vein. Reversed and anastomosed between aorta and distal coronary A. using 5-0 and 7-0 prolene continuous suture. Occasionally we used internal mammary A. as an arterial blood source and anastomosed to the distal coronary A. and to side fashion. 10. The average cardiopulmonary bypass time for every graft was 43.9 min. and aortic clamp time was 23 minute. We could Rt. coronary A. bypass surgery only by stand by the cardiopulmonary machine and in the state of pumping heart. 11. Rates by the noumbers of graft were as follow : 21.8% single, 33.7% double, 26.7% triple, 13.9% quadruple, 3% quintuple and 1% was sixtuple graft. 12. combined procedures with coronary bypass surgery were 6% aneurysmectomy, 3% AVR, 1% MVR, 13% pacer implantation and 1% intraaortic ballon setting. 13. We could see the complete abolition of anginal pain after operation in 68% of patient, improvement 25.8%, no change in 3.1%, and there was unknown in 3%. 14. There were 4% immediate postoperative deaths, 13.5% some kinds of heart complication, 51.3% lung complications 33.3% pleural complications as prognosis.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.5
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• pp.581-600
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• 2013

The current status of atmospheric environmental impact assessment (EIA) has been summerized and future prospective for effective and accurate atmospheric EIA has been proposed by reviewing available papers and reports for the atmospheric EIA. The number of reports for the EIA in the EIA support system which is operated by the Korean Environmental Institute have been dramatically decreased from 282 reports in 2008 to 113 reports in 2012 during recent five years. This is partially due to simplification of the EIA procedure, the contraction of the public development and economic recession. We analyzed details of the EIA report to review how actual atmospheric EIA has preformed according to the EIA guidelines from the Korean Ministry of Environment. The 264 reports of EIA published in 2011 and 2012 had been reviewed especially focusing on the atmospheric evaluation items such as meteorology, air quality measurement and modeling, odor measurement and modeling, wind corridor in urban planning, and climate change. In overall sense, the atmospheric EIA has been performed quite well by abiding the guidelines except for local meteorological data measurement, permit standard for air quality and wind corridor. The new approaches to improve the procedure of atmospheric EIA and to reflect future of national air quality standard of $PM_{2.5}$ have been proposed. The guidelines on how to evaluate the wind corridor, to implement atmospheric EIA for $PM_{2.5}$ permit, and how to acquire local meteorological data by combining local measurement and model prediction are required for the effective and future oriented atmospheric EIA.

• Nutrition Research and Practice
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• v.5 no.4
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• pp.313-321
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• 2011

The Korea Food and Drug Administration (KFDA) revised the Health Functional Food Act in 2008 and extended the form of health functional foods to general food types. Therefore, this study was performed to investigate consumers' perceptions of the expanded form of health functional food and to predict consumption patterns. For this study, 1,006 male and female adults aged 19 years and older were selected nationwide by multi-stage stratified random sampling and were surveyed in 1:1 interviews. The questionnaire survey was conducted by Korea Gallup. The subjects consisted of 497 (49.4%) males and 509 (50.6%) females. About 57.9% of the subjects recognized the KFDA's permission procedures for health functional foods. Regarding the health functional foods that the subjects had consumed, red ginseng products were the highest (45.3%), followed by nutritional supplements (34.9%), ginseng products (27.9%), lactobacillus-containing products (21.0%), aloe products (20.3%), and Japanese apricot extract products (18.4%). Opinions on expanding the form of health functional foods to general food types scored 4.7 points on a 7-point scale, showing positive responses. In terms of the effects of medicine-type health functional foods versus generic health functional foods, the highest response was 'same effects if the same ingredients are contained' at a rate of 34.7%. For intake frequency by food type, the response of 'daily consistent intake' was 31.7% for capsules, tablets, and pills, and 21.7% for extracts. For general food types, 'daily consistent intake' was 44.5% for rice and 22.8% for beverages, which were higher rates than those for medicine types. From the above results, consumers had positive opinions of the expansion of health functional foods to generic forms but are not expected to maintain accurate intake frequencies or amounts. Thus, continuous promotion and education are needed for proper intake of generic health functional foods.