• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.69-76
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• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.

• Atmosphere
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• v.24 no.3
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• pp.303-315
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• 2014

Terrestrial ecosystem plays the important role as carbon sink in the global carbon cycle. Understanding of interactions of terrestrial carbon cycle with climate is important for better prediction of future climate change. In this paper, terrestrial carbon cycle is investigated by Hadley Centre Global Environmental Model, version 2, Carbon Cycle (HadGEM2-CC) that considers vegetation dynamics and an interactive carbon cycle with climate. The simulation for future projection is based on the three (8.5/4.5/2.6) representative concentration pathways (RCPs) from 2006 to 2100 and compared with historical land carbon uptake from 1979 to 2005. Projected changes in ecological features such as production, respiration, net ecosystem exchange and climate condition show similar pattern in three RCPs, while the response amplitude in each RCPs are different. For all RCP scenarios, temperature and precipitation increase with rising of the atmospheric $CO_2$. Such climate conditions are favorable for vegetation growth and extension, causing future increase of terrestrial carbon uptakes in all RCPs. At the end of 21st century, the global average of gross and net primary productions and respiration increase in all RCPs and terrestrial ecosystem remains as carbon sink. This enhancement of land $CO_2$ uptake is attributed by the vegetated area expansion, increasing LAI, and early onset of growing season. After mid-21st century, temperature rising leads to excessive increase of soil respiration than net primary production and thus the terrestrial carbon uptake begins to fall since that time. Regionally the NEE average value of East-Asia ($90^{\circ}E-140^{\circ}E$, $20^{\circ}N{\sim}60^{\circ}N$) area is bigger than that of the same latitude band. In the end-$21^{st}$ the NEE mean values in East-Asia area are $-2.09PgC\;yr^{-1}$, $-1.12PgC\;yr^{-1}$, $-0.47PgC\;yr^{-1}$ and zonal mean NEEs of the same latitude region are $-1.12PgC\;yr^{-1}$, $-0.55PgC\;yr^{-1}$, $-0.17PgC\;yr^{-1}$ for RCP 8.5, 4.5, 2.6.

• Journal of radiological science and technology
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• v.30 no.3
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• pp.205-212
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• 2007

To evaluate the feasibility and efficacy of a pulsatile aortic aneurysm phantoms for in-vitro study. The phantoms consisted of a pulsating motor part(heart part) and an aortic aneurysm part, which mimicked true physiologic conditions. The heart part was created from a high-pressured water pump and a pulsatile flow solenoid valve for the simulation of aortic flow. The aortic aneurysm part was manufactured from paper clay, which was placed inside a acrylic plastic square box, where liquid silicone was poured. After the silicone was formed, the clay was removed, and a silicone tube was used to connect the heart and aneurysm part. We measured the change in pressure as related to the opening time(pulse rate, Kruskal-Wallis method) and pressure before and after the stent-graft implantation(n = 5, Wilcoxon's signed ranks test). The changes in blood pressures according to pulse rate were all statistically significant(p<0.05). The systolic/diastolic pressures at the proximal aorta, the aortic aneurysm, and the distal aorta of the model were $157.80{\pm}1.92/130.20{\pm}1.92$, $159.40{\pm}1.14/134.00{\pm}2.92$, and $147.20{\pm}1.480/129.60{\pm}2.70\;mmHg$, respectively, when the pulse rate was 0.5 beat/second. The pressures changed to $161.40{\pm}1.34/90.20{\pm}1.64$, $175.00{\pm}1.58/93.00{\pm}1.58$, and $176.80{\pm}1.48/90.80{\pm}1.92\;mmHg$, respectively, when the pulse rate was 1.0 beat/second, and $159.40{\pm}1.82/127.20{\pm}1.48$, $166.60{\pm}1.67/138.00{\pm}1.87$, and $161.00{\pm}1.22/135.40{\pm}1.67\;mmHg$, respectively, when it was 1.5 beat/second. When pulse rate was set at 1.0 beat/second, the pressures were $143.60{\pm}1.67/90.20{\pm}1.64$, $147.20{\pm}1.92/84.60{\pm}1.82$, and $137.40{\pm}1.52/88.80{\pm}1.64\;mmHg$ after stent-graft implantation. The changes of pressure before and after stent-graft implantation were statistically significant(p<0.05) except the diastolic pressures at the proximal(p =1.00) and distal aorta(p=0.157). The aortic aneurysm phantoms seems to be useful for the evaluation of the efficacy of stent-graft before animal or clinical studies because of its easy reproducibility and ability to display a wide range of pressures.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.