• Journal of Animal Science and Technology
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• v.45 no.3
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• pp.377-386
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• 2003

A study was conducted to evaluate seasonal variations in chemical composition of food waste (FW) and its feeding effects on growth performance and pork quality in finishing pigs. FW was collected for 1 year (6 times a month) to establish a database for use of FW as a feed ingredient. For a feeding trial (8 weeks), a total of 117 pigs ${\times}$D; 54.80$\pm$4.60kg) were used to evaluate the processing effects of FW. Treatments were: Control (a corn-soybean meal diet without FW), simple dried FW (SD) and vacuum fermented FW (VF). The gross energy, crude protein, crude fat, ash, calcium and phosphorus in FW (DM, average of 4 seasons) were 5,111kcal/kg, 22.92%, 14.31%, 15.48%, 2.7% and 1.05%, respectively. Among seasons, the energy and crude protein contents were the highest (p<0.05) in winter and summer, respectively. In lactic acid bacterial counts, there was no difference between SD and VF. Pigs fed the control diet grew faster (p<0.05) than those fed diets containing food wastes, but not feed conversion ratio. There were no differences in production traits between SD and VF. No differences were also found in dressing percentage, backfat thickness, and pork quality (color, drip loss and TBARS) among treatments. The feed cost (￦/kg body weight) was lower in pigs fed FW than those fed a control diet. In conclusion, a pelleted diet containing food waste less than 20% would reduce feed cost in finishing pigs. However, it seems that a vacuum fermentation of food waste is not necessary for diet processing.

• Journal of Nutrition and Health
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• v.50 no.4
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• pp.336-349
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• 2017

Purpose: The purposes of this study were to describe changes in weight, waist circumference (WC), and prevalence of obesity over 8 years as well as investigate demographic and dietary factors associated with weight gain in Korean adults. Methods: The Korean Genome and Epidemiology Study is an ongoing community-based longitudinal study, which was started in 2001~2002 and repeated every 2 years. Height, weight, and WC were measured, and demographic data and food intake information using the food frequency questionnaire were collected from 10,038 adults aged 40~69 years at baseline. Among those individuals, 3,506 healthy individuals without chronic diseases completed the 4th follow-up survey in 2009~2010. Results: Mean weight decreased by 0.35 kg and 0.65 kg in men and women, respectively, whereas mean WC increased by 1.71 cm and 1.85 cm during the 8-year period. Prevalence of obesity based on body mass index (BMI) decreased from 34.5% to 33.5% in men and from 38.0% to 36.7% in women, whereas abdominal obesity increased from 14.8% to 22.2% in men and from 28.8% to 35.4% in women. Weight change was associated with age and smoking status in men, and residence area, age, education, income, and alcohol drinking in women. Approximately 57.5% maintained their BMI over 8 years (<${\pm}1kg/m^2$, stable weight group), 19.5% showed a BMI increase of ${\geq}1kg/m^2$ (weight gain group), and 23.0% showed a BMI decrease of more than $1kg/m^2$ (weight loss group). There was no significant difference in energy intake calculated as the percentage of estimated energy requirements among the three weight change groups. Intakes of coffee mix and milk were significantly higher in the weight gain group than in the weight loss group in men after controlling for confounding factors. Conclusion: Our results show that higher consumption of coffee mix and milk was associated with weight gain in Korean healthy men.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.241-263
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• 2010

KoFlux is a Korean network of micrometeorological tower sites that use eddy covariance methods to monitor the cycles of energy, water, and carbon dioxide between the atmosphere and the key terrestrial ecosystems in Korea. KoFlux embraces the mission of AsiaFlux, i.e. to bring Asia's key ecosystems under observation to ensure quality and sustainability of life on earth. The main purposes of KoFlux are to provide (1) an infrastructure to monitor, compile, archive and distribute data for the science community and (2) a forum and short courses for the application and distribution of knowledge and data between scientists including practitioners. The KoFlux community pursues the vision of AsiaFlux, i.e., "thinking community, learning frontiers" by creating information and knowledge of ecosystem science on carbon, water and energy exchanges in key terrestrial ecosystems in Asia, by promoting multidisciplinary cooperations and integration of scientific researches and practices, and by providing the local communities with sustainable ecosystem services. Currently, KoFlux has seven sites in key terrestrial ecosystems (i.e., five sites in Korea and two sites in the Arctic and Antarctic). KoFlux has systemized a standardized data processing based on scrutiny of the data observed from these ecosystems and synthesized the processed data for constructing database for further uses with open access. Through publications, workshops, and training courses on a regular basis, KoFlux has provided an agora for building networks, exchanging information among flux measurement and modelling experts, and educating scientists in flux measurement and data analysis. Despite such persistent initiatives, the collaborative networking is still limited within the KoFlux community. In order to break the walls between different disciplines and boost up partnership and ownership of the network, KoFlux will be housed in the National Center for Agro-Meteorology (NCAM) at Seoul National University in 2011 and provide several core services of NCAM. Such concerted efforts will facilitate the augmentation of the current monitoring network, the education of the next-generation scientists, and the provision of sustainable ecosystem services to our society.

• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.2
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• pp.153-165
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• 2014

Carbon dioxide capture and storage (CCS) technology is recognizing one of method responding the climate change with reduction of carbon dioxide in atmosphere. In Korea, due to its geological characteristics, sub-seabed geological $CO_2$ storage is regarded as more practical approach than on-land storage under the goal of its deployment. However, concerns on potential $CO_2$ leakage and relevant acidification issue in the marine environment can be an important subject in recently increasing sub-seabed geological $CO_2$ storage sites. In the present study effect data from literatures were collected in order to conduct an effect assessment of elevated $CO_2$ levels in marine environments using a species sensitivity distribution (SSD) various marine organisms such as microbe, crustacean, echinoderm, mollusc and fish. Results from literatures using domestic species were compared to those from foreign literatures to evaluate the reliability of the effect levels of each biological group and end-point. Ecological effect guidelines through estimating level of pH variation (${\delta}pH$) to adversely affect 5 and 50% of tested organisms, HC5 and HC50, were determined using SSD of marine organisms exposed to the $CO_2$-induced acidification. Estimated HC5 as ${\delta}pH$ of 0.137 can be used as only interim quality guideline possibly with adequate assessment factor. In the future, the current interim guideline as HC5 of ${\delta}pH$ in this study will look forward to compensate with supplement of ecotoxicological data reflecting various trophic levels and indigenous species.

• Journal of Nutrition and Health
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• v.50 no.5
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• pp.504-518
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• 2017

Purpose: The objective of this study was to develop a processed foods database (DB) for estimation of processed food intake in the Korean population using data from the Korea National Health and Nutrition Survey (KNHANES). Methods: Analytical values of processed foods were collected from food composition tables of national institutions (Development Institute, Rural Development Administration), the US Department of Agriculture, and previously reported scientific journals. Missing or unavailable values were substituted, calculated, or imputed. The nutrient data covered 14 nutrients, including energy, protein, carbohydrates, fat, calcium, phosphorus, iron, sodium, potassium, vitamin A, thiamin, riboflavin, niacin, and vitamin C. The processed food DB covered a total of 4,858 food items used in the KNHANES. Each analytical value per food item was selected systematically based on the priority criteria of data sources. Results: Level 0 DB was developed based on a list of 8,785 registered processed foods with recipes of ready-to-eat processed foods, one food composition table published by the national institution, and nutrition facts obtained directly from manufacturers or indirectly via web search. Level 1 DB included information of 14 nutrients, and missing or unavailable values were substituted, calculated, or imputed at level 2. Level 3 DB evaluated the newly constructed nutrient DB for processed foods using the 2013 KNHANES. Mean intakes of total food and processed food were 1,551.4 g (males 1,761.8 g, females 1,340.8 g) and 129.4 g (males 169.9 g, females 88.8 g), respectively. Processed foods contributed to nutrient intakes from 5.0% (fiber) to 12.3% (protein) in the Korean population. Conclusion: The newly developed nutrient DB for processed foods contributes to accurate estimation of nutrient intakes in the Korean population. Consistent and regular update and quality control of the DB is needed to obtain accurate estimation of usual intakes using data from the KNHANES.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.632-645
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• 2021

Satellite sea surface temperature (SST) composites provide important data for numerical forecasting models and for research on global warming and climate change. In this study, six types of representative SST composite database were collected from 2007 to 2018 and the characteristics of spatial structures of SSTs were analyzed in seas around the Korean Peninsula. The SST composite data were compared with time series of in-situ measurements from ocean meteorological buoys of the Korea Meteorological Administration by analyzing the maximum value of the errors and its occurrence time at each buoy station. High differences between the SST data and in-situ measurements were detected in the western coastal stations, in particular Deokjeokdo and Chilbaldo, with a dominant annual or semi-annual cycle. In Pohang buoy, a high SST difference was observed in the summer of 2013, when cold water appeared in the surface layer due to strong upwelling. As a result of spectrum analysis of the time series SST data, daily satellite SSTs showed similar spectral energy from in-situ measurements at periods longer than one month approximately. On the other hand, the difference of spectral energy between the satellite SSTs and in-situ temperature tended to magnify as the temporal frequency increased. This suggests a possibility that satellite SST composite data may not adequately express the temporal variability of SST in the near-coastal area. The fronts from satellite SST images revealed the differences among the SST databases in terms of spatial structure and magnitude of the oceanic fronts. The spatial scale expressed by the SST composite field was investigated through spatial spectral analysis. As a result, the high-resolution SST composite images expressed the spatial structures of mesoscale ocean phenomena better than other low-resolution SST images. Therefore, in order to express the actual mesoscale ocean phenomenon in more detail, it is necessary to develop more advanced techniques for producing the SST composites.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1195-1206
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• 2011

This study was conducted to apply LCA (Life cycle assessment) methodology to lettuce (Lactuca sativa L.) production systems in Namyang-ju as a case study. Five lettuce growing farms with three different farming systems (two farms with organic farming system, one farm with a system without agricultural chemicals and two farms with conventional farming system) were selected at Namyangju city of Gyeonggi-province in Korea. The input data for LCA were collected by interviewing with the farmers. The system boundary was set at a cropping season without heating and cooling system for reducing uncertainties in data collection and calculation. Sensitivity analysis was carried out to find out the effect of type and amount of fertilizer and energy use on GHG (Greenhouse Gas) emission. The results of establishing GTG (Gate-to-Gate) inventory revealed that the quantity of fertilizer and energy input had the largest value in producing 1 kg lettuce, the amount of pesticide input the smallest. The amount of electricity input was the largest in all farms except farm 1 which purchased seedlings from outside. The quantity of direct field emission of $CO_2$, $CH_4$ and $N_2O$ from farm 1 to farm 5 were 6.79E-03 (farm 1), 8.10E-03 (farm 2), 1.82E-02 (farm 3), 7.51E-02 (farm 4) and 1.61E-02 (farm 5) kg $kg^{-1}$ lettuce, respectively. According to the result of LCI analysis focused on GHG, it was observed that $CO_2$ emission was 2.92E-01 (farm 1), 3.76E-01 (farm 2), 4.11E-01 (farm 3), 9.40E-01 (farm 4) and $5.37E-01kg\;CO_2\;kg^{-1}\;lettuce$ (farm 5), respectively. Carbon dioxide contribute to the most GHG emission. Carbon dioxide was mainly emitted in the process of energy production, which occupied 67~91% of $CO_2$ emission from every production process from 5 farms. Due to higher proportion of $CO_2$ emission from production of compound fertilizer in conventional crop system, conventional crop system had lower proportion of $CO_2$ emission from energy production than organic crop system did. With increasing inorganic fertilizer input, the process of lettuce cultivation covered higher proportion in $N_2O$ emission. Therefore, farms 1 and 2 covered 87% of total $N_2O$ emission; and farm 3 covered 64%. The carbon footprints from farm 1 to farm 5 were 3.40E-01 (farm 1), 4.31E-01 (farm 2), 5.32E-01 (farm 3), 1.08E+00 (farm 4) and 6.14E-01 (farm 5) kg $CO_2$-eq. $kg^{-1}$ lettuce, respectively. Results of sensitivity analysis revealed the soybean meal was the most sensitive among 4 types of fertilizer. The value of compound fertilizer was the least sensitive among every fertilizer imput. Electricity showed the largest sensitivity on $CO_2$ emission. However, the value of $N_2O$ variation was almost zero.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.1
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• pp.67-75
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• 2018

Purpose The purpose of this study is to investigate and analyze the external dose rates of $^{18}F-FDG$ Whole Body PET/CT patients by distance, and to identify the main factors that contribute to the reduction of radiation dose by checking the cumulative doses of nuclear medicine technologist(NMT). Materials and Methods After completion of the $^{18}F-FDG$ Whole Body PET/CT scan($75.4{\pm}3.3min$), the external dose rates of 106 patients were measured at a distance of 0, 10, 30, 50, and 100 cm from the chest. Gender, age, BMI(Body Mass Index), fasting time, diabetes mellitus, radiopharmaceutical injection information, creatine value were collected to analyze individual factors that could affect external dose rates from a patient's perspective. From the perspective of NMT, personal pocket dosimeters were worn on the chest to record accumulated dose of NMT who performed the injection task($T_1$, $T_2$ and $T_3$) and scan task($T_4$, $T_5$ and $T_6$). In addition, patient contact time with NMT was measured and analyzed. Results External dose rates from the patient for each distance were calculated as $246.9{\pm}37.6$, $129.9{\pm}16.7$, $61.2{\pm}9.1$, $34.4{\pm}5.9$, and $13.1{\pm}2.4{\mu}Sv/hr$ respectively. On the patient's aspect, there was a significant difference in the proximity of gender, BMI, Injection dose and creatine value, but the difference decreased as the distance increased. In case of dialysis patient, external dose rates for each distance were exceptionally higher than other patients. On the NMT aspect, the doses received from patients were 0.70, 1.09, $0.55{\mu}Sv/person$ for performing the injection task($T_1$, $T_2$, and $T_3$), and were 1.25, 0.82, $1.23{\mu}Sv/person$ for performing the scan task($T_4$, $T_5$, $T_6$). Conclusion we found that maintaining proper distance with patient and reducing contact time with patient had a significant effect on accumulated doses. Considering those points, efforts such as sufficient water intake and encourage of urination, maintaining the proper distance between the NMT and the patient(at least 100 cm), and reducing the contact time should be done for reducing dose rates not only patient but also NMT.

• 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.