• Korean Journal of Soil Science and Fertilizer
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• v.42 no.5
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• pp.399-407
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• 2009

Laser induced breakdown spectroscopy(LIBS) is an simple analysis method for directly quantifying many kinds of soil micro-elements on site using a small size of laser without pre-treatment at any property of materials(solid, liquid and gas). The purpose of this study were to find an optimum condition of the LIBS measurement including wavelengths for quantifying soil elements, to relate spectral properties to the concentration of soil elements using LIBS as a simultaneous un-breakdown quantitative analysis technology, which can be applied for the safety assessment of agricultural products and precision agriculture, and to compare the results with a standardized chemical analysis method. Soil samples classified as fine-silty, mixed, thermic Typic Hapludalf(Memphis series) from grassland and uplands in Tennessee, USA were collected, crushed, and prepared for further analysis or LIBS measurement. The samples were measured using LIBS ranged from 200 to 600 nm(0.03 nm interval) with a Nd:YAG laser at 532 nm, with a beam energy of 25 mJ per pulse, a pulse width of 5 ns, and a repetition rate of 10 Hz. The optimum wavelength(${\lambda}nm$) of LIBS for estimating soil and plant elements were 308.2 nm for Al, 428.3 nm for Ca, 247.8 nm for T-C, 438.3 nm for Fe, 766.5 nm for K, 85.2 nm for Mg, 330.2 nm for Na, 213.6 nm for P, 180.7 nm for S, 288.2 nm for Si, and 351.9 nm for Ti, respectively. Coefficients of determination($r^2$) of calibration curve using standard reference soil samples for each element from LIBS measurement were ranged from 0.863 to 0.977. In comparison with ICP-AES(Inductively coupled plasma atomic emission spectroscopy) measurement, measurement error in terms of relative standard error were calculated. Silicon dioxide(SiO2) concentration estimated from two methods showed good agreement with -3.5% of relative standard error. The relative standard errors for the other elements were high. It implies that the prediction accuracy is low which might be caused by matrix effect such as particle size and constituent of soils. It is necessary to enhance the measurement and prediction accuracy of LIBS by improving pretreatment process, standard reference soil samples, and measurement method for a reliable quantification method.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.18
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• pp.54-87
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• 1975

The effects of various weather factors on yield components of rice, year variation of yield components within regions, and regional differences of yield components within year were investigated at three Crop Experiment Stations O.R.D., Suweon, Iri, Milyang, and at nine provincial Offices of Rural Development for eight years from 1966 to 1973 for the purpose of providing information required in improving cultural practices and predicting the yield level of rice. The experimental results analyzed by standard partial regression analysis are summarized as follows: 1. When rice was grown in ordinary seasonal culture the number of panicles greatly affected rice yield compared to other yield components. However, when rice was seeded in ordinary season and transplanted late, and transplanted in ordinary season in the northern area the ratio of ripening was closely related to the rice yield. 2. The number of panicles showed the greatest year variation when the Jinheung variety was grown in the northern area. The ripening ratio or 1, 000 grain weight also greatly varied due to years. However, the number of spikelets per unit area showed the greatest effects on yield of the Tongil variety. 2. Regional variation of yield components was classified into five groups; 1) Vegetation dependable type (V), 2) Partial vegetation dependable type (P), 3) Medium type (M), 4) Partial ripening dependable type (P.R), and 5) Ripening dependable type (R). In general, the number of kernel of rice in the southern area showed the greatest partial regression coefficient among yield components. However, in the mid-northern part of country the ripening ratio was one of the component!; affecting rice yield most. 4. A multivariate equation was obtained for both normal planting and late planting by log-transforming from the multiplication of each component of four yield components to additive fashion. It revealed that a more accurate yield could be estimated from the above equation in both cases of ordinary seasonal culture and late transplanting. 5. A highly positive correlation coefficient was obtained between the number of tillers from 20 days after transplanting and the number of panicles at each(tillering) stage 20 days after transplanting in normal planting and late planting methods. 6. A close relationship was found between the number of panicles and weather factors 21 to 30 days, after transplanting. 7. The average temperature 31 to 40 days after transplanting was greatly responsible for the maximum number of tillers while the number of duration of sunshine hours per day 11 to 30 days after transplantation was responsible for that character. The effect of water temperature was negligible. 8. No reasonable prediction for number of panicles was calculated from using either number of tillers or climatic factors. The number of panicles could early be estimated formulating a multiple equation using number of tillers 20 days after transplantation and maximum temperature, temperature range and duration of sunshine for the period of 20 days from 20 to 40 days after transplantation. 9. The effects of maximum temperature and day length 25 to 34 days before heading, on kernel number per panicle, were great in the mid-northern area. However, the minimum temperature and day length greatly affected the kernel number per panicle in the southern area. The maximum temperature had a negative relationship with the kernel number per panicle in the southern area. 10. The maximum temperature was highly responsible for an increased ripening ratio. On the other hand, the minimum temperature at pre-heading and early ripening stages showed an adverse effect on ripening ratio. 11. The 1, 000 grain weight was greatly affected by the maximum temperature during pre- or mid-ripening stage and was negatively associated with the minimum temperature over the entire ripening period.

• Research in Community and Public Health Nursing
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• v.5 no.2
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• pp.109-129
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• 1994

Chronic health problems has become a major concern and challenge to the health care professionals today. Especially hypertension, one of the leading primary cause of death in Korea, is a typical chronic disease requiring adequate and continuous management. Though these hypertensives need to maintain desirable health practice by themselves for their life time, many previous studies indicated that most of the essential hypertensives have no specific symptoms and thus, reluctant to follow appropriate medical regimens causing the condition further aggravated and complicated. Self-care is an essential factor that keeps chronic patients in control of their health and wellness. Thus this study was conducted to identify the effect of the comprehensive self-regulation program as a nursing intervention on the promotion self-care performance and improvement in physical parameters of hypertensives. For this purpose, a one group quasi-experimental research with pre and post test design was used. The subjects of the study was consisted of thirty persons with mild or moderate essential hypertension from two companies in Cheong-ju city. The whole program was carried out from October, 1993 to February, 1994. The self-regulation program was consisted with group education on hypertension and self-care, self-regulation including the blood pressure self-monitoring and recording, recording of daily self-care activities, and encouraging and reinforcing self-efficacy through verbal persuation and enactive attainment. The subjects were asked to measure their own blood pressure by themselves twice per day and to record blood pressure and the daily self-care performance according to the instructions provided during the whole period of 9 weeks. The instruments used for data collection in this study were as follows : 1) Instruments used for measuring the knowledge about hypertension, multiple health locus of control, and perceived benifits and barriers were adapted from previous studies and modified by author to be fit for the subjects. 2) Self-efficacy scale and self-care performance record were developed by the author. 3) Physiological parameters included systolic / diastolic blood pressure, body weight, level of blood cholesterol, and 24hour ambulatory blood pressure. The post-experimental Cronbach's Alpha as the reliability test of scales were 0.703-0.897, an appropriate level of confidence. The effect of the program was analyzed by experimental stages ; the first week, the fifth week, and the ninth week since the experimental imput began. Data were analyzed by the SPSS PC+ program with paired t-test and t-test, repeated measure ANOVA, and pearson's correlation to de termine the effect of program. The results were as follows : 1) After the self-regulation program, scores on knowledge(t=-2.41, p=.011), perceived self-efficacy (F=5.60, p=.001), self-care performance(F=22.31, p=.0001) were significantly higher than those before the program. 2) After the program, both systolic and diastolic blood pressure were significantly lower than those before the program(F=10.89 -13.11, p=.0001). However in 24hour ambulatory blood pressure, systolic mean pressure was nearly significantly lower, but not in diastolic mean pressure. 3) After the program, the body weight was significant decresed(t=5.53, p=.0001), but the blood cholesterol level was not decreased significantly except in those cases with higher cholesterol level. 4) There were significant relationships between changes in self-care performance and diastolic pressure at 1st week (r=.3389, p=.033) and changes in self-care performance and systolic pressure at 9th week(r=.3651, p=.024). 5) There were significant relationship between perceived self-efficacy and self-care performance at 5th week(r=.5313, p=.001) and 9th week (r=.3026, p=.052). 6) After the program, internal health locus of control and perceived benefits did not show significant change, but perceived barriers was significantly lower than those before the program (t=3.57, p=.0001). From the above results, it can be concluded that 1) The self-regulation program is an effective nursing strategy to promote self-care performance of hypertensives and to lower the blood pressure. Thus this program can be recommended in the management of the hypertensives in workplaces and community settings. 2) The synthesis of Orem's self-care theory and Bandura's self-regulation & self-efficacy theory in this study was proved to enhance explanation and prediction of the change of self-care behavior. Thus the result of the study would contribute in development of the self-care theory and an expansion of practice-theory.

• Pediatric Infection and Vaccine
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• v.17 no.2
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• pp.169-176
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• 2010

Purpose : We aimed to evaluate predictive parameters for non-response to intravenous immunoglobulin (IVIG) in patients with Kawasaki disease (KD) before IVIG use using two controls. Methods : We evaluated 229 consecutive KD patients who were treated with 2 g/kg of IVIG at a single center. Those who had persistent fever >24 hours after IVIG infusion made up the 23 IVIG non-responders; the first control included a total 206 defervesced cases and the second control included 46 cases that were matched for age and pre-treatment fever duration to non-responders. Results : Demographic and clinical characteristics were similar in IVIG non-responders and responders at presentation. As for laboratory findings, the neutrophil differential, CRP, AST, ALT, and LDH were higher, and lymphocyte differential, total protein, albumin, platelet count, and total cholesterol were significantly lower in IVIG non-responders compared to responders by univariate analysis in both study designs. However in multivariate analysis, non-responders showed a significantly higher neutrophil differential (cutoff value, >77%, sensitivity 68.4% and specificity 79.5%) and lower cholesterol (<124 mg/dL, sensitivity 79% and specificity 70.5%). Whereas plasma albumin (<3.6 g/dL, sensitivity 73.7% and specificity 60%) was the sole laboratory parameter of non-responders in the second study design. Conclusion : Severity of inflammation in KD was reflected by higher or lower laboratory values at presentation. Because the multivariate analysis for these indices may be influenced by some confounding factors, including the numbers of patients of different ages and fever duration, other assessment modalities are needed for KD patients with the greatest risk of coronary artery lesions.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.2
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• pp.89-103
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• 2013

National demands for the ocean forecasting system have been increased to support economic activity and national safety including search and rescue, maritime defense, fisheries, port management, leisure activities and marine transportation. Further, the ocean forecasting has been regarded as one of the key components to improve the weather and climate forecasting. Due to the national demands as well as improvement of the technology, the ocean forecasting systems have been established among advanced countries since late 1990. Global Ocean Data Assimilation Experiment (GODAE) significantly contributed to the achievement and world-wide spreading of ocean forecasting systems. Four stages of GODAE were summarized. Goal, vision, development history and research on ocean forecasting system of the advanced countries such as USA, France, UK, Italy, Norway, Australia, Japan, China, who operationally use the systems, were examined and compared. Strategies of the successfully established ocean forecasting systems can be summarized as follows: First, concentration of the national ability is required to establish successful operational ocean forecasting system. Second, newly developed technologies were shared with other countries and they achieved mutual and cooperative development through the international program. Third, each participating organization has devoted to its own task according to its role. In Korean society, demands on the ocean forecasting system have been also extended. Present status on development of the ocean forecasting system and long-term plan of KMA (Korea Meteorological Administration), KHOA (Korea Hydrographic and Oceanographic Administration), NFRDI (National Fisheries Research & Development Institute), ADD (Agency for Defense Development) were surveyed. From the history of the pre-established systems in other countries, the cooperation among the relevant Korean organizations is essential to establish the accurate and successful ocean forecasting system, and they can form a consortium. Through the cooperation, we can (1) set up high-quality ocean forecasting models and systems, (2) efficiently invest and distribute financial resources without duplicate investment, (3) overcome lack of manpower for the development. At present stage, it is strongly requested to concentrate national resources on developing a large-scale operational Korea Ocean Forecasting System which can produce open boundary and initial conditions for local ocean and climate forecasting models. Once the system is established, each organization can modify the system for its own specialized purpose. In addition, we can contribute to the international ocean prediction community.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.5
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• pp.429-435
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• 2009

Purpose: The purpose of this study was to assess the prognostic value of preoperative FDG-PET in colorectal cancer (CRC) patients with hepatic metastasis (HM). Materials and Methods: 24 CRC patients (M:F=14:10; age, $63{\pm}10$ yrs) with HM who had undergone preoperative FDG PET were included. Cure-intent surgery was performed in all the patients and HMs were controlled using resection (n=13), radio-frequency ablation (RFA) (n=7), and resection plus RFA (n=4). Potential prognostic markers tested were maxSUV of primary tumor, maxSUV of HM, maxSUV ratio of HM over primary tumor (M/P ratio), histologic grade, CEA level, venous/lymphatic/nerve invasion, T stage, N stage, no. of HM, no. of lymph node metastasis, and treatment modality of HM. Results: 14 CRC patients developed a recurrence with a median follow-up duration of 244 days, whereas 10 patients did not develop recurrence with a median follow-up duration of 504 days. M/P ratios but other potential prognostic markers were significantly higher in the recurrent patients ($0.72{\pm}0.14$) than recurrence-free patients ($0.54{\pm}0.23$) (p=0.038). M/P ratio only was found to predict recurrence by Cox multivariate analysis (hazard ratio 37.7, 95% confidence interval 2.01-706.1, p=0.016). The 11 patients with lower M/P ratio of <0.61 had significantly better disease-free survival rate than the 13 patients with higher M/P ratio (${\geq}0.61$) (p=0.026). Conclusion: maxSUV ratio of HM over primary tumor (M/P ratio) may be useful for prognosis prediction of CRC patients with HM. Higher FDG uptake of HM than that of primary tumor may indicate a more advanced status in stage IV CRC.

• Journal of the Korean Chemical Society
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• v.62 no.4
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• pp.299-313
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• 2018

Inquiry-centered education is important in science education, but in the actual education field, scientific research is being done in a uniform manner due to realistic difficulties. In this study, we use RS (Reverse Science) as a secondary chemistry class to provide opportunities for students to engage in inquiry learning and scientific thinking through process-oriented activities. In this study, we developed and applied it to explore the effects on the scientific inquiry abilities of middle school students and checked the students' perception of it. For the application of the program, 128 students were selected from 6 classes of the 2nd grade in D district middle school, 64 from the experimental group and 64 from the comparative group. The experimental group taught RSP-based the chemistry inquiry programs and the comparative group taught instructor-led classes and verification experiments on the same topic over the seventh hour with three themes. In addition, we analyzed the results of the pre- and post-test by using the science inquiry ability test, and discussed the effects of the program based on the students' perceptions through class observation, student activity area, questionnaire and interview. As a result, the class using the program showed statistically significant changes in the science inquiry ability of secondary school students. Specifically, the experimental group was found to be significant in its prediction among the subcomponents of basic exploration ability compared to the comparative group. The differences have also been shown to be significant in terms of data translation, hypothesis setup and variable control, which are subcomponents of integrated exploration capabilities (p <. 05). In addition, students became interested in the process of creating the theory of science, and were highly interested in collaborating with their friends. It also provided students with opportunities to experience scientific thinking through process-oriented inquiry. Finally, based on the positive impact of the RSP-based chemistry inquiry program on students, we were able to identify the potential use of the program.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.562-581
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• 2018

This study was designed to predict the changes in species richness of plants under the climate change in South Korea. The target species were selected based on the Plants Adaptable to Climate Change in the Korean Peninsula. Altogether, 89 species including 23 native plants, 30 northern plants, and 36 southern plants. We used the Species Distribution Model to predict the potential habitat of individual species under the climate change. We applied ten single-model algorithms and the pre-evaluation weighted ensemble method. And then, species richness was derived from the results of individual species. Two representative concentration pathways (RCP 4.5 and RCP 8.5) were used to simulate the species richness of plants in 2050 and 2070. The current species richness was predicted to be high in the national parks located in the Baekdudaegan mountain range in Gangwon Province and islands of the South Sea. The future species richness was predicted to be lower in the national park and the Baekdudaegan mountain range in Gangwon Province and to be higher for southern coastal regions. The average value of the current species richness showed that the national park area was higher than the whole area of South Korea. However, predicted species richness were not the difference between the national park area and the whole area of South Korea. The difference between current and future species richness of plants could be the disappearance of a large number of native and northern plants from South Korea. The additional reason could be the expansion of potential habitat of southern plants under climate change. However, if species dispersal to a suitable habitat was not achieved, the species richness will be reduced drastically. The results were different depending on whether species were dispersed or not. This study will be useful for the conservation planning, establishment of the protected area, restoration of biological species and strategies for adaptation of climate change.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.