• Journal of Radiation Protection and Research
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• v.40 no.4
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• pp.252-260
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• 2015

This paper describes the technical background for the Korean wildlife radiation dose assessment code, K-BIOTA, and the summary of its application. The K-BIOTA applies the graded approaches of 3 levels including the screening assessment (Level 1 & 2), and the detailed assessment based on the site specific data (Level 3). The screening level assessment is a preliminary step to determine whether the detailed assessment is needed, and calculates the dose rate for the grouped organisms, rather than an individual biota. In the Level 1 assessment, the risk quotient (RQ) is calculated by comparing the actual media concentration with the environmental media concentration limit (EMCL) derived from a bench-mark screening reference dose rate. If RQ for the Level 1 assessment is less than 1, it can be determined that the ecosystem would maintain its integrity, and the assessment is terminated. If the RQ is greater than 1, the Level 2 assessment, which calculates RQ using the average value of the concentration ratio (CR) and equilibrium distribution coefficient (Kd) for the grouped organisms, is carried out for the more realistic assessment. Thus, the Level 2 assessment is less conservative than the Level 1 assessment. If RQ for the Level 2 assessment is less than 1, it can be determined that the ecosystem would maintain its integrity, and the assessment is terminated. If the RQ is greater than 1, the Level 3 assessment is performed for the detailed assessment. In the Level 3 assessment, the radiation dose for the representative organism of a site is calculated by using the site specific data of occupancy factor, CR and Kd. In addition, the K-BIOTA allows the uncertainty analysis of the dose rate on CR, Kd and environmental medium concentration among input parameters optionally in the Level 3 assessment. The four probability density functions of normal, lognormal, uniform and exponential distribution can be applied.The applicability of the code was tested through the participation of IAEA EMRAS II (Environmental Modeling for Radiation Safety) for the comparison study of environmental models comparison, and as the result, it was proved that the K-BIOTA would be very useful to assess the radiation risk of the wildlife living in the various contaminated environment.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.57-65
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• 1997

As jet lag of modern travel continues to spread, there has been an exponential growth in popular explanations of jet lag and recommendations for curing it. Some of this attention are misdirected, and many of those suggested solutions are misinformed. The author reviewed the basic science of jet lag and its practical outcome. The jet lag symptoms stemed from several factors, including high-altitude flying, lag effect, and sleep loss before departure and on the aircraft, especially during night flight. Jet lag has three major components; including external de synchronization, internal desynchronization, and sleep loss. Although external de synchronization is the major culprit, it is not at all uncommon for travelers to experience difficulty falling asleep or remaining asleep because of gastrointestinal distress, uncooperative bladders, or nagging headaches. Such unwanted intrusions most likely to reflect the general influence of internal desynchronization. From the free-running subjects, the data has revealed that sleep tendency, sleepiness, the spontaneous duration of sleep, and REM sleep propensity, each varied markedly with the endogenous circadian phase of the temperature cycle, despite the facts that the average period of the sleep-wake cycle is different from that of the temperature cycle under these conditions. However, whereas the first ocurrence of slow wave sleep is usually associated with a fall in temperature, the amount of SWS is determined primarily by the length of prior wakefulness and not by circadian phase. Another factor to be considered for flight in either direction is the amount of prior sleep loss or time awake. An increase in sleep loss or time awake would be expected to reduce initial sleep latency and enhance the amount of SWS. By combining what we now know about the circadian characteristics of sleep and homeostatic process, many of the diverse findings about sleep after transmeridian flight can be explained. The severity of jet lag is directly related to two major variables that determine the reaction of the circadian system to any transmeridian flight, eg., the direction of flight, and the number of time zones crossed. Remaining factor is individual differences in resynchmization. After a long flight, the circadian timing system and homeostatic process can combine with each other to produce a considerable reduction in well-being. The author suggested that by being exposed to local zeit-gebers and by being awake sufficient to get sleep until the night, sleep improves rapidly with resynchronization following time zone change.

• Korean journal of applied entomology
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• v.25 no.4 s.69
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• pp.221-228
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• 1986

An empirical model to predict initial disease occurrence and subsequent progress of Alternaria leaf spot was constructed based on the modified degree day temperature and frequency of rainfall in three years field experiments. Climatic factors were analized 10-day bases, beginning April 20 to the end of August, and were used as variables for model construction. Cumulative degree portion (CDP) that is over $10^{\circ}C$ in the daily average temperature was used as a parameter to determine the relationship between temperature and initial disease occurrence. Around one hundred and sixty of CDP was needed to initiate disease incidence. This value was considered as temperature threshhold. After reaching 160 CDP, time of initial occurrence was determined by frequency of rainfall. At least four times of rainfall were necessary to be accumulated for initial occurrence of the disease after passing temperature threshhold. Disease progress after initial incidence generally followed the pattern of frequency of rainfall accumulated in those periods. Apparent infection rate (r) in the general differential equation dx/dt=xr(1-x) for individual epidemics when x is disease proportion and t is time, was a linear function of accumulation rate of rainfall frequency (Rc) and was able to be directly estimated based on the equation r=1.06Rc-0.11($R^2=0.993$). Disease severity (x) after t time could be predicted using exponential equation $[x/(1-x)]=[x_0/(1-x)]e^{(b_0+b_1R_c)t}$ derived from the differential equation, when $x_0$ is initial disease, $b_0\;and\;b_1$ are constants. There was a significant linear relationship between disease progress and cumulative number of air-borne conidia of Alternaria mali. When the cumulative number of air-borne conidia was used as an independent variable to predict disease severity, accuracy of prediction was poor with $R^2=0.3328$.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.67-72
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• 2010

Purpose: In Asan Medical Center we perform myocardial perfusion SPECT to evaluate cardiac event risk level for non-cardiac surgery patients. In case of patients with cancer, we check tumor metastasis using whole body bone scan and whole body PET scan and then perform myocardial perfusion SPECT to reduce unnecessary exam. In case of short term in patients, we perform $^{201}Tl$ myocardial perfusion SPECT after whole body bone scan a minimum 16 hours in order to reduce hospitalization period but it is still the actual condition in which the evaluation about the affect of the crosstalk contamination due to the each other dissimilar isotope administration doesn't properly realize. So in our experiments, we try to evaluate crosstalk contamination influence on $^{201}Tl$ myocardial perfusion SPECT using anthropomorphic torso phantom and patient's data. Materials and Methods: From 2009 August to September, we analyzed 87 patients with $^{201}Tl$ myocardial perfusion SPECT. According to $^{201}Tl$ myocardial perfusion SPECT yesterday whole body bone scan possibility of carrying out, a patient was classified. The image data are obtained by using the dual energy window in $^{201}Tl$ myocardial perfusion SPECT. We analyzed $^{201}Tl$ and $^{99m}Tc$ counts ratio in each patients groups obtained image data. We utilized anthropomorphic torso phantom in our experiment and administrated $^{201}Tl$ 14.8 MBq (0.4 mCi) at myocardium and $^{99m}Tc$ 44.4 MBq (1.2 mCi) at extracardiac region. We obtained image by $^{201}Tl$ myocardial perfusion SPECT without gate method application and analyzed spatial resolution using Xeleris ver 2.0551. Results: In case of $^{201}Tl$ window and the counts rate comparison result yesterday whole body bone scan of being counted in $^{99m}Tc$ window, the difference in which a rate to 24 hours exponential-functionally notes in 1:0.114 with Ventri (GE Healthcare, Wisconsin, USA), 1:0.249 after the bone tracer injection in 12 hours in 1:0.411 with 1:0.79 with Infinia (GE healthcare, Wisconsin, USA) according to a reduction a time-out was shown (Ventri p=0.001, Infinia p=0.001). Moreover, the rate of the case in which it doesn't perform the whole body bone scan showed up as the average 1:$0.067{\pm}0.6$ of Ventri, and 1:$0.063{\pm}0.7$ of Infinia. According to the phantom after experiment spatial resolution measurement result, and an addition or no and time-out of $^{99m}Tc$ administrated, it doesn't note any change of FWHM (p=0.134). Conclusion: Through the experiments using anthropomorphic torso phantom and patients data, we found that $^{201}Tl$ myocardium perfusion SPECT image later carried out after the bone tracer injection with 16 hours this confirmed that it doesn't receive notable influence in spatial resolution by $^{99m}Tc$. But this investigation is only aimed to image quality, so it needs more investigation in patient's radiation dose and exam accuracy and precision. The exact guideline presentation about the exam interval should be made of the validation test which is exact and in which it is standardized about the affect of the crosstalk contamination according to the isotope use in which it is different later on.