• Communications of Mathematical Education
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• v.38 no.2
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• pp.145-165
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• 2024

The purpose of this study is to analyze the types of errors made by Korean language learners in the use of dual numerals and provides basic data for developing an effective teaching numeration using dual numerals. To this end, a case study was conducted to analyze the types of errors that appear in numeration using dual numerals targeting Korean language learners with diverse linguistic and cultural backgrounds and different academic achievements in Korean and mathematics. Error types that categorized errors made by Korean language learners were used as an analysis framework. The conclusions obtained from the research results are as follows. First, it is necessary to provide students with opportunities to use them frequently so that they can become familiar with the use of native language numerals, which often causes errors. Second, when teaching Korean language learners with low-level Korean language academic achievement how to use Chinese numerals, it is necessary to pay attention to the multiplicative numeral system of Chinese numerals. Third, it is necessary to teach children to accurately read foreign word classifiers used with Chinese numerals accurately in Korean and distinguish between the classifiers 'o'clock' and 'hours'. There is a need to provide guidance so that native language/Chinese numerals can be used appropriately in succession along with Chinese classifiers. The results of this study may contribute to the development of an effective teaching numeration using dual numerals for Korean language learners with diverse linguistic and cultural backgrounds.

• Journal of Radiation Industry
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• v.17 no.3
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• pp.275-282
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• 2023

Workers in nuclear power plants are likely to be exposed to radiation from various geometrical sources. In order to evaluate the exposure level, the point-kernel method can be utilized. In order to perform a dose assessment based on this method, the radiation source should be divided into point sources, and the number of divisions should be set by the evaluator. However, for the general public, there may be difficulties in selecting the appropriate number of divisions and performing an evaluation. Therefore, the purpose of this study is to develop an algorithm for dose assessment for arbitrary shaped sources based on the point-kernel method. For this purpose, the point-kernel method was analyzed and the main factors for the dose assessment were selected. Subsequently, based on the analyzed methodology, a dose assessment algorithm for arbitrary shaped sources was developed. Lastly, the developed algorithm was verified using Microshield. The dose assessment procedure of the developed algorithm consisted of 1) boundary space setting step, 2) source grid division step, 3) the set of point sources generation step, and 4) dose assessment step. In the boundary space setting step, the boundaries of the space occupied by the sources are set. In the grid division step, the boundary space is divided into several grids. In the set of point sources generation step, the coordinates of the point sources are set by considering the proportion of sources occupying each grid. Finally, in the dose assessment step, the results of the dose assessments for each point source are summed up to derive the dose rate. In order to verify the developed algorithm, the exposure scenario was established based on the standard exposure scenario presented by the American National Standards Institute. The results of the evaluation with the developed algorithm and Microshield were compare. The results of the evaluation with the developed algorithm showed a range of 1.99×10^-1~9.74×10^-1 μSv hr^-1, depending on the distance and the error between the results of the developed algorithm and Microshield was about 0.48~6.93%. The error was attributed to the difference in the number of point sources and point source distribution between the developed algorithm and the Microshield. The results of this study can be utilized for external exposure radiation dose assessments based on the point-kernel method.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.3
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• pp.1-13
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• 2024

This paper proposes a cost-effective system design and user-friendly approach for the key technological elements necessary to configure an autonomous mobile robot. To implement a high-precision positioning system using an autonomous mobile robot, we established a Linux-based VRS (virtual reference station)-RTK (real-time kinematic) GNSS (global navigation satellite system) system with NTRIP (Network Transport of RTCM via Internet Protocol) client functionality. Notably, we reduced the construction cost of the GNSS positioning system by performing dynamic location analysis of the established system, without utilizing an RTK replay system. Dynamic location analysis involves sampling each point during the trajectory following of the autonomous mobile robot and comparing the location precision with ground-truth points. The proposed system ensures high positioning performance with fast sampling times and suggests a GPS waypoint system for user convenience. The centimeter-level precision GNSS information is provided at a 30Hz sampling rate, and the dead reckoning function ensures valid information even when passing through tall buildings and dense forests. The horizontal position error measured through the proposed system is 6.7cm, demonstrating a highly precise dynamic location measurement error within 10cm. The VRS network-RTK Linux system, which provides precise dynamic location information at a high sampling rate, supports a GPS waypoint planner function for user convenience, enabling easy destination setting based on GPS information.

• Journal of Korean society of Dental Hygiene
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• v.24 no.4
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• pp.271-280
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• 2024

Objectives: This study aimed to test whether the digital competence of dental hygienists can affect their intraoral scanning potential in terms of scan time and error. Methods: Dental hygienists and dental hygiene students who had never used an intraoral scanner were surveyed to determine their digital competence. Individual data collected using an intraoral scanner was compared with reference data collected using a model scanner to identify scanning errors, and participants' scanning times were measured. Results: A significant decrease in scanning time was observed as the overall level of digital competence increased. The increase in digital skills and digital knowledge led to a decrease in scanning time by 3.73 and 3.98 minutes, respectively. Conclusions: This study found that digital competence was associated with reduced scan times, but less so with scan errors. This may be due to recent advances in scanning software, and future studies may need to develop a digital competence assessment tool that is more appropriate for the dental field.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.17-24
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• 2019

Purpose: The present study aims to assess the level of coherency and the accuracy of Point dose of the Isocenter of VERO, a linear accelerator developed for the purpose of the Stereotactic Body Radiation Therapy(SBRT). Materials and Method: The study was conducted randomly with 10 treatment plans among SBRT patients in Kyungpook National University Chilgok Hospital, using VERO, a linear accelerator between June and December, 2018. In order to assess the equipment's power stability level, we measured the output constancy by using PTW-LinaCheck, an output detector. We also attempted to measure the level of accuracy of the equipment's Laser, kV(Kilo Voltage) imaging System, and MV(Mega Voltage) Beam by using Tofu Phantom(BrainLab, Germany) to assess the accuracy level of geometrical Isocenter. We conducted a comparative analysis to assess the accuracy level of the dose by using an acrylic Phantom($30{\times}30{\times}20cm$), a calibrated ion chamber CC-01(IBA Dosimetry), and an Electrometer(IBA, Dosimetry). Results: The output uniformity of VERO was calculated to be 0.66 %. As for geometrical Isocenter accuracy, we analyzed the error values of ball Isocenter of inner Phantom, and the results showed a maximum of 0.4 mm, a minimum of 0.0 mm, and an average of 0.28 mm on X-axis, and a maximum of -0.4 mm, a minimum of 0.0 mm, and an average of -0.24 mm on Y-axis. A comparison and evaluation of the treatment plan dose with the actual measured dose resulted in a maximum of 0.97 % and a minimum of 0.08 %. Conclusion: The equipment's average output dose was calculated to be 0.66 %, meeting the ${\pm}3%$ tolerance, which was considered as a much uniform fashion. As for the accuracy assessment of the geometric Isocenter, the results met the recommended criteria of ${\pm}1mm$ tolerance, affirming a high level of reproducibility of the patient's posture. The difference between the treatment plan dose and the actual measurement dose was calculated to be 0.52 % on average, significantly less than the 3 % tolerance, confirming that it obtained predicted does. The current study suggested that VERO equipment is suitable for SBRT, and would result in notable therapeutic effect.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Agricultural and Biosystems Engineering
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• v.7 no.1
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• pp.1-7
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• 2006

A need exists to monitor and control the localized high temperatures often experienced in solar grain dryers, which result in grain cracking, reduced germination and loss of cooking quality. A verified finite element model would be a useful to monitor and control the drying process. This study examined the feasibility of the finite element method (FEM) to predict temperature distribution in solar grain dryers. To achieve this, an indirect solar grain dryer system was developed. It consisted of a solar collector, plenum and drying chambers, and an electric fan. The system was used to acquire the necessary input and output data for the finite element model. The input data comprised ambient and plenum chamber temperatures, prevailing wind velocities, thermal conductivities of air, grain and dryer wall, and node locations in the xy-plane. The outputs were temperature at the different nodes, and these were compared with measured values. The ${\pm}5%$ residual error interval employed in the analysis yielded an overall prediction performance level of 83.3% for temperature distribution in the dryer. Satisfactory prediction levels were also attained for the lateral (61.5-96.2%) and vertical (73.1-92.3%) directions of grain drying. These results demonstrate that it is feasible to use a two-dimensional (2-D) finite element model to predict temperature distribution in a grain solar dryer. Consequently, the method offers considerable advantage over experimental approaches as it reduces time requirements and the need for expensive measuring equipment, and it also yields relatively accurate results.

• Preventive Nutrition and Food Science
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• v.10 no.1
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• pp.64-67
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• 2005

Leptin is a small polypeptide hormone secreted primarily by adipocytes. Leptin regulates energy balance by decreasing food intake and increasing energy expenditure. This study investigated the relationships between serum leptin levels and BMI (body mass index) in 49 adults in Pohang, Korea. The subjects were 25 males and 24 females, aged 21 to 64 years attending an outpatient clinic at Handong University Sunlin Presbyterian Hospital. Values are given +/- the standard error of the mean. Our study shows that the serum leptin levels in these subjects were positively correlated with BMI. The leptin levels were higher in females (2.39+/-1.82 ng/mL) than in males (0.43+/-0.455ng/mL), although lower than previously reported. We therefore compared the serum leptin levels from the male Korean subjects (BMI 24.3+/-0.74㎏/㎡) with serum from six British males with a similar BMI (23.4+/-1.48㎏/㎡). The serum leptin concentrations (1.76+/-0.76 ng/mL) were lower than that of plasma (4.28+/-1.66 ng/mL) in the British subjects. The serum leptin in the British subjects (1.76+/-0.76ng/mL) was higher than that in the Koreans. There was no correlation between leptin levels and BMI in either male (slope 0.018 ± 0.036, p=0.624) or female (slope 0.382±0.433, p=0.417) type 2 diabetic patients in Pohang, Korea. Taken together, our study shows that the serum leptin level in Koreans varies with the BMI, but is lower than that of BMI-matched British subjects.

• Analytical Science and Technology
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• v.18 no.2
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• pp.163-167
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• 2005

In this study, a novel analytical method has been developed for the rapid determination of L-carnitine in human plasma using electrospray ionization tandem mass spectrometry. Free carnitine (FC) was analyzed after extraction with 80% methanol and total carnitine (TC) was analyzed after hydrolysis and extraction. Acyl carnitine (AC) was subtracted FC from TC. Analytical methods used multiple reaction monitoring (MRM) scan modes. A correlation coefficient of linear regression ($r^2$) was 0.9995, recovery was 97%, reproducibility was less than 10%, and limit of detection (LOD) was $0.0016{\mu}mol/L$. This method reduced sample preparation time and showed high resolution and good reproducibility compared to that with liquid chromatographic methods. Normal control showed AC was lower than FC. Clinical management of patients with inborn error of metabolism showed FC was lower than AC. Thus, carnitine fraction level was very important to monitoring patients with metabolic disorder.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.5
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• pp.677-684
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• 2008

Accurate estimation of dry matter intake (DMI) is a prerequisite to meet animal performance targets without penalizing animal health and the environment. The objective of the current study was to evaluate some of the existing models in order to predict DMI when lactating dairy cows were offered a total mixed ration containing a high level of concentrates and locally produced agricultural by-products. Six popular models were chosen for DMI prediction (Brown et al., 1977; Rayburn and Fox, 1993; Agriculture Forestry and Fisheries Research Council Secretariat, 1999; National Research Council (NRC), 2001; Cornell Net Carbohydrate and Protein System (CNCPS), Fox et al., 2003; Fuentes-Pila et al., 2003). Databases for DMI comparison were constructed from two different sources: i) 12 commercial farm investigations and ii) a controlled dairy cow experiment. The model evaluation was performed using two different methods: i) linear regression analysis and ii) mean square error prediction analysis. In the commercial farm investigation, DMI predicted by Fuentes-Pila et al. (2003) was the most accurate when compared with the actual mean DMI, whilst the CNCPS prediction showed larger mean bias (difference between mean predicted and mean observed values). Similar results were observed in the controlled dairy cow experiment where the mean bias by Fuentes-Pila et al. (2003) was the smallest of all six chosen models. The more accurate prediction by Fuentes-Pila et al. (2003) could be attributed to the inclusion of dietary factors, particularly fiber as these factors were not considered in some models (i.e. NRC, 2001; CNCPS (Fox et al., 2003)). Linear regression analysis had little meaningful biological significance when evaluating models for prediction of DMI in this study. Further research is required to improve the accuracy of the models, and may recommend more mechanistic approaches to investigate feedstuffs (common to the Asian region), animal genotype, environmental conditions and their interaction, as the majority of the models employed are based on empirical approaches.