• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.61-66
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• 2010

The purpose of this experiment is to analyze the basic status of brain. Which are consist of rest, attention and concentration, of the brain by measuring the temperature of color by changing RGB color after manufacturing LED-illumination stand. Basic status (rest, attention and concentration) of experimenter were measured temperature of colors having three difference temperature like as $2,300^{\circ}K$, $4,000^{\circ}K$ and $6,000^{\circ}K$. The results was shown that experimenter feels more comfortable and relaxation by decreasing the temperature of color. For example we can see the little increase of concentration index at $4,000^{\circ}K$ condition and we can estimate that right brain can be more activated at the $4,000^{\circ}K$ condition. But we can not find out any different at the $6,000^{\circ}K$ condition. Main cause of no difference from the color temperature was the similarity of color temperature under the general fluorescent lamp. And interface temperature of radiant heat design results LED and PCB was approximately 80 degrees to COMSOL Multiphysics, and changed until approximately 50 degrees until a floor plane of PCB, and verification as arranged chip LED to metal PCB, and it was possible, and a near radiant heat design was confirmed to an approximate value of, as a result, acid manufacture.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.599-624
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• 2009

The purpose of this study was to investigate the relationship between error types and Polya's problem solving process. For doing this, we selected 106 sophomore students in a middle school and gave them algebra word problem test. With this test, we analyzed the students' error types in solving algebra word problems. First, We analyzed students' errors in solving algebra word problems into the following six error types. The result showed that the rate of student's errors in each type is as follows: "misinterpreted language"(39.7%), "distorted theorem or solution"(38.2%), "technical error"(11.8%), "unverified solution"(7.4%), "misused data"(2.9%) and "logically invalid inference"(0%). Therefore, we found that the most of student's errors occur in "misinterpreted language" and "distorted theorem or solution" types. According to the analysis of the relationship between students' error types and Polya's problem-solving process, we found that students who made errors of "misinterpreted language" and "distorted theorem or solution" types had some problems in the stage of "understanding", "planning" and "looking back". Also those who made errors of "unverified solution" type showed some problems in "planing" and "looking back" steps. Finally, errors of "misused data" and "technical error" types were related in "carrying out" and "looking back" steps, respectively.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.129-140
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• 2019

The purpose of our study is to confirm the usefulness of our new youth entrepreneurship program. In this study, I suggest design thinking as a new Youth entrepreneurship program which is consist of 9 dimensions: opportunity discovery, opportunity utilization, creativity capacity, career preparation behavior, entrepreneurship, entrepreneurial intention, self-preservation, social problem solving, and educational program objectives. To verify this new program, I conduct a pilot test in middle school and high school; the sample target is randomly selected one class in each school. My main finding is two. First, our new program successfully improves Youth entrepreneurship. Particularly, the improvement of opportunity utilization and entrepreneurial intention are prominent. As reasons, studies about food industry entrepreneurship is an unfamiliar subject for adolescent. Considering that entrepreneurial intention rapidly changes after the experience of entrepreneurial education, researchers need to focus on this variable. Second, I confirm the effects of gender, motivation, prior experience, interest oneself and other's recommendation about the entrepreneurship program. As a result, gender and prior experience do not have an important influence. On the other hand, voluntary interest and other's recommendation are influential. The most important factor is the influence of a teacher. Therefore, researchers need to examine the more specific mechanism of each dimension in the future.

• Journal of Service Research and Studies
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• v.8 no.4
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• pp.77-88
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• 2018

Collaboration works on R&D project have many characteristics in interoranizational relationships. There are many variables on explaining the their relationships. In the previous study, I chose some relevant variables and some hypotheses. Base on service marketing theories and relationship marketing perspective I suggest 5 hypotheses. Independent variables are fairness and quality, and dependent variable is loyalty. And trust are treated as the intervening variable between the independent variables and dependent variable. To test the research model and some hypothesis empirically, I collected the data using the questionnaire. Sample size was 448, it was enough to analyze statistically. Data were analysed using the SPSS and AMOS. In the previous study, H1($fairness{\rightarrow}trust$), H2($quality{\rightarrow}trust$), H5($trust{\rightarrow}loyalty$) were accepted, but H3 and H4 were rejected. The reason H3($fairness{\rightarrow}loyalty$) and H4($quality{\rightarrow}loyalty$) were not accepted might be attributed to the fact that trust was the mediating variable between fairness and loyalty, quality and loyalty. Specific research methodologies and statistical findings from AMOS were referred in the previous study(Jeong 2018). In this study, I suggested some hypotheses on the mediating role trust between fairness and loyalty and service quality and loyalty. Using the PROCESS-macro, I found that trust was the mediating variable between fairness and loyalty as well as service quality and loyalty. This research is the complementary and extended study from previous research.

• Korean Journal of Food Science and Technology
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• v.51 no.3
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• pp.272-277
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• 2019

The effects of medium-chain enriched diacylglycerol (MCE-DAG) oil on hepatic cholesterol homeostasis were investigated. HepG2 hepatocytes were treated with either 0.5, 1.0, or $1.5{\mu}g/mL$ of MCE-DAG for 48 h. There was no evidence of cytotoxicity by MCE-DAG up to $1.5{\mu}g/mL$. The level of proteins for cholesterol uptake including CLATHRIN and LDL receptor increased by MCE-DAG in a dose-dependent manner (p<0.05). Furthermore, proprotein convertase subtilisin/kexin type 9, an inhibitor of LDLR, was dose-dependently diminished (p<0.05), indicating cholesterol clearance raised. MCE-DAG significantly increased 3-hydroxy-3-methylglutaryl-coenzyme A reductase and acetyl-CoA acetyltransferase2 (p<0.05), required for cholesterol synthesis, and their transcriptional regulator sterol regulatory element-binding protein2 (p<0.05). These findings suggest that given conditions of prolonged sterol fasting in the current study activated both hepatic cholesterol synthesis and clearance by MCE-DAG. However, total intracellular level of cholesterol was not altered by MCE-DAG. Taken together, MCE-DAG has the potential to prevent hypercholesterolemia by increasing hepatic cholesterol uptake without affecting intracellular cholesterol level.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.969-983
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• 2021

Evapotranspiration, one of the hydrometeorological components, is considered an important variable for water resource planning and management and is primarily used as input data for hydrological models such as water balance models. The FAO56 PM method has been recommended as a standard approach to estimate the reference evapotranspiration with relatively high accuracy. However, the FAO56 PM method is often challenging to apply because it requires considerable hydrometeorological variables. In this perspective, the Hargreaves equation has been widely adopted to estimate the reference evapotranspiration. In this study, a set of parameters of the Hargreaves equation was calibrated with relatively long-term data within a Bayesian framework. Statistical index (CC, RMSE, IoA) is used to validate the model. RMSE for monthly results reduced from 7.94 ~ 24.91 mm/month to 7.94 ~ 24.91 mm/month for the validation period. The results confirmed that the accuracy was significantly improved compared to the existing Hargreaves equation. Further, the evaporative demand drought index (EDDI) based on the evaporative demand (E₀) was proposed. To confirm the effectiveness of the EDDI, this study evaluated the estimated EDDI for the recent drought events from 2014 to 2015 and 2018, along with precipitation and SPI. As a result of the evaluation of the Han-river watershed in 2018, the weekly EDDI increased to more than 2 and it was confirmed that EDDI more effectively detects the onset of drought caused by heatwaves. EDDI can be used as a drought index, particularly for heatwave-driven flash drought monitoring and along with SPI.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.59-68
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• 2022

This paper presents a method for 1:1 verification by comparing the similarity between the given real product image and the drawing image. The proposed method combines two existing CNN-based deep learning models to construct a Siamese Network. After extracting the feature vector of the image through the FC (Fully Connected) Layer of each network and comparing the similarity, if the real product image and the drawing image (front view, left and right side view, top view, etc) are the same product, the similarity is set to 1 for learning and, if it is a different product, the similarity is set to 0. The test (inference) model is a deep learning model that queries the real product image and the drawing image in pairs to determine whether the pair is the same product or not. In the proposed model, through a comparison of the similarity between the real product image and the drawing image, if the similarity is greater than or equal to a threshold value (Threshold: 0.5), it is determined that the product is the same, and if it is less than or equal to, it is determined that the product is a different product. The proposed model showed an accuracy of about 71.8% for a query to a product (positive: positive) with the same drawing as the real product, and an accuracy of about 83.1% for a query to a different product (positive: negative). In the future, we plan to conduct a study to improve the matching accuracy between the real product image and the drawing image by combining the parameter optimization study with the proposed model and adding processes such as data purification.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.99-107
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• 2021

We live in a myriad of data. Various data are created in all situations in which we work, and we discover the meaning of data through big data technology. Many efforts are underway to find meaningful data. This paper introduces an analysis technique that enables humans to make better choices through the trend and prediction of time series data as a principal component analysis technique. Principal component analysis constructs covariance through the input data and presents eigenvectors and eigenvalues that can infer the direction of the data. The proposed method computes a reference axis in a time series data set having a similar directionality. It predicts the directionality of data in the next section through the angle between the directionality of each time series data constituting the data set and the reference axis. In this paper, we compare and verify the accuracy of the proposed algorithm with LSTM (Long Short-Term Memory) through cryptocurrency trends. As a result of comparative verification, the proposed method recorded relatively few transactions and high returns(112%) compared to LSTM in data with high volatility. It can mean that the signal was analyzed and predicted relatively accurately, and it is expected that better results can be derived through a more accurate threshold setting.

• Journal of Korean Elementary Science Education
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• v.43 no.2
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• pp.269-283
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• 2024

This study compared the characteristics of an experiment f or determining whether gas occupies space presented in elementary school science textbooks to examine the validity of the experiment, identify and improve problems in its presentation, and verify its effectiveness. This study interviewed third-year elementary school teachers who had experience teaching this experiment to students. Based on teachers' opinions and observations, this study identified issues with the current experiment, developed an improved experiment, and determined its effectiveness. Through this analysis, three key findings emerged. First, the study found that the original experiment on gases was presented in the same or highly similar manner in 5 of 7 textbooks (71.4%). Thus, while various textbooks have been developed with the aim of promoting diversity and creativity in scientific literature, most experiments presented in these books are identical. Second, the existing experiment was not suitable for its target audience (third-year elementary school students) and was difficult to observe directly. The interviewed teachers also deemed the validity of the experiment to be considerably low. Finally, the original experiment was improved; this improved version was determined to be highly valid, showing a statistically significant difference compared with the original experiment. The improved experiment was effective for students as it involved activities suitable for their intellectual level and was directly observable through the senses. Thus, the study analyzed and improved an existing science experiment f or elementary students, providing insights into the 2022 revised science authorized textbooks and implications for future textbook development.