• Journal of vocational education research
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• v.37 no.6
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• pp.36-60
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• 2018

The purpose of this study is to explore the occupational personality required for high school graduates and to develop a scale to measure them objectively. In order to achieve the purpose of the study, this study constituted the delphi committee composed of the teacher group and the industrial personnel group. Afterwards, Delphi survey was conducted twice, and it was found that 12 jobs such as sincerity and honesty were related to occupational personality. As a result of the development of the scale based on the previous research and the expert group interview, 12 factors and 116 scales were developed for the pre - occupational personality test tool. In order to verify the validity and reliability of the developed preliminary test tool, we conducted a questionnaire survey of 700 students of vocational high school, and 514 questionnaires were used for final analysis. Parallel analysis was performed to determine the number of factors before exploratory factor analysis. As a result, eight factors were found to be appropriate. As a result of exploratory factor analysis using the 'maximum likelihood method' and 'direct oblimin rotation method', 78 items of 8 factors were found appropriate. However, in order to confirm whether the item reflects the contents of the factors, we conducted a content validity test for the expert group. As a result, feedback was obtained that 19 items were irrelevant or inadequate. Therefore, the validity of the existing job personality test tool and the modified job personality test tool were verified through confirmatory factor analysis. As a result, the fitness of the revised test tool was higher and the fitness level was generally good.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.141-150
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• 2012

This paper suggests a critical path search algorithm that can easily draw PERT/GANTT chart which manages and plans a project schedule. In order to evaluate a critical path that determines the project schedule, Critical Path Method (CPM) is generally utilized. However, CPM undergoes 5 stages to calculate the critical path for a network diagram that is previously designed according to correlative relationship and execution period of project execution activities. And it may not correctly evaluate $T_E$ (The Earliest Time), since it does not suggest the way how to determine the sequence of the nodes activities that calculate the $T_E$. Also, the sequence of the network diagram activities obtained from CPM cannot be visually represented, and hence Lucko suggested an algorithm which undergoes 9 stages. On the other hand, the suggested algorithm, first of all, decides the sequence in advance, by reallocating the nodes into levels after Breadth-First Search of the network diagram that is previously designed. Next, it randomly chooses nodes of each level and immediately determines the critical path only after calculation of $T_E$. Finally, it enables the representation of the execution sequence of the project activity to be seen precisely visual by means of a small movement of $T_E$ of the nodes that are not belonging to the critical path, on basis of the $T_E$ of the nodes which belong to the critical path. The suggested algorithm has been proved its applicability to 10 real project data. It is able to get the critical path from all the projects, and precisely and visually represented the execution sequence of the activities. Also, this has advantages of, firstly, reducing 5 stages of CPM into 1, simplifying Lucko's 9 stages into 2 stages that are used to clearly express the execution sequence of the activities, and directly converting the representation into PERT/GANTT chart.

• Management & Information Systems Review
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• v.36 no.3
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• pp.219-237
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• 2017

Looking into the top-five innovative sectors in Korea's manufacturing and service industries, this study empirically analyzes the effect of innovation performance on R&D investment, which is one of the most important strategic decisions for corporate management. In the midst of an uncertain business environment, R&D investment has been regarded as the most important strategic decision making in corporate management related to innovation. Corporate management, however, tend to be reluctant to make sufficient R&D investment due to the risk of an investment failure. Therefore, having R&D investment by offsetting this risk has been deemed as a key task for corporate management. However, prior studies have failed to identify which factors affect companies' strategic decision making on R&D investment. This study is to remedy this weakness of prior study. Relying on path dependency theory at organization-level and dominant logic at individual-level, this study empirically examines the multiple regression model, which sees entrepreneurial orientation as a positive mediator between innovation performance and R&D investment. The results found in the analysis of 242 local companies in the manufacturing and service sectors represent that innovation performance has a direct and positive effect on R&D investment, while it indirectly affects R&D investment through the mediating roles of entrepreneurial orientation. They also revealed that innovation performance had a meaningful impact on entrepreneurial orientation, which is an inclination to seek innovation, led to R&D investment. The founding of this study imply that innovation performance in the past affects innovation strategies in the future, and such a relationship could be strengthened by entrepreneurial orientation as the dominant logic of corporate management.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1817-1826
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• 2022

After the hydrogen fluoride leak in Gumi in 2012, the government has been systemizing the disaster management system, such as responding to and managing chemical accidents. In particular, the Ministry of the Interior and Safety (MOIS) is in charge of evacuation of residents following chemical accidents based on the Framework Act on Management of Disaster and Safety. In this study, an application plan was presented to support and utilize the decision-making support for evacuation of residents after a chemical accident using the chemical accident investigation equipment of the National Disaster Management Research Institute (NDMI). In the equipment operation system for scientific information collection due to chemical accidents, the roles and purpose of use of long/short distance measurement equipment were presented according to regular and emergency situations. Using the data acquired through long/short distance measurement equipment, it can be used as basic data for resident evacuation decision-making by monitoring whether chemicals are detected in an emergency and managing data on detected substances by company in a regular situation. As a result of measuring chemical substances in order to verify on-site usability by equipment only for the regular operation system, it was confirmed that real-time detection of chemical substances is possible with long distance measuring equipment. In addition, it was confirmed that it was necessary to check the measurable distance and range of the equipment in the future. In the case of short distance measurement equipment, hydrocarbon-based substances were mainly detected, and it was confirmed that it was measured at a higher level in Ulsan-Mipo National Industrial Complex than in Onsan National Industrial Complex. It is expected that it can be used as basic data to support decision-making in the event of chemical accidents through continuous data construction in the future.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.47-66
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• 2023

The purpose of this study was to develop an educational smart app for classes by reconstructing some of the teaching-learning contents of the clothing preparation planning within the 'clothing preparation planning and selection' curriculum unit. To this end, a teaching-learning process plan was planned for the classes, a smart app was developed for classes, and feedback from home economics teachers and app development experts was received for the developed app. The main composition of the developed app consists of five steps. The first step is to set up a profile using a real photo, ZEPETO or Galaxy emoji, or iPhone Memoji. In the second step, students make a list of clothes by figuring out the types, quantities and conditions of their exisitng wardrobe items. Each piece of clothing is assigned an individual registration number, and stduents can take pictures of the front and back, along with describing key attributes such as type, color, season-appropriateness, purchase date, and current status. Step three guides students in deciding which garments to retain and which to discard. Building on the clothing inventory from the previous step, students classify items to keep and items to dispose of. In Step 4, Deciding How to Arrange Clothing, students decide how to arrange clothing by filling out an alternative scorecard. Through this process, students can learn in advance the subsection of resource management and self-reliance, laying the foundationa for future learning in 'Practice of Rational Consumption Life'. Lastly, in the fifth stage of determining the disposal method, this stage is to develop practical problem-oriented classes on how to dispose of the clothes to be discarded in the thirrd stage by exploring various disposal methods, engaging in group discussions, and sharing opinions. This study is meaningful as a case study as an attempt to develop a smart app for education by an instructor to align teaching plans and educational content with achievement standards for the class. In the future, upgrades will have to be made through user application.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.312-318
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• 2010

This study was carried out to develop and test a prototype program that recommends the nitrogen topdressing rate using the color digital camera image taken from rice field at panicle initiation stage (PIS). This program comprises four models to estimate shoot N content (PNup) by color digital image analysis, shoot N accumulation from PIS to maturity (PHNup), yield, and protein content of rice. The models were formulated using data set from N rate experiments in 2008. PNup was found to be estimated by non-linear regression model using canopy cover and normalized green values calculated from color digital image analysis as predictor variables. PHNup could be predicted by quadratic regression model from PNup and N fertilization rate at panicle initiation stage with $R^2$ of 0.923. Yield and protein content of rice could also be predicted by quadratic regression models using PNup and PHNup as predictor variables with $R^2$ of 0.859 and 0.804, respectively. The performance of the program integrating the above models to recommend N topdressing rate at PIS was field-tested in 2009. N topdressing rate prescribed for the target protein content of 6.0% by the program were lower by about 30% compared to the fixed rate of 30% that is recommended conventionally as the split application rate of N fertilizer at PIS, while rice yield in the plots top-dressed with the prescribed N rate were not different from those of the plots top-dressed with the fixed N rates of 30% and showed a little lower or similar protein content of rice as well. And coefficients of variation in rice yield and quality parameters were reduced substantially by the prescribed N topdressing. These results indicate that the N rate recommendation using the analysis of color digital camera image is promising to be applied for precise management of N fertilization. However, for the universal and practical application the component models of the program are needed to be improved so as to be applicable to the diverse edaphic and climatic condition.

• The Korean Journal of Pesticide Science
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• v.19 no.3
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• pp.210-217
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• 2015

Ipfencarbazone is a herbicide of the tetrazolinone class, and is believed to be an inhibitor of very long chain fatty acids (VLCFAs), which control cell division in weeds. The objective of this study was to develop and validate an official analytical method for ipfencarbazone determination in agricultural products. The ipfencarbazone residues in agricultural products were extracted with acetone, partitioned with n-hexane, and then purified through silica SPE cartridge. Finally, the analyte was quantified by gas chromatograph-electron capture detector (GC-ECD) and confirmed with gas chromatograph/mass spectrometer(GC/MS). The linear range of ipfencarbazone was 0.01 to 1.0 mg/L with the coefficient of determination ($r^2$) of 0.9999. The limit of detection (LOD) and quantification (LOQ) was 0.003 and 0.01 mg/kg, respectively. In addition, average recoveries of ipfencarbazone ranged from 80.6% to 112.3% at the different concentration levels LOQ, 10LOQ and 50LOQ, while the relative standard deviation was 2.2-8.6%. All values were consistent with the criteria ranges requested in the CODEX guidelines. Furthermore, and inter-laboratory study was conducted to validate the method. This proposed method for determination of ipfencarbazone residues in agricultural products can be used as an official analytical method.

• Journal of Korean Society of Transportation
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• v.20 no.2
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• pp.105-115
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• 2002

For many rears, traffic accident statistics are the most direct measure of safety for a signalized intersection. However it takes more than 2 or 3 yearn to collect certain accident data for adequate sample sizes. And the accident data itself is unreliable because of the difference between accident data recorded and accident that is actually occurred. Therefore, it is rather difficult to evaluate safety for a intersection by using accident data. For these reasons, traffic conflict technique(TCT) was developed as a buick and accurate counter-measure of safety for a intersection. However, the collected conflict data is not always reliable because there is absence of clear criteria for conflict. This study developed objective and accurate conflict criteria, which is shown below based on traffic engineering theory. Frist, the rear-end conflict is regarded, when the following vehicle takes evasive maneuver against the first vehicle within a certain distance, according to car-following theory. Second, lane-change conflict is regarded when the following vehicle takes evasive maneuver against first vehicle which is changing its lane within the minimum stopping distance of the following vehicle. Third, cross and opposing-left turn conflicts are regarded when the vehicle which receives green sign takes evasive maneuver against the vehicle which lost its right-of-way crossing a intersection. As a result of correlation analysis between conflict and accident, it is verified that the suggested conflict criteria in this study ave applicable. And it is proven that estimating safety evaluation for a intersection with conflict data is possible, according to the regression analysis preformed between accident and conflict, EPDO accident and conflict. Adopting the conflict criteria suggested in this study would be both quick and accurate method for diagnosing safety and operational deficiencies and for evaluation improvements at intersections. Further research is required to refine the suggested conflict criteria to extend its application. In addition, it is necessary to develope other types of conflict criteria, not included in this study, in later study.

• The Journal of Korean Society for Radiation Therapy
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• v.24 no.2
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• pp.189-196
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• 2012

Purpose: The sufficiency of skin dose and the reemergence of patient set-up position to the success of skin cancer radiation treatment is a very important element. But the conventional methods to increase the skin dose were used to vacuum cushion, bolus and water tank have several weak points. For this reason, we producted Foxtail Millet Vacuum Cushion and evaluated the efficiency of the Foxtail Millet Vacuum Cushion in skin cancer Radiation treatment. Materials and Methods: We measured absolute dose for 3 materials (Foxtail Millet Vacuum Cushion, bolus and solid water phantom) and compared each dose distribution. We irradiated 6 MV 100 MU photon radiation to every material of 1 cm, 2 cm, 3 cm thickness at three times. We measured absolute dose and compared dose distribution. Finally we inspected the CT simulation and radiation therapy planing using the Foxtail Millet Vacuum Cushion. Results: Absolute dose of Foxtail Millet Vacuum Cushion was similar to absolute dose of bolus and solid water phantom's result in each thickness. it Showed only the difference of 0.1~0.2% between each material. Also the same result in dose distribution comparison. About 97% of the dose distribution was within the margin of error in the prescribed ranges ($100{\pm}3%$), and achieved the enough skin dose (Gross Tumor Volume dose : $100{\pm}5%$) in radiation therapy planing. Conclusion: We evaluated important fact that Foxtail Millet Vacuum Cushion is no shortage of time to replace the soft tissue equivalent material and normal vacuum cushion at the low energy radiation transmittance. Foxtail Millet Vacuum Cushion can simultaneously achieve the enough skin dose in radiation therapy planing with maintaining normal vacuum cushion' function. Therefore as above We think that Foxtail Millet Vacuum Cushion is very useful in skin cancer radiation treatment.