• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.49-71
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• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.231-241
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• 2021

This study intended to present strategies and implications for attracting new students and customized education to university officials through research on the participation of workers' academic aspirations in university education. Thus, variables were derived by analyzing prior data, and causal settings between variables and questionnaires were developed. Subject to the survey, 331 workers interested in participating in university education were collected through interpersonal interviews. The collected data were dataized, and reliability and feasibility verification and frequency analysis were conducted. Finally, we validate the fit of the structural equation model and the causal relationship for each concept. Therefore, the results of the validation show the following implications. First, university officials should be motivated by a mentor and mentee system with experienced people who have switched to a suitable vocational group through university education. It will also be necessary to develop and disseminate programs so that they can continue to develop themselves for the future. To this end, it will be necessary to help them understand their aptitude and strengths through consultation with experts. Second, university officials should strengthen public relations so that prospective students can know the cases and information of the job transformation of the admitted workers through recommendations. It will also be necessary to develop university education programs that can self-develop, accept various ideas through "public contest", and provide accurate information about university education to workers through re-processing. Third, university officials should provide workers with a program that allows them to catch two rabbits: job transformation and self-improvement through university education. In other words, it is necessary to stimulate the motivation of workers by providing various information such as visiting advanced overseas companies, obtaining various certificates, moving between departments of blue-collar and white-collar, and transfer opportunities. Fourth, university officials should actively promote university education programs related to this by participating in university education and receiving systematic education and the flow of social environment. Finally, university officials will need to consult and promote workers so that they can self-develop when they participate in college education, and they will have to figure out what they need for self-development through demand surveys and analysis.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1423-1444
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• 2022

In this study, the sensitivity of the mid-infrared radiance to atmospheric and surface factors was analyzed using the radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN6)'s simulation data. The possibility of retrieving the land surface temperature (LST) using only the mid-infrared bands at night was evaluated. Based on the sensitivity results, the LST retrieval algorithm that reflects various factors for night was developed, and the level of the LST retrieval algorithm was evaluated using reference LST and observed LST. Sensitivity experiments were conducted on the atmospheric profiles, carbon dioxide, ozone, diurnal variation of LST, land surface emissivity (LSE), and satellite viewing zenith angle (VZA), which mainly affect satellite remote sensing. To evaluate the possibility of using split-window method, the mid-infrared wavelength was divided into two bands based on the transmissivity. Regardless of the band, the top of atmosphere (TOA) temperature is most affected by atmospheric profile, and is affected in order of LSE, diurnal variation of LST, and satellite VZA. In all experiments, band 1, which corresponds to the atmospheric window, has lower sensitivity, whereas band 2, which includes ozone and water vapor absorption, has higher sensitivity. The evaluation results for the LST retrieval algorithm using prescribed LST showed that the correlation coefficient (CC), the bias and the root mean squared error (RMSE) is 0.999, 0.023K and 0.437K, respectively. Also, the validation with 26 in-situ observation data in 2021 showed that the CC, bias and RMSE is 0.993, 1.875K and 2.079K, respectively. The results of this study suggest that the LST can be retrieved using different characteristics of the two bands of mid-infrared to the atmospheric and surface conditions at night. Therefore, it is necessary to retrieve the LST using satellite data equipped with sensors in the mid-infrared bands.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.1
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• pp.38-41
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• 2022

Purpose Testosterone is a steroid hormone synthesized by the Leydig cells of the testes in men, and by the adrenal cortex and ovaries in women. Testosterone production is regulated by luteinzing hormone secreted by the anterior pituitary gland. In this experiment, the effectiveness of testosterone radioimmunoassay (RIA) kits produced by three companies was evaluated and compared in case the production of testosterone kits was stopped or supply problems occurred. Materials and Methods In October 2021, samples were collected from the patients (n=49) who requested the testosterone RIA test. The experiment was conducted by dividing the patient's sample into low concentration (1.0 ng/mL or less), medium concentration (2.0-4.0 ng/mL) and high concentration (6.0 ng/mL or more). The Testosterone RIA test compared and evaluated the validity of Company A kits used in this hospital and those of Company B and C used in other hospitals. The precision, sensitivity, recovery, linearity and correlation were evaluated for each kit. The testosterone RIA test was carried out in accordance with the insert kit manual for each manufacturer. Results As a result of measuring the precision of the intra assay, the Coefficient of Variation (CV) value of the company A kit was high at 11.4% only in the low concentration sample, and in the case of the company B and C kits, the CV value was less than 10% at low, medium, and high concentrations. In the inter-assay precision measurement, the CV value was less than 15% in both A and C kits, but in the case of the B kit, the CV value exceeded 15% at low and medium concentrations. Sensitivity was 0.13 ng/mL for company A, 0.01 ng/mL for company B, and 0.01 ng/mL for company C, and the linearity of all three kits showed excellent linearity. In the case of recovery rate, all of the A, B, and C company kits showed results that were out of 90-110%. In the case of correlation test, when compared with the company A kit currently use in here, the correlation coefficient (R²) value for the company B kit was 0.9508, and for the company C kit was 0.9352 Conclusion As a result, there was a slight difference in precision at the low concentration sample. The correlation test showed an excellent correlation coefficient. However, it was difficult to secure samples of various concentrations because there were not many tests of testosterone requested at this hospital. So, additional experiments should be carried out by acquiring samples of various concentrations on each laboratory later.

• Analytical Science and Technology
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• v.34 no.6
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• pp.259-266
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• 2021

Cannabis (Marijuana) is one of the most widely used drugs in the world, and its distribution has been controlled in South Korea since 1976. Identification of 11-nor-Δ⁹-carboxy-tetrahydrocannabinol (THCCOOH) in urine can provide important proof of cannabis use, and it is considered scientific evidence in the forensic field. In this study, we describe a simultaneous quantitative method for identifying THCCOOH and THCCOOH-glucuronide in urine, using simple liquid-liquid extraction (LLE), and liquid chromatography-tandem mass spectrometry (LC-MS/MS). THCCOOH-D₃ and THCCOOH-glucuronide-D₃ were used as internal standards. Validation results of the matrix effect, as well as recovery, linearity, precision, accuracy, process efficiency, and stability were all satisfactory. No carryover, endogenous or exogenous interferences were observed. The limit of detection (LOD) of THCCOOH and THCCOOH-glucuronide were 0.3 and 0.2 ng/mL, respectively. The developed method was applied to 28 authentic human urine samples that tested positive in immunoassay screening and gas chromatography/mass spectrometry (GC/MS) tests. The ranges of concentrations of THCCOOH and THCCOOH-glucuronide in the samples were less than LOQ~266.90 ng/mL and 6.43~2133.03 ng/mL, respectively. The concentrations of THCCOOH-glucuronide were higher than those of THCCOOH in all samples. This method can be effectively and successfully applied for the confirmation of cannabinoid use in human urine samples in the forensic field.

• Journal of agricultural medicine and community health
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• v.47 no.3
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• pp.166-180
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• 2022

Objective: This study evaluated the effects of a non-face-to-face obesity management program, implemented during the pandemic. Methods: The non-face-to-face obesity management program used the Intervention mapping protocol (IMP). The program was put into effect over the course of eight weeks, from September 14 to November 13, 2020 in 48 overweight and obese adults, who applied to participate through the Daegu Citizen Health Support Center. Results: IMP was first a needs assessment was conducted; second, goal setting for behavior change was established; third, evidence-based selection of arbitration method and performance strategy was performed; fourth, program design and validation; fifth, the program was run; and sixth, the results were evaluated. The average weight after participation in the program was reduced by 1.2kg, average WC decreased by 3cm, and average BMI decreased by 0.8kg/m² (p<0.05). The results of the health behavior survey showed a positive improvement in lifestyle factors, including average daily intake calories, fruit intake, and time spent in walking exercise before and after participation in the program. A statistically significant difference was seen (p<0.05). The satisfaction level for program process evaluation was high, at 4.57±0.63 point. Conclusion: The non-face-to-face obesity management program was useful for obesity management for adults in communities, as it enables individual counseling by experts and active participation through self-body measurement and recording without restriction by time and place. However, the program had some restrictions on participation that may relate to the age of the subject, such as skill and comfort in using a mobile app.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• Korean Journal of School Psychology
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• v.17 no.3
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• pp.401-419
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• 2020

The purpose of this study was to validate the Korean Implementation Fidelity Checklist of Tier 1 School-Wide Positive Behavior Support (KIFC-T1) for use in the Korean educational system. Tier 1 support, which is universal supports, within a multi-tiered, school-wide positive behavior support (SWPBS) model, aims to provide support to and prevent problem behaviors among all students in a school. The initial KIFC-T1 consisted of 48 items and 11 factors and was developed based on a literature review. Its content was validated by experts. The validated KIFC-T1 was introduced to 185 special school teachers who had experience implementing SWPBS and who used the instrument to assess the degree to which their schools had implemented Tier 1 support. Based on their responses, the construct validity of the KIFC-T1 was examined using factor, item, and internal consistency reliability analyses. The concurrent validity of the tool was examined using the PBS Evaluation Tool, School Climate Questionnaire, School Discipline Practice Scale, and PBS Effectiveness Scale. The analyses revealed that KIFC-T1 had a stable five-factor structure with 35 items, had good reliability (Cronbach's α=.956, each factor's Cronbach's α=.834-.951), and its results were statistically significantly correlated with those of the PBS Evaluation Tool, School Discipline Practice Scale, and the PBS Effectiveness Scale. However the KIFC-T1's results were not statistically significantly correlated with the results of the School Climate Questionnaire. These results suggest that KIFC-T1 is a reliable and valid tool for assessing the fidelity of universal support implementations.

• Journal of Korean Home Economics Education Association
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• v.34 no.1
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• pp.59-80
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• 2022

The purpose of this study was to develop a workbook that can support the class and evaluation of the subject, 「Food safety and health」 and to verify its validity. The development direction of the workbook was set by analyzing the 「Food safety and health」 curriculum, dietary education materials, and previous studies related to the workbook, and the overall structure was designed by deriving the activity ideas for each area. Based on this, the draft was developed, and the draft went through several rounds of cross-review by the authors and the examination and revision by the Ministry of Food and Drug Safety, before the final edited version was developed. The workbook was finalized with corrections and enhancements based on the advice of 9 experts and 44 home economics teachers. The workbook consists of 4 areas: the 'food selection' area, with 10 learning topics and 36 lessons, the 'food poisoning and food management' area, with 10 learning topics and 36 lessons, the 'cooking' area, with 11 learning topics and 43 lessons, and the 'healthy eating' area, with 11 learning topics and 55 lessons, resulting in a total of 42 learning topics, 170 lessons. The workbook was designed to evenly cultivate practical problem-solving competency, self-reliance capacity, creative thinking capacity, and community capacity. In-depth inquiry-learning is conducted on the content, and the context is structured so that self-diagnosis can be made through evaluation. According to the validity test of the workbook, it was evaluated to be very appropriate for encouraging student-participatory classes and evaluations, and to create a class atmosphere that promotes inquiry by strengthening experiments and practices. In the current situation where the high school credit system is implemented and individual students' learning options are emphasized, the results of this study is expected to help expand the scope of home economics-based elective courses and contribute to realizing student-led classrooms with a focus on inquiry.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.