• Geophysics and Geophysical Exploration
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• v.24 no.1
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• pp.20-27
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• 2021

Subsurface rock produces heat from the decay of radioactive isotopes in constituent minerals and gamma-ray emissions, of which the magnitude is dominated by the contents of the major radioactive isotopes (e.g., U, Th, and K). The heat production is generally calculated from the rock density and contents of major isotopes, which can be determined by mass spectrometry of drilled core samples or rock fragments. However, such methods are not easily applicable to deep boreholes because core samples recovered from depths of several hundred meters to a few kilometers are rarely available. A geophysical logging technique for boreholes is available where the U, Th, and K contents are measured from the gamma-ray spectrum. However, this technique requires the density to be measured separately, and the measurement depth of the equipment is still limited. As an alternative method, a normal gamma-ray logging tool was adopted to estimate the heat production from the total gamma activity, which is relatively easy to measure. This technical report introduces the development of the proposed method for evaluating the heat production of a granitic rock mass with domestic commercial borehole logging tools, as well as its application to a ~2 km deep borehole for verification.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.49-54
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• 2022

Rapid changes in modern society after the COVID-19 have highlighted artificial intelligence talent as a major influencing factor in determining national competitiveness. Accordingly, the Ministry of Education planned a large-scale SW·AI camp education project to develop the digital capabilities of 4th to 6th grade elementary school students and middle and high school students who are in a vacuum in artificial intelligence education. Therefore, this study aims to develop a camp-type SW·AI education program for students in grades 4-6 of elementary school so that students in grades 4-6 of elementary school can acquire basic knowledge in artificial intelligence. For this, the meaning of SW·AI education in elementary school is defined and SW·AI contents to be dealt with in elementary school are: understanding of SW AI, 'principle and application of SW AI', and 'social impact of SW AI' was set. In addition, an attempt was made to link the set elements of elementary school SW AI education and learning with related subjects and units of textbooks currently used in elementary schools. As for the program used for education, entry, a software coding learning tool based on block coding, is designed to strengthen software programming basic competency, and all programs are designed to be operated centered on experience and experience-oriented participants in consideration of the developmental characteristics of elementary school students. In order for SW·AI education to be organized and operated as a member of the regular curriculum, it is suggested that research based on the analysis of regular curriculum contents and in-depth analysis of SW·AI education contents is necessary.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.44-56
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• 2018

Researches on military weapons are actively studied to improve national defense power of each country. The military weapon system is being used not only as a weapon but also as a reconnaissance and surveillance device for places where it is difficult for people to access. If such a weapon system becomes an object of attack, military data that is important to national security can be leaked. Furthermore, if a device is taken, it can be used as a terrorist tool to threaten its own country. So, security of military devices is necessarily required. In order to enhance the security of a weapon system such as drone, it is necessary to form a chain of trust(CoT) that gives trustworthiness to the overall process of the system from the power on until application is executed. In this paper, by analyzing the trusted computing-based boot technology, we derive trusted boot technology components and classify them based on hardware dependence/independence. We expect our classification of hardware dependence/independence to be applied to the trusted boot technology of our self-development ultraprecision weapon system to improve the defense capability in our military.

• Journal of Korean Dental Hygiene Science
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• v.4 no.1
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• pp.29-37
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• 2021

Background: The selection of an occupation is typically based on individuals' personalities and the characteristics of occupations, which significantly affect occupational consciousness. The present study aimed to enhance the occupational achievement level of and provide fundamental data for student counseling in order to develop competitive professional workers by understanding the occupational consciousness of freshmen and motivating them as dental hygienists with career development plans, as freshmen majoring in dental hygiene eventually play a significant role in the field of dentistry as dental hygienists. Methods: The surveys were distributed to 160 freshmen in the dental hygiene department and were subsequently collected. The data from 142 surveys were used for analysis, as 18 surveys were excluded due to insincere responses. The survey contents included questions related to major selection and satisfaction, including motives for selecting a dental hygiene major, prior knowledge on a dental hygiene major and a career as a dental hygienist, satisfaction level of the major, and reasons for dissatisfaction in cases if applicable, as well as questions related to occupational consciousness, including career prospects for dental hygienists, opinions on the occupation, and conditions of job selection. Results: High employment rate with good salary level ranked highest (43.7%) among motives to apply the dental hygiene major, followed by the desire to be a professional worker (21.1%) and recommendation by acquaintances. Of those who responded, 50.7% indicated a normal level of satisfaction with the major, and 69.9% responded that they had prior knowledge regarding the dental hygiene major and/or field of dental hygiene. These results may be due to youth unemployment and the occurrence of job preparation immediately after students enter university, which is a result of the difficulty in job seeking. In terms of career prospects, 48.6% of students responded with "growing a little bit," followed by "growing a lot" (28.9%), "no difference from now" (21.1%), and "other" (1.4%). Regarding opinions on the occupation, 65.5% responded that occupation was an tool with which to make and income or a living, 23.2% responded that occupation was for dreams and self-realization, and 11.3% responded that occupation was for success in life and maintaining social status. Regarding the conditions of job selection, the responses included that the workplace had good working conditions (39.4%), good interpersonal relationships (21.8%), and a higher salary (18.3%). This may reflect the change in work ethics among university students, according to the trend of the times. Conclusion: Based on the results of the present study, we found that educational guidance to enhance the level of satisfaction with the major, and career guidance to understand and apply the clear vision and long-term job security are necessary.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.1-12
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• 2021

Technologies for an MVDC(medium voltage direct current) distribution system are being considered as an effective alternative to overcome the interconnection delay issues of PV systems. However, the implementation of a DC distribution system might lead to economic problems because of the development of DC devices. Therefore, this paper deals with the scale of a PV plant based on its capacity and proposes hosting-capacity models for PV systems to establish a network to evaluate the feasibility of an MVDC distribution system. The proposed models can be classified as AC and DC distribution systems by the power-supply method. PV systems with hundreds of MW, dozens of MW, and a few MW can be categorized as large-scale, medium-scale, and small-scale models, respectively. This paper also performed modeling for an economic evaluation of MVDC distribution system by considering both the cost of AC and DC network construction, converter replacement, operation, etc. The profit was composed of the SMP and REC rate of a PV plant. A simulation for economic evaluation was done for the MVDC distribution system using the present worth and equal-principal costs repayment method. The results confirmed that the proposed model is a useful tool to evaluate economic issues of a DC distribution system.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.103-112
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• 2021

In recent years, public bicycle systems are widely spread over the world according to the development of ICT technology. Since the public bicycle systems in large cities need to secure both publicity and convenience for citizens, analysis of various their issues from introduction to operation is required. In addition, it is also necessary to prepare for various scenarios for coexistence with the PM business, which is recently in the spotlight as a last mile means and normally managed privately. This paper introduces modeling and simulation for efficient operations of public bicycle systems. In particular, the proposed method was developed in a form that can be easily used in other cities by modeling the general structure and behavior of the public bicycle system, and it was developed to facilitate modification and expansion of the future model with a component-based model configuration. This paper provides a case study of the propose method, which is the public bicycle simulation in Sejong City. The simulation results were derived by applying the data from the public bicycle system of Sejong City, and they were verified with the associated real data of Sejong City. Using the verified model, it is expected that it can be used as a tool to design and analyze various services on the public bicycle systems, which were especially suitable for Sejong City.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1277-1290
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• 2020

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1435-1447
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• 2020

The seasonal variations in weather parameters can significantly affect the atmospheric transmission characteristics. Herein, we propose a novel augmented multiple regression algorithm for the accurate estimation of atmospheric transmittance and solar irradiance over highly localized areas. The algorithm employs 1) adaptive atmospheric model selection using measured meteorological data and 2) multiple linear regression computation augmented with the conventional application of MODerate resolution atmospheric TRANsmission (MODTRAN). In this study, the proposed algorithm was employed to estimate the solar irradiance over Taean coastal area using the 2018 clear days' meteorological data of the area, and the results were compared with the measurement data. The difference between the measured and computed solar irradiance significantly improved from 89.27 ± 48.08σ W/㎡ (with standard MODTRAN) to 21.35 ± 16.54σ W/㎡ (with augmented multiple regression algorithm). The novel method proposed herein can be a useful tool for the accurate estimation of solar irradiance and atmospheric transmission characteristics of highly localized areas with various weather conditions; it can also be used to correct remotely sensed atmospheric data of such areas.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Therapeutic Science for Rehabilitation
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• v.10 no.3
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• pp.97-110
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• 2021

Objective : This study aimed to develop evaluation items based on Parten's social play developmental stages to measure social play in preschool children. Methods : Through a literature review, the investigation items according to Parten's social play developmental stages were collected. Two Delphi surveys were conducted using 22 expert panels. An understanding of the contents of the preliminary items was determined via parents of preschool children. The evaluation method was established through an expert advisory meeting. Results : Through data collection and the Delphi survey, a total of 89 items were drawn, including 12 unoccupied behaviors, 7 onlooker behaviors, 14 solitary play, 16 parallel play, 17 associative play, and 23 cooperative play items. The average content validity ratio of the Delphi survey was 0.85. The stability was 0.15. The consensus was 0.78. The final preliminary evaluation items comprised a total of 40 items, including 17 for associative play and 23 for cooperative play. Conclusion : An evaluation items that can measure social play in preschool children was developed, and its content validity was verified. It is expected to be used as an evaluation tool in clinical practice.