• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.51-75
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• 2022

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation that is able to alter neuronal activity in particular brain regions. Many studies have researched how tDCS modulates neuronal activity and reorganizes neural networks. However it is difficult to conclude the effect of brain stimulation because the studies are heterogeneous with respect to the stimulation parameter as well as individual difference. It is not fully in agreement with the effects of brain stimulation. In particular few studies have researched the reason of variability of brain stimulation in response to time so far. The study investigated individual variability of brain stimulation based on circadian rhythm and chronotype. Participants were divided into two groups which are morning type and evening type. The experiment was conducted by Zoom meeting which is video meeting programs. Participants were sent experiment tool which are Muse(EEG device), tdcs device, cell phone and cell phone holder after manuals for experimental equipment were explained. Participants were required to make a phone in frount of a camera so that experimenter can monitor online EEG data. Two participants who was difficult to use experimental devices experimented in a laboratory setting where experimenter set up devices. For all participants the accuracy of 98% was achieved by SVM using leave one out cross validation in classification in the the effects of morning stimulation and the evening stimulation. For morning type, the accuracy of 92% and 96% was achieved in classification in the morning stimulation and the evening stimulation. For evening type, it was 94% accuracy in classification for the effect of brain stimulation in the morning and the evening. Feature importance was different both in classification in the morning stimulation and the evening stimulation for morning type and evening type. Results indicated that the effect of brain stimulation can be explained with brain state and trait. Our study results noted that the tDCS protocol for target state is manipulated by individual differences as well as target state.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.169-178
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• 2021

The Korean girl group "Kara" has suffered the trilemma of its de facto failure to debut, the crisis of team breakup, and the CEO crisis of the agency. But the group has made an outstanding achievement in the history of Korean pop music after overcoming all odds. Their success strategy has never been disclosed by insiders involved in Kara's total music projects. This study has been carried out in the analysis of the strategy to provide academic implications and to honor the contribution of the late CEO Ho-yeon Lee and Kara's key member Ha-ra Gu. Therefore, between Nov. and Dec. 2020, we conducted in-depth interviews with managers, composers, stylists and Ha-ra Gu(Only in 2019, before her death) who took part in the project. The research model is set up by combining Porter's Competitive Advantage Strategy and the music value chain model into categories of "Product Innovation Differentiation (PD)" (producing, album production, performance activities) and "Marketing Differentiation (MD)" (market targeting, image specialization, promotion and communication). The analysis showed that the PD focused on complete rediscovered harmonization and revalued members' personality and sincerity with peppy songs and dainty dances as well as emission of "bright energy" which caused healing effects instead of mimicking other star singers recklessly. In terms of MD, they selected Japan's 10-20s as their main market, increasing intimacy with fans and media with the image of cute+pretty+classy+sexy. The result suggests that Poter's differentiation can function as a meaningful strategy frame in the fostering, hit, and revival of idol groups. In addition, it reaffirmed that spontaneous and passionate activities of early-stage or celebrity fan may serve as a valid catalyst for realizing differentiation, as Kara's caller of Japanese actor Gekidan Hitori caused a strong "priming effect" that drove Kara's unexpected wonderful success in Japan.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Bio-Environment Control
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• v.31 no.2
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• pp.79-89
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• 2022

With the wide use of greenhouses, the working hours have been increasing inside the greenhouse for workers. In the closed ventilated greenhouse, the internal environment has less affected to external weather during making a suitable temperature for crop growth. Greenhouse workers are exposed to organic dust including soil dust, pollen, pesticide residues, microorganisms during tillage process, soil grading, fertilizing, and harvesting operations. Therefore, the health status and working environment exposed to workers should be considered inside the greenhouse. It is necessary to secure basic data on particulate matter (PM) concentrations in order to set up dust reduction and health safety plans. To understand the PM concentration of working environment in greenhouse, the PM concnentrations were monitored in the cut-rose and Hallabong greenhouses in terms of PM size, working type, and working period. Compare to no-work (move) period, a significant increase in PM concentration was found during tillage operation in Hallabong greenhouse by 4.94 times on TSP (total suspended particle), 2.71 times on PM-10 (particle size of 10 ㎛ or larger), and 1.53 times on PM-2.5, respectively. During pruning operation in cut-rose greenhouse, TSP concentration was 7.4 times higher and PM-10 concentration was 3.2 times higher than during no-work period. As a result of analysis of PM contribution ratio by particle sizes, it was shown that PM-10 constitute the largest percentage. There was a significant difference in the PM concentration between work and no-work periods, and the concentration of PM during work was significant higher (p < 0.001). It was found that workers were generally exposed to a high level of dust concentration from 2.5 ㎛ to 35.15 ㎛ during tillage operation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.162-171
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• 2021

The Korean government proposed the K-SDGs in 2019 to promote the UN SDGs, but the role and tasks of science and technology, an important means of implementing the SDGs, have not been materialized. Accordingly, the role of science and technology ODA for the SDGs was established through the Ministry of Science and ICT's policy research project 'Science and Technology ODA Promotion Roadmap for Spreading the New Southern Policy and Realizing the 2030 SDGs'. In addition, goals, strategies, and core tasks for the next 10 years were derived in 10 fields such as water, climate change, energy, and ICT. In this paper, we analyze 30 key tasks of the ODA promotion roadmap for science and technology for the realization of SDGs, and propose mid- and short-term tasks and implementation plans for effective roadmap promotion. Among the key tasks in each field, four common elements were derived: ICT/smartization, a global problem-solving center, cooperation/communication platform, and business model/startup support platform/living lab that can create and integrate roadmap implementation conditions. In addition, the four mid- and short-term tasks, 1) Establishment of science and technology ODA network, 2) Establishment of living lab business platform linked to start-up support business, 3) Local smartization of recipient countries, and 4) Expand and secure sustainability of global problem-solving centers, were set in relation to the implementation of the detailed roadmap. For the derived mid- and short-term tasks, detailed implementation plans based on the ICTization of global problem-solving centers were presented. The implementation of the mid- and short-term tasks presented in this paper can contribute to the more effective achievement of the science and technology ODA roadmap, and it is expected that Korea's implementation of SDGs will also achieve high performance.

• Korean Journal of Environment and Ecology
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• v.35 no.6
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• pp.621-631
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• 2021

The purpose of this study is to survey and analyze acorn production and characteristics of the Quercus acuta Thunb. according to the need for information on seed supply and seedling cultivation during the restoration of warm broad-leaved forests. For the survey, a total of 30 seed traps with a surface area of 1 m² were set up, 3 in each of 10 quadrats (8 in Wando, 1 in Haenam, and 1 in Jindo). The acorns that fell in the seed trap at the end of each month were collected from August to December each year between 2013 to 2016. The collected acorns were then classified into sound, damaged, decayed, or empty grade, and the number of acorns produced was calculated. In the case of sound acorns, acorn traits, such as length, diameter and weight of acorns without cupule, were measured. Duncan's multiple tests of acorn production and characteristics were conducted for comparative analysis of the annual average values with the values by year, stand, month, and treatment plot. The annual number of acorn dropped into the seed traps in each quadrat from 2013 to 2016 was 5-350 acorns/3 m² in 2013, 17-551 acorns/3 m² in 2014, 5-454 acorns/3 m² in 2015, and 14-705 acorns/3 m² in 2016. There was a large difference in acorn production between the quadrats, presumably attributed to the difference in the amount of light received due to the density of trees in the square. Annual acorn production per area was 335,000 acorns/ha in 2013, 932,000 acorns/ha in 2014, 556,000 acorns/ha in 2015, and 1,037,000 acorns/ha in 2016. That was a sharp variation of acorn production in the two-year cycle. As the fluctuation in the production of Q. acuta showed simultaneity between stands, it is judged that Quercus acuta Thunb. had a clear cycle of fruitfulness and fruitiness between forest objects. September showed the biggest amount of fallen acorns and largest damage from insect pests, indicating that preventing early fall of acorns could increase the fruiting period and enable mass production of sound acorns. There was no significant difference between annual average acorn length in each region. In the case of the acorn diameter and weight, the average values of acorns from Haenam were significantly higher than those from Wando and Jindo. There was no significant difference in the average annual acorn characteristics by month, and the average annual acorn length, diameter, and weight in November were 19.72mm, 12.23mm, and 1.64g, respectively, the highest between August and November.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1041-1052
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• 2022

This study is to estimate the optimal ecological flow and analysis the spatial distribution of fish habitat for Andong dam downstream reach (4,565.7 km²) using PHABSIM (Physical Habiat Simulation System) and River2D. To establish habitat models, the cross-section informations and hydraulic input data were collected uisng the Nakdong river basic plan report. The establishment range of PHABSIM was set up about 410.0 m from Gudam streamflow gauging station (GD) and about 6.0 km including GD for River2D. To select representative fish species and construct HSI (Habitat Suitability Index), the fish survey was performed at Pungji bridge where showed well the physical characteristics of target stream located downstream of GD. As a result of the fish survey, Zacco platypus was showed highly relative abundance resulting in selecting as the representative fish species, and HSI was constructed using physical habitat characteristics of the Zacco platypus. The optimal range of HSI was 0.3~0.5 m/s at the velocity suitability index, 0.4~0.6 m at the depth suitability index, and the substrate was sand to fine gravel. As a result of estimating the optimal ecological flow by applying HSI to PHABSIM, the optimal ecological flow for target stream was 20.0 m³/sec. As a result of analysis two-dimensional spatial analysis of fish habitat using River2D, WUA (Weighted Usable Area) was estimated 107,392.0 m²/1000 m under the ecological flow condition and it showed the fish habitat was secured throughout the target stream compared with Q₃₅₅ condition.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.131-143
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• 2021

In this study, the factors influencing the satisfaction of the online platform musical viewing method were investigated, and the effect of the satisfaction factors on the recommendation intention and rewatching intention of the online platform viewing method for musicals was investigated. In addition, the effect of the survey subjects' degree of involvement to musicals between the satisfaction of the online platform-based musical viewing method and recommendation intention, and rewatching intention was investigated. Satisfaction factors of online platform musicals, which are independent variables, were classified into image quality, convenience, economy, and interactivity, and dependent variables were classified into recommendation intention and rewatching intention of online platform musicals, and moderator variable was set to degree of involvement to musicals, and a total of 20 hypotheses were established. An online survey was conducted on 1,454 audiences who had experience watching musicals through the online platform from August 28 to September 7, 2021, and a total of 1,418 answers were used as valid samples. As a result of the analysis, the factors that make up the satisfaction of online platform musicals appeared in the order of convenience, video quality, economics, and interactivity. It was found that the satisfaction level of watching online platform musicals had a positive effect on the intention to recommend and rewatching online platform musicals in the path of all satisfaction factors. It was found that the moderating effect of the audience's involvement in musicals between online platform musical viewing satisfaction and recommendation intention and rewatching intention had a significant effect only between image quality and recommendation intention. It shows that audiences with high involvement in musicals have intention to recommend only when they are satisfied with the video quality of online platform musicals. Particularly important point is that the convenience factor was found to have the greatest influence on the satisfaction of online platform musical viewing method, but the image quality factor was found to have the greatest influence on the recommendation intention and rewatching intention of online platform musicals.