• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.105-113
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• 2021

Due to the recent coronavirus outbreak, many universities in Korea have started to implement remote education. Accordingly, the Ministry of Education has stated its plans to continuously encourage and maintain remote learning as the future innovation model for education and suggested the need for a diverse range of remote learning models. However, studies on the development of practical learning models have not been carried out actively until now. Particularly, there are not many case studies in the field of design, especially regarding mobile website development. As means to improve the newly designed practice environment, this study therefore proposes the "modoo" project that offers domain creation and online marketing services. As a result of this study, the researcher suggests the use of a mixed(blending) teaching method and realized that the effectiveness of education multiplies when project-based learning and flipped learning is combined appropriately. The research methodology was divided into two big sections, education content and operations, and the effect was evaluated using the course evaluations. The study results confirmed that the applicability will increase given that learning satisfaction levels increased by more than 5% compared to face-to-face learning.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.913-924
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• 2022

In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.639-650
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• 2023

Economic development has resulted in the concentration of population and industry in the metropolitan area. Additionally, the Republic of Korea is experiencing this phenomenon, with more than half of the population living in the Seoul capital area. To alleviate this concentration of population, the Korean government implemented the new town development policy. Unfortunately, this has led to an increase in the commuting population, causing an imbalance in transportation services due to financial and policy differences in each region. This paper analyzes the level of user satisfaction with mega commuting in three aspects: mobility, accessibility, and connectivity. To objectively assess the level of user satisfaction, which is qualitative data, the Rasch Model is used to analyze the collinearity of user data. The results indicate that the level of user satisfaction differs by region, and service satisfaction with mobility is lower than that with accessibility and connectivity. Therefore, prior to the introduction of new town policies, it is necessary to develop metropolitan transportation infrastructure.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.1-18
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• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.611-618
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• 2020

The reliability of the results of LCC analysis is determined by accurate analytical procedures and energy data from which the uncertainty is removed. Until now, systems that can automatically measure these energy data and produce databases have not been commercialized. Therefore this paper proposes a concept model of an S-LCC platform that can automatically collect and analyze electric energy consumption data of equipment systems using the IOT, which is the core tool in the Fourth Industrial Revolution and operates the equipment system efficiently using the analyzed results. The proposed concept model was developed by the convergence of existing BLCS and IOT and was comprised of five modules: Facility Control Module, LCC Analysis Module, Energy Consumption Control Module, Efficiency Analysis Module, and Maintenance Standard Reestablishment Module. Using the results of LCC analysis deduced from this system, the deterioration condition of an equipment system can be identified in real-time. The results can be used as the baseline data to re-establish standards for the maintenance factor, replacement frequency, and lifetime of existing equipment, and establish new maintenance standards for new equipment. If the S-LCC platform is established, it would increase the reliability of LCC analysis, reduce the labor force for entering data and improve accuracy, and would also change disregarded data into big data with high potential.

• Journal of the Korean association of regional geographers
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• v.5 no.1
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• pp.163-185
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• 1999

The purpose of this study is to develop the model of constructivist multimedia-assisted instruction(CMAI) and to analyze the effect of it in the secondary school geography. The main results are as follows : (1) The conceptual model of CMAI can be defined as an instruction aiming at making a person who has self-directed learning ability through constructivism and multimedia. The procedural model of CMAI based on PIDA instructional strategy is divided into four stages : prediction & explanation, inquiry activity, discussion & fixation, application & synthesis stage. (2) CMAI is typed by offline CMAI and online CMAI. that is, O/WCMAI(online CMAI by web-based courseware). Offline CMAI is subdivided into P/TCMAI(offline CMAI by presentation-based courseware) and C/RCMAI(offline CMAI by cd-rom based courseware) according to authoring tool and function. (3) Offline constructivist multimedia course-ware(offline courseware) was developed for 2 periods as the material to analyze the effect of CMAI. Offline courseware is received development level of it. (4) After offline courseware being applied to the class, the effect of it according the types of the CMAI instruction(lecture instruction, whole teaching, individualized learning, cooperative learning) was analyzed. As the result of analyzing the descriptive statistics of the level of learning achievement and instruction response, there isn't big relationship between them. As the result of analyzing the inferential statistics of the level of learning achievement, there wasn't significant difference between the types of CMAI instruction in whole student of the classes and certain students who improved their grades. But as the result of analyzing of the level of instruction response, there was significant difference between lecture instruction and other types of the CMAI instruction(whole teaching, individualized learning, cooperative learning).

• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.5-15
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• 2018

The biggest characteristic of Social Network Game(SNG) is that games are played through competition and cooperation with the actual acquaintances based on SNS. Even though such competition and challenge spirit have been dealt importantly as preceding factors having influence on the flow in games in the existing game area, it is rare to find researches deeply considering the characteristics of ranking competition between acquaintances in SNG. Moreover, it was not considered that such acquaintances could be the targets of competition and also challenge at the same time in SNG. Therefore, this study examined the achievements(big differences in ranking, small differences in ranking) of the targets for comparison and closeness(strong ties, weak ties) with the targets for comparison as factors having influence on competition and challenge spirit, and also empirically analyzed the influence of such factors and interactions between factors on players' competition and challenge spirit in the ranking competitive society, by analyzing the characteristics of ranking competition between acquaintances in the mobile puzzle, SNG based on SNS through the analysis on the preceding research on the self-evaluation maintenance model of the social comparison theory. In the results, when preferentially exposing competitors with small difference in ranking and also exposing competitors with stronger ties, players' competition is stimulated, so that it can improve their challenge spirit. Such results of this study can be expected to a lot contribute to the actual design work of SNG ranking table contents.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.401-405
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• 2006

A uniform velocity field of crust can be obtained by cumulative multi-year GPS data. Then the shortening and sliding of drape zones between Chinese Continent Blocks can be researched through the velocity field and dynamics meaning is also analyzed. A model of movement and strain is created to extract displacing and rotating information of blocks in this paper. On the basis of it, the shortening vectors and sliding states of drape zones between blocks can be obtained by the model of level center of gravity moving velocity vectors between neighboring blocks. Some result show as follows. India plate jostles greatly toward north, so a complicated movement situation is formed for 14 sub-blocks. And self-deformations of inner tectosomes can be greatly reflected according to the characteristics of drape zones between tectosomes. The extrusion deformation exists between Himalaya and Qiangtang blocks. Its contraction ratio is about 20.1 $mm.a^{-1}$. However, it only is $mm.a^{-1}$ between Tarim and Zhungar. The deformation characteristics and contraction ratio of other drape zones are obviously different with the former. The movement characteristics of contraction, shear, dislocation, etc. are showed in these zones. The average contraction ratio is about 5.0 $mm.a^{-1}$. The whole trend in the west continent has a big movement toward north, and in the east continent has a small movement toward south or southeast. The strain of west continent is far bigger than that of east, and the strain of southwest is bigger than that of the southeast. It is whole showed that India plate jostles toward north-east and the south-north zone has cutting and absorbing phenomena. The total characteristics and present-day trends of deformation of inland drape zones are basically described by the sinistrorse dislocation in south-north zone and Arjin fracture, the sinistrorse shear between south china and north china, etc.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.71-80
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• 2023

This study primarily aimed to develop an automated stuttering identification and classification method using artificial intelligence technology. In particular, this study aimed to develop a deep learning-based identification model utilizing the convolutional neural networks (CNNs) algorithm for Korean speakers who stutter. To this aim, speech data were collected from 9 adults who stutter and 9 normally-fluent speakers. The data were automatically segmented at the phrasal level using Google Cloud speech-to-text (STT), and labels such as 'fluent', 'blockage', prolongation', and 'repetition' were assigned to them. Mel frequency cepstral coefficients (MFCCs) and the CNN-based classifier were also used for detecting and classifying each type of the stuttered disfluency. However, in the case of prolongation, five results were found and, therefore, excluded from the classifier model. Results showed that the accuracy of the CNN classifier was 0.96, and the F1-score for classification performance was as follows: 'fluent' 1.00, 'blockage' 0.67, and 'repetition' 0.74. Although the effectiveness of the automatic classification identifier was validated using CNNs to detect the stuttered disfluencies, the performance was found to be inadequate especially for the blockage and prolongation types. Consequently, the establishment of a big speech database for collecting data based on the types of stuttered disfluencies was identified as a necessary foundation for improving classification performance.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.413-426
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• 2023

Species distribution model (SDM) is used to preserve biodiversity and climate change impact. To evaluate biodiversity, various studies are being conducted to utilize and apply SDM. However, there is insufficient research to provide useful information by identifying the current status and recent trends of SDM research and discussing implications for future research. This study analyzed the trends and flow of academic papers, in the use of SDM, published in academic journals in South Korea and provides basic information that can be used for related research in the future. The current state and trends of SDM research were presented using philological methods and text-mining. The papers on SDM have been published 148 times between 1998 and 2023 with 115 (77.7%) papers published since 2015. MaxEnt model was the most widely used, and plant was the main target species. Most of the publications were related to species distribution and evaluation, and climate change. In text mining, the term 'Climate change' emerged as the most frequent keyword and most studies seem to consider biodiversity changes caused by climate change as a topic. In the future, the use of SDM requires several considerations such as selecting the models that are most suitable for various conditions, ensemble models, development of quantitative input variables, and improving the collection system of field survey data. Promoting these methods could help SDM serve as valuable scientific tools for addressing national policy issues like biodiversity conservation and climate change.