• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.49-55
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• 2024

This paper proposes PredFeed Net, a neural network model that mimics the food distribution of fish farming experts. Unlike existing food distribution automation systems, PredFeed Net predicts food distribution by learning the food distribution patterns of experts. This has the advantage of being able to learn using only existing environmental data and food distribution records from food distribution experts, without the need to experiment by changing food distribution variables according to the environment in an actual aquarium. After completing training, PredFeed Net predicts the next food ration based on the current environment or fish condition. Prediction of feed ration is a necessary element for automating feed ration, and feed ration automation contributes to the development of modern fish farming such as smart aquaculture and aquaponics systems.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.1-32
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• 2024

This study aims to explore the utilization of kiosks, a case of self-service technology in library services, by applying task-technology fit theory to reveal the factors that affect the satisfaction and continued use of library kiosk services and to conduct a review of library non-face-to-face services. We organized the kiosk characteristic factors through a literature review and established a research model mediated by related theories. We collected 229 valid questionnaire data from users with experience using library kiosks and analyzed them using SPSS 26.0 and SmartPLS 4.0 programs. The analysis results confirmed that the fit of library services and self-service technology was significantly influenced by the usefulness and enjoyment of kiosk technology characteristics and the kiosk-friendly environment of the usage environment attributes. In addition, we found the fit between library services and self-service technology to significantly affect library kiosk usage satisfaction and intention to continue using the kiosk, so this study proposed a plan for library kiosk services utilizing the significant factors. In addition, to effectively use the kiosks as a non-face-to-face library service, we suggest operating them in line to provide library information materials, install them in various locations within the library to increase accessibility, and provide education on how to use them for learning and to raise positive awareness of the kiosks for the digitally disadvantaged.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.3
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• pp.262-277
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• 2017

Digital textbooks enable learning that is appropriate to the characteristics and level of learners through various interactions. The purpose of this study was to develop an instruction model that can more effectively use digital textbooks in elementary science classes and to verify its effectiveness. The results were as follows. The instruction model for helping learners complete their learning by using digital textbooks needs to receive diagnostic assessment and feedback on entry behavior, to build a self-directed learning environment, and to interact with teachers, students, and digital textbooks as scaffolding. In this study, we developed an instruction model using digital textbooks reflecting these characteristic. The instructional model consists of preparation, practice and solidity step. In the preparation step, the learner performs a diagnostic evaluation using digital textbooks. Based on the results, feedback provided at each level can complement the entry behavior and maintain interest in learning activities. In the practice step, self-directed learning is implemented using diverse functions of digital textbooks and various types of data. In the solidity step, learners can internalize the learning contents by reviewing video clips which are provided by teachers, performing problem-solving activities, and accessing outcomes accumulated by learners in the community online. In order to verify the effectiveness of this model, we selected the "Weather and our Life" unit. This experiment was conducted using 101 students in the 5th grade in B Elementary School in Gwangju Metropolitan City. In the experimental group, 50 students learned using a smart device that embodies digital textbooks applied with the instruction model. In the comparative group, 51 students were taught using the paper textbooks. The results were as follows. First, there was a significant effect on the improvement of the learning achievement in the experimental group with low academic ability compared with the comparative group with low academic ability. Second, there was a significant effect on self-directed learning attitude in the experimental group. Third, in the experimental group, the number of interactions with the learner, teacher, and digital textbook was higher than the comparative group. In conclusion, the digital textbooks based on the instruction model in elementary science classes developed in this study helped to improve learners' learning achievement and self-directed learning attitudes.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.537-542
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• 2021

This paper improves the possibility of recognizing fast-moving objects through the YOLO (You Only Look Once) deep learning recognition model in an application environment for object recognition in images. The purpose was to study the method of collecting semantic data through processing. In the recognition model, the moving object recognition error was identified as unrecognized because of the difference between the frame rate of the camera and the moving speed of the object and a misrecognition due to the existence of a similar object in an environment adjacent to the object. To minimize the recognition errors by compensating for errors, such as unrecognized and misrecognized objects through the proposed data collection method, and applying vision processing technology for the causes of errors that may occur in images acquired for sports (tennis games) that can represent real similar environments. The effectiveness of effective secondary data collection was improved by research on methods and processing structures. Therefore, by applying the data collection method proposed in this study, ordinary people can collect and manage data to improve their health and athletic performance in the sports and health industry through the simple shooting of a smart-phone camera.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.30-30
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• 2023

This study aims to establish the multi-reservoir operation system model in the Upper Mun River Basin which includes 5 main dams namely, Mun Bon (MB), Lamchae (LC), Lam Takhong (LTK), Lam Phraphoeng (LPP), and Lower Lam Chiengkrai (LLCK) Dams. The knowledge and AI technology were applied aiming to develop innovative prototype for SMART dam-reservoir operation in future. Two different sorts of reservoir operation system model namely, Fuzzy Logic (FL) and Constraint Programming (CP) as well as the development of rainfall and reservoir inflow prediction models using Machine Learning (ML) technique were made to help specify the right amount of daily reservoir releases for the Royal Irrigation Department (RID). The model could also provide the essential information particularly for the Office of National Water Resource of Thailand (ONWR) to determine the short-term and long-term water resource management plan and strengthen water security against flood and drought in this region. The simulated results of base case scenario for reservoir operation in the Upper Mun from 2008 to 2021 indicated that in the same circumstances, FL and CP models could specify the new release schemes to increase the reservoir water storages at the beginning of dry season of approximately 125.25 and 142.20 MCM per year. This means that supplying the agricultural water to farmers in dry season could be well managed. In other words, water scarcity problem could substantially be moderated at some extent in case of incapability to control the expansion of cultivated area size properly. Moreover, using AI technology to determine the new reservoir release schemes plays important role in reducing the actual volume of water shortfall in the basin although the drought situation at LTK and LLCK Dams were still existed in some periods of time. Meanwhile, considering the predicted inflow and hydrologic factors downstream of 5 main dams by FL model and minimizing the flood volume by CP model could ensure that flood risk was considerably minimized as a result of new release schemes.

• Knowledge Management Research
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• v.22 no.2
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• pp.19-32
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• 2021

Bitcoin, a representative cryptocurrency, is receiving a lot of attention around the world, and the price of Bitcoin shows high volatility. High volatility is a risk factor for investors and causes social problems caused by reckless investment. Since the price of Bitcoin responds quickly to changes in the world environment, we propose to predict the price volatility of Bitcoin by utilizing news information that provides a variety of information in real-time. In other words, positive news stimulates investor sentiment and negative news weakens investor sentiment. Therefore, in this study, sentiment information of news and deep learning were applied to predict the change in Bitcoin yield. A single predictive model of logit, artificial neural network, SVM, and LSTM was built, and an integrated model was proposed as a method to improve predictive performance. As a result of comparing the performance of the prediction model built on the historical price information and the prediction model reflecting the sentiment information of the news, it was found that the integrated model based on the sentiment information of the news was the best. This study will be able to prevent reckless investment and provide useful information to investors to make wise investments through a predictive model.

• Journal of Environmental Impact Assessment
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• v.31 no.4
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• pp.214-225
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• 2022

This study emphasized the soil of environmental impact assessment to devise measures to minimize the negative impact of project implementation on the environment. As a series of efforts for impact assessment procedures, a national inventory-based database was established for urban development projects, and three machine learning model performance evaluation as well as soil pollutant concentration distribution mapping were conducted. Here, nine soil pollutants were mapped to the metropolitan area of South Korea using the Random Forest model, which showed the best performance. The results of this study found that concentrations of Zn, F, and Cd were relatively concerned in Seoul, where urbanization is the most active. In addition, in the case of Hg and Cr6+, concentrations were detected below the standard, which was derived from a lack of pollutants such as industrial and industrial complexes that affect contents of heavy metals. A significant correlation between land cover and pollutants was inferred through the spatial distribution mapping of soil pollutants. Through this, it is expected that efficient soil management measures for minimizing soil pollution and planning decisions regarding the location of the project site can be established.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.273-278
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• 2024

The purpose of this study is to examine the limitations of university operating system changes as a result of the policy changes and outcomes of the university lifelong education system support project by project period, and based on this, to propose a development plan to support the university's adult learning system. In this study, we sought to investigate changes in the higher education environment and changes in lifelong education in universities through analysis of literature and various data. The changing times of technological innovation and changes in knowledge require continuous learning even after school education, and the need for re-education and improved education is increasing. Therefore, the Ministry of Education and the National Institute for Lifelong Education have been actively carrying out support projects for lifelong learning-centered universities since 2008 to provide adult learners with opportunities to study. This project is centered around universities and the local community, and is promoting various types of changes in educational operation, such as reforming the university's academic system to be adult-friendly and operating night or weekend classes in order to provide educational opportunities for adult learners. Now, universities must play a role as a hub of regional lifelong education for the coexistence of the region and university, and as a key institution responsible for the contemporary tasks of sustainable development and coexistence between the university and the community.