• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.79-86
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• 2019

This paper is an overview of key elements of Korea National Open University's smart mobile learning system, and an attempt to evaluate its main services relative to the FRAME model and the Mobile Learning Development Model for distance learning in higher education. KNOU improved its system architecture to one based on xMOOC e-learning content delivery while also upgrading its PC-based online/mobile learning services to facilitate an easier and more convenient access to lectures and for better interactivity. From the users' viewpoint, the upgraded 'U-KNOU Campus' allows for a more integrated search capability coupled with better course recommendations and a customized notification service. Using the new system, the students can access not only the school- and peer-issued messages via online bulletin boards but also share information and pose questions to others including to the school faculty/officials and system administrators. Additionally, a new mobile payment method has been incorporated into the system so that the students can select and pay for additional courses from anywhere. In spite of these advances, the issue of device usability and content development remain; specifically U-KNOU Campus needs to improve its instructor-learner and learner-to-learner interactivity and mobile evaluation interface.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.6
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• pp.133-139
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• 2008

Cyber university can easily study the lecture only press the couple of mouse click or keyboard button anywhere and anytime, if you have a computer. Many people believe that virtual university can save time and improve learning. To check How many students learn which selected some of the virtual university courses, instructor must know how to the understanding students and find out their difficult problems. Without checking this condition, it will be a very difficult and boring virtual university course. In this paper, we introduce the intelligent learning system. It has a full duplex direction that teaches understanding students and not understanding students. The computer simulation results confirmed that a full duplex virtual learning system has been proven to be much more efficient than one way direction which unfortunately does not consider to understanding problems.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.121-128
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• 2021

In order to design a real time big data collection and analysis system of manufacturing data in a smart factory, it is important to establish an appropriate wired/wireless communication system and protocol. This paper introduces the latest communication protocol, OPC-UA (Open Platform Communication Unified Architecture) based client/server function, applied user interface technology to configure a network for real-time data collection through IoT Integration. Then, Database is designed in MES (Manufacturing Execution System) based on the analysis table that reflects the user's requirements among the data extracted from the new cutting process automation process, bush inner diameter indentation measurement system and tool monitoring/inspection system. In summary, big data analysis system introduced in this paper performs SPC (statistical Process Control) analysis and visualization analysis with interface of OPC-UA-based wired/wireless communication. Through AI learning modeling with XGBoost (eXtream Gradient Boosting) and LR (Linear Regression) algorithm, quality and visualization analysis is carried out the storage and connection to the cloud.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.609-620
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• 2010

In order to provide a tailored education for learners within the ubiquitous environment, it is critical to undertake an analysis of the learning activities of learners. For this purpose, SCORM (Sharable Contents Object Reference Model), IMS LD (Instructional Management System Learning Design) and other standards provide learning design support functions, such as, progress checks. However, in order to apply these types of standards, contents packaging is required, and due to the complicated standard dimensions, the facilitation level is lower than the work volume when developing the contents and this requires additional work when revision becomes necessary. In addition, since the learning results are managed by the server there is the problem of the OS being unable to save data when the network is cut off. In this study, a system is realized to manage the actions of learners through the event interception of a web-browser by using event hooking. Through this technique, all HTMLbased contents can be facilitated again without additional work and saving and analysis of learning results are available to improve the problems following the application of standards. Furthermore, the ubiquitous learning environment can be supported by tracking down learning results when the network is cut off.

• Smart Media Journal
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• v.13 no.5
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• pp.45-51
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• 2024

Recently, realistic contents such as virtual reality(VR) and augmented reality (AR) are widely used for education to provide beneficial learning environments with thee-dimensional(3D) information and interactive technology. Specially, AR technology will be helpful to intuitively understand by adding virtual objects registered in the real learning environment with effective ways. In this paper, we developed an AR learning system using 3D spatial information in the 2D based textbook for studying math related to geometry. In order to increase spatial learning effect, we applied to solid shapes such as prisms and pyramids in mathematics education process. Also, it allows participants to use various shapes and expression methods (e.g., wireframe mode) with interaction. We conducted the experiment with our AR system, evaluated achievement and interest. Our experimental study showed positive results, our results are expected to provide effective learning methods in various classes through realistic visualization and interaction methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.997-1000
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• 2015

스마트폰 및 태블릿 PC 보급의 빠른 확산으로 인해 e-learning 학습환경도 빠른 속도로 모바일 환경으로 전환되고 있다. 이러한 변화에 맞추어 e-learning 서비스업체들도 모바일 서비스를 앞다투어 제공하고 있으며 앞으로도 그 수는 계속 늘어날 것으로 예측된다. 하지만 아직까지는 PC에서 보던 학습 동영상을 단지 모바일 환경에 보는 수준이며 수강생이 온라인 강의를 시청하다 강사에게 실시간으로 질문을 하고 답변을 받는 등의 의사소통은 어렵다. 이러한 단점을 극복하고 강사와 수강생이 웹 환경에서 원활한 의사소통이 가능하며, 많은 수의 수강생이 동시에 접속할 수 있는 HTML5의 WebSocket과 Canvas를 기반으로 한 실시간 문제풀이 시스템을 제안하고자 한다.

• Journal of Industrial Convergence
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• v.17 no.3
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• pp.39-49
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• 2019

This study, as a study on building platforms for smart campus ecosystem, took an approach that reflected the needs of various stakeholders of smart campus, and focused on functions to help them strengthen their competitiveness and advance into society by focusing on the learning of the most important university student users, college life, and social connection. First, we looked at the theories related to smart campus construction through prior research, and next, through domestic and international environmental analysis and trend analysis, we designed and presented a target model for e-portfolio focusing on core competency development and support system for Industry-Academic Cooperation, and proposed the main point for continuous smart campus development model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.532-534
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• 2022

At a time when the 5G era is advancing beyond commercialization, places that used to handle simple logistics warehouse tasks are transforming into smart logistics warehouses by combining IT convergence technology and platforms. Smart logistics warehouses can accurately predict demand and inventory of products with AI, deep learning, and robot technologies based on 5G, and provide information on warehousing and warehousing status in real time. As the e-commerce market grows, the smart logistics sector is also growing rapidly. This paper implements a smart logistics warehouse system and studies and proposes a method of establishing a fast and accurate logistics system by utilizing 5G-based Logistics Automation Robot.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.