• Korean Management Science Review
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• v.10 no.1
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• pp.59-80
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• 1993

Electronic data interchange (EDI) is a new technology of information communication which can make offices paperless. This study develop an EDI system to communicate informations among companies using computer systems. The system consists of five modules-document I/O, translation, system management, communication, and additional modules. The database of EDI documentation standards has been constructed by the table-driven method. The communication module provides three communication modes-RS232C, MODEM, and message handling system (MHS). The system performance has been improved by the method of data compression and data encryption which prevent the communication delay and illegal users. This EDI system can be used as a front-end, back-end, or stand-alone mode.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1353-1361
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• 2018

With the availability of real-time educational data collection and analysis techniques, the education paradigm is shifting from educator-centric to data-driven lectures. However, most offline and online education frameworks collect students' feedback from question-answering data that can summarize their understanding but requires instructor's attention when students need additional help during lectures. This paper proposes a content restructure recommendation framework based on collected student feedback. We list the types of student feedback and implement a web-based framework that collects both implicit and explicit feedback for content restructuring. With a case study of four-week lectures with 50 students, we analyze the pattern of student feedback and quantitatively validate the effect of the proposed content restructuring measured by the level of student engagement.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.1-14
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• 2023

This paper aims to share the course, performance and implications of Project-Based Learning (PBL) education in healthcare data science (HDS). The HDS team of the business group of Soonchunhyang University, which was selected for the health care field of 'University Innovation Project', considered that the health care IT-based education of the current university differs greatly from the rapidly changing health care 3.0 environment of the fourth industry, and emphasized the PBL practice-oriented specialization program as a learning model. The PBL focused on self-directed learning experiences, real analysis problems, and team-oriented classes. In other words, it was implemented with three specialized strategies: 'Field Inside Education', 'Fusion-type Track Education', and 'Training to strengthen resilience and change response'. This collaborative, practical learning experience, etc. resulted in significant results. The results were recognized as being rated A by the Korea Research Foundation and the comprehensive evaluation, and the results were significantly elevated through the analysis of the student survey and the results index.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8128-8139
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• 2015

Recently, the necessity of developing the load and resistance factor design(LRFD) for soft ground improvement method has been raised, since the limit state design is requested as international technical standard for the foundation of structures. In this study, to develop LRFD codes for foundation structures in Korea, target reliability index and resistance factor for static bearing capacity of driven steel pipe piles were calibrated in the framework of reliability theory. The 16 data(in Gwangyang) and the 57 data(Korea Institute of Construction Technology, 2008) sets of static load test and soil property tests conducted in the whole domestic area were collected along with available subsurface investigation results. The resistance bias factors were evaluated for the tow static design methods by comparing the representative measured bearing capacities with the expected design values. Reliability analysis was performed by two types of advanced methods : the First Order Reliability Method (FORM), and the Monte Carlo Simulation (MCS) method using resistance bias factor statistics. As a result, when target reliability indices of the driven pipe pile were selected as 2.0, 2.33, 2.5, resistance factor of two design methods for SPT N at pile tip less than 50 were evaluated as 0.611~0.684, 0.537~0.821 respectively, and STP N at pile tip more than 50 were evaluated as 0.545~0.608, 0.643~0.749 respectively. The result from this research will be useful for developing various foundations and soil structures under LRFD.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.109-119
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• 2015

The interaction between wireless sensors such as Internet of Things (IoT) and Cloud is a new paradigm of communication virtualization to overcome resource and efficiency restriction. Cloud computing provides unlimited platform, resources, services and also covers almost every area of computing. On the other hand, Wireless Sensor Networks (WSN) has gained attention for their potential supports and attractive solutions such as IoT, environment monitoring, healthcare, military, critical infrastructure monitoring, home and industrial automation, transportation, business, etc. Besides, our virtual groups and social networks are in main role of information sharing. However, this sensor network lacks resource, storage capacity and computational power along with extensibility, fault-tolerance, reliability and openness. These data are not available to community groups or cloud environment for general purpose research or utilization yet. If we reduce the gap between real and virtual world by adding this WSN driven data to cloud environment and virtual communities, then it can gain a remarkable attention from all over, along with giving us the benefit in various sectors. We have proposed a Pub/Sub-based sensor virtualization framework Cloud environment. This integration provides resource, service, and storage with sensor driven data to the community. We have virtualized physical sensors as virtual sensors on cloud computing, while this middleware and virtual sensors are provisioned automatically to end users whenever they required. Our architecture provides service to end users without being concerned about its implementation details. Furthermore, we have proposed an efficient content-based event matching algorithm to analyze subscriptions and to publish proper contents in a cost-effective manner. We have evaluated our algorithm which shows better performance while comparing to that of previously proposed algorithms.

• Safety and Health at Work
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• v.12 no.4
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• pp.462-470
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• 2021

Background: Fatigue is pervasive, under-reported, and potentially deadly where flight operations are concerned. The aviation industry appears to lack a standardized, practical, and easily replicable protocol for fatigue risk assessment which can be consistently applied across operators. Aim: Our paper sought to present a framework, supported by real-world data with subjective and objective parameters, to monitor aircrew fatigue and performance, and to determine the safe crew configuration for commercial airline operations. Methods: Our protocol identified risk factors for fatigue-induced performance degradation as triggers for fatigue risk and performance assessment. Using both subjective and objective measurements of sleep, fatigue, and performance in the form of instruments such as the Karolinska Sleepiness Scale, Samn-Perelli Crew Status Check, Psychomotor Vigilance Task, sleep logs, and a wearable actigraph for sleep log correlation and sleep duration and quality charting, a workflow flagging fatigue-prone flight operations for risk mitigation was developed and trialed. Results: In an operational study aimed at occupational assessment of fatigue and performance in airline pilots on a three-men crew versus a four-men crew for a long-haul flight, we affirmed the technical feasibility of our proposed framework and approach, the validity of the battery of assessment instruments, and the meaningful interpretation of fatigue and work performance indicators to enable the formulation of safe work recommendations. Conclusion: A standardized occupational assessment protocol like ours is useful to achieve consistency and objectivity in the occupational assessment of fatigue and work performance.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.2
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• pp.65-80
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• 2019

With the advent of the ICT-based 4th industrial revolution, the convergence of the manufacturing industry and ICT seems to be the new breakthrough for achieving the company's competitiveness and play a role on the key element for accelerating the revival of the manufacturing industry. When the smart factory is implemented, each plant can analyze the quantity of data collected, build the data-driven operation systems which can make decisions, and ultimately discover the correlation among many events in the manufacturing sites. As the customers' needs become diversified more and more, it is required for the company to change its operating method from large quantity batch production systems to customizable and flexible manufacturing systems. For performing this requirements, it is essential for the company to adopt the smart factory. Based on technology acceptance model (TAM), this study investigates the factors influencing continuous use intention and switching intention of the smart factory. To do so, a questionnaire survey is conducted both online and offline. 122 samples are used for the study analysis. The results of this study will provide many implications with many researchers and practitioners relevant smart factories.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.23-32
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• 2023

Interest in Serious Games for Healthcare (SGHs) that can improve health through games is increasing. Digital Therapeutics (DTx) is a treatment that must be approved for effectiveness and safety, so it should follow the traditional drug distribution method, but SGHs are wellness products that are more flexible in terms of adoption and diffusion than DTx. SGHs are effective because it can provide customized services through continuous monitoring and feedback. When SGHs are applied to cognitive impairment treatment or behavioral correction, malfunctions and side effects are minor. This study developed research model based on the Valence Framework, gathered data from 142 undergraduates, and demonstrated that only the perceived benefits have a statistically significant positive (+) effect on SGHs acceptance intentions. Based on these results, this study suggests that SGHs companies should promote benefits in accepting SGHs for general users and they need for a distribution and analytics platform strategy based on a data-driven approach.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.