• Journal of Korea Society of Industrial Information Systems
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• v.6 no.2
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• pp.54-62
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• 2001

In this paper, we proposed multimedia based ExamBank system in web environments. In the proposed system the database was designed based on the object-relation model and the application program was implemented with Java such that independent execution would be possible to guarantee no fault for multi-client in Web environments. We defined the Exam entities as objects, and implemented those inter-relationships as user definition and type. In addition, by mapping the schema object of DBMS and JAVA class, it becomes to possible transferring the object systematically between DHMS and JAVA application server.

• 한국연소학회:학술대회논문집
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• 2013.06a
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• pp.87-90
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• 2013

In pulverized coal fired boilers, slagging and fouling may cause significant effect on the operational life of boiler. As increasing a consumption of low rank coal, slagging and fouling are main issues in pulverized coal combustion. This study predicts ash deposition propensity in a 0.7 MW pilot-scale furnace. Slagging model is employed as a User-Defined Function (UDF) of FLUENT and validated against measurement and prediction. The results show good agreement compared with experiment. There is need to development of a pulverized coal combustion and slagging analysis at low coal.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.73-79
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• 2003

In managing organizational tacit knowledge, recent researches have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise. In this paper, we propose an intelligent expert search framework to provide search capabilities for experts in similar or related fields according to the user's information needs. In enabling intelligent expert searches, Fuzzy Abstraction Hierarchy (FAH) framework has been adopted, through which finding experts with similar or related expertise is possible according to the subject field hierarchy defined in the system. To improve FAH, a text categorization approach called Vector Space Model is utilized. To test applicability and practicality of the proposed framework, the prototype system, "Knowledge Portal for Researchers in Science and Technology" sponsored by the Ministry of Science and Technology (MOST) of Korea, was developed.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.69.5-69
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• 2001

Computer simulations are widely used to analyze passenger safety in traffic accidents. ATB(articulated total body) is a computer simulation model developed to predict gross human body response to such dynamic environments as vehicle crashes and pilot ejections. ATB, whose code is open, has high flexibility and application capability that users can easily insert defined modules and functions. ATB is, however, inconvenient as it was coded in FORTRAN and it needs a formatted input file. Moreover, it takes much time to make input files and to modify coding errors. This study aims to increase user friendliness by adding a preprocessor program, WINATB(WiNdow ATB), to the conventional ATB. WINATB programmed in Visual C++ and OpenGL uses ATB IV as a dynamic solver ...

• Proceedings of the KIEE Conference
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• summer
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• pp.276-279
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• 2004

The purpose of this paper is to describe and demonstrate how a utility-connectable inverter for photovoltaic or fuel-cell applications can be well modeled using PSCAD/EMTDC. In this paper, a single-phase IGBT inverter using SPWM is modeled. Simple voltage magnitude and phase controls are implemented using PSCAD's Pl controller, PLL, and a 'user defined' component called Modulo (found in their extensive collection of example circuits). The circuit model also takes advantage of PSCAD's interpolated firing pulse option, which provides improved simulation results by preventing errors from being introduced when switches fire between time simulation steps. Additionally, SCAD's Online Frequency Scanner for FFT is utilized for a demonstration of PSCAD's frequency-domain analysis capabilities.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.6
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• pp.19-25
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• 2019

We have developed a tool that uses finite element analysis (FEA) to rapidly evaluate a shaft-bearing system of machine tools. We extracted commercial data on suitable clamping units and defined the inner profile of the shaft to avoid needing direct user input to define the profile. We use a splitting algorithm to convert the shaft into beam elements with two diameters and length. To validate the tool, we used it to design and evaluate a shaft-bearing system and found that our tool automated the construction of an FE system model in a commercial FEA package as well as the static stiffness evaluation; both tasks were completed in seconds, demonstrating a significant reduction from the minutes normally required to complete these tasks manually.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.25-32
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• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1153-1160
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• 2021

This paper proposes a routing algorithm that determines the optimal path using deep reinforcement learning in software-defined networks. The deep reinforcement learning model for learning is based on DQN, the inputs are the current network state, source, and destination nodes, and the output returns a list of routes from source to destination. The routing task is defined as a discrete control problem, and the quality of service parameters for routing consider delay, bandwidth, and loss rate. The routing agent classifies the appropriate service class according to the user's quality of service profile, and converts the service class that can be provided for each link from the current network state collected from the SDN. Based on this converted information, it learns to select a route that satisfies the required service level from the source to the destination. The simulation results indicated that if the proposed algorithm proceeds with a certain episode, the correct path is selected and the learning is successfully performed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1181-1191
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• 2021

The pandemic has rapidly developed a non-face-to-face environment. However, the sudden transition to a non-face-to-face environment has led to new security issues in various areas. One of the new security issues is the security threat of insiders, and the zero trust security model is drawing attention again as a technology to defend against it.. Software Defined Perimeter (SDP) technology consists of various security factors, of which device validation is a technology that can realize zerotrust by monitoring insider usage behavior. But the current SDP specification does not provide a technology that can perform device validation.. Therefore, this paper proposes a device validation technology using SVDD-based abnormal behavior detection technology through user behavior monitoring in an SDP environment and presents a way to perform the device validation technology in the SDP environment by conducting performance evaluation.