• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.400-402
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• 1998

최근기업의 경쟁력 확보를 위해 기업내의 분산된 이질적인 정보 시스템을 전사적차원에서 통합을 하려는 노력이 활발히 진행되고 있다. 그 중 많은 경우에 통합 문제를 향후의 IT환경을 고려하여 객체지향 개념의 JAVA 및 CORBA를 기반으로 해결하려고 하고 있다. 하지만 실제 기업 환경에서 적용된 경우는 아직 드물다. 본 논문에서는 바이어가 전자회사에 주문한 물품의 생산 현황, 출하까지의 예상시간, 배송일 등을 실시간으로 확인할 수 있는 시스템인 주문 물품 모니터링 시스템(DW- GOMS: DasWoo -Global Ordering status Monitoring System)의 구현에 대해 설명한다. 이 시스템에 대한 사용자의 요구사항 분석은 Use Case를 이용했고 분석 및 설계는 OMT 프로세스를 도입하였고 그 결과는 UML로 표기하는 등 객체지향 개발 방법론을 적용했으며, 설계된 결과를 바탕으로 JAVA 및 CORBA를 기반으로 구현하였다. 구현된 시스템은 CORBA를 적용하여 개발 속도를 현저히 줄일 수 있으며, 시스템 분석/설계에 객체지향 개발 방법론을 적용함으로써 향후 시스템 변경에 유연하게 대응할 수 있게 된다.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.109-121
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• 2020

As a decrease in labor became a serious issue in the manufacturing industry, smart factory technology, which combines IT and the manufacturing business, began to attract attention as a solution. In this study, we have designed and implemented a real-time remote management system for smart factories, which is connected to an IoT sensor and gateway, for plastic manufacturing plants. By implementing the REST API in which an IoT sensor and smart gateway can communicate, the system enabled the data measured from the IoT sensor and equipment status data to the real-time monitoring system through the gateway. Also, a web-based management dashboard enabled remote monitoring and control of the equipment and raw material processing status. A comparative analysis experiment was conducted on the suggested system for the difference in processing speed based on equipment and measurement data number change. The experiment confirmed that saving equipment measurement data using cache mechanisim offered faster processing speed. Through the result our works can provide the basic framework to factory which need implement remote management system.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.2
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• pp.251-261
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• 2004

In this article, an in-depth review is presented to explain the present status of QC and QA for air quality monitoring, especially with respect to a few pollutants of which measurement requires high standard such as VOC and heavy metals. Upon reviewing the reported results of various studies conducted previously, we attempted to suggest a future direction of QC and QA for the acquisition of the high quality data in the air quality monitoring.

• Journal of the Earthquake Engineering Society of Korea
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• v.6 no.2
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• pp.39-50
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• 2002

Several pre-processing techniques for earthquake data from earthquake monitoring institutes in Korea including Korea Electric Power Research Institute are thoroughly reviewed. Among these techniques for removing an instrumental response, removing the non-causal ringing distortion by FIR filter, checking calibration status of seismic stations, and minimizing the window effect are introduced and applied to real data. It is also recommended that analysts evaluate S/N ratio in the frequency domain and consider the possibility of using the saturated earthquake data.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2811-2813
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• 2000

Nowadays PLC is used for controlling various industrial processes. For its better performance operating status of PLC should be continuously checked. However. it is difficult to monitor the status of PLC owing to the spatial limitation. This paper presents an internet based remote monitoring and control system for PLC and a practical implementation for Tiny PLC is executed for verifying its feasibility.

• Smart Structures and Systems
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• v.21 no.2
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• pp.139-149
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• 2018

Understanding the current status of a construction project is necessary to achieve successful on-site management. Real-time information delivery is a major concern for construction industry practitioners in order to expedite decisions and discussions. We propose the use of a first personal view (FPV) system of a quadcopter drone as a tool for monitoring on-site status and communicating between construction participants. The most important function of the drone FPV system is its ability to visually monitor construction site situations in real time. An on-site management system process is developed, verified, and applied to several construction work tasks after determining factors that affect efficient construction management. The proposed system is expected to assist the construction manager in achieving high efficiency.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.57-65
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• 2011

Monitoring auto correlated processes is prevalent in recent manufacturing environments. As a proactive control for manufacturing processes is emphasized especially in the semiconductor industry, it is natural to monitor real-time status of equipment through sensor rather than resultant output status of the processes. Equipment's sensor data show various forms of correlation features. Among them, considerable amount of sensor data, statistically autocorrelated, is well represented by Box-Jenkins autoregressive moving average (ARMA) model. In this paper, we present a design method of statistical process control (SPC) used for monitoring processes represented by the ARMA model. The proposed method shows benefits in the power of detecting process changes, and considers robustness to ARMA modeling errors simultaneously. We prove benefits through Monte carlo simulation-based investigations.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.62-66
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• 2014

This study aims to implement the emergency monitoring robot system which predicts the current state of the patients without visiting the medical institutions by measuring the basic health status of the user's blood pressure, heartbeat, and basic health status of body temperature in the disaster emergency situation based on the Smart Grid. By arranging a large number of sensor(blood pressure, heartbeat, body temperature sensor) and measuring the bio signs, so the attached wireless XBee sensor can be stored in DB of robot, and it aims to draw the current state of the patients by analysis of stored bio data. Among 300 data obtained from the sensor, 1st data to 100th data were used for learning, and from 101st data to 300th data were used for assessment. 12 results were different among the total 300 assessment data, so it shows about 96% accuracy.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.294-303
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• 2021

Various types of sensors are used at industrial sites to measure vibration. With the increase in the diversity of vibration measurement methods, vibration monitoring methods using camera equipment have recently been introduced. However, owing to the physical limitations of the hardware, the measurement resolution is lower than that of conventional sensors, and real-time processing is difficult because of extensive image processing. As a result, most such methods in practice only monitor status trends. To address these disadvantages, a high-resolution vibration measurement method using image analysis of the edge region of the structure has been reported. While this method exhibits higher resolution than the existing vibration measurement technique using a camera, it requires significant amount of computation. In this study, a method is proposed for rapidly processing considerable amount of image data acquired from vision equipment, and measuring the vibration of structures with high resolution. The method is then verified through experiments. It was shown that the proposed method can fast measure vibrations of structures remotely.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.2
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• pp.84-93
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• 2021

Airline schedule is the most basic data for flight operations and has significant importance to an airline's management. It is crucial to know the airline's current schedule status in order to effectively manage the company and to be prepared for abnormal situations. In this study, machine learning techniques were applied to actual schedule data to examine the possibility of whether the airline's fleet state could be artificially learned without prior information. Given that the schedule is in categorical form, One Hot Encoding was applied and t-SNE was used to reduce the dimension of the data and visualize them to gain insights into the airline's overall fleet status. Interesting results were discovered from the experiments where the initial findings are expected to contribute to the fields of airline schedule health monitoring, anomaly detection, and disruption management.