• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.16-21
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• 2020

How accurately reproducible energy is delivered to the wafer in the process of making thin films using PE-CVD (Plasma enhanced chemical vapor deposition) during the semiconductor process. This is the most important technique, and most of the reaction on the wafer surface is made by thermal energy. In this study, we studied the method of monitoring the change of thermal energy transferred to the wafer surface by monitoring the temperature change according to the change of the thin film formed on the showerhead facing the wafer. Through this research, we could confirm the monitoring of wafer thin-film which is changed due to abnormal operation and accumulation of equipment, and we can expect improvement of semiconductor quality and yield through process reproducibility and equipment status by real-time monitoring of problem of deposition process equipment performance.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.69-80
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• 2014

This paper considers the problem of monitoring the mean of a normally distributed process variable when the objective is to effectively detect both a sustained shift and a linear drift. The design and application of a generalized likelihood ratio (GLR) chart for simultaneously monitoring a sustained shift and a linear drift are evaluated. The GLR chart has the advantage that when we design this chart, we do not need to specify the size of the parameter change. The performance of the GLR chart is compared with that of other control charts, such as the standard cumulative sum (CUSUM) charts and the cumulative score (CUSCORE) charts. And we compare the proposed GLR chart with the GLR charts designed for monitoring only a sustained shift and for monitoring only a linear drift. Finally, we also compare the proposed GLR chart with the chart combinations. We show that the proposed GLR chart has better overall performance for a wide range of shift sizes and drift rates relative to other control charts, when a special cause produces a sustained shift and/or a linear drift in the process mean.

• The KIPS Transactions:PartA
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• v.11A no.3
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• pp.181-188
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• 2004

PDF(Parallel/Distributed Processing) is an internet-based parallel/distributed processing system that utilizes resources from hosts on the internet in idle state to perform large scale application through parallel processing, thus decreasing the total execution time. In this paper. do propose an adaptive method to be changed network environment at any time using realtime monitoring of host. It is found from experiments that parallel/distributed processing has better performance than its without monitoring as an adaptive strategy, which copy with task delay factor by overload and fault of network, be applicable to the cockpits of task allocation algorithm in PDP.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.460-467
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• 2012

Recently, concern over climate change and global politics associated with traditional fossil fuel energy sources has driven significant increase in wind energy utilization over the past decade. Especially, small wind turbine systems below 10kW are actively developed by several companies from all over the world. Generally, for the small wind turbine systems to be installed for commercial purposes, the performance of the system should be certified. For this, wireless remote monitoring system should be developed for efficient monitoring. In this work, a new type of wireless remote monitoring system based on Zigbee communication is proposed. Furthermore, practical experimentations are carried out to verify the feasibility of the proposed scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2185-2191
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• 2012

The monitoring of the existing inverter is being implemented using a local computer of web monitoring, but in this paper, the remote monitoring system of the power condition system was implemented using a Bluetooth communication at a convenient position for the user that can be monitored without the computer. The proposed system was designed to be able to monitor the wanted information by using the protocol of inverter. Also when the power condition system has failed, the fault history and the generated time of inverter were stored in the Bluetooth device. Finally the performance of the proposed system was evaluated through experiments, it showed the good performance and the possibility of commercialization.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.6
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• pp.1-6
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• 2008

This work proposes a fuzzy trend monitoring method for the fault detection of a gas turbine engine through analyzing measured performance data trend. The proposed trend monitoring technique can diagnose the engine status by monitoring major engine measured parameters such as fuel flow rate, exhaust gas temperature, rotor rotational speed and vibration, and then analyzing their time deppendent changes. In order to perform this, firstly the measured engine performance data variation is formulated using Linear Regression, and then faults are isolated and identified using fuzzy logic.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.19-23
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• 2013

This research is about the Implementation of Remote Machine Health Monitoring System using Internet. This research will help users in the un-installed office to save a lot of cost and time from checking and managing machines' condition installed in the factory. We have made an experiment and developed the application program and the monitoring terminal which can monitor the machine's condition. This research will contribute to the development of internet and remote instrumentation engineering in the future. Finally the performance of the proposed system was evaluated through experiments, it showed the good performance and the possibility of commercialization.

• Journal of Sensor Science and Technology
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• v.26 no.6
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• pp.427-431
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• 2017

In this study, a foods radioactivity monitoring sensor was developed as a part of basic research for household radioactivity monitoring, and its performance was evaluated using a calibration source. The prototype of the sensor was based on a CsI:Tl scintillator using a crystal light guide and Si photomultiplier. The light guide was introduced to improve gamma-ray detection efficiency. For quantitative evaluation, tests were conducted using $^{134}Cs$ liquid source. In the performance evaluation, It was confirmed that analysis of $^{134}Cs$: 100 Bq/L(kg) was possible. Thus, result of this study is expected to contribute to research in the development of the household foods radioactivity monitoring system.

• Smart Structures and Systems
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• v.2 no.3
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• pp.253-274
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• 2006

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.