• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.1-5
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• 2007

In this paper, we propose an unmanned patient monitoring system design and performance of a motion capture and sound detection. Unmanned patient monitoring system can be used in the greek koma and meaning deep sleep patient to need 24 hour surveillance. To monitoring, we used laptop, CCTV camera (or PC camera), A/D converter, microphone and detection program. The detection program based on the frame difference method and sound level meter. It had several functions such as data collecting and storing. All of this system was tested in several the simulations of emergency situations. It can be expected that an unmanned patient monitoring system can be used in emergency situation and patient care.

• ETRI Journal
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• v.40 no.5
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• pp.664-676
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• 2018

Given the rapidly increasing market penetration of photovoltaic (PV) systems in many fields, including construction and housing, the effective maintenance of PV systems through remote monitoring at the panel level has attracted attention to quickly detect faults that cause reductions in yearly PV energy production, and which can reduce the whole-life cost. A key point of PV monitoring at the panel level is cost-effectiveness, as the installation of the massive PV panels that comprise PV systems is showing rapid growth in the market. This paper proposes an implementation method that involves the use of a panel-level wireless PV monitoring module (WPMM), and which assesses the cost-effectiveness of this approach. To maximize the cost-effectiveness, the designed WPMM uses a voltage-divider scheme for voltage metering and a shunt-resistor scheme for current metering. In addition, the proposed method offsets the effect of element errors by extracting calibration parameters. Furthermore, a design method is presented for portable and user-friendly PV monitoring, and demonstration results using a commercial 30-kW PV system are described.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.84-88
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results, it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Transactions on Electrical and Electronic Materials
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• v.13 no.2
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• pp.78-84
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• 2012

An in-situ optical monitoring method for real-time process monitoring of electrochemical copper deposition (CED) is presented. Process variables to be controlled in achieving desired process results are numerous in the CED process, and the importance of the chemical bath conditions cannot be overemphasized for a successful process. Conventional monitoring of the chemical solution for CED relies on the pH value of the solution, electrical voltage level for the reduction of metal cations, and gravity measurement by immersing sensors into a plating bath. We propose a nonintrusive optical monitoring technique using three types of optical sensors such as chromatic sensors and UV/VIS spectroscopy sensors as potential candidates as a feasible optical monitoring method. By monitoring the color of the plating solution in the bath, we revealed that optically acquired information is strongly related to the thickness of the deposited copper on the wafers, and that the chromatic information is inversely proportional to the ratio of $Cu$ (111) and {$Cu$ (111)+$Cu$ (200)}, which can used to measure the quality of the chemical solution for electrochemical copper deposition in advanced interconnection technology.

• The Research Journal of the Costume Culture
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• v.19 no.4
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• pp.766-779
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• 2011

The purpose of this study is to find the levels of self-monitoring, self-consciousness and cosmetic attitude of female university students and to analyze the relationship between the levels of self-monitoring, self-consciousness and cosmetic attitude in an effort to strengthen external human beauty. The respondents were 264 university women attending a university Chung-Cheong Province in Korea. In this study, a questionnaire method was used. The questionnaire consisted of SMS(self-monitoring scale), the(self-consciousness scale), and a measurement of the cosmetic attitude. Earlier studies were used to create the measuring instruments with some adjustments for the purpose of this research. Factorial analysis, correlation analysis, multiple regression analysis were carried out with SPSS 18.0. The cosmetic attitude consisted of four factors, and those factors were related to self-monitoring and self-consciousness. The Four factors were happiness with change, manners to others, the instrument of change, and conformity. It was found that the cosmetic attitude reflected the personal internal mental states; hence, the cosmetic attitude was used as method to express the internal mind. Determining the correlations between self-monitoring, self-consciousness and the cosmetic attitude was useful in understanding the personal peculiarity of the cosmetic attitude. Moreover, in the cosmetic industry, it is likely meaningful to investigate the capability whether the variables of self-monitoring and self-consciousness can be applied in an effort to understand consumers' internal character.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.81-86
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• 2019

According to the needs of many users, motor pumps have been steadily developed and widely used in many industries where fluids are used. Motor pumps are used in a variety of environments, degrading quality and performance. It is becoming important to monitor the condition of motor pumps in order to maintain system performance and increase efficiency. This paper presents the method for effectively monitoring the condition of motor pumps. The designed MMI controller receives the collected data through data logging and output and controls the motor pump with touch screen. Implementing a Wi-Fi monitoring system and SCADA monitoring system based on Ethernet and RS485, user can monitor even if the user is far from the MMI controller without time and space limitation. In addition, by implementing a voice output system, the user can immediately recognize the situation through the sound. Therefore, through the proposed method, multi-monitoring system solves the cause of breakdowns and degradation of motor-pump and gives the possibility of commercialization by providing the convenience of maintenance to users.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.5-9
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• 2019

Optical emission spectroscopy is used to identify chemical species and monitor the changes of process results during the plasma process. However, plasma process monitoring or fault detection by using emission signal variation monitoring is vulnerable to background signal fluctuations. IR heaters are used in semiconductor manufacturing chambers where high temperature uniformity and fast response are required. During the process, the IR lamp output fluctuates to maintain a stable process temperature. This IR signal fluctuation reacts as a background signal fluctuation to the spectrometer. In this research, we evaluate the effect of infrared background signal fluctuation on plasma process monitoring and improve the plasma process monitoring accuracy by using simple infrared background signal subtraction method. The effect of infrared background signal fluctuation on plasma process monitoring was evaluated on $SiO_2$ PECVD process. Comparing the $SiO_2$ film thickness and the measured emission line intensity from the by-product molecules, the effect of infrared background signal on plasma process monitoring and the necessity of background signal subtraction method were confirmed.

• Smart Structures and Systems
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• v.20 no.2
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• pp.219-229
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• 2017

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

• YAKHAK HOEJI
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• v.60 no.2
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• pp.85-91
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• 2016

Drying of granules for tablet formulation is one of the important unit operations. The loss on drying method is traditionally used for this purpose. However, it is a time-consuming method, requiring at least 1 h. Moreover, it is ineffective in monitoring the moisture content of granules during the drying process. In this study, online real-time monitoring of moisture content during the drying process was successfully performed using near-infrared (NIR) spectroscopy. NIR spectra were collected during 15 different drying batches for developing a reliable NIR spectroscopic method. Such a large number of batches were used to develop a more robust partial least squares (PLS) model. NIR spectra collected from 12 batches were used for developing the model that was validated by predicting the moisture content of the samples in the remaining 3 batches. The standard errors of predictions (SEPs) in the measurement of batch 1, batch 2, and batch 3 were 0.52%, 0.57%, and 0.56%, respectively. The online NIR spectroscopic method developed in this study was reliable and accurate in monitoring the moisture content during the drying process.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.