• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.464-464
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• 2010

A few years ago, for maintaining high stability and production yield of production equipment in a semiconductor fab, on-line monitoring of wafers is required, so that semiconductor manufacturers are investigating a software based process controlling scheme known as virtual metrology (VM). As semiconductor technology develops, the cost of fabrication tool/facility has reached its budget limit, and reducing metrology cost can obviously help to keep semiconductor manufacturing cost. By virtue of prediction, VM enables wafer-level control (or even down to site level), reduces within-lot variability, and increases process capability, $C_{pk}$. In this research, we have practiced VM on $SiO_2$ etch rate with optical emission spectroscopy(OES) data acquired in-situ while the process parameters are simultaneously correlated. To build process model of $SiO_2$ via, we first performed a series of etch runs according to the statistically designed experiment, called design of experiments (DOE). OES data are automatically logged with etch rate, and some OES spectra that correlated with $SiO_2$ etch rate is selected. Once the feature of OES data is selected, the preprocessed OES spectra is then used for in-situ sensor based VM modeling. ICP-RIE using 葰.56MHz, manufactured by Plasmart, Ltd. is employed in this experiment, and single fiber-optic attached for in-situ OES data acquisition. Before applying statistical feature selection, empirical feature selection of OES data is initially performed in order not to fall in a statistical misleading, which causes from random noise or large variation of insignificantly correlated responses with process itself. The accuracy of the proposed VM is still need to be developed in order to successfully replace the existing metrology, but it is no doubt that VM can support engineering decision of "go or not go" in the consecutive processing step.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.579-593
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• 2010

Low-power radio frequency (RF) chip transceiver technology and the associated structural health monitoring platforms have matured recently to enable high-rate, lossless transmission of measurement data across large-scale sensor networks. The intrinsic value of these advanced capabilities is the allowance for high-quality, rapid operational modal analysis of in-service structures using distributed accelerometers to experimentally characterize the dynamic response. From the analysis afforded through these dynamic data sets, structural identification techniques can then be utilized to develop a well calibrated finite element (FE) model of the structure for baseline development, extended analytical structural evaluation, and load response assessment. This paper presents a case study in which operational modal analysis is performed on a three-span prestressed reinforced concrete bridge using a wireless sensor network. The low-power wireless platform deployed supported a high-rate, lossless transmission protocol enabling real-time remote acquisition of the vibration response as recorded by twenty-nine accelerometers at a 256 Sps sampling rate. Several instrumentation layouts were utilized to assess the global multi-span response using a stationary sensor array as well as the spatially refined response of a single span using roving sensors and reference-based techniques. Subsequent structural identification using FE modeling and iterative updating through comparison with the experimental analysis is then documented to demonstrate the inherent value in dynamic response measurement across structural systems using high-rate wireless sensor networks.

• STI Policy Review
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• v.8 no.2
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• pp.75-93
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• 2017

Recently, global competition in the manufacturing sector is driving firms in the manufacturing sector to conduct product innovation projects to maintain their competitive edge. The key points of product innovation projects are 1) what the purpose of the project is and 2) what expected results in the target market can be achieved by implementing the innovation. Therefore, this study focuses on the performance of innovation projects with a business viewpoint. In this respect, this study proposes the "achievement rate" of product innovation projects as a measurement of project performance. Then, this study finds the best strategies from various innovation activities to optimize the achievement rate of product innovation projects. There are three major innovation activities for the projects, including three types of R&D activities: Internal, joint and external R&D, and five types of non-R&D activities - acquisition of machines, equipment and software, purchasing external knowledge, job education and training, market research and design. This study applies decision tree modeling, a kind of data-mining methodology, to explore effective innovation activities. This study employs the data from the 'Korean Innovation Survey (KIS) 2014: Manufacturing Sector.' The KIS 2014 gathered information about innovation activities in the manufacturing sector over three years (2011-2013). This study gives some practical implication for managing the activities. First, innovation activities that increased the achievement rate of product diversification projects included a combination of market research, new product design, and job training. Second, our results show that a combination of internal R&D, job training and training, and market research increases the project achievement most for the replacement of outdated products. Third, new market creation or extension of market share indicates that launching replacement products and continuously upgrading products are most important.

• Journal of Power Electronics
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• v.6 no.4
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• pp.307-314
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• 2006

This paper presents a wavelet and neural network based technology for the monitoring and classification of various types of power quality (PQ) disturbances. Simultaneous and automatic detection and classification of PQ transients, is recommended, however these processes have not been thoroughly investigated so far. In this paper, the hardware and software of a power quality data acquisition system (PQDAS) is described. In this system, an auto-classifying system combines the properties of the wavelet transform with the advantages of a neural network. Additionally, to improve recognition rate, extraction technology is considered.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1982-1984
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• 2004

Stator coil of rotating machinery has shown different characteristics according to impregnated with coil or not. And this is major determinant of equipment's life. In this paper, PD characteristics is studied as a classification scheme between two specimens. Processing of the coil impregnation is very important thing because that influences on thermal and electrical characteristics of the coil. And then PD is occurring at the coil and causing insulation degradation. For processing statistical processing, PD data acquired from PD detector using PDASDA(partial discharge acquisition, storage and display system). And also these statistical distribution and parameter are applied to classify PD sources by neural networks. As a result of, Neural Networks have a good discrimination rate for classification PD sources.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.171-177
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• 2004

Many instrumentations have been used to acquire the performance data of military systems fer many years. But they could not satisfy environmental specifications(vibration, shock, temperature) and processing speed to apply for the performance test of military systems because of having developed as common vehicles/fixed installation equipments. Thus a new rugged embedded measurement system is required to process large data in high processing speed(Maximum sample rate:1.25Mhz/ch) with rugged environmental specifications. We have developed embedded measurement systems by using PXI(PCI extension for Instrumentation)bus interface composed of a stand alone controller and versatile data acquisition boards(analog, digital, vision, temperature and small signal conditioner) on PC-based environment to solve these problems. Operation programs have been developed using Lab_View and the performances have been validated experimentally.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1241-1248
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• 2004

A controller of wheeled mobile robot(WMR) based on Lyapunov theory is designed and a Fuzzy-Neural Network algorithm is applied to this system to adjust controller gain. In conventional controller of WMR that adopts fixed controller gain, controller can not pursuit trajectory perfectly when initial condition of system is changed. Moreover, acquisition of optimal value of controller gain due to variation of initial condition is not easy because it can be get through lots of try and error process. To solve such problem, a Fuzzy-Neural Network algorithm is proposed. The Fuzzy logic adjusts gains to act up to position error and position error rate. And, the Neural Network algorithm optimizes gains according to initial position and initial direction. Computer simulation shows that the proposed Fuzzy-Neural Network controller is effective.

• Journal of Korean Society for Geospatial Information Science
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• v.9 no.1 s.17
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• pp.59-66
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• 2001

Satellite visibility, Accuracy, and Availability were increased by the combined GPS/GLONASS. But, there are some problems such as differences in the time frame, differences in the coordinate datum, and the problem of solving carrier phase ambiguities in the combined carrier frequency solutions due to different GLONASS frequency. Therefore, the accuracy of single point positioning using the combined GPS/GLONASS will be assessed, and intend to study the characteristics of the combined GPS/GLONASS with considered the rate of data acquisition according to the visibility of satellite and elevation cutoff at the combined GPS/GLONASS.

• Journal of the Korean Society of Industry Convergence
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• v.9 no.4
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• pp.263-268
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• 2006

This paper presents a real-time verification system by extracting a features of multimodal biometrics using hybrid method, which is combined the moment balance and the independent component analysis(ICA). The moment balance is applied to reduce the computation loads by extracting the validity signal due to exclude the needless backgrounds of multimodal biometrics. ICA is also applied to increase the verification performance by removing the overlapping signals due to extract the statistically independent basis of signals. Multimodal biometrics are used both the faces and the fingerprints which are acquired by Web camera and acquisition device, respectively. The proposed system has been applied to the fusion problems of 48 faces and 48 fingerprints(24 persons * 2 scenes) of 320*240 pixels, respectively. The experimental results show that the proposed system has a superior verification performances(speed, rate).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.5
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• pp.523-529
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• 2009

Smart sensor is known as intelligent sensor, it is different with other conventional sensors in the case of intelligent system embedded on it. Smart sensor has many benefits e.g. low-cost in usage, self-decision and self-diagnosis abilities. This sensor consists of perception element(sensing element), signal processing and technology of communication. In this work, a bridge and structure of smart sensor has been investigated to be capable to condition monitoring routine. This investigation involves low power consumption, software programming, fast data acquisition ability, and authoritativeness warranty. Moreover, this work also develops smart sensor to be capable to perform high sampling rate, high resolution of ADC, high memory capacity, and good communication for data transfer. The result shows that the developed smart sensor is promising to be applied to various industrial fields.