• Analytical Science and Technology
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• v.16 no.3
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• pp.226-231
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• 2003

We analyzed the concentrations of heavy metals associated airbone particles by using AAS made by both a domestic manufacturer and one of the foreign manufacturer. One model developed by a domestic manufacture showed excellent results in the selected wavelength with an excellent performance of a monochromator. It abandons a big drop except a fine drop to improve reproduction in atomizer, so that no remains should leave. Using the low pass filter we were able to reduce a noise of detection signal. The performance of our equipment was found to be highly compatible with that of a foreign company as we achieved the detection limit of about $0.015{\mu}g/L$ using a standard solution of Au. The PM samples had been collected from by main observation points in 7 areas of Seoul city from 2001 to the spring in 2002. with these PM sample we analyzed the concentrations of Pb, Cu, Mn, Cd, Ni, Fe, Cr, Co, Mg and Al.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1216-1223
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• 2018

As field of application of AMI, AMR uses the power line as the primary means of communication. PLC has a big merit without installation of the new network for communication in a field using the power line which is the existing equipment. However, there is a serious obstacle in commercialization for the instability by noise and communication environment. Therefore, the technical method for maintaining the communication state which overcome such demerit and was stabilized is required essentially. PLC routing technology is applied with the alternative plan now. The routing technology currently managed by field includes many problems by applying the algorithm of an elementary level. PLC routing path search problem can be modeled with the problem of searching for optimal solution as similar to such as optimal routing problem and TSP(Travelling salesman problem). In this paper, in order to search for a PLC routing path efficiently and to choose the optimal path, GA(Genetic Algorithm) was applied. Although PLC was similar in optimal solution search as compared with typical GA, it also has a difference point by the characteristic of communication, and presented the new methodology over this. Moreover, the validity of application technology was verified by showing the experimental result to which GA is applied and analyzing as compared with the existing algorithm.

• 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.511-520
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• 2000

Ultrasonic pulse echo method comes to be difficult to apply to the multi-layered structure with very thin layer, because the echoes from the top and the bottom of the layer are superimposed. We can easily meet this problem when the silicon chip layer in the semiconductor is inspected by a SAM equipment using fairly low frequency lower than 20MHz by which severe attenuation in the epoxy mold compound of packaging material can be overcome. Conventionally, deconvolution technique has been used for the decomposition of superimposed UT signals, however it has disabilities when the waveform of the transmitted signal is distorted according to the propagation. In this paper, the wavelet transform based deconvolution(WTBD) technique is proposed as a new signal processing method that can decompose the superimposed echo signals with superior performances compared to the conventional deconvolution technique. WTBD method uses the wavelet transform in the pre-stage of deconvolution to extract out the common waveform from the transmitted and received signal with distortion. Performances of the proposed method we shown by through computer simulations using model signal with noise and we demonstrated by through experiments for the fabricated semiconductor sample with partial delamination at the top of silicon chip layer.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.72-80
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• 2019

In this task, the tool dynamometer design and manufacture, and the Ansys S/W structural analysis program for tool attachment that satisfies the cutting force measurement requirements of the tool dynamometer system are used to determine the cutting force generated by metal cutting using 3-axis static structural analysis and the LabVIEW system. The cutting power in a cutting process using a milling tool for processing metals provides useful information for understanding the processing, optimization, tool status monitoring, and tool design. Thus, various methods of measuring cutting power have been proposed. The device consists of a strain-gauge-based sensor fitted to a new design force sensing element, which is then placed in a force reduction. The force-sensing element is designed as a symmetrical cross beam with four arms of a rectangular parallel line. Furthermore, data duplication is eliminated by the appropriate setting the strain gauge attachment position and the construction of a suitable Wheatstone full-bridge circuit. This device is intended for use with rotating spindles such as milling tools. Verification and machining tests were performed to determine the static and dynamic characteristics of the tool dynamometer. The verification tests were performed by analyzing the difference between strain data measured by weight and that derived by theoretical calculations. Processing test was performed by attaching a tool dynamometer to the MCT to analyze data generated by the measuring equipment during machining. To maintain high productivity and precision, the system monitors and suppresses process disturbances such as chatter vibration, imbalances, overload, collision, forced vibration due to tool failure, and excessive tool wear; additionally, a tool dynamometer with a high signal-to-noise ratio is provided.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.5
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• pp.599-606
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• 2019

The Arena Fragmentation Test(AFT) is one of the important tasks for designing a high-explosive warhead. In order to measure the statistics of a warhead in the test, fragments of a warhead that penetrate steel plates are detected by using complex and expensive measuring equipment. In this paper, instead of using specific hardware to measure the statistics of a warhead, we propose to use an image processing based object detection algorithm to detect fragments in AFT. To this end, we use a hard-thresholding method with a brightness feature and apply a morphology filter to remove noise components. We also propose a simple yet effective temporal filtering method to detect only the first penetrating fragments. We show that the performance of the proposed method is comparable to that of a hardware system under the same experimental conditions. Furthermore, the proposed method can produce better results in terms of finding exact positions of fragments.

• Journal of the Korean Magnetics Society
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• v.21 no.5
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• pp.167-173
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• 2011

Underwater static magnetic field signature for the naval ship has been widely used as the detonating source of the influence mine system because it is possible to make an accurate target detection in the near field although the magnetic field falls off relatively fast with distance in comparison with the underwater radiated noise signal. In this paper, we describe the prediction results about the underwater static magnetic field by the ferromagnetic hull, the internal structures and the main on-board equipment for the target vessel using the commercial FEM software. Also we analyze the degaussing effectiveness for the target vessel through the degaussing coils arrangement.

• Journal of Korean Clinical Nursing Research
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• v.17 no.2
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• pp.228-238
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• 2011

Purpose: The purpose of this study was to investigate parent expectation and satisfaction with respect to pediatric inpatient care and to identify the variables related to parent satisfaction. Methods: The study was conducted in pediatric wards of a tertiary children's hospital in Korea. The participants were 361 parents of children who were inpatients. Data were collected using a structured questionnaire (The Pediatric Family Satisfaction Questionnaire) at the time of discharge. Results: The highest parent expectation domain was medical service. The parents were most satisfied with nursing service and least satisfied with general hospital service and accommodation. The parents expressed lower satisfaction with hospital facilities, equipment, noise, cleanliness, and communication by health care professionals. Parents with younger children reported higher expectation from the complete hospital service and those who had a longer length of stay reported higher expectation from the nursing service. Conclusion: To improve the quality of hospital services, we need to understand parent expectation and improve and provide clear communication. In addition, the general hospital service and accommodation should not be overlooked for improvement.

• The Journal of Bigdata
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• v.6 no.1
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• pp.197-206
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• 2021

GPR data is used for underground exploration. The data gathered are interpreted by experts based on experience as the underground facilities often reflect GPR. In addition, GPR data are different in the noise and characteristics of the data depending on the equipment, environment, etc. This often results in insufficient data with accurate labels. Generally, a large amount of training data have to be obtained to apply CNN models that exhibit high performance in image classification problems. However, due to the characteristics of GPR data, it makes difficult to obtain sufficient data. Finally, this makes neural networks unable to learn based on general supervised learning methods. This paper proposes an image classification method considering data characteristics to ensure that the accuracy of each label is similar. The proposed method is based on semi-supervised learning, and the image is classified using clustering techniques after extracting the feature values of the image from the neural network. This method can be utilized not only when the amount of the labeled data is insufficient, but also when labels that depend on the data are not highly reliable.