• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.413-413
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• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.

• Korean Journal of Microbiology
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• v.53 no.2
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• pp.71-78
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• 2017

Raman microspectroscopy is a promising tool for microbial analysis at single cell level since it can rapidly measure the cell materials including lipids, nucleic acids, and proteins by measuring the inelastic scattering of a molecule irradiated by monochromatic lights. Using Raman spectra provides high specificity and sensitivity in classification of bacteria at the strain level. In addition, a Raman approach coupled with stabled isotope such as $^{13}C$ and $^2H$ is able to detect and quantify general metabolic activity at single cell level. After bacterial detection process by Raman microspectroscopy, interested unculturable cell sorting and single cell genomics can be accomplished by combination with optical tweezer and microfluidic devices. In this review, the characteristics and applications of Raman microspectroscopy were reviewed and summarized in order to provide a better understanding of microbial analysis using Raman spectroscopy.

• Applied Chemistry for Engineering
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• v.23 no.6
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• pp.577-582
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• 2012

In this work, vibrational circular dichroism (VCD) technique was applied for the determination of %EE of chiral compounds. It may provide an easy way to determine the %EE with a proper accuracy within 2% error ranges as well as the absolute configuration of enantiomers. We demonstrated herein a flow cell VCD (FT-VCD) technique for time-dependent %EE measurements. The simultaneous monitoring of the mole fraction and %EE for two chiral species (epichlorohydrin and glycidol mixture) in the mixture was shown to be successful without any further separation steps. Thus, we demonstrate that FT-VCD is an appropriate analytical tool to monitor the kinetics of reactions involving chiral molecules. FT-VCD also provides a convenient nondestructive approach for the time dependent determination of the optical purity of individual components in a reaction mixture containing chiral molecules.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.117-125
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• 2002

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. Using an adaptive reconstruction system, dynamic compositing approach was developed to recover missing/bad observations. The reconstruction method incorporates temporal variation in physical properties of targets and anisotropic spatial optical properties into image processing. The adaptive system performs the dynamic compositing by obtaining a composite image as a weighted sum of the observed value and the value predicted according to local temporal trend. The proposed system was applied to the sequence of NDVI images of AVHRR observed on the Korean Peninsula from 1999 year to 2000 year. The experiment shows that the reconstructed series can be used as an estimated series with complete data for the observations including bad/missing values. Additionally, the gradient image, which represents the amount of temporal change at the corresponding time, was generated by the proposed system. It shows more clearly temporal variation than the data image series.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.1
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• pp.35-41
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• 2008

The radiative heat loss from a receiver of a dish solar collector is numerically investigated. The dish solar collector considered in this paper consists of a receiver and multi-faceted mirrors. In order to investigate the performance comparison of dish solar collectors, six different mirror arrays and four different receivers are considered. A parabolic- shaped perfect mirror of which diameter is 1.40 m is considered as the reference for the mirror arrays. The other mirror arrays which consist of twelve identical parabolic-shaped mirror facets of which diameter are 0.405 m are suggested for comparison. Their reflecting areas, which are 1.545 $m^{2}$, are the same. Four different receiver shapes are a conical, a dome, a cylindrical, and a unicorn type. The radiative properties of the mirror surfaces and the receiver surfaces may vary the thermal performance of the dish solar collector so that various surface properties are considered. In order to calculate the radiative heat loss in the receiver, two kinds of methods are used. The Net Radiation Method that is based on the radiation heat balance on the surface is used to calculate the radiation heat transfer rate from the inside surface of the receiver to the environment. The Monte-Carlo Method that is the statistical approach is adopted to predict the radiation heat transfer rate from the reflector to the receiver. The collector efficiency is defined as the results of the optical efficiency and the receiver efficiency. Based on the calculation, the unicorn type has the best performance in receiver shapes and the STAR has the best performance in mirror arrays except the perfect mirror.

• Journal of Powder Materials
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• v.26 no.1
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• pp.34-39
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• 2019

Current synthesis processes for titanium dioxide ($TiO_2$) nanoparticles require expensive precursors or templates as well as complex steps and long reaction times. In addition, these processes produce highly agglomerated nanoparticles. In this study, we demonstrate a simple and continuous approach to synthesize $TiO_2$ nanoparticles by a salt-assisted ultrasonic spray pyrolysis method. We also investigate the effect of salt content in a precursor solution on the morphology and size of synthesized products. The synthesized $TiO_2$ nanoparticles are systematically characterized by X-ray diffraction, transmission electron micrograph, and UV-Vis spectroscopy. These nanoparticles appear to have a single anatase phase and a uniform particle-size distribution with an average particle size of approximately 10 nm. By extrapolating the plots of the transformed Kubelka-Munk function versus the absorbed light energy, we determine that the energy band gap of the synthesized $TiO_2$ nanoparticles is 3.25 eV.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.

• Korean Journal of Optics and Photonics
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• v.30 no.2
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• pp.48-58
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• 2019

We investigate the signal-to-noise ratios (SNRs) of time-domain (i.e. pulsed illumination) and frequency-domain (i.e. chirped illumination) photoacoustic signals measured by a spherically focused ultrasound transducer for spherical absorbers. The simulation results show that the time-domain photoacoustic SNR is higher than that of frequency-domain photoacoustic signals, as reported in the previous literature. We understand the reason for this SNR gap between the two measurement modes by analyzing photoacoustic-signal spectra, considering the incident beam energy controlled by the maximum permissible exposure. As the result of this approach, we find that filtering off the DC term in the chirped signal's spectrum improves frequency-domain photoacoustic SNRs by up to approximately 5 dB. In particular, it is observed that photoacoustic SNRs are highly sensitive to an upper-frequency value of frequency filtering functions, and the optimal upper-frequency values maximizing the SNR are different in time- and frequency-domain photoacoustic measurements.

• Database Research
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• v.34 no.3
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• pp.124-136
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• 2018

There have been active research activities to use neural networks to analyze OCT images and make medical decisions. One requirement for these approaches to be promising solutions is that the trained network must be generalized to new devices without a substantial loss of performance. In this paper, we use a deep convolutional neural network to distinguish AMD from normal patients. The network was trained using a data set generated from an OCT device. We observed a significant performance degradation when it was applied to a new data set obtained from a different OCT device. To overcome this performance degradation, we propose an image normalization method which performs segmentation of OCT images to identify the retina area and aligns images so that the retina region lies horizontally in the image. We experimentally evaluated the performance of the proposed method. The experiment confirmed a significant performance improvement of our approach.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.