• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2022.03a
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• pp.37-38
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• 2022

• Journal of Information Display
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• v.3 no.2
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• pp.1-5
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• 2002

A CMOS driving circuit for active matrix type polymer electroluminescent displays was designed to develop an on-chip microdisplay on the single crystal silicon wafer substrate. The driving circuit is a conventional structure that is composed of the row, column and pixel driving parts. 256 gray scales were implemented using pulse amplitude modulation method. The 2-transistor driving scheme was adopted for the pixel driving part. The layout was carried out considering the compatibility with the standard CMOS process. Judging from the layout of the driving circuit, it turns that it is possible to implement a high-resolution display about 400 ppi resolution. Through the HSPICE simulation, it was verified that this circuit is capable of driving a VGA signal mode display and implementing 256 gray levels.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.208-214
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• 2021

Recently, much of the intelligent security scenario and criminal investigation demands for matching photo and non-photo. Existing face recognition system can not sufficiently guarantee these needs. In this paper, we propose an algorithm to improve the performance of heterogeneous face recognition systems by reducing the different modality between sketches and photos of the same person. The proposed algorithm extracts each image's texture features through texture descriptors (gray level co-occurrence matrix, multiscale local binary pattern), and based on this, generates a transformation matrix through eigenfeature regularization and extraction techniques. The score value calculated between the vectors generated in this way finally recognizes the identity of the sketch image through the score normalization methods.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.70-77
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• 2019

Breast ultrasound readings are very important to diagnose early breast cancer. In Ultrasonic inspection, it shows a significant difference in image quality depending on the ultrasonic equipment, and there is a large difference in diagnosis depending on the experience and skill of the inspector. Therefore, objective criteria are needed for accurate diagnosis and treatment. In this study, we analyzed texture characteristics by applying GLCM (Gray Level Co-occurrence Matrix) algorithm and extracted characteristic parameters and diagnosed breast cancer using neural network classifier. Breast ultrasound images were classified into normal, benign and malignant tumors and six texture parameters were extracted. Fourteen cases of normal, malignant and benign tumor diagnosed by mammography were studied by using the extracted six parameters and learning by multi - layer perceptron neural network back propagation learning method. As a result of classification using 51 normal images, 62 benign tumor images, and 74 malignant tumor images of the learned model, the classification rate was 95.2%.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.599-610
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• 2010

Color laser printers are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use their own printing process, each of printed papers from different printers has a little different invisible noise. After the wiener-filter is used to analyze the invisible noises from each printer, we extract some features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, we use total 2,597 images from 7 color laser printers. The results prove that the presented identification method performs well using the noise features of color printed images.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.433-442
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• 2018

The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

• Journal of Korea Foundry Society
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• v.32 no.5
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• pp.211-218
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• 2012

The effects of thickness, silicon and manganese contents on the mechanical properties of 3.3 wt%C-0.1 wt%S thin-section gray cast iron plates were investigated. The eutectic cell counts and volume fraction of pearlite in the matrix decreased with increased thickness and therefore the strength and hardness decreased with it. Even though the eutectic cell count increased with increased silicon content, the volume fraction of pearlite decreased and the strength and hardness decreased with it. The pearlite was refined more with increased manganese content and therefore the strength and hardness increased with it.

• Structural Monitoring and Maintenance
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• v.10 no.2
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• Journal of the Mineralogical Society of Korea
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• v.24 no.3
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• pp.151-164
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• 2011

Mineralogical and petrological characteristics were investigated on matrix of dense gray, vesiculate gray, brown and black pumice in Ulleung Island by using XRD, FT-IR, XRF, SEM and thermal analysis. According to the analysis, most of pumice matrix are amorphous and include very small amount of sanidine and anorthoclase. Since the adsorption moistures, which commonly observed as O-H peak in FT-IR spectrum, are not identified in thermal analysis, it seems reasonable to conclude that content of the adsorption moisture has very low level. Although pumice has a large specific surface area, with long time elapsed after eruption, pumice matrix shows very low degree of hydration alteration due to the low level of water content. In SEM images, most surfaces of pumice show morphological characteristics such as various shapes of vesicle with wrinkled and thin walls resulted from ductile coalescence. Dense gray pumice formed in the initial stage includes small vesicles less than $15{\mu}m$ in size with subangular to angular shapes, free of ovoid vesicle. These characteristics are interpreted to have related to the hydrous environment derived from phreato-plinian eruption. Submicron particles observed as amorphous alumina silicate assemblages in vesicle surface are considered as particles sticked to the matrix surface through rapidly cooling process during ascent of alkali phonolitic magma. It indicates that these particles coexisted partly with crystallized alkali feldspar.