• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.1987-1996
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• 2009

Recently, the color of vehicle license plate has been changed from green to white. Thus the vehicle plate recognition system used for parking management systems, speed and signal violation detection systems should be robust to the both colors. This paper presents a vehicle license plate recognition system, which works on both of green and white plate at the same time. In the proposed system, the image of license plate is taken from a captured vehicle image by using morphological information. In the next, each character region in the license plate image is extracted based on the vertical and horizontal projection of plate image and the relative position of individual characters. Finally, for the recognition process of extracted characters, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis) are sequentially utilized. In the experiment, vehicle license plates of both green background and white background captured under irregular illumination conditions have been tested, and the relatively high extraction and recognition rates are observed.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.759-765
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• 2006

In this paper, it is proposed an effective face expression recognition LDA mixed mode using a triangularity membership fuzzy function and wavelet basis. The proposal algorithm gets performs the optimal image, fuzzy wavelet algorithm and Expression recognition is consisted of face characteristic detection step and face Expression recognition step. This paper could applied to the PCA and LDA in using some simple strategies and also compares and analyzes the performance of the LDA mixed model which is combined and the facial expression recognition based on PCA and LDA. The LDA mixed model is represented by the PCA and the LDA approaches. And then we calculate the distance of vectors dPCA, dLDA from all fates in the database. Last, the two vectors are combined according to a given combination rule and the final decision is made by NNPC. In a result, we could showed the superior the LDA mixed model can be than the conventional algorithm.

• Conservation Science in Museum
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• v.29
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• pp.1-32
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• 2023

The Paintings of a 60^th Wedding Anniversary Ceremony Created by an Unknown Painter (Deoksu 6375), housed by the National Museum of Korea, is a five-panel painting book depicting scenes from a wedding ceremony. Hoehonrye is a type of repeated wedding ceremony to commemorate a couple's 60th wedding anniversary with congratulations from the community. The paintings of the book record five scenes from the wedding: jeoninrye, a ceremony where the groom brings a wooden wild goose to the bride's house; gyoberye, the groom and the bride bowing to each other; heosurye, pouring liquor to toast to the couple's longevity; jeopbin, offering tea to guests; and a banquet to celebrates the couple's 60th wedding anniversary. The book describes figures, buildings and a variety of items in detail with delicate brushstrokes. The techniques were examined using microscopy, infrared, and X-ray irradiation and hyperspectral imaging analysis. The invisible parts were examined to identify the rough sketch and distinguish pigments and dyes used for each color. The components of the pigments were determined by X-ray fluorescence analysis, while the dyes were identified by UV-vis spectrometry. Microscope observation revealed that the fabric used for the paintings was raw silk thread with almost no fiber twist, and plain silk fabric. Hyperspectral imaging analysis, X-ray fluorescence analysis, and UV-vis spectrometry confirmed that the white pigment was white lead and the black was chinese ink. The red pigments were using red clay, cinnabar, and a mixture of cinnabar and minium. Brown was made using red clay and organic dyes, and yellow using gamboge. Green was identified as indigo, malachite, chrome green, barium sulfide, and blue as azurite, smalt, and indigo. The purple dye was estimated as a mixture of indigo and cochineal, and gold parts were used gold powder. Hyperspectral images were distinguished parts damaged and conservation treatment area.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.509-520
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• 2015

College Scholastic Ability Test(CSAT) is a primary test to evaluate the study achievement of high-school students and used by most universities for admission decision in South Korea. Because its level of difficulty is a significant issue to both students and universities, the government makes a huge effort to have a consistent difficulty level every year. However, the actual levels of difficulty have significantly fluctuated, which causes many problems with university admission. In this paper, we build two types of data-driven prediction models to predict correct answer rate and to identify significant factors for CSAT English test through accumulated test data of CSAT, unlike traditional methods depending on experts' judgments. Initially, we derive candidate question-specific factors that can influence the correct answer rate, such as the position, EBS-relation, readability, from the annual CSAT practices and CSAT for 10 years. In addition, we drive context-specific factors by employing topic modeling which identify the underlying topics over the text. Then, the correct answer rate is predicted by multiple linear regression and level of difficulty is predicted by classification tree. The experimental results show that 90% of accuracy can be achieved by the level of difficulty (difficult/easy) classification model, whereas the error rate for correct answer rate is below 16%. Points and problem category are found to be critical to predict the correct answer rate. In addition, the correct answer rate is also influenced by some of the topics discovered by topic modeling. Based on our study, it will be possible to predict the range of expected correct answer rate for both question-level and entire test-level, which will help CSAT examiners to control the level of difficulties.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Journal of Life Science
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• v.33 no.6
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• pp.481-489
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• 2023

The taste and quality of soy sauce, a fermented liquid condiment, is greatly influenced by microbial metabolism during fermentation. To investigate the microbiological characteristics of ganjang during the initial fermentation process, we prepared meju (fermented soybean) blocks fermented with starter cultures and solar salts and analyzed the microbial community quantitively using 16S rRNA gene profiling from ganjang that had been fermented over a five-week period. The ganjang samples were collected and analyzed after soaking for week one (1W), three (3W), and five (5W) weeks. We found that Halomonadaceae was significantly higher in the 1W group (89.83%) than the 3W and 5W groups (14.46%, and 13.78%, respectively). At a species level, Chromohalobacter beijerinckii and Chromohalobacter canadensis were the dominant species in the 1W group but several taxa such as Bacillus subtilis, Pediococcus acidilactici, and Enterococcus faecalis were more abundant in the 3W and 5W groups. Pearson correlation analysis of the relative abundance of the bacteria showed a negative correlation between Chromohalobacter and two bacterial genera Bacillus and Enterococcus. Beta-diversity showed a statistical distinction between the 1W and the 3W and 5W groups, while no significance was evident between the 3W and 5W groups. Linear discriminant effect size analysis was used to identify biomarkers and significant differences in the relative abundance of several halophilic bacteria, Bacillus sp. and lactic acid bacteria at 1W, 3W, and 5W, recpectively, which indicates the important role of the bacterial community at these time points.

• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.1-8
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• 2014

Clustering is one of the most popular unsupervised learning methods, which is widely used to form clusters with homogeneous data. Clustering was used to extract contexts corresponding to clusters and a classification method was applied to each context or cluster individually. However, it is difficult to say that the unsupervised clustering is the best context forming method from the view of classification. In this paper, a new clustering method considering classification was proposed. The proposed method tries to minimize classification error in each cluster when a classification method is applied to each context locally. For this purpose, the proposed method adds constraints forcing two data points belong to the same class to have small distances, and two data points belong to different classes to have large distances in each cluster like in linear discriminant analysis. The usefulness of the proposed method is confirmed by experimental results.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.7-14
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• 2002

This paper proposes a camera motion estimation technique using special relations of motion vectors of geometrically symmetrical triple points of two consecutive views of single camera. The proposed technique uses camera-induced motion vectors and their relations other than feature points and epioplar constraints. As contrast to the time consuming iterations or numerical methods in the calculation of E-matrix or F-matrix induced by epipolar constraints, the proposed technique calculates camera motion parameters such as panning, tilting, rolling, and zooming at once by applying the proposed linear equation sets to the motion vectors. And by devised background discriminants, it effectively reflects only the background region into the calculation of motion parameters, thus making the calculation more accurate and fast enough to accommodate MPEG-4 requirements. Experimental results on various types of sequences show the validity and the broad applicability of the proposed technique.