• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.116-123
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• 1994

A novel opto-digital pattern recognition method which has shift, rotation, and scale invariant properties is proposed for recognizing two dimensional images having straight lines. The algorithm is composed of three stages. In the first stage the line features of the image are extracted. The second stage imposes the shift, rotation, and scale invariant properties on the extracted features through normalizing procedure. The required normalizing equations are analytically explained. In the last stage, the artificial feedforward neural network is trained with the extracted features. In order to evaluated the proposed algorithm, nine different edge enhnaced binary images composed of straight lines are tested. Thus the proposed algorithm can recognize the patterns event though they are shifted, rotated, and scaled.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1092-1098
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• 2007

Since one decade, the detection of HFPD (High frequency Partial Discharge) has been proposed as one of the effective method for the diagnosis of the power component under service in power grids. As a tool for HFPD detection, Metal Foil sensor based on the embedded technology has been commercialized for mainly power cable due to its advantages. Recently, for the on-site noise discrimination, several PA (Pulse analysis) methods have been reported and the related software, such as Neural Network and Fuzzy, have been proposed to separate the PD (Partial Discharge) signals from the noises since their wave shapes are completely different from each other. On the other hand, the relevant fundamental investigation has not yet clearly made while it is reported that the effectiveness of the current methods based on PA is dependant on the types of sensors. Moreover, regarding the identification of the vital defects introducible into the Power Cable, the direct identification of the nature of defects from the PD signals through Metal Foil coupler has not yet been realized. As a trial for solving above shortcomings, different types of software have been proposed and employed without any convincing probability of identification. In this regards, our novel algorithm 'PA Map' based on the pulse analysis is suggested to identify directly the defects inside the power cable from the HFPD signals which is output of the HFCT and metal foil sensors. This method enables to discriminate the noise and then to make the data analysis related to the PD signals. For the purpose, the HFPD detection and PA (Pulse Analysis) system have been developed and then the effect of noise discrimination has been investigated by use of the artificial defects using real scale mockup. Throughout these works, our system is proved to be capable of separating the small void discharges among the very large noises such as big air corona and ground floating discharges at the on-site as well as of identifying the concerned defects.

• Journal of the Korean Ceramic Society
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• v.42 no.6 s.277
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• pp.391-398
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• 2005

It is known that $BaMgAl_{10}O_{17}:Eu^{2+}(BAM)$ phosphors currently used have a serious thermal degradation problem. We screened $SrO-B_2O_3-P_2O_5$ system by a solution combinatorial chemistry technique in an attempt to search for a thermally stable blue phosphor for PDPs. A Quantitative Structure Activity Relationship (QSAR) was also obtained using an artificial neural network trained by the result fiom the combinatorial screening. As a result, we proposed a promising composition range in the $SrO-B_2O_3-P_2O_5$ ternary library. These compositions crystallized into a single major phase, $Sr_6BP_5O_{20}:Eu^{2+}$. The structure of $Sr_6BP_5O_{20}:Eu^{2+}$ was clearly determined by ab initio calculation. The luminescent efficiency of $Sr_6BP_5O_{20}:Eu^{2+}$ was 2.8 times of BAM at Vacuum Ultra Violet (VUV) excitation. The thermal stability was also good but the CIE color chromaticity was slightly poor.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.327-335
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• 2016

The People's Republic of China has vigorously been pursuing the internationalization of the Chinese Yuan or Renminbi after the financial crisis of 2008. In this view, an abrupt increase of use of the Chinese Yuan in the onshore and offshore markets are important milestones to be one of important currencies. One of the most frequently used methods to forecast volatility is GARCH model. Since a prediction error of the GARCH model has been reported quite high, a lot of efforts have been made to improve forecasting capability of the GARCH model. In this paper, we have proposed MLP-GARCH and a DL-GARCH by employing Artificial Neural Network to the GARCH. In an application to forecasting Chinese Yuan volatility, we have successfully shown their overall outperformance in forecasting over the GARCH.

• Food Science of Animal Resources
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• v.38 no.2
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• pp.362-375
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• 2018

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.739-752
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• 2018

Purpose: The purpose of this study was to investigate the relationship between kiln processing parameters and NOx emissions that occur in the sintering and calcination steps of the cement manufacturing process and to derive the main factors responsible for producing emissions outside emission limit criteria, as determined by category models and classification rules, using data mining techniques. The results from this study are expected to be useful as guidelines for NOx emission control standards. Methods: Data were collected from Precalciner Kiln No.3 used in one of the domestic cement plants in Korea. Thirty-four independent variables affecting NOx generation and dependent variables that exceeded or were below the NOx emiision limit (>1 and <0, respectively) were examined during kiln processing. These data were used to construct a detection model of NOx emission, in which emissions exceeded or were below the set limits. The model was validated using SPSS MODELER 18.0, artificial neural network, decision treee (C5.0), and logistic regression analysis data mining techniques. Results: The decision tree (C5.0) algorithm best represented NOx emission behavior and was used to identify 10 processing variables that resulted in NOx emissions outside limit criteria. Conclusion: The results of this study indicate that the decision tree (C5.0) can be applied for real-time monitoring and management of NOx emissions during the cement firing process to satisfy NOx emission control standards and to provide for a more eco-friendly cement product.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.105-114
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• 2005

This paper describes a new measuring technique by analysing multiple time-series patterns. This paper's goal is that extracts a really measured value having a sample pattern which is the best matched with an inputted time-series, and calculates a difference ratio with the value. Therefore, the proposed technique is not a recognition but a measurement. and not a hardware but a software. The proposed technique is consisted of three stages, initialization, learning and measurement. In the initialization stage, it decides weights of all parameters using importance given by an operator. In the learning stage, it classifies sample patterns using LBG and DTW algorithm, and then creates code sequences for all the patterns. In the measurement stage, it creates a code sequence for an inputted time-series pattern, finds samples having the same code sequence by hashing, and then selects the best matched sample. Finally it outputs the really measured value with the sample and the difference ratio. For the purpose of performance evaluation, we tested on multiple time-series patterns obtained from etching machine which is a semiconductor manufacturing.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.669-677
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• 2020

As the number of Internet users exploded, attacks on the web increased. In addition, the attack patterns have been diversified to bypass existing defense techniques. Traditional web firewalls are difficult to detect attacks of unknown patterns.Therefore, the method of detecting abnormal behavior by artificial intelligence has been studied as an alternative. Specifically, attempts have been made to apply natural language processing techniques because the type of script or query being exploited consists of text. However, because there are many unknown words in scripts and queries, natural language processing requires a different approach. In this paper, we propose a new classification model which uses byte pair encoding (BPE) technology to learn the embedding vector, that is often used for web attack payloads, and uses an attention mechanism-based Bi-GRU neural network to extract a set of tokens that learn their order and importance. For major web attacks such as SQL injection, cross-site scripting, and command injection attacks, the accuracy of the proposed classification method is about 0.9990 and its accuracy outperforms the model suggested in the previous study.

• Journal of Microbiology and Biotechnology
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• v.22 no.8
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• pp.1054-1058
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• 2012

In order to predict biologically significant attributes such as function from protein sequences, searching against large databases for homologous proteins is a common practice. In particular, BLAST and HMMER are widely used in a variety of biological fields. However, sequence-homologous proteins determined by BLAST and proteins having the same domains predicted by HMMER are not always functionally equivalent, even though their sequences are aligning with high similarity. Thus, accurate assignment of functionally equivalent proteins from aligned sequences remains a challenge in bioinformatics. We have developed the FEP-BH algorithm to predict functionally equivalent proteins from protein-protein pairs identified by BLAST and from protein-domain pairs predicted by HMMER. When examined against domain classes of the Pfam-A seed database, FEP-BH showed 71.53% accuracy, whereas BLAST and HMMER were 57.72% and 36.62%, respectively. We expect that the FEP-BH algorithm will be effective in predicting functionally equivalent proteins from BLAST and HMMER outputs and will also suit biologists who want to search out functionally equivalent proteins from among sequence-homologous proteins.

• Smart Structures and Systems
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• v.3 no.3
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• pp.341-356
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• 2007

A good understanding of normal modal variability of civil structures due to varying environmental conditions such as temperature and wind is important for reliable performance of vibration-based damage detection methods. This paper addresses the quantification of wind-induced modal variability of a cable-stayed bridge making use of one-year monitoring data. In order to discriminate the wind-induced modal variability from the temperature-induced modal variability, the one-year monitoring data are divided into two sets: the first set includes the data obtained under weak wind conditions (hourly-average wind speed less than 2 m/s) during all four seasons, and the second set includes the data obtained under both weak and strong (typhoon) wind conditions during the summer only. The measured modal frequencies and temperatures of the bridge obtained from the first set of data are used to formulate temperature-frequency correlation models by means of artificial neural network technique. Before the second set of data is utilized to quantify the wind-induced modal variability, the effect of temperature on the measured modal frequencies is first eliminated by normalizing these modal frequencies to a reference temperature with the use of the temperature-frequency correlation models. Then the wind-induced modal variability is quantitatively evaluated by correlating the normalized modal frequencies for each mode with the wind speed measurement data. It is revealed that in contrast to the dependence of modal frequencies on temperature, there is no explicit correlation between the modal frequencies and wind intensity. For most of the measured modes, the modal frequencies exhibit a slightly increasing trend with the increase of wind speed in statistical sense. The relative variation of the modal frequencies arising from wind effect (with the maximum hourly-average wind speed up to 17.6 m/s) is estimated to range from 1.61% to 7.87% for the measured 8 modes of the bridge, being notably less than the modal variability caused by temperature effect.