• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• Journal of the Semiconductor & Display Technology
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• v.11 no.2
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• pp.45-51
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• 2012

Multilayer co-extrusion blown film construction is a popular technique for producing plastic films for various packaging industries. Automated detection of defective films can improve the quality of film production process. In this paper, we propose a film inspection system that can detect and classify film defects robustly. In our system, first, film images are acquired through a high speed line-scan camera under an appropriate lighting system. In order to detect and classify film defects, an inspection algorithm is developed. The algorithm divides the typical film defects into two groups: intensity-based and texture-based. Intensity-based defects are classified based on geometric features. Whereas, to classify texture-based defects, a texture analysis technique based on local binary pattern (LBP) is adopted. Experimental results revealed that our film inspection system is effective in detecting and classifying defects for the multilayer co-extrusion blown film construction line.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.198-205
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• 2016

In the contemporary world, the breakthrough of the ultra-modern technologies has been blessed with the camera. This device has become omnipresent either integrated on with other devices such as smartphones, notebooks, laptops, handheld devices and so on. In addition, digital signage, display and monitors have also become very widespread around us everywhere. These types of scenarios have made imaginable to imagine about the consuming of the unused resources. The dual use of displayed contents can make it possible to use it as an advertisement as well as a transmitter for camera based communication. In this study, a digital watermarking algorithm based communication method has been analyzed. We have introduced Binary Pattern (BP) based a new message extracting algorithm to extract message in an efficient way from the watermarked image compared to other algorithms. Besides, the previous works using camera and display have been illustrated using comparative analysis. This paper has demonstrated an advantageous overview using the experimental results which reveals that the proposed methodology significantly reduces the complexity while augmenting the advantages of the proposed scheme. Moreover, the simulation results have shown the advantages of the proposed scheme over the other schemes.

• The Journal of Korean Medicine
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• v.40 no.4
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• pp.49-60
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• 2019

Objectives: To develop a standardized diagnostic pattern identification equation for stroke patients, our group conducted a study to derive the predictive logistic equations. However, the sample size was relatively small. In the current study, we aimed to derive new predictive logistic equations for each diagnostic pattern using an expanded number of subjects. Methods: This study was a hospital-based multi-center trial recruited stroke patients within 30 days of symptom onset. Patients' general information, and the variables related to diagnostic pattern identification were measured. The diagnostic pattern of each patient was identified independently by two Korean Medicine Doctors. To derive a predictive model for pattern identification, binary logistic regression analysis was applied. Results: Among the 1,251 patients, 385 patients (30.8%) had the Fire Heat Pattern, 460 patients (36.8%) the Phlegm Dampness Pattern, 212 patients (16.9%) the Qi Deficiency Pattern, and 194 patients (15.5%) the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for each pattern were determined. Conclusion: The predictive equations for Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency would be useful to determine individual stroke patients' pattern identification in the clinical setting. However, further studies using objective measurements are necessary to validate these data.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.943-952
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• 2018

This paper presents a new type of absolute rotary encoder system based on the affine n-digit N-ary gray code. A brief comparison of the existing encoder systems is carried out in terms of resolution, encoding and decoding principles and number of sensor heads needed. Using the proposed method, two different types of encoder disks are designed, namely, color-coded disk and grayscale coded disk. The designed coded disk pattern is used to manufacture 3 digit 3 ary and 2 digit 5 ary grayscale coded disks respectively. The manufactured disk is used with the light emitter and photodetector assembly to design the entire encode system. Experimental analysis is done on the designed prototype with LabVIEW platform for data acquisition. A comparison of the designed system is done with the traditional binary gray code encoder system in terms of resolution, disk diameter, number of tracks and data acquisition system. The resolution of the manufactured system is 3 times higher than the conventional system. Also, for a 5 digit 5 ary coded encoder system, a resolution approximately 100 times better than the conventional binary system can be achieved. In general, the proposed encoder system gives $(N/2)^n$ times better resolution compared with the traditional gray coded disk. The miniaturization in diameter of the coded disk can be achieved compared to the conventional binary systems.

• Journal of Society of Preventive Korean Medicine
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• v.20 no.3
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• pp.67-74
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• 2016

Objectives : The purpose of this study was to analyze the relationship between the Cold-pattern and the quantitative index through the results of an anthropometric method and Bio Impedance Analysis (BIA) of the Haenyeo living in Jeju island. Furthermore, we will examine the effect of Cold-pattern on the quality of life. Methods : BIA indices were acquired directly from Inbody770 and questionnaires were collected by Gallup Korea professional surveyor through face to face interviews. Binary regression analysis and linear regression analysis were used to examine the association between collected data. Results : Total of 175 of people were participated in this study. First, we examined the difference of the indicators in the Cold-pattern group and the non-Cold pattern group by the average comparison of the anthropometry and BIA indices. Most of the non-Cold pattern group showed high quality of life, BIA and anthropometry. In the relationship between Cold-pattern and anthropometry and BIA indices, BMI and PA indices were found to affect the Cold-pattern on a group basis. As the BMI increased by $1kg/m^2$, probability of not being non-Cold pattern was 1.13 times. and as the PA increased by $1^{\circ}$, probability of not being non-Cold pattern was 2.4 times. In the case of EQ5D value, the quality of life of ${\beta}$ was increased by 0.08 in non-Cold pattern (p <.05), EQ5D VAS of ${\beta}$ was also increased by 10.05 (p <.05). Conclusions : This study showed that BMI and PA could be used as a clinical index to evaluate the Cold-pattern as a clinical indicator, and there is a difference in quality of life according to Cold-pattern.

• ETRI Journal
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• v.32 no.5
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• pp.784-794
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• 2010

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

• Korean Journal of Materials Research
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• v.34 no.5
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• pp.235-241
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• 2024

Gas identification techniques using pattern recognition methods were developed from four micro-electronic gas sensors for noxious gas mixture analysis. The target gases for the air quality monitoring inside vehicles were two exhaust gases, carbon monoxide (CO) and nitrogen oxides (NO_x), and two odor gases, ammonia (NH₃) and formaldehyde (HCHO). Four MEMS gas sensors with sensing materials of Pd-SnO₂ for CO, In₂O₃ for NO_X, Ru-WO₃ for NH₃, and hybridized SnO₂-ZnO material for HCHO were fabricated. In six binary mixed gas systems with oxidizing and reducing gases, the gas sensing behaviors and the sensor responses of these methods were examined for the discrimination of gas species. The gas sensitivity data was extracted and their patterns were determined using principal component analysis (PCA) techniques. The PCA plot results showed good separation among the mixed gas systems, suggesting that the gas mixture tests for noxious gases and their mixtures could be well classified and discriminated changes.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.279-291
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• 2012

When fitting a Cox proportional hazards model with missing covariates, it is inefficient to exclude observations with missing values in the analysis. Furthermore, if the missing-data mechanism is not Missing Completely At Random(MCAR), it may lead to biased parameter estimation. Many approaches have been suggested to handle the Cox proportional hazards model when covariates are sometimes missing, but they are based on the selection model. This paper suggest an approach to handle Cox proportional hazards model with missing covariates by using the pattern-mixture model (Little, 1993). The pattern-mixture model is expressed by the joint distribution of survival time and the missing-data mechanism. In the pattern-mixture model, many models can be considered by setting up various restrictions, and different results under various restrictions indicate the sensitivity of the model due to missing covariates. A simulation study was conducted to show the sensitivity of parameter estimation under different restrictions in a pattern-mixture model. The proposed approach was also applied to mouse leukemia data.