• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.747-748
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• 2010

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.233-237
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• 2013

Image recognition is an important research area of pattern recognition. This paper studies that the image segmentation algorithm theory and its application in road signs recognition system. In this paper We studied a systematic study for road signs and we have made the recognition algorithm. This paper is divided in image segmentation part and image recognition part for the road signs recognition. The experimental results show that the road signs recognition model can make effective use in smart phone system, and the model can be used in many other fields.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.25-31
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• 2004

This paper is research about the improvement of recognition rate of the biometrics user identification system using the data previously stored in the non contact Ic smart card. The proposed system identifies the user by analyzing the iris pattern his or her us. First, after extracting the area of the iris from the image of the iris of an eye which is taken by CCD camera, and then we save PCA Coefficient using GHA(Generalized Hebbian Algorithm) into the Smart Card. When we confirmed the users, we compared the imformation of the biometrics of users with that of smart card. In case two kinds of information was the same, we classified the data by using SVM(Support Vector Machine). The Experimental result showed that this system outperformed the previous developed system.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.80-89
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• 2009

The importance of personalized services has been attracted in smart home environments according to the development of ubiquitous computering. In this paper, we proposed the smart home personalized service system based on context information using the speech recognition. The proposed service consists of an OSGi framework based service mobile manager, service manager, voice recognition manager, and location manager. Also, this study defines the smart home space and configures the commands of units, sensor information, and user information that are largely used in the defined space as context information. In particular, this service identifies users who exist in the same space that shows a difficulty in the identification using RFID through the training model and pattern matching in voice recognition and supports the personalized service of smart home applications. In the results of the experiment, it was verified that the OSGi based automated and personalized service can be achieved through verifying users in the same space.

• Smart Media Journal
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• v.8 no.3
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• pp.62-69
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• 2019

Electrocardiogram (ECG) signals cannot be counterfeited and can easily acquire signals from both wrists. In this paper, we propose a method of generating a coupling image using direction information of ECG signals as well as its usage in a personal recognition method. The proposed coupling image is generated by using forward ECG signal and rotated inverse ECG signal based on R-peak, and the generated coupling image shows a unique pattern and brightness. In addition, R-peak data is increased through the ECG signal calculation of the same beat, and it is thus possible to improve the recognition performance of the individual. The generated coupling image extracts characteristics of pattern and brightness by using the proposed convolutional neural network and reduces data size by using multiple pooling layers to improve network speed. The experiment uses public ECG data of 47 people and conducts comparative experiments using five networks with top 5 performance data among the public and the proposed networks. Experimental results show that the recognition performance of the proposed network is the highest with 99.28%, confirming potential of the personal recognition.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• Smart Structures and Systems
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• v.17 no.5
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• pp.725-742
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• 2016

This paper is focused on stiffness ratio effect and a new method to specify the best pattern of piezoelectric patches placement around a hole in a plate under tension to reduce the stress concentration factor. To investigate the stiffness ratio effect, some different values greater and less than unity are considered. Then a python code is developed by using particle swarm optimization algorithm to specify the best locations of piezoelectric actuators around the hole for each stiffness ratio. The results show that, there is a line called "reference line" for each plate with a hole under tension, which can guide the location of actuator patches in plate to have the maximum stress concentration reduction. The reference line also specifies that actuators should be located horizontally or vertically. This reference line is located at an angle of about 65 degrees from the stress line in plate. Finally two experimental tests for two different locations of the patches with various voltages are carried out for validation of the results.

• Smart Media Journal
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• v.7 no.2
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• pp.23-33
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• 2018

A musical sheet is read by optical music recognition (OMR) systems that automatically recognize and reconstruct the read data to convert them into a machine-readable format such as XML so that the music can be played. This process, however, is very challenging due to the large variety of musical styles, symbol notation, and other distortions. In this paper, we present a model for the recognition of musical symbols through the use of a mobile application, whereby a camera is used to capture the input image; therefore, additional difficulties arise due to variations of the illumination and distortions. For our proposed model, we first generate a line adjacency graph (LAG) to remove the staff lines and to perform primitive detection. After symbol segmentation using the primitive information, we use a covariance-matching method to estimate the similarity between every symbol and pre-defined templates. This method generates the three hypotheses with the highest scores for likelihood measurement. We also add a global consistency (time measurements) to verify the three hypotheses in accordance with the structure of the musical sheets; one of the three hypotheses is chosen through a final decision. The results of the experiment show that our proposed method leads to promising results.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.748-757
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• 2017

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.3
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• pp.474-485
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• 2018

Smart sensors and connected devices have changed the concept of garments along with IT technology convergent garments that transform the performance of basic functions. Various types of products have been researched and developed due to the increased interest in smart clothing; in addition, studies based on physical and mechanical properties have also been actively studied to improve accuracy and reliability. This study represents a basic study for the development of smart textiles based on motion recognition for the surfing practice of beginners interested in IT convergence type. A physical durability evaluation of conductive yarn according to sewing method was later carried out. This study is a conditional specimen sewn with cotton lower thread and 100mm pattern length based on the results of previous studies. The durability of the conductive yarn according to the sewing method was evaluated according to the sewing method. Durability was evaluated by two kinds of repeated strain and abrasion tests. The specimen with applied cotton in a lower thread zigzag pattern 2mm stitch size 100mm stitch length was shown to have the most suitable durability for smart textile.