• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.10
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• pp.885-892
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• 2013

The main advantage of ESPAR antenna is that ESPAR antenna requires only a single RF chain for reduction of transceiver's hardware complexity, as compared to conventional MIMO system. In conventional MIMO system, each data symbol is mapped to each antenna. But, each data symbol is mapped to each orthogonal basis pattern in ESPAR antenna system. In this paper, we design beamspace MIMO system using ESPAR antenna with single RF chain for MIMO system of low-complexity and low power consumption. And then, we analyze performance of beamspace MIMO according to each PSK modulation. Performance of beamspace MIMO system is similar to performance of conventional MIMO system. As a result of analyzing the performance of beamspace MIMO system using higher-order PSK modulation. we can confirm that performance characteristic of beamspace MIMO system with low complexity and low power consumption is similar to digital communication of signal domain.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.149-158
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• 2004

This study proposes a method for segmentation and recognition of traffic signs using shape information and edge image in real image. It first segments traffic sign candidate regions by connected component algorithm from binary images, obtained by utilizing the RGB color ratio of each pixel in the image, and then extracts actual traffic signs based on their symmetries on X- and Y-axes. Histogram equalization is performed for unsegmented candidate regions caused by low contrast in the image. In the recognition stage, it utilizes shape information including projection profiles on X- and Y-axes, moment, and the number of crossings and distance which concentric circular patterns and 8-directional rays from region center intersects with edges of traffic signs. It finally performs recognition by measuring similarity with the templates in the database. It will be shown from several experimental results that the system is robust to environmental factors, such as light and weather condition.

• The Journal of Korea Robotics Society
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• v.5 no.2
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• pp.152-159
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• 2010

Finding a head of a person in a scene is very important for taking a well composed picture by a robot photographer because it depends on the position of the head. So in this paper, we propose a robust head tracking algorithm using a hybrid of an omega shape tracker and local binary pattern (LBP) AdaBoost face detector for the robot photographer to take a fine picture automatically. Face detection algorithms have good performance in terms of finding frontal faces, but it is not the same for rotated faces. In addition, when the face is occluded by a hat or hands, it has a hard time finding the face. In order to solve this problem, the omega shape tracker based on active shape model (ASM) is presented. The omega shape tracker is robust to occlusion and illuminationchange. However, whenthe environment is dynamic,such as when people move fast and when there is a complex background, its performance is unsatisfactory. Therefore, a method combining the face detection algorithm and the omega shape tracker by probabilistic method using histograms of oriented gradient (HOG) descriptor is proposed in this paper, in order to robustly find human head. A robot photographer was also implemented to abide by the 'rule of thirds' and to take photos when people smile.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.89-94
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• 2016

This paper proposed a facial expression recognition approach using SIFT feature and SVM classifier. The SIFT was generally employed as feature descriptor at key-points in object recognition fields. However, this paper applied the SIFT descriptor as feature vector for facial expression recognition. In this paper, the facial feature was extracted by applying SIFT descriptor at each sub-block image without key-point detection procedure, and the facial expression recognition was performed using SVM classifier. The performance evaluation was carried out through comparison with binary pattern feature-based approaches such as LBP and LDP, and the CK facial expression database and the JAFFE facial expression database were used in the experiments. From the experimental results, the proposed method using SIFT descriptor showed performance improvements of 6.06% and 3.87% compared to previous approaches for CK database and JAFFE database, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.68-76
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• 1999

The intuitive understanding of the dynamic pattern generation in asymmetric networks may be useful for developing models of dynamic information processing. In this paper, dynamic behavior of the ring connection neural network in which each neuron is only to its nearest neurons with binary synaptic weights of ±1, has been inconnected vestigated Simulation results show that dynamic behavior of the network can be classified into only three categories: fixed points, limit cycles with basin and limit cycles with no basin. Furthermore, the number and the type of limit cycles generated by the networks have been derived through analytical method. The sufficient conditions for a state vector of n-neuron network to produce a limit cycle of n- or 2n-period are also given The results show that the estimated number of limit cycle is an exponential function of n. On the basis of this study, cyclic connection neural network may be capable of storing a large number of dynamic information.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1372-1381
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• 2017

In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.20-26
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• 2021

In this paper, we studied the rotation angle estimation methods required for image alignment in an image recognition environment. In particular, a rotation angle estimation method applicable to a low specification embedded-based environment was proposed and compared with the existing method using complex moment. The proposed method estimates the rotation angle through similarity mathcing of the 1D projection profile along the radial axis after converting an image into polar coordinates. In addition, it is also possible to select a method of using vector sum of the projection profile, which more simplifies the calculation. Through experiments conducted on binary pattern images and gray-scale images, it was shown that the estimation error of the proposed method is not significantly different from that of complex moment-based method and requires less computation and system resources. For future expansion, a study on how to match the rotation center in gray-scale images will be needed.

• Journal of dental hygiene science
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• v.21 no.3
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• pp.133-139
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• 2021

Background: Smoking in adolescence leads to an intensified addiction to nicotine when physical and mental growth has not yet been completed. With the advent of e-cigarettes, the rate of e-cigarette use among Korean adolescents has been steadily increasing. To date, studies on e-cigarettes and oral health, especially on the relationship between smoking styles and oral health in adolescents, are limited. Therefore, this study aimed to identify the risk factors for oral health problems caused by the repeated use of conventional cigarettes and e-cigarettes. Methods: This explanatory research study compared the adolescents' experiences of periodontal disease symptoms according to smoking type through a secondary analysis of the original data from the 15th Adolescent Health Behavior Survey (2019). Cross-analysis was performed to compare the smoking patterns according to the adolescents' general characteristics. Finally, a binary logistic regression analysis was performed to determine how smoking characteristics affect the adolescents' experience of periodontal disease symptoms. Results: In terms of patients' general characteristics, significant differences were observed in sex, school level, grades, household economic status, type of residence, and father's education level between adolescents who smoked conventional cigarettes alone and those who smoked both conventional cigarettes and e-cigarettes (p<0.05). After checking the factors affecting the smoking pattern and the experience of periodontal disease symptoms in adolescents, it was found that the duplicate smoking group was more likely to experience periodontal disease symptoms (odds ratio, 1.20) than the group that smoked conventional cigarettes alone (p<0.05). Conclusion: Duplicate smokers experienced more symptoms of periodontal disease than those who smoked cigarettes alone. Based on the findings of this study, smoking cessation counseling according to the smoking type and differentiated education for oral health promotion should be provided.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Health Policy and Management
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• v.28 no.4
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• pp.369-377
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• 2018

Background: Catastrophic health expenditure (CHE) occurs when medical expenditure of a household passes over a certain ratio of household income. This research studied the effect of gender on CHE based on Korea Health Panel data. Methods: This study implemented binary logistic regression model to figure out whether gender affects CHE and how different gender groups show pattern of CHE process. With gender, age, marital status, income level, economic activity, membership of private insurance, existence of chronic disease, and self-rated health were included in the model. Results: Results showed that females faced CHE 1.5 times more than males (odds ratio, 1.241). Also, main determinants of CHE in female groups were marital status, while age and economic activity status were significant in male groups. Subgroup analysis displayed that married female under 35 years old are located in intersectionality of CHE including pregnancy and delivery, multiple health risk behaviors, mental stress, and relatively vulnerable social status due to lower income. Meanwhile, both gender above 50 years old faced remarkably high chance of CHE, which seems to be caused by complex health risk behaviors and chronic diseases. Conclusion: Such results implied not only that gender is an important determinant of CHE, but also other determinants of CHE differ according to gender, which suggests a necessity of gender-based CHE support and rescue policy.