• Journal of International Society for Simulation Surgery
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• 제2권2호
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• pp.64-66
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• 2015

Purpose : In this paper we proposed cascade feature filter and projection method for rapid human face recognition for the large-scale high-dimensional face database. Materials and Methods : The relevant features are selected from the large feature set using Fast Correlation-Based Filter method. After feature selection, project them into discriminant using Principal Component Analysis or Linear Discriminant Analysis. Their cascade method reduces the time-complexity without significant degradation of the performance. Results : In our experiments, the ORL database and the extended Yale face database b were used for evaluation. On the ORL database, the processing time was approximately 30-times faster than typical approach with recognition rate 94.22% and on the extended Yale face database b, the processing time was approximately 300-times faster than typical approach with recognition rate 98.74 %. Conclusion : The recognition rate and time-complexity of the proposed method is suitable for real-time face recognition system on the large-scale high-dimensional face database.

• Nutrition Research and Practice
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• 제13권6호
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• pp.521-528
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• 2019

BACKGROUND/OBJECTIVES: The aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. MATERIALS/METHODS: We collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of $150{\times}150$ and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition. RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks. CONCLUSION: The results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.

• 한국정밀공학회지
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• 제30권9호
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• pp.901-908
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• 2013

The introduction of industrial intelligent robot using vision sensor has been interested in automated factory. 2D and 3D vision sensors have used to recognize object and to estimate object pose, which is for packaging parts onto a complete whole. But it is not trivial task due to illumination and various types of objects. Object image has distorted due to illumination that has caused low reliability in recognition. In this paper, recognition method of complex shape object has been proposed. An accurate object region has detected from combined binary image, which has achieved using DoG filter and local adaptive binarization. The object has recognized using neural network, which is trained with sub-divided object class according to object type and rotation angle. Predefined shape model of object and maximal slope have used to estimate the pose of object. The performance has evaluated on ETRI database and recognition rate of 96% has obtained.

• Bulletin of the Korean Chemical Society
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• 제29권5호
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• pp.1069-1072
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• 2008

• Bulletin of the Korean Chemical Society
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• 제28권11호
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• pp.2015-2019
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• 2007

The article reports the synthesis of a novel bispyridino-18-crown-6 ether, 7-{[(5S,15S)-5,15-diphenyl- 3,6,14,17-tetraoxa-23,24-diazatricyclo[17.3.1.18,12]tetracosa-1(23),8(24),9,11,19,21-hexaen-10-yl]oxy}heptylferrocenamide 6, bearing the C2-symmetric diphenyl substituents as chiral barriers and the ferrocenyl groups serving as an electrochemical sensor, and its electrochemical study with D- and L-AlaOMe·HCl as the guest by cyclovoltametry.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2821-2823
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• 1999

In this paper we proposed an algorithm for rotation-invariant pattern recognition and rotated angle estimation between two patterns by employing selective template matching. Generally template matching has been used in determining the location of pattern but template matching requires a number of calculating correlation. To reduce the number of correlation we used steady-state genetic algorithm which is effective in optimization problem. We apply this method to distinguish specific pattern from similar coin patterns and estimate rotated angle between patterns. Our result leads us to the conclusion that proposed method performed faster than classical template matching

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.

• 대한화학회지
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• 제61권5호
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• pp.231-237
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• 2017

Efficiently removing Al(III) from rare earth is very significant because even trace amount of Al(III) can cause serious harm to the rare earth materials. In this paper, a nitrogen-containing activated carbon, AC-P700, was synthesized using peas as raw materials. The AC-P700 was characterized by surface area analyzer, FT-IR, and XPS methods. The adsorption and recognition properties of AC-P700 towards Al(III) were investigated, and the recognition mechanism was also analyzed. The BET special surface area of AC-P700 was $1277.1m^2{\cdot}g^{-1}$, and the average pore diameter was 1.90 nm. The AC-P700 possesses strong adsorption affinity and excellent recognition selectivity towards Al(III). The adsorption capacity for Al(III) could reach to $0.53mmol{\cdot}g^{-1}$, and relative selectivity coefficients relative to La(III) and Ce(III) is 9.6 and 8.7, respectively. Besides, AC-P700 possesses better regeneration ability and reusability.

• 대한치과기공학회지
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• 제34권4호
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• pp.423-431
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• 2012

Purpose: The purpose of this study was to examine the recognition of dental technician's about including denture into the coverage of the national health insurance. Methods: This study carried out self-administered questionnaire survey from June 10, 2012 to June 20 by having research subjects as 230 dental technician. Except 22 copies with incomplete response, 208 copies were used as the materials of final analysis. Results: The recognition of dental technician on the national health insurance of denture was 48%, but there was a low recognition on the details. The rates of dental technician who approved of the inclusion of denture into the coverage of the health insurance respectively stood at 59%. Conclusion: The coverage of the health insurance should be extended to dental medicine in a manner to satisfy dental technicians, dental service providers and receivers. Also, further studies for the extending coverage of the details are needed.

• 복식문화연구
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• 제23권3호
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• pp.523-536
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• 2015

Corporate association-which refers to consumers' beliefs, knowledge, perceptions, and evaluations of a corporation -can affect consumers' purchasing intentions. Corporate association consists of corporate ability association and corporate social responsibility association. Corporate ability association refers to a company's product quality, corporate innovation, productivity, consumer orientation, and after service. Corporate social responsibility association, which refers to the social perspective a company has of its responsibility to society, can affect corporate image and consumers' purchasing intentions. Eco-labeling for protecting and sustaining the environment is one of the important green marketing strategies in the fashion business that can influence corporate association and consumers' purchasing intentions. The purpose of this study was to investigate the effect of consumers' eco-label recognition on their corporate association and intentions to purchase eco-friendly fashion products. Questionnaires were distributed to consumers. The 263 usable questionnaires that were returned were analyzed by descriptive statistics, Cronbach's alpha, factor analysis, regression analysis, and t-test. The results were as follows: There was a significant effect of eco-label recognition on corporate association (ability association and social responsibility association). Eco-label recognition and corporate association were found to significantly affect consumers' purchasing intentions. Regarding the eco-friendly fashion product buying experience, there was no significant difference on corporate association and buying intention, but there was significant difference on eco-label recognition.