• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.28-34
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• 2019

The purpose of this study is to analyze the meaning and structure of the experiences of 20 years old women who participated in the pregnancy recognition improvement program developed by JA Kim et al. The participants of the study were interviewed three times in total for 20 years old of 6 women. The interview period was from December 1 to December 30, 2018. The interview data were processed through the analysis and interpretation process using the phenomenological research of Giorgi method. As a result, 33 semantic units were derived, and then divided into 4 subcomponents and divided into 2 categories. After participating in the program, they tried to maintain their health, use appropriate welfare policies, and deeply consider their lives as mysterious mothers. In conclusion, this study suggests that the implementation of the pregnancy awareness improvement program for young women in a small group, more systematically and continuously, effectively implements low fertility measures in Korea.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.192-197
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• 2020

In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

• Journal of the Korean housing association
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• v.13 no.3
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• pp.93-101
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• 2002

The goal of this survey is to propose planning and design informations for the public open spaces in apartment housing, through the observation and analysis of the current situations. For this, the planning information of housing suppliers about public open spaces and the spatial utilization of users were compared and by analyzing facility utilization and resident\`s recognition. This study is also intended to guide the future directions of the research for the improvement of public open spaces. The research follows three phases; \circled1 To understand the conditions of public open spaces in apartment housing sites through survey and analysis of catalogues and references. \circled2 To study on facility utilization and resident's recognition by observation and analysis. \circled3 To propose planning guidelines for the improvement of public open space by recognition differences of facilities.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.228-236
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• 2011

Emotion recognition plays an important role in the research area of human-computer interaction, and it allows a more natural and more human-like communication between humans and computer. Most of previous work on emotion recognition focused on extracting emotions from face, speech or EEG information separately. Therefore, a novel approach is presented in this paper, including face, speech and EEG, to recognize the human emotion. The individual matching scores obtained from face, speech, and EEG are combined using a weighted-summation operation, and the fused-score is utilized to classify the human emotion. In the experiment results, the proposed approach gives an improvement of more than 18.64% when compared to the most successful unimodal approach, and also provides better performance compared to approaches integrating two modalities each other. From these results, we confirmed that the proposed approach achieved a significant performance improvement and the proposed method was very effective.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.143-147
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• 2012

The rapid growth of camera technology can provide various types of information which was not previously provided. Furthermore, IP camera which has rapid data transfer rate and high resolution particularly provide a lot of useful functions beyond the existing simple surveillance capabilities. We are developing Real-Time Face Recognition Access Control System based on the camera technology, and improvement of face detection and recognition algorithms are vitally needed to realize that system. In this paper, we proposes a method to improve the computing speed and detection rate by adding new features to the existing Viola-Jones detection algorithm.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.85-93
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• 2011

Previous existing vocabulary recognition programs calculate general vector values from a database, so they can not process phonemes that form during a search. And because they can not create a model for phoneme data, the accuracy of the Gaussian model can not secure. Therefore, in this paper, we recommend use of the Bhattacharyya distance measurement method based on the features of the phoneme-thus allowing us to improve the recognition rate by picking up accurate phonemes and minimizing recognition of similar and erroneous phonemes. We test the Gaussian model optimization through share continuous probability distribution, and we confirm the heighten recognition rate. The Bhattacharyya distance measurement method suggest in this paper reflect an average 1.9% improvement in performance compare to previous methods, and it has average 2.9% improvement based on reliability in recognition rate.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.237-244
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• 2006

This study proposes an approach to the performance improvement of EMG(Electromyogram) pattern recognition. MFCC(Mel-Frequency Cepstral Coefficients)'s approach is molded after the characteristics of the human hearing organ. While it supplies the most typical feature in frequency domain, it should be reorganized to detect the features in EMG signal. And the dynamic aspects of EMG are important for a task, such as a continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most approaches. Thus, this paper proposes reorganized MFCC and HMM-GMM, which is adaptable for the dynamic features of the signal. Moreover, it requires an analysis on the most suitable system setting fur EMG pattern recognition. To meet the requirement, this study balanced the recognition-rate against the error-rates produced by the various settings when loaming based on the EMG data for each motion.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.335-340
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• 2018

Commercialized speech recognition systems that have an accuracy recognition rates are used a learning model from a type of speaker dependent isolated data. However, it has a problem that shows a decrease in the speech recognition performance according to the quantity of data in noise environments. In this paper, we proposed the vector quantization based speech recognition performance improvement using maximum log likelihood in Gaussian distribution. The proposed method is the best learning model configuration method for increasing the accuracy of speech recognition for similar speech using the vector quantization and Maximum Log Likelihood with speech characteristic extraction method. It is used a method of extracting a speech feature based on the hidden markov model. It can improve the accuracy of inaccurate speech model for speech models been produced at the existing system with the use of the proposed system may constitute a robust model for speech recognition. The proposed method shows the improved recognition accuracy in a speech recognition system.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.1
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• pp.54-68
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• 2015

The purpose of this study was to analyze the effect of environmental perception in neighborhood parks on user's recognition of health improvement. This study used objective field survey data and subjective user survey data at 8 neighborhood parks in Changwon City. According to the results, the perceptions of distance and street environment from home to park were evaluated highly but the perception of water spaces and various attractions within the parks were rated lowly in most of parks. As a result of factor analysis, 23 environmental perception variables were classified into 6 factors such as scenery, comfort, accessibility, activity, convenience and amenity. For the result of regression analysis between environmental perceptions and user's recognition of health improvement based on 6 factors, the recognition of physical health improvement was significantly influenced from environmental perception factors in 4 parks among 8 parks. 'Accessibility' and 'activity' were analyzed as the meaningful factors in 3 parks. Also, recognition of mental health improvement was significantly influenced from environmental perception factors in 5 parks among 8 parks and especially 'accessibility' was showed as the significant factor in 4 parks. The findings from this study can contribute to improve the physical environments of the present parks and can be a basic data for new park development.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.351-359
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• 1991

This paper describes performances of speaker adaptation using a probabilistic spectral mapping matrix in hidden-Markov model(HMM) -based Korean isolated word recognition. Speaker adaptation based on probabilistic spectral mapping uses a well-trained prototype HMM's and is carried out by Viterbi, dynamic time warping, and forward-backward algorithms. Among these algorithms, the best performance is obtained by using the Viterbi approach together with codebook adaptation whose improvement for isolated word recognition accuracy is 42.6-68.8 %. Also, the selection of the initial values of the matrix and the normalization in computing the matrix affects the recognition accuracy.