• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.11
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• pp.1050-1055
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• 2007

P.A.(public address) system is considered as an useful active soundscape appliance which can gives a place identity and vitality by introducing conventional musics, environmental musics, bird singing sounds etc. In this study, the main aim is to know the optimistic distance from the speaker and sound pressure level range of introducing sound. So, the sound pressure level of P.A. system due to distances were measured and subjects' responses with level variations were checked. The main results are as follows. Level range from 64 dB to 71 dB is comfortable for subjects. And the optimal level of introducing sound is related with sound source characteristics. The results of this study could be used for street furniture location design and P.A. system output level.

• Korean Journal of Community Nutrition
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• v.4 no.1
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• pp.95-102
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• 1999

This study used questionnaires to investigate the safety awareness for imported foods by 365 male and female adults in Taejon. The results of the study were as follow : By factor analysis, the subjects' behaviors and awareness of the imported food was grouped into 3 factors such as 'health and quality factor', 'purchasing factor' and 'contamination factor'. 'Health and quality factor' and 'purchasing factor' were not recognized negatively by the subjects, moreover' contamination factor' was recognized very highly. The subjects' concern and worry about the imported food was also very high. The marital status, education level, nutriton knowledge adn recognition level of contamination by pesticides and heavy metals of foods partially affected the recognitio level of imported foods. The major selection criteria of imported food were distribution period(36.3%), price(28.8%) and purchasing experience(17.3%). The imported food mean discrimination score was 8.4±3.1 out of 13. The worst discriminatio score was red pepper. The subjects' experiences with imported foods selection affected the most instead of education level or nutrition knowledge. The higher discrimination score group more negatively recognized imported food and contamination recognition level was higher whereas the lowerdiscrimination score group more positively recognized the purchasing frequency and with to buy more easily. But both groups desired to reinforce contamination control. The better discrimination score of imported food pooring recognized sanitation concerns(p<0.05), quality(p<0.05), cooking convenience(p<0.01), desire for more variety(p<0.05), and the higher contamination recognition level(p<0.05) and desire to reinforce contamination control(p<0.01).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.413-435
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• 2018

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.929-938
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• 2004

This paper proposes a pose-invariant face recognition method using cylindrical model and stereo camera. We divided this paper into two parts. One is single input image case, the other is stereo input image case. In single input image case, we normalized a face's yaw pose using cylindrical model, and in stereo input image case, we normalized a face's pitch pose using cylindrical model with previously estimated pitch pose angle by the stereo geometry. Also, since we have an advantage that we can utilize two images acquired at the same time, we can increase overall recognition performance by decision-level fusion. Through representative experiments, we achieved an increased recognition rate from 61.43% to 94.76% by the yaw pose transform, and the recognition rate with the proposed method achieves as good as that of the more complicated 3D face model. Also, by using stereo camera system we achieved an increased recognition rate 5.24% more for the case of upper face pose, and 3.34% more by decision-level fusion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.209-218
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• 2013

Early researches in human action recognition have focused on tracking and classifying articulated body motions. Such methods required accurate segmentation of body parts, which is a sticky task, particularly under realistic imaging conditions. Recent trends of work have become popular towards the use of more and low-level appearance features such as spatio-temporal interest points. Given the great progress in pose estimation over the past few years, redefined views about pose-based approach are needed. This paper addresses the issues of whether it is sufficient to train a classifier only on low-level appearance features in appearance approach and proposes effective pose-based approach with pose estimation for emotional action recognition. In order for these questions to be solved, we compare the performance of pose-based, appearance-based and its combination-based features respectively with respect to scenario of various emotional action recognition. The experiment results show that pose-based features outperform low-level appearance-based approach of features, even when heavily spoiled by noise, suggesting that pose-based approach with pose estimation is beneficial for the emotional action recognition.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.69-77
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• 1993

Automatic image recognition system is essential for a better man-to machine interaction. Because of the noise and deformation due to the sensor operation, it is not simple to build an image recognition system even for the fixed images. In this paper neural network which has been reported to be adequate for pattern recognition task is applied to the fixed and variational(rotation, size, position variation for the fixed image)recognition with a hope that the problems of conventional pattern recognition techniques are overcome. At fixed image recognition system. ART model is trained with face images obtained by camera. When recognizing an matching score. In the test when wigilance level 0.6 - 0.8 the system has achievel 100% correct face recognition rate. In the variational image recognition system, 65 invariant moment features sets are taken from thirteen persons. 39 data are taken to train multi-layer perceptron and other 26 data used for testing. The result shows 92.5% recognition rate.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.476-488
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• 2017

The object of this study was to survey the relationship between emotional self-support and the level of subjective health recognition of participators in the self support program. The health condition which is recognized subjectively by participators could affect the participation motive and the attitude to the program, and could be connected to the achievement of self-support. Therefore I tried to survey the implications and the relationship between emotional self-support and the level of subjective health recognition in terms of progress for the economic self-support. The objectives were self-support program participators in Wonju, and 127 questionaries were collected. The results of this study were as followings; First, there was the positive relationship between emotional self-support and the level of subjective health recognition. Second, the participation period in the self-support program affected emotional self-support and the level of subjective health recognition negatively, and the affects of the ages were meaningless. The suggestion of this study is that the level of emotional self-support and subjective health recognition could be developed if the emotional and psychological program is developed which makes the conditional pensioners perceive the living attitude and the thoughts to the works positively.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.726-729
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• 2006

As a active soundscape facility, P.A. system is a useful instrument to give place identity and vitality by letting out music, environmental music, bird singing sound etc. In this study, to know the optimistic distance and sound level range of introducing sound, sound levels due to distance were measured and subject responses were checked by questionnaire. Levels from 64dB to 71dB are recommended by subjects. And the optimistic level of introducing level is related with level variance of sound source. The results of this study could used for street furniture location design and P.A. system output level.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.75-80
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• 2010

Noise handing is very important in voice recognition of vehicle environment. that has been studying about to hardware and software approach. hardware method that is noise filter circuit design, basically using Low-pass filter. it was shown a good result. and the side of software that has been developing about to algorithm for Noise canceler, NN(neural network), etc. in this paper we have analysis about to classified parameter pattern matting level for voice recognition on car noise environment that use of DTW(Dynamic Time Warping) which is applicable time series pattern recognition algorithm.