• Phonetics and Speech Sciences
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• v.3 no.4
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• pp.63-70
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• 2011

Recent studies on English $L^*$ (low pitch accent) have revealed the difference of changes in acoustic manifestation between utterances produced by Koreans and those produced by native speakers of English. However, not much effort has been made to compare $L^*$ focused constituents and non-focused constituents. At the same time, most previous works on focus realization are lacking in terms of normalization of acoustic measurement. Therefore, this research is dedicated to comparing the $L^*$ focused items and non-focused items realized by Koreans and Americans and to examining the realization of English $L^*$ produced by the two language groups with improved normalization of the acoustic features (F0, intensity and duration). Within-group analysis comparing focused words and non-focused words showed both Americans and Koreans prolonged the $L^*$ focused syllables but the effect size of syllable lengthening made by Koreans was far less than that made by Americans. Furthermore, significant F0 lowering was found in Americans but not in Koreans. However, the effect of intensity change caused by $L^*$ focus was not significant within each group. The effect of focused words was tested between the two groups revealing that Koreans implemented English $L^*$ focus with higher F0, lower intensity and shorter duration than Americans. In the instances in which a significant Group x Focus Location (initial, middle and final of a sentence) interaction was found, further analysis testing the effect of Group on each Focus Location was conducted. The testing showed that the Koreans produced shorter syllables at initial and middle of a sentence and higher F0 at initial of a sentence than Americans. Implications for the intonation training were also discussed.

• Journal of Energy Engineering
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• v.14 no.2 s.42
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• pp.140-146
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• 2005

In this study, a quantitative environmental impacts assessment was performed for various power technologies with a lift cycle assessment (LCA) method. The LCA is regarded as a useful tool far analyzing diverse environmental impacts at a local, regional, and global aspect. The investigated power plants such as nuclear, coal, and LNC power systems were selected because they took share over $90\%$ of domestic elec-tricity supply in Korea. Furthermore, a wind power technology was included as a representative energy source out of Korean renewable energy systems. According to the three geological aspects, environmental impacts had been categorized into eight types. For these impact categories, characterization had been carried out for comparing environmental burdens of power systems under consideration. Then, normalization had been done in order to gain a better understanding of the relative size among impact categories.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.671-676
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• 2016

Recently, according to the limelight of Fintech, simple payment using biometric at smartphone is widely used. In this paper, we propose a new contactless biometric method using thumb image without additional sensors unlike previous biometrics such as fingerprint, iris, and vein recognition. In our method, length, width, and skin texture information are used as features. For that, illumination normalization, skin region segmentation, size normalization and alignment procedures are sequentially performed from the captured thumb image. Then, correlation coefficient is calculated for similarity measurement. To analyze recognition accuracy, genuine and imposter matchings are performed. At result, we confirmed the FAR of 1.68% at the FRR of 1.55%. In here, because the distribution of imposter matching is almost normal distribution, our method has the advantage of low FAR. That is, because 0% FAR can be achieved at the FRR of 15%, the proposed method is enough to 1:1 matching for payment verification.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.1-7
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• 2004

This paper proposes a new method for facial feature extraction, and the method could be used to normalize face images for 3D face recognition. 3D images are much less sensitive than intensity images at a source of illumination, so it is possible to recognize people individually. But input face images may have variable poses such as rotating, Panning, and tilting. If these variances ire not considered, incorrect features could be extracted. And then, face recognition system result in bad matching. So it is necessary to normalize an input image in size and orientation. It is general to use geometrical facial features such as nose, eyes, and mouth in face image normalization steps. In particular, nose is the most prominent feature in 3D face image. So this paper describes a nose feature extraction method using 3D nasal masks that are similar to real nasal shape.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.525-538
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• 2020

A practical animal face classification system that classifies animals in image and video data is considered as a pivotal topic in machine learning. In this research, we are proposing a novel method of fully connected dual Deep Convolutional Neural Network (DCNN), which extracts and analyzes image features on a large scale. With the inclusion of the state of the art Batch Normalization layer and Exponential Linear Unit (ELU) layer, our proposed DCNN has gained the capability of analyzing a large amount of dataset as well as extracting more features than before. For this research, we have built our dataset containing ten thousand animal faces of ten animal classes and a dual DCNN. The significance of our network is that it has four sets of convolutional functions that work laterally with each other. We used a relatively small amount of batch size and a large number of iteration to mitigate overfitting during the training session. We have also used image augmentation to vary the shapes of the training images for the better learning process. The results demonstrate that, with an accuracy rate of 92.0%, the proposed DCNN outruns its counterparts while causing less computing costs.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.247-258
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• 2003

Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the face recognition literature. PCA is a statistical technique and its incorporation into a face recognition system requires numerous design decisions. We explicitly take the design decisions by in-troducing a generic modular PCA-algorithm since some of these decision ate not documented in the literature We experiment with different implementations of each module, and evaluate the different im-plementations using the September 1996 FERET evaluation protocol (the do facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. The result shows that empirical analysis of preprocessing, feature extraction, and matching performance is extremely important in order to produce optimized performance. In the second experiment, we examine variations in algorithm performance based on 100 randomly generated image sets (galleries) of the same size. The result shows that a reasonable threshold for measuring significant difference in performance for the classifiers is 0.10.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2109-2115
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• 2001

Miniaturized specimen technology is useful to characterize the mechanical behavior when it is difficult to sample the material enough for the test. In this study, two kinds of miniaturized Charpy impact specimens(i.e., miniaturized specimen with side groove and without side groove) of aged 1Cr- lMo-0.25V steel were prepared and tested. The relationship between the extent of degradation in terms of ductile brittle transition temperature(DBTT) and the fracture stress of 1Cr-1Mo-0.25V steel was established. The fracture stress obtained from miniaturized specimen without side groove turned out to be linearly related with the DBTT of standard specimen. Therefore the fracture toughness of aged turbine rotor steel might be evaluated by the fracture stress. In addition, the correlation between DBTT of standard specimen and that of miniaturized specimen was investigated. As the results of normalizing DBTT by maximum elastic tensile stress, the normalized DBTT of miniaturized specimen without side groove allows one to estimate that of standard specimen.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.226-228
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• 2021

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.

• Journal of Pharmaceutical Investigation
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• v.26 no.1
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• pp.33-41
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• 1996

In order to study the effect of phenobarbital(PB) on the hepatic transport of diltiazem(DTZ), $Ca^{2+}$ channel blocker, we used isolated hepatocytes of rat which was intraperitoneally pretreated with phenobarbital sodium(75 mg/kg) for four days once a day. For the isolation of rat liver cells, a modification of the two step procedure of Seglen was used. DTZ was dissolved in incubation buffer to the final DTZ concentrations of 200, 400, 600, 800 and 1000 ng/ml in order to elucidate the uptake characteristics of DTZ by hepatocytes. Reactions were stopped at 10, 20, 30, 45, 60, 90, 120 and 300 sec. The initial velocity was determined by disappearance of diltiazem in the hepatocyte suspension. On the other hand, to determine the effect of PB on the in vitro hepatic intrinsic clearance of DTZ we obtained the metabolism rates of DTZ in the control and the PB-pretreated rat hepatocyte at various time intervals. According to pretreatment with PB, the size of hepatocyte and the amount of protein per $10^6$ cells were significantly (p<0.01) increased from $26.92{\pm}0.1364\;m$ to $35.31{\pm}1.00\;m$ and from $468{\pm}6.5\;{\mu}g/10^6$ cells to $628.8{\pm}12.1{\mu}g/10^6$ cells, respectively. In the case or hepatic uptake of diltiazem, $K_m$ was not different in the normalization by cell numbers and increased from $2.90\;{\mu}M\;to\;13.89\;{\mu}M$ in the normalization by protein amount. $V_max$ was increased regardless of normalization by protein amount and cell numbers, from $1.21\;{\mu}mole/min \;{\cdot}\;mg\;protein\;to\;3.96\;{\mu}mole/min\;{\cdot}\;mg\;protein\;and\;from\;2.38\;{\mu}mole/min\;{\cdot}\;10^6\;cells\;to\;2.83\;{\mu}mole/min\;{\cdot}\;10^6\;cells$, respectively. The in vitro hepatic intrinsic clearance of DTZ was significantly (p<0.01) increased from $0.640{\pm}0.038\;ml/mim\;{\cdot}\;10^6\;cells\;to\;2.385{\pm}0.212\;ml/min\;{\cdot}\;10^6\;cells$ due to PB-pretreatment. These results suggest that the uptake of DTZ by hepatocyte is extremely fast and PB enhances the hepatic intrinsic metabolic clearance of DTZ.

• Journal of IKEEE
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• v.22 no.1
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• pp.14-20
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• 2018

The purpose of training a convolutional neural network (CNN) is to obtain weight factors that give high classification accuracies. The initial values of hyper-parameters affect the training results, and it is important to train a CNN with a suitable hyper-parameter set of a learning rate, a batch size, the initialization of weight factors, and an optimizer. We investigate the effects of a single hyper-parameter while others are fixed in order to obtain a hyper-parameter set that gives higher classification accuracies and requires shorter training time using a proposed VGG-like CNN for training since the VGG is widely used. The CNN is trained for four datasets of CIFAR10, CIFAR100, GTSRB and DSDL-DB. The effects of the normalization and the data transformation for datasets are also investigated, and a training scheme using merged datasets is proposed.