• Proceedings of the KSPS conference
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• 2006.05a
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• pp.123-125
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• 2006

One of the biggest problems unsolved in emotional speech acquisition is how to make or find a situation which is close to natual or desired state from humans. We proposed a method to collect emotional speech data by scriptual context. Several contexts from the scripts of drama were chosen by the experts in the area. Context were divided into 6 classes according to the contents. Two actors, one male and one female, read the text after recognizing the emotional situations in the script.

• Journal of The Korean Association of Information Education
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• v.15 no.1
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• pp.147-154
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• 2011

In this Paper, we suggest the effective pre-correction algorithm on sensor values and the classification algorithm for gesture recognition that use values for each axis of the accelerometer to send data(a number or specific input data) to device. we know that creation of reliable preprocessed data in experimental results through the error rate of X-Axis and Y-Axis for pre-correction and post-correction. we can show high recognition rate through recognizer using the normalization and classification algorithm for the preprocessed data.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.494-497
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• 2006

The objectives of this study are to develop and evaluate the Neural Network algorithm which can predict the inelastic shortening such as the creep strain and the drying shrinkage strain of reinforced concrete members using the previous test data. New learning algorithms for the prediction of creep strain and the drying shrinkage strain are proposed focusing on input layer components and a normalization method for input data and their validity is examined through several test data. In Neural Network algorithm, the main input data to be trained are the compressive strength of the concrete, volume to surface ratio, curing condition, relative humidity, and the applied load. The results show that the new algorithms proposed herein successfully predict creep strain and the drying shrinkage strain.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.23-24
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• 1996

We reexamined CDM texture large-scale structure (LSS) formation model. We confirmed that texture model is consistent with 4-year COBE data both in an open and a critical matter density (${\Omega}_0$ = 1) universes, and then obtained normalization for density perturbation power spectrum. We next compare the power spectrum with LSS observation data. Contrary to the previous literature, we found that texture model matches with these data in an open universe no better than in an ${\Omega}_0$ = 1 universe. We also found that the model is more likely to fit these data in a cosmological constant dominated ($\Lambda-$) universe.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.225-227
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• 1995

An object detection algorithm using a modified IDM(Image Differential Method) is proposed for detecting an object in a level crossing area. The conventional object detection method using LASER light has the deadzone that it cannot detect small objects, while the object detection method using image data in a level crossing area can detect such small objects. But the image data in a level crossing area can be changeable easily because the data is outdoor and sensitive to such surrounding environments as the change of the sun beam, the shadow of cars, and so on. So we resolve these problems by adding the normalization and the process for shadow of the image data in a level crossing area to the basic IDM(Image Differential Method).

• International Journal of Contents
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• v.17 no.4
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• pp.79-90
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• 2021

With the deepening of population aging, pension has become an urgent problem in most countries. Community smart pension can effectively resolve the problem of traditional pension, as well as meet the personalized and multi-level needs of the elderly. To predict the pension intention of the elderly in the community more accurately, this paper uses the decision tree classification method to classify the pension data. After missing value processing, normalization, discretization and data specification, the discretized sample data set is obtained. Then, by comparing the information gain and information gain rate of sample data features, the feature ranking is determined, and the C4.5 decision tree model is established. The model performs well in accuracy, precision, recall, AUC and other indicators under the condition of 10-fold cross-validation, and the precision was 89.5%, which can provide the certain basis for government decision-making.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.190-198
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• 2023

Sentiment analysis can be applied to all texts generated from websites, blogs, messengers, etc. The study fulfills an artificial intelligence sentiment analysis estimating visiting evaluation opinions (reviews) and visitor ratings, and suggests a deep learning model which foretells either an affirmative or a negative inclination for new reviews. This study operates review big data about Jeju tourist attractions which are extracted from Google from October 1st, 2021 to November 30th, 2021. The normalization data used in the propensity prediction modeling of this study were divided into training data and test data at a 7.5:2.5 ratio, and the CNN classification neural network was used for learning. The predictive model of the research indicates an accuracy of approximately 84.72%, which shows that it can upgrade performance in the future as evaluating its error rate and learning precision.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1007-1017
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• 2019

Currently, most biometrics system authenticates users by using single biometric information. This method has many problems such as noise problem, sensitivity to data, spoofing, a limitation of recognition rate. One method to solve this problems is to use multi biometric information. The multi biometric authentication system performs information fusion for each biometric information to generate new information, and then uses the new information to authenticate the user. Among information fusion methods, a score-level fusion method is widely used. However, there is a problem that a normalization operation is required, and even if data is same, the recognition rate varies depending on the normalization method. A rank-level fusion method that does not require normalization is proposed. However, a existing rank-level fusion methods have lower recognition rate than score-level fusion methods. To solve this problem, we propose a rank-level fusion method with higher recognition rate than a score-level fusion method using correlation coefficient. The experiment compares recognition rate of a existing rank-level fusion methods with the recognition rate of proposed method using iris information(CASIA V3) and face information(FERET V1). We also compare with score-level fusion methods. As a result, the recognition rate improve from about 0.3% to 3.3%.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.1
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• pp.93-103
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• 1997

Charpy V-notch impact tests were performed on the full-, half-and third-size specimens from two ferritic SA 508 Cl. 3 steels for nuclear pressure vessel. New normalization factors were proposed to predict the upper shelf energy(USE) and the ductile-brittle transition temperature(DBTT) of full-size specimens from the measured data on sub-size specimens. The factors for the USE and the DBTT are $(Bb^2/Kt); and; (Bb/R)^1/2/, $ respectively, where B the width, b the ligament size, $K_{t}$ the elastic stress concentration factor, and R the notch root radius. These correlations successfully estimated the USE and DBTT of the full-size specimens based on sub-size specimen data. In addition, the size effects were studied to develop the correlations among absorbed energy, lateral expansion(LE) and displacement. It was also found that the LE was able to be estimated from the displacement obtained by the instrumented impact test, and that the displacement would be used as a criterion for the toughness of the steels corresponding to change in their yield strength.h.