• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.1-6
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• 2021

This research examines the stock market index determinants and the prediction using the FFT curve fitting of the Jakarta Stock Exchange (JKSE) Composite Index during the COVID-19 pandemic. This paper has used daily data of Jakarta Stock Exchange (JKSE) Composite Index, interest rate, and exchange rate from 15 October 2019 to 15 September 2020, and a total of 224 observations, retrieved from Indonesia Stock Exchange (IDX), Indonesia Statistics Central Bureau and Observation & Research of Taxation. The study covers descriptive statistics, multicollinearity test, hypothesis tests, determination test, and prediction using FFT curve fitting. The results unveil four fresh and robust evidence. Partially, the interest rate has affected positively and significantly the stock market index. Partially, the exchange rate has affected negatively and significantly the stock market index. The F-test result, interest rate, and exchange rate have significantly affected the stock market index (JKSE) simultaneously. Furthermore, the FFT curve fitting has predicted that the stock market fluctuates and increases over time. The results have shown a strong influence of the independent variables and the dependent variable. The value of Adjusted R-Square is 0.719, which means that the independent variables have simultaneously impacted the dependent variable for 71.9%; other factors have influenced the remaining 28.1%.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.159-166
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• 2021

In order to analyze cell images, accurate segmentation of each cell is indispensable. However, the reality is that accurate cell image segmentation is not easy due to various noises, dense cells, and inconsistent shape of cells. Therefore, in this paper, we propose an algorithm that combines marker-based watershed segmentation and ellipse fitting method for glioblastoma cell segmentation. In the proposed algorithm, in order to solve the over-segmentation problem of the existing watershed method, the marker-based watershed technique is primarily performed through "seeding using local minima". In addition, as a second process, the concave point search using ellipse fitting for final segmentation based on the connection line between the concave points has been performed. To evaluate the performance of the proposed algorithm, we compared three algorithms with other algorithms along with the calculation of segmentation accuracy, and we applied the algorithm to other cell image data to check the generalization and propose a solution.

• Journal of Computational Design and Engineering
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• v.2 no.4
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• pp.218-232
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• 2015

Piecewise linear (G01-based) tool paths generated by CAM systems lack $G_1$ and $G_2$ continuity. The discontinuity causes vibration and unnecessary hesitation during machining. To ensure efficient high-speed machining, a method to improve the continuity of the tool paths is required, such as B-spline fitting that approximates G01 paths with B-spline curves. Conventional B-spline fitting approaches cannot be directly used for tool path B-spline fitting, because they have shortages such as numerical instability, lack of chord error constraint, and lack of assurance of a usable result. Progressive and Iterative Approximation for Least Squares (LSPIA) is an efficient method for data fitting that solves the numerical instability problem. However, it does not consider chord errors and needs more work to ensure ironclad results for commercial applications. In this paper, we use LSPIA method incorporating Energy term (ELSPIA) to avoid the numerical instability, and lower chord errors by using stretching energy term. We implement several algorithm improvements, including (1) an improved technique for initial control point determination over Dominant Point Method, (2) an algorithm that updates foot point parameters as needed, (3) analysis of the degrees of freedom of control points to insert new control points only when needed, (4) chord error refinement using a similar ELSPIA method with the above enhancements. The proposed approach can generate a shape-preserving B-spline curve. Experiments with data analysis and machining tests are presented for verification of quality and efficiency. Comparisons with other known solutions are included to evaluate the worthiness of the proposed solution.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.57-65
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• 2018

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS : For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS : The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination ($R^2$) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as $R^2$ had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.253-263
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• 2021

Machine learning is the process of constructing a cost function using learning data used for learning and an artificial neural network to predict the data, and finding parameters that minimize the cost function. Parameters are changed by using the gradient-based method of the cost function. The more complex the digital signal and the more complex the problem to be learned, the more complex and deeper the structure of the artificial neural network. Such a complex and deep neural network structure can cause over-fitting problems. In order to avoid over-fitting, a weight decay regularization method of parameters is used. We additionally use the value of the cost function in this method. In this way, the accuracy of machine learning is improved, and the superiority is confirmed through numerical experiments. These results derive accurate values for a wide range of artificial intelligence data through machine learning.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.275-290
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• 2008

The size of microarray gene expression data is very big and its observation process is also very complex. Thus missing values are frequently occurred. In this paper we propose the sequential partial least squares(SPLS) regression fitting method to estimate missing values for time course gene expression data that has correlations among observations over time points. The SPLS method is to combine the sequential technique with the partial least squares(PLS) regression fitting method. The usefulness of method proposed is evaluated through some simulation study for three yeast time course data.

• Journal of Fashion Business
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• v.22 no.3
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• pp.122-142
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• 2018

In the virtual fitting system, the use of a 3D avatar is not a simple garment model, but it should be able to reproduce the size and shape of the customer using a fitting system. Although various virtual fitting systems have their own 3D avatar sizing systems and provide 3D avatars that match the size of the customer, there are limitations in realizing the actual body shape in actual use by the consumer. The purpose of this study is to realize a 3D avatar with excellent size and conformity for customer use. Therefore, this study aims to provide basic data for the formation of a 3D standard avatar of Korean women aged in their 20's, by comparing and analyzing the degree of the consumer user friendly system change of a body type, and the consumer's ability in selecting a consumer representative body type. Based on the survey data of 'Size Korea' conducted from 2004 to 2015 at three times, we examined the change of body shape over 10 years. Then, based on the results of 6th and 7th data, 4 factors of the concurrent body shape change of women of the consumer demographic studied were selected through the use of a factor analysis. Following this analysis, the 4 extracted factors were clustered again and finally released 7 representative body types, which were obtained based on height and weight. The size of each representative figure is derived by the use of a regression analysis, and it is used as a basic data for 3D avatar formation of the virtual fitting system.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.3 s.162
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• pp.463-474
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• 2007

With rapid expansion in e-retailing of apparel business, personalized fitting model service shows the possibility as the differentiated marketing strategy in cyber shopping. According as necessity of personalized fitting model construction rises, it is tried personalized fitting model creation in several fields such as computer engineering, mechanical engineering, information engineering. But, because existent study was concentrated only on human body modeling, it does not reflect average morphological characteristics of human body properly. In this study, we wish to examine if morphing is fit for expressing characteristic of average human body shape and suggest desirable morphing. We used 3-D scan data of 254 Korean middle aged men collected by Size Korea 2004. The result of this study are as follows: Lower body types were categorized by height hip girth and lower drop(hip girth-navel girth) which were main factors of lower body shape. Then each factor was divided into 3 groups respectively, 30% in the middle, over 30%, under 30%. In 27 groups, the group which belonged to 30% in the middle of height, 30% in the middle of hip girth, 30% in the middle of lower drop was selected as a representative group. We tested geometrical figure by differ volume, tilt, position of point. And we created a representative type of men's lower bodies by morphing the representative group and analyzed it's horizontal, vertical sections. A representative type which was created by morphing reflected a real body and changed realistically at the part of hip, crotch, calf muscle and so on. A cross sections of a representative type were similar to average cross sections of the representative group in size and shape. So it was proved that morphing was successful.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.500-505
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• 2000

This paper deals with wafer temperature uniformity control essential in rapid thermal processing (RTP). One of the important control objectives of RTP is to keep the temperature over the wafer surface as uniformly as possible. For this, a discrete time state equation around the operating point is first identified by using the subspace fitting method, and a multivariable LQG(Linear Quadratic Gaussian) controller is designed based on the identified model. Simulation and experimental results show improvement in temperature uniformity over the conventional PID method.

• Journal of the Korean Statistical Society
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• v.5 no.1
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• pp.19-28
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• 1976

In empirical study for fitting a multiple linear regression model for successive cross-sections data observed on the same set of independent variables over several time periods, one often faces the problem of poor $R^2$, the multiple coefficient of determination, which provides a standard measure of how good a specified regression line fits the sample data.