• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2265-2267
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• 2007

The purpose of this work is to compare the local fitting and global fitting approaches while applying regression model to the PTT-BP data for the prediction of exercise blood pressures. We used linear and nonlinear regression models to represent the PTT-BP relationship during exercise. PTT-BP data were acquired both under resting state and also after cycling exercise with several load conditions. PTT was calculated as the time between R-peak of ECG and the peak of differential photo-plethysmogram. For the identification of the regression models, we used local fitting which used only the resting state data and global fitting which used the whole region of data including exercise BP. The results showed that the global fitting was superior to the local fitting in terms of the coefficient of determination and the RMS (root mean square) error between the experimental and estimated BP. The nonlinear regression model which used global fitting showed slightly better performance than the linear one (no significant difference). We confirmed that the wide-range of data is required for the regression model to appropriately predict the exercise BP.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.158-169
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• 1999

This paper is concerned with the application of generalized linear interactive modelling(GLIM) to the binary categorical data. To analyze the categorical data given by a contingency table, finding a good-fitting loglinear model is commonly adopted. In the case of a contingency table with a response variable, we can fit a logit model to find a good-fitting loglinear model. For a given $2^4$ contingency table with a binary response variable, we show the process of fitting a loglinear model by fitting a logit model using GLIM and SAS and then we estimate parameters to interpret the nature of associations implied by the model.

• Journal of Fashion Business
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• v.17 no.5
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• pp.57-69
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• 2013

The purpose of this study is to compare the traditional shopping model and virtual avatar fitting model with regards to credibility and favorable impression effects on shopping mall satisfaction, product preferences, and purchasing intentions of apparel e-commerce. Questionnaires are distributed to 10-30s years old consumers who live in Seoul. Data are analyzed by descriptive statistics, Cronbach's ${\alpha}$, and regression analysis. The results are that the provoked credibility and favorable impression from the traditional shopping model affects the consumers' shopping mall satisfaction and buying intention in descending order. In additional, the credibility from traditional shopping model affects the product preference. The provoked credibility from the virtual fitting model influences the consumers' product preferences, and buying intentions. The favorable impression from the virtual fitting model affects shopping mall satisfaction. In general, provoked credibility from virtual avatar fitting model and traditional shopping model play key roles which could influence the consumers' buying intention.

• Applied Chemistry for Engineering
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• v.29 no.6
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• pp.789-798
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• 2018

The role of empirical equation to predict the performance of polymer electrolyte membrane fuel cell is important. The activation, ohmic and mass transfer losses were separated in a polarization curve, and the curve fitting according to each region was performed using Kim's model and Hao's model. Changes of each loss were compared according to operation variables of the temperature, pressure, oxygen concentration and membrane thickness. The existing model showed a good fitting convergence, but less fitting accuracy in the separated loss region. A new model using the convergence coefficient was suggested to improve the accuracy of performance prediction of fuel cells of which results were demonstrated.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.159-161
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• 2003

Fitting the projected wire-frame model to the detected edge pixels on images by using least-squares approach, called Least-squares Model-image Fitting (LSMIF), is the key of the Model-based Building Extraction (MBBE). It is implemented by iteratively adjusting the model parameters to minimize the squares sum of distances from the extracted edge pixels to the projected wire-frame. This paper describes a series of experiments and studies on various factors affect the fitting results, including the edge detectors, the weighting rules, the initial value of parameters, and the number of overlapped images. The experimental result is not only helpful to clarify the influences of each factor, but is also able to enhance the robustness of the LSMIF algorithm.

• Science of Emotion and Sensibility
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• v.5 no.4
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• pp.1-10
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• 2002

A web based virtual try-on provides customers a more enjoyable shopping experience that visualize clothes on personal mannequin. The researchers compared virtual fitting models which were developed in 2000 at Korea and in 2000 and 2002 at U.S. The results of this study as follows: The information about user's body size was required to input for selection of a virtual fitting model. 7 to 19 different body size, shape, and face features including weight and height were needed for visualizing virtual fitting model. The body type of the U.S virtual fitting model(My virtual model) was selected by front view silhouette for women, and by shoulder width and midriff silhouette for men. The more detailed information was required for developing Korean virtual fitting model. The additional body size information required in the site were leg and arm lengths, waist length, and thigh and ankle circumferences. The body proportion of Korean cyber personal mannequin was longer and narrower than the U.S cyber personal mannequin. It was recommended that standardized body length, width, and depth proportions calculated from national anthropometric data must be applied for developing Korean virtual fitting model. With application of more detailed information on face feature and advanced graphic image technology the 'My virtual model in 2002 resembled the human body shape of various race.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.841-849
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• 2001

The influence in fitting an equicorrelation model is investigated using the influence function. The influence functions for the model parameters are derived and its sample versions are used for investigating the influence of observations on the estimators of the parameters. Some relationships among the sample versions are found. We will derive a measure for identifying observations that have a large influence on the test of fitting the equicorrelation model using the influence function method. An example is given for illustration.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.52-57
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• 2006

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

• Current Optics and Photonics
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• v.2 no.1
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• pp.69-78
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• 2018

This paper presents a precise edge detection algorithm for the critical dimension (CD) measurement of a Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) pattern. The sigmoid surface function is proposed to model the blurred step edge. This model can simultaneously find the position and geometry of the edge precisely. The nonlinear least squares fitting method (Levenberg-Marquardt method) is used to model the image intensity distribution into the proposed sigmoid blurred edge model. The suggested algorithm is verified by comparing the CD measurement repeatability from high-magnified blurry and noisy TFT-LCD images with those from the previous Laplacian of Gaussian (LoG) based sub-pixel edge detection algorithm and error function fitting method. The proposed fitting-based edge detection algorithm produces more precise results than the previous method. The suggested algorithm can be applied to in-line precision CD measurement for high-resolution display devices.