• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.66-70
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• 2005

The full automation of welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to select an optimal neural network model.

• Journal of Biomedical Engineering Research
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• v.43 no.3
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• pp.131-135
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• 2022

Due to COVID-19, Correct method of wearing mask is important to prevent COVID-19 and the other respiratory tract infections. And the deep learning technology in the image processing has been developed. The purpose of this study is to create the type of mask wearing dataset for deep learning models and select the deep learning model to detect the wearing mask correctly. The Image dataset is the 2,296 images acquired using a web crawler. Deep learning classification models provided by tensorflow are used to validate the dataset. And Object detection deep learning model YOLOs are used to select the detection deep learning model to detect the wearing mask correctly. In this process, this paper proposes to validate the type of mask wearing datasets and YOLOv5 is the effective model to detect the type of mask wearing. The experimental results show that reliable dataset is acquired and the YOLOv5 model effectively recognize type of mask wearing.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.6-8
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• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.537-544
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• 2015

This paper provided two types of design method on moving core type LOA and one type of design method on moving coil type LOA, and compared and examined each of its characteristics. In order to conduct parametric design process, voltage equation was used to schematize Lmin/K and L/M map, and the schematized map was used to determine Lmin, K or L, M. In order to meet requirements such as thrust force and input voltage and to satisfy the target values of Lmin, K or L, M, the types and sizes of each type were designed using geometry design process. 2-FEA was conducted for each of the designed model. After examining thrust force based on the location of the mover, Type-1 showed radical change in thrust force as movers moved, and Type-2 and Type-3 showed constant appearance of thrust force. The total volume of the designed LOA model was compared to select the model with highest thrust force density. Also, the weight of the mover for each model was compared in order to select the model that was predicted to have highest mechanical responsiveness and stroke characteristics.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.1
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• pp.1-9
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• 2022

In this study, the selection action on the mesh in the net pot for whelk (Buccinum opisthoplectum) is experimentally considered, and the selectivity was compared by the SELECT model and the Nashimoto's method with the probability model according to the contact shape of the mesh and the whelk. The experiments of the mesh size selectivity was conducted for two mesh sizes: 70 mm (inner stretched size 65.4 mm) and 44 mm (inner stretched size 39.5 mm). Selectivity experiments were conducted three times in total for each mesh size used 264 whelks. In addition, Nashimoto's method analyzed the retention probability using probability model for whether the mesh passed or not based on the carapace width of the whelk. As a result of the selectivity analysis, the 50% selection carapace width for the mesh size of 70 mm was similar to 43.62 mm in the SELECT model and 42.64 mm in the Nashimoto's method. However, the 44 mm mesh with relatively small mesh size showed differences of 40.01 mm and 26.80 mm, respectively. As for the mesh size selectivity of whelk, it was found that the smaller the mesh size, the lower the selectivity. In addition, in the selectivity study on the mesh size of whelk, an evaluation method that closely considers the contact shape between the mesh and the target species is required.

• Korean Institute of Interior Design Journal
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• no.38
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• pp.225-232
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• 2003

The purpose of this study is to select the optimal and minimum transmittance for visual amenity in office buildings. This study progressed as follows. The first, we select 5 films with various transmittance based on the current film makers whose films being used on the windows of office building nowadays and the case study of construction. And then we choose 6 kinds of transmittance as evaluation object including plain glass. The second, we made a mock-up models having different transmittance on the southern windows with model of real scale office building. The third, we select 17 pairs of adjectives for the evaluation of visual comfort on interior or exterior conditions with transmittance factor. The fourth, subjective evaluation experiment was done using selected evaluation adjectives and the result was analyzed. The results are as follows : the minimum transmittance appropriate for the office building is 30%∼40% and the optimal transmittance range is 40%∼60%.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.259-270
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• 2013

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.