• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.411-425
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• 2021

Accelerated failure time (AFT) model represents a linear relationship between the log-survival time and covariates. We are interested in the inference of covariate's effect affecting the variation of survival times in the AFT model. Thus, we need to model the variance as well as the mean of survival times. We call the resulting model mean and variance AFT (MV-AFT) model. In this paper, we propose a variable selection procedure of regression parameters of mean and variance in MV-AFT model using penalized likelihood function. For the variable selection, we study four penalty functions, i.e. least absolute shrinkage and selection operator (LASSO), adaptive lasso (ALASSO), smoothly clipped absolute deviation (SCAD) and hierarchical likelihood (HL). With this procedure we can select important covariates and estimate the regression parameters at the same time. The performance of the proposed method is evaluated using simulation studies. The proposed method is illustrated with a clinical example dataset.

• Journal of the East Asian Society of Dietary Life
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• v.22 no.6
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• pp.895-902
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• 2012

This study was conducted to understand the Ramyun's selection attributes of Korean adults and examine differences in demographic characteristics and meal intake behavior among three groups of samples divided based on the Ramyun's selection attributes. Self-administered questionnaires were completed by 702 adults, and data were subjected to frequency analysis, chi-square analysis, factor analysis, reliability tests, cluster analysis, and discriminant analysis using SPSS. The results of the study were as follows. The Ramyun's selection attributes for Korean adults investigated were food quality (four variables), price (three variables), and company reliability (four variables). Cluster analysis resulted in the subjects being divided into three groups according to their Ramyun's selection attributes, a high-selection group, mid-selection group, and low-selection group. Three groups of samples classified by Ramyun's selection attributes differed based on demographic characteristics (gender and education level) and meal intake behavior (meal numbers, reason for meal, meal time, and meal size).

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.5-8
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• 2000

Vehicle travel time (empty travel time pius loaded travel time) is a key parameter for designing AGV-based material handling systems. Especially, the determination of empty vehicle travel time is difficult because of the stochastic nature of the empty vehicle locations. This paper presents a method to estimate vehicle travel times for AGV-based material transport systems. The model considers probabilistic aspects for the travel time and vehicle location under random vehicle selection rule and nearest vehicle selection rule. The estimation of empty travel time is of major effort. Simulation experiments are used to verify the proposed travel time model, and the simulation results show that the presented model provides reasonable travel time estimations.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.533-540
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• 2006

This paper describes the design and implementation of a grid system META (Metacomputing Environment using Test-run of Application) which facilitates the execution of a CFD (Computational Fluid Dynamics) analysis program on distributed environment. The grid system META allows the CFD program developers can access the computing resources distributed over the network just like one computer system. The research issues involved in the grid computing include fault-tolerance, computing resource selection, and user-interface design. In this paper, we exploits an automatic resource selection scheme for executing the parallel SPMD (Single Program Multiple Data) application written in MPI (Message Passing Interface). The proposed resource selection scheme is informed from the network latency time and the elapsed time of the kernel loop attained from test-run. The network latency time highly influences the executional performance when a parallel program is distributed and executed over several systems. The elapsed time of the kernel loop can be used as an estimator of the whole execution time of the CFD Program due to a common characteristic of CFD programs. The kernel loop consumes over 90% of the whole execution time of a CFD program.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1203-1210
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• 2017

This paper presents an improved switching selection method for the direct torque control (DTC) of five-phase induction motors (IMs). The proposed method is conducted using optimal switching selection. A five-phase inverter has 32 voltage vectors which are divided into 30 nonzero voltage vectors and two zero voltage vectors. The magnitudes of the voltage vectors consist of large, medium, and small voltage vectors. In addition, these vectors are related to the torque response and torque ripple. When a large voltage vector is selected in a drive system, the torque response time decreases with an increased torque ripple. On the other hand, when a small voltage vector is selected, the torque response time and torque ripple increase. As a result, this paper proposes an optimal voltage vector selection method for improved DTC of a five-phase induction machine depending on the situation. Simulation and experimental results verify the effectiveness of the proposed control algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.39-42
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• 1990

In recent years, several volume selection techniques have been tried to obtain 31P MR spectroscopy in vivo. Most volume selection techniques, however, suffer from T2 decay in VOI due to the relatively long selection time. In this paper, we propose a new localized 3-D volume selection technique which is specially suitable for 31P spectroscopy. The proposed technique, which uses the radial scans in the k-space, minimizes T2 decay during the selection time and also provides good volume selectivity.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.11-22
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• 2023

In this paper, we propose an efficient satellite selection method for multi-constellation GNSS. The number of visible satellites has increased dramatically recently due to multi-constellation GNSS. By the increased availability, the overall GNSS performance can be improved. Whereas, due to the increase of the number of visible satellites, the computational burden in implementing advanced processing such as integer ambiguity resolution and fault detection can be increased considerably. As widely known, the optimal satellite selection method requires very large computational burden and its real-time implementation is practically impossible. To reduce computational burden, several sub-optimal but efficient satellite selection methods have been proposed recently. However, these methods are prone to the local optimum problem and do not fully utilize the information redundancy between different constellation systems. To solve this problem, the proposed method utilizes the inter-system biases and geometric assignments. As a result, the proposed method can be implemented in real-time, avoids the local optimum problem, and does not exclude any single-satellite constellation. The performance of the proposed method is compared with the optimal method and two popular sub-optimal methods by a simulation and an experiment.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.192-200
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• 2017

With the acceleration of construction industrialization, the buildings that China has adopted the construction of industrialization technology are increasing day by day, and Precast Concrete (PC) Structure technology is one of the main technologies of construction industrialization. Compared with the traditional cast-in-place concrete structure, PC structure is more conducive to shorten the construction period, reduce the number of construction workers and the site construction waste. Nevertheless, PC structure improves the requirements of hoisting machinery in the construction site, and the lay-out and selection of hoisting machinery become an important factor influencing the construction cost. The paper regards the typical tower crane in China as the research object, and establishes the time optimization model for the lifting scheme. The influence of the different precast rate on the selection of the tower crane is analyzed. This paper obtains the time variation of the tower crane under different precast rate, provides a theoretical basis for the design of precast concrete structures under the influence of assembly construction, and lays the foundation for the selection of tower crane under the precast rate.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.724-729
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• 2001

This paper discusses the composition of the theory of reinforcement learning, which is applied in real-time learning, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.