• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.277-285
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• 2020

A differential drive wheeled robot is a kind of mobile robot suitable for indoor navigation. Model predictive control is an optimal control technique with various advantages and can achieve excellent performance. One of the main advantages of model predictive control is that it can easily handle constraints. Therefore, it deals with realistic constraints of the mobile robot and achieves admirable performance for trajectory tracking. In addition, the intention of the robot can be properly realized by adjusting the weight of the cost function component. This control technique is applied to the local planner of the navigation component so that the mobile robot can operate in real environment. Using the Robot Operating System (ROS), which has transcendent advantages in robot development, we have ensured that the algorithm works in the simulation and real experiment.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.13-18
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• 2018

This paper is based on a simple approach that the most efficient learning of a multi-layered network is the process of finding the optimal set of weight vectors. To overcome the disadvantages of general learning problems, the proposed model uses a combination of features of EBP and OVSSA. In other words, the proposed method can construct a single model by taking advantage of each algorithm so that it can escape to the probability theory of OVSSA in order to reinforce the property that EBP falls into local minimum value. In the proposed algorithm, methods for reducing errors in EBP are used as energy functions and the energy is minimized to OVSSA. A simple experimental result confirms that two algorithms with different properties can be combined.

• The KSFM Journal of Fluid Machinery
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• v.16 no.4
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• pp.22-27
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• 2013

The purpose of this paper is to perform the structural design of the small scale vertical-axis wind turbine (VAWT) blade using a response surface method(RSM). First, the four design factors that have a strong influence on the structural response of blade were selected. Analysis conditions were calculated by using the central composite design(CCD), which is a typical design of experiment for the response surface method(RSM). Also, the significance of the central composite design(CCD) was verified using analysis of variance(ANOVA). The finite element analysis was performed for the selected analytical conditions for the application of response surface method(RSM). Finally, a optimization problem was solved with a objective function of blade weight and a constraint of allowable stress to achieve a optimal structural design of blade.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.18 no.3
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• pp.317-327
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• 2011

Purpose: The purpose of this study was to contribute data toward prevention from advancing dementia and also prevention of deterioration in cognitive impairment by constructing an optimal prediction model and verifying factors influencing cognitive impairment in elders with dementia who reside at home. Methods: The participants in this study were 351 elders who were registered at dementia day care centers in 11 regions of Metropolitan Incheon. Collected data were analyzed using SPSS Statistics 17.0 and SAS 9.1. Bootstrap method using the Clementine program 12.0 was applied to build an optimum prediction model. Results: Gender and education (general characteristics), alcohol, urinary/fecal incontinence, exercise, weight, and ADL (state of health), and depression (psychological state) were found to have an affect on cognitive impairment in these elders. Conclusion: Study results indicate nine key factors that affect cognitive impairment of elders with dementia who reside at home and that could be useful in prevention and management nursing plans. These factors could also be used to expand the role of nurses who are working in community day care centers, and can be applied in the development and provision of various programs to aid retention and improve cognitive function as well as preventing deterioration of cognition.

• The Journal of Korean Physical Therapy
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• v.31 no.4
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• pp.161-168
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• 2019

Most athletes with anterior cruciate ligament (ACL) ruptures undergo a surgical ACL reconstruction (ACLR) and rehabilitation. On the other hand, controversy still exists because neither a reconstruction nor rehabilitation have been proven to be superior in the management of ACL injury. This study reviewed the success rates of interventions to provide recommendations for the optimal management after an ACL injury. One of the most important considerations after an ACL injury is the timing and type of intervention. At the early stages, which involve the loss of volume and strength of quadriceps femoral muscle, weight bearing (closed kinetic chain) exercises with pain management followed by high velocity resistance exercises in an open kinetic chain environment are recommended to improve the quadriceps function. After that, it is important to apply intensive isokinetic exercise with a lower extension rate. In this case, it is important to apply overload to the muscles and to simultaneously lead the co-contraction of the hamstrings. Standards are essential because the timing and type of interventions are crucial to prevent re-injury and complications, such as osteoarthritis, as well as to confirm the successful outcome of the treatment. Different interventions recommended for ACL damage have yet to reach consensus. Further studies will be needed to observe the effects of the intervention through multidisciplinary approaches.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.44-51
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• 2019

Designing sophisticate ship structures that satisfy several design criteria simultaneously with minimum weight and cost is an important engineering issue. For a ship structure composed of a shell and stiffeners, this issue is more serious because their mutual effect has to be addressed. In this study, a two-stage optimization method is proposed for the conceptual design of stiffeners in a ship's prow. In the first stage, a topology optimization method is used to determine a potential stiffener distribution based on the optimal results, whereupon stiffeners are constructed according to stiffener generative theory and the material distribution. In the second stage, size optimization is conducted to optimize the plate and stiffener sections simultaneously based on a parametric model. A final analysis model of the ship-prow structure is presented to assess the validity of this method. The analysis results show that the two-stage optimization method is effective for stiffener conceptual design, which provides a reference for designing actual stiffeners for ship hulls.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• Structural Engineering and Mechanics
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• v.84 no.1
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• pp.1-16
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• 2022

An efficient and accurate classification method for failure modes of reinforced concrete (RC) columns was proposed based on key characteristic parameters. The weight coefficients of seven characteristic parameters for failure modes of RC columns were determined first based on the support vector machine-recursive feature elimination. Then key characteristic parameters for classifying flexure, flexure-shear and shear failure modes of RC columns were selected respectively. Subsequently, a support vector machine with key characteristic parameters (SVM-K) was proposed to classify three types of failure modes of RC columns. The optimal parameters of SVM-K were determined by using the ten-fold cross-validation and the grid-search algorithm based on 270 sets of available experimental data. Results indicate that the proposed SVM-K has high overall accuracy, recall and precision (e.g., accuracy>95%, recall>90%, precision>90%), which means that the proposed SVM-K has superior performance for classification of failure modes of RC columns. Based on the selected key characteristic parameters for different types of failure modes of RC columns, the accuracy of SVM-K is improved and the decision function of SVM-K is simplified by reducing the dimensions and number of support vectors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.5
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• pp.203-211
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• 2006

In this paper, the optimization of direct design system of steel frames by genetic algorithm involving advanced analysis are performed. For the analysis of steel frames advanced analysis accounting for geometric nonlinearity and material nonlinearity are executed. The genetic algorithm was used as optimization technique. The weight of structures is treated as the objective function. The constraint functions are defined by load-carrying capacities, deflections, inter-story drifts, and ductility requirement. The effectiveness of the proposed method are verified by comparing the results of the proposed method with those of other method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.11
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• pp.1331-1337
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• 2013

This study deals with the structural optimization of hybrid vertical-axis wind turbine blades using a response surface method (RSM). The structural analysis results suggest that the stress of hybrid vertical-axis wind turbine blades exceeds the yield strength. Optimization techniques are then applied to structural design to ensure a safe structure. First, the design factors that strongly influence the structural response are identified. The RSM was applied based on the design of experiments. The objective function and constraint terms set the weight and allowable stress, respectively. Furthermore, sensitivity analysis was conducted to indicate the effects of the design factors on the stress and weight. Finally, structural design was performed for the hybrid vertical-axis wind turbine blade.