• Animal Bioscience
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• v.34 no.10
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• pp.1623-1631
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• 2021

Objective: This study aimed to validate and evaluate the dry matter (DM) intake prediction model of the Korean feeding standards for dairy cattle (KFSD). Methods: The KFSD DM intake (DMI) model was developed using a database containing the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 287 studies). The development (458 observations from 103 studies) and evaluation databases (168 observations from 74 studies) were constructed from the database. The body weight (kg; BW), metabolic BW (BW^0.75, MBW), 4% fat-corrected milk (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients (% DM) were chosen as possible explanatory variables. A random coefficient model with the study as a random variable and a linear model without the random effect was used to select model variables and estimate parameters, respectively, during the model development. The best-fit equation was compared to published equations, and sensitivity analysis of the prediction equation was conducted. The KFSD model was also evaluated using in vivo feeding trial data. Results: The KFSD DMI equation is 4.103 (±2.994)+0.112 (±0.022)×MBW+0.284 (±0.020)×FCM-0.119 (±0.028)×neutral detergent fiber (NDF), explaining 47% of the variation in the evaluation dataset with no mean nor slope bias (p>0.05). The root mean square prediction error was 2.70 kg/d, best among the tested equations. The sensitivity analysis showed that the model is the most sensitive to FCM, followed by MBW and NDF. With the in vivo data, the KFSD equation showed slightly higher precision (R² = 0.39) than the NRC equation (R² = 0.37), with a mean bias of 1.19 kg and no slope bias (p>0.05). Conclusion: The KFSD DMI model is suitable for predicting the DMI of lactating dairy cows in practical situations in Korea.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.6
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• Korean Journal of Applied Biomechanics
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• v.32 no.4
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• pp.111-120
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• 2022

Objective: This study investigated the effect of pelvic tiltng according to the paralytic side on gait in stroke patients during a 10 m functional movement timed up and go (TUG) test. Method: In this study, gait parameters were measured using a gait analyzer for 20 stroke patients and their gait was analyzed during a 10 m TUG test. For statistical analysis, an independent sample t-test were performed for age, height, and weight among general characteristics of subjects and homogeneity was tested by performing a chi-square test for gender, paralysis side, period of onset, and K-MMSE score. In order to understand the relationship between each variable, Pearson correlation analysis was performed on the variables. Results: First, the right-hand paralyzed group showed correlations in cadence and gait velocity in the up and down tilt of the pelvis, and the left-hand paralyzed group showed correlations in cadence and step length in the anterior and posterior tilt of the pelvis. Second, the tilt of the pelvis was correlated with the Sit to stand, walk forward, walk backwards, turn around at the end point, sit on a chair and the total TUG time in the right hemiplegic group compared to the left hemiplegic group. Conclusion: In this study, a significant correlation was confirmed as a result of gait analysis of right-handed stroke patients divided into a right paraplegic group and a left paraplegic group. In the future, it is suggested that treatment for improving gait of stroke patients should be treated differently for the right and left paralyzed side.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.423-427
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• 2022

A pressure sensor that mimics the sensing ability of human skin has emerged as high-profile technology because it shows remarkable applications in numerous fields such as robotics, human health monitoring, and artificial prosthetics. Whereas recent pressure sensors have achieved high sensitivity similar to that of human skin, they still show limited detection bandwidth. Moreover, once these e-skin are fabricated, their sensitivity and stiffness are fixed; therefore, they can be used for only limited applications. Our study proposes a new adaptive pressure sensor built with uniform gallium microdroplet-elastomer composite. Based on the phase transition of gallium microdroplets, the proposed sensor undergoes mode transformation, enabling it to have a higher sensitivity and wider detection bandwidth compared with those of human skin. In addition, we succeeded in extending a single adaptive pressure sensor to sensor arrays based on its high uniformity, reproducibility, and large-scale manufacturability. Finally, we designed an adaptive e-skin with the sensor array and demonstrated its applications on health monitoring tasks including blood pulse and body weight measurements.

• Computers and Concrete
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• v.29 no.4
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• pp.219-235
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• 2022

Strengthening slender reinforced concrete (RC) columns is a challenge. They are susceptible to overall buckling that induces bending moment and axial compression. This study presents the precise three-dimensional finite element modeling of slender RC columns strengthened with fiber-reinforced polymer (FRP) composites sheets with various patterns under concentric or eccentric compression. The slenderness ratio λ (height/width ratio) of the studied columns ranged from 15 to 35. First, to determine the optimal modeling procedure, nine alternative nonlinear finite element models were presented to simulate the experimental behavior of seven FRP-strengthened slender RC columns under eccentric compression. The models simulated concrete behavior under compression and tension, FRP laminate sheets with different fiber orientations, crack propagation, FRP-concrete interface, and eccentric compression. Then, the validated modeling procedure was applied to simulate 58 FRP-strengthened slender RC columns under compression with minor eccentricity to represent the inevitable geometric imperfections. The simulated columns showed two cross sections (square and rectangular), variable λ values (15, 22, and 35), and four strengthening patterns for FRP sheet layers (hoop H, longitudinal L, partial longitudinal L_w, and longitudinal coupled with hoop LH). For λ=15-22, pattern L showed the highest strengthening effectiveness, pattern L_w showed brittle failure, steel reinforcement bars exhibited compressive yielding, ties exhibited tensile yielding, and concrete failed under compression. For λ>22, pattern L_w outperformed pattern L in terms of the strengthening effectiveness relative to equivalent weight of FRP layers, steel reinforcement bars exhibited crossover tensile strain, and concrete failed under tension. Patterns H and LH (compared with pattern L) showed minor strengthening effectiveness.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.222-228
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• 2022

As a means to cope with the climate change crisis caused by global warming, automobile manufacturers continue to make efforts to use the driving energy of vehicles as electricity. As a result, parts industry such as battery, motor, and controller are attracting attention. China is often seen in large cities, with electric vehicles such as electric bicycles, electric motorcycles, and small electric vehicles popularized and commercialized, mainly in large cities. However, small electric vehicles are not popular in Korea, which is why the country's topography is high in hills. In order to drive the hilly domestic roads, power performance including vehicle climbing ability should be improved. In order to improve the power performance and the climbing capacity of small electric vehicles, the capacity of the motor should be increased. However, when the performance of the motor is improved, the weight of the motor becomes heavy and the price competitiveness is likely to decrease. In addition, in order to operate a high-performance motor, the power consumption of the battery is rapidly increased, so various problems must be solved. In order to commercialize a small electric vehicle for one or two people who do not emit harmful exhaust gas to the human body in a hilly domestic terrain, it is effective to have a separate transmission system. In this study, we were conducted dynamometer test to produce a continuously variable transmission(CVT) system prototype using a spring that can be applied to a small electric vehicle and to install a CVT system prototype manufactured in a small electric vehicle. The dynamometer test results showed that the maximum speed performance, acceleration performance, and climbing performance were improved.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.91-97
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• 2021

This paper uses a data extension technique originally designed for the rule refinement problem to handling incomplete data. This technique is characterized in that each event can have a weight indicating importance, and each variable can be expressed as a probability value. Since the key problem in this paper is to find the probability that is closest to the missing value and replace the missing value with the probability, three different algorithms are used to find the probability for the missing value and then store it in this data structure format. And, after learning to classify each information area with the SVM classification algorithm for evaluation of each probability structure, it compares with the original information and measures how much they match each other. The three algorithms for the imputation probability of the missing value use the same data structure, but have different characteristics in the approach method, so it is expected that it can be used for various purposes depending on the application field.

• Journal of Fashion Business
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• v.26 no.2
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• pp.143-155
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• 2022

The purpose of this study was to develop protective clothing that could alleviate fall impacts. Fall impact protection pants for elderly women were designed, and motion adaptable hip pads and knee pads printed by 3D printing were integrated into the pants and evaluated. First, the design of the fall impact protection pants with variable motion was semi-loose fitting pants that could be worn and detached from the protective pad. A pad pocket was made in the lining inside the pants so that the protective pad could be fixed to the protective area. Second, in the evaluation of the appearance of the fall impact protection pants, the wearer group had a good score of 4.60 or higher for all questions on color, material, ease, and fit. In the evaluation of the insertion method of the protective pad, the flexibility of the pad, and the weight of the pad, the subjects' scores were 4.30~4.80. The fit of the fall impact protection pants was excellent in the texture and elasticity of the outside and inside of the pants. There was no discomfort due to the pad(4.60), and no difficulty in movement during wearing activities was reported. During squatting, it was evaluated as 4.80, indicating that the motion adaptable hip joint and knee pads were highly effective during operation.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.547-559
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• 2023

Proper handling of nonresponse in sample survey improves the accuracy of the parameter estimation. Various studies have been conducted to properly handle MAR (missing at random) nonresponse or MCAR (missing completely at random) nonresponse. When nonresponse occurs, the PSA (propensity score adjusted) estimator is commonly used as a mean estimator. The PSA estimator is known to be unbiased when known sample weights and properly estimated response probabilities are used. However, for MNAR (missing not at random) nonresponse, which is affected by the value of the study variable, since it is very difficult to obtain accurate response probabilities, bias may occur in the PSA estimator. Chung and Shin (2017, 2022) proposed a post-stratification method to improve the accuracy of mean estimation when MNAR nonresponse occurs under a non-informative sample design. In this study, we propose a double post-stratification method to improve the accuracy of the naive PSA estimator for MNAR nonresponse under an informative sample design. In addition, we perform simulation studies to confirm the superiority of the proposed method.