• Geomechanics and Engineering
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• v.20 no.3
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• International Journal of Industrial Entomology and Biomaterials
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• v.11 no.2
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• pp.119-124
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• 2005

To determine the level of heterosis, higher cocoon shell weight multivoltine congenic lines (Con. L) and bivoltine syngenic lines (Syn. L) of silkworm were used for crosses. First filial generations $(F_1s)$ expressed heterobeltiotic genetic interaction at significant magnitude (p < 0.01) for single cocoon shell weight (SCSW). The other linked characters viz., single cocoon weight (SCW) and yield by weight per 10, 000 larvae were also significantly higher (p < 0.01) than the better parental lines. All the hybrids showed significant improvement for these aforesaid characters over standard heterosis (Standard check). The reeling parameters viz., filament length, raw silk, neatness, cohesionstrokes etc, also showed improvement among the hybrids than check in congenial environment. Overall results suggested that the cross between congenic and syngenic lines provide better heterosis with good quality silk than conventional hybrids and may be used for commercial exploitation.

• International Journal of Industrial Entomology and Biomaterials
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• v.2 no.2
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• pp.149-153
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• 2001

The influence of baculovirus Bombyx mori Nuclear Polyhedrosis virus (BmNPV) infection on intermediary metabolic pathways in silkworm Bombyx mori L. was investigated. Studies revealed that NADP-linked malate dehydrogenase activity in hemolymph of infected silkworms at 96 hrs post infection (p.i.) with visible symptoms of infection was enhanced in comparison to healthy larvae of the same age. Also, NADP-dependent MDH activity was significantly lower in fat body cytosol of infected larvae at 96 hrs p.i. when compared to healthy larvae. Similarly, some biometabolic parameters like growth, protein content and cholesterol titer were observed to be influenced by baculovirus infection. While the growth of infected larvae was significantly retardedi protein content was also drastically reduced in both hemolymph and fat body tissues. Cholesterol titers however, was enhanced in infected larvae. The results observed herein point to a significant change in the normal biochemical and biometabolic pathways required for growth and development following BmNPV infection.

• Speech Sciences
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• v.8 no.3
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• pp.173-184
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• 2001

The purpose of this study was to examine the effects of manipulating loudness and pitch in terms of speech intelligibility and voice of a patient with Parkinson's Disease. The subject, who was diagnosed as a patient with Parkinson's disease 11 years ago, demonstrated a severely breath voice with low intensity. The accuracy of articulation in consonants was intelligible only at the single word level, and the overall intelligibility in continuous speech was low. The results showed that the subject's articulation accuracy and speech intelligibility was significantly improved after having loudness and pitch training. Habitual Fo, Jitter, Shimmer, Fo tremor, Amp tremor were decreased after training. In addition, the value of HNR also increased after training. It was shown that the changes of these acoustic parameters were closely related to the decrease of breathiness in Parkinson's voice, and this decrease of breathiness affected speech intelligibility considerably. Based on the experimental results, it was claimed that the vocal training by manipulating the loudness and pitch could be highly effective in improving the voice quality and speech intelligibility in Parkinson's Disease.

• Journal of the military operations research society of Korea
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• v.34 no.1
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• pp.1-11
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• 2008

A methodology for the improvement of simulation based training system for the artillery is proposed in this paper. The complex nonlinear relationship inherent among parameters in artillery firing is difficult to model and analyze. By introducing neural network based simulation, accurate representation of artillery firing is made possible. The artillery training system can greatly benefit from the improved prediction. Neural networks learning is conducted using the conjugate gradient algorithm. The evaluation of the proposed methodology is performed through simulation. Prediction errors of both regression analysis model and neural networks model are analyzed. Implementation of neural networks to training system enables more realistic training, improved combat power and reduced budget.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1621-1633
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• 2022

The present study aims to design an application for anger control training of individuals and test its practical usability with the goal of encouraging preventive training in daily life. This study also investigates, through usability evaluation, whether users can use the application to carry out the actual anger management training program, whether it is useful and convenient, and whether it produces adequate learning effects. In order to conduct usability evaluation, a usability evaluation scale comprised of six factors-utility, reuse intention, learning, error, and reflectivity-was derived, and survey items tailored to each factor were produced. The association between usability evaluation elements, user demographic parameters, mobile usage behavior, and state anger was also examined. The result demonstrated that additional menus and features are necessary to increase the usability of the application for anger management. The result also revealed that it is vital to build an intuitive application interface that users unfamiliar with mobile app functionality can easily navigate, as well as to add entertaining components in the content, as users may be somewhat bored. On the basis of the findings, ideas of modifying and creating anger management training programs were discussed.

• The Korean Journal of Physiology and Pharmacology
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• v.25 no.6
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• pp.545-553
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• 2021

Fixed-dose combinations development requires pharmacokinetic drugdrug interaction (DDI) studies between active ingredients. For some drugs, pharmacokinetic properties such as long half-life or delayed distribution, make it difficult to conduct such clinical trials and to estimate the exact magnitude of DDI. In this study, the conventional (non-compartmental analysis and bioequivalence [BE]) and model-based analyses were compared for their performance to evaluate DDI using amlodipine as an example. Raw data without DDI or simulated data using pharmacokinetic models were compared to the data obtained after concomitant administration. Regardless of the methodology, all the results fell within the classical BE limit. It was shown that the model-based approach may be valid as the conventional approach and reduce the possibility of DDI overestimation. Several advantages (i.e., quantitative changes in parameters and precision of confidence interval) of the model-based approach were demonstrated, and possible application methods were proposed. Therefore, it is expected that the model-based analysis is appropriately utilized according to the situation and purpose.

• PNF and Movement
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• v.19 no.2
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• pp.287-301
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• 2021

Purpose: Gait training for stroke patients focuses on adjusting to new environments to facilitate outdoor walking. Therefore, the purpose of this study was to identify the effects of various ground obstacle walking combined with treadmill walking on the gait parameters and functional gait ability of chronic stroke patients. Methods: Twenty-four chronic stroke patients were divided into two groups: an experimental group (n = 12) and a control group (n = 12). The experimental group received a combined gait training using various ground obstacle walking and treadmill walking (VGOW) five times/week for four weeks. The control group received traditional treadmill training (TW) five times/week for four weeks. Patients were evaluated using the figure-8 walk test (F8WT) and the Functional Gait Assessment (FGA) before and after each intervention. Results: The ANCOVA results showed that both treatments significantly influenced F8WT steps, F8WT time, and FGA score. The paired t-test results showed a significant improvement in F8WT steps, F8WT time, and FGA score in the experimental group compared to those in the control group. Conclusion: Combined gait training using various ground obstacle walking and treadmill walking can improve gait ability in chronic stroke patients.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.62-67
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• 2019

This study was performed to apply the measurement of heart rate and velocity in training horses for assessing race performance. Additionally, we aimed to identify parameters that can be used to evaluate the training level and exercise capacity. Eleven healthy 2- to 6-year-old Thoroughbreds were trained by the standard training program and heart rate and velocity were measured by using heart monitoring system and GPS. Regression analysis in heart rate and velocity data was performed to calculate velocity parameters. The mean maximal heart rate in gallop was $214{\pm}11bpm$. The mean $V_{140}$, $V_{180}$, $V_{200}$ and $VHR_{max}$ were $13.8{\pm}4.3km/h$, $37.5{\pm}3.8km/h$, $49.3{\pm}4.3km/h$ and $57.4{\pm}7.1km/h$ respectively. The mean $V_{140}$ of high performance racehorses was significantly higher than that of low performance racehorses (P < 0.05). Moreover, analyzing the correlation between velocity parameters and racing ability-related categories showed that $V_{140}$ was positively correlated with rating (P < 0.05), $V_{180}$ and $VHR_{max}$ were positively correlated with prize money per race (P < 0.05). Also, $V_{140}$ was significantly correlated with G1F (P < 0.05). The results of this study have shown that the measurement of heart rate and velocity during training could be useful methods to assess fitness for races or performance potential. Especially, $V_{140}$ is a good parameter to evaluate a performance of racehorses in Korea.