• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.1
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• pp.75-87
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• 1993

Four series of fiber reinforced concrete deep beams without shear reinforcement were tested to determine their cracking shear strengths and ultimate shear capacities. Results of tests on 20 reinforced concrete deep beams (including 16 containing steel fibers) are reported. Three parameters were varied in the study, namely, the concrete compressive strength, volume fraction of fibers, and the shear span to depth ratio. The effects of fiber incorporation on failure modes, deflections. strains, cracking shear strength, and ultimate shear strength have been examined. Resistance to shear stresses have been found to be improved by the inclusion of fibers. Based on these investigations, a method of computing the shear stress of steel fiber reinforced concrete deep beam is suggested. The comparisons between computed values and experimentally observed values are shown to validate the proposed theoretical treatment.

• Korea Trade Review
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• v.46 no.1
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• pp.169-187
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• 2021

This paper analyzes the feasibility of using machine learning and deep learning methods to forecast the income and employment using the strategic industries as well as investment, export, and exchange rates. The decision tree, artificial neural network, support vector machine, and deep learning models were used to forecast the income and employment in Busan. The following were the main findings of the comparison of their predictive abilities. First, the decision tree models predict the income and employment well. The forecasting values for the income and employment appeared somewhat differently according to the depth of decision trees and several conditions of strategic industries as well as investment, export, and exchange rates. Second, since the artificial neural network models show that the coefficients are somewhat low and RMSE are somewhat high, these models are not good forecasting the income and employment. Third, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, the deep neural network models show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the employment well, we need to adopt the machine learning and deep learning models to forecast the income and employment.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.923-932
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• 1994

The studies on the bearing capacity of shallow and deep foundations have been made in various fields and formulas for various failure mechanisms have been presented. But, for these models, the method of classification with foundation depth has been obscure and bearing capacity factors have not been uniformly applied. An experiment was performed, in plane strain conditions, with ground model made of carbon rods. The failure mechanism of foundation and ultimate bearing capacity with foundation depth were observed. Based on experimental results the classification between shallow and deep foundations by failure shape was tried. Various present failure mechanisms of foundation were verified through the experiment.

• The korean journal of orthodontics
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• v.21 no.2 s.34
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• pp.399-418
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• 1991

This study was designed to evaluate the relationship of mandibular condylar position to overbite depth and compare the findings from transcranial radiographs and those, from individualized corrected tomographs in determination of condylar position. The subjects consisted of 20 control subjects (male 8, female 12), and 10 open-bite patients (male 3, female 7) and 23 deep-bite patients (male 17, female 6). The mean age was 23.3 years for the control group, 21.5 years for open-bite group, and 23.2 years for deep-bite group. Transcranial radiographys and individualized corrected tomographys in centric occlusion were taken from right and left temporomandibular joints of each sueject. The results were as follows. 1. In the 20 normal subjects showing no symptoms of TM disorder, the incidence of condylar retrusion was $27.5\%$, middle condylar position $60.0\%$, and anterior displacement $12.5\%$. 2. There was significant correlation between the bite depth and observed condylar position. 3. Only $45.2\%$ of the findings from transcranial radiographs coincided with those from individualized corrected tomographs in determining condylar position.

• The Journal of Korean Physical Therapy
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• v.22 no.3
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• pp.55-59
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• 2010

Purpose: This study was designed to investigate the effect of a water exercise program on the pennation angle of the lower-limb muscle in women in their 20s. Methods: Ten female subjects were randomly divided into two groups, with 5 subjects exercising in water 0.7 m deep and 5 subjects exercising in water 1.4 m deep. They did the water exercising program for 40 minute per day, 3 days per week, for total 6 weeks. We measured the pennation angle of lower-limb muscle using ultrasonography. All measurements for each group were performed at pre-training and after 6 weeks of training. Results: The pennation angle was compared before and after the water exercise period for each group, and statistically significant changes within each group in measurements of the rectus femoris and tibialis anterior (p<0.05). However, there was no significant difference in muscle architecture by water depth (p>0.05) between the two groups. Conclusion: These results show that the pennation angle of the lower-limb muscle of women in their 20s changed after 6 weeks of participating in a water exercise program, but these changes were not dependent on the depth of the water in which the exercises were performed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1013-1016
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• 2004

High level radioactive wastes, such as spent fuels generated from nuclear power plant, will be disposed in a deep geological repository. To maintain the integrity of the disposal canister and to carry out the process effectively, the emplacement process for the canister system in borehole of disposal tunnel should be well defined. In this study, the concept of the disposal canister emplacement process for deep geological disposal was established. To do this, the spent fuel arisings and disposal rate were reviewed. Also, not only design requirements, such canister and disposal depth but also preliminary repository layout concept were reviewed. Based on the requirements and the other bases, the canister emplacement process in the borehole of the disposal tunnel was established. The established concept of the disposal canister emplacement process will be improved continuously with the future studies. And this concept can be effectively used in implementing the reference repository system of our own case.

• The korean journal of orthodontics
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• v.17 no.1
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• pp.15-21
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• 1987

The present study was carried out to investigate the relationships between the depth of overbite and the masseteric silent period. Normal subjects of 44 were selected, which consisted of 9 open bites, 24 normal overbites and 11 deep bites, all were 19-29 years of age. EMG activity was recorded on the bilateral masseteric muscles and craniofacial radiography was done. The following results were obtained. 1. The mean duration of masseteric silent period was $18.58{\pm}4.50$ msec in open bite, $17.37{\pm}7.05$ msec in normal overbite and $19.30{\pm}7.62$ msec in deep bite groups. 2. There were no significant differences on masseteric silent period among open bite, normal overbite and deep bite groups. 3. There were no significant correlations between masseteric silent period and craniofacial variables.

• Structural Monitoring and Maintenance
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• v.4 no.3
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• pp.269-299
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• 2017

This paper presents a Level III damage evaluation methodology, which simultaneously, identifies the location, the extent, and the severity of stiffness damage in deep beams. Deep beams are structural elements with relatively high aspect (depth-to-length) ratios whose response are no longer based on the simplified Euler-Bernoulli theory. The proposed methodology is developed on the bases of the force-displacement relations of the Timoshenko beam theory and the concept of invariant stress resultants, which states that the net internal force existing at any cross-section of the beam is not affected by the inflicted damage, provided that the external loadings in the undamaged and damaged beams are identical. Irrespective of the aspect ratios, local changes in both the flexural and the shear stiffnesses of beam-type structures may be detected using the approach presented in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4833-4848
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• 2019

This paper introduces the structure and effects of Kakao's news curation algorithm, which is created based on the Deep Reading Index (DRI). The DRI examines the extent of deep reading through content reading time, that is, the duration of reader engagement with an article. Current news curation algorithms focus on reader choice, with the click-through rate or pageviews as the gauge for consumption frequency. DRI is a product of the challenge of introducing and adopting a new factor called 'consumption time' instead of 'frequency of consumption', which is the basis of existing curation algorithms. The analysis of DRI-based services proves that the new algorithm can act as a curation system that is more effective in providing in-depth and quality news reports.