• Architectural research
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• v.21 no.1
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• pp.21-29
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• 2019

Recent years in Korea, some new developed buildings are only using electricity as power for heating, cooling, bathing and even cooking which means except electricity, there is no natural gas or other kinds of energy used in such kind of building. In vehicle industry area, scientists already invented electric vehicle as an environment friendly vehicle; after that, in architecture design and construction field, buildings only using electricity appeared; the curiosity of the environment impact of energy consumption by such kind of building lead me to do this research. In general, electricity is known as a clean energy resource reasoned by it is noncombustible energy resource; however, although there is no environmental pollution by using electricity, electricity generation procedure in power plant may cause huge amount of environment pollution; especially, electricity generation from combusting coal in power plant could emit enormous air pollutants to the air. In this research, the yearly amount of air pollution by energy using under traditional way in research target building that is using natural gas for heating, bathing and cooking and electricity for lighting, equipment and cooling is compared with yearly amount of air pollution by only using electricity as power in the building; result shows that building that only uses electricity emits much more air pollutants than uses electricity and natural gas together in the building. According to the amount of air pollutants comparison result between two different energy application types in the building, residential SOFC (Solid oxide fuel cell) is simulated to apply in this building for decreasing environment pollution of the building; furthermore, high load factor could lead high efficiency of SOFC, in the scenario of simulating applying SOFC in the building, SOFC is shared by two or three households in spring and autumn to increase efficiency of the SOFC. In sum, this research is trying to demonstrate electricity is a conditioned environment friendly energy resource; in the meanwhile, SOFC is simulated efficiently applying in the building only using electricity as power to decrease the large amount of air pollutants by energy using in the building. Energy consumption of the building is analyzed by calibrated commercial software Design Builder; the calibrated mathematical model of SOFC is referred from other researcher's study.

• Restorative Dentistry and Endodontics
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• v.43 no.4
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• pp.48.1-48.13
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• 2018

Objectives: To analyze the influence of thickness and incisal extension of indirect veneers on the stress and strain generated in maxillary canine teeth. Materials and Methods: A 3-dimensional maxillary canine model was validated with an in vitro strain gauge and exported to computer-assisted engineering software. Materials were considered homogeneous, isotropic, and elastic. Each canine tooth was then subjected to a 0.3 and 0.8 mm reduction on the facial surface, in preparations with and without incisal covering, and restored with a lithium disilicate veneer. A 50 N load was applied at $45^{\circ}$ to the long axis of the tooth, on the incisal third of the palatal surface of the crown. Results: The results showed a mean of $218.16{\mu}strain$ of stress in the in vitro experiment, and $210.63{\mu}strain$ in finite element analysis (FEA). The stress concentration on prepared teeth was higher at the palatal root surface, with a mean value of 11.02 MPa and varying less than 3% between the preparation designs. The veneers concentrated higher stresses at the incisal third of the facial surface, with a mean of 3.88 MPa and a 40% increase in less-thick veneers. The incisal cover generated a new stress concentration area, with values over 48.18 MPa. Conclusions: The mathematical model for a maxillary canine tooth was validated using FEA. The thickness (0.3 or 0.8 mm) and the incisal covering showed no difference for the tooth structure. However, the incisal covering was harmful for the veneer, of which the greatest thickness was beneficial.

• Convergence Security Journal
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• v.18 no.2
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• pp.59-67
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• 2018

The programming language is a core education area of software that is becoming increasingly important in the age of the fourth industrial revolution, but it requires mathematical knowledge and logical thinking skills, so that many local private university and college students with low basic skills are having difficulties learning it. This problem occasionally causes SW-major students to lose interest and confidence in their majors during the introductory course of programming languages; making them change their majors, or give up their studies. In this study, we designed an instructional model using Scratch for educating C-language which is a typical programming introductory language. To do this, we analyzed the concepts that can be trained by Scratch among the programming concepts supported by C-language, and developed the examples of Scratch for exercising the concepts. In addition, we designed an instructional model, by which the programming concepts are first learned through Scratch and then C-language is taught, and conducted an experiment on the SW-major freshman students of a local private university to verify the effectiveness of the model. In the situation where SW education is becoming common, we expect that this study will help programming language education of security IT students.

• Geomechanics and Engineering
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• v.17 no.1
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• pp.31-45
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• 2019

In this study the spatial variability of soils is substantiated physically and numerically by using random field theory. Heterogeneous samples are fabricated by combining nine homogeneous soil clusters that are assumed to be elements of an adopted random field. Homogeneous soils are prepared by mixing different percentages of kaolin and bentonite at water contents equivalent to their respective liquid limits. Comprehensive characteristic laboratory tests were carried out before embarking on direct shear experiments to deduce the basic correlations and properties of nine homogeneous soil clusters that serve to reconstitute the heterogeneous samples. The tests consist of Atterberg limits, and Oedometric and unconfined compression tests. The undrained shear strength of nine soil clusters were measured by the unconfined compression test data, and then correlations were made between the water content and the strength and stiffness of soil samples with different consistency limits. The direct shear strength of heterogeneous samples of different stochastic properties was then evaluated by physical and numerical modelling using FISH code programming in finite difference software of $FLAC^{3D}$. The results of the experimental and stochastic numerical analyses were then compared. The deviation of numerical simulations from direct shear load-displacement profiles taken from different sources were discussed, potential sources of error was introduced and elaborated. This study was primarily to explain the mathematical and physical procedures of sample preparation in stochastic soil mechanics. It can be extended to different problems and applications in geotechnical engineering discipline to take in to account the variability of strength and deformation parameters.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.9-16
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• 2019

In this paper, it will analyze security problems, so technology's potential can apply to business security area. First, in order to deep learning do security tasks sufficiently in the business area, deep learning requires repetitive learning with large amounts of data. In this paper, to acquire learning ability to do stable business tasks, it must detect abnormal IP packets and attack such as normal software with malicious code. Therefore, this paper will analyze whether deep learning has the cognitive ability to detect various attack. In this paper, to deep learning to reach the system and reliably execute the business model which has problem, this paper will develop deep learning technology which is equipped with security engine to analyze new IP about Session and do log analysis and solve the problem of mathematical role which can extract abnormal data and distinguish infringement of system data. Then it will apply to business model to drop the vulnerability and improve the business performance.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.226-234
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• 2022

With advances in autonomous vehicles and networked control, there is a growing interest in the consensus control of a multi-agents system to control multi-agents with distributed control beyond the control of a single agent. Since consensus control is a distributed control, it is bound to have delay in a practical system. In addition, it is often difficult to have a very accurate mathematical model for a system. Even though a reinforcement learning (RL) method was developed to deal with these issues, it often experiences slow convergence in the presence of large uncertainties. Thus, we propose a slide RL which combines the sliding mode control with RL to be robust to the uncertainties. The structure of a sliding mode control is introduced to the action in RL while an auxiliary sliding variable is included in the state information. Numerical simulation results show that the slide RL provides comparable performance to the model-based consensus control in the presence of unknown time-varying delay and disturbance while outperforming existing state-of-the-art RL-based consensus algorithms.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.359-373
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• 2022

The new crown pneumonia (COVID-19) has become a global epidemic. The disease has spread to most countries and poses a challenge to the healthcare system. Contact tracing technology is an effective way for public health to deal with diseases. Many experts have studied traditional contact tracing and developed digital contact tracking. In order to better understand the field of contact tracking, it is necessary to analyze the development of contact tracking in the field of computer science by bibliometrics. The purpose of this research is to use literature statistics and topic analysis to characterize the research literature of contact tracking in the field of computer science, to gain an in-depth understanding of the literature development status of contact tracking and the trend of hot topics over the past decade. In order to achieve the aforementioned goals, we conducted a bibliometric study in this paper. The study uses data collected from the Scopus database. Which contains more than 10,000 articles, including more than 2,000 in the field of computer science. For popular trends, we use VOSviewer for visual analysis. The number of contact tracking documents published annually in the computer field is increasing. At present, there are 200 to 300 papers published in the field of computer science each year, and the number of uncited papers is relatively small. Through the visual analysis of the paper, we found that the hot topic of contact tracking has changed from the past "mathematical model," "biological model," and "algorithm" to the current "digital contact tracking," "privacy," and "mobile application" and other topics. Contact tracking is currently a hot research topic. By selecting the most cited papers, we can display high-quality literature in contact tracking and characterize the development trend of the entire field through topic analysis. This is useful for students and researchers new to field of contact tracking ai well as for presenting our results to other subjects. Especially when comprehensive research cannot be conducted due to time constraints or lack of precise research questions, our research analysis can provide value for it.

• Computers and Concrete
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• v.31 no.5
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• pp.419-432
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• 2023

Beam-column joints are a critical component of reinforced concrete frame structures. They are responsible for transferring forces between adjoining beams and columns while limiting story drifts and maintaining structural integrity. During severe loading, beam-column joints deform significantly, affecting, and sometimes governing, the overall response of frame structures. While most failure modes for beam and column elements are commonly considered in plastic-hinge-based global frame analyses, the beam-column joint failure modes, such as concrete shear and reinforcement bond slip, are frequently omitted. One reason for this is the dearth of published guidance on what type of hinges to use, how to derive the joint hinge properties, and where to place these hinges. Many beam-column joint models are available in literature but their adoption by practicing structural engineers has been limited due to their complex nature and lack of practical application tools. The objective of this study is to provide a comparative review of the available beam-column joint models and present a practical joint modeling approach for integration into commonly used global frame analysis software. The presented modeling approach uses rotational spring models and is capable of modeling both interior and exterior joints with or without transverse reinforcement. A spreadsheet tool is also developed to execute the mathematical calculations and derive the shear stress-strain and moment-rotation curves ready for inputting into the global frame analysis. The application of the approach is presented by modeling a beam column joint specimen which was tested experimentally. Important modeling considerations are also presented to assist practitioners in properly modeling beam-column joints in frame analyses.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.