• International Journal of Concrete Structures and Materials
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• v.11 no.1
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• pp.135-149
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• 2017

The prediction of the actual ultimate capacity of confined concrete columns requires partial confinement utilization under eccentric loading. This is attributed to the reduction in compression zone compared to columns under pure axial compression. Modern codes and standards are introducing the need to perform extreme event analysis under static loads. There has been a number of studies that focused on the analysis and testing of concentric columns. On the other hand, the augmentation of compressive strength due to partial confinement has not been treated before. The higher eccentricity causes smaller confined concrete region in compression yielding smaller increase in strength of concrete. Accordingly, the ultimate eccentric confined strength is gradually reduced from the fully confined value $f_{cc}$ (at zero eccentricity) to the unconfined value $f^{\prime}_c$ (at infinite eccentricity) as a function of the ratio of compression area to total area of each eccentricity. This approach is used to implement an adaptive Mander model for analyzing eccentrically loaded columns. Generalization of the 3D moment of area approach is implemented based on proportional loading, fiber model and the secant stiffness approach, in an incremental-iterative numerical procedure to achieve the equilibrium path of $P-{\varepsilon}$ and $M-{\varphi}$ response up to failure. This numerical analysis is adapted to assess the confining effect in rectangular columns confined with conventional lateral steel. This analysis is validated against experimental data found in the literature showing good correlation to the partial confinement model while rendering the full confinement treatment unsafe.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1055-1060
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• 2018

In the wake of the 4th Industrial Revolution, the problem of career education in schools has become a big issue. While various studies are being conducted on services or technologies to effectively handle artificial intelligence and big data, in the field of education, data on students is simply processed. Therefore, in this paper, we are going to design and present career prediction programs for students using artificial intelligence and big data. Using observational data from students at the institute, the decision tree is constructed with the C4.5 algorithm known to be most intelligent and effective in the decision tree and is used to predict students' path of hope. As a result, the coefficient of kappa exceeded 0.7 and showed a fairly low average error of 0.1 degrees. As shown in this study, a number of studies and data will be deployed to help guide students in their consultation and to provide them with classroom attitudes and directions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.9
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• pp.993-1000
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• 1992

Recently, many researchers have found that the speaker recognition rate is high when they perform the speaker recognition using statistical processing method of orthogonal parameter, which are derived from the analysis of speech signal and contain much of the speaker's identity. This method, however, has problems caused by vocalization speed or time varying feature of speed. Thus, to solve these problems, this paper proposes two methods of speaker recognition which combine DTW algorithm with the method using orthogonal parameters extracted from $Karthumem-Lo\'{e}ve$ Transform method which applies orthogonal parameters as feature vector to ETW algorithm and the other is the method which applies orthogonal parameters to the optimal path. In addition, we compare speaker recognition rate obtained from the proposed two method with that from the conventional method of statistical process of orthogonal parameters. Orthogonal parameters used in this paper are derived from both linear prediction coefficients and partial correlation coefficients of speech signal.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.3
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• pp.343-348
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• 2011

With the rapid and wide-spread use of mobile communications much attention has been focussed on propagation in the urban area crowed with buildings. It is often surrounded by hills, forests, and mountains. The importance of surface scattering interference between transmitters and receivers on the rough surfaces has been interested and investigated. Therefore, a prediction method is necessary to estimate the influence of rough surfaces on microwave radio propagation. Moreover, most of the mobile communications are performed based on the digital communication system rather than the analog one. In this case, we must pay more careful attention to the signal delay caused by the phase delay due to the multi-path propagation. In this paper we have analyzed numerically scattering of electromagnetic waves from Composite structures by using FVTD (Finite Volume Time Domain) method. We consider two different types of rough surfaces such as periodic and composite structures.

• Bulletin of the Korean Chemical Society
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• v.18 no.12
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• pp.1274-1280
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• 1997

The photodissociation dynamics of CH2I2 has been studied at 304 nm by state-selective photofragment translational spectroscopy. Velocity distributions, anisotropy parameters, and relative quantum yields are obtained for the ground I(2P3/2) and spin-orbit excited state I*(2P1/2) iodine atoms, which are produced from photodissociation of CH2I2 at this wavelength. These processes are found to occur via B1 ← A1 type electronic transitions. The quantum yield of I*(2P1/2) is determined to be 0.25, indicating that the formation of ground state iodine is clearly the favored dissociation channel in the 304 nm wavelength region. From the angular distribution of dissociation products, the anisotropy parameters are determined to be β(I)=0.4 for the I(2P3/2) and β(I*)=0.55 for the I*(2P1/2) which substantially differ from the limiting value of 1.13. The positive values of anisotropy parameter, however, show that the primary processes for I and I* formation channels proceed dominantly via a transition which is parallel to I-I axis. The above results are interpreted in terms of dual path formation of iodine atoms from two different excited states, i.e., a direct and an indirect dissociation via curve crossing between these states. The translational energy distributions of recoil fragments reveal that a large fraction of the available energy goes into the internal excitation of the CH2I photofragment; < Eint > /Eavl=0.80 and 0.82 for the I and I* formation channels, respectively. The quantitative analysis for the energy partitioning of available energy into the photofragments is used to compare the experimental results with the prediction of direct impulsive model for photodissociation dynamics.

• Biomedical Engineering Letters
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• v.8 no.4
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• pp.383-392
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• 2018

For prompt gamma ray imaging for biomedical applications and environmental radiation monitoring, we propose herein a multiple-scattering Compton camera (MSCC). MSCC consists of three or more semiconductor layers with good energy resolution, and has potential for simultaneous detection and differentiation of multiple radio-isotopes based on the measured energies, as well as three-dimensional (3D) imaging of the radio-isotope distribution. In this study, we developed an analytic simulator and a 3D image generator for a MSCC, including the physical models of the radiation source emission and detection processes that can be utilized for geometry and performance prediction prior to the construction of a real system. The analytic simulator for a MSCC records coincidence detections of successive interactions in multiple detector layers. In the successive interaction processes, the emission direction of the incident gamma ray, the scattering angle, and the changed traveling path after the Compton scattering interaction in each detector, were determined by a conical surface uniform random number generator (RNG), and by a Klein-Nishina RNG. The 3D image generator has two functions: the recovery of the initial source energy spectrum and the 3D spatial distribution of the source. We evaluated the analytic simulator and image generator with two different energetic point radiation sources (Cs-137 and Co-60) and with an MSCC comprising three detector layers. The recovered initial energies of the incident radiations were well differentiated from the generated MSCC events. Correspondingly, we could obtain a multi-tracer image that combined the two differentiated images. The developed analytic simulator in this study emulated the randomness of the detection process of a multiple-scattering Compton camera, including the inherent degradation factors of the detectors, such as the limited spatial and energy resolutions. The Doppler-broadening effect owing to the momentum distribution of electrons in Compton scattering was not considered in the detection process because most interested isotopes for biomedical and environmental applications have high energies that are less sensitive to Doppler broadening. The analytic simulator and image generator for MSCC can be utilized to determine the optimal geometrical parameters, such as the distances between detectors and detector size, thus affecting the imaging performance of the Compton camera prior to the development of a real system.

• International Journal of Oral Biology
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• v.43 no.4
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• pp.223-230
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• 2018

Exosomes are Nano-sized lipid vesicles secreted from mammalian cells containing diverse cellular materials such as proteins, lipids, and nucleotides. Multiple lines of evidence indicate that in saliva, exosomes and their contents such as microRNAs (miRNAs) mediate numerous cellular responses upon delivery to recipient cells. The objective of this study was to characterize the different expression profile of exosomal miRNAs in saliva samples, periodically isolated from a single periodontitis patient. Unstimulated saliva was collected from a single patient over time periods for managing periodontitis. MicroRNAs extracted from each phase were investigated for the expression of exosomal miRNAs. Salivary exosomal miRNAs were analyzed using Affymetrix miRNA arrays and prediction of target genes and pathways for its different expression performed using DIANA-mirPath, a web-based, computational tool. Following the delivery of miRNA mimics (hsa-miR-4487, -4532, and -7108-5p) into human gingival fibroblasts, the expression of pro-inflammatory cytokines and activation of the MAPK pathway were evaluated through RT-PCR and western blotting. In each phase, 13 and 43 miRNAs were found to be differently expressed $({\mid}FC{\mid}{\geq}2)$. Among these, hsa-miR-4487 $({\mid}FC{\mid}=9.292005)$ and has-miR-4532 $({\mid}FC{\mid}=18.322697)$ were highly up-regulated in the clinically severe phase, whereas hsa-miR-7108-5p $({\mid}FC{\mid}=12.20601)$ was strongly up-regulated in the clinically mild phase. In addition, the overexpression of miRNA mimics in human gingival fibroblasts resulted in a significant induction of IL-6 mRNA expression and p38 phosphorylation. The findings of this study established alterations in salivary exosomal miRNAs which are dependent on the severity of periodontitis and may act as potential candidates for the treatment of oral inflammatory diseases.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.35-44
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• 2019

The purpose of this study is to find out quantitative vulnerability assessment about COTS(Commercial Off The Shelf) O/S based I&C System. This paper analyzed vulnerability's lifecycle and it's impact. this paper is to develop a quantitative assessment of overall cyber security risks and vulnerabilities I&C System by studying the vulnerability analysis and prediction method. The probabilistic vulnerability assessment method proposed in this study suggests a modeling method that enables setting priority of patches, threshold setting of vulnerable size, and attack path in a commercial OS-based measurement control system that is difficult to patch an immediate vulnerability.

• Journal of Ocean Engineering and Technology
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• v.33 no.5
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• pp.427-434
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• 2019

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.5
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• pp.18-26
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• 2021

In this work, we have developed a 1D network model aimed at predicting eigenvalues for resonance frequency analysis in a lab-scale industrial gas turbine single nozzle combustion system. Modern industrial gas turbines generally adopt combustors with very complex geometry and flow path to meet various design requirements simultaneously. The current study has developed a network model for combustion systems with backflow at the same axial location. The modeling results of resonance frequencies and mode distributions for a given system using the network model were validated from comparisons with prediction results using a 3D Helmholtz solver.