• Journal of the Korean Geophysical Society
• /
• v.9 no.3
• /
• pp.231-240
• /
• 2006

The crustal structure of the Korean Peninsula was investigated by analyzing phase velocity dispersion data of Rayleigh waves. Earthquakes recorded by three component broad-band velocity seismographs during 1999-2004 in South Korea were used in this study. The fundamental mode Rayleigh waves were extracted from vertical components of seismograms by multiple filter technique and phase match filter method. Phase velocity dispersion curves of the fundamental mode signal pairs for 14 surface wave propagation paths on the great circle in the range 10 to 80 sec were computed by two-station method. Treating the shear velocity of each layer as an independent parameter, phase velocity data of Rayleigh wave were inverted. All the result models can be explained by a rather homogeneous crust of shear-wave velocity increasing from 2.8 to 3.25 km/sec from top to about 33 km depth without any distinctive crustal discontinuities and an uppermost mantle of shear-wave velocity between 4.55 and 4.67 km/sec. Our results turn out to agree well with recent study of Cho et al. (2006 b) based on the analysis of seismic background noises to recover short-period (0.5-20 sec) Rayleigh- and Love-wave group velocity dispersion characteristics.

• Computers and Concrete
• /
• v.21 no.6
• /
• pp.697-703
• /
• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Journal of Information Processing Systems
• /
• v.17 no.5
• /
• pp.972-988
• /
• 2021

At present, DevOps environments are getting popular in software organizations due to better collaboration and software productivity over traditional software process models. Software artefacts in DevOps environments are vulnerable to frequent changes at any phase of the software development life cycle that create a continuous integration continuous delivery pipeline. Therefore, software artefact traceability management is challenging in DevOps environments due to the continual artefact changes; often it makes the artefacts to be inconsistent. The existing software traceability related research shows limitations such as being limited to few types of artefacts, lack of automation and inability to cope with continuous integrations. This paper attempts to overcome those challenges by providing traceability support for heterogeneous artefacts in DevOps environments using a prototype named SAT-Analyser. The novel contribution of this work is the proposed traceability process model consists of artefact change detection, change impact analysis, and change propagation. Moreover, this tool provides multi-user accessibility and is integrated with a prominent DevOps tool stack to enable collaborations. The case study analysis has shown high accuracy in SAT-Analyser generated results and have obtained positive feedback from industry DevOps practitioners for its efficacy.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.18 no.3
• /
• pp.74-81
• /
• 2019

Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.

• Structural Engineering and Mechanics
• /
• v.68 no.5
• /
• pp.537-547
• /
• 2018

This research presents the effect of anisotropy of the hollow disc mode under Brazilian test using PFC3D. The Brazilian tensile strength test was performed on the hollow disc specimens containing the bedding layers and then these specimens were numerically modeled by using the two dimensional discrete element code (PFC3D) to calibrate this computer code for the simulation of the cracks propagation and cracks coalescence in the anisotropic bedded rocks. The thickness of each layer within the specimens varied as 5 mm, 10 mm and 20 mm and the layers angles were changed as $0^{\circ}$, $25^{\circ}$, $50^{\circ}$, $75^{\circ}$ and $90^{\circ}$. The diameter of internal hole was taken as 15 mm and the loading rate during the testing process kept as 0.016 mm/s. It has been shown that for layers angles below $25^{\circ}$ the tensile cracks produce in between the layers and extend toward the model boundary till interact and break the specimen. The failure process of the specimen may enhance as the layer angle increases so that the Brazilian tensile strength reaches to its minimum value when the bedding layers is between $50^{\circ}$ and $75^{\circ}$ but its value reaches to maximum at a layer angle of $90^{\circ}$. The number of tensile cracks decreases as the layers thickness increases and with increasing the layers angle, less layer mobilize in the failure process.

• The Korean Journal of Physiology and Pharmacology
• /
• v.23 no.5
• /
• pp.295-303
• /
• 2019

A heart simulator, UT-Heart, is a finite element model of the human heart that can reproduce all the fundamental activities of the working heart, including propagation of excitation, contraction, and relaxation and generation of blood pressure and blood flow, based on the molecular aspects of the cardiac electrophysiology and excitation-contraction coupling. In this paper, we present a brief review of the practical use of UT-Heart. As an example, we focus on its application for predicting the effect of cardiac resynchronization therapy (CRT) and evaluating the proarrhythmic risk of drugs. Patient-specific, multiscale heart simulation successfully predicted the response to CRT by reproducing the complex pathophysiology of the heart. A proarrhythmic risk assessment system combining in vitro channel assays and in silico simulation of cardiac electrophysiology using UT-Heart successfully predicted drug-induced arrhythmogenic risk. The assessment system was found to be reliable and efficient. We also developed a comprehensive hazard map on the various combinations of ion channel inhibitors. This in silico electrocardiogram database (now freely available at http://ut-heart.com/) can facilitate proarrhythmic risk assessment without the need to perform computationally expensive heart simulation. Based on these results, we conclude that the heart simulator, UT-Heart, could be a useful tool in clinical medicine and drug discovery.

• Geomechanics and Engineering
• /
• v.25 no.2
• /
• pp.111-121
• /
• 2021

Large deformation and rapid pressure propagation take place inside the rock mass under the dynamic loads caused by the explosives, on quarry faces in order to extract aggregate material. The complexity of the science of rock blasting is due to a number of factors that affect the phenomenon. However, blasting engineering computations could be facilitated by innovative software algorithms in order to determine the results of the violent explosion, since field experiments are particularly difficult to be conducted. The present research focuses on the design of a Finite Element Analysis (FEA) code, for investigating in detail the behavior of limestone under the blasting effect of Ammonium Nitrate & Fuel Oil (ANFO). Specifically, the manuscript presents the FEA models and the relevant transient analysis results, simulating the blasting process for three types of limestone, ranging from poor to very good quality. The Finite Element code was developed by applying the Jones-Wilkins-Lee (JWL) equation of state to describe the thermodynamic state of ANFO and the pressure dependent Drucker-Prager failure criterion to define the limestone plasticity behavior, under blasting induced, high rate stress. A progressive damage model was also used in order to define the stiffness degradation and destruction of the material. This paper performs a comparative analysis and quantifies the phenomena regarding pressure, stress distribution and energy balance, for three types of limestone. The ultimate goal of this research is to provide an answer for a number of scientific questions, considering various phenomena taking place during the explosion event, using advanced computational tools.

• Journal of Korean Society on Water Environment
• /
• v.27 no.4
• /
• pp.413-424
• /
• 2011

The study was aimed to evaluate the applicability of a three-dimensional (3D) hydrodynamic and water quality model, ELCOM-CAEDYM for Yongdam Reservoir, Korea. The model was applied for the simulations of hydrodynamics, thermal stratification processes, stream density flow propagation, and water quality parameters including dissolved oxygen, nutrients, organic materials, and algal biomass (chl-a) for the period of June to December, 2006. The field data observed at four monitoring stations (ST1~ST4) within the reservoir were used to validate the models performance. The model showed reasonable performance nevertheless low frequency boundary forcing data were provided, and well replicated the physical, chemical, and biological processes of the system. Simulated spatial and temporal variations of water temperature, nutrients, and chl-a concentrations were moderately consistent with the field observations. In particular, the model rationally reproduced the succession of different algal species; i.e., diatom dominant during spring and early summer, after then cyanobacteria dominant under warm and stratified conditions. ELCOM-CAEDYM is recommendable as a suitable coupled 3D hydrodynamic and water quality model that can be effectively used for the advanced water quality management of large stratified reservoirs in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.4
• /
• pp.1263-1274
• /
• 2021

In online competitive social networks, each user can be influenced by different competing influencers and consequently chooses different products. But their interest may change over time and may have swings between different products. The existing influence spreading models seldom take into account the time-related shifts. This paper proposes a minimum cost influence maximization algorithm based on the competitive transition probability. In the model, we set a one-dimensional vector for each node to record the probability that the node chooses each different competing influencer. In the process of propagation, the influence maximization on Competitive Linear Threshold (IMCLT) spreading model is proposed. This model does not determine by which competing influencer the node is activated, but sets different weights for all competing influencers. In the process of spreading, we select the seed nodes according to the cost function of each node, and evaluate the final influence based on the competitive transition probability. Experiments on different datasets show that the proposed minimum cost competitive influence maximization algorithm based on IMCLT spreading model has excellent performance compared with other methods, and the computational performance of the method is also reasonable.

• The Journal of Information Systems
• /
• v.30 no.4
• /
• pp.203-226
• /
• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.