• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.407-415
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• 2022

In this study, a square rounding sidewalk block frame was developed considering the simplicity of construction and the superiority of aesthetics. In addition, it is possible to prevent damage, deformation, and settlement of adjacent sidewalk blocks due to plastic deformation during car impact load of installed bollad. A non-linear structural analysis was performed through finite element analysis to examine the performance of a car crash to which this was applied. Structural safety was confirmed through car crash simulation according to the direction of impact, and it is estimated that the function can be restored by replacing some parts in case of damage due to impact.

• Structural Engineering and Mechanics
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• v.85 no.3
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• pp.369-379
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• 2023

Reliability-Based Design Optimization (RBDO) is an appropriate framework for obtaining optimal designs by taking uncertainties into account. Large-scale problems with implicit limit state functions and problems with discrete design variables are two significant challenges to traditional RBDO methods. To overcome these challenges, this paper proposes a hybrid method to perform RBDO of structures that links Firefly Algorithm (FA) as an optimization tool to advanced (finite element) reliability methods. Furthermore, the Genetic Algorithm (GA) and the FA are compared based on the design cost (objective function) they achieve. In the proposed method, Weighted Simulation Method (WSM) is utilized to assess reliability constraints in the RBDO problems with explicit limit state functions. WSM is selected to reduce computational costs. To performing RBDO of structures with finite element modeling and implicit limit state functions, a First-Order Reliability Method (FORM) based on the Direct Differentiation Method (DDM) is utilized. Four numerical examples are considered to assess the effectiveness of the proposed method. The findings illustrate that the proposed RBDO method is applicable and efficient for RBDO problems with discrete and continuous design variables and finite element modeling.

• Structural Engineering and Mechanics
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• v.90 no.4
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• pp.391-401
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• 2024

The present study focuses on the effect of extension-bending coupling on the elastic stability (buckling) of laminated composite plates. These plates will be loaded under uni-axial or bi-axial in-plane mechanical loads, especially in the orthotropic or anti-symmetric cross-angle cases. The main objective is to find a limit where we can approximate the elastic stability behavior of angularly crossed anti-symmetric plates by the simple behavior of specially orthotropic plates. The contribution of my present study is to predict the explicit effect of extension-flexion coupling on the elastic stability of this type of panel. Critically, a parametric study is carried out, involving the search for the critical buckling load as a function of deformation mode, aspect ratio, plate anisotropy ratio and finally the study of the effect of lamination angle and number of layers on the contribution of extension-flexure coupling in terms of plate buckling stability. We use first-order shear deformation theory (FSDT) with a correction factor of 5/6. Simply supported conditions along the four boundaries are adopted where we can develop closed-form analytical solutions obtained by a Navier development.

• Advances in aircraft and spacecraft science
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• v.11 no.2
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• pp.177-194
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• 2024

The objective of this paper is to compute entire flow field over Apollo-II, Aerospace Reentry Demonstrator (ARD), Orbital Experiment (OREX) with sharp shoulder and rounded shape shoulder and Space Recovery Experiment (SRE) at different flare-cone half-angle of 20° and 35°. This paper addresses numerical solutions of the compressible three-dimensional Euler equations on hexahedral meshes for a freestream Mach 6 and at an angle of incidence 5°. Furthermore, spatial discretization is accomplished by a cell centred finite volume formulation solution and advanced in time by an explicit multi-stage Runge-Kutta method. The flow field characteristics, distribution of surface pressure coefficient and Mach number on fore-body and aft-body are presented as a function of the geometrical parameters of many reentry capsules. The surface pressure variation is numerically integrated to obtain the aerodynamic drag and compared well with impact theory. The present numerical study has observed the significant dependence of the blunt body and the aft-body geometry of the vehicle and can be used to study atmospheric conditions during re-entry trajectory. The numerical analysis reveals the significant influence of capsule geometry on the flow characteristics of the mechanism of upstream and structure of the flow near the wake region and aerodynamic drag coefficient.

• The Korean Journal of Pharmacology
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• v.18 no.1
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• pp.1-10
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• 1982

In an attempt to delineate the role of beta-adrenoceptors found to be existing in the brain tissue in the central regulation of renal function, isoproterenol, a ${\beta}-adrenergic$ agonist, was administered directly into a lateral ventricle of the rabbit brain and the changes of renal function were observed. Also, the effects of propranolol, a specific ${\beta}-adrenergic$ blocking agent, and its influence upon the isoproterenol action were studied. Isoproterenol, in doses ranging from 5 to $50\;{\mu}g/kg\;i.c.v.$, elicited antidiuresis which seemed to be related to the decreased renal hemodynamics brought about by the systemic hypotension. With moderate doaes of $15\;{\mu}g/kg$ the antidiuresis was less prominent and there was a tendency toward natriuresis, but with higher doses the natriuretic effect became less evident, overrun by the systemic hypotension. Propranolol, $500\;{\mu}g/kg\;i.c.v.$, produced little effect on the renal function, but it eliminated the antidiuretic action of $50\;{\mu}g/kg$ isoproterenol i.c.v. and reversed it to a diuretic and natriuretic one, along with increases in renal plasma flow and glomerular filtration rate. The systemic hypotension also was markedly attenuated by propranolol pretreatment. Thus, it was evident that the renal action of i.c.v. isoproterenol was not blocked by propranolol and became explicit only when the hypotensive action of isoproterenol which seems to he propranolol-sensitive is removed. Various possibilities to account for this disparity in sensitivity were discussed. It is suggested from these observations that the central ${\beta}-adrenoceptors$ might also be involved in the regulation of renal function along with ${\alpha}-adrenoceptors$, though less significant than the latter.

• Food Science of Animal Resources
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• v.29 no.1
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• pp.132-139
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• 2009

The objective of this study was to investigate the most effective analysis methods for palatability grading of Hanwoo beef by comparing the results of discriminant analysis with sensory data. The sensory data were obtained from sensory testing by 1,300 consumers evaluated tenderness, juiciness, flavor-likeness and overall acceptability of Hanwoo beef samples prepared by boiling, roasting and grilling cooking methods. For the discriminant analysis with one factor, overall acceptability, the linear discriminant functions and the non-parametric discriminant function with the Gaussian kernel were estimated. The linear discriminant functions were simple and easy to understand while the non-parametric discriminant functions were not explicit and had the problem of selection of kernel function and bandwidth. With the three palatability factors such as tenderness, juiciness and flavor-likeness, the canonical discriminant analysis was used and the ability of classification was calculated with the accurate classification rate and the error rate. The canonical discriminant analysis did not need the specific distributional assumptions and only used the principal component and canonical correlation. Also, it contained the function of 3 factors (tenderness, juiciness and flavor-likeness) and accurate classification rate was similar with the other discriminant methods. Therefore, the canonical discriminant analysis was the most proper method to analyze the palatability grading of Hanwoo beef.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.409-422
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• 2013

For stability design and P-${\Delta}$ analysis of steel frames with semi-rigid connections, the explicit form of the exact tangential stiffness matrix of a generalized semi-rigid frame element having rotational and translational connections is firstly derived using the stability functions. And its elastic and geometric stiffness matrix is consistently obtained by Taylor series expansion. Next depending on connection types of semi-rigidity, the corresponding tangential stiffness matrices are degenerated based on penalty method and static condensation technique. And then numerical procedures for determination of effective buckling lengths of generalized semi-rigid frames members and P-${\Delta}$ and shortly addressed. Finally three numerical examples are presented to demonstrate the validity and accuracy of the proposed method. Particularly the minimum braced frames and coupled buckling modes of the corresponding frames are investigated.