• 감성과학
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• 제17권2호
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• pp.13-24
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• 2014

Mobile phone makers compete for market shares through domination in media advertisements. These include domination of advertisements (Ads) in TV and the internet. However, the abundance and complexity of the competitions of Ads in TV does not guarantee advertising success which can influence consumers' emotion and the purchase intention towards the brand. This research analyses the case of a directional model on Attitude-towards-the-Ad model as a baseline into a new proposed correlation models (MacKenzie, Scott, &Lutz, 1989). The survey targets the involvements of Asian smartphone owners' attitude on advertisements, brands and purchase intentions. CFA (Confirmatory factor Analysis) was used in the research experiments, including hypothesis testing, the outcome of model fit which revealed significant levels and were successful. The study revealed that all three paths have consistently high coefficient paths (Attitude to Ads - Attitude to Brands - Purchase Intention), showing significant value of (${\beta}$=>.80), which supported each correlation factors. Therefore, this structural model, could set standards for creative managers and advertising teams to improve the brands visibility and build strong influences on attitudes in advertisements and improve purchase intentions.

• 디지털산업정보학회논문지
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• 제7권2호
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• pp.115-125
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• 2011

An accurate estimation of software development size is an important factor in calculating reasonable cost of project development and determining its success. In this study, we propose estimation models, using function point based on the functional correlation between software, with empirical data. Three models($FP_{est}(I)$, $FP_{est}(II)$, $FP_{est}(III)$) are developed with correlation and regression analysis. The validity of the models is evaluated by the significance test by comparing values of Mean Magnitude of Relative Error (MMRE) and predictions of each model at level n%. Model $FP_{est}(III)$ proved to be superior to other models such as IFPC(Indicative Function Point Count), EFPC(Estimated Function Point Count), EPFS(Early Prediction of Function Size), $FP_{est}(I)$, and $FP_{est}(II)$. As a result, the accuracy of the model appears to be very high to determine the usefulness of the model to finally overcome weakness of other estimation models. The model can be efficiently used to estimate project development size including software size or manpower allocation.

• 한국환경과학회지
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• 제12권9호
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• pp.957-966
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• 2003

Gunsan coastal area is one of region increasing pollution problems. To improve water quality, the reduction of these nutrients loads should be indispensible. In this study, the three-dimensional numerical hydrodynamic and ecosystem model were applied to analyze the processes affecting the eutrophication. In field survey, the average concentrations of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus(DIP) at surface waters were found to be 0.43mg/$\ell$ and 0.03mg/$\ell$ respectively, which were exceeding second grade of water quality criteria. In hydrodynamic modelling, the comparison between the simulated and observed tidal ellipses showed fairly good agreement. The ecosystem model was calibrated with the observed data in study area. The simulated results of DIN were fairly good coincided with the observed values within relative error of 32.39%, correlation coefficient(r) of 0.99. In the case of DIP, the simulated results were fairly good coincided with the observed values within relative error of 24.26%, correlation coefficient(r) of 0.82. The simulations of DIN and DIP concentrations using ecosystem model were performed under the conditions of 20∼80% reductions for pollutant loading. At simulation results, concentration of DIN and DIP were reduced to 20∼80% and under 10% in case of the 80% reduction of pollutant loading, respectively.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1821-1830
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• 2018

With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.

• Advances in Computational Design
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• 제4권4호
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• pp.367-379
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• 2019

Multiple Inputs and Multiple Outputs (MIMO) Fuzzy logic model is developed to predict the engine performance and emission characteristics of pongamia pinnata biodiesel with hydrogen injection. Engine performance and emission characteristics such as brake thermal efficiency (BTE), brake specific energy consumption (BSEC), hydrocarbon (HC), carbon monoxide (CO), carbon dioxide ($CO_2$) and nitrous oxides ($NO_X$) were considered. Experimental investigations were carried out by using four stroke single cylinder constant speed compression ignition engine with the rated power of 5.2 kW at variable load conditions. The performance and emission characteristics are measured using an Exhaust gas analyzer, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends (Diesel, B10, B20 and B30) and engine load conditions. Fuzzy logic model uses triangular and trapezoidal membership function because of its higher predictive accuracy to predict the engine performance and emission characteristics. Computational results clearly demonstrate that, the proposed fuzzy model has produced fewer deviations and has exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.99136 to 1 with experimental values. The developed fuzzy logic model has produced good correlation between the fuzzy predicted and experimental values. So it is found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

• Advances in biomechanics and applications
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• 제1권1호
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• pp.23-40
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• 2014

This paper examines the blood chamber of a left ventricular assist device (LVAD) under static loading conditions and standard operating temperatures. The LVAD's walls are made of a temperature-sensitive polymer (ChronoFlex C 55D) and are covered with a titanium nitride (TiN) nano-coating (deposited by laser ablation) to improve their haemocompatibility. A loss of cohesion may be observed near the coating-substrate boundary. Therefore, a micro-scale stress-strain analysis of the multilayered blood chamber was conducted with FE (finite element) code. The multi-scale model included a macro-model of the LVAD's blood chamber and a micro-model of the TiN coating. The theories of non-linear elasticity and elasto-plasticity were applied. The formulated problems were solved with a finite element method. The micro-scale problem was solved for a representative volume element (RVE). This micro-model accounted for the residual stress, a material model of the TiN coating, the stress results under loading pressures, the thickness of the TiN coating and the wave parameters of the TiN surface. The numerical results (displacements and strains) were experimentally validated using digital image correlation (DIC) during static blood pressure deformations. The maximum strain and stress were determined at static pressure steps in a macro-scale FE simulation. The strain and stress were also computed at the same loading conditions in a micro-scale FE simulation.

• 한국분말재료학회지
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• 제26권4호
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• pp.275-281
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• 2019

An artificial neural network (ANN) model is developed for the analysis and simulation of correlation between flake powder metallurgy parameters and properties of AA2024-SiC nanocomposites. The input parameters of the model are AA 2024 matrix size, ball milling time, and weight percentage of SiC nanoparticles and the output parameters are density and hardness. The model can predict the density and hardness of the unseen test data with a correlation of 0.986 beyond the experimental data. A user interface is designed to predict properties at new instances. We have used the model to simulate the individual as well as the combined influence of parameters on the properties. Moreover, we have analyzed the calculated results from the powder metallurgical point of view. The developed model can be used as a guide for further composite development.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.101-101
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• 2020

Traditional stochastic simulation of hydroclimatological variables often underestimates the variability and correlation structure of larger timescale due to the difficulty in preserving long-term memory. However, the Long Short-Term Memory (LSTM) model illustrates a remarkable long-term memory from the recursive hidden and cell states. The current study, therefore, employed the LSTM model in stochastic generation of hydrologic and climate variables to examine how much the LSTM model can preserve the long-term memory and overcome the drawbacks of conventional time series models such as autoregressive (AR). A trigonometric function and the Rössler system as well as real case studies for hydrological and climatological variables were tested. Results presented that the LSTM model reproduced the variability and correlation structure of the larger timescale as well as the key statistics of the original time domain better than the AR and other traditional models. The hidden and cell states of the LSTM containing the long-memory and oscillation structure following the observations allows better performance compared to the other tested conventional models. This good representation of the long-term variability can be important in water manager since future water resources planning and management is highly related with this long-term variability.

• 수산해양기술연구
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• 제48권1호
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• pp.51-58
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• 2012

As a basic study to improve hooking ability of long line fishing gear, which is widely used around Jeju-do coast, the researcher performed hooking experiment of parrot fish by manufacturing and installing 7 kinds of model long line fishing gears, whose thickness of branch line are different and 8 kinds of model long line fishing gear, whose length of branch line are different, in indoor circular aquarium, which is installed for the model experiment of thickness and length of branch line that are various by fishing implement and improper. The hooking rate depending on thickness and length of branch line was calculated and the effect of thickness and length of branch line on hooking rate was analyzed. Its results are as follows. When branch line was thin and long, high hooking rate appeared. In the scope of value setting, the relationship between thickness ($B_t$) of branch line and total hooking rate ($Th_r$) can be shown as following formula as. In the scope of value setting, the relationship between length ($B_t$) of branch line and total hooking rate ($B_t$) can be shown as $Th_r=-20.83B_t+26.04$. Through Pearson correlation analysis, the coefficient of correlation between thickness of branch line and hooking rate was -0.718. Therefore it showed significance in 0.01 significance level. Through Pearson correlation analysis, the coefficient of correlation between length of branch line and hooking rate was 0.431. Therefore it showed significance in 0.01 significance level.