• Journal of the Korean Applied Science and Technology
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• v.41 no.2
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• pp.472-483
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• 2024

This study evaluated the attitudes of the Korean population towards sunscreen use through the Health Belief Model (HBM) construct and investigated the psychological factors that influence sunscreen use. For this purpose, an online survey was conducted from 1 November 2023 to 1 January 2024, and a total of 303 participants were collected. The collected data were analysed using SPSS v. 25.0 programme using Cronbach's 𝛼, frequency analysis, descriptive statistics, correlation analysis, independent samples t-test, one way ANOVA, Scheffe's test, and multiple regression analysis. The results of the study showed that the mean score of sunscreen use was 3.26±1.384 out of 5, and there was a significant correlation between the variables of the health belief model and sunscreen use (p<.01). Gender, age, and skin colour were also associated with each variable, with women, the elderly, and those with lighter skin tending to be more proactive in sun protection. Multiple regression analyses revealed that self-efficacy (𝛽=.629, p<.001) and perceived vulnerability (𝛽=.139, p<.001), sub-factors of the Health Belief Model, had a statistically significant positive effect on sunscreen use, while perceived barriers (𝛽=-.261, p<.001) had a statistically significant negative effect on sunscreen use. These results may have important theoretical implications for the development and implementation of educational programmes to promote sunscreen use by providing insight into the psychosocial factors that influence sun protection.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.27-40
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• 2020

In this paper, we investigate the correlation between the amount of English sentences that Korean English learners (L2ers) are exposed to and their sentence processing patterns by examining what Long Short-Term Memory (LSTM) language models (LMs) can learn about implicit syntactic relationship: that is, the filler-gap dependency. The filler-gap dependency refers to a relationship between a (wh-)filler, which is a wh-phrase like 'what' or 'who' overtly in clause-peripheral position, and its gap in clause-internal position, which is an invisible, empty syntactic position to be filled by the (wh-)filler for proper interpretation. Here to implement L2ers' English learning, we build LSTM LMs that in turn learn a subset of the known restrictions on the filler-gap dependency from English sentences in the L2 corpus that L2ers can potentially encounter in their English learning. Examining LSTM LMs' behaviors on controlled sentences designed with the filler-gap dependency, we show the characteristics of L2ers' sentence processing using the information-theoretic metric of surprisal that quantifies violations of the filler-gap dependency or wh-licensing interaction effects. Furthermore, comparing L2ers' LMs with native speakers' LM in light of processing the filler-gap dependency, we not only note that in their sentence processing both L2ers' LM and native speakers' LM can track abstract syntactic structures involved in the filler-gap dependency, but also show using linear mixed-effects regression models that there exist significant differences between them in processing such a dependency.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.261-266
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• 2007

In this research, we studied an estimating method in client's sight to estimate the total construction cost which is based on the historical cost data at the early stage of the office buildings as a public phase. It is very difficult to analyze the estimation accurately and logically. When a client estimates a project, he/she has to consider there are many issues at the planning step, according as office buildings become gradually diversified as well as their roles continuously extended. Therefore, those are usually make problems for wasting the budget in accordance with the cost estimation errors. Moreover, many kinds of public construction projects, especially such as school, office, sports complex, and the others, have been invested the private finances defined as BTL(Build Transfer Lease) method that are required to manage the detailed process more strictly from initial planning. In order to make an effective planning, the long-term users amount and the building life cycle at the beginning of project should be considered previously and then it may enable to achieve an appropriate project plan. But actually considering overall variables in a building planning is impossible. Accordingly, suggesting a regression model based on the historical cost data from many similar types of office building to support client's role known as estimating the total cost at the early stage. And then performing the test against the proposed model to research the reasonability as using the historical cost data of Japan office buildings.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.597-604
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• 2004

Accurate software cost estimation is essential to both developers and customers. Most of the cost estimating models based on the size measure methods, such as LOC and FP, are obtained through size estimation. The accuracy of size estimation directly influences the accuracy of cost estimation. As a result, the overall structure of regression-based cost models applies the power function based on software size. Many growth phenomenon in nature such as the growth in living organism, performance of technology, and learning capability of human show an S-shaped curve. This paper proposes a model which estimates the developing effort by using the growth curve. The presented model assumes that the relation cost and size follows the growth curve. The appropriateness of the growth curve model based on Function Point, Full-Function Point and Use-Case Point, which are the general methods in estimating the software size have been confirmed. The proposed growth curve model shows similar performance with power function model. In conclusion, the growth curve model can be applied in the estimation of the software cost.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.12
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• pp.1503-1512
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• 2013

In this study, we proposed an indentation evaluation method for fracture toughness using cohesive finite element simulations. First, we examined the effect of material properties (yield strain, Poisson's ratio, and elastic modulus) on crack size during Vickers indentation and then generated a regression formula that explains the relations among fracture toughness, indentation load, and crack size. We also proposed another indentation formula for fracture toughness evaluation using the contact size a and E/H (H: hardness). Finally, we examined the relation between the crack size and the indenter shapes. Based on this, we can generate from the formula obtained using the Vickers indenter a formula for an indenter of different shapes. Using the proposed method, fracture toughness is directly estimated from indentation data.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.225-232
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• 2009

Function Point Method have been applied to measure software size estimation in industry because it supports to estimate the software's size by user's view not developer's. However, the current function point method has some problems for example complexity's upper limit etc. So, In this paper, an enhanced function point model. Micro FP model, was suggested. Using this model, software effort estimation can be more efficiently because this model has some regression equation. This model specially can be applied to estimate in detail the large application system's size Analysis results show that measured software size by this Micro FP model has the advantage with more correlative between the one of LOC, as of 10 applications operated in an large organization.

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.222-228
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• 2010

In this paper, we propose a optimization approach for the Electric Locomotive Overhaul Maintenance Facility (ELOMF), which aims at the simulation optimization so as to meet the design specification. In simulation design, we consider the critical path and sensitivity analysis of the critical (dependent) factors and the design (independent) parameters for the parameter selection and reduction of the metamodel. Therefore, we construct the multi-objective non-linear programming. The objective function is normalized for the generalization of design parameter while the constraints are composed of the simulation-based regression metamodel for the critical factors and design factor's domain. Then the effective solution procedure based on the pareto optimal solution set is proposed. This approach provides a comprehensive approach for the optimization of Train Overhaul Maintenance Facility(TOMF)'s design parameters using the simulation and metamoels.

• Journal of Science Education
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• v.34 no.2
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• pp.185-192
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• 2010

The purpose of this study was to develope the negative-emotion prediction model by hormonal changes during the scientific inquiry. For this study, biological classification task was developed that are suitable for comprehensive scientific inquiry. Forty-seven 2nd grade secondary school students (boy 18, girl 29) were participated in this study. The students are healthy for measure hormonal changes. The students performed the feathers classification task individually. Before and after the task, the strength of negative emotion was measured using adjective emotion check lists and they extracted their saliva sample for salivary hormone analysis. The results of this study, student's change of negative emotion during the feathers classification process was significant positive correlation(R=0.39, P<0.001) with student's salivary cortisol concentration. According to this results, we developed the negative emotion prediction model by salivary cortisol changes.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.