• Journal of KIISE
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• v.45 no.2
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• pp.141-149
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• 2018

Nowadays, mobile computing has become a common computing paradigm that provides convenience to people's daily life. More and more useful mobile applications' appearance makes it possible for a user to manage personal schedule, enjoy entertainment, and do many useful activities. However, there are some inherent defects in a mobile device that battery constraints and bandwidth limitations. These drawbacks get a user into troubles when to run computationally intensive applications. As a remedy scheme, component offloading makes room for handling mentioned issues via migrating computationally intensive component to the cloud server. In this paper, we will present the predictive offloading method for efficient mobile cloud computing. At last, we will present experiment result for validating applicability and practicability of our proposal.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.41-50
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• 2011

It is well-known that pavement is easily damaged by several factors including permanent deformation and fatigue crack, causing service life of the pavement to be shorter than expected. It is very important to predict amount of permanent deformation for designing pavement and developing design method of pavement. A new model of permanent deformation of pavement materials based on concept of shear stress ratio has been proposed because the lower pavement materials are highly affected by shear strength of the material. In this study a large repetitive triaxial load test has been adapted for performing test of permanent deformation of crushed subbase materials. The test procedure which includes concept of shear stress ratio has been newly developed. Several important model parameters can be obtained from the test that can be used for making correct permanent deformation model of the material.

• Journal of Digital Convergence
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• v.16 no.6
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• pp.151-161
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• 2018

Income is a vital aspect of economic life. Knowing what their income will help people create budgets that allow them to pay for their living expenses. Income data is used by banks, stores, and service companies for marketing purposes and for retaining loyal customers; it is a crucial demographic element used at a wide variety of customer touch points. Therefore, it is essential to be able to make income predictions for existing and potential customers. This paper aims to predict employment earnings or income based on history, and uses machine learning techniques such as SVMs (Support Vector Machines), Gaussian, decision tree and DCNNs (Deep Convolutional Neural Networks) for predicting employment earnings. The results show that the DCNN method provides optimum results with 88% compared to other machine learning techniques used in this paper. Improvement of the data length such PCA has the potential to provide more optimum result.

• Korean Journal of Environment and Ecology
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• v.28 no.4
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• pp.393-403
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• 2014

The aim of this study was to use the information deduced from biotopemap in Boryeong, Chungnam province conducted in 2011 and to select the wildlife survey point. The information used for the study was deduced from the knowledge and experience of wildlife specialists and was realized by 6 environmental variables (Outside distance from food vegetation, Outside distance from farm land, Outside distance from forest, Human density, Outside distance from road, Outside distance from water). 6 environmental variables were modeled by map overlay method and the model could deduce the correlation of 94.72% as a result of comparing with occurrence information. The areas predicted to have many occurrences were rural landscapes, forests, and valleys, and they can be used to deduce the quality wildlife survey results in the limit of survey range (area, schedule, and budget). However, it had the limit points such as the inside of forests was excluded, all species did not prefer the same habitat. The following studies are needed for this part in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.219-225
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• 2019

There is a problem with the existing method of selecting the difficulty levels of Hanja characters. Some Hanja characters selected by the existing methods are different from Sino-Korean words used in real life and it is impossible to know how many times the Hanja characters are used. To solve this problem, we measure the difficulty of Hanja characters using the multiple regression analysis with the frequency as the features. Based on the elementary textbooks, FWS and FHU are counted. A questionnaire is written using the two frequencies and stroke together to answer the appropriate timing of learning the Hanja characters and use them as target variables for regression. Use stepwise regression to select the appropriate features and perform multiple linear regression. The R² score of the model was 0.1105 and the RMSE was 0.1105.

• Fire Science and Engineering
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• v.33 no.3
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• pp.112-120
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• 2019

To improve the reliability of a safety assessment using a fire simulation in domestic PBD, the evaluation method of ASET considering the uncertainties of the input parameters and numerical model of fire simulation was carried out. To this end, a cinema and officetel were selected as the representative fire spaces. The main results were as follows. Considering the uncertainty of the heat release rate, which has the greatest effect on the major physical quantities presented in the life safety standard, significant changes in temperature, CO, and visibility occurred. In addition, when the bias factors reflecting the uncertainty of the numerical model were applied, there were no significant changes in temperature and CO concentration. On the other hand, the visibility was increased considerably due to the low prediction performance of smoke concentration in FDS. Finally, the reason why the physical quantity determining the ASET in domestic PBD is mainly visibility was discussed, and the application of uncertainty of the input parameters and numerical model in a fire simulation was suggested for an accurate ASET evaluation.

• Steel and Composite Structures
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• v.36 no.3
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• pp.321-337
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• 2020

Contemporary design and construction of steel-concrete composite structures employs the use of prefabricated concrete elements and demountable shear connectors in order to reduce the construction time and costs and enable dismantling of elements for their potential reuse at the end of life of buildings. Bolted shear connector with mechanical coupler is presented in this paper. The connector is assembled from mechanical coupler and rebar anchor, embedded in concrete, and steel bolt, used for connecting steel to concrete members. The behaviour and ultimate resistance of bolted connector with mechanical coupler in wide and narrow members were analysed based on push-out tests and FE analyses conducted in Abaqus software, with focus on concrete edge breakout and bolt shear failure modes. The effect of concrete strength, concrete edge distance and diameter and strength of bolts on failure modes and shear resistance was analysed. It was demonstrated that premature failure by breakout of concrete edge occurs when connectors are located 100 mm or closer from the edge in low-strength and normal-strength reinforced concrete. Furthermore, the paper presents a relatively simple model for hand calculation of concrete edge breakout resistance when bolted connectors with mechanical coupler are used. The model is based on the modification of prediction model used for cast-in and post-installed anchors loaded parallel to the edge, by implementing equivalent influence length of connector with variable diameter. Good agreement with test and FE results was obtained, thus confirming the validity of the proposed method.

• Journal of Digital Convergence
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• v.16 no.2
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• pp.195-201
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• 2018

Smartphones and PCs have become essential components of our daily life. People are expressing their opinions freely in SNS by using these devices. We are able to predict public opinions on specific subject by analyzing the related big data in SNS. In this paper, we have collected opinion data in SNS and analyzed reputation by text mining in order to make a prediction for the will of the people before 19th presidential election in South Korea. The result shows that our method makes more accurate estimate than other election polls.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.263-270
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• 2014

This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people's perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.

• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.301-313
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• 2017

Purpose: This study was conducted to discriminate between normal hulled barley and Fusarium (Fusarium asiaticum and Fusarium graminearum) infected hulled barley by using the near-infrared spectroscopy (NIRS) technique. Methods: Fusarium asiaticum and Fusarium graminearum were artificially inoculated in hulled barley and the reflectance spectrum of the barley spike was obtained by using a near-infrared spectral sensor with wavelength band in the range 1,175-2,170 nm. After obtaining the spectrum of the specimen, the hulled barley was cultivated in a greenhouse and visually inspected for infections. Results: From a partial least squares discriminant analysis (PLS-DA) prediction model developed from the raw spectrum data of the hulled barley, the discrimination accuracy for the normal and infected hulled barley was 99.82% (563/564) and 100% (672/672), respectively. Conclusions: NIRS is effective as a quick and nondestructive method to detect whether hulled barley has been infected with Fusarium. Further, it expected that NIRS will be able to detect Fusarium infections in other grains as well.