• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.673-682
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• 2023

This study investigates the delay in setting characteristics of mortar influenced by variations in super retarding agent(SRA) content, curing temperature, and strength levels. Utilizing a settimeter, the research introduces an objective approach to accurately determine the setting time of concrete with SRA under diverse environmental and material mixing conditions at construction sites. The findings indicate that the settimeter, in conjunction with a nonlinear regression model, can effectively estimate the setting time of super retarding mortar. Optimal management of the initial setting is recommended at approximately 45ST and the final setting around 80ST. This methodology enables more effective quality control in the setting times of super retarding concrete.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.260-268
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• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.527-535
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• 2015

With results from previous Korean studies on forest thinning, we conducted a meta-analysis on the effect of thinning on diameter at breast height (DBH) growth and carbon (C) stocks (tree, litter layer, coarse woody debris (CWD), and soils) in Korean forests. Thinning increased the DBH growth and the C stocks in soils by 39.2% and 12.8%, respectively, while it decreased the C stocks in tree by 30.9%. In contrast, thinning had no significant effect on the C stocks in litter layer and CWD. The DBH growth and the C stocks in tree showed significant correlations with thinning intensity and recovery time. The C stocks in litter layer correlated with recovery time while those in CWD and soils did not show significant correlation neither with thinning intensity nor with recovery time. Regression models of the DBH growth and the C stocks in tree were developed to quantify the effect of thinning intensity and recovery time. An integration of the regression model of the tree C stock into forest carbon models is expected to be essential to quantify the effect of thinning on the C stocks in litter layer, CWD, and soils. We also suggested expansion of study species, long-term and frequent monitoring, and investigation on understory vegetation in order to elucidate changes in Korean forests following thinning practices.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.57-64
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• 2021

This study presents a method for realtime fuel consumption prediction using real data collected from OBDII. With the advent of the era of self-driving cars, electronic control units(ECU) are getting more complex, and various studies are being attempted to extract and analyze more accurate data from vehicles. But since ECU is getting more complex, it is getting harder to get the data from ECU. To solve this problem, the firmware was developed for acquiring accurate vehicle data in this study, which extracted 53,580 actual driving data sets from vehicles from January to February 2019. Using these data, the ensemble stacking technique was used to increase the accuracy of the realtime fuel consumption prediction model. In this study, Ridge, Lasso, XGBoost, and LightGBM were used as base models, and Ridge was used for meta model, and the predicted performance was MAE 0.011, RMSE 0.017.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.73-79
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• 2019

This study simulated external ear canal modeling with different external ear canal lengths, vertical flexion angles, and inner/outer diameter ratios using digital imaging and communications in medicine(DICOM) of the head temporal region and measured the acoustic sensitivity. The experiment was performed by increasing the audible frequency for humans by 200 Hz and expressing the frequency constantly transmitted at 1 Pa as the eardrum acoustic volume and presented the measurements by linear and quadratic curve regression analysis. The results showed that the longer the external ear canal length and the higher the ratio of the outer/inner diameter, the faster the acoustic response at lower frequencies. The acoustic sensitivity correlation of the meta-model using regression analysis showed a 77% influence by the external ear canal length and 5% by the external/internal diameter ratio, while the vertical flexion angle did not show a significant relationship. This showed that auditory acoustic sensitivity of humans is a factor that reacts faster at a low frequency when the external ear canal length is longer and when the difference between the outer and inner diameter is higher.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.7
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• pp.601-608
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• 2015

The static aeroelastic analysis and optimization of flexible wings are conducted for steady state conditions while both aerodynamic and structural parameters can be used as optimization variables. The system of multidisciplinary design optimization as a robust methodology to couple commercial codes for a static aeroelastic optimization purpose to yield a convenient adaptation to engineering applications is developed. Aspect ratio, taper ratio, sweepback angle are chosen as optimization variables and the skin thickness of the wing. The real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was used to control the optimization process. The support vector regression(SVR) is applied for optimization, in order to reduce the time of computation. For this multi-objective design optimization problem, numerical results show that several useful Pareto optimal designs exist for the flexible wing.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.105-115
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• 2023

This study seeks to find a way to induce users to expand their direct participation in sports through the acceptance of digital technology. From July 1 to August 30, 2022, a survey was conducted targeting home training users who applied the Internet of Things (IoT). 129 people participated in the survey through non-face-to-face self-administration method. For data processing, frequency analysis, exploratory factor analysis, reliability analysis, correlation analysis, multiple regression analysis, and 3-step mediation regression analysis were conducted using IBM's SPSS 21.0 program. The results of the study are as follows. First, in the relationship between the home training PPM model and direct participation in sports, ease appeared to have a mediating effect. In the factors of push, simple functionality showed a complete mediating effect, and inefficiency showed a partial mediating effect. Among pull factors, enjoyment and possibility of experience showed a complete mediating effect. In the mooring factors, individual innovativeness showed a complete mediating effect. Second, in the relationship between home training PPM model and direct participation in sports, usefulness showed a mediating effect. In the factors of push, simple functionality showed a complete mediating effect, and inefficiency showed a partial mediating effect. Among pull factors, enjoyment and possibility of experience showed a complete mediating effect. Among the mooring factors, individual innovativeness showed a partial mediating effect. Through this research, it is expected that the sports industry will contribute to the expansion of consumption expenditure and economic growth through the expansion of digital technologies such as NFT, Metaverse, and virtual/augmented reality.

• Journal of the Korea Computer Industry Society
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• v.5 no.4
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• pp.491-502
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• 2004

In this paper, we propose a HMM model using FSVQ(First Section VQ), fuzzy theory and doubly spectral feature, as study on the isolated word recognition system of speaker-independent. In the proposed paper, LPC cepstrum coefficients and regression coefficients of LPC cepstrum as doubly spectral feature be used. And, training data are divided several section and first section is generated codebook of VQ, and then is obtained multi-observation sequences by order of large propabilistic values based on fuzzy nile from the codebook of the first section. Thereafter, this observation sequences of first section is trained and is recognized a word to be obtained highest probaility by same concept. Besides the speech recognition experiments of proposed method, we experiment the other methods under the equivalent environment of data and conditions. In the whole experiment, it is proved that the proposed method is superior to the others in recognition rate.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.81-87
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• 2010

In order to calculate the accurate runoff from a basin, nonlinearity in the relationship between rainfall and runoff has to be considered. Many runoff calculation models assume the linearity in the relationship or are too complicated to be analyzed. Therefore, the storage function method has been used in the prediction of flood because of the simplicity of the model. The storage function method has five parameters with related to the basin and rainfall characteristics which can be estimated by the empirical trial and error method. To optimize these parameters, regression method or optimization techniques such as genetic algorithm have been used, however, it is not easy to optimize them because of the complexity of the method. In this study, the metamodel is proposed to estimate those model parameters. The metamodel is the combination of artificial neural network and genetic algorithm. The model is consisted of two stages. In the first stage, an artificial neural network is constructed using the given rainfall-runoff relationship. In the second stage, the parameters of the storage function method are estimated using genetic algorithm and the trained artificial neural network. The proposed metamodel is applied in the Peong Chang River basin and the results are presented.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.6
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• pp.541-549
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• 2008

The fatigue analysis is performed to avoid structural failure in aerospace structures under repeated loads. In this paper, the fatigue life is estimated for the design of tilt rotor UAV. First of all, the fatigue load spectrum for tilt rotor UAV is generated. Fatigue analysis is done for the flaperon joint which may have FCL(fracture critical location). Tilt rotor UAV operates at two modes: helicopter mode such as taking off and landing; fixed wing mode like cruising. To make overall fatigue load spectrum, FELIX is used for helicopter mode and TWIST is used for fixed wing mode. The other hand, the Kriging meta model is used to get S-N regression curve for whole range of material life when S-N test data are analyzed. And then, the second order of S-N curve is accomplished by the least square method. In addition, the coefficient of determination method is used to ensure how accuracy it has. Finally, the fatigue life of flaperon joint is compared with that obtained by MSC. Fatigue.