• Advances in concrete construction
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• v.15 no.2
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• pp.85-96
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• 2023

The characteristics of self-compacting concrete (SCC) made with fly ash and reinforced with polyester fibers were investigated in this research. Polyester fibers of 12 mm long and 15 micrometer diameters were utilized in M40 grade SCC mixtures at five different volume fractions 0.025%, 0.05%, 0.075%, 0.1%, 0.3% as a fiber reinforcement. To understand the influence of polyester fibers on passing ability, flowability, segregate resistance the J ring, L box, V funnel, slump flow and U box tests were performed. Polyester fibers have a direct influence, with a maximum of 0.075% polyester fibers producing excellent characteristics. ANN models were constructed using the testing data as inputs to anticipate the fresh and hardened characteristics as targeted outputs. The research revealed that R² values ranging from 0.900 to 0.997 appears to be a good correlation. The performance of ANN models and regression models for predicting the new characteristics of SCC is also evaluated.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.450-458
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• 2007

This paper presents the results of a statistical analysis and its research findings focusing on the learning aspect in the process of international joint ventures (IJVs). The contents of this paper is derived from a sample of 96 field cases based on a proposed conceptual model of effective learning for international construction joint ventures (ICJVs). The paper presents a brief review on the conceptual model with hypotheses and summarized the key results of statistical analysis including factor and multiple regression analysis for the testing of the validity of the proposed conceptual model and its associated research hypotheses. Among other research findings, the research confirms that ICJVs provides an excellent platform of in-action learning for construction organization and suggests that good outcomes in learning could be reaped by a company who has a clear learning intent from the beginning and subsequently take corresponding learning actions during the full process of the joint venture.

• Annual Conference of KIPS
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• 2010.11a
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• pp.940-943
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• 2010

여러 장치들과 연동하여서 동작하는 네트워크 시스템 개발 시, 회귀테스트는 전체 시스템의 안정성을 보장하기 위한 가장 필요한 테스트이다. 그러나 서비스가 진행 중이거나 연동을 위한 추가적인 장비가 필요할 경우 테스트의 제약을 받게 된다. 본 논문에서는 회귀성 테스트에서 발생하는 시간과 비용을 줄이기 위한 가상 시스템에서 네트워크 시뮬레이터를 제안한다. 네트워크 시뮬레이터는 테스트에 대한 시나리오를 분석하여 테스트에 따른 실제 장비에서의 메시지들을 구성하며, 테스트 시나리오에 맞게 이벤트를 발생시킴으로서 가상으로 회귀성 테스트를 가능하게 한다. 설계된 네트워크 시뮬레이터는 우선적으로 모바일 환경에서 테스트를 시행하여, 가상 이벤트 구성과 동작의 기능을 검증하는데 사용되었다.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.261-267
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• 2023

A common approach to the problem of predicting student test scores is based on the student's previous educational history. In this study, high school transcripts of about two thousand candidates, who took the High-school Student Assessment (HSA) were collected. The data were estimated through building a regression model - Random Forest and optimizing the model's parameters based on Genetic Algorithm (GA) to predict the HSA scores. The RMSE (Root Mean Square Error) measure of the predictive models was used to evaluate the model's performance.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.59-82
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• 2023

The present research aims to examine whether learning from the supervisor influences readiness for change with the mediating impact of planfulness. Drawing upon the theory of planned behavior, it is hypothesized that learning from the supervisor positively impacts planfulness ability in individuals, which in turn enhances the readiness for change. Through using convenience sampling, the sample of 451 was collected from employees working full-time in the manufacturing and I.T. service organizations in India. Structural equation modeling and regression analysis indicate that learning from the supervisor is positively associated with readiness for change and planfulness. Additionally, planfulness fully mediated the relationship between learning from the supervisor and readiness to change. The findings of the present research highlight that continuous support and learning from the supervisor enhances the planfulness ability of the individual and consequently enhances individual readiness for change. The current research is pioneering in testing the hypothetical model associating learning from the supervisor, planfulness, and readiness for change.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Korean Journal of Food Science and Technology
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• v.20 no.3
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• pp.310-316
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• 1988

The gelatinized model food system were prepared by combining moisture, starch and protein, and the textural properties of their gels of different temperatures and times of heating were investigated by the use of Instron Universal Testing Machine. The hardness, springiness, cohesiveness, gumminess and chewiness of model foods had a high correlation with solid content and the regression equations between the hardness of model foods and moisture content heated for 20min. at $80{\circ}C$ were as follows; $H(PS_4)=18.6405-3.8201M+0.1959M^2,\;H(P_1S_1)=244.7933-5.692M+0.0332M^2,\;H(P_4S)=693.0292-16.6884M+0.1005M^2$, The correlation coefficients were $0.996^{**},\;0.998^{**}\;and\;0.998^{**}$, respectively. Total correlations between textural parameters and temperature and heating times were different according to model foods. The correlation between textural parameters was proportional to protein foods, but the hardness and cohesiveness of starch foods showed the relationship of inverse proportion. Under low solid content, the parameters of model foods appeared to decrease as protein content increased. Under high solid content, the parameters of protein foods were higher than those of starch foods above some level of protein content. The regression equation between the hardness and protein content heated for 20min. at $80^{\circ}C$ was as follows; Hardness(20%)=5.6858-13.5670P+$9.7758P^2$ and the correlation coefficient was $0.95^{**}$.

• Korean Journal of Radiology
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• v.24 no.9
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• pp.827-837
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• 2023

Objective: To investigate the predictive value of radiomics features based on cardiac magnetic resonance (CMR) cine images for left ventricular adverse remodeling (LVAR) after acute ST-segment elevation myocardial infarction (STEMI). Materials and Methods: We conducted a retrospective, single-center, cohort study involving 244 patients (random-split into 170 and 74 for training and testing, respectively) having an acute STEMI (88.5% males, 57.0 ± 10.3 years of age) who underwent CMR examination at one week and six months after percutaneous coronary intervention. LVAR was defined as a 20% increase in left ventricular end-diastolic volume 6 months after acute STEMI. Radiomics features were extracted from the oneweek CMR cine images using the least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the selected features was evaluated using receiver operating characteristic curve analysis and the area under the curve (AUC). Results: Nine radiomics features with non-zero coefficients were included in the LASSO regression of the radiomics score (RAD score). Infarct size (odds ratio [OR]: 1.04 (1.00-1.07); P = 0.031) and RAD score (OR: 3.43 (2.34-5.28); P < 0.001) were independent predictors of LVAR. The RAD score predicted LVAR, with an AUC (95% confidence interval [CI]) of 0.82 (0.75-0.89) in the training set and 0.75 (0.62-0.89) in the testing set. Combining the RAD score with infarct size yielded favorable performance in predicting LVAR, with an AUC of 0.84 (0.72-0.95). Moreover, the addition of the RAD score to the left ventricular ejection fraction (LVEF) significantly increased the AUC from 0.68 (0.52-0.84) to 0.82 (0.70-0.93) (P = 0.018), which was also comparable to the prediction provided by the combined microvascular obstruction, infarct size, and LVEF with an AUC of 0.79 (0.65-0.94) (P = 0.727). Conclusion: Radiomics analysis using non-contrast cine CMR can predict LVAR after STEMI independently and incrementally to LVEF and may provide an alternative to traditional CMR parameters.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.123-138
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• 2010

After selecting a group of women with premenstrual syndrome based on daily distress scores of 28 days, one needs to estimate threshold for the change of symptoms, which would be useful for the clinician's diagnosis in hospitals. However, a test of whether a change has occurred has to precede the estimation of the threshold. In this paper, we apply parametric and nonparametric testing methods to an example data obtained from a group of women. Nonparametric method does not assume any distributional form of distress scores and parametric testing method is based on the normal distributions of linear regression lines. Therefore, the optimal situation of both methods would be different and we will assess it with a simulation study.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.41-48
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• 2020

The purpose of this study is testing measurement invariance of the school vitality scale across the level of school. For this study, 3,156 elementary school teachers and 4,411 secondary school teachers were surveyed. As a result, school vitality scale was found to have the same factor structure in the structure regression model. Second, the factor load of the measurement model was found to be the same. Third, the structural path coefficients were the same. Fourth, structural covariance was found to be the same. Fifth, the structural residuals were the same. Based on these findings, it can be concluded that we can use school vitality scale both elementary school and secondary school. This study will contribute to diagnosing school vitality levels and finding ways to improve school management.