• Environmental Engineering Research
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• v.23 no.4
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• pp.374-382
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• 2018

Petroleum hydrocarbons pollutants, such as diesel fuel, have caused ecosystem damage in terrestrial and aquatic habitats. They have been recognized as one of the most hazardous wastes. This study was designed to optimize the effect of Tween 80 concentration, nitrogen (N)/phosphorus (P) ratio and salinity level on diesel biodegradation by Vibrio alginolyticus (V. alginolyticus). Response surface methodology with Box-Behnken design was selected with three factors of Tween 80 concentration (0, 5, 10 mg/L), N/P ratio (5, 10, 15) and salinity level (15‰, 17.5‰, 20‰) as independent variables. The percentage of diesel degradation was a dependent variable for 14 d of the remediation period. The results showed that the percentages of diesel degradation generally increased with an increase in the amount of Tween 80 concentration, N/P ratio and salinity level, respectively. The optimization condition for diesel degradation by V. alginolyticus occurred at 9.33 mg/L of Tween 80, 9.04 of N/P ratio and 19.47‰ of salinity level, respectively, with percentages of diesel degradation at 98.20%. The statistical analyses of the experimental results and model predictions ($R^2=0.9936$) showed the reliability of the regression model and indicated that the addition of biostimulant can enhance the percentage of diesel biodegradation.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.89-95
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• 2019

One benefit of education for self-management of chronic diseases is to increase the use of cognitive techniques for coping with symptoms. Unfortunately, that benefit can deteriorate over time, and that phenomenon, which is sometimes called "decay of impact", has been studied only rarely. This study was done to understand the decay of impact with regard to the use of cognitive techniques for coping with symptoms, and especially to understand how that decay might be predicted. Data were analyzed from 381 adults suffering from chronic medical conditions, all of whom were involved in education to improve their self-management of their chronic condition(s). During the first year after the educational program, coping was measured four times. Variables associated with the decay of impact were found using statistical modeling (logistic regression). Decay of impact was found in almost half of the participants. The analysis provided moderately good predictions regarding the decay of impact. Given this new information, interventions to further improve coping with symptoms can be appropriately targeted to the people for whom they will be most beneficial.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.623-632
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• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

• Clean Technology
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• v.29 no.2
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• pp.145-150
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• 2023

Tele-metering systems have been useful tools for managing domestic wastewater treatment plants (WWTP) over the last decade. They mostly generate water quality data for discharged water to ensure that it complies with mandatory regulations and they may be able to produce every operation parameter and additional measurements in the near future. A sub-big data group, comprised of about 150,000 data points from four domestic WWTPs, was ready to be classified and also analyzed to optimize the WWTP process. We used the Statistical Product and Service Solutions (SPSS) 25 package in order to statistically treat the data with linear regression and correlation analysis. The major independent variables for analysis were water temperature, sludge recycle rate, electricity used, and water quality of the influent while the dependent variables representing the water quality of the effluent included the total nitrogen, which is the most emphasized index for discharged flow in plants. The water temperature and consumed electricity showed a strong correlation with the total nitrogen but the other indices' mutual correlations with other variables were found to be fuzzy due to the large errors involved. In addition, a multilayer perceptron analysis method was applied to TMS data along with root mean square error (RMSE) analysis. This study showed that the RMSE in the SS, T-N, and TOC predictions were in the range of 10% to 20%.

• Management & Information Systems Review
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• v.39 no.3
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• pp.83-98
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• 2020

This study analyzes how investors' irrational behavior (or pessimistic sentiment) affects stock market returns and investors' market activity using mass media that delivered public information from January 1998 to December 2012 as a sample. According to pessimistic investor theory, investor pessimism leads to downward pressure on the price of equity capital, thereby making market sentiment pessimistic and lowering market yields. It also shows that investor pessimism increases transaction costs in the market, which in turn dampens investors' trading activities. In other words, pessimistic reporting on public information disseminated by mass media induces investors to act irrationally, eventually having a direct impact on the stock market. This study conducted an empirical analysis of the existing theoretical and empirical studies using domestic mass media as a sample. First, the study revealed a negative correlation between pessimistic reporting and returns as well as excess returns, while it did not show statistically significant results. Second, evidence has been suggested that pessimistic sentiment in the stock market has a negative impact on future pessimistic reporting by mass media. Third, the analysis of the impact of pessimistic reporting on investors' market activity using proxy variables for various market activities found that pessimism dampens market activity, while it did not show statistically significant results. It is assumed that low statistical significance is due to the fact that sample collection was carried out on a monthly basis. While the results of the study have low statistical significance, statistical signs support predictions of the theory.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of the Korea Concrete Institute
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• v.15 no.6
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• pp.820-828
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• 2003

Recent research has indicated that the current ACI shear provision provides unconservative predictions for large slender beams and beams with low level of longitudinal reinforcement, and conservative results for deep beams. To modify some problems of ACI shear provision, ultimate shear strength equation considering size effect and arch action to compute shear strength in high-strength concrete beams without stirrups is presented in this research. Three basic equations, namely size reduction factor, rho factor, and arch action factor, are derived from crack band model of fracture mechanics, analysis of previous some shear equations for longitudinal reinforcement ratio, and concrete strut described as linear prism in strut-tie model deep beams. Constants of basic equations are determined using statistical analysis of previous shear testing data. To verify proposed shear equation for each variable, effective depth, longitudinal reinforcement ratio, concrete compressive strength and shear span-to-depth ratio, about 300 experimental data are used and proposed shear equation is compared with ACI 318-99 code, CEB-FIP Model code, Kim &Park's equation and Zsutty's equation. The proposed shear equation is not only simpler than other shear equations, it is but also shown to be economical predictions and reasonable safety margin. Hence proposed shear strength equation is expected to be applied to practical shear design.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.4
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• pp.406-412
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• 2009

In this work, a new method to obtain the effective ground conductivity value in the middle frequency band where the informations of soil characteristics are insufficient was proposed. The proposed method obtained the effective ground conductivity values with the measured field strength from sea reference stations and general attenuation model in the middle frequency band. In addition, the proposed method used statistical method to minimize the error between the measurements and the predictions. Then, the effective ground conductivity in Korea peninsular was obtained by using the proposed method. Finally, it was verified that the effective ground conductivity using the proposed method is useful to predict electric field strength in the middle frequency band.

• Structural Engineering and Mechanics
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• v.37 no.4
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• pp.427-442
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• 2011

In fatigue life design of mechanical components, uncertainties arising from materials and manufacturing processes should be taken into account for ensuring reliability. A common practice is to apply a safety factor in conjunction with a physics model for evaluating the lifecycle, which most likely relies on the designer's experience. Due to conservative design, predictions are often in disagreement with field observations, which makes it difficult to schedule maintenance. In this paper, the Bayesian technique, which incorporates the field failure data into prior knowledge, is used to obtain a more dependable prediction of fatigue life. The effects of prior knowledge, noise in data, and bias in measurements on the distribution of fatigue life are discussed in detail. By assuming a distribution type of fatigue life, its parameters are identified first, followed by estimating the distribution of fatigue life, which represents the degree of belief of the fatigue life conditional to the observed data. As more data are provided, the values will be updated to reduce the credible interval. The results can be used in various needs such as a risk analysis, reliability based design optimization, maintenance scheduling, or validation of reliability analysis codes. In order to obtain the posterior distribution, the Markov Chain Monte Carlo technique is employed, which is a modern statistical computational method which effectively draws the samples of the given distribution. Field data of turbine components are exploited to illustrate our approach, which counts as a regular inspection of the number of failed blades in a turbine disk.