• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1664-1673
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• 2022

Achieving sustainable development requires an increasing share of green technologies. World energy demand is expected to rise significantly especially in developing economies. The increasing energy demands will be entertained with conventional energy sources at the cost of higher emissions unless eco-friendly technologies are used. This study examines the asymmetric effects of nuclear energy on carbon emissions for Pakistan from 1974 to 2019. Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) unit root tests suggest that variables are integrated of order one and bound test of Autoregressive Distributed Lag (ARDL) and nonlinear ARDL confirm a long-run relationship among selected variables. The ARDL, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS) results show that the coefficient of nuclear energy has a negative and significant impact on emissions in both short and long run. Further, the NARDL finding shows that there exists an asymmetric long-run association between nuclear energy and CO₂ emissions. The vector error correction method (VECM) results indicate that there exists a bidirectional causal relationship between nuclear energy and carbon emissions in both the short and long run. Additionally, the impact of nuclear energy on ecological footprint has been examined and our findings remain robust.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.203-212
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• 2023

This research work aims to investigate the role of FDI in Economic Development by assessing its relationship with GDP per capita in Vietnam +5 from 1986-2020. Through descriptive statistical, correlation matrix analysis, and econometric models, including Vector Error Correction Model (VECM) and Feasible Generalized Least Squares (FGLS) estimation methods using Stata 15.1. The VECM estimation method results show that FDI positively impacts Economic Development in the short run while not finding a long-run relationship. In addition, it is found that a clear relationship between Exports and Economic Development in both the short run and the long run. Meanwhile, CO2 emissions and Employment Opportunities have no clear relationship with Economic Development in the short run. However, the relationship is reversed in the long run, as the empirical study in Vietnam. The results of the FGLS estimation method show that FDI, CO2 emissions, and Exports have a significant and positive impact on Economic Development in five selected Southeast Asian countries without Employment Opportunities in the long run. From these findings, the author proposes some policy implications of attaching FDI to sustainable Economic Development in Vietnam next time.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.237-245
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• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.155-155
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• 2022

Low head, ogee spillways is popularly used to defense against floods as well as to provide water for irrigation. Spillway is also used to assess compliance with water quality regulations by controlling amount of discharge to the downstream of a channel. For the purpose of water resource management and/or environmental aspects as explained above, the flow discharge through spillways need to be correctly rated as a function of geometry and hydraulic variables. Typically, four flow conditions are encountered during the operation of spillway: (a) uncontrolled free flow (UF); (b) uncontrolled submerged flow (US); controlled free flow (CF); and controlled submerged flow (CS), and each condition has a unique rating equation. However, one of the tricky part of the spillway operation is finding correct flow type over the spillway because structures can operate under both submerged and free flow conditions, and the types are continuously changing over time depending on the amount of discharge, head water and tail water elevation. Quite obviously, if the wrong rating curve relationship is applied because of misjudgment of the flow type due to a transition, a serious error can occur. Thus, an hydraulic model study of one of spillway structure located in South Florida was conducted for the purpose of developing transition relationships. In this presentation, US to UF transition is highlighted.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Steel and Composite Structures
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• v.44 no.6
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• pp.867-882
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• 2022

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.15-20
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• 2023

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically, reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches, and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

• Tribology and Lubricants
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• v.40 no.2
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• pp.67-70
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• 2024

Chemical mechanical planarization (CMP) is an essential process for ensuring high integration when manufacturing semiconductor devices. CMP mainly requires the use of polyurethane-based polishing pads as an ultraprecise process to achieve mechanical material removal and the required chemical reactions. A diamond disk performs pad conditioning to remove processing residues on the pad surface and maintain sufficient surface roughness during CMP. However, the diamond grits attached to the disk cause uneven wear of the pad, leading to the poor uniformity of material removal during CMP. This study investigates the pad wear rate profile according to the swing motion of the conditioner during swing-arm-type CMP conditioning using deep learning. During conditioning, the motion of the swing arm is independently controlled in eight zones of the same pad radius. The experiment includes six swingmotion conditions to obtain actual data on the pad wear rate profile, and deep learning learns the pad wear rate profile obtained in the experiment. The absolute average error rate between the experimental values and learning results is 0.01%. This finding confirms that the experimental results can be well represented by learning. Pad wear rate profile prediction using the learning results reveals good agreement between the predicted and experimental values.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.130-139
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• 2017

The crop damage caused by strong wind was predicted using the wind speed data available from Korean Meteorological Administration (KMA). Wind speed data measured at 19 automatic weather stations in 2012 were compared with wind data available from the KMA's digital forecast. Linear regression equations were derived using the maximum value of wind speed measurements for the three-hour period prior to a given hour and the digital forecasts at the three-hour interval. Estimates of daily maximum wind speed were obtained from the regression equation finding the greatest value among the maximum wind speed at the three-hour interval. The estimation error for the daily maximum wind speed was expressed using normal distribution and Weibull distribution probability density function. The daily maximum wind speed was compared with the critical wind speed that could cause crop damage to determine the level of stages for wind damage, e.g., "watch" or "warning." Spatial interpolation of the regression coefficient for the maximum wind speed, the standard deviation of the estimation error at the automated weather stations, the parameters of Weibull distribution was performed. These interpolated values at the four synoptic weather stations including Suncheon, Namwon, Imsil, and Jangsu were used to estimate the daily maximum wind speed in 2012. The wind damage risk was determined using the critical wind speed of 10m/s under the assumption that the fruit of a pear variety Mansamgil would begin to drop at 10 m/s. The results indicated that the Weibull distribution was more effective than the normal distribution for the estimation error probability distribution for assessing wind damage risk.

• Korean Journal of Health Education and Promotion
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• v.19 no.4
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• pp.77-97
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• 2002

Since growth promotion was defined by Koch(1935), many researches like Benholdt and Thomsen(1942) have conducted studies for understanding problem of puberty growth. Growth promotion means that growth is developed in puberty, and several researchers have reported that the more becomes economic growth, the more becomes growth promotion. Thereupon, this study was attempted to find Maximum Growth Age(M.G.A.), as an index of height growth promotion in Korea, which was obtained by longitudinal observations of the same group. Thus, this study can explain the earlier tendency of growth. To investigate domestic changes in M.G.A., M.G.A. was calculated with the results of cross-sectional researchs using 25 representative papers between 1940-1953 including measurements by Lee(1940) and data by Kim(1953) in this study. Based on the research data published between 1940 and 2000, height and M.G.A. of males and females who were born between 1925 and 1983 were gotten by years, and a trend of growth promotion for height in Koreans was suggested by examining study subjects. Findings of this study are as follows; 1. M.G.A. for height decreased both in males and females; for males, 14.28 years in 1940, 14.24 in 1953, 13.86 in 1967, 12.74 in 1985, and 11.71 in 2000; for females, 12.0 in 1940, 11.52 in 1965, 10.00 in 1978 and 9.77 in 2000. 2. Regression equations and standard errors of estimate concerning M.G.A. for height by years were obtained; for males, Y$_1$(M.G.A.) = 17.21 - 0.059X$_1$, S$_{Y1X1}$(standard error of estimate about the regression line) = ${\pm}$0.62; for females, Y$_2$(M.G.A.) = 13.81-0.042X$_2$, S$_{Y2X2}$(standard error of estimate about the regression line) = ${\pm}$0.64 3. As a result of finding correlation between year and M.G.A. r=-0.763 (p<0.001) for male and r=-0.699(p<0.001) for female were obtained 4. From a view that the growth promotion has been continued before 2000, M.G.A. decreased 0.6 years for male and 0.4 for female per 10 years. 5. M.G.A. for height is as shown in Table 2. 6. It is thought that the future trend of growth promotion for height will follow the progress from 1940s to now. It shall be reviewed again after development of coming several years is investigated.