• Journal of Power System Engineering
• /
• v.6 no.2
• /
• pp.40-47
• /
• 2002

In this paper, friction welding optimization for 1Cr0.5Mo-STS304 (${\phi}14\;mm$), AE applications for the weld quality evaluation and the applications of various life prediction methods such as LMP (Larson-Miller Parameter) and ISM (initial strain method) were investigated : The creep behaviors of those steels and the friction welded joints under static load were examined by ISM combined with LMP at 400, 500, 550 and $600^{\circ}C$, and the relationship between these two kinds of phenomena was studied. The real-time predicting equations of elevated-temperature creep life (rupture time) under any creep stress at any elevated-temperature could be developed by LMP and LMP-ISM. It was confirmed that the life prediction equations by LMP and LMP-ISM are effective only up to 102 h and can not be used for long times of 103-106 h, but by ISM it can be used for long times creep prediction of more than 104 h with most reliability.

• ETRI Journal
• /
• v.35 no.2
• /
• pp.270-280
• /
• 2013

A fast intra-prediction method is proposed for High Efficiency Video Coding (HEVC) using a fast intra-mode decision and fast coding unit (CU) size decision. HEVC supports very sophisticated intra modes and a recursive quadtree-based CU structure. To provide a high coding efficiency, the mode and CU size are selected in a rate-distortion optimized manner. This causes a high computational complexity in the encoder, and, for practical applications, the complexity should be significantly reduced. In this paper, among the many predefined modes, the intra-prediction mode is chosen without rate-distortion optimization processes, instead using the difference between the minimum and second minimum of the rate-distortion cost estimation based on the Hadamard transform. The experiment results show that the proposed method achieves a 49.04% reduction in the intra-prediction time and a 32.74% reduction in the total encoding time with a nearly similar coding performance to that of HEVC test model 2.1.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2001.05a
• /
• pp.307-313
• /
• 2001

In this Paper, friction welding optimization for 1Cr0.5Mo-STS304($\Phi$14mm), AE applications for the weld quality evaluation and the applications of various life prediction methods such as LMP(Larson-Miller Parameter) and ISM(initial strain method) were investigated : the creep behaviors of those steels and the friction welded joints under static load were examined by ISM combined with LMP at 400, 500, 550 and $600^{\circ}C$, and the relationship between these two kinds of phenomena was studied. The real-time predicting equations of elevated-temperature creep life(fracture time) under any creep stress at any elevated- temperature could be developed by LMP and LMP-ISM, It was confirmed that the life prediction equations by LMP and LMP-ISM are effective only up to 10$^2$hrs and can not be used for long times of 10$^3$-10$^{6}$ hrs, but by ISM it can be used for long times creep prediction of more than 10$^4$hrs with most reliability.

• Journal of Ocean Engineering and Technology
• /
• v.15 no.2
• /
• pp.46-52
• /
• 2001

In this paper, the real-time prediction of high temperature creep life was carried out for the friction welded joints of dissimilar heat resisting steels (SUH3-SUH35). various life prediction method such as LMP (Larson_miller Parameter) and ISM (initial strain method) were applied. The creep behaviors of those steels and the welds under static load were examined by ISM combined with LMP at 500, 600 and $700^{\circ}C$, and the relationship between these two methods was investigated. A real-time creep lie (tr, hr) prediction equation by initial strain (${\varepsilon}_0$, %) under any creep stress ($\sigma$, MPa) at any high temperature (T, K) was developed as follows: $t_r={\alpha}{\varepsilon}_0^{\beta}{\sigma}^{-1}$ where, ${\phi}=16: {\alpha}=10^{51.412-0.104T+5.375{\times}10^5T^2}$, $ {\beta}=-83.989+0.180T-9.957{\times}10^{-5}T^2,{\phi}=20:$ ${\alpha}=10^{69.910-0.146T+7.744{\times}10^{-5}T^2$, ${\beta}=-51.442+0.105T-5.595{\times}10^{-5}T^2$ for SUH3-SUH35 friction weld of =16mm and 20mm, respectively.

• Journal of the Ergonomics Society of Korea
• /
• v.27 no.4
• /
• pp.73-83
• /
• 2008

With a rapid extension of functions in mobile phones, text input method has become very important for mobile phone users. Previous studies for text input methods were focused on Fitts' law, emphasizing expert's behaviors with one-handed text input method. However, it was observed that 97% of Korean mobile phone users input texts with two-hands. Therefore, this study was designed to develop a prediction model of two-handed Hangul text entry method including novice users as well as experts for mobile phone. For this study, Fitts' law was hypothesized to predict experts' movement time(MT) whereas Hick-Hyman law for visual search time was hypothesized to be added to MT for novices. The results showed that the prediction model was well fitted with the empirical data for both experts and novices with less than 3% error rates. In conclusion, this prediction model of two-handed Hangul text entry including novice users was proven to be a very effective model for modeling two-handed Hangul text input behavior for both experts.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.236-242
• /
• 2003

ATM ABR service controls network traffic using feedback information on the network congestion situation in order to guarantee the demanded service qualities and the available cell rates. In this paper we apply the control method using queue length prediction to the formation of feedback information for more efficient ABR traffic control. If backward node receive the longer delayed feedback information on the impending congestion, the switch can be already congested from the uncontrolled arriving traffic and the fluctuation of queue length can be inefficiently high in the continuing time intervals. The feedback control method proposed in this paper predicts the queue length in the switch using the slope of queue length prediction function and queue length changes in time-series. The predicted congestion information is backward to the node. NLMS and neural network are used as the predictive control functions, and they are compared from performance on the queue length prediction. Simulation results show the efficiency of the proposed method compared to the feedback control method without the prediction. Therefore, we conclude that the efficient congestion and stability of the queue length controls are possible using the prediction scheme that can resolve the problems caused from the longer delays of the feedback information.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.10
• /
• pp.102-111
• /
• 2021

Maintainability is a major design parameter that includes availability as well as reliability in a RAM (reliability, availability, maintainability) analysis, and is an index that must be considered when developing a system. There is a lack of awareness of the importance of predicting and analyzing maintainability; therefore, it is dependent on past-experience data. To improve the utilization rate, maintainability must be managed as a key indicator to meet the user's requirements for failure maintenance time and to reduce life-cycle costs. To improve the maintainability-prediction accuracy in the detailed design stage, we present a maintainability-prediction method that applies Method B of the Military Standardization Handbook (MIL-HDBK-472) Procedure V, as well as a Korean maintainability-prediction software package that reflects the system complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.1975-1988
• /
• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.150-150
• /
• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• Geomechanics and Engineering
• /
• v.38 no.1
• /
• pp.69-77
• /
• 2024

The development of shield-driven cross-river tunnels in China is witnessing a notable shift towards larger diameters, longer distances, and higher water pressures due to the more complex excavation environment. Complex geological formations, such as fault and karst cavities, pose significant construction risks. Real-time adjustment of shield tunneling parameters based on parameter prediction is the key to ensuring the safety and efficiency of shield tunneling. In this study, prediction models for the torque and thrust of the cutter plate of ultra-large diameter slurry shield TBMs is established based on integrated learning algorithms, by analyzing the real data of Heyan Road cross-river tunnel. The influence of geological complexities at the excavation face, substantial burial depth, and high water level on the slurry shield tunneling parameters are considered in the models. The results reveal that the predictive models established by applying Random Forest and AdaBoost algorithms exhibit strong agreement with actual data, which indicates that the good adaptability and predictive accuracy of these two models. The models proposed in this study can be applied in the real-time prediction and adaptive adjustment of the tunneling parameters for shield tunneling under complex geological conditions.