• Journal of the Earthquake Engineering Society of Korea
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• v.17 no.3
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• pp.133-141
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• 2013

This paper presents the seismic evaluation and prediction of a damaged piloti-type Reinforced Concrete (RC) building before and after post-retrofitting under successive earthquakes. For considering realistic successive earthquakes, the past records measured at the same station were combined. In this study, the damaged RC building due to the first earthquake was retrofitted with a buckling-restrained brace (BRB) before the second earthquake occurred. Nonlinear Time History Analysis (NTHA) was performed under the scaled intensity of the successive ground motions. Based on the extensive structural response data obtained form from the NTHA, the fragility relationships between the ground shaking intensity and the probability of reaching a pre-determined limit state was were derived. In addition, The the fragility curves of the pre-damaged building without and with the BRBs were employed to evaluate the effect of the successive earthquakes and the post-retrofit effect. Through the seismic assessment subjected to the successive records, it was observed that the seismic performance of the pre-damaged building was significantly affected by the severity of the damage from the first earthquake damages and the hysteresis behavior of the retrofit element.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• Smart Structures and Systems
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• v.33 no.5
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• pp.365-374
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• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.

• Transactions of Materials Processing
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• v.17 no.1
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• pp.52-58
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• 2008

Tube hydroforming process is generally consisted with pre-bending, preforming and hydroforming processes. Among forming defects which may occur in tube hydroforming such as buckling, wrinkling and bursting, the wrinkling and bursting by local instability under excessive tensile stress mode were mainly caused by thinning phenomenon in the manufacturing process. Thus the accurate prediction and suitable evaluation of the thinning phenomenon play an important role in designing and producing the successfully hydroformed parts without any failures. In this work, the formability on hydroformed part for automobile, i.e. engine cradle, was evaluated using finite element analysis. The initial tube radius, loading path with axial feeding force and internal pressure, and preformed configuration after preforming process were considered as the dominant process parameters in total tube hydroforming process. The effects on these process parameters could be confirmed through the numerical experiments with respect to several kinds of finite element simulation conditions. The degree of enhancement on formability with each process parameters such as initial tube radius, loading path and preform configuration were also compared. Therefore, it is noted that the evaluation approach of the formability on hydroformed parts for lots of industrial fields proposed in this study will provide one of feasible methods to satisfy the increasing practical demands for the improvement of the formability in tube hydroforming processes.

• Journal of the Korean Society of Tobacco Science
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• v.35 no.1
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• pp.1-6
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• 2013

Near InfraRed Spectroscopy(NIRS) is a quick and accurate analytical method to measure multiple components in tobacco manufacturing process. This study was carried out to develop calibration equation of near infrared spectroscopy for the prediction of the amount of chemical components and hot water solubles(HWS) of reconstituted tobacco leaf. Calibration samples of reconstituted tobacco leaf were collected from every lot produced during one year. The calibration equation was formulated as modified partial least square regression method (MPLS) by analyzing laboratory actual values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with the standard error of prediction(SEP) of chemical components in reconstituted tobacco leaf samples, indicated as coefficient of determination($R^2$) and prediction error of sample unacquainted, followed by the verification of model equation of laboratory actual values and these predicted results. As a result of monitoring, the standard error of prediction(SEP) were 0.25 % for total sugar, 0.03 % for nicotine, 0.03 % for chlorine, 0.16 % for nitrate, and 0.38 % for hot water solubles. The coefficient of determination($R^2$) were 0.98 for total sugar, 0.97 for nicotine, 0.96 for chlorine, 0.98 for nitrate and 0.92 for hot water solubles. Therefore, the NIRS calibration equation can be applicable and reliable for determination of chemical components of reconstituted tobacco leaf, and NIRS analytical method could be used as a rapid and accurate quality control method.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.11-16
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• 2022

As an interest in the development of artificial intelligence(AI) technology in the water supply sector increases, we have developed an AI algorithm that can predict improvement decision-making ratings through repetitive learning using the data of pipe condition evaluation results, and present the most reliable prediction model through a verification process. We have developed the algorithm that can predict pipe ratings by pre-processing 12 indirect evaluation items based on the 2020 Han River Basin's basic plan and applying the AI algorithm to update weighting factors through backpropagation. This method ensured that the concordance rate between the direct evaluation result value and the calculated result value through repetitive learning and verification was more than 90%. As a result of the algorithm accuracy verification process, it was confirmed that all water pipe type data were evenly distributed, and the more learning data, the higher prediction accuracy. If data from all across the country is collected, the reliability of the prediction technique for pipe ratings using AI algorithm will be improved, and therefore, it is expected that the AI algorithm will play a role in supporting decision-making in the objective evaluation of the condition of aging pipes.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.131-144
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• 2020

As the blockchain technology attracts attention, interest in cryptocurrency that is received as a reward is also increasing. Currently, investments and transactions are continuing with the expectation and increasing value of cryptocurrency. Accordingly, prediction for cryptocurrency price has been attempted through artificial intelligence technology and social sentiment analysis. The purpose of this paper is to develop a deep learning ensemble model for predicting the price fluctuations and one-day lag price of cryptocurrency based on the design science research method. This paper intends to perform predictive modeling on Ethereum among cryptocurrencies to make predictions more efficiently and accurately than existing models. Therefore, it collects data for five years related to Ethereum price and performs pre-processing through customized functions. In the model development stage, four LSTM models, which are efficient for time series data processing, are utilized to build an ensemble model with the optimal combination of hyperparameters found in the experimental process. Then, based on the performance evaluation scale, the superiority of the model is evaluated through comparison with other deep learning models. The results of this paper have a practical contribution that can be used as a model that shows high performance and predictive rate for cryptocurrency price prediction and price fluctuations. Besides, it shows academic contribution in that it improves the quality of research by following scientific design research procedures that solve scientific problems and create and evaluate new and innovative products in the field of information systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1328-1334
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• 2009

This paper presents a prediction mechanism of performance for an efficient interleave-division multiple access (IDMA) scheme that is being considered as 4th generation mobile radio system. The scheme is based upon log-likelihood ratio (LLR) to predict the performance of the IDMA. The conventional IDMA system simply passes the LLR values to a coarse estimation process in the receiver over a pre-defined number of iterations for an acceptable performance. The proposed IDMA system uses the LLRs to predict its BER performance and thus the iterative operation at the receiver can significantly be reduced when the performance attains an acceptable level. Performance evaluation shows that the proposed scheme of the IDMA with the LLRs used for the prediction provides a comparable BER performance. The use of the LLRs can facilitate an efficient design of the IDMA system that is a strong candidate system for 4G mobile radio systems.

• Journal of Agricultural Extension & Community Development
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• v.4 no.1
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• pp.305-315
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• 1997

The propose of this study was to analyze the relationships among the levels of training & development evaluation (reaction, learning, transfer). The study has been conducted on 730 trainees who attended in the basic accounting program in L training and development institution through three incidents of tracked research such as reaction survey right after the conclusion of training, learning evaluation through test, and an evaluation of the transferability after 3 months of training. Questionnaires and test papers for analyses were used after their reliability, validity, difficulty, and discrimination have been verified on a pre-test. The research has been conducted for six months from 4 March 1996 to the end of August 1996, and data have been collected through direct research and survey through mail. The collected data have been worked on at SAS program for Windows with a statistical significance level of 5%. Statistical method that had been used was Pearson's correlation coefficient. The result and conclusion acquired from this study were as follows: Between reaction and learning, learning and transfer of training, only a weak positive correlation exists and explanation or prediction variance showing hierarchical relationship was quite weak with 1%. Thus, this research not only does not strongly support Kirkpatrick(1976)'s hierarchical model of $reaction{\rightarrow}learning{\rightarrow}transfer$, but also indicates that the separate measurement on each levels of training evaluation needs to be done. On the other hand, there was a relatively strong positive correlation between reaction and transfer of training. Based on the result, the conclusion, and the restriction perceived through this study, the following suggestions were made. 1. There is a need to empirically analyze and verify the hierarchy of all levels of training evaluation including the evaluation of the fourth level (result) such as organizational productivity, organizational satisfaction, and separation rate. 2. A great deal of efforts will be needed to systematically analyze what the relationships are among the methods measuring the level of evaluation of the training and development, and to apply this result to the training field.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.5
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• pp.82-91
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• 2011

In this paper, a planar model for mechanics of a vehicle/pedestrian collision incorporating road gradient is derived to evaluate the pre-collision speed of vehicle. It takes into account a few physical variables and parameters of popular wrap and forward projection collisions, which include horizontal distance traveled between primary and secondary impacts with the vehicle, launch angle, center-of-gravity height at launch, distance from launch to rest, pedestrian-ground drag factor, the pre-collision vehicle speed and road gradient. The model including road gradient is derived analytically for reconstruction of pedestrian collision accidents, and evaluates the vehicle speed from the pedestrian throw distance. The model coefficients have physical interpretations and are determined through direct calculation. This work shows that the road gradient has a significant effect on the evaluation of the vehicle speed and must be considered in accident cases with inclined road. In additions, foreign/domestic empirical cases and multibody dynamic simulation results are used to construct a least-squares fitted model that has the same structure of the analytical one that provides an estimate of the vehicle speed based on the pedestrian throw distance and the band within which the vehicle speed would be expected to be in 95% of cases.