• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.480-486
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• 2005

This paper proposes a method to consider an aging failure probability and survival probability of power system components, though only aging failure probability has been considered in existing mean life calculation. The estimates of the mean and its standard deviation is calculated by using Weibull distribution, and each estimated parameters is obtained from Data Analytic Method (Type H Censoring). The parameter estimation using Data Analytic Method is simpler and faster than the traditional calculation method using gradient descent algorithm. This paper shows calculation procedure of the mean life and its standard deviation by the proposed method and illustrates that the estimated results are close enough to real historical data of combustion turbine generating units in Korean systems. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an are-related reliability index.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.1-8
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• 2011

Stainless steel sheets are widely used as the structure material for the railroad cars and the commercial vehicles. These kinds structures used stainless steel sheets are commonly fabricated by using the gas welding. Gas welding is very important and useful technology in fabrication of an railroad car and vehicles structure. However fatigue strength of the gas welded joints is considerably lower than parent metal due to stress concentration at the weldment, fatigue strength evaluation of gas welded joints are very important to evaluate the reliability and durability of railroad cars and to establish a criterion of long life fatigue design. In this paper, ${\Delta}-N_f$ curve were obtained by fatigue tests. Using these results, the accelerated life test (ALT) is conducted. From the experimental results, an acceleration model is derived and acceleration factors are estimated. So it is intended to obtain the useful information for the fatigue lifetime of plug and ring gas welded joints and data analysis by statistical reliability method, to save time and cost, and to develop optimum accelerated life prediction plans.

• Structural Engineering and Mechanics
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• v.66 no.2
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• pp.151-160
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• 2018

With the aim to evaluate the fatigue damage accumulation and predict the residual life of engineering components under variable amplitude loadings, this paper proposed a new strain energy-based damage accumulation model by considering both effects of mean stress and load interaction on fatigue life in a low cycle fatigue (LCF) regime. Moreover, an integrated procedure is elaborated for facilitating its application based on S-N curve and loading conditions. Eight experimental datasets of aluminum alloys and steels are utilized for model validation and comparison. Through comparing experimental results with model predictions by the proposed, Miner's rule, damaged stress model (DSM) and damaged energy model (DEM), results show that the proposed one provides more accurate predictions than others, which can be extended for further application under multi-level stress loadings.

• Journal of Applied Reliability
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• v.9 no.4
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• pp.275-290
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• 2009

This paper considers accelerated thermal degradation tests which are performed for rubber seal materials used for undersea tunnels constructed by immersion method. Three types of rubber seals are tested; rubber expansion seal, omega seal, and shock absorber hose. Main ingredient of rubber expansion seal is EPDM(Ethylene Propylene Diene Monomer) and that of both omega seal and shock absorber hose is SBR(Styrene Butadiene Rubber). The accelerated stress is temperature and an Arrhenius model is introduced to describe the relationship between the lifetime and the stress. From the accelerated degradation tests, dominant failure mode of the rubber seals is found to be the loss of elongation. The lifetime distribution and the service life of the rubber seals at use condition are estimated from the test results. The acceleration factor for three types of rubber seals are also investigated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7B
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• pp.659-667
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• 2009

Mobile Ad-hoc Network consists of mobile nodes without immobile base station. In mobile ad-hoc network, network cutting has occurred frequently in node because of energy restriction and frequent transfer of node. Therefore, it requires research for certain techniques that react softly in topology alteration in order to improve reliability of transmission path. This paper proposes path selection techniques to consider mobility of node that respond when search path using AOMDV routing protocol. As applying proposed techniques, We can improve reliability and reduce re-searching number of times caused by path cutting.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.87-91
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• 2019

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.1013-1019
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• 2016

This paper describes a novel Context-awareness Markov Chain Prediction (CMCP) algorithm based on movement prediction using Markov chain in Delay Tolerant Network (DTN). The existing prediction models require additional information such as a node's schedule and delivery predictability. However, network reliability is lowered when additional information is unknown. To solve this problem, we propose a CMCP model based on node behaviour movement that can predict the mobility without requiring additional information such as a node's schedule or connectivity between nodes in periodic interval node behavior. The main contribution of this paper is the definition of approximate speed and direction for prediction scheme. The prediction of node movement forwarding path is made by manipulating the transition probability matrix based on Markov chain models including buffer availability and given interval time. We present simulation results indicating that such a scheme can be beneficial effects that increased the delivery ratio and decreased the transmission delay time of predicting movement path of the node in DTN.

• Geomechanics and Engineering
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• v.35 no.6
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• pp.647-663
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• 2023

The investigation of the disc cutter wear prediction has an important guiding role in TBM equipment selection, project planning, and cost forecasting, especially when tunneling in a long-distance rock formations with high strength and high abrasivity. In this study, a comprehensive database of disc cutter wear data, geological properties, and tunneling parameters is obtained from a 1326 m excavated metro tunnel project in leptynite in Shenzhen, China. The failure forms and wear consumption of disc cutters on site are analyzed with emphasis. The results showed that 81% of disc cutters fail due to uniform wear, and other cutters are replaced owing to abnormal wear, especially flat wear of the cutter rings. In addition, it is found that there is a reasonable direct proportional relationship between the uniform wear rate (WR) and the installation radius (R), and the coefficient depends on geological characteristics and tunneling parameters. Thus, a preliminary prediction formula of the uniform wear rate, based on the installation radius of the cutterhead, was established. The correlation between some important geological properties (K_V and UCS) along with some tunneling parameters (F_n and p) and wear rate was discussed using regression analysis methods, and several prediction models for uniform wear rate were developed. Compared with a single variable, the multivariable model shows better prediction ability, and 89% of WR can be accurately estimated. The prediction model has reliability and provides a practical tool for wear prediction of disc cutter under similar hard rock projects with similar geological conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1562-1582
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• 2024

The prediction of pedestrian trajectory is conducive to reducing traffic accidents and protecting pedestrian safety, which is crucial to the task of intelligent driving. The existing methods mainly use the past pedestrian trajectory to predict the future deterministic pedestrian trajectory, ignoring pedestrian intention and trajectory diversity. This paper proposes a multi-modal trajectory prediction model that introduces pedestrian intention. Unlike previous work, our model makes multi-modal goal-conditioned trajectory pedestrian prediction based on the past pedestrian trajectory and pedestrian intention. At the same time, we propose a novel Gate Recurrent Unit (GRU) to process intention information dynamically. Compared with traditional GRU, our GRU adds an intention unit and an intention gate, in which the intention unit is used to dynamically process pedestrian intention, and the intention gate is used to control the intensity of intention information. The experimental results on two first-person traffic datasets (JAAD and PIE) show that our model is superior to the most advanced methods (Improved by 30.4% on MSE_0.5s and 9.8% on MSE_1.5s for the PIE dataset; Improved by 15.8% on MSE_0.5s and 13.5% on MSE_1.5s for the JAAD dataset). Our multi-modal trajectory prediction model combines pedestrian intention that varies at each prediction time step and can more comprehensively consider the diversity of pedestrian trajectories. Our method, validated through experiments, proves to be highly effective in pedestrian trajectory prediction tasks, contributing to improving traffic safety and the reliability of intelligent driving systems.