• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.417-428
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• 2021

Since future maintenance cost estimation of infrastructure involves uncertainty, it is important to make use of a failure prediction model. However, it is difficult for local governments to develop accurate failure prediction models applicable to infrastructure due to a lack of budget and expertise. Therefore, this study estimated the life expectancy of asphalt road pavement of national highways using the Bayesian Markov Mixture Hazard model. In addition, in order to accurately estimate life expectancy, environmental variables such as traffic volume, ESAL (Equivalent Single Axle Loads), SNP (Structural Number of Pavement), meteorological conditions, and de-icing material usage were applied to retain reliability of the estimation results. As a result, life expectancy was estimated from at least 13.09 to 19.61 years by region. By using this approach, it is expected that it will be possible to estimate future maintenance cost considering local failure characteristics.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.307-319
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• 2021

Although it is currently conducting slope management and research using digital technologies such as drones, big data, and artificial intelligence, it is still somewhat insufficient and is still vulnerable to slope failure. For this reason, it is inevitable to present the development direction for research on prediction and analysis of slope failure using the digital technologies to effectively deal with slope failure, which requires a preemptive understanding of prediction and analysis of slope failure. In this paper, we collected literature data based on the Web of Science for five years from January 1, 2016 to December 31, 2020 and analyzed by co-word analysis to identify the domain structure of research on prediction and analysis of slope failure. Detailed subject areas were identified through network analysis, and the domain relationships between keywords were visualized to derive global and regionally oriented keywords through relationship, centrality analysis. In addition, the clusters formed by performing cluster analysis were displayed on the multidimensional scailing map, and the domain structure according to the correlation between each keyword was presented. The results of this study reveal the domain structure of research on prediction and analysis of slope failure, and are expected to be usefully used to find future research directions.

• Transactions on Electrical and Electronic Materials
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• v.7 no.5
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• pp.217-223
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• 2006

When only partial information is available about equipment failures (installation date and amount, as well as failure and replacement rates), data on sufficiently large number of yearly populations of the components can be combined, and estimation of model parameters may be possible. The parametric models may then be used for forecasting of the system's short term future failure and for formulation of replacement strategies. We employ the Weibull distribution and show how we estimate its parameters from past failure data. Using Monte Carlo simulations, it is possible to assess confidence ranges of the forecasted component performance data.

• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.412-427
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• 2002

This paper proposes a restoration scheme based on mobility learning and prediction in the presence of the failure of mobility databases in mobile communication systems. In mobile communication systems, mobility databases must maintain the current location information of users to provide a fast connection for them. However, the failure of mobility databases may cause some location information to be lost. As a result, without an explicit restoration procedure, incoming calls to users may be rejected. Therefore, an explicit restoration scheme against the failure of mobility databases is needed to guarantee continuous service availability to users. Introducing mobility learning and prediction into the restoration process allows systems to locate users after a failure of mobility databases. In failure-free operations, the movement patterns of users are learned by a Neuro-Fuzzy Inference System (NFIS). After a failure, an inference process of the NFIS is initiated and the users' future location is predicted. This is used to locate lost users after a failure. This proposal differs from previous approaches using checkpoint because it does not need a backup process nor additional storage space to store checkpoint information. In addition, simulations show that our proposal can reduce the cost needed to restore the location records of lost users after a failure when compared to the checkpointing scheme

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.131-139
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• 1993

The newly proposed model of the future reliability results in earlier fault-fixes having a greater effect than the fault which make the greatest contribution to the overall failure rate tend to show themselves earlier, and so are fixed earlier. The suggested model allows a variety of reliability measures to be calculated. Predictions of total execution time(debugging time) is to achieve a target reliability. This model could also apply to computer-hardware reliability growth resulting from the elimination of design error and fault.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.266-266
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• 2019

Marine accidents are increasing year by year, and various accidents occur such as engine failure, collision, stranding, and fire. These marine accidents present a risk of large casualties. It is important to prevent accidents beforehand. In this study, we propose a modeling to predict the occurrence of marine accidents by applying the Markov Chain Process that can predict the future based on past data. Applying the proposed modeling, the probability of future marine accidents was calculated and compared with the actual frequency. Through this, a probabilistic model was proposed to prepare a prediction system for marine accidents, and it is expected to contribute to predicting various marine accidents.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.387-392
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• 2001

This Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.24-32
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• 2024

Despite improvements in operative techniques and perioperative care, post-hepatectomy liver failure (PHLF) remains the most serious cause of morbidity and mortality after surgery, and several risk factors have been identified to predict PHLF. Although volumetric assessment using imaging contributes to surgical simulation by estimating the function of future liver remnants in predicting PHLF, liver function is assumed to be homogeneous throughout the liver. The combination of volumetric and functional analyses may be more useful for an accurate evaluation of liver function and prediction of PHLF than only volumetric analysis. Gadoxetic acid is a hepatocyte-specific magnetic resonance (MR) contrast agent that is taken up by hepatocytes via the OATP1 transporter after intravenous administration. Gadoxetic acid-enhanced MR imaging (MRI) offers information regarding both global and regional functions, leading to a more precise evaluation even in cases with heterogeneous liver function. Various indices, including signal intensity-based methods and MR relaxometry, have been proposed for the estimation of liver function and prediction of PHLF using gadoxetic acid-enhanced MRI. Recent developments in MR techniques, including high-resolution hepatobiliary phase images using deep learning image reconstruction and whole-liver T1 map acquisition, have enabled a more detailed and accurate estimation of liver function in gadoxetic acid-enhanced MRI.

• Journal of the Korean Geosynthetics Society
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• v.16 no.3
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• pp.1-10
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• 2017

Recently, one of the natural disasters, landslide is causing huge damage to people and properties. In order to minimize the damage caused by continuous landslide, a scientific management system is needed for technologies related to measurement and monitoring system. This study aims to establish a management system for landslide damage by prediction of slope failure. Ground behavior was predicted by surface ground deformation in case of slope failure, and the change in ground displacement was observed as slope surface. As a result, during the slope failure, the ground deformation has the collapse section, the after collapse precursor section, the acceleration section and the burst acceleration section. In all cases, increase in displacement with time was observed as a slope failure, and it is very important event of measurement and maintenance of risky slope. In the future, it can be used as basic data of slope management standard through continuous research. And it can contribute to reduction of landslide damage and activation of measurement industry.

• IE interfaces
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• v.12 no.1
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• pp.1-9
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• 1999

This paper presents a method for reliability analysis of the air-conditioner with service data. We explain how to acquire and analyze the service data and some problems in data analysis. We propose two procedures to analyze reliability of air-conditioner using operating time concept and predict the operating times by temperature and failure frequency. Finally, the prediction method for future service is studied by numerical example.