• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.743-748
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• 2010

The stability forecasting of rock slope is more difficult than soil slope because catching the sign of failure in monitoring is not easy and deformation of the rock is small in failure process. But in the rock slope, there is small deformation like crack propagation in rock itself and it accumulates gradually in failure process. If it is possible to detect the small change in the rock slope, we can know the failure time exactly. Because the individual signal is gathered in the acoustic emission monitoring, it is possible to monitoring the slope if many sound signal is accumulated. Detection test of acoustic emission was performed. Uniaxial, two types of bending test, and two plane shear test were done with various cement paste sample. Wave propagation velocity of uniaxial test sample was increased with curing time. Wave Analysis give us the result that there is a AE sign signal before the failure, the AE count is suddenly increased. And frequency level 125kHz before failure is changed to level 200-250kHz after failure. In two plane shear test we can catch the AE signal and can know the failure type from wave shape. Monitoring test site is tunnel slope in Hongcheon but special signal is not collected.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.6
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• pp.781-787
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• 2010

Forecasting possible failure characteristics is very important in maintenance planning because it helps in predicting any future failures and determining the optimum replacement interval. This paper examines the time.to-failure distribution of the transfer gearbox of a J79 engine by using a probability plotting technique which is one of the most convenient techniques for reliability analysis. Various probability distributions are evaluated for determining the suitable probability distribution of the failure data of the transfer gearbox, and the resulting correlation coefficient indicates that failure data have a lognormal distribution. The expected number of unscheduled maintenance actions and the optimum replacement interval for various values of cost ratios are determined.

• Journal of Power System Engineering
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• v.13 no.1
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• pp.59-64
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• 2009

Electronic control system of the engine is composed of various sensors and actuators, This paper is concerned with fault analysis for the stable operation of it. We suggest the technology that can systematically and reliably analyze fault causes of sensors and actuators by using the fault generating program. In results, we can acquire the systematic road map of occurring faults as well as the valuable information related to the operations of sensors and actuators. These results should be very useful to get the classification of fault causes, develop an electronic control system of engine, and review control strategies of it.

• Convergence Security Journal
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• v.12 no.6
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• pp.85-92
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• 2012

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical Process Control (SPC) can monitor the forecasting of software failure and there by contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of log Poission, log-linear and Parto distribution.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Advances in Computational Design
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• v.5 no.4
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• pp.445-465
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• 2020

This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.1-6
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• 2004

For a given plant design, improved decisions on when to shutdown an existing plant may be obtained by making better predictions of failure rates, by exerting efforts to collect more relevant information or by improving decision making models which put that information to best use. It is important that the models include the value of possible loss of lives and fear along with cleanup, decommissioning, relocation if the decisions derived from the model are to be useful. The decision model we have described enables us to investigate a class of optimal decisions on whether to shutdown or continue operating one period of time. The analysis and decision process is repeated at the end of each period with additional information about new costs and risks.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.37-48
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• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.345-353
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• 2015

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do parameter inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision to market software, the conditional failure rate is an important variables. In this case, finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, proposed a control mechanism based on NHPP using mean value function of polynomial hazard function.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.483-490
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• 2015

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do likelihood inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The infinite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, proposed a control mechanism based on NHPP using mean value function of Musa-Okumo and Power law type property.