• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.310-317
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• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.85-90
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• 2015

In this paper, the structural analysis, which used Fuzzy Structural Modeling, was conducted about the 24 core cometencies of CEO of SME. It classified them into five groups. Also, regression analysis was conducted to evaluate the relationship beween the job capability and core competencies of the CEO. The characteristic of this paper is to know the relationship beween the structure and classification of the layers for the core competency of CEO, and is to know that each competency group has an influence on the job capability of CEO.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3039-3056
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• 2020

Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based on Fuzzy theory, and define Situation Awareness for each time. Finally, we propose a beach risk assessment and prediction model based on linear regression using the calculated risk result and pre-defined risk factors. We use national public data of the Korea Meteorological Administration (KMA), and the Korea Hydrographic and Oceanographic Agency (KHOA). The results of the experiment showed the prediction accuracy of beach risk to be 0.90%, and the prediction accuracy of drifting and drowning accidents to be 0.89% and 0.86%, respectively. Also, through factor correlation analysis and risk factor assessment, the influence of each of the factors on beach risk can be confirmed. In conclusion, we confirmed that our proposed model can assess and predict beach risks.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.81-90
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• 2006

It is important to clarify the relationship between traffic accidents and various influencing factors in order to reduce the number of traffic accidents. This study developed a traffic accident frequency prediction model using by multi-linear regression and qualification theories which are commonly applied in the field of traffic safety to verify the influences of various factors into the traffic accident frequency The data were collected on the Korean National Highway 17 which shows the highest accident frequencies and fatality rates in Chonbuk province. In order to minimize the uncertainty of the data, the fuzzy theory and neural network theory were applied. The neural network theory can provide fair learning performance by modeling the human neural system mathematically. Tn conclusion, this study focused on the practicability of the fuzzy reasoning theory and the neural network theory for traffic safety analysis.

• Geomechanics and Engineering
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• v.13 no.2
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• pp.237-254
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• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.723-730
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• 2003

In general, well log and core data have been utilized for reservoir characterization. These well data can provide valuable information on reservoir properties with high vertical resolution at well locations. While the seismic surveys cover large areas of field but give only indirect features about reservoir properties. Therefore it is possible to estimate the reservoir properties guided by seismic data on entire area if a relationship of seismic data and well data can be defined. Seismic attributes calculated from seismic surveys contain the particular reservoir features, so that they should be extracted and used properly according to the purpose of study. The method to select the suitable seismic attributes among enormous ones is needed. The stepwise regression and fuzzy curve analysis based on fuzzy logics are used for selecting the best attributes. The relationship can be utilized to estimate reservoir properties derived from seismic attributes. This methodology is applied to a synthetic seismogram and a sonic log acquired from velocity model. Seismic attributes calculated from the seismic data are reflection strength, instantaneous phase, instantaneous frequency and pseudo sonic logging data as well as seismic trace. The fuzzy curve analysis is used for choosing the best seismic attributes compared to sonic log as well data, so that seismic trace, reflection strength, instantaneous frequency, and pseudo sonic logging data are selected. The relationship between the seismic attribute and well data is found out by the statistical regression method and estimates the reliable well data at a specific field location derived from only seismic attributes. For a future work in this study, the methodology should be checked an applicability of the real fields with more complex and various reservoir features.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.997-1004
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• 2012

In enterprise software projects, performance issues have become more critical during recent decades. While developing software products, many performance tests are executed in the earlier development phase against the newly added code pieces to detect possible performance regressions. In our previous research, we introduced the framework to enable automated performance anomaly detection and reduce the analysis overhead for identifying the root causes, and showed Statistical Process Control (SPC) can be successfully applied to anomaly detection. In this paper, we explain the special performance trend in which the existing anomaly detection system can hardly detect the noticeable performance change especially when a performance regression is introduced and recovered again a while later. Within the fixed number of sampling period, the fluctuation gets aggravated and the lower and upper control limit get relaxed so that sometimes the existing system hardly detect the noticeable performance change. To resolve the issue, we apply dynamically tuned sampling window size based on the performance trend, and Fuzzy theory to find an appropriate size of the moving window.

• Structural Engineering and Mechanics
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• v.56 no.5
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• pp.787-796
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• 2015

When the temperature of a structure varies, there is a tendency to produce changes in the shape of the structure. The resulting actions may be of considerable importance in the analysis of the structures having non-prismatic members. The computation of design forces for the non-prismatic beams having symmetrical parabolic haunches (NBSPH) is fairly difficult because of the parabolic change of the cross section. Due to their non-prismatic geometrical configuration, their assessment, particularly the computation of fixed-end horizontal forces and fixed-end moments becomes a complex problem. In this study, the efficiency of the Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) in predicting the design forces and the design moments of the NBSPH due to temperature changes was investigated. Previously obtained finite element analyses results in the literature were used to train and test the ANN and ANFIS models. The performances of the different models were evaluated by comparing the corresponding values of mean squared errors (MSE) and decisive coefficients ($R^2$). In addition to this, the comparison of ANN and ANFIS with traditional methods was made by setting up Linear-regression (LR) model.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.64-70
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.547-565
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• 2011

Different types of schemes have been used in stage prediction involving conceptual and physical models. Nevertheless, none of these schemes can be considered as a single superior model. To overcome disadvantages of existing physics based rainfall-runoff models for stage predicting because of the complexity of the hydrological process, recently the data-derived models has been widely adopted for predicting flood stage. The objective of this study is to evaluate model performance for stage prediction of the Neuro-Fuzzy and regression analysis stage prediction models in these data-derived methods. The proposed models are applied to the Wangsukcheon in Han river watershed. To evaluate the performance of the proposed models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient(NSEC), mean absolute error(MAE), adjusted coefficient of determination($R^{*2}$). The results show that the Neuro-Fuzzy stage prediction model can carry out the river flood stage prediction more accurately than the regression analysis stage prediction model. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.