• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.14 no.4
• /
• pp.322-331
• /
• 2014

Chaotic dynamics is an active area of research in biology, physics, sociology, psychology, physiology, and engineering. This interest in chaos is also expanding to the social scientific fields such as politics, economics, and argument of prediction of societal events. In this paper, we propose a dynamic model for addiction of tobacco. A proposed dynamical model originates from the dynamics of tobacco use, recovery, and relapse. In order to make an addiction model of tobacco, we try to modify and rescale the existing tobacco and Lorenz models. Using these models, we can derive a new tobacco addiction model. Finally, we obtain periodic motion, quasi-periodic motion, quasi-chaotic motion, and chaotic motion from the addiction model of tobacco that we established. We say that periodic motion and quasi-periodic motion are related to the pre-addiction or recovery stage, respectively. Quasi-chaotic and chaotic motion are related to the addiction stage and relapse stage, respectively.

• Proceedings of the Safety Management and Science Conference
• /
• 1999.11a
• /
• pp.109-121
• /
• 1999

플랜트 및 설비가 대규모, 정교화, 복잡화 될수록 이로 인한 고장 및 오류에 의한 피해가 막대하기 때문에, 시스템의 신뢰성, 보전성 및 안전성 향상과 제품 품질 향상을 추구 및 안전성 유지에 대한 관심이 고조되고 있다. 고장진단은 잠재적으로 노이즈를 가지고 있다고 생각되는 데이터의 해석에 근거하여 시스템의 고장을 찾는 일련의 체계적이고 통합된 방법이다. 그러나 대부분의 방법들이 이진 논리에 기초를 둔 추론으로 불확실성을 제대로 결과에 반영하지 못하고 있다. 본 논문에서는 예방정비의 관점에서 시스템에 내재된 다양한 불확실성을 효율적으로 처리하기 위해 전문가의 직관과 경험등을 기초로 하여 언어학적 변량을 취하고, 이를 퍼지 기법을 이용하여 정량화 함으로써 불확실성을 고려한 판단이 가능하게 하는 퍼지 전문가 시스템을 제안한다.

• Journal of information and communication convergence engineering
• /
• v.9 no.4
• /
• pp.369-374
• /
• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• Steel and Composite Structures
• /
• v.17 no.5
• /
• pp.623-639
• /
• 2014

Due to recent advancements in the area of Artificial Intelligence (AI) and computational intelligence, the application of these technologies in the construction industry and structural analysis has been made feasible. With the use of the Adaptive-Network-based Fuzzy Inference System (ANFIS) as a modelling tool, this study aims at predicting the shear strength of channel shear connectors in steel concrete composite beam. A total of 1200 experimental data was collected, with the input data being achieved based on the results of the push-out test and the output data being the corresponding shear strength which were recorded at all loading stages. The results derived from the use of ANFIS and the classical linear regressions (LR) were then compared. The outcome shows that the use of ANFIS produces highly accurate, precise and satisfactory results as opposed to the LR.

• The Journal of Engineering Geology
• /
• v.20 no.3
• /
• pp.243-255
• /
• 2010

The slope design of an open-pit mine must consider economical efficiency and stability. Thus, the overall slope angle is the principal factor because of limited support or reinforcement options available in such a setting. In this study, slope displacement, as monitored by a GPS system, was analyzed for a coal mine at Pasir, Indonesia. Predictions of failure time by inverse velocity analysis showed good agreement with field observations. Therefore, the failure time of an unstable slope can be roughly estimated prior to failure. A GIS model that combines fuzzy theory and the analytical hierarchy process (AHP) was developed to assess slope instability in open-pit coal mines. This model simultaneously considers seven factors that influence the instability of open-pit slopes (i.e., overall slope gradient, slope height, surface flows, excavation plan, tension cracks, faults, and water body). Application of the proposed method to an open-pit coal mine revealed an enhanced prediction accuracy of failure time and failure site compared with existing methods.

• Geomechanics and Engineering
• /
• v.31 no.2
• /
• pp.129-147
• /
• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Geomechanics and Engineering
• /
• v.36 no.5
• /
• pp.489-509
• /
• 2024

Slope stability analysis and prediction are of critical importance to geotechnical engineers, given the severe consequences associated with slope failure. This research endeavors to forecast the factor of safety (FOS) for slopes through the implementation of six distinct ML techniques, including back propagation neural networks (BPNN), feed-forward neural networks (FFNN), Takagi-Sugeno fuzzy system (TSF), gene expression programming (GEP), and least-square support vector machine (Ls-SVM). 344 slope cases were analyzed, incorporating a variety of geometric and shear strength parameters measured through the PLAXIS software alongside several loss functions to assess the models' performance. The findings demonstrated that all models produced satisfactory results, with BPNN and GEP models proving to be the most precise, achieving an R² of 0.86 each and MAE and MAPE rates of 0.00012 and 0.00002 and 0.005 and 0.004, respectively. A Pearson correlation and residuals statistical analysis were carried out to examine the importance of each factor in the prediction, revealing that all considered geomechanical features are significantly relevant to slope stability. However, the parameters of friction angle and slope height were found to be the most and least significant, respectively. In addition, to aid in the FOS computation for engineering challenges, a graphical user interface (GUI) for the ML-based techniques was created.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.5B
• /
• pp.405-414
• /
• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• Journal of the Korea Concrete Institute
• /
• v.22 no.1
• /
• pp.19-27
• /
• 2010

Aging and severe environments are major causes of damage in reinforced concrete (RC) structures such as buildings and bridges. Deterioration such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for strengthening and rehabilitation of RC structures have been developed in the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to the conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of adaptive neuro-fuzzy inference system (ANFIS) in predicting behavior of circular type concrete column retrofitted with FRP. To construct training and testing dataset, experiment results for the specimens which have different retrofit profile are used. Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber and size of specimens are selected as input parameters to predict strength, strain, and stiffness of post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.11a
• /
• pp.488-491
• /
• 2005

This paper has been proposed an engine oil changing system automatically using artificial intelligence. As you know, It is very difficult to forecast the time that exchange engine oil exactly. Because, It does not necessary to change the engine oil when color of engine is black or distance is more than 3000 km. In order to forecast to optimal engine oil replacement time, We must to consider color of engine oil, greasy, mad condition, quick starting condition and quick braking condition. Therefore, in this paper, to overcome those problems, we, developed an expert system that it can forecast to exchange time of engine oil automatically using fuzzy rules and neural networks.