• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.451-461
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• 2002

Differential evolution(DE) has been preyed to be an efficient method for optimizing real-valued multi-modal objective functions. DE's main assets are its conceptual simplicity and ease of use. However, the convergence properties are deeply dependent on the control parameters of DE. This paper proposes an adaptive differential evolution(ADE) method which combines with a variant of DE and an adaptive mechanism of the control parameters. ADE contributes to the robustness and the easy use of the DE without deteriorating the convergence. 12 optimization problems is considered to test ADE. As an application of ADE the paper presents a supervised clustering method for predicting events, what is called, an evolutionary event clustering(EEC). EEC is tested for 4 cases used widely for the validation of data modeling.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.191-197
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• 2009

Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fatal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input-output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.2
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• pp.56-66
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• 2016

Robustness is essential for model based FDI (Fault Detection and Isolation) and it is inevitable to have modeling errors and sensor signal noises during the process of FDI. This study suggests an improved method by applying NARX (Nonlinear Auto Regressive eXogenous) model and Kalman estimator in order to cope with problems caused by linear model errors and sensor signal noises in the process of fault diagnoses. Fault decision is made by the probability of the trend of gradually accumulated errors applying Fuzzy logic, which are robust to instantaneous sensor signal noises. Reliability of fault diagnosis is verified under various fault simulations.

• Structural Engineering and Mechanics
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• v.23 no.5
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• pp.455-468
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• 2006

Smart structural systems are defined as ones that demonstrate the ability to modify their characteristics and/or properties in order to respond favorably to unexpected severe loading conditions. The performance of such a task requires a set of additional components to be integrated within such systems. These components belong to three major categories, sensors, processors and actuators. It is wellknown that all structural systems entail some level of uncertainty, because of their extremely complex nature, lack of complete information, simplifications and modeling. Similarly, sensors, processors and actuators are expected to reflect a similar uncertain behavior. As it is imperative to be able to evaluate the impact of such components on the behavior of the system, it is as important to ensure, or at least evaluate, the reliability of such components. In this paper, a system model for reliability assessment of smart structural systems is outlined. The presented model is considered a necessary first step in the development of a reliability assessment algorithm for smart structural systems. The system model outlines the basic components of the system, in addition to, performance functions and inter-relations among individual components. A fault tree model is developed in order to aggregate the individual underlying component reliabilities into an overall system reliability measure. Identification of appropriate limit states for all underlying components are beyond the scope of this paper. However, it is the objective of this paper to set up the necessary framework for identifying such limit states. A sample model for a three-story single bay smart rigid frame, is developed in order to demonstrate the proposed framework.

• Geomechanics and Engineering
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• v.27 no.5
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• pp.511-525
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• 2021

In the design of underground excavation, the shear strength (SS) is a key characteristic. It describes the way the rock material resists the shear stress-induced deformations. In general, the measurement of the parameters related to rock shear strength is done through laboratory experiments, which are costly, damaging, and time-consuming. Add to this the difficulty of preparing core samples of acceptable quality, particularly in case of highly weathered and fractured rock. This study applies rock index test to the indirect measurement of the SS parameters of shale. For this aim, two efficient artificial intelligence methods, namely (1) adaptive neuro-fuzzy inference system (ANFIS) implemented by subtractive clustering method (SCM) and (2) support vector regression (SVR) optimized by Harmony Search (HS) algorithm, are proposed. Note that, it is the first work that predicts the SS parameters of shale through ANFIS-SCM and SVR-HS hybrid models. In modeling processes of ANFIS-SCM and SVR-HS, the results obtained from the rock index tests were set as inputs, while the SS parameters were set as outputs. By reviewing the obtained results, it was found that both ANFIS-SCM and SVR-HS models can provide acceptable predictions for interlocking and friction angle parameters, however, ANFIS-SCM showed a better generalization capability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.12
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• pp.1043-1053
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• 2016

In this paper, the application of Parallel Distributed Compensation (PDC) controller for fixed pitch rotary wing system was studied. For nonlinear modeling, T-S fuzzy model was utilized to advance system control including the tilt type UAV. PDC controller was designed through the Linear Matrix Inequality (LMI). Experiments for determining the applicability and feasibility of PDC were performed using the 1 axis attitude control equipment and simulation. To verify the performance and characteristics of the controller, Mathworks Co. Simulink was used. After then, the PDC controller performance was verified and the results with developed controller using a 1 axis attitude control equipment were compared. Verification of the feasibility of PDC controller for the fixed pitch rotary wing system and identification of the overall performance and improvement analysis was conducted based on the experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.232-237
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• 2013

The aim of this study is to structuralize a model of the factors determining the quality of nursing care perceived by the director or manager of a long-term care facilities (hospitalization of patients) using FSM(Fuzzy Structural Modeling), employed in structuralizing social systems. The results were as follows: first, quality in the top tier was shown to be connected with job commitment, commitment to the organization, work experience, care skills, knowledge about the elderly, training and education, which are factors in the middle tier; and second, the structure of the middle tier (job commitment, commitment to the organization, work experience, care skills, knowledge about the elderly, training and education) either showed a connection with the lower tier, which includes employment type, job satisfaction, leadership, relationship with users and workplace relationships, or showed a connection among the factors within. These results confirmed the following: first, care skills and knowledge about the elderly, which demonstrate the job expertise of caregivers, showed a connection with service quality based on work experience; second, job commitment in the middle tier was observed to affect various factors in the same tier such as care skills, knowledge about the elderly, training and education amongst others, and it was determined that it is an important determining factor in service quality. Lastly, a meaningful result was shown in relation to leadership. The leadership skills of the director of the facilities had a connection with the care caregivers' commitment to the organization, which had a connection with service quality. This structure showed the kind of role the director must play in order to improve service quality.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Proceedings of KOSOMES biannual meeting
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• 2003.05a
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• pp.59-67
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• 2003

The prevention of marine accidents has been a major topic in marine society for long time and various safety policies and Countermeasures have been developed and applied to prevent those accidents. In spite of these efforts, however significant marine accidents have taken place intermittently. Ship is being operated under a highly dynamic environments and many factors are related with ship's collision and those factors are interacting. So, the analysis on ship's collision causes are very important to prepare countermeasures which will ensure the safe navigation. This study analysed the ship's collision data over the past 10 years(1991-2000), which is compiled by Korea Marine Accidents Inquiry Agency. The analysis confirmed that ‘ship's collision’ is occurred most frequently and the cause is closely related with human factor. The main purpose of this study is to propose risk control countermeasures of ship's collision. For this, the structure of human factor is analysed by the questionnaire methodology. Marine experts were surveyed based on major elements that were extracted from the human factor affecting to ship's collision. FSM has been widely adopted in modeling a dynamic system which is composed of human factors. Then, the structure analysis on the causes of ship's collision using FSM are performed. This structure model could be used in understanding and verifying the procedure of real ship's collision. Furthermore it could be used as the model to prevent ship's collision and reduce marine accidents.