• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.28-42
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• 1996

This paper describes a new automated simulation system for micromachines whose size range $10^{-6}$ to $10^{-3}$ m. An automic finite element (FE) mesh generation technique, which is bases on the fuzzy knowledge processing and computation al geometry technique, is incorporated into the system, together with one of commerical FE analysis codes, MARC ,and one of commerical solid modelers, Designbase. The system allows a geometry model of concern to be automatically converted to different FE models, depending on physical phenomena of micromachines to be analyzed , i,e. electrostatic analysis, stress analysis, modal analysis and so on. The FEmodels are then automatically analyzed using the FE analysis code, Among a whole process of analysis, the definition of a geometry model, the designation of local node patterns and the assignment of material properties and boundary conditions onto the geometry model are only the interactive process to be done by a user. The interactive operations can be processed in a few minutes. The other processes which are time consuming and labour-intensive in conventional CAE systems are fully automatically performed in a popular engineering workstation environment. This automated simulation system is successfully applied to evaluate an electrostatic micro wobble actuator.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.539-546
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• 2007

The important field of research in ship operation is related to the high efficiency of transportation, the convenience of maneuvering ships and the safety of navigation. For these purposes, many intelligent technologies for ship automation have been required and studied. In this paper, we propose an intelligent voice instruction-based learning (VIBL) method and discuss the building of a ship's steering control system based on this method. The VIBL system concretely consists of two functions: a text conversion function where an instructor's inputted voice is recognized and converted to text, and a linguistic instruction based learning function where the text instruction is understood through a searching process of given meaning elements. As a study method, the fuzzy theory is adopted to build maneuvering models of steersmen and then the existing LIBL is improved and combined with the voice recognition technology to propose the VIBL. The ship steering control system combined with VIBL is tested in a ship maneuvering simulator and its validity is shown.

• Earthquakes and Structures
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• v.14 no.2
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• pp.143-153
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• 2018

Correlation among different factors must be considered for selection of influencing factors in safety monitoring of high dam including positive correlation of variables. Therefore, a new factor selection method was constructed based on Copula entropy and mutual information theory, which was deduced and optimized. Considering the small sample size in high dam monitoring and distribution of daily monitoring samples, a computing method that avoids causality of structure as much as possible is needed. The two-dimensional normal information diffusion and fuzzy reasoning of pattern recognition field are based on the weight theory, which avoids complicated causes of the studying structure. Hence, it is used to dam safety monitoring field and simplified, which increases sample information appropriately. Next, a complete system integrating high dam monitoring and uncertainty prediction method was established by combining Copula entropy theory and information diffusion theory. Finally, the proposed method was applied in seepage monitoring of Nuozhadu clay core-wall rockfill dam. Its selection of influencing factors and processing of sample data were compared with different models. Results demonstrated that the proposed method increases the prediction accuracy to some extent.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.64-69
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• 2006

Due to the inherent nonlinear nature of Electro-rheological (ER) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model is constructed by using a multi-layer perceptron neural network. A quarter-car suspension model is considered in this paper for analysis and simulation. Simulation results show that the proposed inverse model of ER damper can obtain control voltage of ER damper for required damping force.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• Computers and Concrete
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• v.6 no.4
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• pp.281-303
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• 2009

The lack of safety of bridge deck structures causes frequent repair and strengthening of such structures. The repair induces great loss of economy, not only due to direct cost by repair, but also due to stopping the public use of such structures during repair. The major reason for this frequent repair is mainly due to the lack of realistic and accurate assessment system for the bridge decks. The purpose of the present research was to develop a realistic expert system, called Bridge Slab-Expert which can evaluate reasonably the condition as well as the service life of concrete bridge decks, based on the deterioration models that are derived from both the structural and environmental effects. The diagnosis assessment of deck slabs due to structural and environmental effects are developed based on the cracking in concrete, surface distress and structural distress. Fuzzy logic is utilized to handle uncertainties and imprecision involved. Finally, Bridge Slab-Expert is developed for prediction of safety and remaining service life based on the chloride ions penetration and fick's second law. Proposed expert system is based on user-friendly GUI environment. The developed expert system will allow the correct diagnosis of concrete decks, realistic prediction of service life, the determination of confidence level, the description of condition and the proposed action for repair.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.1
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• pp.60-68
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• 1999

This paper deals with the tracking control problem of industrial robotic manipulators with unknown or changing dynamics. The proposed method makes use of multiple moodels and switching mechanism by fuzzy inference of the manipulator in an indirect adaptive controller architecture. The models used for the indmtification of the manipliator are identical, except for the initial estimates of the unknown inertial pararmeters of the manipulator and its load. The torque input that is applied to the joint actuators is determined at every instant by the identification model that best approximates the robot dynamics. Simulation results are also included to dermnstrate the improvement in the tracking perfermance when the proposed method is used.s used.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.577-586
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• 2013

In a shipyard, it is hard to predict block movement due to the uncertainty caused during the long period of shipbuilding operations. For this reason, block movement is rarely scheduled, while main operations such as assembly, outfitting and painting are scheduled properly. Nonetheless, the high operating costs of block movement compel task managers to attempt its management. To resolve this dilemma, this paper proposes a new block movement analysis framework consisting of the following operations: understanding the entire process, log clustering to obtain manageable processes, discovering the process model and detecting exceptional processes. The proposed framework applies fuzzy mining and trace clustering among the process mining technologies to find main process and define process models easily. We also propose additional methodologies including adjustment of the semantic expression level for process instances to obtain an interpretable process model, definition of each cluster's process model, detection of exceptional processes, and others. The effectiveness of the proposed framework was verified in a case study using real-world event logs generated from the Block Process Monitoring System (BPMS).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.