• International Journal of Contents
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• v.2 no.2
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• pp.1-5
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• 2006

The modeling and animation of natural phenomena have received much attention from the computer graphics community. Synthetic of natural phenomena are required for such diverse applications as flight simulators, special effects, video games and other virtual realty. In special effects industry there is a high demand to convincingly mimic the appearance and behavior of natural phenomena such as smoke, waterfall, rain, and fire. Particle systems are methods adequate for modeling fuzzy objects of natural phenomena. This paper presents particle system API(Application Program Interfaces) for generating special effects in virtual reality applications. The API are a set of functions that allow C++ programs to simulate the dynamics of particles for special effects in interactive and non-interactive graphics applications, not for scientific simulation.

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.196-199
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• 1997

This paper describes the implementation process of Virtual Modeling system for a customer-oriented product. The human sense is measured and analyzed by physical design factors and can be applied also for the product design. The first step implementing virtual modeling is to make a human sensibility("Kansei") database. Human sensibility database is constructed with the relational data of Kansei words and design factors. The next step is extraction the design information from the human sensibility database by fuzzy inference algorithm. This design information is used for the input data for the graphic database. Virtual implementation software compounds 3D shape of product. The final product can be modified according to the customer's requirement.quirement.

• Fibers and Polymers
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• v.6 no.4
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• pp.348-354
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• 2005

These days consumers' various demands are accelerating research on apparel manufacturing system including automatic measurement, pattern generation, and clothing simulation. Accordingly, methods of reconstructing human body from point-clouds measured using a three dimensional scanning device are required for apparel CAD system to support these functions. In particular, we present in this study a human body reconstruction method focused on two issues, which are the decision of the number of control point for each sectional curve with error bound and the local knot removal for reducing the unusual concentration of control points. The approximation of sectional curves with error bounds as an approximation criterion leads all sectional curves to their own particular shapes apart from the number of control points. In addition, the application of the local knot removal to construction of human body sectional curves reduces the unusual concentration of control points effectively. The results may be used to produce an apparel CAD system as an automatic pattern generation system and a clothing simulation system through the low level control of NUBS or NURBS.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.505-508
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• 2002

An emotionally modeled robot is dealt in this paper. The emotional model is required especially in the entertainment robot. Recently, the entertainment robots have been developed as the next generation of electronic toys. They require several capabilities such as perceiving, acting, communication, and surviving. The owner recognizes the communication with a entertainment robot by observing its expression and reaction. The expression is realized by emotion-based actions based on moving, dancing, sounding, speaking, and lighting. Therefore, we propose an emotional modeling algorithm, using the fuzzy logic system, in this paper. Good performance of the algorithm is confirmed by the result of a simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.215-221
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• 2003

Generally, we want to be searched the newest as well as some appropriate personalized information from the internet resources. However, it is a complex and repeated procedure to search some appropriate information. Moreover, because the user's interests are changed as time goes, the real time modeling of a user's interests should be necessary. In this paper, I propose PREA system that can search and filter documents that users are interested from the World Wide Web. And then it constructs the user's interest model by a modified spreading neural network. Based on this network, PREA can easily produce some queries to search web documents, and it ranks them. The conventional spreading neural network does not have a visualization function, so that the users could not know how to be configured his or her interest model by the network. To solve this problem, PREA gives a visualization function being shown how to be made his interest user model to many users.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.288-291
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• 2004

This paper proposes an intelligent search modeling using avatar agent. This system consists of some modules such as agent interface, agent management, preprocessor, interface machine. Core-Symbol Database and Spell Checker are related to the preprocessor module and Interface Machine is connected with Best Aggregate Designer. Our avatar agent system does the indexing work that converts user's natural language type sentence to the proper words that is suitable for the specific branch information retrieval. Indexing is one of the preprocessing steps that make it possible to guarantee the specialty of user's input and increases the reliability of the result. It references a database that consists of synonym and specific branch dictionary. The resulting symbol after indexing is used for draft search by the internet search engine. The retrieval page position and link information are stored in the database. We experimented our system with the stock market keyword SAMSUNG_SDI, IBM, and SONY and compared the result with that of Altavista and Google search engine. It showed quite excellent results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.22.1-22
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• 2001

In this paper we present Multiple-Channel Active Noise Control[ANC] system by employing Independent Component Analysis[ICA] and Adaptive Network Fuzzy Inference System[ANFIS]. ICA is widely used in signal processing and communication and it use prewhiting and appropriate choice of non-linearities, ICA can separate mixed signal. ANFIS controller is trained with the hybrid learning algorithm to optimize its parameters for adaptively canceling noise. This new method which minimizes a statistical dependency of mutual information(MI) in mixed low frequency noise signal and there is no need to secondary path modeling. The proposed implementations achieve more powerful and stable noise reduction than Filtered-X LMS algorithms which is needed for LTI assumption and precise secondary error

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.25-36
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• 2005

In this study, we suggested a new approach method forecasting distribution demand of urban rail transit usign fuzzy control, with intend to reflect irregularity and various functional relationship between trip length and distribution demand. To establish fuzzy control model and test this model, the actual trip volume(production, attraction and distribution volume) and trip length (space distance between a departure and arrival station) of Daegu subway line 1 were used. Firstly, usign these data we established a fuzzy control model, nd the estimation accuracy of the model was examined and compared with that of generalized gravity model. The results showed that the fuzzy control model was superior to gravity model in accuracy of estimation. Therefore, wwe found that fuzzy control was able to be applied as a effective method to predict the distribution demand of urban rail transit. Finally, to increase the estimation precision of the model, we expect studies that define membership functions and set up fuzzy rules organized with neural networks.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.108-113
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• 2016

The real-world problems usually show nonlinear and multi-variate characteristics, so it is difficult to establish concrete mathematical models for them. Thus, it is common to practice data-driven modeling techniques in these cases. Among them, most widely adopted techniques are regression model and intelligent model such as neural networks. Regression model has drawback showing lower performance when much non-linearity exists between input and output data. Intelligent model has been shown its superiority to the linear model due to ability capable of effectively estimate desired output in cases of both linear and nonlinear problem. This paper proposes modeling method of daily photovoltaic power systems using ELM(Extreme Learning Machine) based modular networks. The proposed method uses sub-model by fuzzy clustering rather than using a single model. Each sub-model is implemented by ELM. To show the effectiveness of the proposed method, we performed various experiments by dataset acquired during 2014 in real-plant.