• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.441-447
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• 2001

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause tole inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.193-198
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• 2004

This paper describes an automatic finite element (FE) mesh generation for three-dimensional structures consisting of tree-form surfaces. This mesh generation process consists of three subprocesses: (a) definition of geometric model, (b) generation of nodes, and (c) generation of elements. One of commercial solid modelers is employed for three-dimensional solid structures. Node is generated if its distance from existing node points is similar to the node spacing function at the point. The node spacing function is well controlled by the fuzzy knowledge processing. The Voronoi diagram method is introduced as a basic tool for element generation. Automatic generation of FE meshes for three-dimensional solid structures holds great benefits for analyses. Practical performances of the present system are demonstrated through several mesh generations for three-dimensional complex geometry.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.1
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• pp.65-75
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• 1993

Fuzzy data is a phenomenon often occurring in real life. There is the inherent vagueness of classification terms referring to a continuous scale, the uncertainty of linguistic terms such as "I almost agree" or the vagueness of terms and concepts due to the statistical variability in communication [20] and many more. Previously, such fuzzy data was approximated by non-fuzzy (crisp) data, which obviously did not lead to a correct and precise representation of the real world. Fuzzy set theory has been developed to represent and manipulate fuzzy data [18]. Explicitly managing the degree of fuzziness in databases allows the system to distinguish between what is known, what is not known and what is partially known. Systems in the literature whose specific objective is to handle imprecision in databases present various approaches. This paper is concerned with the different ways uncertainty and imprecision are handled in database design. It outlines the major areas of fuzzification in (relational) database systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.52-57
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• 2006

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.481-486
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• 2002

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-5) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for the indirect adaptive fuzzy control. Based on the derived design method, the parameter estimation for controllable canonical T-S fuzzy model is also Presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.264-267
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• 2000

The need for including time variables in various type of modeled Discrete Event Dynamic Systems(DEDSs) is apparent since the modeled systems are real time in nature. In the real world, almost all event is related to time. A Time Petri Net(TPN) is one of methods for model ins and analyzing of DEDSs with real time values. Two time values, ${\alpha}$$\sub$i/ and ${\beta}$$\sub$i/ are defined for each transition. In this paper, Ire present Fuzzy Transition Timed Petri Net(FTTPN) to determine the optimal transition firing time between ${\alpha}$$\sub$i/ and ${\beta}$$\sub$i/ using fuzzy theory. The traffic signal controller in an intersection is modeled and analyzed by FTTPN.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.102-105
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• 2002

다기능 휠체어 시스템의 제어부인 80C196KC를 사용했으나, fuzzy 알고리즘을 적용하는데 있어서 여러 가지 문제점(속도, 연산, 외부장치 등) 때문에 구현1차는데 상당한 어려움이 있었다. 이를 해결하기 위해 20MIPS의 속도인 TMS320F240의 프로세서를 사용하였으며 이로 인해 발생하는 문제점을 거의 해소시켰고, 퍼지 알고리즘을 효율적으로 적용할 수 있었다. 본 시스템의 구성은 주제어기로 퍼지 제어 알고리즘을 사용하여 조이스틱을 제어시켰고, 2개 encoder를 입력을 받아 휠체어 회전수를 이용하여 제어시켰다. 휠체어 모터로는 BLDC(Brushless DC) 모터를 사용했으면, BLDC전용 드라이브단을 이용하여 모터를 구현시켰다. 그리고 3개의 초음파 센서를 부착하여 장애물 회피를 시도하였다. 초음파센서의 효율적인 사용을 위해 스텝 모터(Stepping Motor)와 마이크로 컨트롤러로 PIC을 사용하였다. 본 논문의 실험 결과에서는 이전의 제어기(80C196KC)보다 현저히 성능 면에서 향상되었고, 부수적인 외부장치 처리면에서도 상당한 차이를 볼 수 있었다

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.903-921
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• 2024

Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

• Journal of The Korean Association of Information Education
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• v.5 no.2
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• pp.268-278
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• 2001

This study is design and implementation of diagnosis module using item response theory to assess level of student's knowledge in web-based instruction systems. Item response theory uses responses to items on a test or survey questionnaire to simultaneously locate both the items on the latent trait defined by the set of items while simultaneously scaling each item on the very same dimension. Existing method of measurement in web-based instruction system provided dichromatic learning after to be assess just with the total scores of exam. This measurement has an error that do not consider the level of student's knowledge. Moreover, this method can't perform an exact diagnosis of student knowledge and make student modeling to construct intelligent tutoring system. In this study, we present that design and implement a diagnosis module using item response theory to assess level of student's knowledge in web-based instruction systems