• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.71-76
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• 2023

In this paper, we propose a fuzzy U-Net, a fuzzy deep learning model that applies fuzzy logic to improve performance in image segmentation using deep learning. Fuzzy modules using fuzzy logic were combined with U-Net, a deep learning model that showed excellent performance in image segmentation, and various types of fuzzy modules were simulated. The fuzzy module of the proposed deep learning model learns intrinsic and complex rules between feature maps of images and corresponding segmentation results. To this end, the superiority of the proposed method was demonstrated by applying it to dental CBCT data. As a result of the simulation, it can be seen that the performance of the ADD-RELU fuzzy module structure of the model using the addition skip connection in the proposed fuzzy U-Net is 0.7928 for the test dataset and the best.

• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.1-9
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• 2008

Proposed in the paper is an automated procedure to construct a plant model for PLC simulation. Since PLC programs only contain the control logic without the information on the plant model, it is necessary to build the corresponding plant model to perform simulation. Conventionally, a plant model for PLC simulation has been constructed manually, and it requires much efforts as well as the in-depth knowledge of simulation. As a remedy for the problem, we propose an automated procedure to generate a plant model from the symbol table of a PLC program. To do so, we propose a naming rule for PLC symbols so that the symbol names include enough information on the plant model. By analyzing such symbol names, we extract a plant model automatically. The proposed methodology has been implemented, and test runs were made.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.166-169
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model, and the GA is applied to identify this fuzzy model. The proposed method is compared with the AIMM algorithm in simulations.

• Journal for History of Mathematics
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• v.27 no.1
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• pp.47-57
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• 2014

This paper aims to evaluate works of Frege and G$\ddot{o}$del, who play the trigger role in development of logic, by Knowledge Change Model. It identifies where their positions are in the model respectively. For this purpose I suggest types of knowledge change and their criteria for the evaluation. Knowledge change are classified into five types according to the degree of its change: improvement, weak glorious revolution, glorious revolution, strong glorious revolution, and total revolution. Criteria to evaluate the change are its contents, influence, pervasive effects, and so forth. The Knowledge Change Model consists of the types and the criteria. I argue that in the model Frege belongs to the total revolution and G$\ddot{o}$del to the weak glorious revolution. If we accept that the revolution in logic initiated by Frege was completed by G$\ddot{o}$del, it is a natural conclusion.

• Korean Journal of Computational Design and Engineering
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• v.4 no.3
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• pp.238-246
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• 1999

The modeling method to support product development processes (PDP) must have certain characteristics including the ability to represent multiple viewpoints of the product development and integrate with currently available analysis and design methods based on CE concept. This paper describes the reference model to support multiple viewpoints (PPO: Products, Processes, and Organizations/Resources viewpoints) of the product development processes, from which each model (Products model, Processes model, and Organizations/Resources model) can be extracted, as well as produces PPO data schema. This reference model has associative relationships among the products, processes, and organizations/resources. To allow the extensibility to support design evolution, we propose structured dat representation methods using semantic network, which can be constructed through first-order logic. The product development processes is so represented by specifying entities and semantic relationships among them hat he appropriate information can be accessed and all of the relevant attributes about the entities can be retrieved simultaneously.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.1-11
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• 2014

This paper addresses the emotion computing model for software affective agents. In this paper, emotion is represented in valence-arousal-dominance dimensions instead of discrete categorical representation approach. Firstly, a novel emotion model architecture for affective agents is proposed based on Scherer's componential theories of human emotion, which is one of the well-known emotion models in psychological area. Then a fuzzy logic is applied to determine emotional statuses in the emotion model architecture, i.e., the first valence and arousal, the second valence and arousal, and dominance. The proposed methods are implemented and tested by applying them in a virtual training system for children's neurobehavioral disorders.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.3
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• pp.370-377
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• 2017

This paper presents a simple noise margin (NM) model of MOS current mode logic (MCML) gates especially in CMOS processes where a large device mismatch deteriorates logic reliability. Trade-offs between speed and logic reliability are discussed, and a simple yet accurate NM equation to capture process-dependent degradation is proposed. The proposed NM equation is verified for 130-nm, 110-nm, 65-nm, and 40-nm CMOS processes and has errors less than 4% for all cases.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.44-47
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• 1996

Bang-bang control law provides the optimal solution for a minimum-time control problem, but ignores the intermediate path except for the initial and final points. In this paper, a near minimum-time suboptimal fuzzy logic controller is introduced that can control the intermediate path. A dynamic model for a system is established using the average dynamics method of linearization. System model is continuously updated over the control time periods. This makes it suitable for high speed or variable payload applications. Bang-bang control theory is modified and used to derive the preliminary control law. A fuzzy logic algorithm is then applied to adjust and find the best solution. The solution will provide the suboptimal minimum-time control law which can avoid obstacles in the workspace.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.5
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• pp.691-696
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• 2012

In this study, a rigid multi-body dynamic model has been developed for mechatronic analysis to evaluate dynamic behavior of a machine tool. The development environment was the commercialized analysis tool, ADAMS, for rigid multi-body dynamic analysis. A simplified servo control logic was implemented in the tool using its functions in order to negate any external tool of control definition. The advantage of the internal implementation includes convenience of the analysis process by saving time and efforts. Application of this development to a machine tool helps to evaluate its dynamic behavior against feeding its component, to calculate the motor torque, and to optimize parameters of the control logic.