• Nonlinear Functional Analysis and Applications
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• v.26 no.5
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• pp.971-983
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• 2021

In this paper, we prove common fixed point theorems for six weakly compatible mappings in G-fuzzy metric spaces introduced by Sun and Yang [16] which is actually generalization of G-metric spaces. G-metric spaces coined by Mustafa and Sims [13]. The paper concerns our sustained efforts for the materialization of G-fuzzy metric spaces and their properties. We also exercise the concept of symmetric G-fuzzy metric space, 𝜙-function and weakly compatible mappings. The results present in this paper generalize the well-known comparable results in the literature. We justify our results by suitable examples. Some applications are also given in support of our results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.133-138
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• 2002

In this paper, we propose an integrated GUI environment of parallel fuzzy inference system fur pattern classification of remote sensing data. In this, as 4 fuzzy variables in condition part and 104 fuzzy rules are used, a real time and parallel approach is required. For frost fuzzy computation, we use the scan line conversion algorithm to convert lines of each fuzzy linguistic term to the closest integer pixels. We design 4 fuzzy processor unit to be operated in parallel by using FPGA. As a GUI environment, PCI transmission, image data pre-processing, integer pixel mapping and fuzzy membership tuning are considered. This system can be used in a pattern classification system requiring a rapid inference time in a real-time.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.40-47
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• 2009

Recently, dynamic proxy caching has been proposed on the distributed environment so that media objects by user's requests can be served directly from the proxy without contacting the server. However, it makes caching challenging due to multimedia large sizes, low latency and continuous streaming demands of media objects. To solve the problems caused by streaming demands of media objects, this paper has been proposed the grouping scheme with fuzzy filtering based on partition and mapping. For partition and mapping, this paper divides media block segments into fixed partition reference block(R$_f$P) and variable partition reference block(R$_v$P). For semantic relationship, it makes fuzzy relationship to performs according to the fixed partition temporal synchronization(T$_f$) and variable partition temporal synchronization(T$_v$). Simulation results show that the proposed scheme makes streaming service efficiently with a high average request response time rate and cache hit rate and with a low delayed startup ratio compared with other schemes.

• Journal of Power System Engineering
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• v.2 no.2
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• pp.47-54
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• 1998

Underwater robotic vehicles(URVs) have been an important tool for various underwater tasks such as pipe-lining, data collection, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system is one of the most critical subsystems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. It is desirable to have an intelligent vehicle control system because the fixed-parameter linear controller such as PID may not be able to handle these changes promptly and result in poor performance. In this paper we described and analyzed a new type of fuzzy model-based controller which is designed for underwater robotic vehicles and based on Takagi-Sugeno-Kang(TSK) fuzzy model. The proposed fuzzy controller: 1) is a nonlinear controller, but a linear state feedback controller in the consequent of each local fuzzy control rule; 2) can guarantee the stability of the closed-loop fuzzy system; 3) is relatively easy to implement. Its good performance as well as its robustness to parameter changes will be shown and compared with those of the PID controller by simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.223-231
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• 2006

In high-speed fuzzy control systems applied to intelligent systems such as robot control, one of the most important problems is the improvement of the execution speed of the fuzzy inference. In particular, it is more important to have high-speed operations in the consequent part and the defuzzification stage. To improve the speedup of fuzzy controllers for intelligent systems, this paper presents an integer line mapping algorithm to convert [0, 1] real values of the fuzzy membership functions in the consequent part to a $400{\times}30$ grid of integer values. In addition, this paper presents a method of eliminating the unnecessary operations of the zero items in the defuzzification stage. With this representation, a center of gravity method can be implemented with only integer additions and one integer division. The proposed system is analyzed in the air conditioner control system for execution speed and COG, and applied to the truck backer-upper control system. The proposed system shows a significant increase in speed as compared with conventional methods with minimal error; simulations indicate a speedup of an order of magnitude. This system can be applied to real-time high-speed intelligent systems such as robot arm control.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.262-266
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• 1993

The development of COPS(Computer aided Operation Planning System) needs data mapping paradigm which provides intelligent determonation of cutting conditions from the requirements of process planning side. We proposed the idea of multi-level mapping by the combination of heuristics of domain experts and mathematical abstraction of cutting condition and requirements. Mathematical mathods for the generalization of heuristics were constructed by multi-layer perceptron. DBMS for determination of cutting conditions was constructed by classification and combination of best fitted models. Triangular fuzzy number was used to process the uncertainties in heuristics of experts.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.620-623
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• 2003

The TAM (Topographic Attentive Mapping) network is a biologically-motivated neural network. Fuzzy rules are acquired from the TAM network by the pruning algorithm. In this paper we formulate a new input layer using Gabor function for TAU network to realize receptive field of human visual cortex.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.559-565
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• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1037-1041
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• 2004

This paper examines the problem of designing an $H_{\infty}$ fuzzy state-feedback controller for a class of uncertain nonlinear descriptor systems which is described by a Takagi-Sugeno (TS) fuzzy model. Based on a linear matrix inequality (LMI) approach, we develop an $H_{\infty}$ state-feedback controller which guarantees the $L_2$-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value for this class of systems. A numerical example is provided to illustrate the design developed in this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.488-492
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• 1993

This paper proposes a new approach to the optimization method of a blending process with neural network. The method is based on the error backpropagation learning algorithm for neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a system solver. A fuzzy membership function is used in parallel with the neural network to minimize the difference between measurement value and input value of neural network. As a result, we can guarantee the reliability and stability of blending process by the help of neural network and fuzzy membership function.