• Journal of the Institute of Convergence Signal Processing
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• v.1 no.2
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• pp.160-168
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• 2000

In this paper, we study how the remote controllable mobile robot which could come to many via points with FLC(Fuzzy Logic Control) efficiently. The fabricated robot stop after the movement of single path method by four kinds of commands (forward, backward, turn left, turn right). To reduce disadvantages of this driving type, this paper reduce via points to goal position base on map which get from senor, let robot drive via point to via point on optimized path. An algorithm for the avoidance of unexpected obstacles by FLC is developed. And these algorithms are confirmed by computer simulations

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.3
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• pp.205-211
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• 2013

Recently, the needs of system performances such as working speed and processing accuracy in machine tools have been increased. Especially, the working speed increment generates harmful heat at both moving part of the machine tools and handicrafts. The heat is a main drawback to progress accuracy of the processing. Hence, a oil cooler to control temperature is inevitable for the machine tools. In general, two representative control schemes, hot-gas bypass and variable speed control of a compressor, have been adopted in the oil cooler system. This paper deals with design and implementation method of fuzzy controller for obtaining precise temperature characteristic of HB oil cooler system in machine tools. The opening angle of an electronic expansion valve are controlled to keep reference value and room temperature of temperature at oil outlet. Especially, the fuzzy controller is added to suppress temperature fluctuation under abrupt disturbances. Through some experiments, the suggested method can control the target temperature within steady state error of ${\pm}0.22^{\circ}C$.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.45-50
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• 2019

When PPG signal is used in mobile healthcare devices, the accuracy of the measured heartbeat decreases from the influence by the movement of the user. The reason is that the frequency band of the noise overlaps the frequency band of the PPG signal. In order to remove these same noises, the methods using frequency analysis method or application of acceleration sensor have been investigated and showed excellent performance. However, in applying these methods to low-cost healthcare devices, it is difficult to apply these methods because of much processing time and sensor's cost. In order to solve these problems, this study proposed the filter design method using an adaptive notch filter and the fuzzy inference system to extract more accurate heart rate in real time and evaluated its performance. As results, it showed better results than the other methods. Based on the results, when applying the proposed method to design the mobile healthcare device, it is possible to measure the heartbeat more accurately in real time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.223-229
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• 2018

Recently, the necessity of safety diagnosis of electrical devices has been increasing as the fire caused by electric devices has increased rapidly. This study is concerned with the safety diagnosis of electric equipment using intelligent Fuzzy technology. It is used as a diagnostic input for the multiple electrical safety factors such as the use current, cumulative use time, deterioration and arc characteristics inherent to the equipment. In order to extract these information in real time, a device composed of various sensor circuits, DSP signal processing, and communication circuit is implemented. The fuzzy logic algorithm using the Gaussian function for each information is designed and compiled to be implemented on a small DSP board. The fuzzy logic receives the four diagnostic information, deduces it by the fuzzy engine, and outputs the overall safety status of the device as a 100-step analog fuzzy value familiar to human sensibility. By experiments of a device that combines hardware and fuzzy algorithm implemented in this study, it is verified that it can be implemented in a small DSP board with human-friendly fuzzy value, diagnosing real-time safety conditions during operation of electric equipment. In the future, we expect to be able to study more intelligent diagnostic systems based on artificial intelligent with AI dedicated Micom.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.263-268
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• 2010

The premature convergence of genetic algorithms (GAs) is the most major factor of slow evolution of GAs. In this paper we propose a novel method to solve this problem through competition of multiple offspring of in dividuals. Unlike existing methods, each parents in our method generates multiple offspring and then generated multiple offspring compete each other, finally winner offspring become to real offspring. From this multiple offspring competition, our GA rarel falls into the premature convergence and easily gets out of the local optimum areas without negative effects. This makes our GA fast evolve to the global optimum. Experimental results with four function optimization problems showed that our method was superior to the original GA and had similar performances to the best ones of queen-bee GA with best parameters.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.201-204
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• 2000

This paper describes a study on the time-varying control of parameters in learning of the neural network. Elman recurrent neural network (RNN) is used to implement the control of parameters. The parameters of learning and momentum rates In the error backpropagation algorithm ate updated at every iteration using fuzzy rules based on performance index. In addition, the gain and slope of the neuron's activation function are also considered time-varying parameters. These function parameters are updated using the gradient descent algorithm. Simulation results show that the auto-tuned learning algorithm results in faster convergence and lower system error than regular backpropagation in the system identification.

• Annals of Fuzzy Mathematics and Informatics
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• v.16 no.3
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• pp.337-361
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• 2018

We introduce and study some basic properties of rough $I_{\lambda}$-statistical convergent of weight g (A), where $g:{\mathbb{N}}^3{\rightarrow}[0,\;{\infty})$ is a function statisying $g(m,\;n,\;k){\rightarrow}{\infty}$ and $g(m,\;n,\;k){\not{\rightarrow}}0$ as $m,\;n,\;k{\rightarrow}{\infty}$ and A represent the RH-regular matrix and also prove the Korovkin approximation theorem by using the notion of weighted A-statistical convergence of weight g (A) limits of a triple sequence of Bernstein-Stancu polynomials.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.277-283
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• 2014

Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.