• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1176-1182
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• 2021

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.407-409
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• 2021

Under the influence of the 4th industrial revolution, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. Digital images may generate noise due to various reasons, and may affect various systems such as image recognition and classification and object tracking. To compensate for these shortcomings, we propose an image restoration algorithm based on pattern information of non-noise pixels. According to the distribution of non-noise pixels inside the filtering mask, the proposed algorithm switched the filtering process by dividing the interpolation method into a pattern that can be applied, a pattern based on region division, and a randomly arranged pixel pattern. preserves and restores the image. The proposed algorithm showed superior performance compared to the existing impulse noise removal algorithm.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.755-766
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• 2008

This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.

• Advances in robotics research
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• v.2 no.2
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• pp.113-127
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• 2018

Robotics and automation are rapidly growing in the industries replacing human labor. The idea of robots replacing humans is positively influencing the business thereby increasing its scope of research. This paper discusses the development of an experimental platform controlled by a robotic arm through Robot Operating System (ROS). ROS is an open source platform over an existing operating system providing various types of robots with advanced capabilities from an operating system to low-level control. We aim in this work to control a 7-DOF manipulator arm (Robai Cyton Gamma 300) equipped with an external vision camera system through ROS and demonstrate the task of balancing a ball on a plate-type end effector. In order to perform feedback control of the balancing task, the ball is designed to be tracked using a camera (Sony PlayStation Eye) through a tracking algorithm written in C++ using OpenCV libraries. The joint actuators of the robot are servo motors (Dynamixel) and these motors are directly controlled through a low-level control algorithm. To simplify the control, the system is modeled such that the plate has two-axis linearized motion. The developed system along with the proposed approaches could be used for more complicated tasks requiring more number of joint control as well as for a testbed for students to learn ROS with control theories in robotics.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.42-42
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• 2017

Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.430-443
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• 2005

A general construction algorithm of the Grassmann space parameters in linear systems - so-called, the Plucker matrix, 'L' in m-input, p-output, n-th order static output feedback systems and the Plucker matrix, $'L^{aug}'$ in augmented (m+d)-input, (p+d)-output, (n+d)-th order static output feedback systems - is presented for numerical checking of necessary conditions of complete static and complete minimum d-th order dynamic output feedback pole-assignments, respectively, and also for discernment of deterministic computation condition of their pole-assignable real solutions. Through the construction of L, it is shown that certain generically pole-assignable strictly proper mp > n system is actually none pole-assignable over any (real and complex) output feedbacks, by intrinsic rank deficiency of some submatrix of L. And it is also concretely illustrated that this none pole-assignable mp > n system by static output feedback can be arbitrary pole-assignable system via minimum d-th order dynamic output feedback, which is constructed by deterministic computation under full­rank of some submatrix of $L^{aug}$.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3
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• pp.402-407
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• 2013

In this paper, the image processing for flexible disk grinding and the effect of the grinding conditions on the flat zone length of a workpiece are investigated, with the purpose of automating the grinding process. To accomplish this, three issues should be carefully studied. The first is finding the relationship between the flat zone length and the grinding conditions such as the cutting speed and feeding speed. The second is developing a neural network algorithm to predict the flat zone. The third is developing an image processing algorithm to measure the flat zone length of a workpiece. Slope analysis is used to determine straight and curved sections during the image processing. For verification, the estimated length and the length from the image processing are compared with the length measured by a projector. There is a minimum difference of 1.7% between the predicted and measured values. The results of this paper will be useful in compiling a database for process automation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1299-1302
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• 2005

Current mechanical devices are trending toward being a small size, high speedy, automation. For performing these functions, machinery elements organizing a machine should be designed exactly. Cams have high confidence and economics in ablility to transmit a motion. Accordingly, A cam mechanism is very important for processing the machine automatically. This paper introduce an inverse cam mechanism. A square shaped cam which cannot be commonly analyzed is designed and manufactured by using the NURBS interpolation algorithm. The objective of this paper is to develop a computer-aided design program. In this paper, a displacement curve of oscillating motion inverse cam mechanism with square shaped follower is analyzed. The data is redistibuted by the NURBS algorithm. A cam shape is designed by the instant velocity center method, and simulated to verify the validity of the operation state.