• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.253-255
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• 2022

Thanks to the 4th industrial revolution and the development of various communication media, technologies such as artificial intelligence and automation are being grafted into industrial sites in various fields, and accordingly, the importance of data processing is increasing. Image noise removal is a pre-processing process for image processing, and is mainly used in fields requiring high-level image processing technology. Various studies have been conducted to remove noise, but various problems arise in the process of noise removal, such as image detail preservation, texture restoration, and noise removal in a special area. In this paper, we propose a switching mask filter based on the noise intensity to preserve the detailed image information during the impulse noise removal process. The proposed filter algorithm obtains the final output by switching to the extended mask when it is determined that the density is higher than the reference value when noise is determined in the area designated as the filtering mask. Simulation was conducted to evaluate the performance of the proposed algorithm, and the performance was analyzed compared to the existing method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.84-88
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• 2023

This study proposes an automated tilt measurement method using point cloud for buildings. The proposed method consists of two main steps: 1) exterior wall plane extraction, and 2) edge estimation and angle calculation. To validate the performance of the proposed method, the algorithm is applied to a target building, of which the estimated tilt values are compared with those obtained from a total station, a commonly used tool for tilt measurement. The result shows that most estimated tilt values are within the maximum and minimum ranges of the total station measurement, suggesting that the proposed algorithm provides sufficient measurement accuracy. Furthermore, the proposed method is shown to be automated and reliable as well as free from human-induced errors compared to the total station.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.321-331
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• 2003

Experimental software datasets describing software projects in terms of their complexity and development time have been the subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such approaches as neural networks, fuzzy, and fuzzy neural network models. In this study, we introduce the concept of the Rule-based fuzzy polynomial neural networks (RFPNN) as a hybrid modeling architecture and discuss its comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We discuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, it is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the NASA dataset concerning software cost estimation and the one describing software modules of the Medical Imaging System (MIS).

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.263-270
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• 2003

This paper deals with a control technique of eliminating the transient vibration of a waist axis of an articulated robot. This technique is based on a model-based control in order to establish the damping effect on the mechanical part. The control model is related to the velocity control loop, and it is composed of reduced-order electrical and mechanical parts. Using this model, the velocity of the load is estimated, which is converted to the motor shaft. The difference between the estimated load speed and the motor speed is calculated dynamically, and it is added to the velocity command to suppress the transient vibration of a waist axis of the robot arm. The function of this technique is to increase the cut-off frequency of the system and the damping ratio at the driven machine part. This control model is easily obtained from design or experimental data and its algorithm can be easily installed in a DSP. This control technique is applied to a waist axis of an articulated robot composed of a harmonic drive gear reducer and a robot arm with 5 degrees of freedom. Simulations and experiments show satisfactory control results to reduce the transient vibration at the end-effector.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.689-696
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• 2006

This paper presents a new algorithm for the closed-loop $H_{\infty}$ composite control of weakly coupled bilinear systems with time-varying parameter uncertainties and exogenous disturbance using the successive Galerkin approximation(SGA). By using weak coupling theory, the robust $H_{\infty}$ control can be obtained from two reduced-order robust $H_{\infty}$ control problems in parallel. The $H_{\infty}$ control theory guarantees robust closed-loop performance but the resulting problem is difficult to solve for uncertain bilinear systems. In order to overcome the difficulties inherent in the $H_{\infty}$ control problem, two $H_{\infty}$ control laws are constructed in terms of the approximated solution to two independent Hamilton-Jacobi-Isaac equations using the SGA method. One of the purposes of this paper is to design a closed-loop parallel robust $H_{\infty}$ control law for the weakly coupled bilinear systems with parameter uncertainties using the SGA method. The other is to reduce the computational complexity when the SGA method is applied to the high order systems.

• Journal of Ship and Ocean Technology
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• v.6 no.4
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• pp.1-12
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• 2002

A control system for an automated line heating process is developed by use of object-oriented methodology. The main function of the control system is to provide real-time heating information to technicians or automated machines. The information includes heating location, torch speed, heating order, and others. The system development is achieved by following the five steps in the object-oriented procedure. First, requirements are specified and corresponding objects are determined. Then, the analysis, design, and implementation of the proposed system are sequentially carried out. The system consists of six subsystems, or modules. These are (1) the inference module with an artificial neural network algorithm, (2) the analysis module with the Finite Element Method and kinematics analysis, (3) the data access module to store and retrieve the forming information, (4) the communication module, (5) the display module, and (6) the measurement module. The system is useful, irrespective of the heating sources, i.e. flame/gas, laser, or high frequency induction heating. A newly developed automated line heating machine is connected to the proposed system. Experiments and discussions follow.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2002.04a
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• pp.67-75
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• 2002

Digital photogrammetry is one of the powerful tools for surveying in more perceptual ways and exploiting the continuously developing computer technology. Nowadays, digital photogrammetry is being used for a number of industrial measurements and inspections but the automation aspect of this technique is not fully developed yet. Photogrammetric work, which is obtained through usual workflow, delays for a big amount of CGP surveying, interpretation and cadastral information. Therefore through studying ways of reducing the volume of photogrammetric works, financial opportunities for digital photogrammetry can be found. This research is focused on the development of the new workflow and study algorithm in digital photogrammetry. Using this result we can reduce financial expenses and improve technologies of topographic and cadastral plans creation.

• Journal of Mechanical Science and Technology
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• v.20 no.9
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• pp.1418-1427
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• 2006

Windshield wipers play a key role in assuring the driver's safety during precipitation. The traditional wiper systems, however, requires driver's constant attention in adjusting the wiper speed and the intermittent wiper interval because the amount of precipitation on the windshield constantly varies according to time and vehicle's speed. Because the manual adjustment of the wiper distracts driver's attention, which may be a direct cause of traffic accidents, many companies have developed automatic wiper systems using some optical sensors with various levels of success. This paper presents the development of vision-based smart windshield wiper system that can automatically adjust its speed and intermittent interval according to the amount of water drops on the windshield. The system employs various image processing algorithms to detect water drops and fuzzy logic to determine the speed and the interval of the wiper.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.83-91
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• 2004

The purpose of this paper is to obtain an accurate nonlinear system model to test various control schemes for a motion control system that requires high speed, robustness and accuracy. An industrial sewing machine equipped with a Brushless DC motor is considered. It is modeled by a neural network that is configured as an output-error dynamical system. The identified model is essentially a one step ahead prediction structure in which past inputs and outputs are used to calculate the current output. Using the model, a 2 degree-of-freedom PID controller to compensate the effects of disturbance without degrading tracking performance has been de-signed. In this experiment, it is not preferable for safety reasons to tune the controller online on the actual machinery. Experimental results confirm that the model is a good approximation of sewing machine dynamics and that the proposed control methodology is effective.