• Journal of Mechanical Science and Technology
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• v.16 no.12
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• pp.1633-1642
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• 2002

Uninterrupted power supply has become indispensable during the maintenance task of active electric power lines as a result of today's highly information-oriented society and increasing demand of electric utilities. The maintenance task has the risk of electric shock and the danger of falling from high place. Therefore it is necessary to realize an autonomous robot system using electro-hydraulic manipulator because hydraulic manipulators have the advantage of electric insulation. Meanwhile it is relatively difficult to realize autonomous assembly tasks particularly in the case of manipulating flexible objects such as electric lines. In this report, a discrete event control system is introduced for automatic assembly task of electric lines into sleeves as one of the typical task of active electric power lines. In the implementation of a discrete event control system, LVQNN (linear vector quantization neural network) is applied to the insertion task of electric lines to sleeves. In order to apply these proposed control system to the unknown environment, virtual learning data for LVQNN is generated by fuzzy inference. By the experimental results of two types of electric lines and sleeves, these proposed discrete event control and neural network learning algorithm are confirmed very effective to the insertion tasks of electric lines to sleeves as a typical task of active electric power maintenance tasks.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.359-362
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using multi adaptive fuzzy learning controller(AFLC). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.91-98
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• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• Spatial Information Research
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• v.12 no.3
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• pp.263-274
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• 2004

The purpose of this study is to develop of red tide information system for spatial and temporal analysis of red tide including the outbreak season of red tide and biological-oceanography parameters using GIS techniques. The outbreaks of red tide were sporadic in the South Sea until 1994, but became frequent and widespread in whole coastal waters of the South Sea and East Sea since 1995. Therefore, the research fields of red tide has undergone a major changes. For monitoring of red tide, many kinds of techniques were carried out such as remote sensing, GIS and fuzzy model system. In this research, the development methods of red tide information system were suggested. For construction of the CIS based Red Tide database, spatial distribution area, species of red tide plankton and physical environment were analyzed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.161-166
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• 2006

Localization of mobile agent within a sensing network is a fundamental requirement for many applications, using networked navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems, This paper describes a networked sensor-based navigation method in an indoor environment for an autonomous mobile robot which can navigate and avoid obstacle. In this method, the self-localization of the robot is done with a model-based vision system using networked sensors, and nonstop navigation is realized by a Kalman filter-based STSF(Space and Time Sensor Fusion) method. Stationary obstacles and moving obstacles are avoided with networked sensor data such as CCD camera and sonar ring. We will report on experiments in a hallway using the Pioneer-DX robot. In addition to that, the localization has inevitable uncertainties in the features and in the robot position estimation. Kalman filter scheme is used for the estimation of the mobile robot localization. And Extensive experiments with a robot and a sensor network confirm the validity of the approach.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1074-1081
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• 2010

Today, the research related to the robot is achieved in various part. With the high interest in means of transport, various researches about autonomous mobile robot and next generation transport is continuing. The unicycle robot among these needs much control technique like balance control model and driving model. For autonomous driving of this unicycle robot, from the basic balance control to direction switching control and velocity control are needed. But the environment elements like a gradient and frictional force or unbalanced elements from the structural feature. The unicycle needs the real time balance control so more complex, harder to control. And when functional addition is made, the problem that fall entire reaction velocity or accuracy would be happen. This paper introduces entire dynamics modeling of the unicycle robot and reduced model. And propose the new balance control algorithm using fuzzy controller. Also the evaluation about performance would be made through the test.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.6 s.312
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• pp.9-15
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• 2006

The recent development in robot field shows that service robot which interacts with human and provides specific service to human has been researched continually. Especially, robot for human welfare becomes the center of public concern. At present time, guide robot is priority field of general welfare robot and helps the blind keep safe path when he walks outdoor. In this paper, guide robot provides not only collision avoidance but also the best walking direction and velocity to blind people while recognizing environment information from various kinds of sensors. In addition, it is able to provide the most safe path planing on behalf of blind people.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.140-146
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• 2013

In this paper, we describe a vision sensor-based driving algorithm for indoor automatic guided vehicles (AGVs) that facilitates a path tracking task using two mono cameras for navigation. One camera is mounted on vehicle to observe the environment and to detect markers in front of the vehicle. The other camera is attached so the view is perpendicular to the floor, which compensates for the distance between the wheels and markers. The angle and distance from the center of the two wheels to the center of marker are also obtained using these two cameras. We propose five movement patterns for AGVs to guarantee smooth performance during path tracking: starting, moving straight, pre-turning, left/right turning, and stopping. This driving algorithm based on two vision sensors gives greater flexibility to AGVs, including easy layout change, autonomy, and even economy. The algorithm was validated in an experiment using a two-wheeled mobile robot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.91-99
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• 2013

An autonomous unmanned underwater vehicle is a type of marine self-propelled robot that executes some specific mission and returns to base on completion of the task. In order to successfully execute the requested operations, the vehicle must be guided by an effective navigation algorithm that enables it to avoid obstacles and follow the best path. Architectures and principles for intelligent dynamic systems are being developed, not only in the underwater arena but also in related areas where the work does not fully justify the name. The problem of increasing the capacity of systems management is highly relevant based on the development of new methods for dynamic analysis, pattern recognition, artificial intelligence, and adaptation. Among the large variety of navigation methods that presently exist, the dynamic window approach is worth noting. It was originally presented by Fox et al. and has been implemented in indoor office robots. In this paper, the dynamic window approach is applied to the marine world by developing and extending it to manipulate vehicles in 3D marine environments. This algorithm is provided to enable efficient avoidance of obstacles and attainment of targets. Experiments conducted using the algorithm in MATLAB indicate that it is an effective obstacle avoidance approach for marine vehicles.

• Journal of Power Electronics
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• v.16 no.2
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• pp.654-665
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• 2016

The pumping of electric power from photovoltaic (PV) farms is normally carried out using transformers, which require heavy mounting structures and are thus costly, less efficient, and bulky. Therefore, transformerless schemes are developed for the injection of power into the grid. Compared with the H4 inverter topology, the H6 topology is a better choice for pumping PV power into the grid because of the reduced common mode current. This paper presents how the perturb and observe (P&O) algorithm for maximum power point tracking (MPPT) can be implemented in the H6 inverter topology along with the improved sinusoidal current injected to the grid at unity power factor with the average current mode control technique. On the basis of the P&O MPPT algorithm, a power reference for the present insolation level is first calculated. Maintaining this power reference and referring to the AC sine wave of bus bars, a sinusoidal current at unity power factor is injected to the grid. The proportional integral (PI) controller and fuzzy logic controller (FLC) are designed and implemented. The FLC outperforms the PI controller in terms of conversion efficiency and injected power quality. A simulation in the MATLAB/SIMULINK environment is carried out. An experimental prototype is built to validate the proposed idea. The dynamic and steady-state performances of the FLC controller are found to be better than those of the PI controller. The results are presented in this paper.