• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.721-724
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• 2014

Ultrasonic sensor is used widely for many applications because low cost, simple structure, and low restriction. There are many difficulties to recognize an object by use an ultrasonic sensor, because of low resolution, poor direction, and measurement error. To improve the these problem, we use the various kinds of sensor arrangement methods, large amount of sensor, and change the arrangement pattern of sensor. In this paper, to obtain the most basic parameters for pattern recognition such as distance, dimension of the object, an angle of the object, we get the improved results by use the intelligent calculation algorithm based on Neuro-Fuzzy. This method use the multifarious output voltage of ultrasonic sensor by simple electronic circuit.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.673-674
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• 2008

This paper presents a new navigation algorithm of an autonomous mobile robot with vision and IR sensors, Zigbee Sensor Network using fuzzy rules. We also show that the developed mobile robot with the proposed algorithm is navigating very well in complex unknown environments.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.635-641
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• 2008

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.293-298
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• 2010

The key of USN(Ubiquitous Sensor Network) technology is low power wireless communication technology and proper resource allocation technology for efficient routing. The distinguished resource allocation method is needed for efficient routing in sensor network. To solve this problems, we propose an algorithm that can be adopted in USN with making up for weak points of PQ and WRR in this paper. The proposed algorithm produces the control discipline by the fuzzy theory to dynamically assign the weight of WRR scheduler with checking the Queue status of each class in sensor network. From simulation results, the proposed algorithm improves the packet loss rate of the EF class traffic to 6.5% by comparison with WRR scheduling method and that of the AF4 class traffic to 45% by comparison with PQ scheduling method.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.66-72
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• 2016

In this paper, we proposed the clustering algorithm using fuzzy inference system for improving adaptability the cluster head selection of TEEN. The stochastic selection method cannot guarantee available of cluster head. Furthermore, because the formation of clusters is not optimized, the network lifetime is impeded. To improve this problem, we propose the algorithm that gathers attributes of sensor node to evaluate probability to be cluster head.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.2
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• pp.310-316
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• 2003

One of the significant subject in the automatic arc welding is to establish control system of the welding parameters for controlling bead geometry as a criterion to evaluate the quality of arc welding. This paper proposes an inference algorithm for bead geometry in CMA Welding using Neuro-Fuzzy algorithm. The characteristic welding parameters are measured by the circuit composed of hall sensor, voltage divider tachometer, etc. and then the bead geometry of each weld pool is calculated and detected by an image processing with CCD camera and a measuring with microscope. The relationships between the characteristic welding parameters and the bead geometry have been arranged empirically. From the result of experiments, membership functions and fuzzy rules are tuned and determined by the learning of neural network, and then the relationship between actual bead geometry and inferred bead geometry are concluded by fuzzy logic controller. In the applied inference system of bead geometry using Neuro-Fuzzy algorithm, the inference error percent is within -5%∼+4% in case of bead width, -10%∼+10% in bead height, -5%∼+6% in bead area, -10%∼+10% in penetration. Use of the Neuro-Fuzzy algorithm allows the CMA Welding system to evaluate the quality in bead geometry in real time as the welding parameters change.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.2
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• pp.95-101
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• 2018

This paper presents a collision free mobile robot navigation based on the fuzzy inference fusion model in unkonown environments using multi-ultrasonic sensor. Six ultrasonic sensors are used for the collision avoidance approach where CCD camera sensors is used for the trajectory following approach. The fuzzy system is composed of three inputs which are the six distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot's wheels, and three cost functions for the robot's movement, direction, obstacle avoidance, and rotation. For the evaluation of the proposed algorithm, we performed real experiments with mobile robot with ultrasonic sensors. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.3
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• pp.125-131
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• 2018

In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles using multi-ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Proceedings of the KIEE Conference
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• 2003.07e
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• pp.38-41
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• 2003

An Autonomous Mobile Robot(AMR) performs duty by sensing a recognized situation and controlling suitably. The existing algorithm has some advantages that it is possible to express the obstacle exactly and the robot is sensitive to the change of environment. However, this algorithm needs to control repeatedly according to the modelling and working environment that requires a great quantity of calculations. In this paper, We supplement shortcoming and designed direction algorithm of AMR using fuzzy controller. Fuzzy controller does not derive special quality spinning expression for system, and uses rules by value expressed by language. It is used extensively to non-linear, plant which mathematical modelling is difficult etc... Fuzzy control algorithm of AMR that is used by this research applies obstacle position, distance of obstacle, Progress direction of robot, speed of robot, Perception area of sensor, etc... by fuzzy control and decide steering angle of robot.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.514-517
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• 1995

A new stroke output control algorithm with a fuzzy logic for an electrohydraulic left ventricular assist device(EH-LVAD) was developed. The EH-LVAD pumps out blood from left atrium actively. Excessive suction of blood may cause fatal damage in left atrium. The LVAD has to provide a maximal stroke output without collapse of left atrium. In this study a new fuzzy algorithm for predicting and detecting suction and doing proper action on LVAD without using an extra pressure sensor but with bellows pressure signal and motor current signal is developed. The performance of the fuzzy control algorithm is demonstrated by the results from mock circulatory experiments.