• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.173-177
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• 1992

The Helicopter has a lot of flight modes. The most characteristic flight mode is Hovering. It enables the helicopter to be used in many situations. However, a helicopter has nonlinear dynamics so its mathematical modeling is very difficult. Hence it is not easy to control helicopter in hover. In this paper, RC model helicopter is selected as a plant. To stabilize the behavior of RC model helicopter, Fuzzy alogrithm is used as a controller and one camera is used as a sensor. To get proper Information from camera Image, three characteristic points are attatched to the helicopter and a position recognition algorithm is developed. Experiments are performed to stabilize 3 rotational motions synchronousely with fuzzy control algorithm. As a result, Fuzzy control represents better performances than the conventional PID control.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.1
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• pp.81-88
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• 2011

A real time monitoring system from a PC has been developed which can be accessed through transmitted data, which incorporates an established low powered transport system equipped with a single chip combined with wireless sensor network technology from a three-axis acceleration sensor. In order to distinguish between static posture and dynamic posture, the extracted parameter from the rapidly transmitted data needs differentiation of movement and activity structures and status for an accurate measurement. When results interpret a static formation, statistics referring to each respective formation, known as the K-mean algorithm is utilized to carry out a determination of detailed positioning, and when results alter towards dynamic activity, fuzzy algorithm (fuzzy categorizer), which is the relationship between speed and ISVM, is used to categorize activity levels into 4 stages. Also, the ISVM is calculated with the instrumented acceleration speed on the running machine according to various speeds and its relationship with kinetic energy goes through correlation analysis. With the evaluation of the proposed system, the accuracy level stands at 100% at a static formation and also a 96.79% accuracy with kinetic energy and we can easily determine the energy consumption through the relationship between ISVM and kinetic energy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.126-131
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• 2008

Wireless Sensor Network(WSNs) consists of small sensor nodes with sensing, computation, and wireless communication capabilities. It has new information collection scheme and monitoring solution for a variety of applications. Faults occurring to sensor nodes are common due to the limited resources and the harsh environment where the sensor nodes are deployed. In order to ensure the network quality of service it is necessary for the WSN to be able to detect the faulty sensors and take necessary actions for the reconstruction of the lost sensor data caused by fault as earlier as possible. In this paper, we propose an recursive PCA-based fault detection and lost data reconstruction algorithm for sensor networks. Also, the performance of proposed scheme was verified with simulation studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.49-54
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• 2011

In this paper, we represent a mobile robot which can recognize chemical odor, measure concentration, and track its source indoors. The mobile robot has the function of smell that can sort several gases in experiment such as ammonia, ethanol, and their mixture with neural network algorithm and measure each gas concentration with fuzzy rules. In addition, it can not only navigate to the desired position with vision system by avoiding obstacles but also transmit odor information and warning messages earned from its own operations to other nodes by multi-hop communication in wireless sensor network. We suggest the way of odor sorting, concentration measurement, and source tracking for a mobile robot in wireless sensor network using a hybrid algorithm with vision system and gas sensors. The experimental studies prove that the efficiency of the proposed algorithm for odor recognition, concentration measurement, and source tracking.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.71.3-71
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• 2001

Passive Stabilization method and active stabilization method are mainly used to comprise a control system of platform stabilizer. Passive Stabilization method has demerits because of size and weight except that control structure is simple while active stabilization method using sensors can reduce size and weight, it requires high sensor technique and control algorithm. In this paper, a stabilizing controller using adaptive fuzzy control technique and floating-point processor(DSP) is suggested.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.198-203
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• 2002

A fuzzy logic controller design technique is proposed to apply for the control of flexible system under irregular disturbance. The fuzzy rules of $\ulcorner$Mode Selecting Fuzzy Controller$\lrcorner$ are constructed using displacement, velocity information and modal characteristics of the system. The frequency information of flexible system is picked up from $\ulcorner$Mode Selecting Unit$\lrcorner$ based on Fast-Fourier transform algorithm. Experiment is conducted to verify the proposed theoretical approach, Piezo ceramic and laser accelerometer are used as actuator and sensor in the experiments respectively

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.434-443
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• 2008

This paper develops a novel automatic parking algorithm based on a fuzzy logic controller with the vehicle pose for the input and the steering rate for the output. It localizes the vehicle by using only external sensors - a vision sensor and ultrasonic sensors. Then it automatically learns an optimal fuzzy if-then rule set from the training data, using an evolutionary fuzzy system. Furthermore, it also finds the green zone for the ready-to-reverse position in which parking is possible just by reversing. It has been tested on a 4-wheeled Pioneer mobile robot which emulates the real vehicle.