• Proceedings of the KIPE Conference
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• 2008.10a
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• pp.199-201
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• 2008

In this paper proposed the solar tracking system to use direct fuzzy control order to increase an output of the PV (Photovoltaic) array. The solar tracking system operated two DC motors driving by signal of photo sensor. The control of dual axes is not an easy task due to nonlinear dynamics and unavailability of the parameters. Recently, artificial intelligent control of the fuzzy control, neural-network and genetic algorithm etc. have been studied. The fuzzy control made a nonlinear dynamics to well perform and had a robust and highly efficient characteristic about a parameter variable as well as a nonlinear characteristic. Hence the fuzzy control was used to perform the tracking system after comparing with error values of setting-up, nonlinear altitude and azimuth. In this paper designed a DFC(Direct Fuzzy Control)controller for improving output of PV array and evaluated comparison with efficient of conventional PI controller. The data which were obtained by experiment were able to show a validity of the proposed controller.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.28-36
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• 2006

In this paper, an algorithm to classify the 4 motions of arm and a control system to position control the prosthesis are studied. To classify the 4 motions, we use flex sensors which is electrical resistance type sensor that can measure warp of muscle. The flex sensors are attached to the biceps brchii muscle and coracobrachialis muscle and the sensor signals are passed the sensing system. 4 motion of the forearm - flexion and extension, the pronation and supination are classified from this. Also position of forearm is measured from the classified signals. Finally, A two D.O.F prosthesis arm with RC servo-motor is designed to verify the validity of the algorithm. At this time, fuzzy controller is used to reduce the position error by rotary inertia and noise. From the experiment, the position error had occurred within about 5 degree.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1392-1401
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• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.845-858
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• 2020

This paper proposes a modified end-to-end secure low energy adaptive clustering hierarchy (ME-LEACH) algorithm for enhancing the lifetime of a wireless sensor network (WSN). Energy limitations are a major constraint in WSNs, hence every activity in a WSN must efficiently utilize energy. Several protocols have been introduced to modulate the way a WSN sends and receives information. The end-to-end secure low energy adaptive clustering hierarchy (E-LEACH) protocol is a hierarchical routing protocol algorithm proposed to solve high-energy dissipation problems. Other methods that explore the presence of the most powerful nodes on each cluster as cluster heads (CHs) are the sparsity-aware energy efficient clustering (SEEC) protocol and an energy efficient clustering-based routing protocol that uses an enhanced cluster formation technique accompanied by the fuzzy logic (EERRCUF) method. However, each CH in the E-LEACH method sends data directly to the base station causing high energy consumption. SEEC uses a lot of energy to identify the most powerful sensor nodes, while EERRCUF spends high amounts of energy to determine the super cluster head (SCH). In the proposed method, a CH will search for the nearest CH and use it as the next hop. The formation of CH chains serves as a path to the base station. Experiments were conducted to determine the performance of the ME-LEACH algorithm. The results show that ME-LEACH has a more stable and higher throughput than SEEC and EERRCUF and has a 35.2% better network lifetime than the E-LEACH algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.1
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• pp.1-10
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• 2003

This paper is concerned with the fuzzy control scheme and PID controller for the vibration suppression control of a cantilever beam equipped with a laser sensor and an electromagnetic actuator. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriate PID gains in nonlinear systems and systems with time variant characteristic and so on. In this paper, we design the fuzzy based PID controller of which output gains are adjusted automatically and the designed controller is applied to active vibration control of a cantilever beam using electromagnetic actuator with strong nonlinearity. The tuning PID parameters of proposed controller are determined by using Fuzzy algorithm. Effectiveness and performance of the designed controller are verified by both simulation and experiment results. Experimental results demonstrate that better control performance can be achieved in comparison with the PID cotroller.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.93-100
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• 2007

The mobile robots to rescue a life in a disaster area and to explore planets demand high mobility as well as recognition of the environment. To avoid unknown obstacles exactly in unknown environment, accurate sensing is required. This paper proposes a sensor fusion to recognize unknown obstacles accurately by using low-cost sensors. Ultrasonic sensors and infrared sensors are used in this paper to avoid obstacles. If only one of these sensors is used alone, it is not useful fer the mobile robots to complete their tasks in the real world since the surrounding environment in the real world is complex and composed of many kinds of materials. So infrared sensor may not recognize transparent or reflective obstacles and ultrasonic sensor may not recognize narrow obstacles, far example, columns of small diameter. Therefore, I selected six ultrasonic sensors and five infrared sensors to detect obstacles. Then, I fused ultrasonic sensors with infrared sensors in order that both advantages and disadvantages of each sensor are utilized together. In fusing sensors, fuzzy algorithm is used to cope with the uncertainties of each sensor. TAMRY which is terrain-adaptive mobile robot is used as the mobile robot for experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.28-36
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• 2020

As many types of magnetic sensors are widely applied in various industries, the analysis and processing of magnetic sensor data need to be accurate. On the other hand, owing to the complexity of the magnetic field line caused by a moving magnet, the magnetic data generated by magnetic sensors are unpredictably nonlinear. Many industry systems using magnetic sensors have struggled with the nonlinear nature of magnetic sensor data. To reduce the effect of the nonlinearity, they have the target objects fixed firmly. Therefore, to collect accurate and reliable data, considerable efforts have been made to resolve the issues with the expensive tools and systems required. Through this paper, to tackle the issues, the data analysis and methodologies, including intelligent algorithms, are presented for the wrist rehabilitation system using magnetic sensors while being implemented without using expensive tools or systems. On processing magnetic sensor data, this paper adopted an intelligent algorithm, fuzzy logic, and compared the performance of other algorithms for comparison.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.119-125
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• 2012

This paper proposes a model for a chainless power assistor system(PAS) that can provide the required power based upon operational status by designing a chainless electric bicycle that can be substituted for a general chain type bicycle and which is also an environmentally friendly means of transportation. This paper designed a fuzzy control algorithm that can intelligently examine operational status through the stopping force sensor of a chainless intelligently auxiliary power system it and also have the power of an auxiliary power system to be controlled by the vehicle's operational status. This paper designed an intelligent electric bicycle that provides auxiliary power to a general bicycle system relying only upon the stepping force of a human and systems to provide auxiliary power to the intelligent chainless bicycle model designing presented.

• Journal of Sensor Science and Technology
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• v.22 no.2
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• pp.162-173
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• 2013

The purpose of this paper is to classify VOC gases by emulating the characteristics found in biological olfaction. For this purpose, we propose new signal processing method based a polymeric chemical sensor array consisting of 4096 sensors which is created by NEUROCHEM project. To remove unstable sensors generated in the manufacturing process of very large scaled chemical sensor array, we used discrete wavelet transformation and cosine similarity. And, to remove the supernumerary redundancy, we proposed the method of selecting candidates of representative sensor representing sensors with similar features by Fuzzy c-means algorithm. In addition, we proposed an improved algorithm for selecting representative sensors among candidates of representative sensors to better enhance classification ability. However, Classification for very large scaled sensor array has a great deal of time in process of learning because many sensors are used for learning though a redundancy is removed. Throughout experimental trials for classification, we confirmed the proposed method have an outstanding classification ability, at transient state as well as steady state.