• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1092-1098
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• 2007

Since one decade, the detection of HFPD (High frequency Partial Discharge) has been proposed as one of the effective method for the diagnosis of the power component under service in power grids. As a tool for HFPD detection, Metal Foil sensor based on the embedded technology has been commercialized for mainly power cable due to its advantages. Recently, for the on-site noise discrimination, several PA (Pulse analysis) methods have been reported and the related software, such as Neural Network and Fuzzy, have been proposed to separate the PD (Partial Discharge) signals from the noises since their wave shapes are completely different from each other. On the other hand, the relevant fundamental investigation has not yet clearly made while it is reported that the effectiveness of the current methods based on PA is dependant on the types of sensors. Moreover, regarding the identification of the vital defects introducible into the Power Cable, the direct identification of the nature of defects from the PD signals through Metal Foil coupler has not yet been realized. As a trial for solving above shortcomings, different types of software have been proposed and employed without any convincing probability of identification. In this regards, our novel algorithm 'PA Map' based on the pulse analysis is suggested to identify directly the defects inside the power cable from the HFPD signals which is output of the HFCT and metal foil sensors. This method enables to discriminate the noise and then to make the data analysis related to the PD signals. For the purpose, the HFPD detection and PA (Pulse Analysis) system have been developed and then the effect of noise discrimination has been investigated by use of the artificial defects using real scale mockup. Throughout these works, our system is proved to be capable of separating the small void discharges among the very large noises such as big air corona and ground floating discharges at the on-site as well as of identifying the concerned defects.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.17-26
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• 1998

Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But for the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. The time-of-flight(TOF) method generally used for distance measurement can not distinguish small object patterns of plane, corner or edge. To resolve the problem, an increased number of the sensors in the forms of a linear array or 2-dimensional array of the sensors has been used. Also better resolution has been obtained by shifting the array in several steps using mechanical actuators. Also simple patterns are classified based on analyzing signal reflections. In this paper we propose a method of a sensor array system with improved capability in pattern distinction using electronic circuits accompanying the sensor array, and intelligent algorithm based on neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. A set of different return signals from neighborhood sensors is manipulated to provide enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.359-364
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• 2011

The study uses department of the sensitivity and fuzzy reasoning, one of artificial intelligence algorithms, so that develop LED lighting system based on fuzzy reasoning for systematical control of the LED color temperature. In the area of sensitivity engineering, by considering the relation between color and emotion expressed as an adjective word, the corresponding sensitivity word can be determined, By taking into consideration the relation between the brain wave measured from the human brain and the color temperature, the preferred lesson subject can be determined. From the decision of the sensitivity word and the lesson subject, we adjust the color temperature of RGB (Red, Green, Blue) LED. In addition, by using the information of the latitude and the longitude from GPS(Global Positioning System), we can calculate the on-line moving altitude of sun. By using the sensor information of both temperature and humidity, we can calculate the discomfort index. By considering the altitude of sun as well as the value of the discomfort index, the illumination of W(white) LED and the color temperature of RGB LED can be determined. The (LED) sensitivity lighting control system is bulit up by considering the sensitivity word, the lesson subject, the altitude of sun, and the discomfort index The developed sensitivity lighting control system leads to more suitable atmosphere and also the enhancement of the efficiency of lesson subjects as well as business affairs.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.388-397
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• 2009

There are many tasks which require robotic manipulators interaction with environment. It consists of three control problems, i.e., position control, impact control and force control. The position control means the way of reaching to the environment. The moment of touching to the environment yields the impact control problem and the force control is to maintain the desired force trajectory after the impact with the environment. These three control problems occur in sequence, so each control algorithm can be developed independently. Especially for redundant manipulators, each of these three control problems has been important independent research topic. For example, joint torque minimization and impulse minimization are typical techniques for such control problems. The three control problems are considered as a single task in this paper. The position control strategy is developed to improve the performance of the task, i.e., minimization of the individual joint torques and impulse. Therefore, initial conditions of the impact control problem are optimized at the previous position control algorithm. Such a control strategy yields improved result of the impact control. Similarly, the initial conditions for the force control problem are indirectly optimized by the previous position control and impact control strategies. The force control algorithm uses the individual joint torque minimization concept. It also minimizes the force disturbances. The simulation results show the proposed control strategy works well.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.561-568
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• 2006

WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.571-579
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• 2005

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.233-242
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• 2018

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 feature point based on only R peak through optimal R wave. 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 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.

• Archives of design research
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• v.20 no.3 s.71
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• pp.203-214
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• 2007

This study proposes an information retrieval method reflecting the user's preferences based on the fuzzy set theory to develop information contents which support pedestrian's navigation. Firstly, the research evaluated subjects' preferences on commercial spaces set to a hypothetical destination. Also it surveyed the causal relationship between the visual characteristics and the emotional characteristics to propose methods of Navigation Knowledge Base (NKB). The NKB was composed of three elements; 1. the correlation model between emotional characteristics, 2. the causal relationship between visual characteristics and emotional characteristics, 3. the transformation model between visual characteristics and the physical characteristics. Secondly, this study classified the pedestrian's destination search into 4 types with his or her preferences and the time conditions limited during navigation. For each type it presented the Destination Search Algorithm (DSA). Finally, the research simulated the destination search in 4 navigation types using NKB and DSA and verified the availability of the information retrieval method reflecting pedestrian's preferences. In conclusion, the proposed information search method will be applied to reflect the user's preferences to develop information appliances.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1588-1593
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• 2005

In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant. Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator's knowledge for plant control. To perform fuzzy clustering, there are some coefficients to be determined and these kinds of studies have been performed over decades such as clustering indices. In this study, statistical indices were taken to calculate the number of clusters. Simultaneously, seed points were found out based on hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.155-164
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.