• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.69-71
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• 2005

This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.36-39
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• 2006

This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurement data. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of PMU for monitoring power system dynamics. The methodology for fast monitoring of the system is initiated locally which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper briefly describes the mathematical background of SVM, and explains the procedure for fast classification of voltage stability using the SVM algorithm. To illustrate the effectiveness of the classifier, this paper includes numerical examples with a 11-bus test system.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.779-784
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• 2004

This paper describes a sonic polygonal multiple reflection range sensor (SPMRS), which uses multiple reflection properties usually ignored in ultrasonic sensors as disturbances or noises. Targets such as a plane, corner, edge, or cylinder in indoor environments can easily be detected by the multiple reflection patterns obtained with a SPMRS system. Target classification and feature data extraction, such as distance and azimuth to the target, are computed simultaneously by considering the geometrical relationships between the detected targets, and finally the environment model is generated by refining the detected targets. In addition, the narrow field of view of a sonar range sensor is increased and the scanning time is reduced by active motion of the SPMRS stepping servomechanism.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.487-489
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• 2005

This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and In an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.830-835
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• 2003

This paper describes a sonic polygonal multiple reflection range sensor (SPMRS), which uses multiple reflection properties usually ignored in ultrasonic sensors as disturbances or noises. Targets such as a plane, corner, edge, or cylinder in indoor environments can easily be detected by the multiple reflection patterns obtained with a SPMRS system. Target classification and feature data extraction, such as distance and azimuth to the target, are computed simultaneously by considering the geometrical relationships between the detected targets, and finally the environment model is generated by refining the detected targets. In addition, the narrow field of view of a sonar range sensor is increased and the scanning time is reduced by active motion of the SPMRS stepping servomechanism.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.24 no.2
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• pp.227-236
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• 1994

The purpose of this study was to observe mandibular condyle shape in an asymptomatic population. In order to carry out this study, 96 temporomandibular joints in 48 adults(22 males, 26 females), who were asymptomatic for temporomandibular disturbances and had no history of prosthodontic or orthodontic treatments, were selected, and radiographed using the Sectograph(Denar Co., U.S.A.) for lateral and frontal individualized corrected TMJ tomograph and submentovertex radiograph. Mandibular condyles were classified morphologically, and measured medioateral and anteroposterior dimensions and condylar angulation. The obtained results were as follows. 1. In the classification of condyle shape on lateral tomographs, 94.8% were convex type and 5.2% were angled type. 2. In the classification of condyle shape on frontal tomographs, 45.3% were convex type, 32.0% were round type, 16.0% were flat type, and 6.7% were angled type. 3. In the classification of condyle shape on submentovertex radiographs, 34.5% were flat-convex type, 22.9% were flat-flat type, 20.8% were concave-convex type, 19.8% were convex-convex type, and 1.0% were concave-flat type and convex-flat type. Concave-concave type, convex-concave type, and flat-concave type were not observed. 4. The average mediolateral legth of the condyle was 19.3㎜ and the average anteroposterior length was 9.4㎜. The average angle between the long axis of condyle and the coronal plane made on submentovertex view was 19.6 degrees.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.637-650
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• 2007

A new on-line fault detection and isolation(FDI) scheme has been proposed for engines using an adaptive neural network classifier; this paper investigates the robustness of this scheme by evaluating in a wide range of operational modes. The neural classifier is made adaptive to cope with the significant parameter uncertainty, disturbances, and environmental changes. The developed scheme is capable of diagnosing faults in the on-line mode and can be directly implemented in an on-board diagnosis system(hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes, including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all changes occurring simultaneously. The evaluations are performed using a mean value engine model(MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.348-357
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• 2008

The classification of the effect of ionospheric disturbances on the radio occultation signal amplitude has been introduced based on an analysis of more than 2000 seances of radio occultation measurements per formed with the help of the CHAMP German satellite. The dependence of the histograms of variations in the radio occultation signal amplitude on the IMF variation index has been revealed. It has been indicated that it is possible to introduce the radio occultation index characterizing the relation between ionospheric disturbances and solar activity. An amplitude radio occultation (RO) method is proposed to study connection between the ionospheric and solar activity on a global scale. Sporadic amplitude scintillation observed in RO experiments contain important information concerning the seasonal, geographical, and temporal distributions of the ionospheric disturbances and depend on solar activity. The probability of strong RO amplitude variations (RO $S_4$ index greater than 0.2) in the CHAMP RO signals diminishes sharply with the weakening of solar activity from 2001 to 2008. The general number of RO events with strong amplitude variations can be used as an indicator of the ionospheric activity. We found that during 2001-2008 the daily globally averaged RO $S_{4a}$ index depends essentially on solar activity. The maximum occurred in January 2002, minimum has been observed in summer 2008. Different temporal behavoir of $S_{4a}$ index has been detected for polar (with latitude greater than $60^{\circ}$) and low latitude (moderate and equatorial) regions. For polar regions $S_{4a}$ index is slowly decreasing with solar activity. In the low latitude areas $S_{4a}$ index is sharply oscillating, depending on the solar ultraviolet emission variations. The different geographical behavoir of $S_{4a}$ index indicates different origin of ionospheric plasma disturbances in polar and low latitude areas. Origin of the plasma disturbances in the polar areas may be connected with influence of solar wind, the ultraviolet emission of the Sun may be the main cause of the ionospheric irregularities in the low latitude zone. Therefore, the $S_{4a}$ index of RO signal is important radio physical indicator of solar activity.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.712-714
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• 2003

The modifications taking place within the alluvial plains impart a larger extent of disturbances to hydrologic systems. The objective of the present investigation is to detect the sensitivity of multi-temporal image data from Landsat TM (Thematic Mapper) for finding out the land-cover/land-use changes associated with alluvial low land. The eastern coast of Chiba Prefecture, Japan, forms a very important geographic unit owing to the existence of a unique alluvial landform. The alluvial plain occupied in the study area is widely known as 'Kujukuri Plain'. The TM images have been classified by means of maximum likelihood supervised classifier and the extent of changes has been estimated.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.774-775
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• 2011

Recently, by increasing of devices which are sensitive to power quality, microprocessor and power electronics, the deterioration of power quality is accelerated. Accordingly, various methods for analysis, detection, compensation and countermeasure on power quality are proposed. For this, the study of distinction of various disturbances and fault which is occurred in power system is necessary to prior. Therefore, in this paper, the classification algorithm among steady state and single line to ground fault which is commonly occurred fault is proposed using S-Transform.