• Journal of Power Electronics
• /
• v.2 no.1
• /
• pp.1-10
• /
• 2002

An advanced active power filter for the compensation of instantaneous harmonic current components in nonlinear current load is presented in this paper. A novel signal processing technique using an adaptive neural network algorithm is applied for the detection of harmonic components generated by three-phase nonlinear current loads and this method can efficiently determine the instantaneous harmonic components in real time. The control strategy of the switching signals to compensate current harmonics of the three-phase inverter is also discussed and its switching signals are generated with the space voltage vector modulation scheme. The validity of this active filtering processing system to compensate current harmonics is substantiated on the basis of simulation results.

• The Bulletin of The Korean Astronomical Society
• /
• v.37 no.2
• /
• pp.123.1-123.1
• /
• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• The Bulletin of The Korean Astronomical Society
• /
• v.40 no.2
• /
• pp.36.2-36.2
• /
• 2015

We report the first detection of linearly polarized emission at an observing wavelength of 350 mum from the radio-loud active galactic nucleus 3C 279. We conducted polarization observations for 3C 279 using the SHARP polarimeter in the Caltech Submillimeter Observatory on 2014 March 13 and 14. For the first time, we detected the linear polarization with the degree of polarization of $13.3%{\pm}3.4%$ (3.9sigma) and the electric vector position angle (EVPA) of $34.^{\circ}7{\pm}5.^{\circ}6$. We also observed 3C 279 simultaneously at 13, 7, and 3.5 mm in dual polarization with the Korean very long baseline interferometry (VLBI) Network on 2014 March 6 (single dish) and imaged in milliarcsecond (mas) scales at 13, 7, 3.5, and 2.3 mm on March 22 (VLBI). We found that the degree of linear polarization increases from 10% to 13% at 13 mm to 350 mum and the EVPAs at all observing frequencies are parallel within < $10^{\circ}$ to the direction of the jet at mas scale, implying that the integrated magnetic fields are perpendicular to the jet in the innermost regions. We also found that the Faraday rotation measures RM are in a range of $-6.5{\times}102{\sim}-2.7{\times}103$ rad m-2 between 13 and 3.5 mm, and are scaled as a function of wavelength:| {RM}| ${\backslash}propto$ {lambda }-2.2. These results indicate that the millimeter and sub-millimeter polarization emission are generated in the compact jet within 1 mas scale and affected by a Faraday screen in or in the close proximity of the jet.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.9
• /
• pp.930-935
• /
• 2014

A Gabor filter is a linear filter used for edge detectionas frequency and orientation representations of Gabor filters are similar to those of the human visual system. In this thesis, we propose a pedestrian detection algorithm using a Gabor filter bank. In order to extract the features of the pedestrian, we use various image processing algorithms and data structure algorithms. First, color image segmentation is performed to consider the information of the RGB color space. Second, histogram equalization is performed to enhance the brightness of the input images. Third, convolution is performed between a Gabor filter bank and the enhanced images. Fourth, statistical values are calculated by using the integral image (summed area table) method. The calculated statistical values are used for the feature matrix of the pedestrian area. To evaluate the proposed algorithm, the INRIA pedestrian database and SVM (Support Vector Machine) are used, and we compare the proposed algorithm and the HOG (Histogram of Oriented Gradient) pedestrian detector, presentlyreferred to as the methodology of pedestrian detection algorithm. The experimental results show that the proposed algorithm is more accurate compared to the HOG pedestrian detector.

• Journal of Power Electronics
• /
• v.14 no.5
• /
• pp.967-979
• /
• 2014

This paper focuses on speed and current sensor fault detection and isolation (FDI) for induction motor (IM) drives. A new, accurate and high-efficiency FDI approach is proposed so that a system can continue operating with good performance even in the presence of speed sensor faults, current sensor faults or both. The proposed three paralleled adaptive observers are capable of current sensor fault detection and localization. By using observers, the rotor flux and rotor speed can be estimated which allows the system to run under the speed sensorless vector control mode when a speed sensor fault occurs. In order to detect speed sensor faults, a threshold-based scheme is proposed. To verify the feasibility and effectiveness of the proposed FDI strategy, experiments are carried out under different conditions based on a dSPACE DS1104 induction motor drive platform.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.6
• /
• pp.21-28
• /
• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.21 no.11
• /
• pp.997-1004
• /
• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• The KIPS Transactions:PartC
• /
• v.14C no.1 s.111
• /
• pp.1-6
• /
• 2007

In order to operate a secure network, it is very important for the network to raise positive detection as well as lower negative detection for reducing the damage from network intrusion. By using SVM on the intrusion detection field, we expect to improve real-time detection of intrusion data. However, due to classification based on calculating values after having expressed input data in vector space by SVM, continuous data type can not be used as any input data. Therefore, we present the hybrid model between SVM and decision tree method to make up for the weak point. Accordingly, we see that intrusion detection rate, F-P error rate, F-N error rate are improved as 5.6%, 0.16%, 0.82%, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.50 no.1
• /
• pp.21-30
• /
• 2022

The restart of scramjet engine is almost impossible in case its unstart happens during engine operation. Therefore, it is essential to prognosticate the scramjet engine unstart phenomena. A numerical simulation of a scramjet engine is conducted to investigate the unstart process of the scramjet engine by adjusting the backpressure at the isolator outlet to the engine analysis environment. The start and unstart of the engine are identified by applying a support vector machine (SVM) through the data measured by wall pressure so that the locations of the pressure sensors most suitable for the unstart detection are selected. And the operation conditions in which the engine is avoid to be unstarted are quantified to perceive the safety margin.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.239-245
• /
• 2004

A hue-based noise-tolerant corner detector is proposed for the exact detection of the real corners in spite of the shadows and random noise. Based on the fact that the hue gradient at the border of the opaque objects' shadow is smaller than the intensity gradient in HSI (hue-saturation-intensity) color space, the effects of shadow are eliminated by introducing the hue-weighted combination of vector gradient to the proposed corner detector. Furthermore, the proposed corner detector is robust to random noise by offsetting the contribution to the corner candidate when the polarities of the color gradients of the pixel pairs are out of phase each other. Results of the experiment show that the proposed corner detector can effectively detect the real corners.