• Progress in Superconductivity
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• v.3 no.1
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• pp.49-55
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• 2001

Josephson junction arrays were fabricated by DC magnetron sputtering, self-aligning and reactive ion etching technique. The Al native oxide, formed by thermal oxidation, was used as the tunneling barrier of Nb/$Al-A1_2$$O_3$Nb trilayer. The arrays have 2,000 Josephson junctions with the area of $14\mu\textrm{m}$ $\times$ $46\mu\textrm{m}$. The gap voltages were in the range of 2.5 ~2.6 mV and the spread of critical current was $\pm$11~14%. When operated at 70~94 ㎓, the arrays generated zero-crossing steps up to 2.1~2.4 V. To improve transmission of microwave power and prevent diffusion of oxygen into Nb ground-plane while depositing $SiO_2$dielectric, we applied a plasma nitridation process to the Nb ground-plane. The microwave power was well propagated in Josephson junction arrays with nitridation. The difference in microwave transmission 7an be interpreted by the surface impedance change depending on nitridation.

• Journal of Ocean Engineering and Technology
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• v.22 no.1
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• pp.30-36
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• 2008

Underwater ambient noise originating from geophysical, biological, and man-made acoustic sources contains information on the source and the ocean environment. Such noise affectsthe performance of sonar equipment. In this paper, three steps are used to identify the ambient noise source, detection, feature extraction, and similarity measurement. First, we use the zero-crossing rate to detect the ambient noisesource from background noise. Then, a set of feature vectors is proposed forthe ambient noise source using the Hilbert-Huang transform and the Karhunen-Loeve transform. Finally, the Euclidean distance is used to measure the similarity between the standard feature vector and the feature vector of the unknown ambient noise source. The developed algorithm is applied to the observed ocean data, and the results are presented and discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.914-921
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• 2014

In this paper, an algorithm is suggested to detect an offset angle of the absolute rotor position sensor after the initial assembly of a PMSG. Unlike previous studies in a stationary state, this one is not designed to detect an electrical angle but rather the absolute position of the rotor is detected while operating the generator. Also,a position sensor, current sensors and voltage sensor were used to ensure reliability. This technique completes the detection of the sensor offset in two steps. In the first step, a zero-crossing of the EMF is measured using a voltage sensor to detect the electrical angle offset when the alternator is actuated by the engine. In the second step, a high frequency current is injected along the d-axis on-line during the control of the generation, eventually to obtain the inductance using a DFT (Discrete Fourier Transform), and then to ultimately extract the final electrical angle offset through the comparison of the inductance magnitude. The suggested algorithm was validated with PSIM simulation and, furthermore, was tested with actual experiments on a dynamometer.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.193-197
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• 2012

This paper focuses on identifying which appliance is currently operating by analyzing electrical load signature for home energy monitoring system. The identification framework is comprised of three steps. Firstly, specific appliance features, or signatures, were chosen, which are DC (Duty Cycle), SO (Slope of On-state), VO (Variance of On-state), and ZC (Zero Crossing) by reviewing observations of appliances from 13 houses for 3 days. Five appliances of electrical rice cooker, kimchi-refrigerator, PC, refrigerator, and TV were chosen for the identification with high penetration rate and total operation-time in Korea. Secondly, K-NN and Naive Bayesian classifiers, which are commonly used in many applications, are employed to estimate from which appliance the signatures are obtained. Lastly, one of candidates is selected as final identification result by majority voting. The proposed identification frame showed identification success rate of 94.23%.

• Proceedings of the IEEK Conference
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• summer
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.