• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.3
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• pp.227-234
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• 2012

This paper describes the remote monitoring of breaking ocean waves generated by Typhoon Nabi, whose name means butterfly in Korean, using a marine X-band radar in the Yongho Man, Busan, Korea. The basic purpose of this study is to investigate the dynamic behavior and to estimate the periods of breaking waves across the surf zone from radar image sequences. In these experiments, the land-based radar system imaged the inshore zone of three miles from the coastline to a isobath of 30 meters. The wave period and the dominant wave direction for breaking ocean waves extracted directly from radar image sequences were 157.4 meters and 298 degrees, respectively. However, the result calculated quantitatively by the continuous wavelet transform (CWT) showed that the period of breaking waves was 154.3 meters. The average difference in breaking wave periods between the value extracted by using EBRL (electronic bearing and range line) of radar and the calculated value by CWT was 3.1 meters, showing that the CWT method is also accurate. These results suggest that a marine X-band radar system is a viable method of monitoring the breaking ocean waves.

• Journal of Communications and Networks
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• v.12 no.2
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• pp.168-173
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• 2010

Error concealment techniques are significant due to the growing interest in imagery transmission over error-prone channels. This paper presents a spatial error concealment technique for losslessly compressed images using least significant bit (LSB)-based data hiding to reconstruct a close approximation after the loss of image blocks during image transmission. Before transmission, block description information (BDI) is generated by applying quantization following discrete wavelet transform. This is then embedded into the LSB plane of the original image itself at the encoder. At the decoder, this BDI is used to conceal blocks that may have been dropped during the transmission. Although the original image is modified slightly by the message embedding process, no perceptible artifacts are introduced and the visual quality is sufficient for analysis and diagnosis. In comparisons with previous methods at various loss rates, the proposed technique is shown to be promising due to its good performance in the case of a loss of isolated and continuous blocks.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.120-121
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• 2006

In this paper, We have designed a face recognition system based on the embedded Linux. This paper has an aim in embedded system to recognize the face more exactly. At first, the contrast of the face image is adjusted with lightening compensation method, the skin and lip color is founded based on YCbCr values from the compensated image. To take advantage of the method based on feature and appearance, these methods are applied to the eyes which has the most highly recognition rate of all the part of the human face. For eyes detecting, which is the most important component of the face recognition, we calculate the horizontal gradient of the face image and the maximum value. This part of the face is resized for fitting the eye image. The image, which is resized for fit to the eye image stored to be compared, is extracted to be the feature vectors using the continuous wavelet transform and these vectors are decided to be whether the same person or not with PNN, to miminize the error rate, the accuracy is analyzed due to the rotation or movement of the face. Also last part of this paper we represent many cases to prove the algorithm contains the feature vector extraction and accuracy of the comparison method.

• Journal of IKEEE
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• v.26 no.2
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• pp.319-328
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• 2022

Bearing plays an important role in the operation of most machinery, Therefore, when a defect occurs in the bearing, a fatal defect throughout the machine is generated. In this reason, bearing defects should be detected early. In this paper, we describe a method using Convolutional Neural Networks (SN-CNNs) based on continuous wavelet transformations and Switchable normalization for bearing defect detection models. The accuracy of the model was measured using the Case Western Reserve University (CWRU) bearing dataset. In addition, batch normalization methods and spectrogram images are used to compare model performance. The proposed model achieved over 99% testing accuracy in CWRU dataset.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.10
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• pp.981-989
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• 2011

In this paper, we performed radar target classification for the five scale models using ultra-wideband measured signal. In order to compare the performance, the 2 GHz(2~4 GHz), 4 GHz(2~6 GHz), and 6 GHz(2~8 GHz) bandwidth were used. Short time Fourier transform(STFT) and continuous wavelet transform(CWT) are used for target feature extraction. Extracted feature vectors are used as input for the multi-layerd perceptron(MLP) neural network classifier. The results show that as the bandwidth is wider, the performance is better.

• Computers and Concrete
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• v.8 no.5
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• pp.563-581
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• 2011

The signal-based acoustic emission (AE) characterization of concrete fracture process utilizing home-programmed AE monitoring system was performed for three kinds of static loading tests (Cubic-splitting, Direct-shear and Pull-out). Each test was carried out to induce a distinct fracture mode of concrete. Apart from monitoring and recording the corresponding fracture process of concrete, various methods were utilized to distinguish the characteristics of detected AE waveform to interpret the information of fracture behavior of AE sources (i.e. micro-cracks of concrete). Further, more signal-based characters of AE in different stages were analyzed and compared in this study. This research focused on the relationship between AE signal characteristics and fracture processes of concrete. Thereafter, the mode of concrete fracture could be represented in terms of AE signal characteristics. By using cement-based piezoelectric composite sensors, the AE signals could be detected and collected with better sensitivity and minimized waveform distortion, which made the characterization of AE during concrete fracture process feasible. The continuous wavelet analysis technique was employed to analyze the wave-front of AE and figure out the frequency region of the P-wave & S-wave. Defined RA (rising amplitude), AF (average frequency) and P-wave & S-wave importance index were also introduced to study the characters of AE from concrete fracture. It was found that the characters of AE signals detected during monitoring could be used as an indication of the cracking behavior of concrete.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.98-105
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• 2003

We propose a new technique of deinterleaving multiple radar pulse sequences by means of genetic algorithm for threat identification in electronic warfare(EW) system. The conventional approaches based on histogram or continuous wavelet transform are so deterministic that they are subject to failing in detection of individual signal characteristics under real EW signal environment that suffers frequent signal missing, noise, and counter-EW signal. The proposed algorithm utilizes the probabilistic optimization procedure of genetic algorithm. This method, a time-of-arrival(TOA) only strategy, constructs an initial chromosome set using the difference of TOA. To evaluate the fitness of each gene, the defined pulse phase is considered. Since it is rare to meet with a single radar at a moment in EW field of combat, multiple solutions are to be derived in the final stage. Therefore it is designed to terminate genetic process at the prematured generation followed by a chromosome grouping. Experimental results for simulated and real radar signals show the improved performance in estimating both the number of radar and the pulse repetition interval.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.35-44
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• 2009

The emerging notion of data stream has brought many new challenges to the research communities as a consequence of its conceptual difference with conventional concepts of just data. One typical example is data stream processing in sensor networks. The range of data processing considerations in a sensor network is very wide, from physical resource restrictions such as bandwidth, energy, and memory to the peculiarities of query processing including continuous and specific types of queries. In this paper, as one of the physical constraints in data stream processing, we consider the problem of limited memory and propose a new scheme for data stream reduction based on the Principal Component Analysis (PCA) technique. PCA can transform a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables. We adapt PCA for the data stream of a sensor network assuming the cooperation of a query engine (or application) with a network base station. Our method exploits the spatio-temporal correlation among multiple measurements from different sensors. Finally, we present a new framework for data processing and describe a number of experiments under this framework. We compare our scheme with the wavelet transform and observe the effect of time stamps on the compression ratio. We report on some of the results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.12
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• pp.1460-1466
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• 2010

In this paper, we analyzed the performance of radar target classification using the monostatic and bistatic radar cross section(RCS) for four different wire targets. Short time Fourier transform(STFT) and continuous wavelet transform (CWT) were used for feature extraction from the monostatic RCS and the bistatic RCS of each target, and a multi-layered perceptron(MLP) neural network was used as a classifier. Results show that CWT yields better performance than STFT for both the monostatic RCS and the bistatic RCS. And, when STFT was used, the performance of the bistatic RCS was slightly better than that of the monostatic RCS. However, when CWT was used, the performance of the monostatic RCS was slightly better than that of the bistatic RCS. Resultingly, it is proven that bistatic RCS is a good cadndidate for application to radar target classification in combination with a monostatic RCS.