• The Journal of the Acoustical Society of Korea
• /
• v.31 no.8
• /
• pp.532-541
• /
• 2012

An underwater transient signal is distinguished from an ambient noise. Database for the underwater transient signal is required since the underwater transient signal shows various characteristics depending on acoustic features. In the paper, hence, sound mask-filter was applied to extract the transient signals which exist temporally and locally in the ocean. The standard signal was chosen and cross-correlated with the raw signal. A mask-filter for a transient signal was obtained using the threshold which was decided by the maximum likelihood method in the envelope of the cross-correlated signal. Using the sound mask-filter, the transient signal of a sea catfish {Galeichthys felis (Linnaeus)} was extracted from the underwater ambient noise. Similarly, the man-made signal was added into the noise and it was extracted by the same method. We also have demonstrated the significance of the transient signal through comparing the extracted signals depending on the standard signal. In the results, the proposed method, sound mask-filtering, could be utilized as a database construction of the transient signals in underwater noise. Particularly, this study would be useful to extract the wanted signal from arbitrary signals.

• Journal of Ocean Engineering and Technology
• /
• v.26 no.4
• /
• pp.57-63
• /
• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• Journal of Ocean Engineering and Technology
• /
• v.28 no.6
• /
• pp.552-559
• /
• 2014

In general, the number of underwater transient signals is very limited for research on automatic recognition. Data-dependent feature extraction is one of the most effective methods in this case. Therefore, we suggest WPCC (Wavelet packet ceptsral coefficient) as a feature extraction method. A wavelet packet best tree for each data set is formed using an entropy-based cost function. Then, every terminal node of the best trees is counted to build a common wavelet best tree. It corresponds to flexible and non-uniform filter bank reflecting characteristics for the data set. A GMM (Gaussian mixture model) is used to classify five classes of underwater transient data sets. The error rate of the WPCC is compared using MFCC (Mel-frequency ceptsral coefficients). The error rates of WPCC-db20, db40, and MFCC are 0.4%, 0%, and 0.4%, respectively, when the training data consist of six out of the nine pieces of data in each class. However, WPCC-db20 and db40 show rates of 2.98% and 1.20%, respectively, while MFCC shows a rate of 7.14% when the training data consists of only three pieces. This shows that WPCC is less sensitive to the number of training data pieces than MFCC. Thus, it could be a more appropriate method for underwater transient recognition. These results may be helpful to develop an automatic recognition system for an underwater transient signal.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.27 no.3
• /
• pp.291-298
• /
• 2016

In this paper, we studied about the radiated emission of EFT(Electrical Fast Transient)/Burst signal, referenced IEC-61000-4-4 of the international EMC standard and its effect on EUT(Equipment Under the Test) when EFT/Burst signal (Conducted noise) induced on a power cable to EUT. Firstly, we set up test sample by the standard and +4 kV standard signal is induced by EFT/Burst signal generator with 1 m length power cable for 1 minute. Consequently, we found not only induced EFT/Burst signal along power line, but also directly radiated emission signal from EFT/Burst signal generator (Radiated emission noise of EFT/Burst). The radiated emission signal of EFT/Burst was 30 dB which is higher than limit of CISPR 22 which is international EMC standard. Moreover, we measured the electric field strength by changing distance from maximum radiated emission point to electric field strength tester. As a result, this electric field strength was 23 V/m which is higher than international EMC standard test level(CISPR 24). Therefore, it was confirmed that the probability of malfunction may be high when radiated emission of EFT/Burst noise exists near the EUT.

• Journal of the Society of Naval Architects of Korea
• /
• v.57 no.3
• /
• pp.175-181
• /
• 2020

For the success of a structural integrity management, it is essential to acquire structural response data at some critical locations with limited number of sensors. In this study, the structural response of numerical model was estimated by data fusion approach based on the Kalman filter known as stochastic recursive filter. Firstly, transient direct analysis was conducted to calculate the acceleration and strain of the numerical standing beam model, then the noise signals were mixed to generate the numerical measurement signals. The acceleration measurement signal was provided to the Kalman filter as an information on the external load, and the displacement measurement, which was transformed from the strain measurement by using strain-displacement conversion relationship, was provided into the Kalman filter as an observation information. Finally, the Kalman filter estimated the displacement by combining both displacements calculated from each numerically measured signal, then the estimated results were compared with the results of the transient direct analysis.

• Journal of Positioning, Navigation, and Timing
• /
• v.10 no.3
• /
• pp.229-234
• /
• 2021

In this paper, a study was conducted on the power amplifier control required to design an eLoran transmitter system using a low-height antenna. The eLoran transmitter developed during the eLoran technology development project conducted in Korea used a small 35 m antenna due to the difficulty of securing a site for antenna installation. This antenna height is very low compared to the height of 750 m which is required for eLoran 100 kHz signal transmission without any radiation loss. In the case of using such a small antenna, not only the radiation efficiency of the transmission is lowered, but also the power module control must be performed more precisely in order to transmit the eLoran standard signal. The equivalent RLC circuit of the transmitter system was implemented and transient analysis was conducted to derive the input required voltage for satisfying the output requirement. The voltage waveform was also generated by the RLC circuit analysis to generate the eLoran signal. Furthermore, we suggest power width modulation method to control eLoran power amplifier module more sophisticatedly.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.7
• /
• pp.209-220
• /
• 2014

In this paper, we analyze the discriminability of features for the classification of transient signals with an ambient noise in a shallow water. For the classification of the transient signals, robust features for the variance of a noise are required due to a low SNR under a marine environment. In the modelling the ambient noise in shallow water, theoretical noise model, Wenz's observation data from the shallow water, and Yule-walker filter are used. Discrimination of each feature of the transient signals with an additive ambient noise is analyzed by utilizing a Fisher score. As the analysis of a classification accuracy about the transient signals of 24 classes using the selected features with a high discriminability, the features selected in the environment without a noise relatively have a good classification accuracy. From the analyzed results, we finally select a total 16 features out of 28 features. The recognition using the selected features results in the classification accuracy of 92% in SNR 20dB using Multi-class SVM.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.6
• /
• pp.201-208
• /
• 2014

In this paper, we propose the classifier structure design algorithm using DTW. Proposed algorithm uses DTW result to design the binary tree architecture based on the SVM which classify the multi-class data. Design the binary tree architecture for Support Vector Machine(SVM-BTA) using the threshold criterion calculated by the sum columns in square matrix which components are the reference data from each class. For comparison the performance of the proposed algorithm, compare the results of classifiers which binary tree structure are designed based on database and k-means algorithm. The data used for classification is 333 signals from 18 classes of underwater transient noise. The proposed classifier has been improved classification performance compared with classifier designed by database system, and probability of detection for non-biological transient signal has improved compare with classifiers using k-means algorithm. The proposed SVM-BTA classified 68.77% of biological sound(BO), 92.86% chain(CHAN) the mechanical sound, and 100% of the 6 kinds of the other classes.