• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.7 no.3
• /
• pp.95-99
• /
• 2002

3.5KHz subbottom profiling systems are useful for delineating of shallow (up to 10~100m below the sea bottom) geological structure. These systems are generally used to image geological structures with less than 1m of vertical resolution. However swell in the sea is quite often higher than 1m, causing degradation in the quality of the 3.5KHz subbottom profiles. In this paper, we show the quality of digitally recorded data can be enhanced by the suppression of swell effect. Prior to suppression of swell effect, sea bottom detection procedure was applied using the characteristics that the amplitude of sea bottom reflection is high. To suppress the swell effect, we applied moving average method and high-cut filtering method using the extracted water depth of adjacent traces. Acceptable results were obtained from both methods. In the case of bad quality data or shallow data interfered with direct wave, the suppression of swell effect is difficult due to incorrect sea bottom detection.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.11C
• /
• pp.1076-1084
• /
• 2009

We propose an ATI (Adaptive Tone Injection) scheme based on clipping noise for PAPR (Peak-to-Average Power Ratio) reduction of OFDM (Orthogonal Frequency Division Multiplexing) signals. The proposed scheme is composed of three steps: clipping, tone selection, and TI procedures. In the first step, the peak samples in the IFFT (Inverse Fast Fourier Transform) outputs are scaled down by clipping. In the second step, the sub-carrier position where the power of the clipping noise is the maximum, is selected. Finally, the generic TI procedure is performed. Simulation results show that the proposed scheme does not require all the possible combinations of the original TI procedures, while maintaining the PAPR reduction performance.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.5 s.43
• /
• pp.95-103
• /
• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.6
• /
• pp.75-82
• /
• 2005

The robust feature vector selection method for the multiple frontal face detection is proposed in this paper. The proposed feature vector for the training and classification are integrated by means, amplitude projections, and its 1D Harr wavelet of the input image. And the statistical modeling is performed both for face and nonface classes. Finally, the estimated probability density functions (PDFs) are applied for the detection of multiple frontal faces in the still image. The proposed method can handle multiple faces, partially occluded faces, and slightly posed-angle faces. And also the proposed method is very effective for low quality face images. Experimental results show that detection rate of the propose method is $98.3\%$ with three false detections on the testing data, SET3 which have 227 faces in 80 images.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.5
• /
• pp.817-824
• /
• 2019

We propose an ATR (Adaptive Tone Reservation) scheme based on clipping noise for PAPR (Peak-to-Average Power Ratio) reduction of OFDM (Orthogonal Frequency Division Multiplexing) signals. The proposed scheme is composed of three steps: clipping, tone selection, and TR procedures. In the first step, the peak samples in the IFFT (Inverse Fast Fourier Transform) outputs are scaled down by clipping. In the second step, the sub-carrier position where the power of the clipping noise is the maximum, is selected. Finally, the generic TR procedure is performed. Simulation results show that the proposed scheme does not require all the possible combinations for the original TR procedures, while maintaining the PAPR reduction performance.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.1
• /
• pp.192-200
• /
• 2015

Several algorithms have been developed to classify arrhythmia which rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

• Journal of Internet of Things and Convergence
• /
• v.10 no.3
• /
• pp.57-64
• /
• 2024

In this paper, we propose a Line-of-Sight (LoS)/Non-Line-of-Sight (NLoS) identification- based Human Activity Recognition (HAR) system using Channel State Information (CSI) to improve the accuracy of HAR, which dynamically changes depending on the reception environment. to consider the reception environment of HAR system, the proposed system includes three operational phases: Preprocessing phase, Classification phase, and Activity recognition phase. In the preprocessing phase, amplitude is extracted from CSI raw data, and noise in the extracted amplitude is removed. In the Classification phase, the reception environment is categorized into LoS and NLoS. Then, based on the categorized reception environment, the HAR model is determined based on the result of the reception environment categorization. Finally, in the activity recognition phase, human actions are classified into sitting, walking, standing, and absent using the determined HAR model. To demonstrate the superiority of the proposed system, an experimental implementation was performed and the accuracy of the proposed system was compared with that of the existing HAR system. The results showed that the proposed system achieved 16.25% higher accuracy than the existing system.

• Geophysics and Geophysical Exploration
• /
• v.16 no.4
• /
• pp.240-249
• /
• 2013

The seismic data quality of marine geological and engineering survey deteriorates because of the sea swell. We often conduct a marine survey when the swell height is about 1 ~ 2 m. The swell effect correction is required to enhance the horizontal continuity of seismic data and satisfy the resolution less than 1 m. We applied the swell correction to the 8 channel high-resolution airgun seismic data and 3.5 kHz subbottom profiler (SBP) data. The correct sea bottom detection is important for the swell correction. To detect the sea bottom, we used maximum amplitude of seismic signal around the expected sea bottom, and picked the first increasing point larger than threshold value related with the maximum amplitude. To find sea bottom easily in the case of the low quality data, we transformed the input data to envelope data or the cross-correlated data using the sea bottom wavelet. We averaged the picked sea bottom depths and calculated the correction values. The maximum correction of the airgun data was about 0.8 m and the maximum correction of two kinds of 3.5 kHz SBP data was 0.5 m and 2.0 m respectively. We enhanced the continuity of the subsurface layer and produced the high quality seismic section using the proper methods of swell correction.

• Korean Journal of Remote Sensing
• /
• v.27 no.5
• /
• pp.521-534
• /
• 2011

The high-resolution X-band SAR systems such as COSMO-SkyMED and TerraSAR-X have been launched recently. In addition KOMPSAT-5 will be launched in the early of 2012. In this study we developed the new method for persistent scatterer candidate (PSC) selection and network construction, which is more suitable for PSInSAR analysis using multi-temporal X-band SAR data. PSC selection consists in two main steps: first, selection of initial PSCs based on amplitude dispersion index, mean amplitude, mean coherence. second, selection of final PSCs based on temporal coherence directly estimated from network analysis of initial PSCs. To increase the stability of network the Multi- TIN and complex network for non-urban area were addressed as well. The proposed algorithm was applied to twenty-one TerraSAR-X SAR of New Orleans. As a result many PSs were successfully extracted even in non-urban area. This research can be used as the practical application of KOMPSAT-5 for surface displacement monitoring using X-band PSInSAR.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.6 no.2
• /
• pp.95-106
• /
• 2007

This paper proposes an effective tempo feature extraction method for music information retrieval. The tempo information is modeled by the narrow-band temporal modulation components, which are decomposed into a modulation spectrum via joint frequency analysis. In implementation, the tempo feature is directly extracted from the modified discrete cosine transform coefficients, which is the output of partial MP3(MPEG 1 Layer 3) decoder. Then, different features are extracted from the amplitudes of modulation spectrum and applied to different music information retrieval tasks. The logarithmic scale modulation frequency coefficients are employed in automatic music emotion classification and music genre classification. The classification precision in both systems is improved significantly. The bit vectors derived from adaptive modulation spectrum is used in audio fingerprinting task That is proved to be able to achieve high robustness in this application. The experimental results in these tasks validate the effectiveness of the proposed tempo feature.