• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.3
• /
• pp.74-85
• /
• 2021

Global Navigation Satellite System (GNSS) receivers are intrinsically vulnerable to radio frequency jamming signals due to the fundamental property of radio navigation systems. A GNSS jamming monitoring system that is capable of jamming detection, classification and localization is essential for infrastructure for autonomous driving systems. For these 3 functionalities, a GNSS jamming monitoring network consisting of a multiple of low-cost GNSS receivers distributed in a certain area is needed, and the precise time synchronizaion between multiple independent GNSS receivers in the network is an essential element. This paper presents a precise time synchronization method based on the direct use of Time Difference of Arrival (TDOA) technique in signal domain. A block interpolation method is additionally incorporated into the method in order to maintain the precision of time synchronization even with the relatively low sampling rate of the received signals for computational efficiency. The feasibility of the proposed approach is verified in the numerical simualtions.

• Journal of Advanced Navigation Technology
• /
• v.20 no.4
• /
• pp.367-373
• /
• 2016

We propose an improved direction of arrival (DoA) estimation algorithm based on co-prime array and propagator method. The propagator method with co-prime array does not require singular value decomposition (SVD) requiring much less computational complexity but exhibiting somewhat worse performance in comparison with MUSIC based on co-prime array. We notice that one cause of the performance degradation was in the avoidance of the usage of the diagonal elements of the signal autocorrelation matrix that contains the noise power spectral density. So we propose an algorithm with the diagonal elements of the signal autocorrelation matrix based on the fact that the noise power spectral density can be estimated using noise observation over a long period of time. We observe, through simulations, that the proposed scheme in this paper improves the performance, with 4 times more computational requirement, by signal-to-noise ratio of 1.5dB and by DoA resolution of $0.7^{\circ}$ at the detection probability of 95% compared with the previously introduced co-prime array propagator scheme, resulting in performance much closer to that of co-prime array-based MUSIC scheme.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.6
• /
• pp.679-685
• /
• 2011

This paper presents an algorithm identifying devices that generate observed mixed signals that are collected at main power-supply line. The proposed algorithm, which is necessary for low cost electric power monitoring system at appliance-level, that is non-intrusive load monitoring system, divides incoming mixed signal into multiple time intervals, calculating difference-signals between consecutive time interval, and identifies which device is operating at the time interval by analysing the difference-signals. Since the features of one device can remain when the time interval is short enough and the features are independent and additive, well-known classification algorithms can be used to classify the difference-signals with features of N individual devices, otherwise $2^N$ features might be necessary. The proposed algorithm was verified using data mixed in a laboratory with individual devices's data collected from field. When maximum 4 devices operate or stop sequentially and when features satisfy the requirements of proposed algorithm, the proposed algorithm resulted nearly 100% success rate under the constrained test condition. In order to apply the proposed algorithm in real world, the number devices shall increase, the time interval shall be smaller and the pattern of mixture shall be more diverse. However we can expect, if features used follow guidelines of proposed algorithm, future system could have certain level of performance without the guideline.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.44 no.11
• /
• pp.137-141
• /
• 2007

This paper proposes a cost-effective architecture design of an angle-of-arrival (AOA) estimator based on the multiple signal identification and classification (MUSIC) algerian in UWB systems adapting Multi-band OFDM (MB-OFDM) techniques with two-receive antennas. In the proposed method, by modifying the equations of algorithm in order to remove the high computational functions, the computation power can be significantly reduced without significant performance degradation. The proposed architecture is designed and verified by Verilog HDL, and implemented into 0.13um CMOS standard cell and Xilinx FPGA circuits for the estimation of hardware complexity and computation power. From the results of the implementations, we can find that the proposed circuits reduces the hardware complexity by about 43% and the estimated computation power by about 23%, respectively, compared to the architecture employing the original MUSIC algorithm.

• Journal of Advanced Navigation Technology
• /
• v.20 no.3
• /
• pp.258-264
• /
• 2016

In this paper, we propose a computationally efficient direction of arrival (DoA) estimation algorithm based on propagator method with non-uniform array. While the co-prime array techniques can improve the resolution of DoA, they generally lead to high computational complexity as the length of the coarray aperture. To reduce the complexity we use the propagator method that does not require singular value decomposition (SVD). Through simulations, we compare MUSIC with uniform lineary array, propagator method with uniform linear array, MUSIC with co-prime array, and the proposed scheme and observe that the performance of the proposed scheme is significantly better than MUSIC or propagator method with uniform linear array while it is slightly worse than computationally much more expensive co-prime array MUSIC scheme.

• The Journal of the Acoustical Society of Korea
• /
• v.14 no.1
• /
• pp.31-39
• /
• 1995

In this paper, we have constructed phoneme discriminant filters [PDF] according to the linear discriminant function. These discriminant filters do not follow the heuristic rules by the experts but the mathematical methods in iterative learning. Proposed system. is based on the piecewise linear classifier and error correction learning method. The segmentation of speech and the classification of phoneme are carried out simutaneously by the PDF. Because each of them operates independently, some speech intervals may have multiple outputs. Therefore, we introduce the unified coefficients by the output unification process. But sometimes the output has a region which shows no response, or insensitive. So we propose time windows and median filters to remove such problems. We have trained this system with the 549 monosyllables uttered 3 times by 3 male speakers. After we detect the endpoint of speech signal using threshold value and zero crossing rate, the vowels and consonants are separated by the PDF, and then selected phoneme passes through the following PDF. Finally this system unifies the outputs for competitive region or insensitive area using time window and median filter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.30 no.4
• /
• pp.275-281
• /
• 2019

A synthetic aperture radar(SAR) satellite is more effective than an optical satellite for target analysis because an SAR satellite can provide two-dimensional electromagnetic scattering distribution of a target during all-weather and day-and-night operations. To conduct target analysis while considering the earth observation interval of an SAR satellite, observing a specific area as wide as possible would be advantageous. However, wider the observation area, worse is the resolution of the associated SAR satellite image. Although conventional methods for improving the resolution of radar images can be employed for addressing this issue, few studies have been conducted for improving the resolution of SAR satellite images and analyzing the performance. Hence, in this study, the applicability of conventional methods to SAR satellite images is investigated. SAR target detection was first applied to Korea Multipurpose Satellite-5(KOMPSAT-5) SAR images provided by Korea Aerospace Research Institute for extracting target responses. Extrapolation, RELAX, and MUSIC algorithms were subsequently applied to the target responses for improving the resolution, and the corresponding performance was thereby analyzed.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.181-193
• /
• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.35 no.6
• /
• pp.109-120
• /
• 2023

The High-Frequency Radar (HFR) is an equipment designed to measure real-time surface ocean currents in broad maritime areas.It emits radio waves at a specific frequency (HF) towards the sea surface and analyzes the backscattered waves to measure surface current vectors (Crombie, 1955; Barrick, 1972).The Seasonde HF Radar from Codar, utilized in this study, determines the speed and location of radial currents by analyzing the Bragg peak intensity of transmitted and received waves from an omnidirectional antenna and employing the Multiple Signal Classification (MUSIC) algorithm. The generated currents are initially considered ideal patterns without taking into account the characteristics of the observed electromagnetic wave propagation environment. To correct this, Antenna Pattern Measurement (APM) is performed, measuring the strength of signals at various positions received by the antenna and calculating the corrected measured vector to radial currents.The APM principle involves modifying the position and phase information of the currents based on the measured signal strength at each location. Typically, experiments are conducted by installing an antenna on a ship (Kim et al., 2022). However, using a ship introduces various environmental constraints, such as weather conditions and maritime situations. To reduce dependence on maritime conditions and enhance economic efficiency, this study explores the possibility of using unmanned aerial vehicles (drones) for APM. The research conducted APM experiments using a high-frequency radar installed at Dangsa Lighthouse in Dangsa-ri, Wando County, Jeollanam-do. The study compared and analyzed the results of APM experiments using ships and drones, utilizing the calculated radial currents and surface current fields obtained from each experiment.

• Journal of Korean Neurosurgical Society
• /
• v.60 no.5
• /
• pp.577-583
• /
• 2017

Objective : Laminoplasty is an effective surgical method for treating cervical degenerative disease. However, postoperative complications such as kyphosis, restriction of neck motion, and instability are often reported. Despite sufficient preoperative lordosis, this procedure often aggravates the lordotic curve of the cervical spine and straightens cervical alignment. Hence, it is important to examine preoperative risk factors associated with postoperative kyphotic alignment changes. Our study aimed to investigate preoperative radiologic parameters associated with kyphotic deformity post laminoplasty. Methods : We retrospectively reviewed the medical records of 49 patients who underwent open door laminoplasty for cervical spondylotic myelopathy (CSM) or ossification of the posterior longitudinal ligament (OPLL) at Pusan National University Yangsan Hospital between January 2011 and December 2015. Inclusion criteria were as follows : 1) preoperative diagnosis of OPLL or CSM, 2) no previous history of cervical spinal surgery, cervical trauma, tumor, or infection, 3) minimum of one-year follow-up post laminoplasty with proper radiologic examinations performed in outpatient clinics, and 4) cases showing C7 and T1 vertebral body in the preoperative cervical sagittal plane. The radiologic parameters examined included C2-C7 Cobb angles, T1 slope, C2-C7 sagittal vertical axis (SVA), range of motion (ROM) from C2-C7, segmental instability, and T2 signal change observed on magnetic resonance imaging (MRI). Clinical factors examined included preoperative modified Japanese Orthopedic Association scores, disease classification, duration of symptoms, and the range of operation levels. Results : Mean preoperative sagittal alignment was $13.01^{\circ}$ lordotic; $6.94^{\circ}$ lordotic postoperatively. Percentage of postoperative kyphosis was 80%. Patients were subdivided into two groups according to postoperative Cobb angle change; a control group (n=22) and kyphotic group (n=27). The kyphotic group consisted of patients with more than $5^{\circ}$ kyphotic angle change postoperatively. There were no differences in age, sex, C2-C7 Cobb angle, T1 slope, C2-C7 SVA, ROM from C2-C7, segmental instability, or T2 signal change. Multiple regression analysis revealed T1 slope had a strong relationship with postoperative cervical kyphosis. Likewise, correlation analysis revealed there was a statistical significance between T1 slope and postoperative Cobb angle change (p=0.035), and that there was a statistically significant relationship between T1 slope and C2-C7 SVA (p=0.001). Patients with higher preoperative T1 slope demonstrated loss of lordotic curvature postoperatively. Conclusion : Laminoplasty has a high probability of aggravating sagittal balance of the cervical spine. T1 slope is a good predictor of postoperative kyphotic changes of the cervical spine. Similarly, T1 slope is strongly correlated with C2-C7 SVA.