• Smart Structures and Systems
• /
• v.30 no.1
• /
• pp.75-88
• /
• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.19 no.8
• /
• pp.816-827
• /
• 2009

This paper deals with a bearing faults localization technique based on holographic approach by visualizing sound radiated from the faults. The main idea stems from the phenomenon that bearing faults in a moving vehicle generate impulsive sound. To visualize fault signal from the moving vehicle, we can use the moving frame acoustic holography [Kwon, H.-S. and Kim, Y.-H., 1998, "Moving Frame Technique for Planar Acoustic Holography," J. Acoust. Soc. Am. Vol. 103, No. 4, pp. 1734${\sim}$1741]. However, it is not easy to localize faults only by applying the method. This is because the microphone array measures noise(for example, noise from other parts of the vehicle and the wind noise) as well as the fault signal while the vehicle passes by the array. To reduce the effect of noise, we propose two ideas which utilize the characteristics of fault signal. The first one is to average holograms for several frequencies to reduce the random noise. The second one is to apply the partial field decomposition algorithm [Nam, K.-U., Kim, Y.-H., 2004, "A Partial Field Decomposition Algorithm and Its Examples for Near-field Acoustic Holography," J. of Acoust. Soc. Am. Vol. 116, No. 1, pp. 172${\sim}$185] to the moving source, which can separate the fault signal and noise. Basic theory of those methods is introduced and how they can be applied to localize bearing faults is demonstrated. Experimental results via a miniature vehicle showed how well the proposed method finds out the location of source in practice.

• Journal of the Institute of Convergence Signal Processing
• /
• v.9 no.4
• /
• pp.295-303
• /
• 2008

In general, transform domain adaptive filters show faster convergence speed than the time domain adaptive filters, but the amount of calculation increases dramatically as the filter order increases. This problem can be solved by making use of the subband structure in transform domain adaptive filters. In this paper, to increase the convergence speed on the generalized subband decomposition FIR adaptive filters, a structure of the adaptive filter with subfilter of dyadic sparsity factor in wavelet transform domain is designed. And, in this adaptive filter, the equivalent input in transform domain is derived and, by using the input, the convergence properties for the LMS algorithm is analyzed and evaluated. By using this sub band adaptive filter, the inverse system modeling and the periodic noise canceller were designed, and, by computer simulation, the convergence speeds of the systems on LMS algorithm were compared with that of the subband adaptive filter using DFT(discrete Fourier transform).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.1C
• /
• pp.43-50
• /
• 2007

In the paper, we address QRM-MLD (Maximum Likelihood Detection with QR Decomposition and M-algorithm) signal detection method for spatially multiplexed MIMO (Multiple Input Multiple Output) systems. Recently, the QRM-MLD signal detection method which can achieve 1Gbps transmission speed for next generation mobile communication was implemented in a MIMO testbed for the mobile moving at a pedestrian speed. In the paper, we propose a novel signal detection method 'reduced complexity QRM-MLD' that achieves identical error performance as the QRM-MLD while reducing the computational complexity significantly. We rigorously compare the two detection methods in terms of computational complexity to show the complexity reduction of the proposed method. We also perform a set of computer simulations to demonstrate that two detection methods achieve identical error performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.3
• /
• pp.15-20
• /
• 2024

Path planning for unmanned aerial vehicles (UAV) is crucial in avoiding collisions with obstacles in complex environments that include both static and dynamic obstacles. Path planning algorithms like RRT and A^* are effectively handle static obstacle avoidance but have limitations with increasing computational complexity in high-dimensional environments. Reinforcement learning-based algorithms can accommodate complex environments, but like traditional path planning algorithms, they struggle with training complexity and convergence in higher-dimensional environment. In this paper, we proposed a reinforcement learning model utilizing a cell decomposition algorithm. The proposed model reduces the complexity of the environment by decomposing the learning environment in detail, and improves the obstacle avoidance performance by establishing the valid action of the agent. This solves the exploration problem of reinforcement learning and improves the convergence of learning. Simulation results show that the proposed model improves learning speed and efficient path planning compared to reinforcement learning models in general environments.

• Journal of KIISE:Databases
• /
• v.33 no.7
• /
• pp.708-714
• /
• 2006

The World Wide Web has become an entrenched global medium for storing and searching information. Most people begin at a Web search engine to find information, but the user's pertinent search results are often greatly diluted by irrelevant data or sometimes appear on target but still mislead the user in an unwanted direction. One of the intentional, sometimes vicious manipulations of Web databases is Web spamming as Google bombing that is based on the PageRank algorithm, one of the most famous Web structuring techniques. In this paper, we regard the Web as a directed labeled graph that Web pages represent nodes and the corresponding hyperlinks edges. In the present work, we define the label of an edge as having a link context and a similarity measure between link context and the target page. With this similarity, we can modify the transition matrix of the PageRank algorithm. A motivating example is investigated in terms of the Singular Value Decomposition with which our algorithm can outperform to filter the Web spamming pages effectively.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.6
• /
• pp.981-989
• /
• 2015

In a log likelihood ratio(LLR) calculation of the detected symbol, multiple-input multiple-output(MIMO) system applying an optimal or suboptimal algorithm such as a maximum likelihood(ML) detection, sphere decoding(SD), and QR decomposition with M-algorithm Maximum Likelihood Detection(QRM-MLD) suffers from exponential complexity growth with number of spatial streams and modulation order. In this paper, we propose a LLR calculation method with very low complexity in the QRM-MLD based symbol detector for a high order modulation based $N_T{\times}N_R$ MIMO system. It is able to approach bit error rate(BER) performance of full maximum likelihood detector to within 1 dB. We also analyze the BER performance through computer simulation to verify the validity of the proposed method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.30 no.6
• /
• pp.479-486
• /
• 2019

The fully digital active array radar uses a wide beam for effective mission performance within a limited time. This paper presents a convex optimization algorithm that adjusts only the phase of an array element. First, the algorithm applies a semidefinite relaxation technique to relax the constraint and convert it to a convex set. Then, the constraint is set so that the amplitude is fixed to some extent and the phase is variable. Finally, the optimization is performed to minimize the sum of the eigenvalues obtained through eigenvalue decomposition. Compared to the application results of the existing genetic algorithm, the proposed algorithm is more effective in wide beam design for a fully digital active array radar.

• Journal of the Society of Disaster Information
• /
• v.19 no.1
• /
• pp.10-17
• /
• 2023

Purpose: In this paper, a distance estimation technique for indoor location estimation using IR-UWB is proposed and researched. We propose an algorithm that can increase the SNR lowered due to clutter or noise in an indoor environment. Method: In order to clutter suppression and detect weak signals, we analyze the existing studies of background remover, correlation, and singular vector decomposition techniques and propose an algorithm. Result: The proposed algorithm, the average error was 0.57m up to 11.5m, and the error were 0.49m from 6m to 11.5m. the average error rate was reduced by about 1m compared to the previous study. Conclusion: It can be used as a technique for detecting weak signals in clutter and noise environments for distance or location estimation, and can also be used as a human life detection technique to reduce damage to people in a disaster situation by using UWB radar which has highly transparent.

• Structural Engineering and Mechanics
• /
• v.39 no.6
• /
• pp.813-831
• /
• 2011

This paper presents an efficient version of Hilbert-Huang transform for nonlinear non-stationary systems analyses. An ensemble empirical mode decomposition (EEMD) is introduced to alleviate the problem of mode mixing between intrinsic mode functions (IMFs) decomposed by EMD. Yet the problem has not been fully resolved when a signal of a similar scale resides in different IMF components. Instead of using a trial and error method to select the "best" outcome generated by EEMD, a hybrid algorithm based on EEMD and EMD is proposed for multi-mode signal processing. The developed approach comprises the steps from a bandpass filter design for regrouping modes of the IMFs obtained from EEMD, to the mode extraction using EMD, and to the assessment of each mode in the marginal spectrum. A simulated two-mode signal is tested to demonstrate the efficiency and robustness of the approach, showing average relative errors all equal to 1.46% for various noise levels added to the signal. The developed approach is also applied to a real bridge structure, showing more reliable results than the pure EMD. Discussions on the mode determination are offered to explain the connection between modegrouping form on the one hand, and mode-grouping performance on the other.