• Title, Summary, Keyword: Signal reconstruction

Search Result 366, Processing Time 0.044 seconds

Input signal reconstruction for nonlinear systems using iterative learning procedures (반복 학습법에 의한 비선형 계의 입력신호 재현)

  • Seo, Jong-Soo;S. J. Elliott
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
    • /
    • /
    • pp.855-861
    • /
    • 2002
  • This paper demonstrates the reconstruction of input signals from only the measured signal for the simulation and endurance test of automobiles. The aim of this research is concerned with input signal reconstruction using various iterative teaming algorithm under the condition of system characteristics. From a linear to nonlinear systems which provides the output signals are estimated in this algorithm which is based on the frequency domain. Our concerns are that the algorithm can assure an acceptable stability and convergence compared to the ordinary iterative learning algorithm. As a practical application, a f car model with nonlinear damper system is used to verify the restoration of input signal especially with a modified iterative loaming algorithm.

  • PDF

An Efficient Model Based on Smoothed ℓ0 Norm for Sparse Signal Reconstruction

  • Li, Yangyang;Sun, Guiling;Li, Zhouzhou;Geng, Tianyu
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.13 no.4
    • /
    • pp.2028-2041
    • /
    • 2019
  • Compressed sensing (CS) is a new theory. With regard to the sparse signal, an exact reconstruction can be obtained with sufficient CS measurements. Nevertheless, in practical applications, the transform coefficients of many signals usually have weak sparsity and suffer from a variety of noise disturbances. What's worse, most existing classical algorithms are not able to effectively solve this issue. So we proposed an efficient algorithm based on smoothed ${\ell}_0$ norm for sparse signal reconstruction. The direct ${\ell}_0$ norm problem is NP hard, but it is unrealistic to directly solve the ${\ell}_0$ norm problem for the reconstruction of the sparse signal. To select a suitable sequence of smoothed function and solve the ${\ell}_0$ norm optimization problem effectively, we come up with a generalized approximate function model as the objective function to calculate the original signal. The proposed model preserves sharper edges, which is better than any other existing norm based algorithm. As a result, following this model, extensive simulations show that the proposed algorithm is superior to the similar algorithms used for solving the same problem.

Block Sparse Signals Recovery via Block Backtracking-Based Matching Pursuit Method

  • Qi, Rui;Zhang, Yujie;Li, Hongwei
    • Journal of Information Processing Systems
    • /
    • v.13 no.2
    • /
    • pp.360-369
    • /
    • 2017
  • In this paper, a new iterative algorithm for reconstructing block sparse signals, called block backtracking-based adaptive orthogonal matching pursuit (BBAOMP) method, is proposed. Compared with existing methods, the BBAOMP method can bring some flexibility between computational complexity and reconstruction property by using the backtracking step. Another outstanding advantage of BBAOMP algorithm is that it can be done without another information of signal sparsity. Several experiments illustrate that the BBAOMP algorithm occupies certain superiority in terms of probability of exact reconstruction and running time.

Variable-magnitude Voltage Signal Injection for Current Reconstruction in an IPMSM Sensorless Drive with a Single Sensor

  • Im, Jun-Hyuk;Kim, Sang-Il;Kim, Rae-Young
    • Journal of Electrical Engineering and Technology
    • /
    • v.13 no.4
    • /
    • pp.1558-1565
    • /
    • 2018
  • Three-phase current is reconstructed from the dc-link current in an AC machine drive with a single current sensor. Switching pattern modification methods, in which the magnitude of the effective voltage vector is secured over its minimum, are investigated to accurately reconstruct the three-phase current. However, the existing methods that modify the switching pattern cause voltage and current distortions that degrade sensorless performance. This paper proposes a variable-magnitude voltage signal injection method based on a high frequency voltage signal injection. The proposed method generates a voltage reference vector that ensures the minimum magnitude of the effective voltage vector by varying the magnitude of the injection signal. This method can realize high quality current reconstruction without switching pattern modification. The proposed method is verified by experiments in a 600W Interior permanent magnet synchronous machine (IPMSM) drive system.

Phase Retrieval Using an Additive Reference Signal: II. Reconstruction (더해지는 기준신호를 이용한 위성복원: II. 복원)

  • Woo Shik Kim
    • Journal of the Korean Institute of Telematics and Electronics B
    • /
    • v.31B no.5
    • /
    • pp.34-41
    • /
    • 1994
  • Phase retrieval is concerned with the reconstruction of a signal from its Fourier transform magnitude (or intensity), which arises in many areas such as X-ray crystallography, optics, astronomy, or digital signal processing In such areas, the Fourier transform phase of the desired signal is lost while measuring Fourier transform magnitude (F.T.M.). However, if a reference 'signal is added to the desired signal, then, in the Fourier trans form magnitude of the added signal, the Fourier transform phase of the desired signal is encoded This paper addresses uniqueness and retrieval of the encoded Fourier phase of a multidimensional signal from the Fourier transform magnitude of the added signal along with Fourier transform magnitude of the desired signal and the information of the additive reference signal In Part I, several conditions under which the desired signal can be uniquely specified from the two Fourier transform magnitudes and the additive reference signal are presented In Part II, the development of non-iterative algorithms and an iterative algorithm that may be used to reconstruct the desired signal (s) is considered

  • PDF

An intelligent sensor system with reconstruction mechanism of faulty signal

  • Jung, Young-Su;Hyun, Woong-Keun;Yoon, In-Mo;Jung, Young-Kee;Kim, C.S.;Kim, Nam-Ho
    • 제어로봇시스템학회:학술대회논문집
    • /
    • /
    • pp.1231-1234
    • /
    • 2003
  • A sensor working in outdoor may generate some faulty signal owing to dust and high temperature. This paper describes an intelligent sensor system and controller which has a reconstruction mechanism for faulty signal. The faulty signals are dievided into two types as linear distortion and non linear distortion, respectively. The linear distorted signal is due to dust, and non linear distorted signal is due to physical breakdown of sensor or high temperature. These distorted signal have been reconstructed by the proposed method based on polynomial regression method and principal component analysis approach.. The proposed method has been applied to sun tracking system working in outdoor. For a robust and precision control of sun tracker, a fuzzy controller was also proposed. The fuzzy controller controls the tracker by using the collected sensor signal. The tolerance of the position control is within 1.5 degree. To show the validity of the developed system, some experiments in the field were illustrated.

  • PDF

Power Signal Monitering System with Compression Storage and Reconstruction (압축 저장 및 복원기능을 가지는 전력신호 모니터링 시스템)

  • Bae, Hyeon-Deok
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
    • /
    • v.9 no.2
    • /
    • pp.148-154
    • /
    • 2016
  • In recent year, the interests of PQ is increase due to the increasing of non-linear load and distributed power sources in power system. For the parameters detection and feature extraction of PQ, and the PQ improvement method, continuous power signal monitering is needed. In this paper, the power signal compression and reconstruction method is suggested for power signal monitering. The power signal is compressed using DCT that has good compression performance, and the compressed signal is reconstructed through IDCT. And for the higher compression rate, DCT coefficients are arranged by magnitude in compression process, and in recouction process DCT coefficients are rearranged to original frequency position. The synthesized signal according to the IEC standard is used used in compression and reconstruction simulations. The performances of the proposed method are verified by comparing the error between synthesized signal and reconstructed signal.

Time Reversa1 Reconstruction of Ultrasonic Waves in Anisotropic Media

  • Jeong, Hyun-Jo
    • Journal of the Korean Society for Nondestructive Testing
    • /
    • v.28 no.1
    • /
    • pp.54-58
    • /
    • 2008
  • Time reversal (TR) of body waves in fluids and isotropic solids has been used in many applications including ultrasonic NDE. However, the study of the TR method for anisotropic materials is not well established. In this paper, the full reconstruction of the input signal is investigated for anisotropic media using an analytical formulation, called a modular Gaussian beam (MGB) model. The time reversal operation of this model in the frequency domain is done by taking the complex conjugate of the Gaussian amplitude and phase received at the TR mirror position. A narrowband reference signal having a particular frequency and number of cycles is then multiplied and the whole signal is inverse Fourier transformed. The original input signal is seen to be fully restored by the TR process of MGB model and this model can be more generalized to simulate the spatial and temporal focusing effects due to TR process in anisotropic materials.

Probabilistic Exclusion Based Orthogonal Matching Pursuit Algorithm for Sparse Signal Reconstruction (희소 신호의 복원을 위한 확률적 배제 기반의 직교 정합 추구 알고리듬)

  • Kim, Seehyun
    • Journal of IKEEE
    • /
    • v.17 no.3
    • /
    • pp.339-345
    • /
    • 2013
  • In this paper, the probabilistic exclusion based orthogonal matching pursuit (PEOMP) algorithm for the sparse signal reconstruction is proposed. Some of recent greedy algorithms such as CoSaMP, gOMP, BAOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. They still often fail to converge to the solution because the support set could not escape from a local minimum. PEOMP helps to escape by excluding a random atom in the support set according to a well-chosen probability function. Experimental results show that PEOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

Performance Analysis of Compressed Sensing Given Insufficient Random Measurements

  • Rateb, Ahmad M.;Syed-Yusof, Sharifah Kamilah
    • ETRI Journal
    • /
    • v.35 no.2
    • /
    • pp.200-206
    • /
    • 2013
  • Most of the literature on compressed sensing has not paid enough attention to scenarios in which the number of acquired measurements is insufficient to satisfy minimal exact reconstruction requirements. In practice, encountering such scenarios is highly likely, either intentionally or unintentionally, that is, due to high sensing cost or to the lack of knowledge of signal properties. We analyze signal reconstruction performance in this setting. The main result is an expression of the reconstruction error as a function of the number of acquired measurements.