• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1498-1509
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• 2004

In this paper, we evaluate the performance of a noncoherent OOK (On-Off Keying) UWB (Ultra Wide Band) system based on power detection with noise power calibration and noise power windowing for ubiquitous sensor network applications in typical indoor wireless channels. Utilizing noise power calibration and noise power windowing, the current noise information can be initially or adaptively provided to determine suitable detection threshold value for signal demodulation. Simulation results show that the noncoherent OOK UWB system using noise power calibration achieves good BER (Bit Error Rate) performance which is favorably comparable to that of the system using the ideal adaptive threshold, while maintaining simple receiver structure. However, despite the serious loss of the data transmission rate, the performance improvement by noise power windowing is not so remarkable. furthermore, these performance results are similarly maintained in BEE 802.15 Task Group 3a UWB indoor channel model, and it is also revealed that the BER performance can be significantly improved by increasing the pulse repetition rate.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.721-730
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• 2010

In satellite remote sensing, irregular temporal sampling is a common feature of geophysical and biological process on the earth's surface. Lee (2008) proposed a feed-back system using a harmonic model of single period to adaptively reconstruct observation image series contaminated by noises resulted from mechanical problems or environmental conditions. However, the simple sinusoidal model of single period may not be appropriate for temporal physical processes of land surface. A complex model of multiple periods would be more proper to represent inter-annual and inner-annual variations of surface parameters. This study extended to use a multi-periodic harmonic model, which is expressed as the sum of a series of sine waves, for the adaptive system. For the system assessment, simulation data were generated from a model of negative errors, based on the fact that the observation is mainly suppressed by bad weather. The experimental results of this simulation study show the potentiality of the proposed system for real-time monitoring on the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.928-934
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• 2002

A natural way to cope with fault tolerant control (FTC) problems is to modify the control parameters according to an online identification of the system parameters when a fault occurs. However. due to not only difficulties Inherent to the online multivariable identification in closed-loop systems, such as modeling errors, noise or the lack of excitation signals, but also long time requirement to identify the post-fault system and implemeutation of control problems during the identification process, we propose an alternative approach based on the observer-based fault detection and isolation (FDI) and model reference adaptive control (MRAC). The proposed robust fault diagnosis method is based on a bank of observers. We also propose a model reference adaptive control with changeable reference models according to the occurred faults. Simulation results of a flight control example show the validity and applicability of the proposed algorithms.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1048-1053
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• 2003

This paper proposed a MR(Magneto-rheological) seat damper for a commercial vehicle. After formulating the governing equation of motion, an appropriate size of damper is designed and manufactured. Following the equation of fie d-dependent damping force characteristics, a semi-active seat suspension installed with the proposed MR-damper is constructed and its dynamic model id established, Subsequently, vibration isolation performance of the semi-active suspension system is demonstrated by incorporating with a MRAC(Model referenced adaptive control) fer the MR Seat Damper

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.69-74
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• 1999

Adaptive algorithms based on gradient adaptation have been extensively investigated and successfully joined with active noise/vibration control applications. The Filtered-X LMS algorithm became one of the basic feedforward algorithms in such applications, but is not fully understood yet. Effects of cancellation path model on the Filtered-X LMS algorithm have investigated and some useful properties related to stability were discovered. Most of the results stated that the error in the cancellation path model is undesirable to the Filtered X LMS. However, we started convergence analysis of Filtered-X LMS based on the assumption that erroneous model does not always degrade its performance. In this paper, we present a way of optimizing the cancellation path modern in order to enhance the convergence speed by introducing intentional phase error. Carefully designed intentional phase error enhances the convergence speed of the Filtered X LMS algorithm for pure tone noise suppression application without any performance loss at steady state.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.555-572
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• 2018

Noise reduction processes that reduce or eliminate noise (caused by a variety of reasons) in noise contaminated image is an important theme in image processing fields. Many studies are being conducted on noise removal processes due to the importance of distinguishing between noise added to a pure image and the unique characteristics of original images. Adaptive filter and sigma filter are typical noise reduction filters used to reduce or eliminate noise; however, their effectiveness is affected by accurate noise estimation. This study generates a distribution of noise contaminating image based on a Dirichlet normal mixture model and presents a Bayesian approach to distinguish the characteristics of an image against the noise. In particular, to distinguish the distribution of noise from the distribution of characteristics, we suggest algorithms to develop a Bayesian inference and remove noise included in an image.

• Journal of Broadcast Engineering
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• v.8 no.3
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• pp.278-287
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• 2003

Noise in an image degrades image quality and deteriorates coding efficiency. Recently, various edge-preserving noise filtering methods based on the nonstationary image model have been proposed to overcome this problem. In most conventional nonstationary image models, however, pixels are assumed to be uncorrelated to each other in order not to Increase the computational burden too much. As a result, some detailed information is lost in the filtered results. In this paper, we propose a computationally feasible adaptive noise smoothing algorithm which considers the nonstationary correlation characteristics of images. We assume that an image has a nonstationary mean and can be segmented into subimages which have individually different stationary correlations. Taking advantage of the special structure of the covariance matrix that results from the proposed image model, we derive a computationally efficient FFT-based adaptive linear minimum mean-square-error filter. Justification for the proposed image model is presented and effectiveness of the proposed algorithm is demonstrated experimentally.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.723-724
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• 2012

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.308-318
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• 2022

Noise is a serious issue. While sending images via electronic communication, Impulse noise, which is created by unsteady voltage, is one of the most common noises in digital communication. During the acquisition process, pictures were collected. It is possible to obtain accurate diagnosis images by removing these noises without affecting the edges and tiny features. The New Average High Noise Density Median Filter. (HNDMF) was proposed in this paper, and it operates in two steps for each pixel. Filter can decide whether the test pixels is degraded by SPN. In the first stage, a detector identifies corrupted pixels, in the second stage, an algorithm replaced by noise free processed pixel, the New average suggested Filter produced for this window. The paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. In this paper the comparison of known image denoising is discussed and a new decision based weighted median filter used to remove impulse noise. Using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structure Similarity Index Method (SSIM) metrics, the paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the HNDMF model has reached to a better performance with the maximum picture quality. images affected by various amounts of pretend salt and paper noise, as well as speckle noise, are calculated and provided as experimental results. According to quality metrics, the HNDMF Method produces a superior result than the existing filter method. Accurately detect and replace salt and pepper noise pixel values with mean and median value in images. The proposed method is to improve the median filter with a significant change.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.377-382
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• 2012

In this paper, the echo noise robust CHMM learning model using echo cancellation average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise. For improving the performance of a continuous speech recognition, CHMM models were constructed using echo noise cancellation average estimator LMS algorithm. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 1.93dB, recognition rate improved as 2.1%.