• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.63-68
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• 2017

In this paper, Distance recognition measurement using distance calculation correction algorithm, was realization through LED dimming control. The calculation values for the RSSI average filtering and the RSSI feedback filtering were calculated and applied to reduce the error of the RSSI value measured from a long distance. It was confirmed that the RSSI values through the average filtering and the RSSI values measured by setting the coefficient value of the feedback filtering to 0.5 were ranged from -61 dBm to - 52.5 dBm, which shows irregular and high values decrease slightly as much as about -2 dBm to -6 dBm as compared to general measurements. A distance calculation correction algorithm to improve the accuracy was applied, which confirmed that as the distance increases, the range of errors decreases. In conclusion, unstable signals were corrected using the RSSI measurement result filtering, and the distance calculation correction algorithm was applied and performed to reduce the range of errors. In addition, RGB colors were implemented by LED to indicate the distance determination and the signal stability.

• 한국ITS학회:학술대회논문집
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• v.2006 no.10
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• pp.37-44
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• 2006

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.351-363
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• 2006

The 8-VSB modulation, the transmission standard for the terrestrial digital television(DTV) of the Advanced Television Systems Committee(ATSC), suffers from multipath fading because it conveys information on the amplitude. To solve this problem, decision feedback equalizers(DFE's) have been commonly used in terrestrial DTV receivers. However, under severe channels, such as a 0 dB ghost channel or a single frequency network (SFN) channel, the DFE shows unstable convergence due to the error propagation caused by decision errors. Instead of unstable time-domain DFE schemes, by proposing a frequency-domain direct-inversion equalization method, we try to guarantee stable equalization and achieve low symbol error rates. To secure the existence of a channel inverse, channel-matched filtering and noncausal filtering are carried out prior to equalization. Simulation results show that the proposed method performs much better than existing DFE schemes in terms of both the stability and the symbol error rate.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.613-617
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• 2000

This paper descries the effect of the stylus tip size on the measuring error in surface topography measurement. To analyze the distortional effect of an actual surface geometry originating from the finite stylus size, the surface is modeled as a sinusoid and the stylus tip as a circle. the measuring error is defined as the ratio of the standard deviation of a tracing profile and an original profile. It is shown that this measuring error depends on the amplitude and wavelength of an original profile. In this paper, the spectrum analysis is applied to investigate the distortional effect due to the mechanical filtering of the stylus in the frequency domain. and, the cumulative power spectrum is applied to determinate the minimum wavelength limits to be measured with the various stylus tip radius from these results, a new method to select proper stylus tip radius is proposed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.5
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• pp.318-326
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• 1991

A Quantizer reconstruction level control method for block artifact reduction in DCT image coding is described. In our scheme, quantizer reconstruction level control is obtained by adding quantization level step size to the optimum quantization level in the direction of reducing the block artifact by minimizing the mean square error(MSE) and error difference(EDF) distribution in boundary without the other additional bits. In simulation results, although the performance in terms of signal to noise ratio is degraded by a little amount, mean square of error difference at block boundary and mean square error having relation block artifact is greatly reduced. Subjective image qualities are improved compared with other block artifact reduction method such as postprocessing by filtering and trasform coding by block overlapping. But the addition calculations of 1-dimensional DCT become to be more necessary to coding process for determining the reconstruction level.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.560-567
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• 1998

This paper concerned about a imputed color printed material image in camera to decrease noise and distortion by processing median filtering with input image to identical condition. Also this paper proposed the way of compares a normal printed material with an abnormal printed material color tone with trained a learning of the error back-propagation to block classification by extracting five place from identical block(3${\times}$3) of color printed material R, G, B value. As a representative algorithm of multi-layer perceptron the error Back-propagation technique used to solve complex problems. However, the Error Back-propagation is algorithm which basically used a gradient descent method which can be converged to local minimum and the Back Propagation train include problems, and that may converge in a local minimum rather than get a global minimum. The network structure appropriate for a given problem. In this paper, a good result is obtained by improve initial condition and adjust th number of hidden layer to solve the problem of real time process, learning and train.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1598-1605
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• 1993

In this paper, the performance degradation caused by the band pass filter and the data sampling timing error which are generally considered ideal for the simplicity was analyzed. The well known intersymbol interference theory can be applicable to calculate only the upper bound of a system performance. Therefore to obtain the average error probability, a method average intersymbol interference was proposed. This method can be applicable to obtain average error probablity with a computer simulation and with a measured value in test lab. easily.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.270-277
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• 2002

Text filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. The aim of this paper is to improve the accuracy of text filtering systems by using machine learning techniques. We apply AdaBoost algorithms to the filtering task. An AdaBoost algorithm generates and combines a series of simple hypotheses. Each of the hypotheses decides the relevance of a document to a topic on the basis of whether or not the document includes a certain word. We begin with an existing AdaBoost algorithm which uses weak hypotheses with their output of 1 or -1. Then we extend the algorithm to use weak hypotheses with real-valued outputs which was proposed recently to improve error reduction rates and final filtering performance. Next, we attempt to achieve further improvement in the AdaBoost's performance by first setting weights randomly according to the continuous Poisson distribution, executing AdaBoost, repeating these steps several times, and then combining all the hypotheses learned. This has the effect of mitigating the ovefitting problem which may occur when learning from a small number of data. Experiments have been performed on the real document collections used in TREC-8, a well-established text retrieval contest. This dataset includes Financial Times articles from 1992 to 1994. The experimental results show that AdaBoost with real-valued hypotheses outperforms AdaBoost with binary-valued hypotheses, and that AdaBoost iterated with random weights further improves filtering accuracy. Comparison results of all the participants of the TREC-8 filtering task are also provided.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.5
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• pp.455-463
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• 2003

Adaptive noise cancellation techniques are ideally suitable for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation. Grain noises have an un-correlation property, while flaw echoes are correlated. Thus, adaptive filtering algorithms use the correlation properties of signals to enhance the signal-to-noise ratio (SNR) of the output signal. In this paper, a multi-stage adaptive noise cancellation (MANC) method using adaptive least mean square error (LMSE) filter for enhancing flaw detection in ultrasonic signals is proposed.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.363-371
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• 2010

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate for the speaker-independent speech recognition, we study the effect of spectral tilt on the Fourier magnitude spectrum en route to the extraction of MFCC. The effect of FIR filtering on the speech signal on the speech recognition is also investigated in parallel. Evaluation of the proposed methods are performed by two independent ways of the Fisher discriminant objective function and speech recognition test by hidden Markov model with fuzzy vector quantization. From the experiments, the recognition error rate is found to show about 10% relative improvements over the conventional method by an appropriate choice of the tilt factor.