• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.4
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• pp.10-15
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• 1983

In this paper effective methods of demodulation and equalization in a 9600 bps modem have been studied. To reduce the number of multiplications required per symbol in demodula-tion, the method of using a decimation filter is presented. In the equalizer the optimum step size and the steady state mean-squared error (MSE) are obtained from computer simulation results. The performance of the first-order carrier phase tracking loop is compared with that of the second-order loop when carrier frequency offset exists. In addition, the finite word length effects in the equalizer are studied.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.275-280
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• 2021

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.523-532
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• 1997

We propose several estimators of the reliability function R of the two-parameter exponential distribution, and then compare those estimator in terms of the mean square error (MSE) through Monte Carlo method. We also consider the parametric bootstrap estimation. Using the parametric bootstrap estimator, we obtain the bootstrap confidence intervals for reliability function and compare the proposed bootstrap confidence intervals in terms of the length and the coverage probability through Monte Carlo method.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.56-59
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• 2017

CNN(Convolutional Neural Network)을 이용하여 발화 주제 다중 분류 task를 multi-labeling 방법과, cluster 방법을 이용하여 수행하고, 각 방법론에 MSE(Mean Square Error), softmax cross-entropy, sigmoid cross-entropy를 적용하여 성능을 평가하였다. Network는 음절 단위로 tokenize하고, 품사정보를 각 token의 추가한 sequence와, Naver DB를 통하여 얻은 named entity 정보를 입력으로 사용한다. 실험결과 cluster 방법으로 문제를 변형하고, sigmoid를 output layer의 activation function으로 사용하고 cross entropy cost function을 이용하여 network를 학습시켰을 때 F1 0.9873으로 가장 좋은 성능을 보였다.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.59-70
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• 1997

We introduce several estimators of the location and the scale parameters of the two-parameter exponential distribution, and then compare these estimators by the mean square error (MSE). Using the parametric bootstrap estimators and the delete-d jackknife, we obtain the bootstrap and the delete-d jackknife confidence intervals for the location and the scale parameters and compare the bootstrap confidence intervals with the delete-d jackknife confidence intervals by length and coverage probability through Monte Carlo method.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1986.04a
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• pp.1-3
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• 1986

We are concerned with transmitting numerical source data of {0, 1, 2, ..., 2k-1} through a channel coding system. The rate 1/v dual-k convolutional code with the orthogonal MFSK modulation and the Viterbl decoding is employed for the implementation of the channel coding system. The mean square error of the dual-k convolutional code is evaluated for the numerical source transmitted over an additive white Gaussian noise channel with Rayleigh fading.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.1997-2003
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• 2010

Mean Residual Life (MRL) function plays a very important role in the area of engineering, medical science, survival studies, social sciences, and many other fields. Specially, in the reliability study of technical systems, the MRL estimation of a component is very important because the sudden stop of a system brings a serious problem. So, many simulation studies of MRL estimation have been done considering various situation variables. In this paper, four estimators of MRL are proposed under random censoring and their performances re compared through bias and Mean Square Error (MSE) by Monte Carlo simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.467-472
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• 2002

In this paper, we propose a Fuzzy Cellular Neural Network(FCNN) that is based on a-Least Trimmed Square Hausdorff distance(a-LTSHD) which applies Hausdorff distance(HD) to the FCNN structure in order to remove the impulse noise of images effectively and also improve the speed of operation. FCNN incorporates Fuzzy set theory to Cellular Neural Network(CNN) structure and HD is used as a scale which computes the distance between set or two pixels in binary images without confrontation of the feature object. This method has been widely used with the adjustment of the object. For performance evaluation, our proposed method is analyzed in comparison with the conventional FCNN, with the Opening-Closing(OC) method, and the LTSHD based FCNN by using Mean Square Error(MSE) and Signal to Noise Ratio(SNR). As a result, the performance of our proposed network structure is found to be superior to the other algorithms in the removal of impulse noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.11B
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• pp.1884-1894
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• 2000

광대역 무선 통신 시스템에서는 주파수 선택적 다중경로 페이딩 채널상에서 발생하는 인접 심볼간의 간섭에 의한 성능 열화를 극복하기 위해서 채널 등화 기술의 도입이 필연적이다. 그러나, 데이터 전송률이 증가함에 따라 채널 등화에 필요한 훈련열의 길이가 늘어나고, 이로 인한 오버헤드를 감소하기 위해서는 보다 수렴률이 높은 적응 알고리즘이 요구된다. 본 논문에서는 주파수 영역의 적응 여파 기법 중의 하나인 자기 직교환 방식의 결정궤환 등화기를 고려한다. 이 채널 등화기에서는 적응 알고리즘으로 DCT-LMS (discrete cosine transform least mean square) 알고리즘을 채택함으로써 수렴률과 MSE (mean square error) 성능을 향상시켜 결과적으로 광대역 무선통신에서 요구되는 훈련열에 따른 오버헤드를 감소시킬 수 있게 된다. 시뮬레이션을 통해 주파수 선택적 다중경로 페이딩 채널에서의 제안된 채널 등화 기법에 대한 성능을 분석한다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.188-190
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• 2011

Image interpolation is a technique that pervades many an application. Interpolation is almost never the goal in itself, yet it affects both the desired results and the ways to obtain them. In this paper, we proposed a technique that is capable to find out the error when the common two methods (bilinear and nearest neighbor interpolation) are applied on an image for rotation. The proposed technique also includes the comparison results of bilinear interpolation and nearest neighbor interpolation. Among them nearest neighbor interpolation gives us a better result regarding to the enhancement and due to least error. The error is found by using Mean Square Error (MSE).