• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• The Journal of the Acoustical Society of Korea
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• v.19 no.1
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• pp.97-102
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• 2000

Subband coding is to divide the signal frequency band into a set of uncorrelated frequency bands by filtering and then to encode each of these subbands using a bit allocation rationale matched to the signal energy in that subband. The actual coding of the subband signal can be done using waveform encoding techniques such as PCM, DPCM and vector quantizer(VQ) in order to obtain higher data compression. Most researchers have focused on the error in the quantizer, but not on the overall reconstruction error and its dependence on the filter bank. This paper provides a thorough analysis of subband codecs and further development of optimum filter bank design using vector quantizer. We compute the mean squared reconstruction error(MSE) which depends on N the number of entries in each code book, k the length of each code word, and on the filter bank coefficients. We form this MSE measure in terms of the equivalent quantization model and find the optimum FIR filter coefficients for each channel in the M-band structure for a given bit rate, given filter length, and given input signal correlation model. Specific design examples are worked out for 4-tap filter in 2-band paraunitary filter bank structure. These optimum paraunitary filter coefficients are obtained by using Monte Carlo simulation. We expect that the results of this work could be contributed to study on the optimum design of subband codecs using vector quantizer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3672-3688
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• 2016

The nonorthogonality between the real and imaginary FBMC/OQAM modulated signals complicates the channel estimation (CE) process, and conventional OFDM CE methods cannot be directly applied to FBMC/OQAM. The conventional preamble-based CE schemes in FBMC/OQAM systems are mainly based on the interference approximation method (IAM) to improve the estimation performance. In this paper, we develop a novel preamble structure to improve the CE performance. We exploit the symmetry pattern to cancel interference and take into account the interference weights in this symmetric structure. The conventional preamble and the proposed preamble are compared via simulations in the IEEE 802.22, 3GPP Vehicular A and Pedestrian A channels. Numerical simulation results demonstrate that the proposed preamble can achieve better bit error ratio (BER) and mean squared error (MSE) performance under the three channel models considered.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.23-30
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• 1997

In this paper, a decision feedback recurrent neural equalization (DFRNE) scheme is proposed for adaptive equalization problems. The proposed equalizer models a nonlinear infinite impulse response (IIR) filter. The modified Real-Time recurrent Learning Algorithm (RTRL) is used to train the DFRNE. The DFRNE is applied to both linear channels with only intersymbol interference and nonlinear channels for digital video cassette recording (DVCR) system. And the performance of the DFRNE is compared to those of the conventional equalizaion schemes, such as a linear equalizer, a decision feedback equalizer, and neural equalizers based on multi-layer perceptron (MLP), in view of both bit error rate performance and mean squared error (MSE) convergence. It is shown that the DFRNE with a reasonable size not only gives improvement of compensating for the channel introduced distortions, but also makes the MSE converge fast and stable.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.3
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• pp.216-223
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• 1987

This paper presents an analysis of the effects of statistically independent channel errors on the mean-squared error performance of image transform coding systems. The analysis is discussed for several different stochasic statistics of the quantizer input valuse. The stochastic distributions under consideration here are Laplacian, Gaussian and uniform. For each case of the distributions, we evaluate the MSE performance when NBC, FBC, MDC and Gray code respectively is employed for encoding the quantized values of the transformed coeffecients into the corresponding code words. The result of this study shows that what code is desired to be chosen to minimize the MSE for the given stochastic distributions of the quantizer input values.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.279-289
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• 2012

Characteristics, such as the support limit and distortions, of minimum mean-squared error (MSE) N-level uniform and nonuniform scalar quantizers are studied for the family of the generalized gamma density functions as N increases. For the study, MSE-optimal scalar quantizers are designed at integer rates from 1 to 16 bits/sample, and their characteristics are compared with corresponding asymptotic formulas. The results show that the support limit formulas are generally accurate. They also show that the distortion of nonuniform quantizers is observed to converge to the Panter-Dite asymptotic constant, whereas the distortion of uniform quantizers exhibits slow or even stagnant convergence to its corresponding Hui-Neuhoff asymptotic constant at the studied rate range, though it may stay at a close proximity to the asymptotic constant for the Rayleigh and Laplacian pdfs. Additional terms in the asymptote result in quite considerable accuracy improvement, making the formulas useful especially when rate is 8 or greater.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.61-73
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• 2004

Rotation sampling designs may be classified into two categories. The first type uses the same sample unit for the entire life of the survey. The second type uses the sample unit only for a fixed number of times. In both type of designs, the entire sample is partitioned into a finite number(=G) of rotation groups. This paper is generalization of the first type designs. Since the generalized design can be identified by only G rotation groups and recall level 1, we denote this rotation system as l/G rotation design. Under l/G rotation design, variance and mean squared error (MSE) of generalized composite estimator are derived, incorporating two type of biases and exponentially decaying correlation pattern. Compromising MSE's of some selected l/G designs, we investigate design efficiency, design gap effect, ans the effects of correlation and bias.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.294-297
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• 2004

Mean squared error (MSE) is an effective criterion to combine the mean and the standard deviation responses in dual response surface optimization. The bias and variance components of MSE need to be weighted properly in the given problem situation. This paper proposes a systematic method to determine the relative weights of bias and variance in accordance with a decision maker's prior and posterior preference structure.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2176-2179
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• 2003

본 논문에서는 적은 계산량을 갖는 Fast Kalman/LMS 복합 구조 등화기를 제안한다. HDTV (High Definition Television)의 채널은 긴 지연을 가지는 다중경로가 존재하기 때문에 등화기에 많은 수의 탭이 필요하다. 그러나 실제로 다중경로에 영향을 받는 심볼은 몇 개의 탭에 의해서만 발생한다 본 논문에서는 훈련기간 초기에 Fast Kalman 알고리즘을 이용하여 MSE(Mean Squared Error) 값이 특정 임계치 이하가 될 때까지 빠르게 수렴을 시키고, 심볼들에 영향을 주지 않는 탭을 제외한 나머지 탭만을 LMS (Least Mean Squre) 알고리즘으로 갱신시킴으로써 계산량을 줄이는 새로운 방법을 제안한다. 시뮬레이션 결과 제안한 방법이 기존의 Fast Kalman/LMS 복합 구조에 비해 적은 계산량으로 비슷한 수렴 속도와 MSE를 갖는 것을 보여준다.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.4
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• pp.212-218
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• 2010

This paper shows accuracy comparison results of reliability estimation methods for one-shot systems with respect to sample sizes. To compare accuracy in reliability estimation methods, quantal-response data, characterizing one-shot systems, were simulated using failure times of LED obtained through the accelerated life test, and then the true reliability over time was evaluated using the failure times. The simulated quantal-response data were used to estimate the true reliability through applying reliability estimation methods in open literature. Accuracy of each reliability estimation method was compared in terms of both SSE (Sum of Squared Error) and MSE (Mean Squared Error), and then estimation trend for each method is found. Feasible bounds which true reliability would exist within were estimated through applying the found trends to quantal-response data set of a real weapon system.