• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1041-1045
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• 2010

Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.579-585
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• 2006

In this study, we analysis an optical receivers using the optical preamplifier in a spectrum-sliced WDM systems. The average numbers of photons/bit, for an $10^{-9}$ error probability, counts using the OOK and FSK transmission. As a result, the theoretical sensitivity for PIN receiver and optical preamplifier receiver are approximately $9.2\times10^4\;and\;7.2\times10^2$ in the m=20, respectively. Also, the average numbers of photons/bit, for and given error probability, theoretical receiver sensitivity for Gaussian method and k-square method are approximately $9\times10^2\;and\;2.16\times10^2$ in the m=40, respectively. And the average numbers of photons/bit, for an given error probability, theoretical receiver sensitivity, OOK and FSK transmission are approximately $1.9\times10^2\;and\;3.1\times10^2$ in the m=20, respectively.

• Journal of KIISE:Information Networking
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• v.30 no.6
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• pp.755-763
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• 2003

To improve performance over noisy wireless channels, mobile wireless networks employ forward error correction(FEC) techniques. The performance of static FEC algorithms, however, degrades by poorly matching the overhead of their correction code to the degree of the fluctuating underlying channel error. This paper proposes an adaptive FEC technique called FECA(FEC-level Adaptation), which dynamically tunes FEC strength to the currently estimated channel error rate at the data link layer. FECA is suitable for wireless networks whose error rate is high and slowly changing compared to the round-trip time between two communicating nodes. One such example network would be a sensor network in which the average bit error rate is higher than $10^{-6}$ and the detected error rate at one time lasts a few hundred milliseconds on average. Our experiments show that FECA performs 15% in simulations with theoretically modeled wireless channels and in trace-driven simulations based on the data collected from real sensor networks better than any other static FEC algorithms.

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.401-406
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• 2004

Among many algorithms for the integration problems in which one wants to compute the approximation to the definite integral in the average case setting, we study the average case errors of numerical integration rules using interpolation. In particular, we choose the composite Newton-Cotes quadratures and the function values at equally spaced sample points on the given interval as information. We compute the average case error of composite Newton-Cotes quadratures and show that it is minimal(modulo a multiplicative constant).

• Journal of IKEEE
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• v.15 no.3
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• pp.198-204
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• 2011

In this letter, another analytical approach for the opportunistic amplify-and-forward (OAF) relay systems is proposed over Rayleigh fading channels. Based on set-operation at the selected relay node, its selection probability as the best relay is derived and then, the probability density function (PDF) of the received instantaneous signal-to-noise ratio (SNR) is expressed as a more tractable form in which the number of summations and the length of each summation are specified. Then, the average error rate, outage probability, and average channel capacity are obtained as approximated closed-forms. Simulation results are finally presented to validate that the proposed analytical expressions can be a unified frame work covering all Rayleigh fading channel conditions. Furthermore, it is confirmed that OAF schemes can outperform the other non-selective schemes on the average error rate, outage probability, and average channel capacity.

• The Research Journal of the Costume Culture
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• v.2 no.2
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• pp.385-394
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• 1994

Data of the body surface is a necessary unit for the measuring of metabolism energy and activity energy. And also, these data are referring to check the degree of retaining warmth of clothes, to find the effect of heat insulation according to body surface, to calculate an average temperature of skin, and to study the several fields of clothing. In measuring of body surface, it si actually impossible to measure a subject's body surface in each experiment. As the experimental method, both gypsum method, by which the shape of body an be copied as it is, and the weighting method from which planed body surface area can be measured with consistent thickness of polyprophylene film as used. In fact, every female subject feels uncomfortable to measure her body surface as a naked body. There, it is providing a simple, accurate regressive equation with weight & height as variable factors in this study. This equation is as=117.02W+77.31H-3344.94 with average error : 0.1%, absolute average error : 2.07%.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.316-324
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• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1296-1309
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• 2023

In this paper, based on the soft cancellation (SCAN) bit-flipping (SCAN-BF) algorithm, a generalized SCAN bit-flipping (GSCAN-BF-Ω) decoding algorithm is carried out, where Ω represents the number of bits flipped or corrected at the same time. GSCAN-BF-Ω algorithm corrects the prior information of the code bits and flips the prior information of the unreliable information bits simultaneously to improve the block error rate (BLER) performance. Then, a joint threshold scheme for the GSCAN-BF-2 decoding algorithm is proposed to reduce the average decoding complexity by considering both the bit channel quality and the reliability of the coded bits. Simulation results show that the GSCAN-BF-Ω decoding algorithm reduces the average decoding latency while getting performance gains compared to the common multiple SCAN bit-flipping decoding algorithm. And the GSCAN-BF-2 decoding algorithm with the joint threshold reduces the average decoding latency further by approximately 50% with only a slight performance loss compared to the GSCAN-BF-2 decoding algorithm.

• Journal of Sasang Constitutional Medicine
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• v.18 no.2
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• pp.41-49
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• 2006

1. Objectives The Face is an important standard for the classification of Sasang Contitutions. We are developing 3D Face Automatic Recognition Apparatus to analyse the facial characteristics. So We should examine a shape demobilization error of 3D Face Automatic Recognition Apparatus. 2. Methods We compared facial shape data be demobilized by 3D Face Automatic Recognition Apparatus with facial shape data that be demobilized by 3D laser scanner. The subject was two korean men. And We analysed the average error and the maximum error of two data. In this process, We used one datum point(the peak of nose) and two datum line(vertical section and horizontal section). 3. Results and Conclusions In each this comparison, the average error of vertical section was 1.962574mm and 2.703814mm. and the maximum error of vertical section was 16.968249mm and 18.61464mm. the average error of horizontal section was 4.173203mm and 21.487479mm. and the maximum error of horizontal section was 3.571210mm and 17.13255mm. Also We complemented this apparatus a little and We reexamined a shape demobilization error of 3D Face Automatic Recognition Apparatus again. Accuracy of a shape demobilization was improved a little. From now on We complement accuracy of a shape demobilization in 3D Face Recognition Apparatus.