• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.07a
• /
• pp.319-322
• /
• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pass- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this Prediction system was acquired ${\pm}$3.2% error range.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.619-622
• /
• 2000

This research presents an analytical model to predict the instruction fetch rate on superscalar Processors. The proposed model is also able to analyze the performance relationship between cache miss and branch prediction miss. The proposed model takes into account various kind of architectural parameters such as branch instruction probability, cache miss rate, branch prediction miss rate, and etc.. To prove the correctness of the proposed model, we performed extensive simulations and compared the results with those of the analytical models. Simulation results showed that the pro-posed model can estimate the instruction fetch rate accurately within 10% error in most cases. The model is also able to show the effects of the cache miss and branch prediction miss on the performance of instruction fetch rate, which can provide an valuable information in designing a balanced system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.20 no.12
• /
• pp.1287-1296
• /
• 2009

It is very important to estimate the Signal to Noise Ratio(SNR) of received signal in time varying channel state. Most SNR estimation techniques derive the SNR estimates solely from the samples of the received signal after the matched filter. In the severe distorted wireless channel, the performance of these estimators become unstable and degraded. LP-based SNR estimator which can operate on data samples collected at the front-end of a receiver shows more stable performance than other SNR estimator. In this paper, we study an efficient SNR estimation algorithm based on LP and propose a new estimation method to decrease the computation complexity. Proposed algorithm accomplishes the SNR estimation process efficiently because it uses the forward prediction error and its conjugate value during the linear prediction error update. Via the computer simulation, the performance of this proposed estimation method is compared and discussed with other conventional SNR estimators in digital communication channels.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.12
• /
• pp.108-115
• /
• 1994

Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.

• Smart Structures and Systems
• /
• v.15 no.3
• /
• pp.717-733
• /
• 2015

Modal parameters of a structure are commonly used quantities for system identification and damage detection. With a limited number of studies on the statistics assessment of modal parameters, this paper presents procedures to properly account for the uncertainties present in the process of extracting modal parameters. Particularly, this paper focuses on how to deal with the measurement error in an ambient vibration test and the modeling error resulting from a modal parameter extraction process. A bootstrap approach is adopted, when an ensemble of a limited number of noised time-history response recordings is available. To estimate the modeling error associated with the extraction process, a model prediction expansion approach is adopted where the modeling error is considered as an "adjustment" to the prediction obtained from the extraction process. The proposed procedures can be further incorporated into the probabilistic analysis of applications where the modal parameters are used. This study considers the effects of the measurement and modeling errors and can provide guidance in allocating resources to improve the estimation accuracy of the modal data. As an illustration, the proposed procedures are applied to extract the modal data of a damaged beam, and the extracted modal data are used to detect potential damage locations using a damage detection method. It is shown that the variability in the modal parameters can be considered to be quite low due to the measurement and modeling errors; however, this low variability has a significant impact on the damage detection results for the studied beam.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1903-1906
• /
• 2005

We proposed a new variant of correlation approach for ARMA model. The proposed method is is intended to make the current prediction error uncorrelated with the past one. In the investigation of the properties, the uniqueness, consistency and asymptotic normality of the estimate are shown. Via simulation results, we show that the proposed method give good estimates for various systems.

• Journal of the Korean Solar Energy Society
• /
• v.38 no.1
• /
• pp.1-11
• /
• 2018

In this work, we propose weather prediction models to estimate hourly outdoor temperatures and solar irradiance in the next day using forecasting information. Hourly weather data predicted by the proposed models are useful for setting system operating strategies for the next day. The outside temperature prediction model considers 3-hourly temperatures forecasted by Korea Meteorological Administration. Hourly data are obtained by a simple interpolation scheme. The solar irradiance prediction is achieved by constructing a dataset with the observed cloudiness and correspondent solar irradiance during the last two weeks and then by matching the forecasted cloud factor for the next day with the solar irradiance values in the dataset. To verify the usefulness of the weather prediction models in predicting a short-term building load, the predicted data are inputted to a TRNSYS building model, and results are compared with a reference case. Results show that the test case can meet the acceptance error level defined by the ASHRAE guideline showing 8.8% in CVRMSE in spite of some inaccurate predictions for hourly weather data.

• New & Renewable Energy
• /
• v.18 no.1
• /
• pp.17-28
• /
• 2022

This paper presents an effective Markov transition matrix (EMTM), which will be used to calculate the wind speed at the target site in a wind farm to accurately predict wind energy production. The existing MTS prediction method using a Markov transition matrix (MTM) exhibits a limitation where significant prediction variations are observed owing to random selection errors and its bin width. The proposed method selects the effective states of the MTM and refines its bin width to reduce the error of random selection during a gap filling procedure in MTS. The EMTM reduces the level of variation in the repeated prediction of wind speed by using the coefficient of variations and range of variations. In a case study, MTS exhibited better performance than other MCP models when EMTM was applied to estimate a one-day wind speed, by using mean relative and root mean square errors.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.8
• /
• pp.1531-1536
• /
• 2016

In this paper, we propose a fast motion estimation algorithm which is important in performance of video encoding. Even though so many fast algorithms for motion estimation have been published due to its tremendous computational amount of for full search algorithm, efforts for reducing computations in motion estimation still remain. In the paper, we propose an algorithm that reduces unnecessary computations only, while keeping prediction quality the same as that of the full search. The proposed algorithm does not calculate block matching error for each candidate at once to find motion vectors but divides the calculation procedure into several steps and calculates partial sum of block errors. By doing that, we can estimate the minimum error point early and get the enhancement of calculation speed by reducing unnecessary computations. The proposed algorithm uses smaller computations than conventional fast search algorithms with the same prediction quality as full search.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.10
• /
• pp.1317-1326
• /
• 1999

As the integration ratio and operation speed increase, it has become an important problem to estimate the dissipated power during the design procedure as a method to reduce the TTM(time to market). This paper proposed a prediction model to estimate the maximum dissipated power of a CMOS logic gate. This model uses a calculational method. It was formed by including the characteristics of MOSFETs of which a CMOS gate consists, the operational characteristics of the gate, and the characteristics of the input signals. As the modeling process, a maximum power estimation model for CMOS inverter was formed first, and then a conversion model to convert a multiple input CMOS gate into a corresponding CMOS inverter was proposed. Finally, the power model for inverter was applied to the converted result so that the model could be applied to a general CMOS gate. For experiment, several CMOS gates were designed in layout level by $0.6{\mu}m$ layout design rule. The result by comparing the calculated results with those from HSPICE simulations for the gates showed that the gate conversion model has within 5% of the relative error rate to the SPICE and the maximum power estimation model has within 10% of the relative error rate. Thus, the proposed models have sufficient accuracies. Also in calculation time, the proposed models was more than 30 times faster than SPICE simulation. Consequently, it can be said that the proposed model could be used efficiently to estimate the maximum dissipated power of a CMOS logic gate during the design procedure.