• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.129-134
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• 1987

In this paper, "Revised two-level costate prediction method" is developed to optimize the quadratic performance of a class of nonlinear dynamic systems. To show the merit, of this algorithm, the proposed algorithm is compared With "The new prediction method" and "Two-level costate prediction method". Advantages of this algorithm are illustrated by applying it to three examples, turbine generator system, fermentation Process, power control system in nuclear reactor.

• Journal of Platform Technology
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• v.11 no.6
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• pp.3-12
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• 2023

In today's system operation, it is difficult to detect failures and take immediate action in the case of a shortage of manpower compared to the number of equipment or failures in vulnerable time zones, which can lead to delays in failure recovery. In addition, various algorithms exist to detect abnormal symptom data, and it is important to select an appropriate algorithm for each problem. In this paper, an ensemble-based isolation forest model was used to efficiently detect multivariate point anomalies that deviated from the mean distribution in the data set generated to predict system failure and minimize service interruption. And since significant changes in memory space usage are observed together with changes in CPU usage, the problem is solved by using LSTM-Auto Encoder for a collective anomaly in which another feature exhibits an abnormal pattern according to a change in one by comparing two or more features. did In addition, evaluation indicators are set for the performance evaluation of the model presented in this study, and then AI model evaluation is performed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.471-476
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• 2016

This paper presents our study on speech recognition performance prediction. Our initial study shows that a combination of speech quality measures effectively improves correlation with Word Error Rate (WER) compared to each speech measure alone. In this paper we demonstrate a new combination of various types of speech quality measures shows more significantly improves correlation with WER compared to the speech measure combination of our initial study. In our study, SNR, PESQ, acoustic model score, and MFCC distance are used as the speech quality measures. This paper also presents our speech database verification system for speech recognition employing the speech measures. We develop a WER prediction system using Gaussian mixture model and the speech quality measures as a feature vector. The experimental results show the proposed system is highly effective at predicting WER in a low SNR condition of speech babble and car noise environments.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.5
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• pp.24-30
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• 2010

In this paper, we described intra prediction which is the one of techniques to be used for higher compression performance in H.264/AVC and proposed the design of intra predictor for efficient intra prediction mode processing. The proposed system is consist of processing elements, precomputation processing elements, an intra prediction controller, an internal memory and a register controller. The proposed system needs the reduced the computation cycles by using processing elements and precomputation processing element and also needs the reduced the number of access time to external memory by using internal memory and registers architecture. We designed the proposed system with Verilog-HDL and verified with suitable test vectors which are encoded YUV files. The proposed architecture belongs to the baseline profile of H.264/AVC decoder and is suitable for portable devices such as cellular phone with the size of $176{\times}144$. As a result of experiment, the performance of the proposed intra predictor is about 60% higher than that of the previous one.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.2
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• pp.84-94
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• 2018

In order to overcome the limitation of deterministic forecast, an ensemble forecasting system for regional ocean wave is developed. This system predicts ocean wind waves based on the meteorological forcing from the Ensemble Prediction System for Global of the Korea Meteorological Administration, which is consisted of 24 ensemble members. The ensemble wave forecasting system is evaluated by using the moored buoy data around Korea. The root mean squared error (RMSE) of ensemble mean showed the better performance than the deterministic forecast system after 2 days, especially RMSE of ensemble mean is improved by 15% compared with the deterministic forecast for 3-day lead time. It means that the ensemble method could reduce the uncertainty of the deterministic prediction system. The Relative Operating Characteristic as an evaluation scheme of probability prediction was bigger than 0.9 showing high predictability, meaning that the ensemble wave forecast could be usefully applied.

• Journal of Environmental Science International
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• v.26 no.10
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• pp.1167-1180
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• 2017

Numerical Weather Prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are essential for forecasting one-day-ahead solar irradiance. In order to evaluate the performance of the WRF in forecasting solar irradiance over the Korean Peninsula, we compared WRF prediction data from 2008 to 2010 corresponding to weather observation data (OBS) from the Korean Meteorological Administration (KMA). The WRF model showed poor performance at polluted regions such as Seoul and Suwon where the relative Root Mean Square Error (rRMSE) is over 30%. Predictions by the WRF model alone had a large amount of potential error because of the lack of actual aerosol radiative feedbacks. For the purpose of reducing this error induced by atmospheric particles, i.e., aerosols, the WRF model was coupled with the Community Multiscale Air Quality (CMAQ) model. The coupled system makes it possible to estimate the radiative feedbacks of aerosols on the solar irradiance. As a result, the solar irradiance estimated by the coupled system showed a strong dependence on both the aerosol spatial distributions and the associated optical properties. In the NF (No Feedback) case, which refers to the WRF-only stimulated system without aerosol feedbacks, the GHI was overestimated by $50-200W\;m^{-2}$ compared with OBS derived values at each site. In the YF (Yes Feedback) case, in contrast, which refers to the WRF-CMAQ two-way coupled system, the rRMSE was significantly improved by 3.1-3.7% at Suwon and Seoul where the Particulate Matter (PM) concentrations, specifically, those related to the $PM_{10}$ size fraction, were over $100{\mu}g\;m^{-3}$. Thus, the coupled system showed promise for acquiring more accurate solar irradiance forecasts.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.7
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• pp.527-534
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• 2018

The endurance of the multi-copter is one of the important variables that determine the mission performance. Therefore, accurate endurance should be defined as essential for performing effective missions. In this paper, we present the results of the study on the flight performance of the aircraft, especially the hovering of the drone(multi-copter). Unlike conventional aircraft, which consider aerodynamic performance by the fuselage, the multi-copter is mostly determined by the propulsion system. Therefore, the research method classifies the various parts constituting the drone system into functions, analyzes the performance of the unit parts and obtains the experimental data by sorting out the specifications and functions at the component level and mathematical formulation, The results of this study are as follows. In addition, the 5kg class quad copter was used to predict and verify the voltage change with endurance through analysis of in situ flight. By predicting endurance under various conditions, it can help design/build the right Multi-copter for mission.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.623-637
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• 2018

This study investigates the effects of horizontal resolution, cumulus parameterization scheme (CPS), and probability forecasting on precipitation forecasts over the Korean Peninsula from 00 UTC 15 August to 12 UTC 14 September 2013, using the limited-area ensemble prediction system (LEPS) of the Korea Meteorological Administration. To investigate the effect of resolution, the control members of the LEPS with 1.5- and 3-km resolution were compared. Two 3-km experiments with and without the CPS were conducted for the control member, because a 3-km resolution lies within the gray zone. For probability forecasting, 12 ensemble members with 3-km resolution were run using the LEPS. The forecast performance was evaluated for both the whole study period and precipitation cases categorized by synoptic forcing. The performance of precipitation forecasts using the 1.5-km resolution was better than that using the 3-km resolution for both the total period and individual cases. The result of the 3-km resolution experiment with the CPS did not differ significantly from that without it. The 3-km ensemble mean and probability matching (PM) performed better than the 3-km control member, regardless of the use of the CPS. The PM complemented the defect of the ensemble mean, which better predicts precipitation regions but underestimates precipitation amount by averaging ensembles, compared to the control member. Further, both the 3-km ensemble mean and PM outperformed the 1.5-km control member, which implies that the lower performance of the 3-km control member compared to the 1.5-km control member was complemented by probability forecasting.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.147-158
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• 2021

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.