• 유체기계공업학회:학술대회논문집
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• 1998.02a
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• pp.63-72
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• 1998

This study Is on the performance prediction and design of sirocco fan. Slip coefficient is very important factor for the performance analysis of centrifugal-type fan. Because generally used slip coefficient equations of backward curved centrifugal fan are not appropriate for forward curved sirocco fan, in this study a proper slip coefficient equation for sirocco fan is suggested. Using this equation performance prediction program for sirocco fan is composed and also included the total noise prediction that include turbulent noise at the fan Inlet and boundary layer noise. A comparison between the values obtained from performance prediction program and experimental values shows that the program predicts the sirocco fan performance in a practical rate.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.5
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• pp.637-653
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• 1999

In this study well-known burned rate expressions of Weibe function and double Wiebe function have been adopted for the combustion analysis of large two stroke marine diesel engine. A cycle simulation program was also developed to predict the performance and pressure waves in pipes using validated burned rate function,. Levenberg-Marquardt iteration method was applied to cali-brate the shape coefficients included in double Wiebe function for the performance prediction of two-stroke marine diesel engine. As a result the performance prediction using double Wiebe func-tion is well correlated withexperimental dta with the accuracy of 5% and pressure waves in intake and transport pipe are well predicted. From the results of this study it can be confirmed that the shape coefficients of burned rate function should be modified using the numerical method suggested for the accurated prediction and double Wiebe function is more suitable than Wiebe func-tion for combustion analysis of large two stroke marine engine.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.191-196
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• 2015

There have been many studies on statistical forecasting on firm's performance and stock price by applying various financial indicators such as debt ratio and sales growth rate. Selecting predictors for constructing a prediction model among the various financial indicators is very important for precise prediction. Most of the previous studies applied variable selection algorithms for selecting predictors. However, the variable selection algorithm is considered to be at risk of eliminating certain amount of information from the indicators that were excluded from model construction. Therefore, we propose a firm's performance prediction model which principal component analysis is applied instead of the variable selection algorithm, in order to reduce dimensionality of input variables of the prediction model. In this study, we constructed the proposed prediction model by using financial data of American IT companies to empirically analyze prediction performance of the model.

• Atmosphere
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• v.31 no.5
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• pp.511-523
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• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• ETRI Journal
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• v.42 no.3
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• pp.366-375
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• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.12
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• pp.439-446
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• 2016

In this paper, an efficient prediction accuracy enhancement strategy is proposed for improving the performance of the prediction-based parallel event-driven gate-level timing simulation. The proposed new strategy adopts the static double prediction and the dynamic prediction for input and output values of local simulations. The double prediction utilizes another static prediction data for the secondary prediction once the first prediction fails, and the dynamic prediction tries to use the on-going simulation result accumulated dynamically during the actual parallel simulation execution as prediction data. Therefore, the communication overhead and synchronization overhead, which are the main bottleneck of parallel simulation, are maximally reduced. Throughout the proposed two prediction enhancement techniques, we have observed about 5x simulation performance improvement over the commercial parallel multi-core simulation for six test designs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.969-972
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• 2016

In this paper, we propose an algorithm of angular mode selection for high-performance HEVC intra prediction. HEVC intra prediction is used to remove the spatial redundancy. Intra prediction has a total of 35 modes and block size of $64{\times}64$ to $4{\times}4$. Intra prediction has a high amount of calculation and operational time due to performing all 35 modes for each block size for the best cost. The angular mode algorithm proposed has a simple difference between pixels of the original image and the selected angular mode. A decision is made to select one angular mode plus planar mode and DC mode to perform the intra prediction and determine the mode with the best cost. In effect, only three modes are executed compared to the traditional 35 modes. Performance evaluation index used are BD-PSNR and BD-Bitrate. For the proposed algorithm, BD-PSNR results averagely increased by 0.035 and BD-Bitrate decreased by 0.623 relative to the HM-16.9 intra prediction. In addition, the encoding time is decreased by about 6.905%.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.1-10
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• 2009

Precise branch predictor has a profound impact on system performance in modern processor architectures. Recent works show that prediction latency as well as prediction accuracy has a critical impact on overall system performance as well. However, prediction latency tends to be overlooked. In this paper, we propose Branch Pre-Prediction policy to tolerate branch prediction latency. The proposed solution allows that branch predictor can proceed its prediction without any information from the fetch engine, separating the prediction engine from fetch stage. In addition, we propose newly modified BTE structure to support our solution. The simulation result shows that proposed solution can hide most prediction latency with still providing the same level of prediction accuracy. Furthermore, the proposed solution shows even better performance than the ideal case, that is the predictor which always takes a single cycle prediction latency. In our experiments, IPC improvement is up to 11.92% and 5.15% in average, compared to conventional predictor system.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.6
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• pp.499-508
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• 2016

This paper describes a study of performance prediction of an electric propulsion system for multirotor UAVs. The electric propulsion system consists of motors, propellers, batteries and speed controllers, and significantly affects performance characteristics of the platform. The performance of the electric propulsion system for multirotor UAVs was predicted using an analytical model derived from the characteristics of each component, operation experiments and statistical analyses. Ground performance tests and endurance flights were performed to verify the reliability of the proposed performance prediction method. A quadrotor platform was designed to demonstrate the parcel delivery service used in the endurance flight. From the result of verification tests, it was confirmed that the proposed method has a good agreement.