• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3612-3624
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• 2021

At present, in the case of pump fast optimization, there is a problem of rapid, accurate and effective prediction of cavitation performance. In "A Cavitation Performance Prediction Method for Pumps PART1-Proposal and Feasibility" [1], a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments of a mixed flow pump. However, whether this method is applicable to vane pumps with different specific speeds and whether the prediction results of this method are accurate is still worthy of further study. Combined with the experimental results, the research evaluates the sensitivity and accuracy at different flow rates. For a certain operating condition, the method has better sensitivity to different flow rates. This is suitable for multi-parameter multi-objective optimization of pump impeller. For the test mixed flow pump, the method is more accurate when the area ratios are 13.718% and 13.826%. The cavitation vortex flow is obtained through high-speed camera, and the correlation between cavitation flow structure and cavitation performance is established to provide more scientific support for cavitation performance prediction. The method is not only suitable for cavitation performance prediction of the mixed flow pump, but also can be expanded to cavitation performance prediction of blade type hydraulic machinery, which will solve the problem of rapid prediction of hydraulic machinery cavitation performance.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.377-386
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• 2004

A technique that provides prediction intervals based on a model called an empirical linear model is discussed. The technique, high-dimensional empirical linear prediction (HELP), involves principal component analysis, factor analysis and model selection. HELP can be viewed as a technique that provides prediction (and confidence) intervals based on a factor analysis models do not typically have justifiable theory due to nonidentifiability, we show that the intervals are justifiable asymptotically.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.24-32
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• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

• Journal of Korean Society for Quality Management
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• v.22 no.3
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• pp.179-190
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• 1994

Financial variables have been used in bankruptcy prediction. Despite of possible errors in prediction, most existing approaches do not consider the causal time sequence of prediction activity and bankruptcy phenomena. This paper proposes a prediction method using Neyman-Pearson Theorem and Bayes' rule. The proposed method uses posterior probability concept and determines a prediction policy with appropriate error rate.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.189-191
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• 2005

In the scalable extension of H.264/AVC, the codec is based on a layered approach to enable spatial scalability. In each layer, the basic concepts of motion compensated prediction and intra prediction are employed as in standard H.264/AVC. Additionally inter-layer prediction algorithm between successive spatial layers is applied to remove redundancy. In the inter-layer prediction, as the prediction we can use the signal that is the upsampled signal of the lower resolution layer. In this case, coding efficiency can be variable as the kinds of interpolation filter. In this paper, we investigate the approach to select the interpolation filter for residual signal in order to optimal prediction.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.723-726
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• 2009

This paper describes a non-causal interpolative prediction method for B-picture encoding. Interpolative prediction uses correlations between neighboring pixels, including non-causal pixels, for high prediction performance, in contrast to the conventional prediction, using only the causal pixels. For the interpolative prediction, the optimal quantizing scheme has been investigated for preventing conding error power from expanding in the decoding process. In this paper, we extend the optimal quantization sceme to inter-frame prediction in video coding. Unlike H.264 scheme, our method uses non-causal frames adjacent to the prediction frame.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.137-139
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• 2017

Modern manufacturing fields have been changed to use optimal manufacturing volume on the optimal demand prediction. This research is to compare the prediction capability of various prediction methods. And then, it is to suggest a flexible selection of the optimal prediction method according to optimal prediction capability.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.1-6
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• 2021

For competitive hotel business, the hotel must have a sound prediction capability to balance the demand and supply of hospitality products. To have a sound prediction capability in the hotel, it should be prepared to be equipped with a new technology such as quantum computing. The quantum computing is a brand new cutting-edge technology. It will change hotel business and even the whole world too. Therefore, we study the impact of quantum computing on supply chain management (SCM) and hotel performance. Toward the goal we have developed the research model including six constructs: quantum (computing) prediction, communication, supplier relationship, service quality, non-financial performance, and financial performance. The result of the study shows a significant influence of quantum (computing) prediction on hotel performance through the mediating role of SCM in the hotel. Quantum prediction is highly significant in enhancing the SCM in the hotel. However, the direct effect between the quantum prediction and hotel performance is not significant. The finding indicates that hotels which would install the quantum computing technology and utilize the quantum prediction could hugely benefit from the performance improvement.

• Atmosphere
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• v.27 no.2
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• pp.235-249
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• 2017

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.