• Journal of the Korean Society for Railway
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• v.11 no.5
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• pp.465-470
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• 2008

Sound transmission characteristics are investigated on the honeycomb panels used for railway vehicles. Equivalent orthotropic plate model and equivalent mass law are applied to predict the sound transmission loss (STL) of the honeycomb panels. The predicted values of the STL are compared with the measured values. The reliability and the limitation of the prediction models are investigated. Coincidence effect and local resonance effect on STL are considered. The result of the study shows that the equivalent orthotropic plate model can be used as a good prediction model, if the local resonance frequency is properly applied. finally, ways to improve the severe STL drop by local resonance are proposed and the effect on the sound insulation performance is analysed.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.236-244
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• 2024

A representative problem in domestic chestnut industry is the high loss of flesh due to excessive knife peeling in order to increase the peeling rate, resulting in a decrease in production efficiency. In this study, a prediction model for weight loss rate of chestnut by stage of knife peeling process was developed as undergarment study to optimize conditions of the machine. 51 control conditions of the two-stage blade peeler used in the experiment were derived and repeated three times to obtain a total of 153 data. Machine learning(ML) models including artificial neural network (ANN) and random forest (RF) were implemented to predict the weight loss rate by chestnut peel stage (after 1st peeling, 2nd peeling, and after final discharge). The performance of the models were evaluated by calculating the values of coefficient of determination (R), normalized root mean square error (nRMSE), and mean absolute error (MAE). After all peeling stages, RF model have better prediction accuracy with higher R values and low prediction error with lower nRMSE and MAE values, compared to ANN model. The final selected RF prediction model showed excellent performance with insignificant error between the experimental and predicted values. As a result, the proposed model can be useful to set optimum condition of knife peeling for the purpose of minimizing the weight loss of domestic chestnut flesh with maximizing peeling rate.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.580-585
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• 2000

Recently TEIS model, Two-zone model aid Stanitz equations are often used for off-design performance prediction of centrifugal compressor and pump. The prediction results often agree well with experimental data. However these models and equations have some important variables which have a great influence on overall performance prediction me. But no systematic study about these variables has been performed. So, in this paper, a systematic study about these variables influence on overall performance prediction owe is peformed. Finally the meaning of the variables and the research to be undertaken are discussed.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.1-12
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• 2024

Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.87-92
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• 2011

In this paper, we got the 800 ~ 900 MHz path loss model for Russia PNG area using the free space model and the Okumura-Hata Model. In order to add new regional properties to the existing path loss model, the mean square error technique is used to obtain the correction factor. The correction factors for the free space and the Hata model are 28, 13 dB respectively. By applying this correction factors, the new Russain PNG path loss model is proposed.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.985-987
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• 2011

This paper includes a development of a starting time prediction model for a derivative engine. For this derivative engine design, a new map expansion method, Modified Pump Scaling Law(MPS), has been applied and expand the maps to sub-idle range. From loss characteristics of the reference engine, loss models for the derivative engine have been developed considering different pressure, temperature, and engine configurations. Starting time predictions of the derivative engine shows preferable results comparing test results.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.123-128
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• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.

• Food Science and Biotechnology
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• v.14 no.5
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• pp.628-633
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• 2005

Muscle fiber characteristics were evaluated for predictability of meat quality traits using 231 crossbred pigs. Muscle $pH_{45min}$, R-value, and $pH_{24hr}$ were selected to estimate regression equation model of drip loss and lightness, although variances of coefficient estimates could only account for small part of drip loss (about 16.3 to 25.3%) and lightness (about 16.9 to 31.7%). Muscle $pH_{24hr}$ was represented to drip loss and lightness, which explained corresponding 25.3 and 31.7% of estimation in drip loss and lightness, respectively. Area percentage of type IIb fiber significantly contributed to prediction of metabolic rate and meat quality. However, equations predicting meat quality traits based on area percentage of type IIb fiber alone are less useful than ones based on early postmortem parameters. These results suggest estimated model using both metabolic properties of muscle and postmortem metabolic rate could be used for prediction of pork quality traits.

• Applied Chemistry for Engineering
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• v.29 no.6
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• pp.789-798
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• 2018

The role of empirical equation to predict the performance of polymer electrolyte membrane fuel cell is important. The activation, ohmic and mass transfer losses were separated in a polarization curve, and the curve fitting according to each region was performed using Kim's model and Hao's model. Changes of each loss were compared according to operation variables of the temperature, pressure, oxygen concentration and membrane thickness. The existing model showed a good fitting convergence, but less fitting accuracy in the separated loss region. A new model using the convergence coefficient was suggested to improve the accuracy of performance prediction of fuel cells of which results were demonstrated.