• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1382-1387
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• 2010

Recently, there has been a growing interest in the environmental conservation. Accordingly, problems related to the audible noise of transformers have became more frequent. Therefore, it is urgent to find a fundamental solution about the audible noises in the substations. This paper described a sort of fundamental solution to solve the noise problem. As a fundamental solution, we suggested the proper audible noise level of transformers through noise prediction in the substation construction phase. And we applied the low noise transformers which have the predicted noise level. As the result, we are able to satisfy the noise regulation through measuring 43.6dBA at the boundary of substation. It is confirmed that the average error rate of prediction was within 3 percent.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.853-856
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• 2002

Rocket propulsion systems generate very high-level noise (acoustic loads), which is due to supersonic jet emitted by rocket engine. In practice, the sound power level of rocket propulsion systems is over 180 dB. This high level noise excites rocket structures and payloads, so that it causes the structural failure and electronic malfunction of payloads. Prediction method of acoustic loads of rocket enables us to determine the safety of payloads. A popular prediction method is based on NASA SP-8072. This method was used to predict the acoustic loads of KSR-III rocket. Measurement of acoustic loads by KSR-III propulsion system was performed in the stage qualification test. The predicted results were compared with the measured ones.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.1157-1161
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• 2007

A construction noise is the main reason for people's petition among the pollution. The purpose of this study is to develop the noise prediction program to see the level of the noise on the construction site more accurately. For this purpose, the database of the power level on the various equipments was made. The noise reduction by distance and the noise reduction by diffraction of barrier were mainly considered and calculated. The simple noise prediction program will provide the information about proper height and length of the potable barrier which satisfies noise criteria of the construction sites from a construction planning stage. To investigate the reliability of this program, the predicted data was compared with the measured data. An average of difference between measured data and predicted data is 1.3 dB(A) and a coefficient of correlation is about 0.95.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.06b
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• pp.270-275
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• 1998

It was assumed that the voice of cattle is generated with the virtual white noise through the digital filter called the linear prediction filter, and filter parameters (prediction coefficients) were estimated by the maximum entropy method (MEM) , using the sound signal of the animal . The feature planes were defined by the pairs of two parameters selected appropriately from these parameters. The cattle voices were divided into three levels, that is the high, medium and low levels according to their total power equivalent to the variances of the sound signal . It was found that the straight lines could be used for recognizing tow cow and one calf for high level voices. For high and medium level voices, however, it was difficult or impossible to recognize individual cattle on the parameters planes.

• Journal of KSNVE
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• v.9 no.6
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• pp.1116-1122
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• 1999

HRSG, which is one of main components of the combined cycle power plant,is composed of an inlet duct, a main body and casing, an outlet duct and a stack. It is important to design HRSG wihtin the allowable noise limit. For this purpose, it is necessary to analyze and predict the noise reduction and radiation at HRSG. In this paper, the technology for the noise prediction at each part of HRSG has been based on the empirical and field data, and also the HRSG noise prediction program has been developed. In order to verify the developed technology and program a field test is conducted. The results of noise prediction show good agreement with the measured.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.11
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• pp.2837-2845
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• 1994

Several methods have been developed to predict on the remaining life of the old power plants. However, Larson-Miller parameter, one of existing creep life prediction methods, has not reflected the effect of material degradatioin and grain size. So this study has been carried out to research the effects of material degradation and austenite grain coarsening on the life prediction of 3.5Ni-Cr-Mo-V steel. An experimental result shows that carbide coarsening has no significant effects on the creep rupture life and the Larson-Miller parameter, but grain coarsening has an important influence on the creep ruptrure life and the Larson-Miller parameter. Therefore Larson-Miller constant, K should be determined to consider on the chemical composition and the grain size of materials.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• Korean Journal of Applied Biomechanics
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• v.34 no.2
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• pp.71-80
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• 2024

Objective: This study aimed to explore the brain connectivity between brain and biomechanical variables by exploring motion recognition through FFT (fast fourier transform) analysis and AI (artificial intelligence) focusing on quiet standing movement patterns. Method: Participants included 12 young adult males, comprising university students (n=6) and elite gymnasts (n=6). The first experiment involved FFT of biomechanical signals (f_CoP, f_AJtorque and f_EEG), and the second experiment explored the optimization of AI-based GRU (gated recurrent unit) using f_EEG data. Results: Significant differences (p<.05) were observed in frequency bands and maximum power based on group and posture types in the first experiment. The second study improved motion prediction accuracy through GRU performance metrics derived from brain signals. Conclusion: This study delved into the movement pattern of upright standing posture through the analysis of bio-signals linking the cerebral cortex to motor performance, culminating in the attainment of motion recognition prediction performance.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.6
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• pp.501-514
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• 2017

In the previous research, a study was carried out to estimate the actual performance such as the propeller Revolution Per Minute (RPM) and engine power of a Liquefied Natural Gas Carrier (LNGC) using the full-scale measurement data. After the predicted RPM and engine power were verified by comparing those with the measured values, the suggested method was regarded to be acceptable. However, there was a limitation to apply the method on the prediction of the RPM and engine power of a ship. Since the information of route, speed, and environmental conditions required for estimating the RPM and engine power is generally regarded as the intellectual property of a shipping company, it is difficult to secure the information on a shipyard. In this paper, the RPM and engine power of the 151K LNGC was estimated using the combination of Automatic Identification System (AIS) and European Centre for Medium-Range Weather Forecasts (ECMWF) database in order to replace the full-scale measurement. The simulation approach, which was suggested in the previous research, was identically applied to the prediction of RPM and engine power. After the results based on the AIS and ECMWF database were compared with those obtained from the full-scale measurement data, the feasibility was briefly reviewed.