• Advances in environmental research
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• v.4 no.4
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• pp.211-218
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• 2015

Traffic noise estimation models are useful in evaluation of the noise pollution in current circumstances. They are helpful tools for design and planning new roads and highways. Measurement of average traffic noise level is possible when traffic speed and volume are known. The objective of this study was to devise a model for prediction of highway traffic noise levels based on current traffic variables in Iran. The design of this model was to take the impact of traffic congestion into consideration and to be field tested. This study is a library research augmented by field study conducted on Saeedi Highway located south west of Tehran. The period for the field study lasted 5 days from 7-12 February, 2013. This study examined liner and non-liner methods in formulation of its model. Liner method without a fixed coefficient was the best fit for the intended model. The proposed model can serve as a decision making tool to estimate the impact of key influential factors on sound pressure levels in urban areas in Iran.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.241-248
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• 1998

A Systematic approach to estimate an interior noise level for a railway passenger car has been proposed. The prediction is based on the sound power values obtained from measured sound pressure lever, when a high speed train runs at 300km/hr. Then, the exterior sound pressure value is calculated by using the BEM code. After that, an interior sound lever is estimated, considering the transmission loss of body structures and absorption effect inside of the train. In this application, the estimated noise level is between 66 dBA and 74 dBA. The proposed approach could be useful for rough estimation of a noise level inside a passenger car at the design stage, a]though the method has some limitation to be implement for a general situation.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1413-1419
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• 2008

As the improvements of service quality is becoming an important issue the, interior noise level of a train is an important factor of comfortable ride. To reduce the interior noise level, noise sources of the train need to be removed. However, in case with a structure of large scale and multiple noise sources, an estimation of influences of major noise sources, with indentification of its traveling paths needs to be performed. In current state, to improve the interior noise reduction, consideration of sound transmission loss of the train body prior to manufacturing is usually performed. In this study, the sound transmission loss of the train body of new model train of seoul metro's line no.2 under test opeation is measured and modeling of the train body is performed. And train interior noise is predicted using the measured values.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.1
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• pp.10-16
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• 2013

This paper discussed a procedure for noise certification of Aircraft and predicting the full scale over-flight noise of propeller from acoustic wind tunnel measurement of small scale propeller. Noise Certification Procedures is established from International Civil Aviation Organization(ICAO). The data manipulations are then discussed in extrapolation to simulation flight distance and flight simulation. One of the most important point of flight simulation is adjustments for differences between wind tunnel test conditions and flight test conditions. To simulated the noise level estimation procedure for noise data post-process, simulate procedures from data of the wind tunnel noise measurement and the flight noise measurement by using a 7kg degree UAV. This study confirmed an effectively noise estimation procedures by wind tunnel noise test and flight noise test.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.270-277
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• 2010

The BAMS filter is a kind of wavelet shrinkage filter based on the Bayes estimators with no simulation, therefore it can be used for a real time filter. The denoising efficiency of BAMS filter is seriously affected by the estimated noise variance in each wavelet band. To remove noise in signals in existing BAMS filter, the noise variance is estimated by using the quartile of the finest level of details in the wavelet decomposition, and with this variance, the noise of the level is removed. In this paper, to remove the image noise includingodified quartile of the level of detail is proposed. And by these techniques, the image noises of mid and high frequency bands are removed, and the results showed that the increased PSNR of ab the midband noise, the noise variance estimation method using the monotonic transform and the mout 2[dB] and the effectiveness in denosing of low noise deviation images.

• Journal of KSNVE
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• v.9 no.6
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• pp.1131-1136
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• 1999

NIC@E is the software developed by authors. The program provides the noise level in outdoors due to various noise source types : construction machines including blast sources, railroad vehicles and automobiles. It operates in the Windows system. In this paper, a highway traffic noise has been evaluated using various types of approach : Ray-tracing method, NIRI method, JAS method. In order to compare the noise estimation performance for various models, a measurement is conducted on a 8 lane express highway at the distance of 25 m and 50 m from the lane. The result shows that the ray tracing and JAS model predict the measured value well within 2dB deviaton. The NIRI model, however, underestimates the highway noise level, as the distance between the source and receiver increases.

• Journal of Environmental Impact Assessment
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• v.14 no.1
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• pp.25-36
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• 2005

Actually, prediction formula of road traffic noise for EIA(Environmental Impact Assessment) has been used that proposed by National Institute of Environmental Research in 1999. The prediction formula, however, was calculated predicted noise level according to noise level producing on first floor, then needs to correct noise level at each floor in the case of apartment building. The investigation was carried out to calculate the correction coefficient for commonly using in EIA of large scaled apartment development areas. The noised level at each floor were measured from August 2001 to March 2002 at 31 investigation points of large scaled apartment development area in national wide. Measured data were divided and treated with 4 types as 3th floor, 5th floor, 7th floor and 10th floor and then the correction coefficients of each floor were calculated using by correlation formula according to each floor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.503-504
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• 2014

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.255-263
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• 2012

This paper presents the smart interacting multiple model (SIMM) using the concept of predicted point and maximum noise level. Maximum noise level means the largest value of the mere noises. We utilize the positional difference between measured point and predicted point as acceleration. Comparing this acceleration with the maximum noise level, we extract the acceleration to recognize the characteristics of the target. To estimate the acceleration, we propose an optional algorithm utilizing the proposed method and the Kalman filter (KF) selectively. Also, for increasing the effect of estimation, the weight given at each sub-filter of the interacting multiple model (IMM) structure is varying according to the rate of noise scale. All the procedures of the proposed algorithm can be implemented by an on-line system. Finally, an example is provided to show the effectiveness of the proposed algorithm.

• Explosives and Blasting
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• v.18 no.2
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• pp.14-22
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• 2000

The estimation of proper prediction method and the alteration of transformation method of environmental regulation standard were carried out by measuring blasting noise in construction field. The correlation of scaled distance with sound pressure level were better than with sound level, but it was proved to be difficult to control blasting noise because the correlation factor was too 1ow. three methods to transform sound pressure levee to sound level were examined. The method is the transformation by correlation equation of sound pressure level and sound level which are measured at the same time, and simplified transformation of A-weighting network corresponding to dominant frequency, and the transformation of sound pressure level by FFT. There were many errors to transform. The best effective method is the transformation using correlation equation of sound pressure level and sound level which are measured at the same time.