• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.843-844
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.159-160
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.751-756
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• 2006

The purpose of this study is to grasp present condition of railroad noise, and forecast noise and presented basis data that ready noise reduction countermeasure. This study has predicted the level of noise reduction according to barrier by considering the characteristics of the cause of noise. These result drew using 'Prediction Model of NIER' and 'Shall03 of Germany'. This paper found important factors in railroad noise using this results and become forward syudy direction.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.1
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• pp.94-100
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• 2008

Noise barriers are being used more often to solve problems of noise pollution from traffic noise. Several types of noise barriers are being installed to increase the cost-effectiveness of noise barrier installation. In this study, the insertion loss is analyzed to evaluate the effectiveness of the noise barrier by using the BEM. In order to check the validity of the BEM, the BEM and Lam's theoretical analysis are compared with measurement, which is performed in the anechoic chamber for the 1/10 scale-down model, and good agreements are obtained. By using the two dimensional boundary element method, the insertion loss is calculated and analyzed for several typical noise barriers such as the vertical barrier, the barrier with an oblique edge on top, the T-shaped barrier and the barrier with interference device on top. With these analyses, it is possible to design more cost-effective noise barriers appropriate for a particular area.

• ETRI Journal
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• v.46 no.3
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• pp.379-391
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• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.5
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• pp.400-407
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• 2011

The suggested method of previous Son's study dichotomized subjective response data to modeling noise exposure-response. The method used maximum liklihood estimation instead of least square estimation and the noise exposure-response curve of the study was logistic regression analysis result. The method was originated to modeling community response rate such as %HA or %A. It can be useful when the subjective response was investigated based on predicted noise level. It is difficult to measure the single source emitting noise such as railway because various traffic noise sources combined in our life. The suggested method was adopted to model in this study and railway noise-exposure response curves were modeled because the noise level of this area was predicted data. The data of this study was used by previous Ko's paper but he dealt the area as combined noise area and divided the data by dominant noise source. But this study used all data of this area because the annoyance response to railway noise was higher than other noise according to the result of correlation analysis. The trend of the %HA and %A prediction model to train noise of this study is almost same as the model based on measured noise of previous Lim's study although the investigated areas and methods were different.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• Journal of Environmental Impact Assessment
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• v.15 no.2
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• pp.93-100
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• 2006

In general, the verification to prediction formula in a national road and the main street of a town has been used recklessly in Korea. Therefore we investigated the validity of an existing prediction formula (NIER(87, 99), TR-Noise, KLC(2002)) with correction relationship which was based on both the prediction formular from apartment complex in the field and height 1.5m from the surface level. On the results of measuring the noise level form an isolated distance, the noise level showed that it was 4.5~5.5dB(A) by reason of becoming 2 folder far from a source. From the distribution of noise level measured by the apartment floors, the measurement point (1st floor) was 58.7~71.4dB(A) at its lowest level and the middle floors (3, 5, 7 and 10) were the highest distribution of noise level. From the analysis results on the application validity to an existing prediction formular (NIER(87, 99), TR-Noise, KLC(2002)) in the height 1.5m, the correction coefficients were 0.95~0.96 and the measured values were reasonably close to the predicted values, indicating the validity and adequacy of the predicted models. KLC(2002) model was found accurate within 3dB(A) with 36 data out of the total 42 data, showing the most accuracy among the predict models. However, the developed models have to improve the accuracy with a various of factors.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.153-158
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• 2012

Noise maps are produced according to 'The Method of making a Noise Map' in order to noise control efficiently, and prediction model to predict road traffic noise which may apply to Korean situation, include CRTN, RLS 90, NMPB, Nord 2000 and ASJ 2003. Of them, ASJ 2003, Japan's prediction model has not been verified for the application to Korean situation according to the classification of vehicle. In addition, ASJ 2003 was revised to ASJ 2008 recently, a classification for motorcycle was added. This study attempts to check the classification of vehicle in ASJ 2008 and 'The Method of making a Noise Map' to confirm the suitability of the application of them to Korean situation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.161-166
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• 2012

This Study, as part of a study on the application plan of overseas noise prediction models suitable for making domestic noise maps, analyzed the correlation between the differences in predicted noise levels by individual noise prediction model and surveyed data on General roads and Expressways. Separation distances of 5m and 10m, respectively were set from the ends of the general roads and the expressways at the points of measurements and to check the distribution patterns of sound power levels, the levels were measured at the heights of 1.5m and 3m, respectively. The latest revised versions of the five models (CRTN, RLS90, NMPB, Nord2000, ASJ2008) suggested in The Method of making Noise Maps were used as prediction models, and predicted noise levels were calculated by using commercial software SoundPLAN (Ver 7.1).