• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.204-206
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• 2021

Real-time drilling parameters prediction is a considerably important study from the viewpoint of maximizing drilling efficiency. Among the methods of maximizing drilling, the method of improving the drilling speed is common, which is related to the rate of penetration, drillstring rotational speed, weight on bit, and drilling mud flow rate. This study proposes a method of predicting the drilling rate, one of the real-time drilling parameters, using a recurrent neural network-based deep learning model, and compares the existing physical-based drilling rate prediction model with a prediction model using deep learning.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.545-546
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• 2008

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2844-2851
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• 2011

The purpose of this study is to set up an acoustic prediction technique and to perform the IL test of scale down noise reducing device for the development of the noise reducing device as the development of 400km/h class high speed train. First of all, the IL prediction of noise reducing device was performed with the 2D BEM method. And the noise test of scale down noise reducing device in anechoic chamber was performed for the verification of acoustic prediction technique and IL performance evaluation. As the results, the acoustic prediction technique for the development of noise reducing device was verified because the averaged IL difference between prediction and test is in 2dB(A). And the measured IL value of noise reducing device is less than 2dB(A), and additional IL with polyester absorption material is increased about 0.5dB(A).

• Journal of Applied Reliability
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• v.11 no.4
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• pp.399-407
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• 2011

Reliability evaluation of One-Shot system which flies at speed of Mach must be evaluated as the result of many firing tests. But many firing tests are impossible because of budget deficit. Consequently the reliability prediction which substitutes firing tests is used. The accuracy of reliability prediction is decided according to a quantity of accumulated test data. If the test data is insufficient, the direction of prediction can not be set. So we propose the reliability prediction method which applies MIL-HDBK-217 Plus. MIL-HDBK-217 Plus is described about reliability prediction method without sufficient test data. So we apply MIL-HDBK-217 Plus to the rocket motor system, and we accomplish a modeling and a reliability prediction about the system.

• Journal of Environmental Science International
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• v.17 no.2
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• pp.195-201
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• 2008

Damage from typhoon disaster can be mitigated by grasping and dealing with the damage promptly for the regions in typhoon track. What is this work, a technique to analyzed dangerousness of typhoon should be presupposed. This study estimated 10 m level wind speed using 700 hPa wind by typhoon, referring to GPS dropwindsonde study of Franklin(2003). For 700 hPa wind, 30 km resolution data of Regional Data Assimilation Prediction System(RDAPS) were used. For roughness length in estimating wind of 10 m level, landuse data of USGS are employed. For 10 m level wind speed of Typhoon Rusa in 2002, we sampled AWS site of $7.4{\sim}30km$ distant from typhoon center and compare them with observational data. The results show that the 10 m level wind speed is the estimation of maximum wind speed which can appear in surface by typhoon and it cannot be compared with general hourly observational data. Wind load on domestic buildings relies on probability distributions of extreme wind speed. Hence, calculated 10 m level wind speed is useful for estimating the damage structure from typhoon.

• Journal of the Korean Society for Railway
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• v.2 no.3
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• pp.18-25
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• 1999

A high speed train named Korean High Speed Train (KHST) with its maximum operating speed 350 km/h is now under development. For the successful design of a quiet and comfortable high speed train, it is important to understand the general features of the noise and vibration characteristics of high speed train. This paper reviews extensively collected materials on the noise and vibration characteristics of existing high speed trains. Based on the noise and vibration characteristics of TGV systems, a simple indoor-noise prediction rule is proposed for the TGV-type high speed trains such as KHST The indoor-noise of KHST at 350km/h is expected to be larger than at least 71 dBA. Some important researches conducted for designing KHST during last two years under the G-7 project are also briefly introduced at the last part of this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.50-57
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• 1997

A 600HP class high-speed gear driven 3-stage turbo-compressor (IGCC : Integrally Geared Centrifugal Compressor) driven by a 3600 rpm AC induction motor has been designed, of which low speed pinion runs at 35000 rpm and high speed pinion at 50000 rpm nominally. Due to its high speed operation, the system requires very reliable bearing selection and design as well as accurate rotordynamic analysis and prediction of its dynamic behavior to secure the operating reliability. Rotordaynamic analyses of the IGCC rotor-bearing system predicted that the low speed pinion rotor mounted on 5-pad tilting pad bearings has two critical speeds before its design speed and high speed pinion rotor only one critical speed, and estimated critical speeds of both pinion shafts are away from the continuous operating speed enough to satisfy the corresponding API requirement. The forced response analysis with API specified maximum allowable unbalances also showed that unbalance responses are small enough for smooth operation of the system.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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• pp.554-569
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• 2017

Sensitivity analysis on $PM_{10}$ forecasting simulations was carried out by using two different initial and boundary conditions of meteorological fields: NCEP/FNL (National Centers for Environmental Prediction/Final Analysis) reanlaysis data and NCEP/GFS (National Centers for Environmental Prediction/Global Forecast System) forecasting data, and the comparisons were made between two different simulations. The two results both yielded lower $PM_{10}$ concentrations than observations, with relatively lower biased results by NCEP/FNL than NCEP/GFS. We explored the detailed individual meteorological variables to associate with $PM_{10}$ prediction performance. With the results of NCEP/FNL outperforming GFS, our conclusion is that no particular significant bias was found in temperature fields between NCEP/FNL and NCEP/GFS data, while the overestimated wind speed by NCEP/GFS data influenced on the lower $PM_{10}$ concentrations simulation than NCEP/FNL, by decreasing the duration time of high-$PM_{10}$ loaded air mass over both coastal and metropolitan areas. These comparative characteristics of FNL against GFS data such as maximum 3~4 m/s weaker wind speed, $PM_{10}$ concentration control with the highest possible factor of 1.3~1.6, and one or two hour difference of peak time for each case in this study, were also reflected into the results of statistical analysis. It is implying that improving the surface wind speed fluctuation is an important controlling factor for the better prediction of $PM_{10}$ over Korean Peninsula.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.9-15
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• 2023

In this paper, we propose a method predicting the speed of each lane from the link speed using a neural network. We took three measures for configuring learning data to increase prediction accuracy. The first one is to expand the spatial range of the data source by including 14 links connected to the beginning and end points of the link. We also increased the time interval from 07:00 to 22:00 and included the data generation time in the feature data. Finally, we marked weekdays and holidays. Results of experiments showed that the speed error was reduced by 21.9% from 6.4 km/h to 5.0 km/h for straight lane, by 12.9% from 8.5 km/h to 7.4 km/h for right turns, and by 5.7% from 8.7 km/h to 8.2 km/h for left-turns. As a secondary result, we confirmed that the prediction accuracy of each lane was high for city roads when the traffic flow was congested. The feature of the proposed method is that it predicts traffic conditions for each lane improving the accuracy of prediction.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.8
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• pp.703-709
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• 2010

An analysis tool for predicting transmission loss in high speed trains based on combined use of the statistical energy analysis and the finite element methods has been proposed. The analysis utilizes a commercially available numerical solver VA ONE with imbedded NASTRAN module. The proposed analysis tool is first verified by comparing numerically predicted transmission loss of a light rail transport(LRT) structure with experimental results. The comparison shows that the numerically predicted transmission loss is similar to the experimental data. The analysis tool is then applied to the prediction of transmission loss in the high speed train(HST) currently under development. Various sub-structures such as the floor, side panel and ceiling have been numerically analyzed to predict their transmission losses. The results obtained here can be used as input data for predicting the interior noise level of the HST at design stage.