• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.17 no.11
• /
• pp.1021-1027
• /
• 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 $0.1{\sim}2.8\;dB(A)$ and a coefficient of correlation is about $0.85{\sim}0.95$.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2011.10a
• /
• pp.843-848
• /
• 2011

Planning and construction of railway for high speed trains up to 400 km/h are recently driven in Korea. High speed train is one of the environment-friendly fastest mass transportation means but its noise generated by rolling, traction and aerodynamic mechanism can cause public complaints of residents nearby railways. To cost-effectively prevent the troublesome noise in a railway planning stage, the rational railway noise prediction method considering the characteristics of trains as well as railway structures should be required but it is difficult to find authentic methods for Korean high speed trains such as KTX and KTX-II. In this study, we propose a framework of our own railway noise prediction model emitted by Korean high speed trains over 250 km/h based on the recent research results carried out in EU countries. The model considers railway sound power level using several point sources distributed in heights as well as tracks, whose detail speed- and frequency-dependent emission characteristics of Korean high speed trains should be determined in near future by measurement or numerical analysis. The attenuation during propagation outdoors is calculated by the well-known ISO 9613-2 and auxiliary methods to consider undulated terrain and wind effect.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.23 no.8
• /
• pp.707-716
• /
• 2013

Tire noise is divided into two parts. One is pattern noise the other one is road noise. Pattern noise primarily occurs in over 500 Hz frequency but road noise occurs mainly in low frequency. It is important to develop a technology to predict the pattern noise at the design stage. Prediction technology of pattern noise has been developed by using image processing. Shape of tire pattern is computed by using imaging signal processing. Its results are different with the measured one. Therefore, the prediction of actual measured pattern noise is valuable. In the signal processing theory is applied to calculate the impulse response for the measurement environment. This impulse response used for the prediction of pattern noise by convolving this impulse response by the results of image processing of tire pattern.

• Journal of Advanced Marine Engineering and Technology
• /
• v.23 no.4
• /
• pp.453-461
• /
• 1999

The estimation of fatigue life at the design stage is very important in order to arrive at feasible and cost effective solutions considering the total lifetime of the structure and machinery compo-nents. In this study the practical procedure of prediction of fatigue life by use of cumulative damage factors based on Miner-Palmgren hypothesis and probability density function is shown with a $135,000m^3$ LNG tank being used as an example. In particular the parameters of Weibull distribution taht determine the stress spectrum are dis-cussed. At the end some of uncertainties associated with fatigue life prediction are discussed. The main results obtained from this study are as follows: 1. The practical procedure of prediction of fatigue life by use of cumulative damage factors expressed in combination of probability density function and S-N data is proposed. 2. The calculated fatigue life is influenced by the shape parameter and stress block. The conser-vative fatigue design can be achieved when using higher value of shape parameter and the stress blocks divded into more stress blocks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.623-625
• /
• 2018

As the issue of particulate matter spreads, services for providing particulate matter information in real time are increasing. However, when a sensor node for collecting particulate matter is defective, a corresponding service may not be provided. To solve these problems, it is necessary to predict and deduce particulate matter. In this paper, a particulate matter prediction model is designed using artificial neural network algorithm based on past particulate matter and meteorological data to predict particulate matter. Also, the prediction results are compared by learning the input data of the model in the design stage.

• Journal of the Society of Disaster Information
• /
• v.9 no.1
• /
• pp.11-21
• /
• 2013

This study proposed cost prediction equation model by considering duration, construction, size, actual cost with the subway construction started by the actual cost system which was introduced since 2004. Costs - scale exponent n(confidence range: 0.5 to 0.7) for cost prediction of subway construction was drawn total cost(0.713), net cost(0.77) in point of the 11 subway construction data. The cost prediction equation model of the subway construction which was presented in this study is able to effectively apply to business planning, preliminary investigation, feasibility study, basic design stage to estimate the approximate cost in the future.

• Nuclear Engineering and Technology
• /
• v.52 no.1
• /
• pp.14-18
• /
• 2020

Korea Atomic Energy Research Institute (KAERI) has carried out various neutronics experiments in the commissioning stage of the Jordan Research and Training Reactor (JRTR), and this paper introduces the results of first criticality prediction and experiment for the JRTR. The Monte Carlo Code for Advanced Reactor Design and analysis (McCARD) with the ENDF/B-VII.0 nuclear library was used for prediction calculations in the process of the first criticality approach, which was performed to provide reference for the first criticality experiment. In the experiment, fuel loading was carried out by measuring the inverse multiplication factor (1/M) to predict the number of fuel assemblies at the first criticality, and the first critical was reached on April 25, 2016. Comparing the first criticality prediction and experiment, the calculated and measured CAR (Control Absorber Rod) heights for the first criticality were 575 mm and 570.5 mm, respectively, that is, the difference between the two results was approximately 5 mm. From this result, it was confirmed that JRTR manufacturing and various experiments had successfully progressed as designed.

• Journal of the Society of Naval Architects of Korea
• /
• v.59 no.4
• /
• pp.243-250
• /
• 2022

In the design stage of hull forms, a fast prediction method of resistance performance is needed. In these days, large test matrix of candidate hull forms is tested using Computational Fluid Dynamics (CFD) in order to choose the best hull form before the model test. This process requires large computing times and resources. If there is a fast and reliable prediction method for hull form performance, it can be used as the first filter before applying CFD. In this paper, we suggest the offset-based performance prediction method. The hull form geometry information is applied in the form of 2D offset (non-dimensionalized by breadth and draft), and it is studied using Convolutional Neural Network (CNN) and adapted to the model test results (Residual Resistance Coefficient; C_R). Some additional variables which are not included in the offset data such as main dimensions are merged with the offset data in the process. The present model shows better performance comparing with the simple regression models.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.196-202
• /
• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1357-1369
• /
• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.