• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.759-764
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• 2000

The accurate prediction of aeroacoustic analysis is necessary for designers to control and reduce airflow-induced sound pressure levels in high speed rotating DVD drives. This paper focuses on the numerical prediction of airflow-induced sound in DVD drives. Computational fluid dynamics(CFD) is first conducted to evaluate flow field characteristics due to the high-speed disk rotation, and to support the acoustic analysis. The acoustic analogy based on Ffowcs Williams-Hawkings(FW-H) equation is adopted to predict aeroacoustic noise patterns. The integral solution for quadrupole volume source is included to identify the turbulence noise generated inside the DVD tray. The strength of sound pressure level with respect to rotating speed is discussed to meet upfront demand on the high fidelity product development. The present study also focuses on the noise directivity and examines how much the sound noise is sensitive to change in rotating speed. Near-field noise is strongly affected by the flow field characteristic, which is caused by the complex shape of the tray. For a mid-field, the quadrupole noise play as a counterpart of thickness noise or loading noise, so it generates different sound noise patterns compared with those in the near field.

• International Journal of High-Rise Buildings
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• v.7 no.2
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• pp.171-183
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• 2018

Turner and Penn (1) from UCL have proved that Visibility Graph Analysis (VGA) can be used as a more accurate method to predict the pedestrian distribution in building spaces. However, this methodology neglects certain elements that are of special influence on pedestrian distribution in buildings, especially the entrances and exits. Based on Space Syntax, this dissertation improves on the traditional method of Visibility Graph Analysis, using three shopping centers in Shenzhen as examples, attempts to explore a new parameter - "attenuation index of pedestrians at the entrances and exits" - using relevant data of the entrances and exits of the three cases, and combines it with traditional VGA analysis through weighted calculation, in order to provide more accurate predictions of pedestrian patterns in shopping centers.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.901-902
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• 2006

In high-speed PM machines, rotor losses form a larger proportion of the total losses than usual in conventional low speed machines. In order to maintain the mechanical integrity of a high-speed PM rotor intended for high-speed operation, the rotor assembly is often retained within a sleeve or can. The sleeve is exposed to field produced by the stator from either the slotting or the mmf harmonics that are not synchronous with the rotor. These non-synchronous fields cause the significant rotor losses. An optimum design of high-speed PM machines requires the accurate prediction for these rotor losses. On the basis of analytical field analysis and 2D finite element analysis (FEA), this paper deals with the rotor losses.

• Korean Journal of Materials Research
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• v.13 no.1
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• pp.11-17
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• 2003

DC characteristics of recessed gate 4H-SiC MESFET were investigated using the device/circuit simulation tool, PISCES. Results of theoretical calculation were compared with the experimental data for the extraction of modeling parameters which were implemented for the prediction of DC and gate leakage characteristics at high temperatures. The current-voltage analysis using a fixed mobility model revealed that the short channel effect is influenced by the defects in SiC. The incomplete ionization models are found out significant physical models for an accurate prediction of SiC device performance. Gate leakage is shown to increase with the device operation temperatures and to decrease with the Schottky barrier height of gate metal.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.154-159
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• 2011

As the need for predicting the flood stage of river from torrential downpouring caused by climate change is increasingly emphasized, the study, centered on the area of Gangwon-do Inje-gun and Jeongseon-gun of local river, is to develop peak water level regression equation by rainfall. Through the correlation between rainfall and peak water level, it is confirmed that rainfall according to duration and peak water level have a high correlation coefficient. Based on this, a relational expression of rainfall and peak water level is verified and then the adequacy of the calculated expression is analyzed and the result shows that a very accurate prediction is not easy to achieve but a rough prediction of the change of water level at each point is possible.

• Smart Structures and Systems
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• v.16 no.3
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• pp.497-519
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• 2015

This study discusses the use of Adaptive-Network-Based-Fuzzy-Inference-System (ANFIS) in predicting the shear strength of reinforced-concrete deep beams. 139 experimental data have been collected from renowned publications on simply supported high strength concrete deep beams. The results show that the ANFIS has strong potential as a feasible tool for predicting the shear strength of deep beams within the range of the considered input parameters. ANFIS's results are highly accurate, precise and therefore, more satisfactory. Based on the Sensitivity analysis, the shear span to depth ratio (a/d) and concrete cylinder strength ($f_c^{\prime}$) have major influence on the shear strength prediction of deep beams. The parametric study confirms the increase in shear strength of deep beams with an equal increase in the concrete strength and decrease in the shear span to-depth-ratio.

• Journal of Industrial Technology
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• v.31 no.A
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• pp.71-78
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• 2011

As the need for predicting the flood stage of river from torrential downpouring caused by climate change is increasingly emphasized, the study, centered on the area of Gangwon-do Inje-gun and Jeongseon-gun of local river, is to develop peak water level regression equation by rainfall. Through the correlation between rainfall and peak water level, it is confirmed that rainfall according to duration and peak water level have a high correlation coefficient. Based on this, a relational expression of rainfall and peak water level is verified and then the adequacy of the calculated expression is analyzed and the result shows that a very accurate prediction is not easy to achieve but a rough prediction of the change of water level at each point is possible.

• Computers and Concrete
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• v.24 no.5
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• pp.399-412
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• 2019

In this investigation, Elman neural networks were utilized for predicting the mechanical properties of Self-Compacting Concretes (SCCs). Elman models were designed by using experimental data of many different concrete mixdesigns of various types of SCC that were collected from the literature. In order to investigate the effectiveness of the selected input variables on the network performance in predicting intended properties, utilized data in artificial neural networks were considered in two sets of 8 and 140 input variables. The obtained outcomes showed that not only can the developed Elman ANNs predict the mechanical properties of SCCs with high accuracy, but also for all of the desired outputs, networks with 140 inputs, compared to ones with 8, have a remarkable percent improvement in the obtained prediction results. The prediction accuracy can significantly be improved by using a more complete and accurate set of key factors affecting the desired outputs, as input variables, in the networks, which is leading to more similarity of the predicted results gained from networks to experimental results.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.25-26
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• 2020

In this study, by applying the concrete compressive strength estimation system Concrete IoT Management System (hereinafter referred to as CIMS) to the concrete slab concrete in the domestic field, the purpose of this study is to confirm the practical use of CIMS and to verify the accuracy of estimating the initial strength of concrete. As a result, it shows a high correlation when the compressive strength and CIMS estimated strength of the specimen for structural management are converted and compared with the integrated temperature. However, in order to determine a more accurate experimental constant, it is necessary to consider the results up to 28 days.

• ETRI Journal
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• v.45 no.4
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• pp.557-569
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• 2023

Traditional deterministic channel modeling is accurate in prediction, but due to its complexity, improving computational efficiency remains a challenge. In an alternative approach, we investigated a multilayer artificial neural network (ANN) to predict large-scale and small-scale channel characteristics in metro tunnels. Simulated high-precision training datasets were obtained by combining measurement campaign with a ray tracing (RT) method in a metro tunnel. Performance on the training data was used to determine the number of hidden layers and neurons of the multilayer ANN. The proposed multilayer ANN performed efficiently (10 s for training; 0.19 ms for prediction), and accurately, with better approximation of the RT data than the single-layer ANN. The root mean square errors (RMSE) of path loss (2.82 dB), root mean square delay spread (0.61 ns), azimuth angle spread (3.06°), and elevation angle spread (1.22°) were impressive. These results demonstrate the superior computing efficiency and model complexity of ANNs.