• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1026-1031
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• 2003

Numbers of people living in high rise apartments are growing due to the overcrowding in urban area. Acoustic environment in those residential buildings has been seriously deteriorated by the increase of wheeled transports. Commonly used sound barriers have a limitation in controlling noise influencing higher part of a residential building. The use of noise barrier tunnels can be an alternative to supplement the defects of conventional noise barriers. But intensive measurements on noise levels at apartments vicinity of current noise barrier tunnels show that the tunnel now has a limited advantage on reducing the noise levels from arterial roads. The present work aims at providing an useful design tool In designing noise barrier tunnels for residential areas adjacent to roads. Number of field measurements, scale model measurements, and computer simulations were performed to ensure whether the prediction from scale model and computer simulation are appropriate. Result shows that the predictions from scale models and computer simulations could be valid prediction tools for designing sound barrier tunnels.

• Wind and Structures
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• v.10 no.5
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• pp.401-420
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• 2007

Field measurements of the wind-induced response of two residential reinforced concrete buildings, among the tallest in the world, have been performed during two typhoons. Natural periods and damping values have been determined and compared with other field measurements and empirical predictors. Suitable and common empirical predictors of natural period and structural damping have been obtained that describe the trend of tall, reinforced concrete buildings whose structural vibrations have been measured in the collection of studies in Hong Kong compiled by the authors. This data is especially important as the amount of information known about the dynamic parameters of buildings of these heights is limited. Effects of the variation of the natural period and damping values on the alongwind response of a tall building for serviceability-level wind conditions have been profiled using the gust response factor approach. When using this approach on these two buildings, the often overestimated natural periods and structural damping values suggested by empirical predictors tended to offset each other. Gust response factors calculated using the natural periods and structural damping values measured in the field were smaller than if calculated using design-stage values.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.1-9
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• 2023

The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.

• Carbon letters
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• v.21
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• pp.16-22
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• 2017

Cobalt was electrodeposited onto chemical vapor deposition (CVD) graphene/Si/$SiO_2$ substrates, during different time intervals, using an electrolyte solution containing a low concentration of cobalt sulfate. The intention was to investigate the details of the deposition process (and the dissolution process) and the resulting magnetic properties of the Co deposits on graphene. During and after electrodeposition, in-situ magnetic measurements were performed using an (AGFM). These were followed by ex situ morphological analysis of the samples with ${\Delta}t_{DEP}$ 30 and 100 s by atomic force microscopy in the non-contact mode on pristine CVD graphene/$SiO_2$/Si. We demonstrate that it is possible to electrodeposit Co onto graphene, and that in-situ magnetic measurements can also help in understanding details of the deposition process itself. The results show that the Co deposits are ferromagnetic with decreasing coercivity ($H_C$) and demonstrate increasing magnetization on saturation ($M_{SAT}$) and electric signal proportional to remanence ($M_r$), as a function of the amount of the electrodeposited Co. It was also found that, after the end of the dissolution process, a certain amount of cobalt remains on the graphene in oxide form (this was confirmed by X-ray photoelectron spectroscopy), as suggested by the magnetic measurements. This oxide tends to exhibit a limited asymptotic amount when cycling through the deposition/dissolution process for increasing deposition times, possibly indicating that the oxidation process is similar to the graphene surface chemistry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.944-951
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• 2011

This paper reports results of a joint experimental and numerical investigation of the causes of near-transducer errors due to the combined effect of acoustic and ADCP-induced flow disturbance near the ADCP transducer. The laboratory study focused on an isolated ADCP (deployment without boat). Measurements of the flow disturbance produced by the ADCP in vertical and horizontal planes were obtained acquiring measurements with an Acoustic Doppler Velocimeter (ADV). Concurrent measurements with ADCP and ADV were made to infer additional near-transducer effects in the ADCP measurements. The numerical investigation was designed to extend the inquiry on the near-transducer potential errors when the ADCP is deployed from a boat. Large Eddy Simulation (LES) was conducted to obtain the extent and magnitude of the disturbances induced by the drag acting on a boat-mounted ADCP and by the blockage effect of the instrument and boat. It is found the velocities measured by the ADCP are biased low and differ substantially from the undisturbed channel flow solution within a limited layer beneath the instrument.

• Smart Structures and Systems
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• v.25 no.3
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Smart Structures and Systems
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• v.31 no.2
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• pp.131-140
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• 2023

As well-known, the extended Kalman filter (EKF) is a powerful tool for parameter identification with limited measurements. However, traditional EKF is not applicable when the external excitation is unknown. By using least-squares estimation (LSE) for force identification, an EKF with unknown input (EKF-UI) approach was recently proposed by the authors. In this approach, to ensure the influence matrix be of full column rank, the sensors have to be deployed at all the degrees-of-freedom (DOFs) corresponding to the unknown excitation, saying collocated measurements are required. However, it is not easy to guarantee that the sensors can be installed at all these locations. To circumvent this limitation, based on the idea of first-order-holder discretization (FOHD), an improved EKF with unknown input (IEKF-UI) approach is proposed in this study for the simultaneous identification of structural parameters and unknown excitation. By using projection matrix, an improved observation equation is obtained. Few displacement measurements are fused into the observation equation to avoid the so-called low-frequency drift. To avoid the ill-conditioning problem for force identification without collocated measurements, the idea of FOHD is employed. The recursive solution of the structural states and unknown loads is then analytically derived. The effectiveness of the proposed approach is validated via several numerical examples. Results show that the proposed approach is capable of satisfactorily identifying the parameters of linear and nonlinear structures and the unknown excitation applied to them.

• Computers and Concrete
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• v.13 no.5
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• pp.659-677
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• 2014

A Bayesian response surface updating procedure is applied in order to update the parameters of the covariance function of a random field for concrete properties based on a limited number of available measurements. Formulas as well as a numerical algorithm are presented in order to update the parameters of response surfaces using Markov Chain Monte Carlo simulations. The parameters of the covariance function are often based on some kind of expert judgment due the lack of sufficient measurement data. However, a Bayesian updating technique enables to estimate the parameters of the covariance function more rigorously and with less ambiguity. Prior information can be incorporated in the form of vague or informative priors. The proposed estimation procedure is evaluated through numerical simulations and compared to the commonly used least square method.

• International Journal of Automotive Technology
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• v.3 no.1
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• pp.1-8
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• 2002

There is Increasing world-wide interest in diesel particulate filters (DPF) because of their proven effectiveness in reducing exhaust smoke and particulate emissions. Fine particulates have been linked to human health . DPF use requires a means to secure the bum-out of the accumulated soot, a process called regeneration. If this is not achieved, the engine cannot continue to operate. A number of techniques are available, but most are complex, expensive or have a high electrical demand. The use of fuel additives to catalyse soot bum-out potentially solves the problem of securing regeneration reliably and at low cost. Work on organo-metallic fuel additives has shown that certain metals combine to glove exceptional regeneration performance. Best performance was achieved with a combination of iron and strontium based compounds. Tests were carried out un a bed engine and on road vehicles, which demonstrated effective and reliable regeneration from a tow dose fuel additive, using a single passive DPF. No control valves, flow diverters. heaters or other devices were employed to assist regeneration. Independent particle size measurements showed that there were no harmful side effects from the use of the iron-strontium fuel additive.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2859-2870
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• 2022

Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN.