• Journal of the Institute of Convergence Signal Processing
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• v.10 no.2
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• pp.127-131
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• 2009

In the digital waveguide theory, traveling waves are represented by general solution to the wave equation that is second-order linear partial differential equation. The movement of these waves can be implemented using only delay lines. An unit delay in the general digital waveguide describes a sampling time interval. However, in the space-based digital waveguide the unit delay implies the spatial sampling distance. In consideration of these differences between two models, it is known that the space-based digital waveguide model is adequate to synthesize pitch-shifted sounds such as vibrato because the propagation distance can be directly control. In this paper, the time-based digital waveguide model which also synthesizes pitch-shifted sounds is proposed and compared with space-based digital waveguide.

• Steel and Composite Structures
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• v.47 no.5
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• pp.569-585
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• 2023

This paper proposes an efficient approach for the structural topology optimization of bi-directional functionally graded structures by incorporating popular radial basis functions (RBFs) into an implicit level set (ILS) method. Compared to traditional element density-based methods, a level set (LS) description of material boundaries produces a smoother boundary description of the design. The paper develops RBF implicit modeling with multiquadric (MQ) splines, thin-plate spline (TPS), exponential spline (ES), and Gaussians (GS) to define the ILS function with high accuracy and smoothness. The optimization problem is formulated by considering RBF-based nodal densities as design variables and minimizing the compliance objective function. A LS-RBF optimization method is proposed to transform a Hamilton-Jacobi partial differential equation (PDE) into a system of coupled non-linear ordinary differential equations (ODEs) over the entire design domain using a collocation formulation of the method of lines design variables. The paper presents detailed mathematical expressions for BiDFG beams topology optimization with two different material models: continuum functionally graded (CFG) and mechanical functionally graded (MFG). Several numerical examples are presented to verify the method's efficiency, reliability, and success in accuracy, convergence speed, and insensitivity to initial designs in the topology optimization of two-dimensional (2D) structures. Overall, the paper presents a novel and efficient approach to topology optimization that can handle bi-directional functionally graded structures with complex geometries.

• Steel and Composite Structures
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• v.6 no.6
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• pp.459-477
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• 2006

A theoretical research project is undertaken to develop integrated analysis and design tools for long span composite beams in modern high-rise buildings, and it aims to develop non-linear finite element models for practical design of composite beams. As the first paper in the series, this paper presents the development study as well as the calibration exercise of the proposed finite element models for simply supported composite beams. Other practical issues such as continuous composite beams, the provision of web openings for passage of building services, the partial continuity offered by the connections to columns as well as the behaviour of both unprotected and protected composite beams under fires will be reported separately. In this paper, details of the finite elements and the material models for both steel and reinforced concrete are first described, and finite element studies of composite beams with full details of test data are then presented. It should be noted that in the proposed finite element models, both steel beams and concrete slabs are modelled with two dimensional plane stress elements whose widths are assigned to be equal to the widths of concrete flanges, and the flange widths and the web thicknesses of steel beams as appropriate. Moreover, each shear connector is modelled with one horizontal spring and one vertical spring to simulate its longitudinal shear and pull-out actions based on measured load-slippage curves of push-out tests of shear connectors. The numerical results are then carefully analyzed and compared with the corresponding test results in terms of load mid-span deflection curves as well as load end-slippage curves. Other deformation characteristics of the composite beams such as stress and strain distributions across the composite cross-sections as well as distributions of shear forces and slippages in shear connectors along the beam spans are also examined in details. It is shown that the numerical results of the composite beams compare well with the test data in terms of various load-deformation characteristics along the entire deformation ranges. Hence, the proposed analysis and design tools are considered to be simple and yet effective for composite beams with practical geometrical dimensions and arrangements. Structural engineers are strongly encouraged to employ the models in their practical work to exploit the full advantages offered by composite construction.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• Journal of the Korean Chemical Society
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• v.26 no.3
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• pp.128-134
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• 1982

The electrical conductivity of n-type polycrystalline MnOx-Ti$O_2$ system containing 0.40, 0.80, and 1.60 mol % of manganese oxide has been measured from 100 to 400$^{\circ}$C and 1100 to 1300$^{\circ}$C under oxygen partial pressures of$10^{-8}\;to\;10^{-1}$ atm. Plots of log conductivity vs. reciprocals of absolute temperature at constant $Po_2$'s are found to be linear with an inflection, and the activation energies obtained from the slopes appear to be an enough average 0.18eV for the extrinsic and 3.70eV for the intrinsic. The log $\sigma$ vs. log $Po_2$ are found to be linear at $Po_2$'s of $10^{-8}\;to\;10^{-1}$atm. The conductivity dependences on $Po_2$at the two temperature regions are closely approximated by $\sigma{\propto}$Po_2$-1}6$ for the extrinsic and $${\sigma}{\propto}Po_2^{-1}4}$$ for the intrinsic, respectively. The predominant defects are believed to be Vo-2e' and $Ti^3$${\cdot}$interstitial at the extrinsic and intrinsic. From the interpretations of conductivity dependences on temperature and$Po_2$ , the conduction mechanisms and possible band models are proposed.

• Korea Trade Review
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• v.47 no.4
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• pp.215-231
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• 2022

Analysis and forecasting of the Baltic Capsize Index (BCI) is important for managing an entity's losses and risks from the uncertainty and volatility of the fast-changing maritime transport market in the future. This study conducted volatility transition analysis through the GARCH model, using BCI which is highly related to steel raw materials. As for the data, 2,385 monthly data were used from March 1999 to March 2021. In this study, after basic statistical analysis, unit root and cointegration test, the GARCH, EGARCH, and DCC-GARCH models were used for volatility transition analysis. As the results of GARCH and EGARCH model, we confirmed that all variables had no autocorrelation between the standardized residuals for error terms and the square of residuals, that the variability of all variables at this time was likely to persist in the future, and that the variability of the time-series error term impact according to Iron ore trade (IoT). In addition, through the EGARCH model, the magnitude convenience of all variables except the Iron ore price (IOP) and Capesize bulk fleet (BCF) variables was greater than the positive value (+). As a result of analyzing the DCC-GARCH (1,1) model, partial linear combinations were confirmed over the entire period. Estimating the effect of variability transition on BCF and C5 with statistically significant linear combinations with BCI confirmed that the impact of BCF on BCI was greater than the impact of BCI itself.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4112-4112
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• 2001

The feasibility of NIR spectroscopy as a quick and nondestructive method for quality control of uniformity of coating thickness of pharmaceutical tablets was investigated. Near infrared spectra of a set of pharmaceutical tablets with varying coating thickness were measured with a diffuse reflectance fiber optic probe connected to a Broker IFS 28/N FT-NIR spectrometer. The challenging issues encountered in this study included: 1. The similarity of the formulation of the core and coating materials, 2. The lack of sufficient calibration samples and 3. The non-linear relationship between the NIR spectral intensity and coating: thickness. A peak at 7184 $cm^{-1}$ was identified that differed for the coating material and the core material when M spectra were collected at 2 $cm^{-1}$ resolution (0.4 nm at 7184 $cm^{-1}$). The study showed that the coating thickness can be analyzed by polynomial fitting of the peak area of the selected peak, while least squares calibration of the same data failed due to the lack of availability of sufficient calibration samples. Samples of coal powder and solid pieces of coal were analyzed by FT-NIR diffuse reflectance spectroscopy with the goal of predicting their ash content, percentage of volatile components, and energy content. The measurements were performed on a Broker Vector 22N spectrometer with a fiber optic probe. A partial least squares model was constructed for each of the parameters of interest for solid and powdered sample forms separately. Calibration models varied in size from 4 to 10 PLS ranks. Correlation coefficients for these models ranged from 86.6 to 95.0%, with root-mean-square errors of cross validation comparable to the corresponding reference measurement methods. The use of FT-NIR diffuse reflectance measurement techniques was found to be a significant improvement over existing measurement methodologies in terms of speed and ease of use, while maintaining the desired accuracy for all parameters and sample forms.(Figure Omitted).

• Journal of the Korean Institute of Gas
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• v.6 no.1 s.17
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• pp.1-9
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• 2002

This paper presents simple models for flow and the PIG dynamics when it passes through a $90^{\circ}$ curved section of pipeline. The simulation has been done with two different operational boundary conditions. The solution fur non-linear hyperbolic partial equations for flow is given by using MOC. The Runge-Kuta method is used to solve the initial condition equation fur flow and the PIG dynamics equation. The simulation results show that the proposed model and solution can be used fur estimating the PIG dynamics when the pig runs in the pipeline including curved section. In this paper, dynamic modeling and its analysis for the PIG flow through $90^{\circ}$ curved pipe with compressible and unsteady flow are studied. The PIG dynamics model is derived by using Lagrange equation under assumption that it passes through 3 different sections in the curved pipeline such that it moves into, inside and out of the curved section. The downstream and up stream flow dynamics including the curved sections are solved using MOC. The effectiveness of the derived mathematical models is estimated by simulation results fur a low pressure natural gas pipeline including downward and upward curved sections. The simulation results show that the proposed model and solution can be used for estimating the PIG dynamics when we pig the pipeline including curved section.

• Smart Structures and Systems
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• v.13 no.2
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• pp.257-280
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• 2014

In this paper, a novel PARAllel FACtor (PARAFAC) decomposition based Blind Source Separation (BSS) algorithm is proposed for modal identification of structures equipped with tuned mass dampers. Tuned mass dampers (TMDs) are extremely effective vibration absorbers in tall flexible structures, but prone to get de-tuned due to accidental changes in structural properties, alteration in operating conditions, and incorrect design forecasts. Presence of closely spaced modes in structures coupled with TMDs renders output-only modal identification difficult. Over the last decade, second-order BSS algorithms have shown significant promise in the area of ambient modal identification. These methods employ joint diagonalization of covariance matrices of measurements to estimate the mixing matrix (mode shape coefficients) and sources (modal responses). Recently, PARAFAC BSS model has evolved as a powerful multi-linear algebra tool for decomposing an $n^{th}$ order tensor into a number of rank-1 tensors. This method is utilized in the context of modal identification in the present study. Covariance matrices of measurements at several lags are used to form a $3^{rd}$ order tensor and then PARAFAC decomposition is employed to obtain the desired number of components, comprising of modal responses and the mixing matrix. The strong uniqueness properties of PARAFAC models enable direct source separation with fine spectral resolution even in cases where the number of sensor observations is less compared to the number of target modes, i.e., the underdetermined case. This capability is exploited to separate closely spaced modes of the TMDs using partial measurements, and subsequently to estimate modal parameters. The proposed method is validated using extensive numerical studies comprising of multi-degree-of-freedom simulation models equipped with TMDs, as well as with an experimental set-up.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.