• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.157-164
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• 2003

Introduction of wave model, considered the effect of tide, wind and wave induced currents at the coastal waters of complex bathymetry, is a very important factor for most coastal engineering design and disaster protection problems. As the steady state spectral wave model could simulate depth induced wave shoaling and refraction, current induced refraction effect, steepness induced wave breaking, diffraction, wind wave growth, and wave-wave interaction that redistribute energy, this would support and compensate the gap in the real field of design where other wave models could not deal and cause wrong estimation. In this study, for that sense, we applied the spectral wave model t the large coastal waters near Gaduck Island where the Busan new port construction project is going on, for better understanding and analysis of wave transformation process. We also compared the simulation results with the calculated from the existing model. From such a trial of this study, we hope that broader and sager use of the spectral model in the area of port design and disaster prevention system come through in near future.

• Korean Journal of Geomatics
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• v.4 no.1
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• pp.1-8
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• 2004

In the middle-resolution remote sensing, the Ground Sampled Distance (GSD) that the detector senses and samples is generally larger than the actual size of the objects (or materials) of interest, and so several objects are embedded in a single pixel. In this case, as it is impossible to detect these objects by the conventional spatial-based image processing techniques, it has to be carried out at sub-pixel level through spectral properties. In this paper, we explain the sub-pixel analysis algorithm, also known as the Linear Spectral Mixing (LSM) model, which has been experimented using the Hyperion data. To find Endmembers used as the prior knowledge for LSM model, we applied the concept of the convex geometry on the two-dimensional scatter plot. The Atmospheric Correction and Minimum Noise Fraction techniques are presented for the pre-processing of Hyperion data. As LSM model is the simplest approach in sub-pixel analysis, the results of our experiment is not good. But we intend to say that the sub-pixel analysis shows much more information in comparison with the image classification.

• Structural Engineering and Mechanics
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• v.86 no.2
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• pp.237-247
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• 2023

Fourier series-based models in the time domain are frequently established to represent individual bouncing loads, which neglects the stochastic property of human bouncing activity. A power spectral density (PSD) model in the frequency domain for individual bouncing loads is developed herein. An experiment was conducted on individual bouncing loads, resulting in 957 records linked to form long samples to achieve a fine frequency resolution. The Welch method was applied to the linked samples to obtain the experimental PSD, which was normalized by the bouncing frequency and the harmonic order. The energy, energy distribution center, and energy distribution shape of the experimental PSD were investigated to establish the PSD model. The proposed model was used to analyze structural vibration responses using stochastic vibration theory, which was verified via field measurements. It is believed that this framework can evaluate the vibration capacity of structures excited by bouncing crowds, such as concert halls and grandstands.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.138-147
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• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.145-149
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• 1997

This paper describes a methodology for the development of models of discrete event system. The methodology is based on transformation of continuous state space into discrete one to homomorphically represent dynamics of continuous processes in discrete events. This paper proposes a formal structure which can coupled discrete event system models within a framework. The structure employs the discrete event specification formalism for the discrete event system models. The proposed formal structure has been applied to develop a discrete event specification model for the complex spectral density analysis of strip for urin analyzer system. For this, spectral density data of strip is partitioned into a set of Phases based on events identified through urine spectrophotometry. For each phase, a continuous system of the continuous model for the urine spectral density analysis has been simulated by programmed C++. To validate this model, first develop the discrets event specification model, then simulate the model in the DEVSIM++ environment. It has the similar simulation results for the data obtained from the continuous system simulation. The comparison shows that the discrete event specification model represents dynamics of the urine spectral density at each phase.

• Atmosphere
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• v.16 no.4
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• pp.359-370
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• 2006

Terrain height variance spectra for the Korean mountain region are calculated in order to determine an adequate grid size required to resolve terrain forcing on mesoscale model simulation. One-dimensional spectral analysis is applied to specifically the central-eastern part of the Korean mountain region, where topographical-scale forcing has an important effect on mesoscale atmospheric flow. It is found that the terrain height variance spectra in this mountain region has a wavelength dependence with the power law exponents of 1.5 at the wavelength near 30 km, but this dependence is steeply changed to 2.5 at the wavelength less than 30 km. For the adequate horizontal grid size selection on mesoscale simulation two-dimensional terrain height spectral analysis is also performed. There is no directionality within 50% of spectral energy region, so one-dimensional spectral analysis can be reasonably applied to the Korea Peninsula. According to the spectral analysis of terrain height variance, the finer grid size which is higher than 6 km is required to resolve a 90% of terrain variance in this region. Numerical simulation using WRF (Weather Research and Forecasting Model) was performed to evaluate the effect of different terrain resolution in accordance with the result of spectral analysis. The simulated results were quantitatively compared to observations and there was a significant improvement in the wind prediction across the mountain region as the grid space decreased from 18 km to 2 km. The results will provide useful guidance of grid size selection on mesoscale topographical simulation over the Korean mountain region.

• Journal of Biosystems Engineering
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• v.40 no.2
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• pp.153-158
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• 2015

Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data from line-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models were developed to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals were line-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region of Interest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) were selected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA) methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctly classify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showed that the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1% for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCA models for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.44.4-45
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• 2020

The solar chromosphere can be observed well through strong absorption lines. We infer the physical parameters of chromospheric plasmas from these lines using a multilayer spectral inversion. This is a new technique of spectral inversion. We assume that the atmosphere consists of a finite number of layers. In each layer the absorption profile is constant and the source function is allowed to vary with optical depth. Specifically, we consider a three-layer model of radiative transfer where the lowest layer is identified with the photosphere and the two upper layers are identified with the chromosphere. This three-layer model is fully specified by 13 parameters. Four parameters can be fixed to prescribed values, and one parameter can be determined from the analysis of a satellite photospheric line. The remaining eight parameters are determined from a constrained least-squares fitting. We applied the multilayer spectral inversion to the spectral data of the Hα and the Ca II 854.21 nm lines taken in a quiet region by the Fast Imaging Solar Spectrograph (FISS) of the Goode Solar Telescope (GST). We find that our model successfully fits most of the observed profiles and produces regular maps of the model parameters. We conclude that our multilayer inversion is useful to infer chromospheric plasma parameters on the Sun.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1983.04a
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• pp.16-17
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• 1983

In Speech signal processing, ARMA spectral estimation method is used. It has been demonstrated that the ARMA model provides better spectral estimation then the more specialized AR model and MA model. Dynamic program is used to achieve time algnment. Speech sound similarity is defined to be proportional to the distance seperating to sound in a vector space defined by ARMA model. AS a result, the recognition rate of 97.3% for three speaker is obtained.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.3-8
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• 1999

In speech recognition for real-world applications, the performance degradation due to the mismatch introduced between training and testing environments should be overcome. In this paper, to reduce this mismatch, we provide a hybrid method of spectral subtraction and residual noise masking. We also employ multiple model approach to obtain improved robustness over various noise environments. In this approach, multiple model sets are made according to several noise masking levels and then a model set appropriate for the estimated noise level is selected automatically in recognition phase. According to speaker independent isolated word recognition experiments in car noise environments, the proposed method using model sets with only two masking levels reduced average word error rate by 60% in comparison with spectral subtraction method.