• YAKHAK HOEJI
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• v.49 no.1
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• pp.1-5
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• 2005

Near-infrared (NIR) spectroscopy has been widely applied in various field, since it is nondestructive and no sample preparation is required. In this paper, NIR spectroscopy was used for the determination of naproxen in a commercial pharmaceutical preparation. NIR spectroscopy was used to determine the content of naproxen in intact naproxen tablets containing 250 mg ($65.8\%$ nominal concentration) by collecting NIR spectra in the range of $1100{\sim}1750nm$. The laboratory-made samples had $46.1{\sim}85.5\%$ nominal naproxen concentration. The measurements were made by reflection using a fiber-optic probe and calibration was carried out by partial least square regression (PLSR). Model validation was performed by randomly splitting the data set into calibration and validation data set (63 samples as a calibration data set and 42 samples as a validation data set). The developed NIR calibration gave results comparable to the known values of tablets in a laboratorial manufacturing process with standard error of calibration (SEC) and standard error of prediction (SEP) of $1.06\%\;and\;1.04\%$, respectively. The NIR method showed good accuracy and repeatability. NIR spectroscopic determination in intact tablets allowed the potential use of real time monitoring for a running production process.

• Journal of Biosystems Engineering
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• v.35 no.6
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• pp.450-457
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• 2010

Although cherry tomato is one of major vegetables consumed in fresh vegetable market, the quality grading method is mostly dependant on size measurement using drum shape sorting machines. Using Visible/Near-infrared spectroscopy, apparatus to be able to acquire transmittance spectrum data was made and used to estimate firmness, sugar content, and acidity of cherry tomatoes grown at hydroponic and soil culture. Partial least square (PLS) models were performed to predict firmness, sugar content, and acidity for the acquired transmittance spectra. To enhance accuracy of the PLS models, several preprocessing methods were carried out, such as normalization, multiplicative scatter correction (MSC), standard normal variate (SNV), and derivatives, etc. The coefficient of determination ($R^2_p$) and standard error of prediction (SEP) for the prediction of firmness, sugar, and acidity of cherry tomatoes from green to red ripening stages were 0.859 and 1.899 kgf, with a preprocessing of normalization, 0.790 and $0.434^{\circ}Brix$ with a preprocessing of the 1st derivative of Savitzky Golay, and 0.518 and 0.229% with a preprocessing normalization, respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.902-907
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• 2008

Shape of the pulse waveform is affected by the visco-elasticity characteristics of the arterial wall and the reflection waves generated at the bifurcations of arterial branches. This study was designed to improve the accuracy for the extraction of pulse wave features, then proved the superiority of the developed algorithm by clinical evaluation. Upstroke point of the pulse wave was used as an extraction feature since it is minimally affected by the waveform variation. R-peak of the ECG was used as a reference to decide the minimum level, then intersection of the least squares of regression line was used as an upstroke point. Developed algorithm was compared with the existing minimum value detection algorithm and tangent-intersection algorithm using data obtained from 102 subjects. Developed algorithm showed the least standard deviation of $0.29{\sim}0.44\;m/s$ compared with that of the existing algorithms, $0.91{\sim}3.66\;m/s$. Moreover, the rate of standard deviation of more than 1.00m/s for the PWV values reduced with the range of $29.0{\sim}42.4%$, which proved the superiority of the newly developed algorithm.

• Journal of the Korean Society of Tobacco Science
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• v.17 no.2
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• pp.177-183
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• 1995

This study was conducted to develop the most precise NIR(near infrared spectrometric) calibration for rapid determination of chemical composition in ground samples of toasted burley tobacco using stepwise, stepup, principal component regression(PCR), partial least square(PLS) and modified partial least square(MPLS) calibration method. The number of wavelength(W) selected by stepup multiple linear regression using: second derivative spectra was as follows: total sugar(TS)-4 W, nicotine-9 W, total nitrogen(TN)-2 W, ash-8 W, total volatile base(TVB)-5 W, chlorine4 W, L of color-6 W, a of color-6 W and b of color-7 W. Comparing the calibration equations followed by each chemical components, the most precise calibration equation was MPLS for 75, a and b of color, PLS for nicotine, ash, TVB, chlorine and L of color and stepup for TN. The standard error of calibration(SEC) and standard error of performance(SEP) between result of near infrared analysis and standard laboratory analysis were 0.18, 0.40% for 75, 0.06, 0.08% for nicotine, 0.18, 0.16% for TN, 0.33, 0.46% for ash, 0.04, 0.03% for TVB, 0.08, 0.06% for chlorine, 0.54, 0.58 for L of color, 0.22, 0.22 for a of color and 0.27, 0.27 for b of color, respectively. The SEC and SEP of ash and TVB were within allowable error of standard laboratory analysis, nicotine, TN and chlorine were 1.2-2.0 times and 75 were 2.1-4.0 times larger than allowable error of standard laboratory analysis. The ratio of SEC and SEP to mean were 1.5, 1.6% for L of color, 3.7, 3.8% for a of color and 1.8, 1.8% for b of color, respectively. Key words : burley tobacco chemistry, near infrared spectroscopy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.1
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• pp.110-115
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• 2002

The tunnels built in recent years are equipped with traffic counters and pollution sensors (mostly, CO and Vl sensors). Utilizing these built-in sensors, it is possible to develop an algorithm to estimate the amount of pollutants exhausted from the each class of cars passing through the tunnel. These estimated data can be effectively utilized not only for ventilation control but also for designing ventilation facilities. The diffusion of pollutants in a tunnel can be described with one-dimensional diffusion-convection equation. This equation is approximated with interpolation functions and weighted residual method converting to adequate form for standard state estimate algorithms. With this converted equations, a least square optimization based algorithm is developed, whose outputs are the estimated amounts of pollutants emitted from each class of cars. In order to verify the feasibility of the developed algorithms, simulations are performed with the real data acquisitioned from the Tunnae tunnel located in Young-Dong highway in Korea.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.188-195
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• 2000

This study was conducted to measure the moisture content of powdered food using RF impedance spectroscopic method. In frequency range of 1.0 to 30㎒, the impedance such as reactance and resistance of parallel plate type sample holder filled with wheat flour and red-pepper powder of which moisture content range were 5.93∼-17.07%w.b. and 10.87 ∼ 27.36%w.b., respectively, was characterized using by Q-meter (HP4342). The reactance was a better parameter than the resistance in estimating the moisture density defined as product of moisture content and bulk density which was used to eliminate the effect of bulk density on RF spectral data in this study. Multivariate data analyses such as principal component regression, partial least square regression and multiple linear regression were performed to develop one calibration model having moisture density and reactance spectral data as parameters for determination of moisture content of both wheat flour and red-pepper powder. The best regression model was one by the multiple linear regression model. Its performance for unknown data of powdered food was showed that the bias, standard error of prediction and determination coefficient are 0.179% moisture content, 1.679% moisture content and 0.8849, respectively.

• Aerospace Engineering and Technology
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• v.10 no.2
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• pp.170-176
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• 2011

This paper analyzes the orbit determination results using azimuth and elevation angle from ground tracking data, which has the standard data interface format, GEOS-C. The ground tracking data is very useful for initial orbit determination after a satellite launch. In this paper, the quality of the measurement data has been investigated using a variety of real tracking passes, compared with the high precision orbit data of KOMPSAT-2. The accumulated tracking data from consecutive satellite-ground passes is processed for orbit determination using least square method. The accuracy of orbit determination result is also presented.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.383-386
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• 1986

In this paper, ultrasonic range finding module for the self-contained robot, INHAE-1, is presented. This system is processed, using Z-80 microprocessor, a much of information on the surrounding condition in real time and is realted a sensor for many side data acqusition with a stepping motor. Also this system can obtain the more correct edge of the obstacle using the standard deviation of the least-square method. In this experiment, it gives more correct information to mobile robot of the blind guidance and improves the orientation of the robot path.

• Journal of Astronomy and Space Sciences
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• v.7 no.2
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• pp.73-103
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• 1990

Morphological luminosity parameters$(\mu_e,\;r_e,\;\mu_0,\;\alpha^{-1})$ and D/B were estimated from the decomposition of surface brightness distributions of 28 extra galaxies. Decomposition was made using the standard non-linear least square fitting method and we used the seeing convolved model to get the central brightness of these galaxies. Masses and $M/L_B$ were calculated using rotational velocities of these galaxies from the fitting to the generalized Toomre's mass model.