• 제목/요약/키워드: bias coefficient

검색결과 302건 처리시간 0.028초

USING TRMM SATELLITE C BAND DATA TO RETRIEVE SOIL MOISTURE ON THE TffiETAN PLATEAU

  • Chang Tzu-Yin;Liou Yuei-An
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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    • pp.737-740
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    • 2005
  • Soil moisture, through its dominance in the exchange of energy and moisture between the land and atmosphere, plays a crucial role in influencing atmospheric circulation. To identify the crucial role, it is a common agreement that knowledge of land surface processes and development of remote sensing techniques are of great important scientific issues. This research uses TRMM satellite C band (10.65 GHz) data to retrieve soil moisture on the Tibetan Plateau in Mainland China. Two retrieval schemes that are implemented include the t-(J) model and the R model. The latter one is developed based on a land surface process and radiobrightness (R) model for bare soil and vegetated terrain. Compared with the in situ ground measurements, the soil moisture retrieved from the R model and the t-(J) model with vegetation information obviously appear more accurate than that derived from bare soil model. Retrieved soil moisture contents from the two inversion models, R model and t-(J) model, have a similar trend, but the former appears to be superior in terms of correlation coefficient and bias compared with in situ data. In the future, we will apply the R model with the TRMM 10.65 GHz brightness temperature to monitor long-term soil moisture variation over Tibet Plateau.

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Determination of Protein and Oil Contents in Soybean Seed by Near Infrared Reflectance Spectroscopy

  • Choung, Myoung-Gun;Baek, In-Youl;Kang, Sung-Taeg;Han, Won-Young;Shin, Doo-Chull;Moon, Huhn-Pal;Kang, Kwang-Hee
    • 한국작물학회지
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    • 제46권2호
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    • pp.106-111
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    • 2001
  • The applicability of near infrared reflectance spectroscopy(NIRS) was tested to determine the protein and oil contents in ground soybean [Glycine max (L.) Merr.] seeds. A total of 189 soybean calibration samples and 103 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of protein, the most accurate equation was obtained at 2, 8, 6, 1(2nd derivative, 8 nm gap, 6 points smoothing and 1 point second smoothing) math treatment condition with SNV-D (Standard Normal Variate and Detrend) scatter correction method and entire spectrum by using MPLS (Modified Partial Least Squares) regression. In the case of oil, the best equation was obtained at 1, 4, 4, 1 condition with SNV-D scatter correction method and near infrared (1100-2500nm) region by using MPLS regression. Validation of these NIRS equations showed very low bias (protein:-0.016%, oil : -0.011 %) and standard error of prediction (SEP, protein: 0.437%, oil: 0.377%) and very high coefficient of determination ($R^2$, protein: 0.985, oil : 0.965). Therefore, these NIRS equation seems reliable for determining the protein and oil content, and NIRS method could be used as a mass screening method of soybean seed.

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The prediction of atmospheric concentrations of toluene using artificial neural network methods in Tehran

  • Asadollahfardi, Gholamreza;Aria, Shiva Homayoun;Mehdinejad, Mahdi
    • Advances in environmental research
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    • 제4권4호
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    • pp.219-231
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    • 2015
  • In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

Alternative robust estimation methods for parameters of Gumbel distribution: an application to wind speed data with outliers

  • Aydin, Demet
    • Wind and Structures
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    • 제26권6호
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    • pp.383-395
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    • 2018
  • An accurate determination of wind speed distribution is the basis for an evaluation of the wind energy potential required to design a wind turbine, so it is important to estimate unknown parameters of wind speed distribution. In this paper, Gumbel distribution is used in modelling wind speed data, and alternative robust estimation methods to estimate its parameters are considered. The methodologies used to obtain the estimators of the parameters are least absolute deviation, weighted least absolute deviation, median/MAD and least median of squares. The performances of the estimators are compared with traditional estimation methods (i.e., maximum likelihood and least squares) according to bias, mean square deviation and total mean square deviation criteria using a Monte-Carlo simulation study for the data with and without outliers. The simulation results show that least median of squares and median/MAD estimators are more efficient than others for data with outliers in many cases. However, median/MAD estimator is not consistent for location parameter of Gumbel distribution in all cases. In real data application, it is firstly demonstrated that Gumbel distribution fits the daily mean wind speed data well and is also better one to model the data than Weibull distribution with respect to the root mean square error and coefficient of determination criteria. Next, the wind data modified by outliers is analysed to show the performance of the proposed estimators by using numerical and graphical methods.

(Bi,La)FeO3-PbTiO3 세라믹스의 자전효과 (Magnetoelectric Effects in (Bi,La)FeO3-PbTiO3 Ceramics)

  • 이은구;이종국;장우양;김선재;이재갑
    • 한국재료학회지
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    • 제15권2호
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    • pp.121-125
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    • 2005
  • Magnetoelectric (ME) effects for lanthanum modified $BiFeO_3-PbTiO_3\;(BE-_xPT)$ solid solutions have been investigated. The value of magnetoelectric polarization coefficient, up is 10 times greater than that of $Cr_2O_3$. The results also show that up is due to a linear coupling between polarization and magnetization, and that up is independent of do magnetic bias and ac magnetic field. The ME effect is believed to be significantly enhanced due to breaking of the cycloidal spin state of a long-period spiral spin structure, via randomly distributed charged imperfections.

운량 및 일조시간을 이용한 우리나라의 시간당 전일사량의 평가 (Global Hourly Solar Irradiation Estimation using Cloud Cover and Sunshine Duration in South Korea)

  • 이관호
    • KIEAE Journal
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    • 제11권1호
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    • pp.15-20
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    • 2011
  • Computer simulation of buildings and solar energy systems is being used increasingly in energy assessments and design. For the six locations (Seoul, Incheon, Daejeon, Deagu, Gwangju and Busan) in South Korea where the global hourly solar irradiation (GHSI) is currently measured, GHSI was calculated using a comparatively simple cloud cover radiation model (CRM) and sunshine fraction radiation model (SFRM). The result was that the measured and calculated values of GHSI were similar for the six regions. Results of cloud cover and sunshine fraction models have been compared with the measured data using the coefficient of determination (R2), root-mean-square error (RMSE) and mean bias error (MBE). The strength of correlation R2 varied within similar ranges: 0.886-0.914 for CRM and 0.908-0.934 for SFRM. Average MBE for the CRM and SFRM were 6.67 and 14.02 W/m2, respectively, and average RMSE 104.36 and 92.15 W/m2. This showed that SFRM was slightly accurate and used many regions as compared to CRM for prediction of GHSI.

PZT 강유전체 박막의 마이크로파 유전특성 (Microwave Dielectric Properties of Ferroelectric PZT Thin Films)

  • 곽민환;문승언;류한철;김영태;이상석;이수재
    • 한국전기전자재료학회:학술대회논문집
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    • 한국전기전자재료학회 2003년도 하계학술대회 논문집 Vol.4 No.2
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    • pp.719-722
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    • 2003
  • Ferroelectric $Pb(Zr_{1-x}Ti_x)O_3$ (PZT) films were deposited on (001) MgO single crystals using sol-gel method. Structural properties and surface morphologies of PZT films were investigated using an X-ray diffractometer and a scanning electron microscopy, respectively. The dielectric properties of PZT films were investigated with the dc bias field using interdigitated capacitors (IDC) which were fabricated on PZT films using a thick metal layer by photolithography and dry etching process. The small signal dielectric properties of PZT films were calculated by a modified conformal mapping method with low and high frequency data, such as capacitance measured by an impedance gain/phase analyzer at 100 kHz and reflection coefficient (S-parameter) measured by a HP 8510C vector network analyzer at 1 -20 GHz. The IDC on PZT films exhibited about 67% of capacitance change with an electric field of 135 kV/cm at 10 GHz. These PZT thin films can be applied to tunable microwave devices such as phase shifters, tunable resonators and tunable filters.

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Mean estimation of small areas using penalized spline mixed-model under informative sampling

  • Chytrasari, Angela N.R.;Kartiko, Sri Haryatmi;Danardono, Danardono
    • Communications for Statistical Applications and Methods
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    • 제27권3호
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    • pp.349-363
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    • 2020
  • Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

근적외선을 이용한 사과의 당도예측 (I) - 다중회귀모델 - (Predicting the Soluble Solids of Apples by Near Infrared Spectroscopy (I) - Multiple Linear Regression Models -)

  • 이강진;;;노상하
    • Journal of Biosystems Engineering
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    • 제23권6호
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    • pp.561-570
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    • 1998
  • The MLR(Multiple Linear Regression) models to estimate soluble solids content non-destructively were presented to make a selection of optimal photosensor utilized to measure the soluble solids content of apples. Visible and NIR absorbance in the 400 to 2498 nanometer(nm) wavelength region, soluble solids content(sugar content), hardness, and weight were measured for 400 apples(gala). Spectrophotometer with fiber optic probe was utilized for spectrum measurement and digital refractometer was used for soluble solids content. Correlation between absorbance spectrum and soluble solids content was analyzed to pick out the optimal wavelengths and to develop corresponding prediction model by means of MLR. For the coefficient of determination($R^2$) to be over 0.92, the MLR models out of the original absorbance were built based on 7 wavelengths of 992, 904, 1096, 1032, 880, 824, 1048nm, and the ones of the second derivative absorbance based on 5 wavelengths of 784, 1056, 992, 808, 872nm. The best model of the second derivative absorbance spectrum had $R^2$=0.91, bias= -0.02bx, SEP=0.28bx for unknown samples.

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MOISTURE CONTENT MEASUREMENT OF POWDERED FOOD USING RF IMPEDANCE SPECTROSCOPIC METHOD

  • Kim, K. B.;Lee, J. W.;S. H. Noh;Lee, S. S.
    • 한국농업기계학회:학술대회논문집
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    • 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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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.

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