Minimization of Percent Root-Mean-Square Difference in the Generation of Wavelets Using Genetic Algorithm

2019 ◽  
pp. 319-325
Author(s):  
Vinícius Suterio ◽  
Paulo R. Scalassara ◽  
Cristiano M. Agulhari ◽  
Fábio R. Durand
Keyword(s):  
Genetic Algorithm ◽  
Root Mean Square ◽  
Root Mean Square Difference ◽  
Mean Square ◽  
Mean Square Difference

scholarly journals Reconstruction of the Surface Inshore Labrador Current from SWOT Sea Surface Height Measurements

2019 ◽  
Vol 11 (11) ◽  
pp. 1264 ◽  
Author(s):  
Zhimin Ma ◽  
Guoqi Han
Keyword(s):  
High Resolution ◽  
Root Mean Square ◽  
Surface Current ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Root Mean Square Difference ◽  
Optimal Interpolation ◽  
Mean Square ◽  
Labrador Current ◽  
Mean Square Difference

Utilizing a high-resolution (2-km) coastal ocean model output off Eastern Newfoundland, this paper explores the potential for reconstructing the sea surface height (SSH) and the surface inshore Labrador Current from high-resolution SSH data of the upcoming Surface Water and Ocean Topography (SWOT) satellite mission. The model results are evaluated against in-situ data from tide gauges and nadir altimetry for the period from June to October, 2010. The hourly model SSH output is used as true SSH and sampled along-swath with expected measurement errors by using a SWOT simulator, which produces SWOT-like data. We reconstruct half-day SSH fields from the SWOT-like data using optimal interpolation and average them into weekly fields. The average normalized root-mean-square difference between the weekly reconstructed SSH field and the model SSH filed is 0.07 for the inshore Labrador Current. Between the geostrophic surface current derived from the reconstructed SSH field and the model surface current, the average normalized root-mean-square difference is 0.26 for the inshore Labrador Current. For the surface unit-depth transport of the inshore Labrador Current, the normalized root-mean-square differences are 0.32–0.38 between the reconstructed current and the model current.

scholarly journals High-resolution satellite-based cloud-coupled estimates of total downwelling surface radiation for hydrologic modelling applications

2009 ◽  
Vol 13 (7) ◽  
pp. 969-986 ◽  
Author(s):  
B. A. Forman ◽  
S. A. Margulis
Keyword(s):  
High Resolution ◽  
Root Mean Square ◽  
Mean Difference ◽  
Lookup Table ◽  
Root Mean Square Difference ◽  
Mean Square ◽  
Radiation Model ◽  
Radiative Fluxes ◽  
Mean Square Difference ◽  
Sky Conditions

Abstract. A relatively simple satellite-based radiation model yielding high-resolution (in space and time) downwelling longwave and shortwave radiative fluxes at the Earth's surface is presented. The primary aim of the approach is to provide a basis for deriving physically consistent forcing fields for distributed hydrologic models using satellite-based remote sensing data. The physically-based downwelling radiation model utilises satellite inputs from both geostationary and polar-orbiting platforms and requires only satellite-based inputs except that of a climatological lookup table derived from a regional climate model. Comparison against ground-based measurements over a 14-month simulation period in the Southern Great Plains of the United States demonstrates the ability to reproduce radiative fluxes at a spatial resolution of 4 km and a temporal resolution of 1 h with good accuracy during all-sky conditions. For hourly fluxes, a mean difference of −2 W m−2 with a root mean square difference of 21 W m−2 was found for the longwave fluxes whereas a mean difference of −7 W m−2 with a root mean square difference of 29 W m−2 was found for the shortwave fluxes. Additionally, comparison against advanced downwelling longwave and solar insolation products during all-sky conditions showed comparable uncertainty in the longwave estimates and reduced uncertainty in the shortwave estimates. The relatively simple form of the model enables future usage in ensemble-based applications including data assimilation frameworks in order to explicitly account for input uncertainties while providing the potential for conditioning estimates from other readily available products derived from more sophisticated retrieval algorithms.

scholarly journals High-resolution satellite-based cloud-coupled estimates of total downwelling surface radiation for hydrologic modelling applications

2009 ◽  
Vol 6 (2) ◽  
pp. 3041-3087
Author(s):  
B. A. Forman ◽  
S. A. Margulis
Keyword(s):  
High Resolution ◽  
Root Mean Square ◽  
Mean Difference ◽  
Lookup Table ◽  
Root Mean Square Difference ◽  
Mean Square ◽  
Radiation Model ◽  
Radiative Fluxes ◽  
Mean Square Difference ◽  
Sky Conditions

Abstract. A relatively simple satellite-based radiation model yielding high-resolution (in space and time) downwelling longwave and shortwave radiative fluxes at the Earth's surface is presented. The primary aim of the approach is to provide a basis for deriving physically consistent forcing fields for distributed hydrologic models using satellite-based remote sensing data. The physically-based downwelling radiation model utilises satellite inputs from both geostationary and polar-orbiting platforms and requires only satellite-based inputs except that of a climatological lookup table derived from a regional climate model. Comparison against ground-based measurements over a 14-month simulation period in the Southern Great Plains of the United States demonstrates the ability to reproduce radiative fluxes at 4 km/h resolution with good accuracy during all-sky conditions. For hourly fluxes, a mean difference of −2 W m−2 with a root mean square difference of 21 W m−2 was found for the longwave fluxes whereas a mean difference of −7 W m−2 with a root mean square difference of 29 W m−2 was found for the shortwave fluxes. Additionally, comparison against advanced downwelling longwave and solar insolation products during all-sky conditions showed comparable uncertainty in the longwave estimates and reduced uncertainty in the shortwave estimates. The relatively simple form of the model enables future usage in ensemble-based applications including data assimilation frameworks in order to explicitly account for input uncertainties while providing the potential for conditioning estimates from other readily available products derived from more sophisticated retrieval algorithms.

scholarly journals KESTABILAN PENYETARAAN HORIZONTAL DENGAN METODE LINIER BERDASARKAN PROPORSI KELOMPOK GANDENG

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Yustiandi Yustiandi
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Difference ◽  
Mean Square ◽  
Mean Square Difference

Penelitian ini bertujuan untuk mengetahui perbedaan kestabilan penyetaraan horizontal metode linier dengan proporsi kelompok gandeng 15%, 20%, 25%, dan 30% dengan replikasi sebanyak 50 kali. Penelitian ini dilakukan dengan menganalisis sekor hasil belajar fisika siswa. Populasi pada penelitian ini adalah seluruh siswa SMA kelas XI program IPA di Kabupaten Pandeglang, sedangkan sampel penelitian adalah siswa kelas XI IPA di SMAN 1 Pandeglang, SMAN 2 Pandeglang, SMAN 4 Pandeglang, SMAN 6 Pandeglang, dan SMAN CMBBS yang berjumlah 800 orang yang diambil secara acak sederhana dengan pengembalian. Metode penelitian yang digunakan adalah metode eksperimen dengan membandingkan nilai Root Mean Square Difference (RMSD) dari hasil penyetaraan berdasarkan perbedaan proporsi kelompok gandeng. Hasil penelitian menunjukkan bahwa terdapat perbedaan kestabilan penyetaraan horizontal dengan metode linier dengan proporsi kelompok gandeng 15%, 20%, 25%, dan 30% dengan replikasi sebanyak 50 kali. Uji lanjut digunakan untuk mengetahui ada tidaknya perbedaan antar kelompok jika masing-masing kelompok dibandingkan. Berdasarkan uji lanjut, diketahui bahwa penyetaraan dengan proporsi kelompok gandeng 15% paling stabil dibandingkan dengan penyetaraan dengan proporsi kelompok gandeng 20%, 25%, dan 30% dengan replikasi sebanyak 50 kali.

scholarly journals KESTABILAN PENYETARAAN VERTIKAL DENGAN METODE EKUIPERSENTIL BERDASARKAN PROPORSI KELOMPOK GANDENG

2017 ◽  
Vol 8 (2) ◽  
pp. 63-66
Author(s):  
Eva Puspita Dewi
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Difference ◽  
Mean Square ◽  
Mean Square Difference

Tujuan penelitian ini untuk mengetahui: Keakurasian penyetaraan vertikal dengan metode ekuipersentil untuk proporsi kelompok gandeng 10%, 20%, dan 30%. Metode penelitian yang digunakan adalah metode eksperimen dengan variabel bebas adalah proporsi kelompok gandeng dan variabel terikatnya adalah keakurasian penyetaraan vertikal dengan metode ekuipersentil yang dinyatakan dengan Root Mean Square Difference (RMSD) yang diperoleh dari 40 replikasi. Data penelitian diperoleh dari sekor tes hasil belajar Matematika untuk topik operasi hitung pada bilangan bulat dan pecahan di kelas VI dan kelas VII semester 2 tahun ajaran 2016/2017. Hasil penelitian menunjukkan keakurasian penyetaraan vertikal metode ekuipersentil dengan proporsi kelompok gandeng 20% lebih akurat dibandingkan dengan proporsi kelompok gandeng 10% dan 30%.

DESIGN AND IMPLEMENTATION OF ECG COMPRESSION ALGORITHM WITH CONTROLLABLE PERCENT ROOT-MEAN-SQUARE DIFFERENCE

2007 ◽  
Vol 19 (04) ◽  
pp. 259-268 ◽  
Author(s):  
Chwan-Lu Tseng ◽  
Chun-Chieh Hsiao ◽  
I-Chi Chou ◽  
Chia-Jung Hsu ◽  
Yi-Ju Chang ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Energy Equation ◽  
Wavelet Coefficient ◽  
Computation Time ◽  
Threshold Value ◽  
Compression Algorithm ◽  
Root Mean Square Difference ◽  
Mean Square ◽  
Ecg Compression ◽  
Mean Square Difference

In this paper, the orthogonality of coefficient matrices of wavelet filters is utilized to derive the energy equation for the relation between time-domain signal and its corresponding wavelet coefficients. Using the energy equation, the relationship between the wavelet coefficient error and the reconstruction error is obtained. The errors considered in this paper include the truncation error and quantization error. This not only helps to control the reconstruction quality but also brings two advantages: (1) It is not necessary to perform inverse transform to obtain the distortion caused by compression using wavelet transform and can thus reduce computation efforts. (2) By using the energy equation, we can search for a threshold value to attain a better compression ratio within the range of a pre-specified percent root-mean-square difference (PRD) value. A compression algorithm with run length encoding is proposed based on the energy equation. In the end, the Matlab software and MIT-BIH database are adopted to perform simulations for verifying the feasibility of our proposed method. The algorithm is also implemented on a DSP chip to examine the practicality and suitability. The required computation time of an ECG segment is less than 0.0786 ,s which is fast enough to process real-time signals. As a result, the proposed algorithm is applicable for implementation on mobile ECG recording devices.

TEST OF CONTOUR ACCURACY ON A PHOTOGRAMMETRIC MAP OF ATHABASCA GLACIER

1966 ◽  
Vol 3 (6) ◽  
pp. 909-915 ◽  
Author(s):  
W. S. B. Paterson
Keyword(s):  
Root Mean Square ◽  
Standard Error ◽  
Large Scale ◽  
Root Mean Square Difference ◽  
Surface Markers ◽  
Mean Square ◽  
Theoretical Error ◽  
Upper Limit ◽  
Contour Accuracy ◽  
Mean Square Difference

An independent survey of the positions and elevations of 59 surface markers set in the lower part of Athabasca Glacier permits assessment of the accuracy of contours on a large-scale map of the glacier. The root mean square difference between the elevation at each marker as determined from the survey and from the map was 49 cm. This is an upper limit to the standard error of the contours. It is less than three times the theoretical error and about 15% of the contour interval.

scholarly journals A Rat Liver Transcriptomic Point of Departure Predicts a Prospective Liver or Non-liver Apical Point of Departure

2020 ◽  
Vol 176 (1) ◽  
pp. 86-102 ◽  
Author(s):  
Kamin J Johnson ◽  
Scott S Auerbach ◽  
Eduardo Costa
Keyword(s):  
Root Mean Square ◽  
Evaluation System ◽  
Human Health Risk ◽  
Root Mean Square Difference ◽  
Strong Positive Correlation ◽  
Mean Square ◽  
Exposure Study ◽  
Short Term Exposure ◽  
Mean Square Difference ◽  
Point Of Departure

Abstract Identifying a toxicity point of departure (POD) is a required step in human health risk characterization of crop protection molecules, and this POD has historically been derived from apical endpoints across a battery of animal-based toxicology studies. Using rat transcriptome and apical data for 79 molecules obtained from Open TG-GATES (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) (632 datasets), the hypothesis was tested that a short-term exposure, transcriptome-based liver biological effect POD (BEPOD) could estimate a longer-term exposure “systemic” apical endpoint POD. Apical endpoints considered were body weight, clinical observation, kidney weight and histopathology and liver weight and histopathology. A BMDExpress algorithm using Gene Ontology Biological Process gene sets was optimized to derive a liver BEPOD most predictive of a systemic apical POD. Liver BEPODs were stable from 3 h to 29 days of exposure; the median fold difference of the 29-day BEPOD to BEPODs from earlier time points was approximately 1 (range: 0.7–1.1). Strong positive correlation (Pearson R = 0.86) and predictive accuracy (root mean square difference = 0.41) were observed between a concurrent (29 days) liver BEPOD and the systemic apical POD. Similar Pearson R and root mean square difference values were observed for comparisons between a 29-day systemic apical POD and liver BEPODs derived from 3 h to 15 days of exposure. These data across 79 molecules suggest that a longer-term exposure study apical POD from liver and non-liver compartments can be estimated using a liver BEPOD derived from an acute or subacute exposure study.

Root-mean-square difference between the intensities of non-Gaussian speckle at two different wavelengths

1981 ◽  
Vol 71 (2) ◽  
pp. 155 ◽  
Author(s):  
Mitrajyoti Deka ◽  
Silverio P. Almeida ◽  
Hitoshi Fujii
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Difference ◽  
Mean Square ◽  
Mean Square Difference ◽  
Non Gaussian
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