A no-reference bitstream-based perceptual model for video qualityestimation of videos affected by coding artifacts and packet losses
NASA Astrophysics Data System (ADS)
Pandremmenou, K.; Shahid, M.; Kondi, L. P.; Lövström, B.
2015-03-01
In this work, we propose a No-Reference (NR) bitstream-based model for predicting the quality of H.264/AVC video sequences, affected by both compression artifacts and transmission impairments. The proposed model is based on a feature extraction procedure, where a large number of features are calculated from the packet-loss impaired bitstream. Many of the features are firstly proposed in this work, and the specific set of the features as a whole is applied for the first time for making NR video quality predictions. All feature observations are taken as input to the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. LASSO indicates the most important features, and using only them, it is possible to estimate the Mean Opinion Score (MOS) with high accuracy. Indicatively, we point out that only 13 features are able to produce a Pearson Correlation Coefficient of 0.92 with the MOS. Interestingly, the performance statistics we computed in order to assess our method for predicting the Structural Similarity Index and the Video Quality Metric are equally good. Thus, the obtained experimental results verified the suitability of the features selected by LASSO as well as the ability of LASSO in making accurate predictions through sparse modeling.
Estimates of residence time and related variations in quality of ground water beneath Submarine Base Bangor and vicinity, Kitsap County, Washington
USGS Publications Warehouse
Cox, S.E.
2003-01-01
Estimates of residence time of ground water beneath Submarine Base Bangor and vicinity ranged from less than 50 to 4,550 years before present, based on analysis of the environmental tracers tritium, chlorofluorocarbons (CFCs), and carbon-14 (14C), in 33 ground-water samples collected from wells tapping the ground-water system. The concentrations of multiple environmental tracers tritium, CFCs, and 14C were used to classify ground water as modern (recharged after 1953), pre-modern (recharged prior to 1953), or indeterminate. Estimates of the residence time of pre-modern ground water were based on evaluation of 14C of dissolved inorganic carbon present in ground water using geochemical mass-transfer modeling to account for the interactions of the carbon in ground water with carbon of the aquifer sediments. Ground-water samples were obtained from two extensive aquifers and from permeable interbeds within the thick confining unit separating the sampled aquifers. Estimates of ground-water residence time for all ground-water samples from the shallow aquifer were less than 45 years and were classified as modern. Estimates of the residence time of ground water in the permeable interbeds within the confining unit ranged from modern to 4,200 years and varied spatially. Near the recharge area, residence times in the permeable interbeds typically were less than 800 years, whereas near the discharge area residence times were in excess of several thousand years. In the deeper aquifers, estimates of ground-water residence times typically were several thousand years but ranged from modern to 4,550 years. These estimates of ground-water residence time based on 14C were often larger than estimates of ground-water residence time developed by particle-tracking analysis using a ground-water flow model. There were large uncertainties?on the order of 1,000-2,000 years?in the estimatesbased on 14C. Modern ground-water tracers found in some samples from large-capacity production wells
A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.
PubMed
Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao
2018-01-02
In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.
Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking.
PubMed
Zhou, Ping; Guo, Dongwei; Wang, Hong; Chai, Tianyou
2017-09-29
Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVR (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. This indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.
Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking
DOE PAGES
Zhou, Ping; Guo, Dongwei; Wang, Hong; ...
2017-09-29
Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore » (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.« less
Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking
SciTech Connect
Zhou, Ping; Guo, Dongwei; Wang, Hong
Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore » (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.« less
Estimates of tracer-based piston-flow ages of groundwater from selected sites-National Water-Quality Assessment Program, 1992-2005
USGS Publications Warehouse
Hinkle, Stephen R.; Shapiro, Stephanie D.; Plummer, Niel; Busenberg, Eurybiades; Widman, Peggy K.; Casile, Gerolamo C.; Wayland, Julian E.
2011-01-01
This report documents selected age data interpreted from measured concentrations of environmental tracers in groundwater from 1,399 National Water-Quality Assessment (NAWQA) Program groundwater sites across the United States. The tracers of interest were chlorofluorocarbons (CFCs), sulfur hexafluoride (SF6), and tritium/helium-3 (3H/3He). Tracer data compiled for this analysis primarily were from wells representing two types of NAWQA groundwater studies - Land-Use Studies (shallow wells, usually monitoring wells, in recharge areas under dominant land-use settings) and Major-Aquifer Studies (wells, usually domestic supply wells, in principal aquifers and representing the shallow, used resource). Reference wells (wells representing groundwater minimally impacted by anthropogenic activities) associated with Land-Use Studies also were included. Tracer samples were collected between 1992 and 2005, although two networks sampled from 2006 to 2007 were included because of network-specific needs. Tracer data from other NAWQA Program components (Flow System Studies, which are assessments of processes and trends along groundwater flow paths, and various topical studies) were not compiled herein. Tracer data from NAWQA Land-Use Studies and Major-Aquifer Studies that previously had been interpreted and published are compiled herein (as piston-flow ages), but have not been reinterpreted. Tracer data that previously had not been interpreted and published are evaluated using documented methods and compiled with aqueous concentrations, equivalent atmospheric concentrations (for CFCs and SF6), estimates of tracer-based piston-flow ages, and selected ancillary data, such as redox indicators, well construction, and major dissolved gases (N2, O2, Ar, CH4, and CO2). Tracer-based piston-flow ages documented in this report are simplistic representations of the tracer data. Tracer-based piston-flow ages are a convenient means of conceptualizing groundwater age. However, the piston
Beef quality parameters estimation using ultrasound and color images
PubMed Central
2015-01-01
Background Beef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef qualityestimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat. Proposal An algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis. Conclusions The proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat. PMID:25734452
Observations-based GPP estimates
NASA Astrophysics Data System (ADS)
Joiner, J.; Yoshida, Y.; Jung, M.; Tucker, C. J.; Pinzon, J. E.
2017-12-01
We have developed global estimates of gross primary production based on a relatively simple satellite observations-based approach using reflectance data from the MODIS instruments in the form of vegetation indices that provide information about photosynthetic capacity at both high temporal and spatial resolution and combined with information from chlorophyll solar-induced fluorescence from the Global Ozone Monitoring Experiment-2 instrument that is noisier and available only at lower temporal and spatial scales. We compare our gross primary production estimates with those from eddy covariance flux towers and show that they are competitive with more complicated extrapolated machine learning gross primary production products. Our results provide insight into the amount of variance in gross primary production that can be explained with satellite observations data and also show how processing of the satellite reflectance data is key to using it for accurate GPP estimates.
Normal tissue complication probability model parameter estimation for xerostomia in head and neck cancer patients based on scintigraphy and quality of life assessments
PubMed Central
2012-01-01
Background With advances in modern radiotherapy (RT), many patients with head and neck (HN) cancer can be effectively cured. However, xerostomia is a common complication in patients after RT for HN cancer. The purpose of this study was to use the Lyman–Kutcher–Burman (LKB) model to derive parameters for the normal tissue complication probability (NTCP) for xerostomia based on scintigraphy assessments and quality of life (QoL) questionnaires. We performed validation tests of the Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) guidelines against prospectively collected QoL and salivary scintigraphic data. Methods Thirty-one patients with HN cancer were enrolled. Salivary excretion factors (SEFs) measured by scintigraphy and QoL data from self-reported questionnaires were used for NTCP modeling to describe the incidence of grade 3+ xerostomia. The NTCP parameters estimated from the QoL and SEF datasets were compared. Model performance was assessed using Pearson’s chi-squared test, Nagelkerke’s R2, the area under the receiver operating characteristic curve, and the Hosmer–Lemeshow test. The negative predictive value (NPV) was checked for the rate of correctly predicting the lack of incidence. Pearson’s chi-squared test was used to test the goodness of fit and association. Results Using the LKB NTCP model and assuming n=1, the dose for uniform irradiation of the whole or partial volume of the parotid gland that results in 50% probability of a complication (TD50) and the slope of the dose–response curve (m) were determined from the QoL and SEF datasets, respectively. The NTCP-fitted parameters for local disease were TD50=43.6 Gy and m=0.18 with the SEF data, and TD50=44.1 Gy and m=0.11 with the QoL data. The rate of grade 3+ xerostomia for treatment plans meeting the QUANTEC guidelines was specifically predicted, with a NPV of 100%, using either the QoL or SEF dataset. Conclusions Our study shows the agreement between the NTCP
Normal tissue complication probability model parameter estimation for xerostomia in head and neck cancer patients based on scintigraphy and quality of life assessments.
PubMed
Lee, Tsair-Fwu; Chao, Pei-Ju; Wang, Hung-Yu; Hsu, Hsuan-Chih; Chang, PaoShu; Chen, Wen-Cheng
2012-12-04
With advances in modern radiotherapy (RT), many patients with head and neck (HN) cancer can be effectively cured. However, xerostomia is a common complication in patients after RT for HN cancer. The purpose of this study was to use the Lyman-Kutcher-Burman (LKB) model to derive parameters for the normal tissue complication probability (NTCP) for xerostomia based on scintigraphy assessments and quality of life (QoL) questionnaires. We performed validation tests of the Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) guidelines against prospectively collected QoL and salivary scintigraphic data. Thirty-one patients with HN cancer were enrolled. Salivary excretion factors (SEFs) measured by scintigraphy and QoL data from self-reported questionnaires were used for NTCP modeling to describe the incidence of grade 3+ xerostomia. The NTCP parameters estimated from the QoL and SEF datasets were compared. Model performance was assessed using Pearson's chi-squared test, Nagelkerke's R2, the area under the receiver operating characteristic curve, and the Hosmer-Lemeshow test. The negative predictive value (NPV) was checked for the rate of correctly predicting the lack of incidence. Pearson's chi-squared test was used to test the goodness of fit and association. Using the LKB NTCP model and assuming n=1, the dose for uniform irradiation of the whole or partial volume of the parotid gland that results in 50% probability of a complication (TD50) and the slope of the dose-response curve (m) were determined from the QoL and SEF datasets, respectively. The NTCP-fitted parameters for local disease were TD50=43.6 Gy and m=0.18 with the SEF data, and TD50=44.1 Gy and m=0.11 with the QoL data. The rate of grade 3+ xerostomia for treatment plans meeting the QUANTEC guidelines was specifically predicted, with a NPV of 100%, using either the QoL or SEF dataset. Our study shows the agreement between the NTCP parameter modeling based on SEF and QoL data, which gave a
Quality analysis of population-based information on cancer stage at diagnosis across Europe, with presentation of stage-specific cancer survival estimates: A EUROCARE-5 study.
PubMed
Minicozzi, Pamela; Innos, Kaire; Sánchez, Maria-José; Trama, Annalisa; Walsh, Paul M; Marcos-Gragera, Rafael; Dimitrova, Nadya; Botta, Laura; Visser, Otto; Rossi, Silvia; Tavilla, Andrea; Sant, Milena
2017-10-01
Cancer registries (CRs) are fundamental for estimating cancer burden, evaluating screening and monitoring health service performance. Stage at diagnosis-an essential information item collected by CRs-has been made available, for the first time, by CRs participating in EUROCARE-5. We analysed the quality of this information and estimated stage-specific survival across Europe for CRs with good data quality. Sixty-two CRs sent stage (as TNM, condensed TNM or extent of disease) for 15 cancers diagnosed in 2000-2007. We assessed the quality, partly by comparing stage according to the three systems. We also developed procedures to reconstruct stage (categories: local, regional, metastatic and unknown) using information from all three systems, thus minimising the amount of missing information. Moderate-to-excellent stage concordance was found for practically all 24 CRs, for which it was possible to compare at least two staging systems. However, since stage was often incorrectly assigned, and information on the presence/absence of metastases was often lacking, data on only 7/15 cancers from 34/62 CRs (15 countries) were of sufficient quality for further analysis. Cases diagnosed ≥70 years had more advanced (or lacking) stage- and worse stage-specific survival than those
Optimizing Radiometric Processing and Feature Extraction of Drone Based Hyperspectral Frame Format Imagery for Estimation of Yield Quantity and Quality of a Grass Sward
NASA Astrophysics Data System (ADS)
Näsi, R.; Viljanen, N.; Oliveira, R.; Kaivosoja, J.; Niemeläinen, O.; Hakala, T.; Markelin, L.; Nezami, S.; Suomalainen, J.; Honkavaara, E.
2018-04-01
Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10 % better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40 % compared to approaches where only spectral features were applied. The best estimation RMSE of 239 kg/ha (6.0 %) was obtained with multiview anisotropy corrected data set and the 3D features.
Improving the quality of parameter estimates obtained from slug tests
USGS Publications Warehouse
Butler, J.J.; McElwee, C.D.; Liu, W.
1996-01-01
The slug test is one of the most commonly used field methods for obtaining in situ estimates of hydraulic conductivity. Despite its prevalence, this method has received criticism from many quarters in the ground-water community. This criticism emphasizes the poor quality of the estimated parameters, a condition that is primarily a product of the somewhat casual approach that is often employed in slug tests. Recently, the Kansas Geological Survey (KGS) has pursued research directed it improving methods for the performance and analysis of slug tests. Based on extensive theoretical and field research, a series of guidelines have been proposed that should enable the quality of parameter estimates to be improved. The most significant of these guidelines are: (1) three or more slug tests should be performed at each well during a given test period; (2) two or more different initial displacements (Ho) should be used at each well during a test period; (3) the method used to initiate a test should enable the slug to be introduced in a near-instantaneous manner and should allow a good estimate of Ho to be obtained; (4) data-acquisition equipment that enables a large quantity of high quality data to be collected should be employed; (5) if an estimate of the storage parameter is needed, an observation well other than the test well should be employed; (6) the method chosen for analysis of the slug-test data should be appropriate for site conditions; (7) use of pre- and post-analysis plots should be an integral component of the analysis procedure, and (8) appropriate well construction parameters should be employed. Data from slug tests performed at a number of KGS field sites demonstrate the importance of these guidelines.
Speed estimation for air quality analysis.
DOT National Transportation Integrated Search
2005-05-01
Average speed is an essential input to the air quality analysis model MOBILE6 for emission factor calculation. Traditionally, speed is obtained from travel demand models. However, such models are not usually calibrated to speeds. Furthermore, for rur...
Noise Estimation and Quality Assessment of Gaussian Noise Corrupted Images
NASA Astrophysics Data System (ADS)
Kamble, V. M.; Bhurchandi, K.
2018-03-01
Evaluating the exact quantity of noise present in an image and quality of an image in the absence of reference image is a challenging task. We propose a near perfect noise estimation method and a no reference image quality assessment method for images corrupted by Gaussian noise. The proposed methods obtain initial estimate of noise standard deviation present in an image using the median of wavelet transform coefficients and then obtains a near to exact estimate using curve fitting. The proposed noise estimation method provides the estimate of noise within average error of +/-4%. For quality assessment, this noise estimate is mapped to fit the Differential Mean Opinion Score (DMOS) using a nonlinear function. The proposed methods require minimum training and yields the noise estimate and image quality score. Images from Laboratory for image and Video Processing (LIVE) database and Computational Perception and Image Quality (CSIQ) database are used for validation of the proposed quality assessment method. Experimental results show that the performance of proposed quality assessment method is at par with the existing no reference image quality assessment metric for Gaussian noise corrupted images.
Estimates of tracer-based piston-flow ages of groundwater from selected sites: National Water-Quality Assessment Program, 2006-2010
USGS Publications Warehouse
Shapiro, Stephanie D.; Plummer, Niel; Busenberg, Eurybiades; Widman, Peggy K.; Casile, Gerolamo C.; Wayland, Julian E.; Runkle, Donna L.
2012-01-01
Piston-flow age dates were interpreted from measured concentrations of environmental tracers from 812 National Water-Quality Assessment (NAWQA) Program groundwater sites from 27 Study Units across the United States. The tracers of interest include chlorofluorocarbons (CFCs), sulfur hexafluoride (SF6), and tritium/helium-3 (3H/3He). Tracer data compiled for this analysis were collected from 2006 to 2010 from groundwater wells in NAWQA studies, including: * Land-Use Studies (LUS, shallow wells, usually monitoring wells, located in recharge areas under dominant land-use settings), * Major-Aquifer Studies (MAS, wells, usually domestic supply wells, located in principal aquifers and representing the shallow drinking water supply), * Flow System Studies (FSS, networks of clustered wells located along a flowpath extending from a recharge zone to a discharge zone, preferably a shallow stream) associated with Land-Use Studies, and * Reference wells (wells representing groundwater minimally impacted by anthropogenic activities) also associated with Land-Use Studies. Tracer data were evaluated using documented methods and are presented as aqueous concentrations, equivalent atmospheric concentrations (for CFCs and SF6), and tracer-based piston-flow ages. Selected ancillary data, such as redox data, well-construction data, and major dissolved-gas (N2, O2, Ar, CH4, and CO2) data, also are presented. Recharge temperature was inferred using climate data (approximated by mean annual air temperature plus 1°C [MAAT +1°C]) as well as major dissolved-gas data (N2-Ar-based) where available. The N2-Ar-based temperatures showed significantly more variation than the climate-based data, as well as the effects of denitrification and degassing resulting from reducing conditions. The N2-Ar-based temperatures were colder than the climate-based temperatures in networks where recharge was limited to the winter months when evapotranspiration was reduced. The tracer-based piston-flow ages
Method of estimation of scanning system quality
NASA Astrophysics Data System (ADS)
Larkin, Eugene; Kotov, Vladislav; Kotova, Natalya; Privalov, Alexander
2018-04-01
Estimation of scanner parameters is an important part in developing electronic document management system. This paper suggests considering the scanner as a system that contains two main channels: a photoelectric conversion channel and a channel for measuring spatial coordinates of objects. Although both of channels consist of the same elements, the testing of their parameters should be executed separately. The special structure of the two-dimensional reference signal is offered for this purpose. In this structure, the fields for testing various parameters of the scanner are sp atially separated. Characteristics of the scanner are associated with the loss of information when a document is digitized. The methods to test grayscale transmitting ability, resolution and aberrations level are offered.
Regression estimators for generic health-related quality of life and quality-adjusted life years.
PubMed
Basu, Anirban; Manca, Andrea
2012-01-01
To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimatorsbased on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.
Why is qualityestimation judgment fast? Comparison of gaze control strategies in quality and difference estimation tasks
NASA Astrophysics Data System (ADS)
Radun, Jenni; Leisti, Tuomas; Virtanen, Toni; Nyman, Göte; Häkkinen, Jukka
2014-11-01
To understand the viewing strategies employed in a qualityestimation task, we compared two visual tasks-qualityestimation and difference estimation. The estimation was done for a pair of natural images having small global changes in quality. Two groups of observers estimated the same set of images, but with different instructions. One group estimated the difference in quality and the other the difference between image pairs. The results demonstrated the use of different visual strategies in the tasks. The qualityestimation was found to include more visual planning during the first fixation than the difference estimation, but afterward needed only a few long fixations on the semantically important areas of the image. The difference estimation used many short fixations. Salient image areas were mainly attended to when these areas were also semantically important. The results support the hypothesis that these tasks' general characteristics (evaluation time, number of fixations, area fixated on) show differences in processing, but also suggest that examining only single fixations when comparing tasks is too narrow a view. When planning a subjective experiment, one must remember that a small change in the instructions might lead to a noticeable change in viewing strategy.
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