Total Estimation Error Research Articles

Volumetric mass flow sensor for citrus mechanical harvesting machines

A volume-based mass flow sensor system was developed to estimate the total mass of fruit travelling on the inclined and horizontal conveyors of two citrus mechanical harvesting systems. A LIDAR (light detection and ranging) sensor was used to scan the conveyor cross-sectional area and use the distance related-information to estimate the bulk volume and total mass of fruit on the conveyor by integrating the data over time. A custom algorithm was developed to analyze the data and calculate the fruit volume. Fruit volume was used to estimate the total fruit mass.Sensor system was tested in the laboratory on two different conveyor systems: an inclined conveyor used in a mechanical harvester (conveyor flap height=8cm), and a horizontal conveyor used in a debris removal system (conveyor flap height ⩽3cm). The tested conveyor speeds ranged from 0.59m/s to 1.71m/s. The system performance for the tested speed ranges was an average error of 7% (standard deviation [SD]±7%) and 7% (SD±5%) for the inclined and horizontal conveyors, respectively. The average total fruit mass estimation error during mechanical harvesting was 10% (SD±6%) for 11 field trails that involved scanning 512–1356kg of the fruit on a horizontal conveyor of a debris removal system. Overall, the developed sensor system can be used for volumetric yield monitoring of citrus. The sensor system performance will likely be improved using instant conveyor speed during the estimation of total fruit mass.

A two-stage approach for formulating fuzzy regression models

Fuzzy regression models have been widely applied to explain the relationship between explanatory variables and responses in fuzzy environments. This paper proposes a simple two-stage approach for constructing a fuzzy regression model based on the distance concept. Crisp numbers representing the fuzzy observations are obtained using the defuzzification method, and then the crisp regression coefficients in the fuzzy regression model are determined using the conventional least-squares method. Along with the crisp regression coefficients, the proposed fuzzy regression model contains a fuzzy adjustment variable so that the model can deal with the fuzziness from fuzzy observations in order to reduce the fuzzy estimation error. A mathematical programming model is formulated to determine the fuzzy adjustment term in the proposed fuzzy regression model to minimize the total estimation error based on the distance concept. Unlike existing approaches that only focus on positive coefficients, the problem of negative coefficients in the fuzzy regression model is taken into account and resolved in the solution procedure. Comparisons with previous studies show that the proposed fuzzy regression model has the highest explanatory power based on the total estimation error using various criteria. A real-life dataset is adopted to demonstrate the applicability of the proposed two-stage approach in handling a problem with negative coefficients in the fuzzy regression model and a large number of fuzzy observations.

Distributed Estimation for Moving Target Based on State-Consensus Strategy

This technical note studies the distributed estimation problem for a continuous-time moving target under switching interconnection topologies. A recursive distributed estimation algorithm is proposed by using state-consensus strategy, where a common gain is assigned to adjust the innovative and state-consensus information for each sensor in the network. Under mild conditions on observability and connectivity, the stability of the distributed estimation algorithm is analyzed. An upper bound and lower bound for the total mean square estimation error (TMSEE) are obtained by virtue of the common Lyapunov method and Kalman-Bucy filtering theory, respectively. Then a numerical simulation is given to verify the effectiveness of the proposed algorithm.

Strato-mesospheric ClO observations by SMILES: error analysis and diurnal variation

Abstract. Chlorine monoxide (ClO) is the key species for anthropogenic ozone losses in the middle atmosphere. We observed ClO diurnal variations using the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) on the International Space Station, which has a non-sun-synchronous orbit. This includes the first global observations of the ClO diurnal variation from the stratosphere up to the mesosphere. The observation of mesospheric ClO was possible due to 10–20 times better signal-to-noise (S/N) ratio of the spectra than those of past or ongoing microwave/submillimeter-wave limb-emission sounders. We performed a quantitative error analysis for the strato- and mesospheric ClO from the Level-2 research (L2r) product version 2.1.5 taking into account all possible contributions of errors, i.e. errors due to spectrum noise, smoothing, and uncertainties in radiative transfer model and instrument functions. The SMILES L2r v2.1.5 ClO data are useful over the range from 0.01 and 100 hPa with a total error estimate of 10–30 pptv (about 10%) with averaging 100 profiles. The SMILES ClO vertical resolution is 3–5 km and 5–8 km for the stratosphere and mesosphere, respectively. The SMILES observations reproduced the diurnal variation of stratospheric ClO, with peak values at midday, observed previously by the Microwave Limb Sounder on the Upper Atmosphere Research Satellite (UARS/MLS). Mesospheric ClO demonstrated an opposite diurnal behavior, with nighttime values being larger than daytime values. A ClO enhancement of about 100 pptv was observed at 0.02 to 0.01 hPa (about 70–80 km) for 50° N–65° N from January–February 2010. The performance of SMILES ClO observations opens up new opportunities to investigate ClO up to the mesopause.

The information content of capacity utilization for detrending total factor productivity

In the production function approach, an accurate output gap assessment requires a careful evaluation of the total factor productivity (TFP) cycle. We build a common cycle model that links TFP to capacity utilization and we show that, in almost all of the pre-enlargement EU countries, using information about capacity utilization reduces both the total estimation error and the revisions in real-time estimates of the concurrent TFP cycle compared to a univariate decomposition. We also argue that relaxing the constant drift hypothesis in favour of a non-linear specification helps to offset a general tendency to underestimate the TFP cycle in the last decade.

Role of proficiency testing in monitoring of standardization of hemoglobin A1c methods

After Hemoglobin A1c (HbA1c), therapeutic targets for monitoring diabetes therapy were recommended, first, National Glycohemoglobin Standardization Program (NGSP), then, the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) developed standardization initiatives. The aim of this article is to demonstrate the role of a proficiency testing (PT) programs in monitoring the long-term effect of these initiatives and the current status of HbA1c measurement. Measurement precision as a coefficient of variation (CV), measurement bias (bias), and satisfactory HbA1c result rates in proficiency testing (PT) surveys were evaluated using fresh single donor whole blood PT items and assigned values from a NGSP-certified secondary reference laboratory. Between 2000 and 2010, both CV and bias of the IC measurement method showed a decreasing trend. While the CV of the HPLC measurement method decreased, no significant change was observed in its bias. The rates of satisfactory HbA1c results in PT surveys were higher in HPLC users than IC users. In 2010, the average CVs in HPLC and IC groups were 2.6 and 3.4 %, biases were 2.7 and 1.8 %, and corresponding total error (TE) estimates were 7.8 and 8.5 %, respectively. These TE values were higher than the maximum permissible measurement error of 7 %, developed based on clinical use of the test. The NGSP and the IFCC networks have promoted improvements in HbA1c testing; however, tightening of NGSP method certification criteria seems to be necessary to achieve a maximum permissible measurement error of 7 %.

Self-monitoring of blood glucose

Self-monitoring of blood glucose

Robust H2/H∞ Filter Design for a Class of Nonlinear Stochastic Systems with State‐Dependent Noise

This paper investigates the problem of robust filter design for a class of nonlinear stochastic systems with state‐dependent noise. The state and measurement are corrupted by stochastic uncertain exogenous disturbance and the dynamic system is modeled by Itô‐type stochastic differential equations. For this class of nonlinear stochastic systems, the robust H∞ filter can be designed by solving linear matrix inequalities (LMIs). Moreover, a mixed H2/H∞ filtering problem is also solved by minimizing the total estimation error energy when the worst‐case disturbance is considered in the design procedure. A numerical example is provided to illustrate the effectiveness of the proposed method.

An Accurate and Efficient Two-Stage Channel Estimation Method Utilizing Training Sequences with Closed Form Expressions

A novel two-stage frequency domain channel estimation method especially suitable for the estimation of long channels such as ultra wide band channels is proposed. The proposed method can efficiently use the sequences with closed form analytical expressions such as the Legendre sequences. (The suggested method does not require a computationally intense search for good training sequences which is infeasible for long training sequences.) The method is shown to present a minor improvement in the total estimation error variance when compared with the conventional single stage frequency domain channel estimation. In addition, the proposed method has a very efficient time domain implementation requiring at most 2N multiplications, where N is the training sequence length, in comparison to O(N log N) multiplications required for the conventional method.

Effects of Vertical Scaling Methods on Linear Growth Estimation

Vertical scaling is necessary to facilitate comparison of scores from test forms of different difficulty levels. It is widely used to enable the tracking of student growth in academic performance over time. Most previous studies on vertical scaling methods assume relatively long tests and large samples. Little is known about their performance when the sample is small or the test is short, challenges that small testing programs often face. This study examined effects of sample size, test length, and choice of item response theory (IRT) models on the performance of IRT-based scaling methods (concurrent calibration, separate calibration with Stocking–Lord, Haebara, Mean/Mean, and Mean/Sigma transformation) in linear growth estimation when the 2-parameter IRT model was appropriate. Results showed that IRT vertical scales could be used for growth estimation without grossly biasing growth parameter estimates when sample size was not large, as long as the test was not too short (≥20 items), although larger sample sizes would generally increase the stability of the growth parameter estimates. The optimal rate of return in total estimation error reduction as a result of increasing sample size appeared to be around 250. Concurrent calibration produced slightly lower total estimation error than separate calibration in the worst combination of short test length (≤20 items) and small sample size ( n ≤ 100), whereas separate calibration, except in the case of the Mean/Sigma method, produced similar or somewhat lower amounts of total error in other conditions.

Effects of Adjusting for Instrumental Variables on Bias and Precision of Effect Estimates

Recent theoretical studies have shown that conditioning on an instrumental variable (IV), a variable that is associated with exposure but not associated with outcome except through exposure, can increase both bias and variance of exposure effect estimates. Although these findings have obvious implications in cases of known IVs, their meaning remains unclear in the more common scenario where investigators are uncertain whether a measured covariate meets the criteria for an IV or rather a confounder. The authors present results from two simulation studies designed to provide insight into the problem of conditioning on potential IVs in routine epidemiologic practice. The simulations explored the effects of conditioning on IVs, near-IVs (predictors of exposure that are weakly associated with outcome), and confounders on the bias and variance of a binary exposure effect estimate. The results indicate that effect estimates which are conditional on a perfect IV or near-IV may have larger bias and variance than the unconditional estimate. However, in most scenarios considered, the increases in error due to conditioning were small compared with the total estimation error. In these cases, minimizing unmeasured confounding should be the priority when selecting variables for adjustment, even at the risk of conditioning on IVs.

Formal Estimation of Errors in Computed Absolute Interaction Energies of Protein-ligand Complexes.

A largely unsolved problem in computational biochemistry is the accurate prediction of binding affinities of small ligands to protein receptors. We present a detailed analysis of the systematic and random errors present in computational methods through the use of error probability density functions, specifically for computed interaction energies between chemical fragments comprising a protein-ligand complex. An HIV-II protease crystal structure with a bound ligand (indinavir) was chosen as a model protein-ligand complex. The complex was decomposed into twenty-one (21) interacting fragment pairs, which were studied using a number of computational methods. The chemically accurate complete basis set coupled cluster theory (CCSD(T)/CBS) interaction energies were used as reference values to generate our error estimates. In our analysis we observed significant systematic and random errors in most methods, which was surprising especially for parameterized classical and semiempirical quantum mechanical calculations. After propagating these fragment-based error estimates over the entire protein-ligand complex, our total error estimates for many methods are large compared to the experimentally determined free energy of binding. Thus, we conclude that statistical error analysis is a necessary addition to any scoring function attempting to produce reliable binding affinity predictions.

Towards more complete specifications for acceptable analytical performance – a plea for error grid analysis

Abstract We examine limitations of common analytical performance specifications for quantitative assays. Specifications can be either clinical or regulatory. Problems with current specifications include specifying limits for only 95% of the results, having only one set of limits that demarcate no harm from minor harm, using incomplete models for total error, not accounting for the potential of user error, and not supplying sufficient protocol requirements. Error grids are recommended to address these problems as error grids account for 100% of the data and stratify errors into different severity categories. Total error estimation from a method comparison can be used to estimate the inner region of an error grid, but the outer region needs to be addressed using risk management techniques. The risk management steps, foreign to many in laboratory medicine, are outlined.

Glyoxal vertical columns from GOME-2 backscattered light measurements and comparisons with a global model

Abstract. Glyoxal vertical column densities have been retrieved from nadir backscattered radiances measured from 2007 to 2009 by the spaceborne GOME-2/METOP-A sensor. The retrieval algorithm is based on the DOAS technique and optimized settings have been used to determine glyoxal slant columns. The liquid water absorption is accounted for using a two-step DOAS approach, leading to a drastic improvement of the fit quality over remote clear water oceans. Air mass factors are calculated by means of look-up tables of weighting functions pre-calculated with the LIDORT v3.3 radiative transfer model and using a priori glyoxal vertical distributions provided by the IMAGESv2 chemical transport model. The total error estimate comprises random and systematic errors associated to the DOAS fit, the air mass factor calculation and the cloud correction. The highest glyoxal vertical column densities are mainly observed in continental tropical regions, while the mid-latitude columns strongly depend on the season with maximum values during warm months. An anthropogenic signature is also observed in highly populated regions of Asia. Comparisons with glyoxal columns simulated with IMAGESv2 in different regions of the world generally point to a missing glyoxal source, most probably of biogenic origin.

Estimates of Total Analytical Error in Consumer and Hospital Glucose Meters Contributed by Hematocrit, Maltose, and Ascorbate

Patients and physicians expect accurate whole blood glucose monitoring even when patients are anemic, are undergoing peritoneal dialysis, or have slightly elevated ascorbate levels. The objective of this study was to estimate analytical error in two consumer and two hospital glucose meters contributed by variations in hematocrit, maltose, ascorbate, and imprecision. The influence of hematocrit (20-60%), maltose, and ascorbate were tested alone and in combination with each glucose meter and with a reference plasma glucose method at three concentrations of glucose. Precision was determined by consecutive analysis (n=20) at three levels of glucose. Multivariate regression analysis was used to estimate the bias associated with the interferences, alone and in combination. Total analytical error was estimated as |% bias|+1.96 (% imprecision). Three meters demonstrated hematocrit bias that was dependent upon glucose concentration. Maltose had profound concentration-dependent positive bias on the consumer meters, and the extent of maltose bias was dependent on hematocrit. Ascorbate produced small but statistically significant biases on three meters. Coincident low hematocrit, presence of maltose, and presence of ascorbate increased the observed bias and was summarized by estimation of total analytical error. Among the four glucose meter devices assessed, estimates of total analytical error in glucose measurement ranged from 6 to 68% under the conditions tested. The susceptibility of glucose meters to clinically significant analytical biases is highly device-dependent, and low hematocrit exacerbated the observed analytical error.

Metrological support for the ASKRO spectrometric channel

The metrological support for monitoring of ground layer of the atmosphere with ASKRO gamma spectrometers requires determining their sensitivity in the measurement geometry of technogenic radionuclides. The application of numerical modeling, the method of similarity of radiation fields, and a combined method in evaluating sensitivity of a spectrometer is examined. It is shown that the model method overstates the sensitivity and does not permit checking the spectrometer. To decrease the error of estimation a combination of measurements of the efficiency of the spectrometer for a point source and a Monte Carlo calculation of the incident flux is used. An estimate of the total relative error of determination of the sensitivity is given and it is shown that it can be verified. The estimates obtained for the sensitivity increase the minimum measured volume activity of the spectrometer at energies less than 1 MeV.

Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery

The spatial behavior of numerous fishing fleets is nowadays well documented thanks to satellite Vessel Monitoring Systems (VMS). Vessel positions are recorded on a frequent and regular basis which opens promising perspectives for improving fishing effort estimation and management. However, no specific information is provided on whether the vessel is fishing or not. To answer that question, existing works on VMS data usually apply simple criteria (e.g. threshold on speed). Those simple criteria generally focus in detecting true positives (a true fishing set detected as a fishing set); conversely, estimation errors are given no attention. For our case study, the Peruvian anchovy fishery, those criteria overestimate the total number of fishing sets by 182%. To overcome this problem an artificial neural network (ANN) approach is presented here. In order to set both the optimal parameterization and use “rules” for this ANN, we perform an extensive sensitivity analysis on the optimization of (1) the internal structure and training algorithm of the ANN and (2) the “rules” used for choosing both the relative size and the composition of the databases (DBs) used for training and inferring with the ANN. The “optimized” ANN greatly improves the estimates of the number and location of fishing events. For our case study, ANN reduces the total estimation error on the number of fishing sets to 1% (in average) and obtains 76% of true positives. This spatially explicit information on effort, provided with error estimation, should greatly reduce misleading interpretations of catch per unit effort and thus significantly improve the adaptive management of fisheries. While fitted on Peruvian anchovy fishery data, this type of neural network approach has wider potential and could be implemented in any fishery relying on both VMS and at-sea observer data. In order to increase the accuracy of the ANN results, we also suggest some criteria for improving sampling design by at-sea observers and VMS data.

SU-GG-T-15: Variation Estimation of Parotids - Towards More Accurate Image Guided Adaptive Radiation Therapy of H&n Cancers

Purpose: Patient anatomical variation identification and treatmentdose estimation are the main feedbacks in image guided adaptive radiation therapy (IGART) process. The identification/estimation can be updated during the treatment course with using online treatment observations obtained from onboard CBCTimaging. In this study, we modeled the geometric variation of parotids using a nonstationary stochastic process and utilized the time‐varying least‐square method to estimate the characteristics of the variation. Method and Materials: An estimation process with the time‐varying least‐square method was used to predict nonstationary stochastic organ variation and applied to evaluate the variation of parotids for 30 h&n cancer patients. The variations observed during the previous treatment days were wascompared to predict those of the remaining treatment. The recursive least‐square polynomial regression with exponential forgetting factor was employed to predict the mean and deviation of parotid positions. The order of polynomial and the forgetting factor were optimized based on the total estimation errors. Results: The optimized order of polynomial was zero and the optimized forgetting factor was 0.74 for the mean position estimation. The corresponding averaged total error of all patients was 1.27mm. The optimized factor for the deviation estimation was 0.94 and the corresponding averaged error was 1.27mm. The estimation errors were approximately isotropic. The predicted results were not sensitive to the patient pool. Conclusion: The developed procedure has been successfully applied to the prediction of the parotid variation. The predicted mean position was closer to the true mean position than the initial position by a factor of 2. Since the estimation process does not utilize any special futures of parotids it can be applied to other organs. Conflict of Interest: Supported by Elekta Oncology System, Inc

Sampling to Reduce Respondent Burden in Personal Network Studies and Its Effect on Estimates of Structural Measures

Recently, researchers have been increasingly interested in collecting personal network data. Collecting this type of data is particularly burdensome on the respondents, who need to elicit the names of alters, answer questions about each alter (network composition), and evaluate the strength of possible relationships among the named alters (network structure). In line with McCarty et al.'s (2007) research, we propose reducing respondent burden by randomly sampling a smaller set of alters from those originally elicited. Via simulation, we assess the estimation error we incur when measures of the network structure are computed on a random sample of alters and illustrate the trade-offs between reduction in respondent burden (measured with the amount of interview time saved) and total estimation error incurred. Researchers can use the provided trade-offs figure to make an informed decision regarding the number of alters to sample when in need to reduce respondent burden.

Fuzzy Regression Models Using the Least-Squares Method Based on the Concept of Distance

Fuzzy regression models are developed to construct the relationship between explanatory variables and responses in a fuzzy environment. In order to increase the explanatory performance of the model, the least-squares method is applied to determine the numeric coefficients based on the concept of distance. Unlike most existing approaches, the numeric coefficients in the proposed model can have negative values. The proposed model minimizes total estimation error in terms of the sum of the average squared distance between the observed and estimated responses based on a few alpha-cuts. The proposed approach is not limited to triangular fuzzy numbers; it can be used to carry out a large number of fuzzy observations efficiently because the model is based on traditional statistical methods. Comparisons with existing methods show that based on the total estimation error using the mean squared error and Kim and Bishu's criterion, the explanatory performance of the proposed model is satisfactory.