Acceptable Estimation Accuracy Research Articles

Estimation accuracy of bean bags as portion size estimation aids for amorphous foods

Objective: A study was undertaken to explore the portion size estimation accuracy of bean bags as low-cost volumetric portion size estimation aids for amorphous foods. Designs: Three observational, cross-sectional and three experimental/quasi-experimental developmental evaluation sub-studies were carried out. Settings: Observational, cross-sectional: one retirement village and four schools. Experimental/quasi-experimental: one South African university. Participants: Observational, cross-sectional: elderly persons, adult women (school educators), schoolchildren. Experimental/quasi-experimental: university students. In total N = 541; > 3 800 observations. Variables measured: Using a standardised set of bean bags (test object; volume range: 60–625 ml), volumes of different amorphous foods (reference objects: actual foods or representations) in varying portion sizes had to be estimated. Accuracy (outcome measure) was perfect if volumes of test and reference object were identical. Acceptable estimation accuracy allowed for misestimation by one bean bag size. Test–retest reproducibility was also assessed. Analysis: Descriptive statistics (proportions perfect and acceptable accuracy). Results: Across the sub-studies, perfect accuracy ranged from 22–65% depending on participants, reference food and portion size. Irrespective thereof, acceptable accuracy was noted in > 70% of observations. Reproducibility varied (range: 28–67% agreement). Conclusions and implications: Perfect portion size estimation of amorphous foods remains challenging. When misestimation by 60–125 ml still serves the purpose of a dietary assessment, bean bags show promise for cost-effective food volume quantification, especially on group level in resource-limited settings.

End-to-end learning for joint depth and image reconstruction from diffracted rotation

Monocular depth estimation is an open challenge due to the ill-posed nature of the problem at hand. Deep learning techniques proved capable of producing acceptable depth estimation accuracy but the lack of robust depth cues within RGB images severally limits their performance. Coded aperture-based methods using phase and amplitude masks encode strong depth cues within 2D images by means of depth-dependent Point Spread Functions (PSFs) at the price of a reduced image quality. In this paper, we propose a novel end-to-end learning approach for depth from diffracted rotation. A phase mask that produces a Rotating Point Spread Function (RPSF) as a function of defocus is jointly optimized with the weights of a depth estimation neural network. To this aim, we introduce a differentiable physical model of the aperture mask and exploit an accurate simulation of the camera imaging pipeline. Our approach requires a significantly less complex model and less training data, yet it outperforms existing methods for monocular depth estimation on indoor benchmarks. In addition, we address the image degradation problem by incorporating a non-blind and nonuniform image deblurring module to recover the sharp all-in-focus image from its blurred counterpart.

Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model

The maximal oxygen uptake (VO2max) estimation has been a subject of research for many years. Cardiorespiratory measurements during incremental tests until exhaustion are considered the golden yard stick to assess VO2max. However, precise VO2max determination based on submaximal tests is attractive for athlete as well for clinical populations. Here, we propose and verify such a method based on experimental data. Using a recently developed model of heart rate (HR) and VO2 kinetics in graded exercise tests, we applied a protocol, which is terminated at 80% of the estimated maximal HR during ergometer cycling. In our approach, initially, formula for maximal HR is selected by retrospective study of a reference population (17 males, 23.5 ± 2.0 years, BMI: 23.9 ± 3.2 kg/m2). Next, the subjects for experimental group were invited (nine subjects of both sexes: 25.1 ± 2.1 years, BMI 23.2 ± 2.2 kg/m2). After calculation of maximal HR using cardiorespiratory recordings from the submaximal test, VO2max is predicted. Finally, we compared the prediction with the values from the maximal exercise test. The differences were quantified by relative errors, which vary from 1.2% up to 13.4%. Some future improvements for the procedure of VO2max prediction are discussed. The experimental protocol may be useful for application in rehabilitation assessment and in certain training monitoring settings, since physical exertion is not a prerequisite and the approach provides an acceptable VO2max estimation accuracy.

Item selection methods in multidimensional computerized adaptive testing for forced-choice items using Thurstonian IRT model.

Multidimensional computerized adaptive testing for forced-choice items (MFC-CAT) combines the benefits of multidimensional forced-choice (MFC) items and computerized adaptive testing (CAT) in that it eliminates response biases and reduces administration time. Previous studies that explored designs of MFC-CAT only discussed item selection methods based on the Fisher information (FI), which is known to perform unstably at early stages of CAT. This study proposes a set of new item selection methods based on the KL information for MFC-CAT (namely MFC-KI, MFC-KB, and MFC-KLP) based on the Thurstonian IRT (TIRT) model. Three simulation studies, including one based on real data, were conducted to compare the performance of the proposed KL-based item selection methods against the existing FI-based methods in three- and five-dimensional MFC-CAT scenarios with various test lengths and inter-trait correlations. Results demonstrate that the proposed KL-based item selection methods are feasible for MFC-CAT and generate acceptable trait estimation accuracy and uniformity of item pool usage. Among the three proposed methods, MFC-KB and MFC-KLP outperformed the existing FI-based item selection methods and resulted in the most accurate trait estimation and relatively even utilization of the item pool.

Estimation of airflow distribution in cubic building group model using POD-LSE and limited sensors

Urban airflow has strong turbulence characteristics, and its distribution varies with time. Urban airflow distribution can be measured with sensors or simulated using Computational Fluid Dynamics (CFD). However, CFD is considered still expensive for the rapid calculation of unsteady airflow distribution and the measurement value of sensors is often inferior in terms of spatial resolution. In this study, the high spatial resolution airflow distribution in the cubic building group model was rapidly estimated from limited sensors by combining Proper Orthogonal Decomposition (POD) with Linear Stochastic Estimation (LSE). Validated airflow data simulated by Large-Eddy Simulation (LES) in a cubic building group model were selected as the airflow database. This study was conducted with two main objectives: 1) to verify the basic performance of the POD-LSE; and 2) to analyze the effects of changing the number of sensors and POD analysis regions on the estimation accuracy. By comparing the results of the POD-LSE estimations with the LES data as the true value in the reference region, the following conclusions were obtained: 1) The airflow distribution in the cubic building group model can be reconstructed and predicted by the POD-LSE; 2) nine sensors can provide acceptable estimation accuracy; 3) the velocity value of a point located in the wake region is difficult to estimate; 4) the accuracy does not increase as the POD region expands (without changing the number of sensors); and 5) with an increasing number of sensors, the accuracy of the velocity estimation is improved in reconstruction.

Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Québec, Canada)

In the terrestrial cryosphere, freeze/thaw (FT) state transitions play an important and measurable role in climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. Active and passive microwave remote sensing has shown a principal capacity to provide effective monitoring of landscape FT dynamics. The study presents a seasonal threshold approach, which examines the timeseries progression of remote sensing measurements relative to signatures acquired during seasonal frozen and thawed reference states. This is used to estimate the FT state from the Sentinel-1 database and applied and evaluated for the region of Eastern Nunavik (Québec, Canada). An optimization process of the threshold is included. In situ measurements from the meteorological station network were used for the validation process. Overall, acceptable estimation accuracy (>70%) was achieved in most tests; on the best-performing sites, an accuracy higher than 90% was reached. The performance of the seasonal threshold approach over the study region was further discussed with consideration of land cover, spatial heterogeneity, and soil depth. This work is dedicated to providing more accurate data to capture the spatiotemporal heterogeneity of freeze/thaw transitions and to improving our understanding of related processes in permafrost landscapes.

Wi-Fruit

People usually assess fruit qualities from external features such as color, shape, size, and texture. However, it is quite common that we select fruits with perfect appearances but rotten inside, especially for fruits with thick pericarps. Thus the accurate measurement is desirable to evaluate the internal conditions of fruits. As two key features of fruit internal qualities, existing methods on measuring fruit moisture and soluble solid contents (SSC) are either destructive or costly, limiting their adoption in daily life. In this paper, we propose Wi-Fruit, a non-destructive and low-cost fruit moisture and SSC measurement system leveraging Wi-Fi channel state information (CSI). First, to cope with the fruit structure dependency challenge, we propose a double-quotient model to pre-process CSI on adjacent antennas. Second, to address the fruit size and type dependency challenges, a lightweight artificial neural network (ANN) model with visual information fusion is proposed for fruit moisture and SSC estimations. Extensive evaluations are conducted on 6 types of fruits with both thick (i.e., watermelon and grapefruit) and thin pericarps (i.e., dragon fruit, apple, pear, and orange) over a month in either an empty laboratory room or a library with massive books. Results demonstrate that Wi-Fruit achieves an acceptable estimation accuracy (RMSE=0.319). It is independent of various fruit structures, sizes, and types, while also robust to time and environmental changes. The fruit internal sensing capabilities of Wi-Fruit can help fruit saving and safety in both pre-harvest and post-harvest applications.

Diffusion bias-compensated recursive maximum correntropy criterion algorithm with noisy input

In recent years, the distributed estimation problem based on the diffusion strategy has received more and more attention, where the node filters cooperate with other in-network node filters to estimate the unknown parameter vector. In this paper, we propose a Diffusion Bias Compensated Recursive Maximum Correntropy Criterion (DBCRMCC) algorithm based on the idea of distributed estimation for adaptive filtering in network containing input noise and non-Gaussian output noise. The Recursive Maximum Correntropy Criterion (RMCC) is an adaptive filtering algorithm with Maximum Correntropy Criterion (MCC) as the cost function which is robust to large outliers. Considering that the estimates directly obtained by the RMCC algorithm are biased when the input is disturbed by noise, this paper proposes the DBCRMCC algorithm under some reasonable assumptions by using the principle of unbiased estimation and combining it with a diffusion strategy. Through bias-compensation for biased estimates and cooperation among node filters, the asymptotic unbiased estimates of the unknown parameters can be obtained. The simulation results show that the proposed DBCRMCC algorithm has acceptable convergence speed and estimation accuracy in the environment where the input signal is noisy and the output noise is non-Gaussian.

Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis

Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.

Investigating Bias of DCFT, DPT and Promoted DPT Methods in terms of Phase Parameters Estimation of Chirp Signal

Amongst the approaches proposed to estimate parameters of a chirp signal sequentially, i.e., the central frequency and the chirp rate, algorithms, such as discrete polynomial-phase transform (DPT) and promoted DPT, exhibit acceptable estimation accuracy. Algorithms intended to estimate phase parameters sequentially, diminish the order of polynomials in complex exponential power to lower-order polynomials, and then estimate these two parameters using the NLS method in a given single exponential mode. The NLS method, which uses FFT to decrease the computational load of frequency domain search, encounters predicaments. In this work, we assessed the bias of algorithms intended for estimating of phase parameters sequentially using the RBF method. The results of investigating the bias of estimators indicated the improved accuracy of the DPT and promoted DPT algorithms in estimation using the RBF method instead of NLS and also than DCFT method.

Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data

Abstract Worldwide proliferation of cyanobacteria blooms in inland waters not only affects the intended use of water but potentially threatens human and animal health. In this study, a stacked autoencoder-deep neural network (SAE-DNN) was developed to estimate phycocyanin (PC) concentration by using in situ reflectance spectra in productive inland water. The estimated PC using the SAE-DNN was in close agreement with the measured PC, with an R2 of 0.87, root mean square error (RMSE) of 14.45 μg/L, and relative RMSE of 86.42%. The performance of the SAE-DNN was superior to that of the DNN and band-ratio algorithms. An analysis on the deep spectral features extracted using the SAE yielded the most useful spectral bands, namely 538, 596, and 735 nm, for the retrieval of PC. The estimation accuracy of the SAE-DNNPeaks, using only the aforementioned spectral bands as input variables, was comparable to that of the SAE-DNN, demonstrating that the high-level of abstraction using the SAE facilitated the improvement in feature learning. The application of the SAE-DNNPeaks to airborne hyperspectral image data resulted in an acceptable estimation accuracy, despite a bias toward underestimation, potentially arising from uncertainty associated with atmospheric correction, at high PC concentrations. Our results suggest that simple, empirical-based approaches, such as the SAE-DNNPeaks, have the potential to serve as a rapid assessment tool for the abundance and spatial distribution of cyanobacteria.

An information aware event-triggered scheme for particle filter based remote state estimation

The remote state estimation problem is considered for general non-Gaussian systems. The estimator runs particle filtering algorithm to track the non-Gaussian probability density function (PDF) of the target state. We are concerned with the reduction of sensor-to-estimator communication while maintaining acceptable estimation accuracy. For this purpose, a novel event-based transmission scheme is proposed where the Kullback–Leibler divergence is used to identify informative measurements. We develop a two-step approximation procedure to obtain a parametric form for the event generator function, thereby enabling each sensor to quantify the informativeness of its current measurement without running a copy of the estimator. Furthermore, a Monte Carlo method is proposed to evaluate the likelihood function of the set-valued measurements. Simulation results demonstrate the effectiveness of our scheme, especially when the predictive PDF of the measurement is strongly non-Gaussian.

Retrieval of narrow-range LAI of at multiple lidar point densities: Application on Eucalyptus grandis plantation

Leaf area index (LAI) is an important forest structural parameter that can be used to characterize various biophysical processes such as photosynthesis, evapotranspiration, and carbon flux. Accurate monitoring of LAI therefore is crucial for efficient management of managed and natural vegetation ecosystems. Remote sensing techniques have proved useful in the quantification and monitoring of LAI in different vegetation types; however, most of the focus has been on vegetation with relatively large LAI ranges. This study aimed to investigate the utility of airborne light detection and ranging (lidar) data to estimate narrow-range LAI (min = 0.71, max = 1.56, mean = 1.08 ± 0.18) of intensively-managed Eucalyptus grandis plantations. The secondary aim of the study was to assess the effect of lidar point density on LAI retrieval. Reference LAI was quantified in 15 m radius sample plots (n = 46) using hemispherical photography. Akaike Information Criterion (AIC) regression was used to build candidate models that estimate LAI from lidar-derived height and density metrics. The correlations were investigated at different point densities, including the original (>6 points/m2) and reduced density levels (0.25–5 points/m2). Candidate models returned adjusted coefficient of determination (adj. R2) ranging between 0.65–0.83 (RMSE 7.0-10.0% of observed mean) depending on the number of predicting metrics included in the models. A model that had two non-collinear metrics was selected as a compromise model (adj. R2 = 0.67; RMSE = 9.7%); this model was comparable to the best model, which had many collinear metrics. Estimation accuracies were similar for lidar densities of the original, 2–5 points/m2 and less accurate for 0.25–1 point/m2. These findings demonstrated the capability of lidar in estimating observed LAI with low range and variation. The study also suggests the efficacy of moderate lidar point densities acquired at relatively low-cost surveys in attaining acceptable LAI estimation accuracy.

Accuracy Assessment of Multi-Source Gridded Population Distribution Datasets in China

Population is one of the core elements of sustainable development. Quantifying the estimation accuracy of population spatial distribution has been recognized as a critical and challenging task. This study aims to evaluate the data accuracy of four population datasets in China, including three global gridded population datasets, the Gridded Population of the World (GPW), Global Rural and Urban Mapping Project (GRUMP), and WorldPop project (WorldPop), and a Chinese regional gridded population dataset, the China 1 km Gridded Population (CnPop) dataset. These datasets are assessed using a specific method based on a GIS-linked 2000 census dataset at the township level in China. The results indicate that WorldPop had the highest estimation accuracy, estimating about 60% of the total population. CnPop accurately estimated about half of the total population, showing a good mapping performance. The GPW had an acceptable estimation accuracy in a few plain and basin areas, accounting for about 30% of the total population. Compared to the GPW, GRUMP accurately estimated about 40% of the total population. The relative estimation error analysis discovered the disadvantages of the generation strategies of these datasets. The conclusions are expected to serve as a quality reference for potential dataset users and producers, and promote accuracy assessment for population datasets in other regions and globally.

A Fast Process-Variation-Aware Mask Optimization Algorithm With a Novel Intensity Modeling

With the continuous shrinkage of advanced technology nodes into the sub-16-nm regime, optical proximity correction (OPC) is still the main stream to preserve acceptable wafer image quality under lithographic process variations in the foreseeable future. However, OPC is getting more aggressive to keep pace with advanced technology nodes. This results in complex mask solutions and long computation time. In this paper, we propose a novel-intensity-based OPC algorithm to find mask solutions with minimal edge placement error and process variability band area within a short computation time. This is achieved through exploiting a fast novel intensity estimation model with acceptable estimation accuracy to guide the OPC response including two-fragment shifting, corner hammering, and subresolution assist feature insertion for better convergence. Moreover, our algorithm is extended to satisfy the mask notch rule and reduce shot count for a lower mask manufacturing cost. The experimental results show that our algorithm outperforms recently published algorithms on the public benchmarks.

Online Calibration of Polytomous Items Under the Generalized Partial Credit Model.

Online calibration is a technology-enhanced architecture for item calibration in computerized adaptive tests (CATs). Many CATs are administered continuously over a long term and rely on large item banks. To ensure test validity, these item banks need to be frequently replenished with new items, and these new items need to be pretested before being used operationally. Online calibration dynamically embeds pretest items in operational tests and calibrates their parameters as response data are gradually obtained through the continuous test administration. This study extends existing formulas, procedures, and algorithms for dichotomous item response theory models to the generalized partial credit model, a popular model for items scored in more than two categories. A simulation study was conducted to investigate the developed algorithms and procedures under a variety of conditions, including two estimation algorithms, three pretest item selection methods, three seeding locations, two numbers of score categories, and three calibration sample sizes. Results demonstrated acceptable estimation accuracy of the two estimation algorithms in some of the simulated conditions. A variety of findings were also revealed for the interacted effects of included factors, and recommendations were made respectively.

Systematic literature review of ensemble effort estimation

The need to overcome the weaknesses of single estimation techniques for prediction tasks has given rise to ensemble methods in software development effort estimation (SDEE). An ensemble effort estimation (EEE) technique combines several of the single/classical models found in the SDEE literature. However, to the best of our knowledge, no systematic review has yet been performed with a focus on the use of EEE techniques in SDEE. The purpose of this review is to analyze EEE techniques from six viewpoints: single models used to construct ensembles, ensemble estimation accuracy, rules used to combine single estimates, accuracy comparison of EEE techniques with single models, accuracy comparison between EEE techniques and methodologies used to construct ensemble methods. We performed a systematic review of EEE studies published between 2000 and 2016, and we selected 24 of them to address the questions raised in this review. We found that EEE techniques may be separated into two types: homogeneous and heterogeneous, and that the machine learning single models are the most frequently employed in constructing EEE techniques. We also found that EEE techniques usually yield acceptable estimation accuracy, and in fact are more accurate than single models.

Viewing urban decay from the sky: A multi-scale analysis of residential vacancy in a shrinking U.S. city

Demographic shifts result in not only urban sprawl but also urban decay. Although urban vacant land is a sign of economic decline, it is beneficial to the improvement of urban ecology since it provides a variety of critical ecosystem services. It is unclear how socioeconomic/ecological drivers collectively affect the emergence and dynamics of vacant land within the urban context of population decrease and economic decline in different shrinking cities. To address this issue, this research performed comprehensive analyses to examine and quantify the impacts of social processes, economic conditions, urban ecological and spatial proximity factors on residential vacant land at both the block group level and the parcel level through incorporating high spatial resolution multispectral aerial photographs and GIS datasets. Results indicate three major conclusions. First, the mean lot NDVI is found to be a consistently effective measurement of residential landscape management behaviors at both levels, which is significantly and positively related to residential vacancy. Second, proximity factors indirectly and partially characterize a number of socioeconomic variables. Third, a ratio index called neighborhood vacancy prevalence was proposed to capture the neighborhood blight effect, and a potentially optimal neighborhood size was obtained among different neighborhood radii. By considering different categories of variables, residential vacancy can be explained and modeled adequately at both the aggregate and fine levels. Urban planners, geographers and social scientists could benefit from the explanatory and predictive model at the fine scale to yield the most updated parcel data with acceptable estimation accuracy for various practical applications.

A Low-Switching-Frequency Flux Observer and Torque Model of Deadbeat–Direct Torque and Flux Control on Induction Machine Drives

This paper presents a digital implementation of deadbeat–direct torque and flux control (DB-DTFC) for induction machines at low switching frequencies (SFs). For high SFs, existing discrete-time flux observers and Volt–second-based inverse torque models used for DB-DTFC achieve acceptable flux estimation accuracy and fast torque response. However, the flux estimate is less accurate when the SF is reduced and the DB-DTFC performance degrades. This paper develops a more suitable flux observer and Volt–second-based inverse torque model that minimizes flux estimation error and improves torque control for DB-DTFC. Simulation and experimental results are provided to evaluate the performance of the proposed observer and torque model at very low SFs. Consequently, digital implementation of low-SF DB-DTFC on high-power induction machines is feasible.

A Novel Online Estimation Scheme for Static Voltage Stability Margin Based on Relationships Exploration in a Large Data Set

A novel integrated scheme based on relationships exploration (RE) is presented for the online estimation of the relative voltage stability margin (VSM). The proposed scheme can select the optimal variables as input features for estimation by detecting the relationship between each operation variable and the relative VSM in a large data set. Each relationship is given scores by the maximal information coefficient (MIC) and the Pearson correlation coefficient (PCC). The variables selected for estimation are corresponding to the relationships highly ranked by MIC and PCC, including linear relationships and nonlinear functional ones. Some of the highly ranked relationships are shown, curve fitted and described from the perspective of power system operation. If the data of the selected variables are obtained online from the wide area measurement system (WAMS), the relative VSM can be online estimated based on the explored relationships. The integrated scheme using RE-based process is examined on a 21-bus test system and a practical 1648-bus system provided by the software PSS/E, and it presents acceptable estimation accuracy. The impacts of training set size, selected relationships' types, total number and ranks on estimation accuracy are analyzed. The robustness of the scheme to measurement errors and topology variation is studied.