Maximum Allowable Error Research Articles

An adaptive ensemble Kalman filter for assimilation of multi-sensor, multi-modal water temperature observations into hydrodynamic model of shallow rivers

There is lack of knowledge in assimilation of multi-sensor, multi-modal water temperature observations into hydrodynamic models of shallow rivers, specifically, (a) how does accuracy of shallow river model improve after assimilation of in-situ and remotely sensed temperature observations, and (b) how can data from disparate sensors and sources be assimilated into the prediction model without significant increase in computational burden. Multi-sensor, multi-modal observations used in this study are obtained from in-situ monitoring devices in the river with limited spatial coverage but dense temporal resolution, and from Landsat-7 satellite that has better spatial coverage but limited temporal coverage. Use of remotely-sensed observations from satellites poses the challenge that the physical region represented by satellite data does not directly correspond with the physical domain of the river simulated in hydrodynamic models. Satellites detects the skin water temperature, whereas numerical models estimate the bulk temperature in the surface layer that may be multiple meters in thickness. Furthermore, for rivers narrower than the resolution of satellite data, the temperature of each cell represents the weighted average temperature of land and water. These factors introduce biases into the updated numerical model, thereby impeding appropriate management of temperature, water quality, and aquatic ecology. We implemented an efficient ensemble Kalman filter method using Latin-hypercube sampling to assimilate multi-sensor water temperature observations into the hydrodynamic model of the Lower Klamath River located in northern California. Assimilation of remote sensing data from Landsat-7 improved the model prediction for the entire river. The average spatial error was reduced from 2.59 °C to 0.66 °C (i.e., 75% improvement). In-situ data assimilation reduced the error at the observation location, however, error in the water temperature predicted by the updated model reverted in less than two days to the same level as that of an un-updated model. On the other hand, it is not computationally efficient to assimilate all of the available data into the model as they become available. In order to overcome these challenges, in-situ data were adaptively assimilated into the model whenever the error exceeded a maximum allowable error. Adaptive assimilation of in-situ data for the Lower Klamath river application occurred one to three times per day, and reduced the average daily error up to 58% compared to assimilation of in-situ data only once each day.

Performance of two portable blood glucose meters for measuring blood glucose concentration in tigers (Panthera tigris) and lions (Panthera leo).

OBJECTIVE To investigate the performance of a portable blood glucose meter (PBGM) designed for use in humans (hPBGM) and a PBGM designed for use in dogs and cats (vPBGM) when measuring blood glucose (BG) concentration in tigers (Panthera tigris) and lions (Panthera leo). DESIGN Method comparison and diagnostic accuracy study. SAMPLES 53 blood samples from tigers (n = 27) and lions (26). PROCEDURES BG concentration was measured with 2 identical hPBGMs, 2 identical vPBGMs, and a reference laboratory analyzer. Bland-Altman bias plots and Passing-Bablok regression analysis were used to assess agreement. Sensitivity, specificity, and positive and negative predictive values with corresponding 95% confidence intervals were calculated for use in assessing diagnostic accuracy of the investigated PBGMs. RESULTS Bias (95% limits of agreement) was -4.3 mg/dL (-46.3 to 37.6 mg/dL) for the hPBGM, -9.3 mg/dL (-64.6 to 46.0 mg/dL) for the vPBGM on canine setting, and 2.3 mg/dL (-47.9 to 52.6 mg/dL) for the vPBGM on feline setting. The hPBGM had better overall repeatability (coefficient of variation, 3.73%) than the vPBGM on canine (9.29%) or feline (9.44%) setting. Total error for the hPBGM, vPBGM on canine setting, and vPBGM on feline setting was 11.8%, 27.7%, and 20.9%, respectively. None of the PBGMs complied with the maximum allowable total error suggested by current guidelines when measuring BG in tigers and lions with hypo-, normo-, or hyperglycemia. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that the PBGMs evaluated were inadequate for measuring BG concentration in tigers and lions.

Common Criterion of Privacy Metrics and Parameters Analysis Based on Error Probability for Randomized Response

Privacy security issues under the classic randomized response (RR) model proposed by Warner and its extended $K$ -RR model are studied. First, in order to provide references for the accuracy of the private distribution estimation problem under RR mechanism, the lower bounds of the differential privacy parameter and the number of participates are deduced for an accuracy objective of $({\alpha }, {\delta })$ – accurate in statistics. Second, when the prior distribution is nonuniform, the data utility has a ceiling effect in the high privacy region by taking the prior into account. In this case, the average distortion, which is defined as the expected Hamming distance between the input and output data, is no longer feasible to measure the data utility. Motivated by this, the error probability is proposed as a measure of data utility for unifying different privacy metrics, where the error probability is defined to be the expected Hamming distance between the input and reconstructed data based on maximum a posteriori estimation. Third, under a unified privacy-preserving framework using RR mechanism based on error probability criterion, the relationship among differential privacy, identifiability privacy, and mutual information privacy is established. Given a maximum allowable error probability $P_{E}^{\mathrm {max}}$ , the optimal privacy parameters of these three privacy notions are derived with the full consideration of the prior distribution. Then, a Bayes-based utility function, which corresponds to the converse of the Bayes risk, is constructed to measure the degree of privacy leakage. Given a maximum allowable correct probability $P_{C}^{\mathrm {max}}$ , the accuracy objective of the statistical estimate is considered to derive the range of local differential privacy parameter from the perspective of security. Fourth, all the research results above are further extended to $K$ -RR mechanism too. Finally, the correctness and effectiveness are further verified by simulation experiments. The results reveal that the error probability can be applied to any prior distribution case, while the average distortion criterion is only a special case with uniform distribution. Therefore, the error probability proposed in the paper is more reasonable to be used as a common criterion to measure the data utility for the RR model so as to unify different privacy metrics.

Lower bound estimation of the maximum allowable initial error and its numerical calculation

In the numerical prediction of weather or climate events, the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty. Due to the absence of true stat...

Development of 10 MN Hydraulic-amplification Force Standard Machine and establishment of metrological standard

This paper mainly describes the structure principle, performance characteristic, main technical index, uncertainty evaluation and the metrological standard establishment of 10 MN hydraulic-amplification force standard machine. The force value range of the force standard machine is 200kN ∼10MN, which is reproduced by divided into two spans: 200kN ∼1MN and 1MN∼10MN. Its repeatability of indication is 0.015%, and the maximum allowable error of indication is ±0.03%.

Laboratory sample stability. Is it possible to define a consensus stability function? An example of five blood magnitudes.

The stability limit of an analyte in a biological sample can be defined as the time required until a measured property acquires a bias higher than a defined specification. Many studies assessing stability and presenting recommendations of stability limits are available, but differences among them are frequent. The aim of this study was to classify and to grade a set of bibliographic studies on the stability of five common blood measurands and subsequently generate a consensus stability function. First, a bibliographic search was made for stability studies for five analytes in blood: alanine aminotransferase (ALT), glucose, phosphorus, potassium and prostate specific antigen (PSA). The quality of every study was evaluated using an in-house grading tool. Second, the different conditions of stability were uniformly defined and the percent deviation (PD%) over time for each analyte and condition were scattered while unifying studies with similar conditions. From the 37 articles considered as valid, up to 130 experiments were evaluated and 629 PD% data were included (106 for ALT, 180 for glucose, 113 for phosphorus, 145 for potassium and 85 for PSA). Consensus stability equations were established for glucose, potassium, phosphorus and PSA, but not for ALT. Time is the main variable affecting stability in medical laboratory samples. Bibliographic studies differ in recommedations of stability limits mainly because of different specifications for maximum allowable error. Definition of a consensus stability function in specific conditions can help laboratories define stability limits using their own quality specifications.

Automatic measurement of anthropometric dimensions using frontal and lateral silhouettes

Anthropometric dimensions, such as lengths, heights, breadths, circumferences and their ratios are highly significant in healthcare, security, sports, clothing, tools and equipment industry. In this study, an automatic and precise method for anthropometric dimensions of human body using two‐dimensional images is proposed. The dimensions are obtained by using fiducial points that are detected from frontal and lateral views of body silhouettes. Primary anthropometric dimensions, which include heights, breadths, depths and lengths, are obtained by calculating the difference between two relevant fiducial points. The secondary dimensions: ratios are obtained directly from primary dimensions, and circumference dimensions are estimated precisely using ellipsoid model. A total of 75, i.e. 51 primary and 24 secondary dimensions are obtained, which are three times the number acquired by the state‐of‐the‐art method. The accuracy of acquired dimensions is verified by comparing it with the manual measurements by using the standard parameter of maximum allowable error. It is found that mean absolute difference of all the dimensions, obtained by the proposed method, lie within the limits of maximum allowable error. More importantly, the mean absolute difference for the majority of dimensions (20 out of 24) is significantly less for proposed method as compared with the best method in existing literature.

Aplicación de metas analíticas y modelo Seis Sigma en la evaluación del control de calidad de Química Clínica

IntroductionWith the aim of providing clinically relevant results, the clinical laboratory must implement a sufficiently good internal quality control system in order to achieve analytical goals with the highest possible standards. Materials and methodsA retrospective, descriptive, and longitudinal study was performed in order to study the internal quality control results of the Clinical Chemistry area of a Mexican laboratory obtained during the period from July to December 2016. ResultsThe Clinical Laboratory Improvement Amendments (CLIA) goals of maximum total allowable error were achieved in 100% of the analytes studied. In the case of biological variation goals, desirable or optimal goals were achieved for in 89% of the analytes studied, with inferior results being obtained in these analytes with a low biological variability index. Finally, as regards the results of sigma values, our results varied considerably depending on the analytical specifications used, achieving the lowest values in those analytes with the lowest biological variability index. ConclusionsBased on our results we can suggest that an adequate quality control model in Clinical Chemistry could initially include the establishment of analytical specifications based on CLIA for those analytes with low biological variation, and the desirable goals of biological variation for the rest of the analytes. Both goals can be achieved by the instruments used in this study.

Impact of spot charge inaccuracies in IMPT treatments.

Spot charge is one parameter of pencil-beam scanning dose delivery system whose accuracy is typically high but whose required value has not been investigated. In this work we quantify the dose impact of spot charge inaccuracies on the dose distribution in patients. Knowing the effect of charge errors is relevant for conventional proton machines, as well as for new generation proton machines, where ensuring accurate charge may be challenging. Through perturbation of spot charge in treatment plans for seven patients and a phantom, we evaluated the dose impact of absolute (up to 5× 106 protons) and relative (up to 30%) charge errors. We investigated the dependence on beam width by studying scenarios with small, medium and large beam sizes. Treatment plan statistics included the Γ passing rate, dose-volume-histograms and dose differences. The allowable absolute charge error for small spot plans was about 2× 106 protons. Larger limits would be allowed if larger spots were used. For relative errors, the maximum allowable error size for small, medium and large spots was about 13%, 8% and 6% for small, medium and large spots, respectively. Dose distributions turned out to be surprisingly robust against random spot charge perturbation. Our study suggests that ensuring spot charge errors as small as 1-2% as is commonly aimed at in conventional proton therapy machines, is clinically not strictly needed.

Manual Validation and Calibration Method for All-or-Nothing Traffic Assignment

Transport Modelling is capital for transportation planning. All-or-Nothing Traffic Assignment is used quite a lot. The model can not be considered valid without passing through validation and calibration process. A validation and calibration method, for the all-or-nothing traffic assignment, need to be developed. The research produced simple practical validation and calibration method. The calculated model traffic volumes are compared against the real traffic volumes. Simple validation method was developed by just setting a maximum allowable error, measured in precentage. Simple calibration method consists of correcting the model traffic volume by correcting the corresponding OD Matrix cell values. The calibration consists of 4 basic tasks : 1. identifying the traffic volume need to be calibrated, 2. identifying corresponding OD matrix cells corresponding to the traffic volume need to be calibrated, 3. distributing traffic volume error to the coresponding OD matrix cells, and 4. developing the calibrated OD matrix. Validation and Calibration are a pair of an iterative process.

A memory efficient implementation scheme of Gauss error function in a Laguerre-Volterra network for neuroprosthetic devices.

Cognitive neural prosthesis is a manmade device which can be used to restore or compensate for lost human cognitive modalities. The generalized Laguerre-Volterra (GLV) network serves as a robust mathematical underpinning for the development of such prosthetic instrument. In this paper, a hardware implementation scheme of Gauss error function for the GLV network targeting reconfigurable platforms is reported. Numerical approximations are formulated which transform the computation of nonelementary function into combinational operations of elementary functions, and memory-intensive look-up table (LUT) based approaches can therefore be circumvented. The computational precision can be made adjustable with the utilization of an error compensation scheme, which is proposed based on the experimental observation of the mathematical characteristics of the error trajectory. The precision can be further customizable by exploiting the run-time characteristics of the reconfigurable system. Compared to the polynomial expansion based implementation scheme, the utilization of slice LUTs, occupied slices, and DSP48E1s on a Xilinx XC6VLX240T field-programmable gate array has decreased by 94.2%, 94.1%, and 90.0%, respectively. While compared to the look-up table based scheme, 1.0×1017 bits of storage can be spared under the maximum allowable error of 1.0×10-3. The proposed implementation scheme can be employed in the study of large-scale neural ensemble activity and in the design and development of neural prosthetic device.

Variation range based simplification for meaningful symbolic analysis of analog integrated circuits

This paper presents a variation range based symbolic simplification technique, where each circuit parameter is represented via a range of variation. We generate a database to handle the variation ranges of the circuit parameters and perform simulated annealing algorithm to achieve the most compact symbolic expressions. In contrast to the existing techniques, the main advantage of the proposed method is that the simplified symbolic expressions have an acceptable generalizability in the whole ranges of variations of the circuit parameters. The maximum allowable simplification error can be controlled by the user based on the required accuracy level that he needs. Simulation results demonstrate that the proposed method outperforms the conventional techniques in terms of expression complexity, accuracy and generalizability of the simplified expressions. The resultant expressions are only as complicated as necessary to give the required accuracy in the results, which help in giving a better insight into the circuit behavior.

An affordable cuff-less blood pressure estimation solution.

This paper presents a cuff-less hypertension pre-screening device that non-invasively monitors the Blood Pressure (BP) and Heart Rate (HR) continuously. The proposed device simultaneously records two clinically significant and highly correlated biomedical signals, viz., Electrocardiogram (ECG) and Photoplethysmogram (PPG). The device provides a common data acquisition platform that can interface with PC/laptop, Smart phone/tablet and Raspberry-pi etc. The hardware stores and processes the recorded ECG and PPG in order to extract the real-time BP and HR using kernel regression approach. The BP and HR estimation error is measured in terms of normalized mean square error, Error Standard Deviation (ESD) and Mean Absolute Error (MAE), with respect to a clinically proven digital BP monitor (OMRON HBP1300). The computed error falls under the maximum standard allowable error mentioned by Association for the Advancement of Medical Instrumentation; MAE <; 5 mmHg and ESD <; 8mmHg. The results are validated using two-tailed dependent sample t-test also. The proposed device is a portable low-cost home and clinic bases solution for continuous health monitoring.

A Four Degree of Freedom Robot for Positioning Ultrasound Imaging Catheters.

In this paper, we present the design, fabrication, and testing of a robot for automatically positioning ultrasound (US) imaging catheters. Our system will point US catheters to provide real-time imaging of anatomical structures and working instruments during minimally invasive procedures. Manually navigating US catheters is difficult and requires extensive training in order to aim the US imager at desired targets. Therefore, a four-degree-of-freedom (4DOF) robotic system was developed to automatically navigate US imaging catheters for enhanced imaging. A rotational transmission enables 3DOF for pitch, yaw, and roll of the imager. This transmission is translated by the 4DOF. An accuracy analysis calculated the maximum allowable joint motion error. Rotational joints must be accurate to within 1.5 deg, and the translational joint must be accurate within 1.4 mm. Motion tests then validated the accuracy of the robot. The average resulting errors in positioning of the rotational joints were 0.04-0.22 deg. The average measured backlash was 0.18-0.86 deg. Measurements of average translational positioning and backlash errors were negligible. The resulting joint motion errors were well within the required specifications for accurate robot motion. The output of the catheter was then tested to verify the effectiveness of the handle motions to transmit torques and translations to the catheter tip. The catheter tip was navigated to desired target poses with average error 1.3 mm and 0.71 deg. Such effective manipulation of US imaging catheters will enable better visualization in various procedures ranging from cardiac arrhythmia treatment to tumor removal in urological cases.

RADON IN GROUNDWATERS IN THE BAIKAL REGION AND TRANSBAIKALIA: VARIATIONS IN SPACE AND TIME

RADON IN GROUNDWATERS IN THE BAIKAL REGION AND TRANSBAIKALIA: VARIATIONS IN SPACE AND TIME

Influences of extinction coefficient profile on the estimation of Slant Visual Range

Abstract Slant Visual Range (SVR) is defined as the distance at which the contrast of a given object with respect to its background is just equal to the contrast threshold of an observer in slant direction. In this study, estimation methods and errors of SVR are explored in lidar-free circumstances where Extinction Coefficient Profile (ECP), vertical distribution of Single Scattering Albedo (SSA) and asymmetry parameter (ASY) cannot be acquired. Statistical characteristics of aerosol optical properties in the Taklimakan Basin for sand-dust weather are derived from the CALIPSO daytime aerosol product from 2011 to 2014. SSA and ASY are approximated as 0.92 and 0.7 because aerosol types are mainly dust and polluted dust throughout layers. Besides, ECP can be categorized into exponential, Gaussian and other patterns. Based on whether the determination of real ECP into one of the three patterns is feasible, two SVR estimation methods are introduced and termed the accurate estimation method (AEM) and the blind estimation method (BEM), both methods are performed using SBDART radiative transfer model. For the AEM, analysis of estimated SVR and real SVR reveals a minimum linear correlation coefficient of 0.98 and a maximum root mean square error of 0.07, and the hit rate (R) of SVR estimation increases from 86% to nearly 100% when the maximum allowable relative error (MARE) increases from 10% to 25%. Validation of the BEM shows that R varies from 78% to 100% for MARE of 25% and falls drastically with the decrease of MARE, with the highest R value in spring and summer for the Gaussian pattern and the lowest values in fall and winter for exponential and other patterns. This study is among the first to explore the feasibility and methodology of deriving SVR in lidar-free circumstances.

A 4-DOF Robot for Positioning Ultrasound Imaging Catheters.

In this paper we present the design, fabrication, and testing of a robot for automatically positioning ultrasound imaging catheters. Our system will point ultrasound (US) catheters to provide real-time imaging of anatomical structures and working instruments during minimally invasive surgeries. Manually navigating US catheters is difficult and requires extensive training in order to aim the US imager at desired targets. Therefore, a four DOF robotic system was developed to automatically navigate US imaging catheters for enhanced imaging. A rotational transmission enables three DOF for pitch, yaw, and roll of the imager. This transmission is translated by the fourth DOF. An accuracy analysis was conducted to calculate the maximum allowable joint motion error. Rotational joints must be accurate to within 1.5° and the translational joint must be accurate within 1.4 mm. Motion tests were then conducted to validate the accuracy of the robot. The average resulting errors in positioning of the rotational joints were measured to be 0.28°-0.38° with average measured backlash error 0.44°. Average translational positioning and backlash errors were measured to be significantly lower than the reported accuracy of the position sensor. The resulting joint motion errors were well within the required specifications for accurate robot motion. Such effective navigation of US imaging catheters will enable better visualization in various procedures ranging from cardiac arrhythmia treatment to tumor removal in urological cases.

Optimal calibration of the learning rate in closed-loop adaptive brain-machine interfaces.

Closed-loop decoder adaptation (CLDA) can improve brain-machine interface (BMI) performance. CLDA methods use batches of data to refit the decoder parameters in closed-loop operation. Recently, dynamic state-space algorithms have also been designed to fit the parameters of a point process decoder (PPF). A main design parameter that needs to be selected in any CLDA algorithm is the learning rate, i.e., how fast should the decoder parameters be updated on the basis of new neural observations. So far, the learning rate of CLDA algorithms has been selected empirically using ad-hoc methods. Here we develop a principled framework to calibrate the learning rate in adaptive state-space algorithms. The learning rate introduces a trade-off between the convergence rate and the steady-state error covariance of the estimated decoder parameters. Hence our algorithm first finds an analytical upper-bound on the steady-state error covariance as a function of the learning rate. It then finds the inverse mapping to select the optimal learning rate based on the maximum allowable steady-state error. Using numerical BMI experiments, we show that the calibration algorithm selects the optimal learning rate that meets the requirement on steady-state error level while achieving the fastest convergence rate possible corresponding to this steady-state level.

Real-time human pose estimation and gesture recognition from depth images using superpixels and SVM classifier.

In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

A linear model for estimating optimal service error fraction in volleyball

AbstractVolleyball coaches are frequently forced to address the question of athlete service errors as a part of their overall service strategy. This is usually done in an ad hoc fashion with an arbitrarily selected maximum allowable service error fraction or maximum allowable service error-to-ace ratio. In this article, an analysis of service outcomes leads to a mathematical expression for the point-scoring fraction in terms of service ace fraction, service error fraction, and opponent modified sideout fraction. These parameters are assumed to be monotonic functions of an athlete or team’s serving aggressiveness and a linear model for the service error-to-ace ratio is used to close the point-scoring optimization problem. The model provides estimates of the optimal service error fraction for individual athletes based on their service ace fraction and the opponent modified sideout fraction against the server overall and also when restricted to only serves that led to perfect passes. Case studies of the Bay to Bay 17 Black Boys’ USAV Juniors team and the Brigham Young University Men’s NCAA Division I team are used to demonstrate the application of the model and standard errors for the predicted optimal service error fractions are calculated with bootstrap resampling.