Hybrid Calibration Approach Research Articles

Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy.

Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction ( ), which in turn is key for noninvasive estimation of the axonal volume fraction ( ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation of . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics of . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmental and can substantially enhance the comparability between EM- and DWI-based metrics of . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-based . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.

Multifunctional Structure Calibration by Optimal Measurement Using Hybrid Approach for Response Monitoring

Multifunctional structure calibration by optimal measurement using hybrid approach is developed for the conventional strain gauges for response monitoring. In the hybrid calibration approach, we have used the finite element analysis and experimental modal technique to quantify the measured output of distributed sensors. Smart materials such as piezoelectric (PZT and PVDF) in thin film form are widely appreciated for distributed sensing application in composite structures. The light weight composite structures can be made multi-functional by incorporating the piezoelectric materials to have non-structural functionalities such as sensing, actuation, energy harvesting and health monitoring. In the present study, in addition to load carrying and damping, which are the essential structural functions, the composite plate is made to have self sensing capability with four distributed strain gauges and PZT patches respectively. Further, it has been shown that using a single strain measurement, the finite element strains can be updated at different assumed strain gauge locations in order to calibrate the PZT patch sensors placed nearby to output engineering strains.

Hybrid calibration procedure for structured light field system

A typical structured light field system consists of a light field camera and a pattern projector. By combining Fringe Projection Profilometry and light field imaging technology, the phase distribution characteristics of each position and each angle of the scene under test could be obtained, thus providing more information dimensions, and therefore improving the robustness and dynamic range of three-dimensional (3D) measurement. System calibration is a decisive factor for high-efficiency and high precision measurement. However, the complicated structure and working method make the calibration of structured light field system usually costly and cumbersome. In this paper, we propose a hybrid calibration approach combining the flexibility of the stereo vision method and high precision characteristics of the phase-coordinate mapping method. The feasibility of the proposed method has been verified in actual experiments with typical 3D targets.

A Hybrid Calibration Method for the Binocular Omnidirectional Vision System

Omnidirectional three-dimensional (3D) measurement based on the binocular omnidirectional vision system attracts increasing attention in computer vision. A 360° horizontal field of view (FOV) and 180° vertical FOV are obtained by the binocular omnidirectional vision system with only a pair of images. However, a nonlinear relationship exists between points on image planes and in 3D space for the binocular omnidirectional vision system, and traditional calibration methods are no longer applicable. This study proposes a hybrid calibration approach, which effectively fuses the unified camera model and the backpropagation neural network with a virtual 3D target, in which the neural network improved with genetic algorithm compensates all kinds of distortions and errors. First, the unified camera model is used to calibrate the intrinsic parameters, extrinsic parameters, and a transformation matrix. Second, the pixel coordinates on image planes are converted to 3D coordinates by 3D reconstruction through binocular vision. The errors between actual and calculated 3D coordinates are obtained. In addition, the neural network improved with genetic algorithm is established and trained with pixel coordinates on image planes and errors. Experimental results indicate that the average measurement errors in the X and Y directions of the central area are respectively reduced to 0.1057 and 0.1548 mm, and those of the edge area are reduced to 0.1992 and 0.2748 mm. Compared with mathematical and neural network methods, the hybrid calibration method provides more stable, robust, and accurate results. It is proven suitable for complex calibration scenes with large FOV, high distortion, and strong nonlinearity.

Positioning Accuracy Improvement of Industrial Robots Considering Configuration and Payload Effects Via a Hybrid Calibration Approach

Robot calibration of an industrial robot is of importance for those applications requiring high positioning accuracy. The use of numerical optimization techniques to identify the accurate kinematic parameters and tackle the residual errors for improving positioning accuracy is still challenging. The difficulties in using the conventional optimization techniques is associated with the expensive computation of the second derivative terms in the Hessian matrix and even more linked to the additional improvement of the residual errors induced by the configuration and payload effects for the practical applications. A hybrid calibration procedure, which is robust and efficient, is proposed to handle the said problems in this study. The first step is to utilize the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm together with the quasi-Newton method to optimize the cost function with non-smooth nature established using massive measurement data. In the second step, two kinds of artificial neural network (ANN) algorithms are employed to further improve the nonlinear residual errors induced by payload and configuration effects. After the completion of the hybrid calibration, improvements of 88.1% and 80.1% have been attained, respectively, for the mean and maximum positioning errors of the robot end-effector, i.e., the mean/maximum positioning errors are reduced from 2.613mm/6.294mm to 0.310mm/1.255mm for 840 untrained measurement data. The experimental results also validated the effectiveness of the proposed method.

Combining BOUNCE and X-ORCA: Improving their real-world utility

Asynchronous time measurement systems, such as tapped delay lines and BOUNCE, consist of a rather large number of independently operating elements, which all have to be calibrated in a rather tedious, preliminary setup process. The required calibration time grows linearly in the number of instantiated elements, which can be as large as thousands of elements in practically relevant systems. Recent research has suggested to seamlessly integrate a second time measurement system, known as X-ORCA, to make the required calibration time constant, i.e., independent of the number of elements. Even though X-ORCA is operating asynchronously as well, it is able to determine the phase shifts between two signals at various on-board locations simultaneously. This paper integrates X-ORCA to calibrate BOUNCE. This hybrid calibration approach improves BOUNCE’s precision from 170ps to 30ps. This paper describes this approach in detail, and provides the results of intensive tests, which include different FPGAs, temperature changes, and validation by means of external laboratory equipment.

Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration

Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.

Hybrid calibration method for six-component force/torque transducers of wind tunnel balance based on support vector machines

A hybrid calibration approach based on support vector machines (SVM) is proposed to characterize nonlinear cross coupling of multi-dimensional transducer. It is difficult to identify these unknown nonlinearities and crosstalk just with a single conventional calibration approach. In this paper, a hybrid model comprising calibration matrix and SVM model for calibrating linearity and nonlinearity respectively is built up. The calibration matrix is determined by linear artificial neural network (ANN), and the SVM is used to compensate for the nonlinear cross coupling among each dimension. A simulation of the calibration of a multi-dimensional sensor is conducted by the SVM hybrid calibration method, which is then utilized to calibrate a six-component force/torque transducer of wind tunnel balance. From the calibrating results, it can be indicated that the SVM hybrid calibration method has improved the calibration accuracy significantly without increasing data samples, compared with calibration matrix. Moreover, with the calibration matrix, the hybrid model can provide a basis for the design of transducers.

Parameter calibration framework for environmental emergency models

Simulation systems that predict the propagation of environmental emergencies have to satisfy hard real-time constraints to prevent tragedy. To obtain more reliable forecasts, input parameter calibration mechanisms should be integrated because it is often impossible to measure highly dynamic input parameters in a correct and timely manner. Evolutionary optimisation methods showed promising calibration potential but involve numerous expensive fitness evaluations. This makes their application to time-restricted real-world problems impractical, especially when only limited computing resources are available. We therefore propose a framework for efficient parameter calibration based on evolutionary intelligent systems. A genetic algorithm is joined with a case-based reasoner to form a hybrid calibration approach. The suggested framework allows the user to select the configuration of the calibration process according to emergency and prediction characteristics and available computing resources. The possibility to generate quick calibration estimates thus minimising the additional computational effort caused by the introduction of parameter calibration is highlighted. The framework was tested in the area of forest fire spread prediction. A case base was generated from real historical and synthetical forest fires. Experiments show that case-based reasoning generates results comparable to pure evolutionary optimisation approaches, clearly outperforming the latter in runtime.

A PARSIMONIOUS MULTI-ASSET HESTON MODEL: CALIBRATION AND DERIVATIVE PRICING

We propose a parsimonious multi-asset Heston model and provide an easy-to-implement calibration algorithm. The model is customized to pricing multi-asset options in markets with liquidly traded single-asset options but no liquidly traded cross-asset options. In this situation, single-asset model parameters can be calibrated from option price data, however, cross-asset parameters cannot. We formulate a parsimonious model specification such that all single-asset models are Heston models, which are affine allowing for efficient calibration of the respective parameters. The single-asset models are correlated using cross-asset correlations only. Cross-asset correlations are observable, in contrast to correlations of latent variables such as volatilities, and serve as basis for calibration. A hybrid calibration approach for identifying the model parameters consistent with option price data and asset price data is outlined and illustrated by a case study. In banking practice the approach is referred to as correlation adjustment.

Formulation of a hybrid calibration approach for a physically based distributed model with NEXRAD data input

This paper describes the background, formulation and results of an hourly input–output calibration approach proposed for the Soil and Water Assessment Tool (SWAT) watershed model, presented for 24 representative storm events occurring during the period between 1994 and 2000 in the Blue River watershed (1233 km 2 located in Oklahoma). This effort is the first follow up to the participation in the National Weather Service-Distributed Modeling Intercomparison Project (DMIP), an opportunity to apply, for the first time within the SWAT modeling framework, routines for hourly stream flow prediction based on gridded precipitation (NEXRAD) data input. Previous SWAT model simulations, uncalibrated and with moderate manual calibration (only the water balance over the calibration period), were provided for the entire set of watersheds and associated outlets for the comparison designed in the DMIP project. The extended goal of this follow up was to verify the model efficiency in simulating hourly hydrographs calibrating each storm event using the formulated approach. This included a combination of a manual and an automatic calibration approach (Shuffled Complex Evolution Method) and the use of input parameter values allowed to vary only within their physical extent. While the model provided reasonable water budget results with minimal calibration, event simulations with the revised calibration were significantly improved. The combination of NEXRAD precipitation data input, the soil water balance and runoff equations, along with the calibration strategy described in the paper, appear to adequately describe the storm events. The presented application and the formulated calibration method are initial steps toward the improvement of the simulation on an hourly basis of the SWAT model loading variables associated with the storm flow, such as sediment and pollutants, and the success of Total Maximum Daily Load (TMDL) projects.