Manual Calibration Research Articles

Development of a calibration approach using DNDC and PEST for improving estimates of management impacts on water and nutrient dynamics in an agricultural system

Calibration and validation are standardized practices to establish the credibility of biogeochemical models for understanding agroecosystem nutrient dynamics. We evaluated three automatic calibration approaches, including simultaneous, sequential, and separate, for the calibration of model parameters of the biogeochemical DeNitrification DeComposition (DNDC) model through inverse modeling using PEST, open-source parameter estimation, and uncertainty analysis software. While manual calibration by experts performed the best during calibration period, followed by simultaneous calibration, sequential calibration had the best model performance during the validation period. Model sensitivity analyses demonstrated water leaching to be sensitive to curve number and drain spacing, nitrate leaching to be sensitive to porosity and clay content, and corn yield to be sensitive to accumulative temperature and grain C/N ratio. While some level of expertise is required to inform the automated calibration procedure, it represents a more efficient and robust approach toward increasing the performance of biogeochemical models.

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability.

Automated measurement of dairy cows body size via 3D point cloud data analysis

Body measurement plays an important role in the breeding and production of dairy cows. Body measurement is mainly done manually, which is laborious, imprecise, and prone to stressful behavior in animals. A portable non-contact 3D measurement system of dairy cow body using smartphones was proposed in this study. Reconstruction of 3D point clouds scene containing the cow through a Structure-from-Motion (SfM) photogrammetry, the RANdom SAmple Consensus (RANSAC), and the Euclidean clustering were used to segment the scene and extract cow’s point clouds. Aiming to solve the holes in the cow point cloud caused by the barrier and legs occlusion, a completion method based on the spline curve with a smoothing factor was proposed. The automatic measurement of body size was realized through morphological features. The accuracy of the proposed system was evaluated in a commercial dairy farm. Compared with mesh-based method, the proposed point cloud hole completion method reduced the maximum relative errors of chest girth and chest width by 2.22% and 2.18%. Under different cow postures, the average relative error between automatic body size measurement algorithm and manual calibration was less than 4.67%. The average relative error between the measured value of the non-contact measurement system and the actual value of withers height, body length, chest girth, and chest width were 2.41%, 3.18%, 4.37%, and 6.12%, respectively. The results showed that the proposed method can be used as an automatic and non-contact approach for measuring animal body size.

Overview of methods and means of implementation of information and measuring components cyber-physical systems for electromagnetic probing

The object of research is cyber-physical systems for the study of inhomogeneous environments, in particular, such as land and water depths, in order to find in them the leading bodies. One of the most problematic places in the creation of such systems is a highly sensitive meter of small phase shifts with a low sensitivity threshold. During the study, a review of methods for achieving a low threshold of sensitivity to phase shift. The main one is the method of converting the phase shift into the amplitude modulation coefficient. The implementation of the method is based on the sum-difference transformation with the preliminary introduction of the quadrature phase shift between the signals. The results concerning the methods of solving the following problems are obtained: – increasing the temperature stability of the quadrature phase shifter; – reducing the non-identity of the transmission coefficients of the sumo-difference scheme channels; – allocation of the amplitude of the amplitude-phase-modulated signal at low ratios of the frequencies of the input signals and switching. The first problem is partially solved by dividing the quadrature phase shifter into two equal halves, placed in different channels, with approximately the same phase temperature coefficients. The second problem is also solved by manual calibration before measurement. The most difficult task is to extract the bypass from the amplitude-phase-modulated signal, in which the carrier frequency is only several times greater than the bypass. The difficulty is that the detection produces combinational frequencies due to phase modulation. Therefore, it is difficult to separate them. Several methods of allocating the bypass, if the frequency of the input signals is fixed, are considered. This allows to get the sensitivity threshold(1·10–4÷3·10–5) of advice. Reducing the operating frequency of the signals increases the depth of research, but increases the impact of flicker noise. The minimum frequency is 10 Hz. Therefore, these methods are not suitable for frequencies of Hz units. Methods to reduce the impact of flicker noise will be considered in another paper.

Collision-free trajectory planning for multi-robot simultaneous motion in preforms weaving

AbstractIn this paper, an automatic obstacle avoidance trajectory planning strategy is proposed for the simultaneous motion of multi-robots, which perform anthropomorphic skill operations in a large curved preformed three-dimensional (3D) weaving environment with multiple obstacles and limited space, to eliminate tedious manual calibration work of robot path in engineering. Firstly, an Adaptive Goal-guided Rapidly-exploring Random Trees (AGG-RRT) algorithm is proposed, combined with the robot obstacle avoidance strategy, to search the discrete position of the collision-free path of the end-effector gradually from the starting point to the ending point. Then the optimization of the path is completed by bidirectional pruning of redundant nodes and cubic non-uniform rational B-spline (NURBS) curve fitting. And finally, the robot trajectory is interpolated based on S-shaped acceleration/deceleration planning to ensure smooth robot joint motion. The simulation results demonstrate the superiority of the AGG-RRT algorithm over the basic RRT algorithm and related improved algorithms in terms of search time and success rate. The simulated experiments also achieve the smooth trajectory planning of multiple robotic arms with the synchronous obstacle avoidance motion, which shows that the AGG-RRT algorithm is successfully applied and the collision-free trajectory planning strategy is effective.

Loss of street trees predicted to cause 6000 L/tree increase in leaf-on stormwater runoff for Great Lakes urban sewershed

Urban forests are recognized as a nature-based solution for stormwater management. This study assessed the underlying processes and extent of runoff reduction due to street trees with a paired-catchment experiment conducted in two sewersheds of Fond du Lac, Wisconsin. Computer models are flexible, fast, and low-cost options to generalize and assess the hydrologic processes determined in field studies. A state-of-the-art, public-domain model, which explicitly simulates urban tree hydrology, i-Tree Hydro, was used to simulate the paired-catchment experiment, and results from field observations and simulation predictions were compared to assess model validity and suitability as per conditions in the broader Great Lakes basin. Model parameters were aligned with observed conditions using automatic and manual calibration. Model performance metrics were used to quantify the weekly performance of calibration and to validate predictions. Those calibration metrics differed substantially between the two periods simulated, but most calibration metrics remained positive, indicating the model was not fitting only the period used for calibration. Predicted avoided runoff for a five-month leaf-on period was 64 L/m2 of canopy, 4 % lower than the field-estimated avoided runoff of 66 L/m2 of canopy. Interception was the most directly comparable process between the model and field observations. Based on 5 storms sampled, field estimation of precipitation intercepted and retained on trees averaged 63 % and ranged from 22 % to 81 %, while model estimation averaged 61 % and ranged from 36 % to 99 %. This model was able to fit predictions to observed catchment discharge but required extensive manual calibration to do so. The i-Tree Hydro model predicted avoided runoff comparable with the field study and earlier assessments. Additional field studies in similar settings are needed to confirm findings and improve transferability to other tree species and environmental settings.

Machine-guided exploration and calibration of astrophysical simulations

ABSTRACT We apply a novel method with machine learning to calibrate sub-grid models within numerical simulation codes to achieve convergence with observations and between different codes. It utilizes active learning and neural density estimators. The hyper parameters of the machine are calibrated with a well-defined projectile motion problem. Then, using a set of 22 cosmological zoom simulations, we tune the parameters of a popular star formation and feedback model within Enzo to match observations. The parameters that are adjusted include the star formation efficiency, coupling of thermal energy from stellar feedback, and volume into which the energy is deposited. This number translates to a factor of more than three improvements over manual calibration. Despite using fewer simulations, we obtain a better agreement to the observed baryon makeup of a Milky Way (MW)-sized halo. Switching to a different strategy, we improve the consistency of the recommended parameters from the machine. Given the success of the calibration, we then apply the technique to reconcile metal transport between grid-based and particle-based simulation codes using an isolated galaxy. It is an improvement over manual exploration while hinting at a less-known relation between the diffusion coefficient and the metal mass in the halo region. The exploration and calibration of the parameters of the sub-grid models with a machine learning approach is concluded to be versatile and directly applicable to different problems.

DRIIS: MapReduce Parameter Optimization of Hadoop Using Genetic Algorithm

In this era of massive knowledge, Hadoop, jointly of the foremost unremarkably used massive processing platforms, features a variety of parameters that are closely associated with resource utilization, particularly mainframe and memory. Calibration of these parameters through improvement may increase Hadoop’s resource utilization. Manual calibration of those parameters is just about not possible, thanks to the time price. Within the massive knowledge business, there’s a requirement to mechanically set up parameters and thereby maximize resource usage. The previous automatic calibration strategies take an extended time to realize the optimum configuration, reducing the cluster’s overall performance. With the help of a novel perceptive procedure, each genetic programming and a genetic rule are supported with a view to enhancing the performance of a Hadoop MapReduce work, we propose an assistant in nursing the best configuration finder. We have a tendency to use the algorithms on top to search out the simplest values for parameter settings. Experiments were performed on four common applications, WordCount, TeraSort, Index and Grep, and eight virtual machines (VMs) in a very typical Hadoop cluster. Our projected methodology will increase MapReduce job potency by 53.63% for a one GB dataset and by 67.4% for a five GB dataset, in keeping with the findings, and by 73.68% on a 10 GB dataset; additionally, for a TeraSort programmed application, MapReduce job potency will increase by 52.62% for an one GB dataset, 61.2% for a five GB dataset, and 55.17% for a 10[Formula: see text]GB dataset. MapReduce jobs boost the performance of Grep applications by 44.4% for a one GB dataset, 56.25% for a five GB dataset, and 49.44% for a 10 GB dataset.

A Switchable Metamaterial Absorber for Fine-Tuning of the Coherence Bandwidth in a Reverberation Chamber

Since wireless communication technology has been widely applied in our daily lives, wireless devices exhibit explosive growth. A reliable, efficient, and repeatable over-the-air (OTA) testing method is vital for the research and development of wireless devices. Due to the particular advantages of the reverberation chamber ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> ), it has become a popular facility to perform OTA tests nowadays. In the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> -based OTA tests, loading the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> with radio frequency (RF) absorbers is a standardized procedure to control the frequency selectivity of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> channel according to the cellular telecommunications industry association (CTIA). However, this procedure involves iterative manual calibrations and, therefore, is tedious and time-consuming. Moreover, it may be difficult to tune the coherence bandwidth (CBW) to a desired value accurately given fixed RF absorbers. In this work, a locally switchable metamaterial absorber (MA) is proposed for fine-tuning of the CBW of the channel in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> . It is experimentally verified that the CBW can be easily controlled by switching on and off of the absorbing/reflection units of the MA. The proposed MA can provide fine-tuning of the CBW in an efficient and convenient manner.

A Dynamic Calibration of Optical Fiber DTS Measurements Using PEST and Reference Thermometers.

Temperature measurements are widely used in structural health monitoring. Optical fiber distributed temperature sensors (DTS) are developed, based on Raman spectroscopy, to measure temperature with relatively high accuracy and short temporal and spatial resolutions. DTS systems provide an extensive number of temperature measurements along the entire length of an optical fiber that can be extended to tens of kilometers. The efficiency of the temperature measurement strongly depends on the calibration of the DTS data. Although DTS systems internally calibrate the data, manual calibration techniques were developed to achieve more accurate results. Manual calibration employs reference sections or points with known temperatures and the DTS scattering data to estimate the calibration parameters and calculate temperature along the optical fiber. In some applications, manual calibration is subjected to some shortages, based on the proposed fiber installation configuration and continuity of calibration. In this article, the manual calibration approach was developed using the model-independent Parameters Estimation (PEST), together with the external temperature sensors as references for the DTS system. The proposed method improved manual calibration in terms of installation configuration, continuity of dynamic calibration, and estimation of the calibration parameters.

A Comparison of Adaptive Ultrasound Reflectometry Calibration Methods for Use in Lubrication Films

Adaptive calibration for ultrasound reflectometry methods used in the detection of lubrication film thickness is of great research interest. This is mainly due to the versatile non-destructive implementation of the technology in industrial applications, allowing for measurements of the lubrication film thickness, which directly relates to the friction, wear, and overall efficiency of the system. This study reviews and compares a curve fitting, extended Kalman filter, and resonance frequency detection adaptive calibration approach. Furthermore, the study compares two different regression models, used for the curve fitting and the extended Kalman filter approach. The study compares the methods and regression models based on both a theoretical and experimental analysis. The experimental analysis is based on data with varying lubrication film thickness. The lubrication film thickness is varied such that the resonance frequency is both detectable and non-detectable within the ultrasound transducer bandwidth. It is found that all three methods give results very similar to those achieved through manual calibration. However, since the extended Kalman filter approach is the only method that allows for continuous calibration, this method is found to have the greatest potential of the methods compared.

Manual calibration of TETIS model in the Mar Menor lagoon subcatchment

Manual calibration of TETIS model in the Mar Menor lagoon subcatchment

Development and Evaluation of a Saturated Zone Module in an Integrated Urban Hydrological Model

Shallow urban groundwater interacts with surface water and underground infrastructures. Low-impact development in urban water management by at-source infiltration should consider shallow urban groundwater in a holistic manner. Traditional hydrological models, however, rarely detail groundwater flows and their interaction with urban runoff and the water budget. In the present study, a new approach is proposed, using the integration of a flow module WTI for the saturated zone in a distributed urban hydrological model URBS-MO. This integration is carried out by paying attention to retaining the initial waterflow subsurface parameterization. The performance of the integrated model is evaluated by piezometric and runoff data in an experimental urban catchment, through a sensitivity analysis and a manual calibration of the main model parameters, as well as a validation step. The new module shows its capacity to improve groundwater flow simulation by assessing more realistic water table variations, along with a very small improvement of flowrate simulation. The bias on the average groundwater level was reduced from +14 to +7% for the one-year validation period. The modelling results show the importance of parameter calibration for distributed physically-based hydrological models. Difficulties in the calibration of parameter values due to spatial heterogeneities are also revealed, as the use of piezometric data for the calibration of a hydrological model is rather innovative.

Optimization and uncertainty quantification of in situ combustion chemical reaction models

Heavy oil, deep, and waterflooded reservoirs contain significant petroleum resources but present many unique challenges for oil recovery. In situ combustion (ISC) is a thermal enhanced oil recovery method that has been proposed for such reservoirs. A persistent challenge in understanding ISC is modeling the underlying chemical reactions. While many different reaction models have been proposed, these models are typically developed on an ad-hoc basis for individual crude oil samples and manually calibrated to experimental data. While some work exists on applying optimization-based approaches to reaction calibration, existing approaches are not fully-generalized nor do they quantify uncertainty in the estimated parameters. Here we present such an approach for calibrating and quantifying uncertainty in ISC chemical reaction parameters. The workflow presented is generalized to any hydrocarbon pseudocomponent-based reaction model of ISC, provides a framework for quantification of uncertainty in estimated parameters, and is fully automated. Thus, no parameter initialization or manual calibration is needed. We apply this workflow to characterization of a heavy crude oil sample and demonstrate the capability of this workflow to predict ISC oxidation kinetics and upscale to a combustion tube simulation. Overall, this work provides an algorithmic approach to calibrating ISC kinetics that enables quantitative analysis and comparison of oil types, reaction models, and reservoir conditions for ISC.

Application of a Non-Invasive Technique for the Preservation of a Fortified Masonry Tower

The Cultural heritage spread all over the World needs to be preserved with systems that do not compromise its architectural and historical value. Nowadays, the most advanced technology available is the ambient vibration test. It allows to obtain the current frequencies, modal shapes, and damping of structures, without being invasive. The comparison between different monitoring campaigns and their use in combination with Finite Element models can give an insight into the state of structures’ health. This paper presents two ambient vibration tests performed on a fortified masonry tower in the Marche region of Italy, carried out after one year each other, with different temperature and humidity conditions. To extract the structure’s dynamic parameters both a time and a frequency domain approaches were used. The comparison between the parameters obtained during the two experimental campaigns showed similar frequencies and modal shapes underlining that no damage occurred and that the dynamic response of the tower does not suffer temperature and humidity variations. In addition, the steps carried out for a first attempt manual calibration of the tower’s Finite Element model are shown. The match between the numerical model and the experimental data is evaluated through the absolute frequencies’ errors and the Modal Assurance Criteria between the modal vectors. The calibrated numerical model can be used for future and accurate assessment of the tower’s structural capacity.

Automatic phantom-less QCT system with high precision of BMD measurement for osteoporosis screening: Technique optimisation and clinical validation.

BackgroundCurrently dual-energy X-ray absorptiometry (DXA) and phantom-based quantitative computed tomography (PB-QCT) have been utilized to diagnose osteoporosis widely in clinical practice. While traditional phantom-less QCT (PL-QCT) is limited by the precision of manual calibration using body tissues, such as fat and muscle.ObjectiveThe aim of this study is to validate the accuracy and precision of one newly-developed automatic PL-QCT system to measure spinal bone mineral density (BMD) and diagnose osteoporosis.MethodsA total of 36 patients were enrolled for comparison of BMD measurement between DXA and QCT. CT images of 63 patients were analyzed by both PB-QCT and newly developed automatic PL-QCT system, then the BMD results generated by the automatic PL-QCT were utilized to diagnose osteoporosis. The diagnostic outcomes were compared with that of DXA and PB-QCT to assess the performance of the new system.ResultsBMD test results showed that the automatic PL-QCT system had higher precision than previous studies performed with QCT, while maintaining similar capability to diagnose osteoporosis as DXA and PB-QCT. Area under curve (AUC) result of PL-QCT was larger than 0.8 for predicting spine DXA T-score in receiver operating characteristic (ROC) analysis. Pearson correlation analysis (r ​= ​0.99) showed strong linear correlation and Bland-Altman analysis (bias ​= ​3.0mg/cc) indicated little difference between the two methods. The precision result (CV ​= ​0.89%) represented good reproducibility of the new system.ConclusionThe traditional PL-QCT system has relatively low reproducibility due to the manual selection of the region of interest (ROI) of body tissues. Automatic selection of ROI in this new system makes the BMD testing more convenient and improves precision significantly. Compared with traditional BMD measurement methods, the automatic PL-QCT system had higher precision in accurate diagnosis of osteoporosis with great potential in translational research and wide clinical application.Translational potential statementWith high accuracy and precision, the automatic PL-QCT system could serve as an opportunistic screening tool for osteoporosis patients in the future. It could also facilitate related researches by providing more reliable data collection, both retrospectively and longitudinally.

A MEMS Rate-Integrating Gyroscope (RIG) With In-Run Automatic Mode-Matching

Toward the objective of eliminating the angle error and rate threshold of the rate integrating gyroscope (RIG) caused by stiffness asymmetry, a novel in-run automatic mode-matching method is presented in this paper. Although other manual mode-matching methods can reduce the stiffness asymmetry temporarily, the environmental factors will interfere the manual calibration. Combined with virtual rotation, the processing circuits obtain the resonant frequencies of RIG operation modes and a controller is used to reduce the frequency difference. Meanwhile, an innovative method is proposed for RIG angle error analysis, avoiding the drawbacks of the traditional analysis methods. An FPGA based platform using a MEMS disk resonator is tested and the angle error is reduced to 1 degree compared with the manual calibration result 6.1 degrees.

A fast and effective parameterization of water quality models

Water quality (WQ) models parameterization remains a challenging task, as these models are typically characterized by a high number of parameters. The objective of this study was to present a solution to the WQ parameterization problem by the use of a fast sensitivity analysis (SA) method and a manual calibration. For this purpose, we applied the simple screening LH-OAT method to the conceptual WQ model of the River Dender, Belgium. To evaluate the effectiveness of LH-OAT to identify the influential parameters, the advanced PAWN method was applied. A manual calibration was done using the influential parameters.LH-OAT provided a parameter ranking that was very similar to the one of PAWN but in a much more efficient way. The Bayesian uncertainty assessment showed the effectiveness of the LH-OAT results. To conclude, a fast screening method is preferred over an advanced SA method to identify the influential parameters for the calibration.

Self-Supervised Segmentation for Terracotta Warrior Point Cloud (EGG-Net)

At present, our team focuses on cultural relics restoration and fragment splicing research. In the research process of terracotta warrior splicing, we find that the existing calibrated fragment data is relatively small, which is not enough for related research. Therefore, we need to calibrate and segment different parts of the intact terracotta warrior data and extract some data we need to use in the future. However, at present, we are short of human resources. If we want to carry out manual calibration, it will take much time, bringing trouble to our future work. Therefore, we hope to design a method to automatically calibrate the terracotta warrior dataset with a small amount of calibrated data. The existing 3D neural network research mainly focuses on supervised classification, segmentation, and unsupervised reconstruction. We cannot find enough schemes to refer to, and the existing methods do not perform well on our terracotta warrior dataset. Therefore, in this article, we propose EGG-Net to solve this problem. EGG-Net is an end-to-end self-supervised model, and it consists of two modules. The first module is an encoder based on dynamic graph and edge convolution. We can extract point cloud features with this module. The second module, called segmenter, is based on multi-layer perceptron, adding labels to points and segmenting the point cloud. After the neural network, we add point refinement operation to the pipeline. Point refinement can adjust the cluster label estimated by the neural network with superpoint, which can optimize the loss function and help us train the neural network. Our EGG-Net can back-propagate with the refinement operation. We evaluated EGG-Net on the terracotta warrior data and ShapeNet Part by measuring the accuracy and the latency. The experiment result shows that our EGG-Net outperforms the state-of-the-art methods.

Design of Automatic Batch Calibration and Correction System for IMU

Thanks to its light weight, low power consumption, and low price, the inertial measurement units (IMUs) have been widely used in civil and military applications such as autopilot, robotics, and tactical weapons. The calibration is an essential procedure before the IMU is put in use, which is generally used to estimate the error parameters such as the bias, installation error, scale factor of the IMU. Currently, the manual one-by-one calibration is still the mostly used manner, which is low in efficiency, time-consuming, and easy to introduce mis-operation. Aiming at this issue, this paper designs an automatic batch calibration method for a set of IMUs. The designed automatic calibration master controller can control the turntable and the data acquisition system at the same time. Each data acquisition front-end can complete data acquisition of eight IMUs one time. And various scenarios of experimental tests have been carried out to validate the proposed design, such as the multi-position tests, the rate tests and swaying tests. The results illustrate the reliability of each function module and the feasibility automatic batch calibration. Compared with the traditional calibration method, the proposed design can reduce errors caused by the manual calibration and greatly improve the efficiency of IMU calibration.