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The numerical simulation of boundary value problems involving a high number of pile-type structures, such as pile foundation systems of high-rise buildings, represents a standard task in computational geotechnics. Due to their ability to simplify the underlying pile modeling process and decrease the simulation runtime embedded beam formulations (EBFs) have attracted widespread interest from the geotechnical community. State-of-the art EBFs are typically equipped with nonlinear interface constitutive models to capture the soil–structure interaction behavior with reasonable accuracy. However, reliable information concerning the calibration of related parameters is limited, which decreases the confidence in the results obtained with EBFs. The present work addresses this research aspect for the first time, along with a theoretical discussion of inherent simplifications in the numerical description of the soil–structure contact associated with EBFs. The significance of selecting adequate embedded interface constitutive model parameters to ensure EBF predictions with high credibility is demonstrated by means of parametric studies. This has motivated the development of an automatic calibration software, leveraged by particle swarm optimization, that provides guidance in selecting suitable values. The effectiveness of the calibration software is confirmed by back-calculation of different pile problems with default and calibrated parameter sets. Likewise, the results provide insight into crucial factors driving the calibration framework, with a view to increasing its potential for take-up in engineering practice. Potential lines of research in the context of the automatic calibration software are explored throughout this work and may serve as valuable reference in future research. Practical Applications: The design of geotechnical problems involving a high number of pile-type structures, such as pile foundation systems of high-rise buildings or wind turbines, is routinely assisted by finite-element analyses. The computational expense of these simulations is significantly influenced by the pile modeling technique. Due to their exceptional ability to simplify the underlying modeling process and reduce the runtime to an acceptable limit, embedded beam formulations have therefore attracted widespread interest for geotechnical design tasks. As with all soil–structure interaction problems, it is essential in embedded beam formulations to accurately describe the interface behavior between the soil and the structure. This work presents the first attempt to highlight the relevance of this concern and visually describes relevant limitations resulting from an inadequate selection of interface constitutive model parameters. This observation has motivated the development of an automatic calibration software to increase the confidence in the parameter selection. The high potential of this software to increase the credibility of numerical predictions obtained with embedded beam formulations is demonstrated based on pile problems with different soil stratification layers, pile geometries, and pile arrangements. In all cases considered, the results confirm that the numerical fidelity could be significantly increased by employing calibrated parameter sets. [ABSTRACT FROM AUTHOR]

The Sand Hypoplastic (SH) constitutive law by von Wolffersdorff (1996) is a widely used hypoplastic model for soil mechanics. This model includes eight parameters, usually calibrated using the oedometric (OE) and the drained isotropically consolidated triaxial tests (CD). However, previous studies show that the SH models calibration in the CD test has conflicting requirements in predicting the evolution of stresses and strains. In this work, we study the SH model calibration over a wide range of testing conditions using 12 OE and 25 CD tests by Wichtmann and Triantafyllidis (2016) on the Karlsruhe sands. The parameter space is extensively explored via genetic algorithm optimization (GA) using the recently developed open-source software GA-cal (available at https://github.com/FraJoMen/GA-cal). This exploration allowed us to study the SH model's predictive limits and to identify, using a multi-objective analysis, the main parameters governing the compromise between the accurate prediction of stresses versus strain in the CD tests. [ABSTRACT FROM AUTHOR]

Structured light sensing systems, as one of the most common optical-based nondestructive evaluation techniques, have been widely applied for inline pipeline inspection. The sensor can be inserted inside the pipe to generate 3D visualisation and evaluate the cracks in the materials. The precise calibration of the camera-projector measurement system is of great significance to ensure the measurement accuracy of the 3D sensing system. Conventional calibration methods for structured light sensors involve complicated and time-consuming procedures and are easily affected by ambient light. The paper presents a novel algorithm to automatically calibrate the projection module and estimate the stereo parameters between the camera and the projector. The calibration algorithm exploits the cylindrical nature of the inspected pipe to create a set of geometric constraints and automatically calibrate the sensor without the need for reference calibration points. Experimental and simulation results showed that the algorithm could successfully estimate the projector's intrinsic and extrinsic parameters by simply acquiring the data inside a cylindrical pipe with a known diameter. The proposed algorithm highly reduces the data collection time for the calibration (only 53 s), improves the accuracy, and simplifies the calibration process. [ABSTRACT FROM AUTHOR]

The prediction of the asphalt dynamic modulus (E*), which measures the material's ability to withstand changes in shape or structure, is important. Previous studies indicated that the well-known Witczak 1-40D model for E* is outperformed by machine learning models. Additionally, the application of machine learning algorithms requires manual fine-tuning of their hyperparameters. In this study, the artificial Hummingbird and Harris Hawks optimization algorithms were employed in the automatic calibration of the Random Forest and Gradient Boost algorithms' hyperparameters for modeling E* using the Witczak 1-40D model and additional parameters. In addition, the model was interpreted using the Shapley value and permutation feature importance. The results indicate that the optimized artificial hummingbird algorithm model performed better, with R² reaching 0.97. The interpretability of the model suggests that the binder parameters exhibited the highest effect on the variance of E*. [ABSTRACT FROM AUTHOR]

Calibrating hydrological model parameters is crucial for ensuring the models' predictive accuracy, which is fundamental for informed decision-making. We provide a toolkit that can perform multi-objective automatic calibration for the parameters of a variable infiltration capacity (VIC) model, with the purpose of locating robust calibration parameters while lowering the threshold for beginners as much as possible. We mainly use the C language to establish the internal dependency relationship between the streamflow generation and routing process through direct function calls without iterative edits to soil parameter files. The Fortran language-based streamflow routing program, previously run independently, is encapsulated in the form of a shared library and then coupled to the streamflow generation module, thus simplifying the design of the calibration program. Then, we further encapsulate the coupled VIC module into a multi-objective function and embed it into the Borg multi-objective optimization algorithm to achieve loose coupling between the Borg algorithm and the VIC model. A case study shows that VIC-Borg requires no additional effort for environment configuration in the operating system and greatly simplifies the automatic calibration of multi-objective parameter optimization. At the same time, it follows the established convention of configuring the VIC streamflow generation module, routing module, and Borg algorithm, making it easier for beginners to use.

Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras’ extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess "better or worse" system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption. [ABSTRACT FROM AUTHOR]

This research addresses the essential need for assessing and managing water quality in reservoirs, which play a pivotal role in various socioeconomic aspects. To achieve this, a stepwise automatic calibration approach is proposed, specifically targeting the CE-QUAL-W2 model. This two-dimensional hydrodynamic and water quality model is widely employed in studies worldwide. Calibration, a fundamental aspect of model development, is complex and traditionally relies on manual, trial-and-error methods, which can be time-consuming and require substantial expertise. In this study, an alternative approach is introduced, incorporating the JAYA optimization algorithm, which reduces the complexity associated with fine-tuning optimization parameters. Furthermore, a clustering framework is adopted, grouping related variables for independent calibration. The research is conducted on the Dez reservoir in southwest Iran, using a two-step calibration process. The first step focuses on hydrodynamics, while the second step addresses water quality variables, including phosphate, ammonium, nitrate, and dissolved oxygen. The proposed methodology applied to Dez reservoir and tested against the observed data, where it demonstrates promising results.

Abstract Background The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses. Methods We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss. Results Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients. Conclusions Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts’ employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters. Trial registration: ClinicalTrial.gov (Identifier: NCT04217005)

Accurately calibrating camera–LiDAR systems is crucial for achieving effective data fusion, particularly in data collection vehicles. Data-driven calibration methods have gained prominence over target-based methods due to their superior adaptability to diverse environments. However, current data-driven calibration methods are susceptible to suboptimal initialization parameters, which can significantly impact the accuracy and efficiency of the calibration process. In response to these challenges, this paper proposes a novel general model for the camera–LiDAR calibration that abstracts away the technical details in existing methods, introduces an improved objective function that effectively mitigates the issue of suboptimal parameter initialization, and develops a multi-level parameter optimization algorithm that strikes a balance between accuracy and efficiency during iterative optimization. The experimental results demonstrate that the proposed method effectively mitigates the effects of suboptimal initial calibration parameters, achieving highly accurate and efficient calibration results. The suggested technique exhibits versatility and adaptability to accommodate various sensor configurations, making it a notable advancement in the field of camera–LiDAR calibration, with potential applications in diverse fields including autonomous driving, robotics, and computer vision. [ABSTRACT FROM AUTHOR]

This research addresses the essential need for assessing and managing water quality in reservoirs, which play a pivotal role in various socioeconomic aspects. To achieve this, a stepwise automatic calibration approach is proposed, specifically targeting the CE-QUAL-W2 model. This two-dimensional hydrodynamic and water quality model is widely employed in studies worldwide. Calibration, a fundamental aspect of model development, is complex and traditionally relies on manual, trial-and-error methods, which can be time-consuming and require substantial expertise. In this study, an alternative approach is introduced, incorporating the JAYA optimization algorithm, which reduces the complexity associated with fine-tuning optimization parameters. Furthermore, a clustering framework is adopted, grouping related variables for independent calibration. The research is conducted on the Dez reservoir in southwest Iran, using a two-step calibration process. The first step focuses on hydrodynamics, while the second step addresses water quality variables, including phosphate, ammonium, nitrate, and dissolved oxygen. The proposed methodology applied to Dez reservoir and tested against the observed data, where it demonstrates promising results. [ABSTRACT FROM AUTHOR]

Risley-prism-based image sensors can expand the imaging field of view through beam control. The larger the top angle of the prism, the higher the magnification of the field of view, but at the same time, it aggravates the problem of imaging aberrations, which also puts higher requirements on the aberration correction method for the Risley-prism-based image sensor. To improve the speed, accuracy, and stability of the aberration correction process, an automatic calibration method for the Risley-prism-based image sensor is proposed based on a two-axis turntable. The image datasets of the calibration plate with different prism rotation angles and object distances are acquired using a two-axis turntable. Then, the images of the calibration plate are pre-processed using the bicubic interpolation algorithm. The calibration parameters are finally calculated, and parameter optimization is performed. The experimental results verify the feasibility of this automated calibration method. The reprojection error of the calibration is within 0.26 pixels when the distance of the imaging sensor is 3.6 m from the object, and the fine aberration correction results are observed. [ABSTRACT FROM AUTHOR]

Contributions to numerical modelling of geotechnical boundary value problems – automatic parameter calibration, earthquake‐induced liquefaction and pull‐out resistance of grouted anchors In this paper, new developments within the finite element program numgeo are presented and their applicability and robustness are shown by simulating an embankment subjected to earthquake loading and the pull‐out of (grouted) anchors. The simulation of water‐saturated embankments subjected to seismic loading places high demands on the available element formulations and constitutive models, for which numgeo was specially developed. The robustness and applicability of the latter is considerably improved by the automatic calibration software numgeo‐ACT. For the simulation of the pull‐out resistance of grouted anchors, the contact behaviour between grouted material and surrounding soil plays a decisive role. In this paper, it is demonstrated through the back‐calculation of model tests that a simple Coulomb friction model for the description of the contact behaviour is not sufficient to adequately determine the pull‐out resistance. With the help of the hypoplastic contact model implemented in numgeo, much better predictions can be achieved. [ABSTRACT FROM AUTHOR]

Background: The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses. Methods: We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss. Results: Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients. Conclusions: Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts’ employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters. Trial registration: ClinicalTrial.gov (Identifier: NCT04217005) [ABSTRACT FROM AUTHOR]

One of the fundamental sensors utilized in the Advanced Driver Assist System (ADAS) is the radar sensor. Automotive-related functions need highly precise detection and range of traffic and surroundings; otherwise, the whole ADAS performance suffers. The radar placement beneath a bumper or a cover, the age or exposure to accidents or vehicle vibration, vehicle integration, and mounting tolerances will impact the angular performance of the radar sensor. In this research, we present an unsupervised online method for elevation mounting angle error compensation and a method for bumper and environmental error compensation in the azimuth direction. The proposed methods need no specific calibration jig and may be used to replace traditional initial calibration methods; they also enable ongoing calibration throughout the sensor's lifespan. A first proposed standalone method for vertical alignment uses stationary radar targets reflected from the environment to calculate a vertical misalignment angle with a line-fitting algorithm. The vertical mounting error compensation approach delivers two types of correction values: a dynamic value that converges quickly in the case of minor accidents and a more stable correction value that converges slowly but offers a long-term compensation value over the sensor's lifespan. A second proposed solution uses the vehicle velocity and radar targets properties, like relative velocity and measured azimuth angle, to calculate an individual azimuth correction curve. Real-world data collected from drive testing with a 77 GHz series automobile radar was used to analyze the performance of the proposed methods, yielding encouraging results. [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow manual palletizing, high risk and easy damage to products after the assembly of microchannel heat exchanger, a palletizing system based on 3D visual guidance is presented in this paper. Firstly, an automatic calibration method for RGB-D camera extrinsic parameters was adopted: used random sample consensus algorithm RANSAC for plane fitting, and the pallet was idealized as a plane. The camera extrinsic parameters were calculated according to the rotation relationship between the plane normal vector and the camera coordinate system. When it was performing detection, converted depth data into point cloud data and preprocessed, including coordinate transformation, downsampling, etc. After that, according to the characteristics of stacking, a point cloud segmentation algorithm based on depth and the number of points was used, which could not only segment the pallet and sponge strips, but also effectively detect obstacles. For different parts segmented, the pallet was positioned by fitting the minimum bounding rectangle, and the stacking depths were obtained by calculating the centroid of the sponge strips. Finally, a series of constraints were used to determine whether the unloading conditions were met. The experimental results show that the automatic calibration is effective, the time consuming is about 0.43 s, and the depth range of the pallet is 11 mm after calibration. The algorithm can accurately identify various situations above the stacking and the time consuming is about 1.63 s.

The article contains the operational principle of adaptive active wideband acoustic noise cancellation system. The algorithm of operation of these systems provides to work with changes of the medium, acoustic or electrical parameters of their elements, is described. A modification of this algorithm is proposed, which can increase the efficiency of the system and reduce the level of residual noise during system calibration. The results of simulation the modified algorithm of an active noise cancellation system are presented. [ABSTRACT FROM AUTHOR]

This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 km) over Southern Italy, to be used for operational runs. The objective of the optimization is to reduce the distance between observed meteorological variables and modeled data. This distance is measured by an opportunely designed objective function. This work represents a preparatory step, since the input parameters considered are only a reduced number with respect to the huge amount of parameters potentially involved. First, domain size sensitivity was performed to choose the optimal domain. Then, the optimization was conducted by means of an Efficient Global Optimization algorithm relying on a Gaussian-based metamodel. The four parameters considered control the heat transfer in the turbulent layer, the laminar resistance and the snow vertical velocity. They were optimized over a week in November 2018, a period characterized by extreme events in the region considered. The results demonstrated the effectiveness of the proposed approach, reducing the distance from observed data, and the method can be considered promising from the perspective taking into account a larger set of physical parameters, and validation over a wider time-window. [ABSTRACT FROM AUTHOR]

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess “better or worse” system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption.

The current terrestrial laser scanners (TLS) are generally equipped with digital cameras which can capture the scene along with the scanner. These two types of sensors offer complementary properties in modeling and visualization of real-world scenes. TLSs can provide geometric information of the real scene with accurate 3D coordinates of the point clouds; cameras are used to acquire high-resolution images and provide good texture information of the environment. Fusing the extracted information from these two sensors helps to create a better virtual representation of the real-world. For a TLS with several external cameras, their acquisition centers are not identical and the axis of their coordinate systems are not aligned either. This paper proposes an automatic camera and TLS extrinsic calibration approach using correspondences extracted from both measurements. To overcome the intrinsic difference between back-projected images of point clouds colored by intensities and the RGB camera images, we innovatively generate both magnitude of gradient images, enabling effective image correlation and accurate correspondence extraction. The 3 external cameras mounted on top, side and bottom of Leica RTC360 3D laser scanner are calibrated. Dependent on the distribution of observations, we achieve different calibration accuracy for each camera. With scans from multiple stations, the cameras obtain an offset accuracy of 0.12 – 0.36 mm and angular accuracy of 3.7 – 8.3″. After calibration, the excellent overlap of images from the two sensors further verifies the proposed method's success. The idea of correspondence identification demonstrated in this study can also be applied to the extrinsic calibration/registration of other types of scanner and digital cameras.

This paper describes an affordable robotic mobile 3D mapping system. It is built with Livox Mid–40 lidar with a conic field of view extended by a custom rotating planar reflector. This 3D sensor is compared with the more expensive Velodyne VLP 16 lidar. It is shown that the proposed sensor reaches satisfactory accuracy and range. Furthermore, it is able to preserve the metric accuracy and non–repetitive scanning pattern of the unmodified sensor. Due to preserving the non–repetitive scan pattern, our system is capable of covering the entire field of view of 38.4 × 360 degrees, which is an added value of conducted research. We show the calibration method, mechanical design, and synchronization details that are necessary to replicate our system. This work extends the applicability of solid–state lidars since the field of view can be reshaped with minimal loss of measurement properties. The solution was part of a system that was evaluated during the 3rd European Robotics Hackathon in the Zwentendorf Nuclear Power Plant. The experimental part of the paper demonstrates that our affordable robotic mobile 3D mapping system is capable of providing 3D maps of a nuclear facility that are comparable to the more expensive solution. [ABSTRACT FROM AUTHOR]

We propose a novel high-accuracy active hand-eye calibration approach. In our method, the robot movement and camera view selection are both driven by and targeting the improvement of calibration accuracy. During the calibration process, the data acquisition is guided by an online estimated discrete viewing quality field (DVQF), representing the calibration quality of different views in various 3D locations. The view quality is measured by how much it reduces the uncertainty of calibration results and increases the diversity of robot poses, contributing to the calibration precision. Based on DVQF, we select the next-best-view as the target moving pose for each time step. A fully automatic system is presented to perform the overall hand-eye calibration process without any human intervention. Numerous experiments are conducted both in real-world and simulated scenarios. The proposed algorithm outperforms other approaches and shows much superiority in accuracy and robustness. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of the existing gas meter reading methods, this paper introduces an automatic reading method and calibration device based on the projection segmentation method, which uses the color difference between the character part and the rest part of the last code wheel of the gas meter counter to realize image recognition of the turned characters, and then calculates the cumulative volume indication of gas meter based on the number of turned characters. The pixel projection value scanned by the camera device at the horizontal centerline of the last code wheel changes alternately when the code wheel rotates. The proposed projection segmentation method does not require recognition of specific characters, simplifying the algorithm and making it suitable for most calibration devices. Experiments show that the accuracy rate of the proposed method is 100% even under low-light conditions, which is a great improvement compared with the traditional character recognition method. Additionally, the reading resolution of the proposed method is improved by 10 times compared with the existing photoelectric sampling method and template matching method, and the total calibration time can be reduced by 6.7%–58.7%, which significantly enhances the calibration efficiency. • The principle of the proposed projection segmentation technique is illustrated. • An image recognition method and device for gas meters are designed. • The proposed method improves reading resolution, accuracy, and efficiency. [ABSTRACT FROM AUTHOR]

This paper develops a novel approach for automatic parameter calibration of soil constitutive models by combining Bayesian optimization (BO) and genetic algorithm (GA). The BO method enables locating the possible point over a large multi-parameter search domain quickly, by which the search domain is no longer required to be accurately pre-defined. Thereafter, the GA is employed to find the best solution within the BO-optimized search space. This calibration procedure can quickly give the best-matched model parameters for both sands and clays even with the same large search space. Taking a critical state-based soil model (Clay And Sand Model (CASM)) as an example, the superiority and capability of the proposed calibration method are examined by comparing experimental data with predicted results for both clays and sands. It is shown that the BO-GA-based approach can reduce errors by 46.4% for clays and 41.2% for sands compared with the conventional GA method and outperforms other traditional optimization algorithms. Eventually, the evolution process of BO and GA is visualized to better understand the principles of automatic parameter calibration, which is followed by model uncertainty analyses. [ABSTRACT FROM AUTHOR]

Accurately calibrating camera–LiDAR systems is crucial for achieving effective data fusion, particularly in data collection vehicles. Data-driven calibration methods have gained prominence over target-based methods due to their superior adaptability to diverse environments. However, current data-driven calibration methods are susceptible to suboptimal initialization parameters, which can significantly impact the accuracy and efficiency of the calibration process. In response to these challenges, this paper proposes a novel general model for the camera–LiDAR calibration that abstracts away the technical details in existing methods, introduces an improved objective function that effectively mitigates the issue of suboptimal parameter initialization, and develops a multi-level parameter optimization algorithm that strikes a balance between accuracy and efficiency during iterative optimization. The experimental results demonstrate that the proposed method effectively mitigates the effects of suboptimal initial calibration parameters, achieving highly accurate and efficient calibration results. The suggested technique exhibits versatility and adaptability to accommodate various sensor configurations, making it a notable advancement in the field of camera–LiDAR calibration, with potential applications in diverse fields including autonomous driving, robotics, and computer vision.

To assist in the implementation of a fine 3D terrain reconstruction of the scene in remote sensing applications, an automatic joint calibration method between light detection and ranging (LiDAR) and visible light camera based on edge points refinement and virtual mask matching is proposed in this paper. The proposed method is used to solve the problem of inaccurate edge estimation of LiDAR with different horizontal angle resolutions and low calibration efficiency. First, we design a novel calibration target, adding four hollow rectangles for fully automatic locating of the calibration target and increasing the number of corner points. Second, an edge refinement strategy based on background point clouds is proposed to estimate the target edge more accurately. Third, a two-step method of automatically matching between the calibration target in 3D point clouds and the 2D image is proposed. Through this method, i.e., locating firstly and then fine processing, corner points can be automatically obtained, which can greatly reduce the manual operation. Finally, a joint optimization equation is established to optimize the camera's intrinsic and extrinsic parameters of LiDAR and camera. According to our experiments, we prove the accuracy and robustness of the proposed method through projection and data consistency verifications. The accuracy can be improved by at least 15.0% when testing on the comparable traditional methods. The final results verify that our method is applicable to LiDAR with large horizontal angle resolutions. [ABSTRACT FROM AUTHOR]

Predicting the spread of infectious diseases is an urgent task, since it allows for an assessment of the current situation and making informed decisions on the disease stemming measures to be taken. However, predictive models need constant adjustment and validation of the data obtained according to current data on infection spread dynamics. The present research aims to select and integrate a calibration method for the epidemiological Kermak-McKendrick SEIR model with additional factors. This paper provides an overview and analysis of calibration algorithms for the required parameters of the epidemiological model, as well as numerical experiments comparing the accuracy of the results. The resulting calibration method is the least squares method, since it allows considering boundary values and searching for a local minimum, spending the least amount of time compared to other algorithms. Automatic calibration of the model parameters allows for up-to-date predictions on the spread of infectious diseases with minimal time resources in response to changes in disease data and various quarantine measures. The developed solution can be tailored to other infection spread models. [ABSTRACT FROM AUTHOR]

This paper presents an interactive three‐dimensional (3D) touch and gesture user interface that utilizes a light‐field display and a 3D sensor. In contrast to conventional gesture interfaces, in which usually the image space and gesture space are separated, a user can directly interact with the 3D image in this interface system. To achieve this trait, the 3D registration between the sensor and display coordinate systems is necessary. In this study, we propose a method to automatically match the position of the reconstructed 3D image with the position of the hand of the user based on scattered light detection. The proposed calibration process is automatic and enables self‐calibration during practical operation without laborious measurement work. The method allows us to use the gesture detection capabilities of the leap motion controller to develop an interface where content and gesture positions match. We show some examples of 3D touch and gesture interactions in which the gesture position and the generated content match on a light field display. [ABSTRACT FROM AUTHOR]

This paper presents an approach for the automatic parameter calibration (AC) of a hypoplastic constitutive soil model. The calibration software developed in this work simplifies the parameter calibration, reduces the subjective "human" factor on the calibration result and lowers the entry hurdle for the use of the hypoplastic constitutive model. The performance of the software was demonstrated by comparing automatically calibrated parameter sets for two sands and their related simulations of the underlying experimental data with simulations using two reference parameter sets. The first reference parameter set was calibrated the classical way, "by hand", and the second was calibrated using the AC tool ExCalibre. Two different optimization methods were used, namely the Differential Evolution (DE) and the Particle Swarm Optimization (PSO). The simulations performed with the parameters obtained from the AC agree well with the experimental data and show improvements over the reference parameter sets. With respect to the optimization method, the performance of the DE proved superior to that of the PSO. Various measures of comparison were examined to quantify the discrepancy between experiment and simulation. By repeating 500 calibration runs, the dispersion of parameters was determined and correlations between different parameters of the hypoplastic model were found. [ABSTRACT FROM AUTHOR]

Most sensor setups for onboard autonomous perception are composed of LiDARs and vision systems, as they provide complementary information that improves the reliability of the different algorithms necessary to obtain a robust scene understanding. However, the effective use of information from different sources requires an accurate calibration between the sensors involved, which usually implies a tedious and burdensome process. We present a method to calibrate the extrinsic parameters of any pair of sensors involving LiDARs, monocular or stereo cameras, of the same or different modalities. The procedure is composed of two stages: first, reference points belonging to a custom calibration target are extracted from the data provided by the sensors to be calibrated, and second, the optimal rigid transformation is found through the registration of both point sets. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups. In order to assess the performance of the proposed method, a novel evaluation suite built on top of a popular simulation framework is introduced. Experiments on the synthetic environment show that our calibration algorithm significantly outperforms existing methods, whereas real data tests corroborate the results obtained in the evaluation suite. Open-source code is available at https://github.com/beltransen/velo2cam_calibration. [ABSTRACT FROM AUTHOR]

This study aims to investigate the effectiveness of the low impact development (LID) practices on sustainable urban flood storm water management. We applied three LID techniques, i.e. green roof, permeable pavements and bioretention cells, on a highly urbanized watershed in Istanbul, Turkey. The EPA-SWMM was used as a hydrologic-hydraulic model and the model calibration was performed by the well-known Parameter ESTimation (PEST) tool. The rainfall-runoff events occurred between 2012 and 2020. A sensitivity analysis on the parameter selection was applied to reduce the computational cost. The Nash-Sutcliffe efficiency coefficient (NSE) was used as the objective function and it was calculated as 0.809 in the model calibration. The simulations were conducted for six different return periods of a storm event, i.e. 2, 5, 10, 25, 50 and 100 years, in which the synthetic storm event hyetographs were produced by means of the alternating block method. The results revealed that the combination of green roof and permeable pavements have the major impact on both the peak flood reduction and runoff volume reduction compared to the single LIDs. The maximum runoff reduction percentage was obtained as 56.02% for a 10 years return period of a storm event in the combination scenario. HIGHLIGHTS The PEST software is used for the auto-calibration of US EPA-SWMM and the calibration results are statistically significant.; The green roof application has more impact on both the peak discharge reduction and the volume reduction than both the permeable pavements and bioretention cells in the highly urbanized Ayamama watershed.; The combination of green roof and permeable pavements has substantial effect on flood mitigation.;

Risley-prism-based image sensors can expand the imaging field of view through beam control. The larger the top angle of the prism, the higher the magnification of the field of view, but at the same time, it aggravates the problem of imaging aberrations, which also puts higher requirements on the aberration correction method for the Risley-prism-based image sensor. To improve the speed, accuracy, and stability of the aberration correction process, an automatic calibration method for the Risley-prism-based image sensor is proposed based on a two-axis turntable. The image datasets of the calibration plate with different prism rotation angles and object distances are acquired using a two-axis turntable. Then, the images of the calibration plate are pre-processed using the bicubic interpolation algorithm. The calibration parameters are finally calculated, and parameter optimization is performed. The experimental results verify the feasibility of this automated calibration method. The reprojection error of the calibration is within 0.26 pixels when the distance of the imaging sensor is 3.6 m from the object, and the fine aberration correction results are observed.

The wobble errors caused by the imperfect integration of motion sensors and transducers in multibeam echo-sounder systems (MBES) manifest as high-frequency wobbles in swaths and hinder the accurate expression of high-resolution seabed micro-topography under a dynamic marine environment. There are many types of wobble errors with certain coupling among them. However, those current calibration methods ignore the coupling and are mainly manual adjustments. Therefore, we proposed an automatic calibration method with the coupling. First, given the independence of the transmitter and the receiver, the traditional georeferenced model is modified to improve the accuracy of footprint reduction. Secondly, based on the improved georeferenced model, the calibration model associated with motion scale, time delay, yaw misalignment, lever arm errors, and soundings is constructed. Finally, the genetic algorithm (GA) is used to search dynamically for the optimal estimation of the corresponding error parameters to realize the automatic calibration of wobble errors. The simulated data show that the accuracy of the calibrated data can be controlled within 0.2% of the water depth. The measured data show that after calibration, the maximum standard deviation of the depth is reduced by about 5.9%, and the mean standard deviation of the depth is reduced by about 11.2%. The proposed method has significance in the precise calibration of dynamic errors in shallow water multibeam bathymetries. [ABSTRACT FROM AUTHOR]

The main advantage of phase noise meters with photonic (fiber) delay lines is that they do not require high-performance, low-noise reference oscillators. On the other hand, some additional calibrations are required, which are the subject of this paper. First, the quadrature must be maintained on the mixer by precise adjustment of the phase and/or delay. Next, since the response of the mixer is proportional to the square of the input test signal, a precise amplitude calibration is required. Finally, the frequency response of the FFT spectrum analyzer and its corresponding anti-aliasing low-pass filter needs to be known precisely. In this paper, to the best of our knowledge, we present innovative solutions for all three calibrations. All three calibrations were built in and tested in our phase noise meter. The result is a simple and robust phase noise meter suitable for non-laboratory environments. [ABSTRACT FROM AUTHOR]

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive for automatic parameter estimation. In this paper, we present a global optimization-based algorithm to calibrate a fully distributed hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed data (i.e., discharge, or pollutant concentration) as input. The algorithm finds near-optimal set of parameters to explain observed gauged data. This framework, although applied in a poorly-gauged urban catchment, is adapted for catchments with more detailed observations. The results of the automatic calibration indicate NSE = 0.99 for the V-Tilted catchment, RMSE = 830 mg L-1 for salt concentration pollutograph in a wooden-plane (i.e., 8.3% of the event mean concentration), and NSE = 0.89 in a urban real-world catchment. This paper also explores the issue of equifinality (i.e., multiple parameters giving the same calibration performance) in model calibration indicating the performance variation of calibrating only with an outlet gauge or with multiple gauges within the catchment. [Display omitted] • An automatic calibration algorithm for distributed flood and water quality modeling is developed. • It uses HydroPol2D model and calibrate water quantity and quality parameters globally. • Data from observed gauges such as discharges, depths, and concentration is used for calibration. • Poorly placed gauges and low runoff events can increase equifinality during calibration. [ABSTRACT FROM AUTHOR]

This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 km) over Southern Italy, to be used for operational runs. The objective of the optimization is to reduce the distance between observed meteorological variables and modeled data. This distance is measured by an opportunely designed objective function. This work represents a preparatory step, since the input parameters considered are only a reduced number with respect to the huge amount of parameters potentially involved. First, domain size sensitivity was performed to choose the optimal domain. Then, the optimization was conducted by means of an Efficient Global Optimization algorithm relying on a Gaussian-based metamodel. The four parameters considered control the heat transfer in the turbulent layer, the laminar resistance and the snow vertical velocity. They were optimized over a week in November 2018, a period characterized by extreme events in the region considered. The results demonstrated the effectiveness of the proposed approach, reducing the distance from observed data, and the method can be considered promising from the perspective taking into account a larger set of physical parameters, and validation over a wider time-window.

Visual sensor plays an important part in intelligentized welding systems, and the calibration of the vision sensor is the indispensable part of visual systems. Aiming at the problem of the tedious calibration process, this paper describes an automatic calibration algorithm. First, the robot motion equation and the motion range constraint equation are proposed to ensure that the collected images of calibration grid and laser line meet the calibration requirements. Based on these two equations, the automatic collection procedure can be realized. Second, the simplified visual servoing method and the Extended Kalman filter were used to adjust the images and rectify system parameters, respectively, which will improve the stability of the calibration motion. Third, to reduce the impact of the complex welding environments, a robustness feature extraction algorithm based on local threshold is studied. And then, the laser plane and hand-eye matrix are fitted with optimization algorithms to ensure calibration accuracy. Finally, the simulation experiments prove the feasibility and stability of the proposed algorithm. And the actual calibration tests suggest that the algorithm can significantly improve calibration efficiency. Moreover, the experimental results of welding guidance and seam tracking confirm that the calibration precision has met the welding requirement. [ABSTRACT FROM AUTHOR]

The quality defects of hazelnut fruits comprise changes in morphology and taste, and their intensity mainly depends on seasonal environmental conditions. The strongest off-flavor of hazelnuts is known as rotten defect, whose candidate causal agents are a complex of fungal pathogens, with Diaporthe as the dominant genus. Timely indications on the expected incidence of rotten defect would be essential for buyers to identify areas where hazelnut quality will be superior, other than being useful for farmers to have the timely indications of the risk of pathogens infection. Here, we propose a rotten defect forecasting model, and we apply it in the seven main hazelnut producing municipalities in Turkey. We modulate plant susceptibility to fungal infection according to simulated hazelnut phenology, and we reproduce the key components of the Diaporthe spp. epidemiological cycle via a process-based simulation model. A model sensitivity analysis has been performed under contrasting weather conditions to select most relevant parameters for calibration, which relied on weekly phenological observations and the post-harvest analyses of rotten incidence in the period 2016–2019, conducted in 22 orchards. The rotten simulation model reproduced rotten incidence data in calibration and validation datasets with a mean absolute error below 1.8%. The dataset used for model validation (321 additional sampling locations) has been characterized by large variability of rotten incidence, in turn contributing to decrease the correlation between reference and simulated data (R 2 = 0.4 and 0.21 in West and East Black Sea region, respectively). This denotes the key effect of other environmental and agronomic factors on rotten incidence, which are not yet taken into account by the predictive workflow and will be considered in further improvements. When applied in spatially distributed simulations, the model differentiated the rotten incidence across municipalities, and reproduced the interannual variability of rotten incidence. Our results confirmed that the rotten defect is strictly dependent on precipitation amount and timing, and that plant susceptibility is crucial to trigger fungal infections. Future steps will envisage the application of the rotten simulation model to other hazelnut producing regions, before being operationally used for in-season forecasting activities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the suitability of literature parameter values for the Green–Ampt infiltration model to be used in hydrodynamic rainfall–runoff simulations. The outcome of this study supports to decide which literature values should be taken if observed data for model calibration is not available. Different laboratory experiments, a plot-scale experiment in the Thiès catchment in Senegal, and a flash flood in the region of El Gouna in Egypt, have been simulated with the 2D shallow water model Hydroinformatics Modeling System (hms) incorporating the Green–Ampt model. For four test cases with available runoff data, the results of the calibrated models were compared to those obtained from average values after Rawls et al. (Journal of Hydraulic Engineering 1:62–70, 1) and Innovyze (Help documentation of XPSWMM and XPStorm, 2). The results showed a clear underestimation of infiltration in two of three considered laboratory experiments, while for a field experiment in Senegal, average values after Rawls et al. (Journal of Hydraulic Engineering 1:62–70, 1) led to a strong overestimation and the ones after Innovyze (Help documentation of XPSWMM and XPStorm, 2) to an underestimation of infiltration. In a case study on flash floods in an ungauged region in Egypt, the values of both sources led to a strong overestimation of infiltration, when the simulation results are compared to observed flooding areas. It can be concluded, that the values after Innovyze (Help documentation of XPSWMM and XPStorm, 2) lead to overall better results than the ones after Rawls et al. (Journal of Hydraulic Engineering 1:62–70, 1). According to the results, the hydraulic conductivity in ungauged areas with bare sandy soil should be reduced by about 90–100 % compared to the value after Rawls et al. (Journal of Hydraulic Engineering 1:62–70, 1). [ABSTRACT FROM AUTHOR]