Acceleration Measurements Research Articles

Beyond i.i.d. in the Resource Theory of Asymmetry: An Information-Spectrum Approach for Quantum Fisher Information.

Energetic coherence is indispensable for various operations, including precise measurement of time and acceleration of quantum manipulations. Since energetic coherence is fragile, it is essential to understand the limits in distillation and dilution to restore damage. The resource theory of asymmetry (RTA) provides a rigorous framework to investigate energetic coherence as a resource to break time-translation symmetry. Recently, in the independent and identically distributed (i.i.d.) regime where identical copies of a state are converted into identical copies of another state, it was shown that the convertibility of energetic coherence is governed by a standard measure of energetic coherence, called the quantum Fisher information (QFI). This fact means that QFI in the theory of energetic coherence takes the place of entropy in thermodynamics and entanglement entropy in entanglement theory. However, distillation and dilution in realistic situations take place in regimes beyond i.i.d., where quantum states often have complex correlations. Unlike entanglement theory, the conversion theory of energetic coherence in pure states in the non-i.i.d. regime has been an open problem. In this Letter, we solve this problem by introducing a new technique: an information-spectrum method for QFI. Two fundamental quantities, coherence cost and distillable coherence, are shown to be equal to the spectral QFI rates for arbitrary sequences of pure states. As a consequence, we find that both entanglement theory and RTA in the non-i.i.d. regime are understood in the information-spectrum method, while they are based on different quantities, i.e., entropy and QFI, respectively.

Orientation resolved measurements of accelerations with sensor particles in bioreactors

Abstract The measurement of the spatially distributed flow field in large industrial vessels, e.g. biogas fermenters, is not possible with commercially available measurement equipment. Therefore the concept of flow following sensor particles has been developed. They act as data loggers within the vessel. The sensor particles are equipped with a pressure sensor and an inertial measurement unit to measure their motion. The measurements are taken in the coordinate frame of the sensor particle and need to be transformed into the vessel’s coordinate frame to analyze the flow field. This is done by an error-state Kalman filter which estimates the orientation of the sensor particle with respect to the vessel based on the direction of the gravitational acceleration and the local magnetic field. Since no other aiding sensors are available or usable within an industrial vessel, particular emphasis was given to the inertial sensors’ stochastic modelling and the accelerometer’s calibration. Based on the orientation estimation, the measured acceleration is transformed into the vessel frame. We performed two experiments in a 1.4 m3 lab reactor to qualify this approach. The results show, that the transformed acceleration is in good qualitative agreement with the known flow field inside the lab reactor.

Printing MEMS: Application of Inkjet Techniques to the Manufacturing of Inertial Accelerometers.

In the last few years, the manufacturing of microelectromechanical systems (MEMS) by means of innovative tridimensional and bidimensional printing technologies has significantly catalyzed the attention of researchers. Inkjet material deposition, in particular, can become a key enabling technology for the production of polymer-based inertial sensors characterized by low cost, high manufacturing scalability and superior sensitivity. In this paper, a fully inkjet-printed polymeric accelerometer is proposed, and its manufacturing steps are described. The manufacturing challenges connected with the inkjet deposition of SU-8 as a structural material are identified and addressed, resulting in the production of a functional spring-mass sensor. A step-crosslinking process allows optimization of the final shape of the device and limits defects typical of inkjet printing. The resulting device is characterized from a morphological point of view, and its functionality is assessed in performing optical readout. The acceleration range of the optimized device is 0-0.7 g, its resolution is 2 × 10-3 g and its sensitivity is 6745 nm/g. In general, the work demonstrates the feasibility of polymeric accelerometer production via inkjet printing, and these characteristic parameters demonstrate their potential applicability in a broad range of uses requiring highly accurate acceleration measurements over small displacements.

Structural system modeling from base excitation measurements using Swarm Intelligence

The problem of system identification is classified as an inverse problem, and it concerns the derivation of mathematical models from experimental data. Problems of this kind can be addressed using optimization techniques. This paper utilizes a hybrid form of the well-known Particle Swarm Algorithm (hPSO) to produce a mathematical model representative of the dynamic behaviour of a generic structure subjected to a base excitation, directly from acceleration measurements. The proposed algorithm is specifically tailored for this application; physical properties of the matrices are assured introducing a 'healing' process. Problem-specific operators from Evolutionary Strategies are used to improve the solution and avoid a local minimum. The final adjustment of the solution is delegated to a local optimization algorithm.

MagLoc-AR: Magnetic-based Localization for Visual-free Augmented Reality in Large-scale Indoor Environments.

Accurate localization of a display device is essential for AR in large-scale environments. Visual-based localization is the most commonly used solution, but poses privacy risks, suffers from robustness issues and consumes high power. Wireless signal-based localization is a potential visual-free solution, but its accuracy is not enough for AR. In this paper, we present MagLoc-AR, a novel visual-free localization solution that achieves sufficient accuracy for some AR applications (e.g., AR navigation) in large-scale indoor environments. We exploit the location-dependent magnetic field interference that is ubiquitous indoors as a localization signal. Our method requires only a consumer-grade 9-axis IMU, with the gyroscope and acceleration measurements used to recover the motion trajectory, and the magnetic measurements used to register the trajectory to the global map. To meet the accuracy requirement of AR, we propose a mapping method to reconstruct a globally consistent magnetic field of the environment, and a localization method fusing the biased magnetic measurements with the network-predicted motion to improve localization accuracy. In addition, we provide the first dataset for both visual-based and geomagnetic-based localization in large-scale indoor environments. Evaluations on the dataset demonstrate that our proposed method is sufficiently accurate for AR navigation and has advantages over the visual-based methods in terms of power consumption and robustness. Project page: https://github.com/zju3dv/MagLoc-AR/.

An Improved Method for Swing State Estimation in Multirotor Slung Load Applications

A method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm is developed to estimate cable swing angle and rate from acceleration measurements available from an onboard Inertial Measurement Unit, without the need for extra sensors. The estimation problem is addressed according to the Extended Kalman Filter structure. With respect to the classical linear formulation, the proposed approach allows for improved estimation accuracy in both stationary and maneuvering flight. As an additional contribution, filter performance is enhanced by accounting for aerodynamic disturbance force, which largely affects the estimation accuracy in windy flight conditions. The validity of the proposed methodology is demonstrated as follows. First, it is applied to an octarotor platform where propellers are modeled according to blade element theory and the load is suspended by an elastic cable. Numerical simulations show that estimated swing angle and rate represent suitable feedback variables for payload stabilization, with benefits on flying qualities and energy demand. The algorithm is finally implemented on a small-scale quadrotor and is investigated through an outdoor experimental campaign, thus proving the effectiveness of the approach in a real application scenario.

The Pace of Biological Aging Predicts Nonspecific Chronic Low Back Pain Severity

This study aimed to determine if and how the pace of biological aging was associated with nonspecific chronic low back pain (cLBP) and compare what measure of epigenetic age acceleration most strongly predicts cLBP outcomes. We used the Dunedin Pace of Aging from the Epigenome (DunedinPACE), Horvath’s, Hannum’s, and PhenoAge clocks to determine the pace of biological aging in 69 cLBP, and 49 pain-free controls (PFCs) adults, ages 18 to 85 years. On average, participants with cLBP had higher DunedinPACE (P < .001) but lower Horvath (P = .04) and Hannum (P = .02) accelerated epigenetic age than PFCs. There was no significant difference in PhenoAge acceleration between the cLBP and PFC groups (P = .97). DunedinPACE had the largest effect size (Cohen’s d = .78) on group differences. In univariate regressions, a unit increase in DunedinPACE score was associated with 265.98 times higher odds of cLBP than the PFC group (P < .001). After controlling for sex, race, and body mass index (BMI), the odds ratio of cLBP to PFC group was 149.62 (P < .001). Furthermore, among participants with cLBP, DunedinPACE scores positively correlated with pain severity (rs = .385, P = .001) and interference (rs = .338, P = .005). Epigenetic age acceleration from Horvath, Hannum, and PhenoAge clocks were not significant predictors of cLBP. The odds of a faster pace of biological aging are higher among adults with cLBP, and this was associated with greater pain severity and disability. Future interventions to slow the pace of biological aging may improve cLBP outcomes. PerspectiveAccelerated epigenetic aging is common among adults with nonspecific cLBP. Higher DunedinPACE scores positively correlate with pain severity and interference, and better predict cLBP than other DNA methylation clocks. Interventions to slow the pace of biological aging may be viable targets for improving pain outcomes.

Epigenetic Age Acceleration and Chronological Age: Associations With Cognitive Performance in Daily Life.

DNA methylation-derived epigenetic clocks offer the opportunity to examine aspects of age acceleration (ie, the difference between an individual's biological age and chronological age), which vary among individuals and may better account for age-related changes in cognitive function than chronological age. Leveraging existing ambulatory cognitive assessments in daily life from a genetically diverse sample of 142 adults in midlife, we examined associations between 5 measures of epigenetic age acceleration and performance on tasks of processing speed and working memory. Covarying for chronological age, we used multilevel models to examine associations of epigenetic age acceleration (Horvath 1, Horvath 2, Hannum, PhenoAge, and GrimAge clocks) with both average level and variability of cognitive performance. Positive age acceleration (ie, epigenetic age greater than chronological age) was associated with poorer mean processing speed (Horvath 1 and 2) and working memory (GrimAge). Higher chronological age was also associated with poorer mean processing speed and working memory performance. Further, positive age acceleration was generally associated with greater intraindividual variability in working memory and processing speed tasks, whereas being chronologically older was associated with less intraindividual variability. Although further work is needed, our results indicate age acceleration effects have comparable or greater size as those for chronological age differences, suggesting that epigenetic age acceleration may account for additional risk and interindividual variation in cognitive performance above chronological age.

Improvement of an Algebraic Observer for the Control of a One-Story Building

This article presents an improvement to an algebraic observer to estimate the state of a mechanical system whose only feedback variable is acceleration. This problem appears in different mechanical systems, e.g., structures subject to the effects of earthquakes. The algebraic observer that motivates this work is constrained to a finite time range; it cannot observe for time very close to zero, nor beyond a large value, because it would make singular the matrix involved in the estimation. In this proposal these disadvantages are eliminated by obtaining a matrix with independent-of-time determinant. The objective of the observer is to estimate the position and velocity that correspond to the acceleration measure, for use in a recently published control law of the semi-active control system of a single-story building by means of a magnetorheological damper.

An iterative augmented unscented Kalman filter for simultaneous state-parameter-input estimation for systems with/without direct feedthrough

A few unscented Kalman filters (UKFs) have been developed for simultaneous state-parameter-input estimation, however, these UKFs often have at least one of these limitations: requiring ad hoc procedures; requiring displacement measurements, which may be inconvenient to obtain, to fuse with acceleration measurements to avoid the “drift phenomenon” in the estimated displacement responses and unknown inputs; requiring a (full-rank) feedthrough matrix; and undesirable computational efficiency. To overcome these limitations, a novel iterative augmented unscented Kalman filter (IAUKF) for simultaneous state-parameter-input estimation for systems either with or without direct feedthrough is developed and examined in this study. The IAUKF closely follows the Bayes’ theorem with no ad hoc procedure, featuring theoretical simplicity. An iterative strategy is introduced to improve the accuracy of the unknown input estimation at each time step, so that the “drift phenomenon” encountered by the existing augmented Kalman filter is avoided. Further, the IAUKF requires no prior models for the time histories of the unknown inputs and applies to systems with/without direct feedthrough. With realistic assumptions, the steps for computationally efficient implementations of the IAUKF are proposed. Numerical examples of nonlinear structural systems with/without direct feedthrough of the unknown inputs are adopted to examine the effectiveness and the computational efficiency of the IAUKF.

Real-Time AI-Driven Fall Detection Method for Occupational Health and Safety

Fall accidents in industrial and construction environments require an immediate reaction, to provide first aid. Shortening the time between the fall and the relevant personnel being notified can significantly improve the safety and health of workers. Therefore, in this work, an IoT system for real-time fall detection is proposed, using the ASSIST-IoT reference architecture. Empowered with a machine learning model, the system can detect fall accidents and swiftly notify the occupational health and safety manager. To train the model, a novel multimodal fall detection dataset was collected from ten human participants and an anthropomorphic dummy, covering multiple types of fall, including falls from a height. The dataset includes absolute location and acceleration measurements from several IoT devices. Furthermore, a lightweight long short-term memory model is proposed for fall detection, capable of operating in an IoT environment with limited network bandwidth and hardware resources. The accuracy and F1-score of the model on the collected dataset were shown to exceed 0.95 and 0.9, respectively. The collected multimodal dataset was published under an open license, to facilitate future research on fall detection methods in occupational health and safety.

An improved Kalman filter with dummy measurement for identification of structural load and unknown parameters

In this paper, a three-stage Kalman filter is proposed for identifying both the load and the unknown parameters in structures. To suppress the load drift caused by using only acceleration measurements, the dummy measurement is introduced. Given the poor accuracy and stability in dealing with a multitude of unknowns, the local multiple-iteration method is proposed to enhance the robustness of the Kalman filter. In addition, by means of an index reflecting the changes in the identified values, a three-step strategy is proposed to track the time-varying parameters. The results demonstrate that the local multiple-iteration method significantly improves the accuracy of the Kalman filter and reduces the difficulty of parameter assignment. The introduction of the dummy measurement can suppress the load drift but may increase the identified errors of the unknown parameters. Discussions on the cases of low sampling frequencies and few measurement points illustrate that the former has a smaller impact on the results than the latter. The results of a long response duration case indicate that the identified errors of the damping may increase with the response duration. Additionally, the results of an abrupt damage case illustrate that the three-step strategy can automatically and accurately track the time-varying parameters. Finally, an experimental example is conducted, and the results further demonstrate the applicability and feasibility of the proposed method for practical structures.

Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients

Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states. Physics informed neural networks (PINNs) leverage deep learning methods to provide an alternative approach to RT modeling. Applications of PINNs in monitoring of RTs for operator support requires near real-time model performance. However, as with all machine learning models, development of a PINN involves time-consuming model training. Here, we show that a transfer learning (TL-PINN) approach achieves significant performance gain, as measured by reduction of the number of iterations for model training. Using point kinetic equations (PKEs) model with six neutron precursor groups, constructed with experimental parameters of the Purdue University Reactor One (PUR-1) research reactor, we generated different RTs with experimentally relevant range of variables. The RTs were characterized using Hausdorff and Fréchet distance. We have demonstrated that pre-training TL-PINN on one RT results in up to two orders of magnitude acceleration in prediction of a different RT. The mean error for conventional PINN and TL-PINN models prediction of neutron densities is smaller than 1%. We have developed a correlation between TL-PINN performance acceleration and similarity measure of RTs, which can be used as a guide for application of TL-PINNs.

Tribological Behavior Characterization and Fault Detection of Mechanical Seals Based on Face Vibration Acceleration Measurements

A mechanical seal is a common type of rotating shaft seal in rotating machinery and plays a key role in the fluid seal of rotating machinery, such as centrifugal pumps and compressors. Given the performance degradation caused by the wear to the face of the contact mechanical seal during operation and the lack of effective predictive maintenance monitoring methods and evaluation indexes, a method for measuring the acceleration of the mechanical seal face’s vibration was pro-posed. The influence of face performance degradation and rotational speed change on the tribo-logical regime of the mechanical seal was investigated. The proposed fault detection model based on support vector data description (SVDD) was constructed. A mechanical seal face degradation test rig verifies the usability of the proposed method. The results show that in the mixed lubrication (ML) regime, the vibration sensitivity of the face increases with the increase in rotational speed. With the decrease in the face performance, the vibration-sensitive characteristic parameters of the face in-crease and change from the ML regime to the boundary lubrication (BL) regime. The incipient fault detection model can warn about incipient faults of mechanical seals. Here, the axial detection result predicted that maintenance would be required 10.5 months earlier than the actual failure time, and the radial and axial detection results predicted required maintenance 12 months earlier than the actual failure.

A methodology for dynamic calibration of inertial dynamometers for wheelchairs

Every day, wheelchair users face countless obstacles that make their social inclusion a real challenge. In this way, it is important that assistive technologies be constantly developed and improved. An example is the dynamometer, which helps in training to characterize the performance of the wheelchair user. In this work, we present a methodology for the dynamic calibration of an inertial dynamometer for wheelchair users, which aims to assist in the rehabilitation and to enable them to perform day-to-day activities independently. The dynamometer is placed on a structure elevated in relation to the ground, at a known height. At the upper end, a wire is attached to the dynamometer cylinder and at the lower end, a known weight is used to enforce acceleration on the cylinder. The wheelchair is placed on top of the cylinder with known different weights corresponding to the user's weight for each repetition of the experiment. The wire is then wrapped around the cylinder and the object is dropped freely, rotating the cylinder. The measurement of the system acceleration is given by an encoder. Data collected in the tests enable to get the system inertial characteristics for each applied weight, allowing to set calibration curve and to evaluate the performance when the propulsion in the dynamometer is imposed by the wheelchair user. This work describes the methodology proposed for calibrating the complete device allowing the quantification of measured values such as power, torque and speed imposed by the user. The calibration steps can be easily reproduced at a low cost and very good performance.

A Soft Sensor for Estimating Tire Cornering Properties for Intelligent Tires

Intelligent tire systems are promising solutions for achieving precise vehicle state estimations, localization, and motion control in the context of autonomous driving. Tire cornering properties, namely, lateral force, aligning moment, and pneumatic trail, are crucial factors that should be accurately estimated for vehicle dynamics control purposes. In this work, a soft sensor for estimating tire cornering properties based on intelligent tire and machine learning is developed. The intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread, which provides acceleration measurements from the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$</tex-math> </inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$</tex-math> </inline-formula> directions. Partial least squares and variable importance in the projection scores (PLS-VIP) are used in the feature extraction of the acceleration signals over the contact patch. A Gaussian process regression (GPR) model is trained to predict the cornering properties with confidence intervals under different input conditions. Based on the variances in the GPR predictions and minimum mean-square error criterion, a data fusion method for pneumatic trail estimation is proposed. It is demonstrated that the developed GPR models for cornering properties and the data fusion method for pneumatic trail estimation have satisfactory accuracy and reliability. The experimental results show that the soft sensor proposed in this work is a strong candidate for further applications in the development of vehicle state estimation and control algorithms.

Application of Negative Poisson’s Ratio Honeycomb Structure in Acceleration Measurements

The measurement of acceleration plays an essential role in evaluating the state of mechanical equipment. The acceleration transducers are the most widely used devices for measuring vibration, motion, and shock. This paper proposes a novel method of acceleration measurement based on auxetic honeycomb structure. By using this method, the change in the auxetic honeycomb structure’s transmittance caused by elastic deformation under acceleration can be converted into a change in solar cell’s output. Geometric analysis and experiments are performed to investigate the effectiveness of the proposed method. The results provide a new structural insight for acceleration sensors. In order to assess the efficacy of the proposed approach, geometric analysis and experiments are conducted. The resonance frequency of an acceleration measurement device using an auxetic structure is approximately 100Hz, while for a device using a non-auxetic structure, it is about 40Hz, both reaching the maximum wavelength variation. The results give potential paths for future study as well as new structural insights for acceleration sensors.

Singular Perturbation-Based Adaptive Integral Sliding Mode Control for Flexible Joint Robots

The flexible joint robot (FJR) typically experiences parametric variations, non-linearities, underactuation, noise propagation, and external disturbances which seriously degrade the FJR tracking. This paper proposes an adaptive integral sliding mode controller (AISMC) based on a singular perturbation method and two state observers for the FJR to achieve high performance. Firstly, the underactuated FJR is modelled into two simple second order fast and slow subsystem by using Olfati transformation and singular perturbation method, which handles underactuation while reducing noise amplification. Then, the AISMC is proposed to effectively accomplish the desired tracking performance, in which the integral sliding surface is designed to reduce chattering based on two state observers with no requirements of the velocity and acceleration measurements in the FJR system. Furthermore, an adaptive laws for switching gains are proposed for both slow and fast subsystems in the FJR to remove the requirements of knowing the up-bound of the disturbances and uncertainties. The closed loop stability of not only slow and fast subsystems but also the overall FJR is proved using the Lyapunov theorem. Finally, the simulation and experimental results demonstrate the superiority of proposed control in terms of less tracking error, significant noise suppression, and strong robustness in comparison with existing controllers.

Active Physical Interaction Control for Aerial Manipulator Based on External Wrench Estimation

This article investigates the active physical interaction problem of an aerial manipulator in terms of capability, reliability, and costs. Then, an active physical interaction control architecture is presented for the aerial manipulator to achieve both stable motion and interaction behaviors with external wrench estimation. Specifically, an external wrench estimator in absence of the acceleration and wrench measurements is designed to regulate the interaction point of the aerial manipulator with a minimal sensor condition. Next, a force tracking impedance control strategy with variable stiffness is presented to guarantee the contact stability and force tracking of the aerial manipulator with uncertain contact targets. Further, utilizing the knowledge of prescribed performance and terminal sliding mode surface, a pose controller is proposed to implement the dynamic response speed and accuracy control of the aerial manipulator, which provides a prerequisite for the realization of reliable physical interaction tasks. The stability of the proposed control architecture is analyzed through Lyapunov tools. Moreover, the feasibility and performance of the proposed control architecture are validated via simulations and real-world contact experiments.

Displacement Estimation of a Skyscraper during Super Typhoon Mangkhut Based on Limited Measurements of Accelerations and Strains

Displacement Estimation of a Skyscraper during Super Typhoon Mangkhut Based on Limited Measurements of Accelerations and Strains