Estimation Of Acceleration Research Articles

Estimation of Head Accelerations in Crashes Using Neural Networks and Sensors Embedded in the Protective Helmet.

Traumatic Brain Injuries (TBIs) are one of the most frequent and severe outcomes of a Powered Two-Wheeler (PTW) crash. Early diagnosis and treatment can greatly reduce permanent consequences. Despite the fact that devices to track head kinematics have been developed for sports applications, they all have limitations, which hamper their use in everyday road applications. In this study, a new technical solution based on accelerometers integrated in a motorcycle helmet is presented, and the related methodology to estimate linear and rotational acceleration of the head with deep Artificial Neural Networks (dANNs) is developed. A finite element model of helmet coupled with a Hybrid III head model was used to generate data needed for the neural network training. Input data to the dANN model were time signals of (virtual) accelerometers placed on the inner surface of the helmet shell, while the output data were the components of linear and rotational head accelerations. The network was capable of estimating, with good accuracy, time patterns of the acceleration components in all impact conditions that require medical treatment. The correlation between the reference and estimated values was high for all parameters and for both linear and rotational acceleration, with coefficients of determination () ranging from 0.91 to 0.97.

Active space control of underactuated robots: From adaptation to robustness to optimality

SummaryThis article develops a controller for the active space control of underactuated robotic systems with the ability to adapt to unknown parameters, be robust to unmodeled dynamics and disturbances, and generate optimal control policies in a pointwise fashion. To achieve the set forth objectives, the model dynamics are first partitioned into active and passive spaces. As a remedy for nonlinear coupling between these spaces, the system's acceleration is estimated for use in the control algorithm. The modeling uncertainty associated with both unknown system parameters and unknown control map is then estimated by proposing a concurrent learning adaptive approach. An online quadratic program (QP) is synthesized utilizing an intelligent robust control Lyapunov function (IRCLF) constraint to ensure the system stability with minimal control effort while using the estimates of the unknown dynamics. The proposed IRCLF scheme automatically compensate for acceleration estimation errors, unmodeled dynamics, and disturbances without the knowledge of their bounds a priori. The uniform ultimate boundedness of all system signals is proven using the Lyapunov stability argument and Barbalat's lemma. The performance of the proposed QP‐AIR‐CLF control framework is validated on two different underactuated systems: a foot‐leg model on a deformable ground, and an overhead crane system. Simulation results show the benefits of the proposed controller over the baseline QP‐RCLF and an adaptive QP‐RCLF in terms of the control optimality, tracking accuracy, dynamic estimation performance, and robustness to disturbances.

Real-Time Adaptive Dynamics Based State Estimation Scheme for Unmanned Aircrafts

In this paper we present a state estimation scheme for Unmanned Aircrafts (UAs) utilizing dynamics based models and multi-sensor data fusion. Employing the UA dynamics in estimation can substantially enhance the estimator performance, but obtaining accurate dynamics parameters for each UA is computationally costly and complex. To eliminate these issues, we propose two decoupled Extended Kalman Filters (EKFs), namely the Rotational Decoupled Extended Kalman Filter (RDEKF) and the Translational Decoupled Extended Kalman Filter (TDEKF). The dynamics parameters in these filters are identified in real-time using the Deep Neural Network and the Modified Relay Feedback Test (DNN-MRFT) approach. This approach doesn’t demand prior knowledge of the UA physical parameters, requiring only an Inertial Measurement Unit (IMU) and a positioning system for model classification. Our estimation scheme provides position, velocity and attitude estimates, in addition to smooth lag-free inertial acceleration estimates. We show experimentally the advantages of our approach on trajectory tracking problems that uses low rate position sensors. We also demonstrate how utilizing the estimated acceleration in feedback control can reduce the tracking error of an optimally tuned system by 43%. Moreover, the proposed estimator produces smooth estimates that leads to a reduction of controller action by 6.6%, when compared to kinematic based estimators. We compare the achieved results against other methods that require full prior knowledge of the UA parameters or the noise models, and show advantages in performance and real-time capability.

Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept.

Detecting fatigue during training sessions would help riders and trainers to optimize their training. It has been shown that fatigue could affect movement patterns. Inertial measurement units (IMUs) are wearable sensors that measure linear accelerations and angular velocities, and can also provide orientation estimates. These sensors offer the possibility of a non-invasive and continuous monitoring of locomotion during training sessions. However, the indicators extracted from IMUs and their ability to show these locomotion changes are not known. The present study aims at defining which kinematic variables and indicators could highlight locomotion changes during a training session expected to be particularly demanding for the horses. Heart rate and lactatemia were measured to attest for the horse’s fatigue following the training session. Indicators derived from acceleration, angular velocities, and orientation estimates obtained from nine IMUs placed on 10 high-level dressage horses were compared before and after a training session using a non-parametric Wilcoxon paired test. These indicators were correlation coefficients (CC) and root mean square deviations (RMSD) comparing gait cycle kinematics measured before and after the training session and also movement smoothness estimates (SPARC, LDLJ). Heart rate and lactatemia measures did not attest to a significant physiological fatigue. However, the statistics show an effect of the training session (p < 0.05) on many CC and RMSD computed on the kinematic variables, indicating a change in the locomotion with the training session as well as on SPARCs indicators (p < 0.05), and revealing here a change in the movement smoothness both in canter and trot. IMUs seem then to be able to track locomotion pattern modifications due to training. Future research should be conducted to be able to fully attribute the modifications of these indicators to fatigue.

A systematic review and meta-analysis of epigenetic clocks in schizophrenia

IntroductionEvidence suggests that schizophrenia (SZ) is associated with accelerated biological aging. DNA methylation can be used as an indicator of biological aging by means of epigenetic clock estimates. ObjectiveThe aim of this systematic review and meta-analysis was to investigate the association between SZ and different epigenetic clocks. MethodsSearch terms were applied in different databases: Embase, MEDLINE (EBSCO), Cochrane Central Register of Controlled Trials, PubMed, PsychINFO and Web of Science. To assess for risk of bias we utilized an adapted version of the Newcastle-Ottawa Scale. Meta-analyses were conducted using the random effects model and meta-regressions were used to assess factors associated with heterogeneity. ResultsEight studies were included (Controls, n = 3394; SZ subjects, n = 3096), which analyzed five different epigenetic clocks. Overall meta-analysis revealed no significant differences between SZ and controls on epigenetic aging (Standardized Mean Difference – SMD = −0.21; p = 0.13). However, epigenetic clock method was a significant moderator of heterogeneity (p = 0.004). Using Horvath's clock as reference, higher SMD's were found for PhenoAge and Intrinsic epigenetic age acceleration (IEAA) clocks. In a stratified meta-analysis restricted to the two clocks mentioned above, a significant accelerating effect was found in patients with SZ when compared to controls (SMD = 0.29; p = 0.003). ConclusionOur findings suggest that the method of epigenetic clocks is a critical factor associated with estimates of aging acceleration in SZ. However, more studies are needed to confirm these findings and in order to evaluate a possible minor effect in overall analysis.

Seismic demand analysis using environmental contours of vector-valued ground motion intensity measures

A study is presented on seismic demand analysis using environmental contours of vector-valued ground motion intensity measures. The environmental contours specify combinations of intensity measures for estimating an engineering demand parameter with a given exceedance probability. Copulas are employed to model the joint distribution of vector-valued ground motion intensity measures for developing the environmental contours. A framework is presented for copula estimation of spectral accelerations using ground motion records from stations on stratified sand and silt deposits in Mexico City. The statistical dependence between pairs of spectral accelerations at a range of periods is analyzed in terms of Kendall’s τ and used for estimating parametric copula models. Goodness of fit of the estimated copulas is examined and the Akaike information criteria is applied for selection of copula models. Environmental contours of spectral accelerations are developed adopting the inverse FORM approach and employed to estimate mean exceedance rates of maximum interstory drift of a building model. Both, the cases of deterministic and stochastic response models of interstory drift are examined. The accuracy of the environmental contour estimates is assessed by comparison with Monte Carlo simulations and estimates from long-term analyses. It is found that the approximation of the failure surface in the inverse FORM approach is quite appropriate for a range of return periods for which very precise or reasonably accurate response estimates were obtained from the environmental contours.

Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods

Among the electromechanical components of the rail system, the rail system vehicle is one of the most important units that carrying the passenger load. In terms of the efficiency of the signalization system, it is very critical to create the optimum vehicle driving profile. While many parameters of the vehicle come into play while designing the driving profile, determining the acceleration and braking accelerations directly affects this characteristic. With the developing technology in rail transportation systems, the use of programmable devices and software instead of human factors is becoming more widespread day by day. Among the software used, artificial intelligence and machine learning applications constitute a large share in the general distribution. Especially if driverless (GOA4) signaling systems are preferred, these software become more important. In this study, the estimation of Vehicle Acceleration and Braking Acceleration with travel time has been carried out by using Machine Learning Methods. The ideal results obtained were given comparatively and interpreted on the graphics.

Data-Driven Synthesis of Broadband Earthquake Ground Motions Using Artificial Intelligence

ABSTRACTRobust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in generative adversarial networks (GANs) to develop a new framework for synthesizing earthquake acceleration time histories. Our approach extends the Wasserstein GAN formulation to allow for the generation of ground motions conditioned on a set of continuous physical variables. Our model is trained to approximate the intrinsic probability distribution of a massive set of strong-motion recordings from Japan. We show that the trained generator model can synthesize realistic three-component accelerograms conditioned on magnitude, distance, and VS30. Our model captures most of the relevant statistical features of the acceleration spectra and waveform envelopes. The output seismograms display clear P- and S-wave arrivals with the appropriate energy content and relative onset timing. The synthesized peak ground acceleration estimates are also consistent with observations. We develop a set of metrics that allow us to assess the training process’s stability and to tune model hyperparameters. We further show that the trained generator network can interpolate to conditions in which no earthquake ground-motion recordings exist. Our approach allows for the on-demand synthesis of accelerograms for engineering purposes.

Airspeed-Aided State Estimation Algorithm of Small Fixed-Wing UAVs in GNSS-Denied Environments.

Aimed at improving the navigation accuracy of the fixed-wing UAVs in GNSS-denied environments, this paper proposes an algorithm of nongravitational acceleration estimation based on airspeed and IMU sensors, which use a differential tracker (TD) model to further supplement the effect of linear acceleration for UAVs under dynamic flight. We further establish the mapping relationship between vehicle nongravitational acceleration and the vehicle attitude misalignment angle and transform it into the attitude angle rate deviation through the nonlinear complementary filtering model for real-time compensation. It can improve attitude estimation precision significantly for vehicles in dynamic conditions. Furthermore, a lightweight complementary filter is used to improve the accuracy of vehicle velocity estimation based on airspeed, and a barometer is fused on the height channel to achieve the accurate tracking of height and the lift rate. The algorithm is actually deployed on low-cost fixed-wing UAVs and is compared with ACF, EKF, and NCF by using real flight data. The position error within 30 s (about 600 m flying) in the horizontal channel flight is less than 30 m, the error within 90 s (about 1800 m flying) is less than 50 m, and the average error of the height channel is 0.5 m. The simulation and experimental tests show that this algorithm can provide UAVs with good attitude, speed, and position calculation accuracy under UAV maneuvering environments.

Airway Aging and Methylation Disruptions in HIV-associated Chronic Obstructive Pulmonary Disease.

Rationale: Age-related diseases like chronic obstructive pulmonary disease (COPD) occur at higher rates in people living with human immunodeficiency virus (PLWH) than in uninfected populations. Objectives: To identify whether accelerated aging can be observed in the airways of PLWH with COPD, manifest by a unique DNA methylation signature. Methods: Bronchial epithelial brushings from PLWH with and without COPD and HIV-uninfected adults with and without COPD (N = 76) were profiled for DNA methylation and gene expression. We evaluated global Alu and LINE-1 methylation and calculated the epigenetic age using the Horvath clock and the methylation telomere length estimator. To identify genome-wide differential DNA methylation and gene expression associated with HIV and COPD, robust linear models were used followed by an expression quantitative trait methylation (eQTM) analysis. Measurements and Main Results: Epigenetic age acceleration and shorter methylation estimates of telomere length were found in PLWH with COPD compared with PLWH without COPD and uninfected patients with and without COPD. Global hypomethylation was identified in PLWH. We identified 7,970 cytosine bases located next to a guanine base (CpG sites), 293 genes, and 9 expression quantitative trait methylation-gene pairs associated with the interaction between HIV and COPD. Actin binding LIM protein family member 3 (ABLIM3) was one of the novel candidate genes for HIV-associated COPD highlighted by our analysis. Conclusions: Methylation age acceleration is observed in the airway epithelium of PLWH with COPD, a process that may be responsible for the heightened risk of COPD in this population. Their distinct methylation profile, differing from that observed in patients with COPD alone, suggests a unique pathogenesis to HIV-associated COPD. The associations warrant further investigation to establish causality.

Differentiator‐based time delay control for uncertain robot manipulators

AbstractHigh‐quality acceleration signal plays a significant role in fast and precise trajectory tracking of robot manipulators via time delay control (TDC). This paper proposes a fast transient tracking differentiator (FTD) for obtaining the noise‐less time derivative from a noisy measurement within the framework of tracking differentiator (TD) design methodology. Global asymptotic convergence of the proposed FTD is proven by Lyapunov's direct method and TD theory. The proposed FTD is cascaded to construct an acceleration estimation and is integrated with the commonly used TDC for an improved trajectory tracking of robot manipulators in the presences of parametric uncertainties and bounded disturbances. Numerical simulations and real‐time experimental validation comparisons demonstrate that the proposed approach provides an easy‐going model‐free improved design for fast and accurate trajectory tracking of robot manipulators with position measurement only.

Epigenetic biomarkers of ageing, physiological frailty, and mortality in people with HIV: a longitudinal cohort study

BackgroundApproaches that provide insight into ageing mechanisms for people with HIV are needed. Epigenetic modifications such as DNA methylation have been operationalised as epigenetic clocks, providing an estimate of age acceleration in years (DNA methylation age acceleration; DNAmAA). Our objective was to determine if measures of DNAmAA predict physiological frailty, as measured by the Veterans Aging Cohort Study (VACS) Index and all-cause mortality, in people with HIV. MethodsAs part of the observational VACS, we profiled epigenome-wide DNA methylation on peripheral blood samples collected from people with HIV as part of a biomarker cohort. We generated six established measures of DNAmAA in samples from 531 participants with VACS Index data at the time of sample collection. All-cause mortality was assessed over 10 years. For each DNAmAA measure, the hazard ratio (HR) per increased unit (1 year) was determined using Cox regression. To test the association of DNAmAA measures with the VACS Index 2.0, linear regression was performed. All regression models were performed with and without adjustment for chronological age. FindingsParticipants were mostly men (98·5% [n=523]; 1·5% [n=8] women); 83·4% (n=448) were Black, 10·5% (n=56) were White, and 5·1% (n=27) were Hispanic; and their mean age was 52·4 years (SD 7·8). There were 136 deaths during a median follow-up of 8·7 years (IQR 7·7–9·4). Hannum's DNAm age acceleration (Hannum AA), extrinsic epigenetic age acceleration (EEAA), Grim age acceleration (Grim AA), and Pheno age acceleration (Pheno AA) were significantly associated with all-cause mortality, but Horvath's DNAm age acceleration (Horvath AA) and intrinsic epigenetic age acceleration (IEAA) were not in unadjusted and age-adjusted models. GrimAA was the strongest predictor (adjusted HR 1·10, 95% CI 1·06–1·14; p<0·0001). Similarly, four of the six DNAmAA measures were significantly associated with the VACS Index in unadjusted and age-adjusted models. Each year increase in GrimAA was associated with a point increase in VACS Index (adjusted β 1·16, 95% CI 0·905–1·42; p<0·0001). InterpretationAmong people with HIV, several DNAmAA measures predicted all-cause mortality and physiological frailty. Each 1-year increase in GrimAA was associated with a 10% increased risk of death. A 5-year increase in GrimAA was associated with a clinically meaningful 5·8-point increase in VACS Index. GrimAA might provide key insights into the molecular mechanisms mediating biological ageing and physiological frailty, while informing the design of novel strategies to reduce mortality and morbidity for people with HIV. FundingKKO received funding from the US Department of Veterans Affairs (grant number I01 RX002790). VCM received funding from Emory (grant number CFAR P30 AI050409). ZW received funding from NIDDK (grant number R01DK125187). KX received funding from NIDA (grant number R01DA042691). MM received funding from NIA (grant numbers P30 AG031679 and P30AI060354). ACJ received funding from NIAAA (grant numbers U24-AA020794, U01-AA020790, U24-AA022001, U10 AA013566 [completed], in kind by US Department of Veterans Affairs). YVS received funding from NIDDK (grant number R01-DK125187) and Emory (grant number CFAR P30 AI050409).

A vectorization of iterative computational processes and time acceleration estimates

O b j e c t i v e s. The problem of efficient organization of the execution of sequential computational processes in the vector mode is solved, taking into account the capabilities of modern high-performance vector-pipeline computers. The relevance of the problem under consideration is due to the fact that processes that occur during cyclic data processing and in iterative algorithms are the most difficult to parallelize. In solving the problem, three main objectives were set. Construction of a mathematical model that takes into account the main architectural and computational features of modern vector-conveyor computers. Calculation of the optimal total execution time of vector operations. Evaluation of the time gain compared to the sequential mode of data processing.M e t h o d s. To achieve the objectives and to prove the main and auxiliary statements, an original method was used, including establishing the validity of the inductive assumptions in the cases under consideration, as well as an illustrative method of scheduling theory using Gantt charts.R e s u l t s . A vector model for the implementation of sequential calculations is proposed, which takes into account the main features of vector-conveyor computers. The optimal total execution time of sequential calculations in the vector mode is determined, and a lower estimate of the time gain is obtained in comparison with the sequential mode of their execution.C o n c l u s i o n. It has been established that when processing scalar input data in the sequential mode by vector operations with a pipeline length k, acceleration is possible by at least a factor nN (nk+N) , where N is the size of the input, n is the number of vector and corresponding scalar operations. The estimation of the time acceleration in the vectorization of calculations is compared with the sequential mode of their execution.

Estimation of Arias intensity and peak ground acceleration (PGA) using probabilistic seismic hazard assessment of Uttarakhand state (India)

Estimation of Arias intensity and peak ground acceleration (PGA) using probabilistic seismic hazard assessment of Uttarakhand state (India)

A two-stage sparse algorithm for localization and characterization of local nonlinear structures

This paper considers the localization and characterization of nonlinear components in local nonlinear structures with noisy measurements, and a novel two-stage sparse identification method is proposed. In this algorithm, one set of candidate nonlinear basis functions are leveraged to characterize system nonlinearities. To effectively localize and characterize nonlinear components, the algorithm introduces two sparse stages. At the first sparse stage, the adaptive group Lasso is adopted to localize nonlinear components in structures. At the second sparse stage, the adaptive Lasso is used to further characterize the contributing nonlinear basis functions in the identified nonlinear positions. Since the first sparse stage can greatly decrease the number of candidate nonlinear basis functions, the contributing functions can be effectively selected and characterized at the second sparse stage. Compared with the one-stage sparse algorithms, the proposed algorithm is more robust to noise, i.e., the new method can correctly identify the local nonlinear structures with a lower Signal to Noise Ratio (SNR). In addition, many existed nonlinear identification methods, simultaneously require the synchronous data of vibration displacements, velocities and accelerations. If only vibration displacements are measured, the first and second order derivatives of displacement are conducted to estimate velocities and accelerations, respectively. Nevertheless, the noise mixed into the displacement measurement will be severely amplified in the process of derivative calculation, which will make the estimation of velocities and accelerations deviating from the actual value and reduce the identification accuracy. To avoid performing the second order derivative, the Duhamel's integral is adopted to rewrite the dynamic relationship between the system input and output. In addition, a non-parametric de-noising method based on reproducing kernel Hilbert space (RKHS) is further developed to reduce the effect of noise in vibration displacements and obtain vibration velocities, which can effectively reduce the effect of noise and improve the identification accuracy. The numerical and experimental studies verify the potential and effectiveness of the proposed algorithm in localizing and characterizing nonlinear components in local nonlinear structures.

로봇팔의 시간지연 제어기에서 각 가속도 추정에 대한 추종 성능 비교연구

This paper presents a comparative study of tracking performances between several methods of estimating acceleration signals for a time-delayed controller(TDC) of a robot manipulator. To implement the time-delayed controller completely, there are three requirements: 1) Fast sampling time, 2) constant inertia value, and 3) angular acceleration signals. This paper focuses on the acceleration estimation for TDC. Several methods for angular acceleration estimation are tested: 1) Finite difference method with low pass filtering, 2) Kalman filtering, 3) desired angular acceleration, 4) control input, 5) Jacobian relationship. We experimented five methods on one arm of a dual arm robot to follow a circular trajectory and their tracking performances are compared.

Earthquake Early Warning for Estimating Floor Shaking Levels of Tall Buildings

ABSTRACTThis article investigates methods to improve earthquake early warning (EEW) predictions of shaking levels for residents of tall buildings. In the current U.S. Geological Survey ShakeAlert EEW system, regions far from an epicenter will not receive alerts due to low predicted ground-shaking intensities. However, residents of tall buildings in those areas may still experience significant shaking due to the acceleration amplification caused by tall buildings’ dynamic behavior, as recently experienced by residents of the 52-story building in downtown Los Angeles (DTLA) during the 2019 M 7.1 Ridgecrest earthquake. Using more than 400 recorded response data acquired from 77 instrumented buildings in California, here we compare the Federal Emergency Management Agency (FEMA) P-58 and American Society of Civil Engineers (ASCE) 7-16 simplified equations for peak floor acceleration (PFA), finding that the ASCE estimation is close to the median of data recorded in large and long-distance events, whereas the current FEMA estimation is not suitable. In the second part of this article, four instrumented tall buildings in DTLA are extensively studied, and the performance of the simplified and response spectrum (RS) methods giving both an estimation of the free-field horizontal peak ground acceleration (PGA) and pseudospectral acceleration is evaluated. The results show that the RS method is as accurate as the response history analysis as long as the ground-motion RS is accurate, whereas the ASCE 7-16 prediction is conservative. However, when ground-motion RS or PGA is estimated for DTLA using a ground-motion model (GMM), the performance of the RS method significantly degrades due to underestimation by the GMM at long periods. The results of this study imply that a nonergodic GMM, which may give more accurate prediction in Los Angeles, could improve the results for PFA when the building’s behavior is dominated by a few long-period fundamental modes, as is the case for the 52-story building in DTLA.

High-Resolution Permanent Magnet Drive Using Separated Observers for Acceleration Estimation and Control.

This paper proposes a high-resolution permanent magnet (PM) motor drive based on acceleration estimation and control. The PM motor is widely implemented in the printed circuit board (PCB) manufacturing process. To achieve the demanded 1 μm drilling resolution, a sine/cosine incremental encoder is usually installed for motion control. In this paper, several improvements are developed to increase the motion control steady-state accuracy balancing transient response. First, the interpolation of every two encoder counts is proposed to increase the position sensing resolution. In this case, the transient response is improved through the high-resolution position feedback. Second, a closed-loop observer with two independent bandwidths is proposed for acceleration estimation. By using the interpolated position for acceleration estimation, the vibration-reflected high-frequency torque harmonics can be compensated through the acceleration closed-loop control. It reduces the steady-state error under the same sensing hardware. According to experimental results, both transient response and steady-state error can be improved on a PM motor using the proposed position interpolation and acceleration control.

歩幅推定の精度向上のための脚部慣性センサの位置推定

Human gait analysis generally needs acceleration at the center of joints where inertial measurement units (IMU) cannot be attached. The joint acceleration estimation requires the position vectors from the IMU to the center of the joint in the sensor coordinate system. Past studies have proposed a method to estimate the position vector of the joint center from two IMUs attached to two rigid bodies, respectively. The idea is based on the equality of a joint center acceleration estimated from two IMUs. However, the estimation accuracy of the position vector depends on the calibration movement. The purpose of this study is to verify the accuracy of the position vector from a lower leg IMU to the ankle joint with calibration movements. We determined the movements as a squat movement and a unidirectional knee rotating movement and compared their accuracy. We assumed that the ankle joint keeps resting throughout the calibration movements. The result demonstrated that the accuracy was better for the unidirectional knee rotation movement and dependent on the quality of the calibration movement.

Estimation and Verification of Maximum Response Acceleration Based on Spectral Modal Analysis of a High-rise Building Using WRF Simulation Data

Estimation and Verification of Maximum Response Acceleration Based on Spectral Modal Analysis of a High-rise Building Using WRF Simulation Data