Force Identification Research Articles

Characteristics and predictive factors of visual function improvements after monocular perceptual learning in amblyopia

Monocular perceptual learning has shown promising performance in restoring visual function in amblyopes beyond the critical period in the laboratory. However, the treatment outcome is variable and indeterminate in actual clinical and neuroscientific practice. We aimed to explore the efficacy of monocular perceptual learning in the clinical setting. By combining continuous monitoring of perceptual learning and clinical measurements, we evaluated the efficacy and characteristics of visual acuity and contrast sensitivity function improvement and further explored the individualized effect after perceptual learning. Amblyopes (average age:17 ± 7 years old) were trained in a monocular two-alternative forced choice identification task at the 50% contrast threshold of the amblyopic eye for 10–15 days. We found that monocular perceptual learning improves both visual acuity and contrast sensitivity function in amblyopia. The broader activation of spatial contrast sensitivity, with a significant improvement in lower spatial frequencies, contributed to improving visual acuity. Visual acuity changes in the early stage can predict the endpoint treatment outcomes. Our results confirm the efficacy of monocular perceptual learning and suggest potential predictors of training outcomes to assist in the future management of clinical intervention and vision neuroscience research in amblyopia beyond the critical period of visual plasticity.

Dynamic force identification in milling based on IRLS using acceleration signals

Measurement of cutting force is important for in-process monitoring, control and optimization. However, it is very difficult to measure the cutting force directly using invasive sensor in industrial scene. This paper develops an infinite-time recursive identification method of cutting force under finite buffer length condition, called the improved recursive least squares (IRLS) method. A cutting force identification framework based on time-domain convolutional theory is introduced, where the cutting force is identified using the acceleration signal easily obtained from industrial scene. Then, the decay characteristic of the impulse response function and the recursive characteristic of the transfer matrix are used to improve the recursive least squares method. The IRLS method overcomes the data saturation problem of the recursive least squares method and can identify the cutting force in infinite time. To reduce the error amplification of the proposed cutting force identification method, the averaged transfer function is used to determine the length of the buffer. In addition, the compensated values of the Kistler 9129AA dynamometer measurement results are used as a reference. Finally, several sets of milling experiments are performed to verify the effectiveness and rationality of the IRLS method.

Dynamic bridge weigh-in-motion using estimated modal parameters from ambient vibration tests

Dynamic bridge weigh in motion systems use models that can simulate the dynamic behaviour of a bridge subjected to moving traffic. Currently available models use complex and computationally expensive Moving Force Identification (MFI) methods that utilize finite element models to estimate the bridge torsional and transverse dynamic behaviour. This paper presents a novel dynamic parametric BWIM method that utilizes the experimentally estimated modal parameters for simulating the response of a bridge structure to moving loads. The estimated modes of an in-situ structure inherently capture the true bridge behaviour for any generalized geometry, boundary conditions and load position. Therefore, it enables the complex torsional and transverse bending behaviour of the bridge to be captured. In this paper, the mathematical derivation of the analytical model is presented in detail, then model calibration and weighing procedures are outlined, followed by the validation of the proposed method using a full-scale case study arterial highway bridge in the Canadian Province of New Brunswick. The proposed dynamic BWIM method offers a novel solution for the development of real-time BWIM systems that is efficient to calibrate and accurate in vehicle identification.

A Numerical Analysis of the Nonlinear Dynamics of Shimmed Conical Gas Foil Bearings

Conical gas foil bearings (CGFBs) have the potential to halve the number of necessary bearings in a conventional rotor system supported by gas foil bearings. Transient simulations of gas foil journal bearings and gas foil thrust bearings have proven their necessity to accurately predict the safe operating range of such bearings. This work presents the first transient model able to simulate the three-dimensional dynamics of CGFBs. The static behaviour of a single CGFB with a uniform bearing clearance is compared to a CGFB modified by thin metal shims and the resulting advantages of shimmed CGFBs are discussed. An investigation of the linear stability behaviour shows that the axial load of the bearing determines the stability of the equilibrium position. Furthermore, three transient simulations demonstrate the capability of the presented model to describe the nonlinear dynamics of a shimmed CGFB such as the occurrence of stable limit cycles and self-excited sub- and super-synchronous vibrations with and without a rotor unbalance. Additionally, waterfall diagrams are used to investigate the frequency response for different rotational speeds. The novel findings of this work are the importance of a non-uniform bearing clearance for the functionality of a CGFB and the identification of the axial force as a critical factor in maintaining bearing stability. These findings are specific to CGFBs and have not been discussed or mentioned in previous works.

Wheel-rail force identification for high-speed railway based on a modified weighted l1-norm regularization with optimal strain sensors

Wheel-rail force identification is an important inverse problem for structural health monitoring of high-speed railway train-track-bridge system. It is widely used for dynamic response reconstruction of bridges and early safety warning of dynamic reduction rate of wheel weight for the train. It can also serve as a reference for the calibration of wheel-rail force model. This paper proposes a wheel-rail force identification method for high-speed railway based on a modified weighted l1-norm regularization. Based on numerical simulations, it is found that variations of dynamic component of wheel-rail forces are very small. Therefore, the problem is transformed into the identification of three categories of static wheel weights and two categories of average dynamic components, which greatly simplifies the degree of freedom of the solution compared to traditional methods, thereby improving the solution accuracy and saving the number of sensors needed. The wheel-rail forces are decomposed into the product of a series of redundant basis functions and corresponding coefficients. The fast iterative shrinkage threshold algorithm (FISTA) and the Bayesian information criterion (BIC) are used to seek the solution of l1-norm regularization and to select the optimal regularization coefficients, respectively. Since the number of sensors and their locations have a significant impact on the accuracy of reconstructed wheel-rail forces, they are optimized using a genetic algorithm (GA), aiming to accurately reconstruct the wheel-rail forces with the minimum number of sensors at corresponding optimal positions. Numerical simulation of a 32 m box girder standard beam for high-speed railway shows that the reconstructed wheel-rail forces are in good agreement with the simulated results, which verifies the accuracy of the proposed method. In addition, the results of dynamic load test indirectly demonstrate the effectiveness of the proposed method.

Nonlinear dynamic modeling and body injuries analysis of human/seat system under vertical impact

Predicting the responses and injuries of seated pilots caused by emergency landing or ejection is significant for the design of ejection seats. A nonlinear two-dimensional multi-body human/seat model is proposed to predict the responses and body injuries exposed to vertical impact. Then a new force identification method is presented and applied to obtain the external excitation of the system. In addition, the parameters of this model are optimized based on the Error Assessment of Response Time Histories (EARTH) metric and Non-dominated Sorting Genetic Algorithm II (NSGA-II). Furthermore, utilizing the proposed model, this research predicts body injuries with different impact intensities. With body injuries as output, the influence of input parameters is assessed by global sensitivity analysis (GSA) methods. The result shows that the backrest angle plays a crucial role in body injuries. Finally, the influence of backrest angle is further analyzed to obtain the variation of body injuries with backrest angle. This research provides guiding principles for the subsequent design of ejection seats.

Noise-enhanced effect in moving dynamic force identification

It is well-known that the response matrix in moving force identification (MFI) is ill-posed, so the identified force is apt to be affected by measurement noise. The effect of noise could be mitigated to some extent by traditional regularization methods, which do not distinguish between white noise and colored noise. However, this paper addresses that adding certain types of white noise can be helpful in suppressing the influence of colored noise because stochastic resonance (SR) can occur due to the interaction between the white noise and the nonlinear process of the MFI. With these understandings, this paper proposes a new MFI method that has two levels of regularization. In the first level, the response matrix is transformed to the time-frequency domain by wavelets with high time-frequency resolution. Thus, the new response matrix is more robust because the major frequency components are more prominent. In the second level, the element-wise Bayesian regularization is adopted to apply regularization on each force element. As regularization weights are determined by the measurement with noise, optimal weights can be obtained by adding certain white noise. This noise enhancement effect is demonstrated by several numerical simulations. Then, the performance of the proposed method is further validated in experimental studies.

Implicit inverse force identification method for vibroacoustic finite element model

The two-field vibroacoustic finite-element (FE) model requires a relatively large number of degrees of freedom compared to the monophysics model, and the conventional force identification method for structural vibration can be adjusted for multiphysics problems. In this study, an effective inverse force identification method for an FE vibroacoustic interaction model of an interior fluid–structure system was proposed. The method consists of: (1) implicit inverse force identification based on the Newmark-β time integration algorithm for stability and efficiency, (2) second-order ordinary differential formulation by avoiding the state-space form causing large degrees of freedom, (3) projection-based multiphysics reduced-order modeling for further reduction of degrees of freedom, and (4) Tikhonov regularization to alleviate the measurement noise. The proposed method can accurately identify the unmeasured applied forces on the in situ application and concurrently reconstruct the response fields. The accuracy, stability, and computational efficiency of the proposed method were evaluated using numerical models and an experimental testbed. A comparative study with the augmented Kalman filter method was performed to evaluate its relative performance.

An optimization neural network model for bridge cable force identification

Accurate determination of cable force values is the most important technical means to avoid damage to the cable bridge. In order to avoid the influence of the difficulty in distinguishing the boundary conditions and the lack of low-order natural frequency on the cable force determination results, an intelligent method for determining the bridge cable force based on the vibration method is proposed. With the cable length, linear density, flexural stiffness and input frequency as input units and the cable force as output unit, a neural network is established to identify the cable force by combining the finite element simulation data, and the model is optimized using the intelligent swarm optimization algorithm. The results show that compared with the cable force prediction models using generalized regression neural network (GRNN) and GRNN optimized using particle swarm optimization (PSO-GRNN) and canonical identification methods, the GRNN optimized using sparrow search algorithm (SSA-GRNN) proposed in this paper has a better identification effect. The prediction error of short cables is essentially within 10%, and that of long cables is within 5%. It can not only realize the accurate identification of bridge cable force by ignoring the boundary conditions and vibration frequency order of cables, but also has a wide range of applications.

Impact force identification on composite panels using fully overlapping group sparsity based on Lp-norm regularization

Impact force identification is of great importance for composite structural health monitoring due to the poor impact resistance of composite materials. Convex sparse regularization method based on L1-norm tends to underestimate the amplitude of the impact force. This paper proposes a novel method using fully overlapping group sparsity based on Lp-norm regularization (FOGSLp) for impact force identification, which can localize the impact force and reconstruct its time history simultaneously with limited measurements in under-determined cases. The FOGSLp method takes more sparse prior information into account by combining the non-convex Lp-norm () and fully overlapping group sparsity to promote sparse solutions, thus improving the accuracy of the reconstructed impact force. An accelerated grouped shrinkage-thresholding algorithm is employed to solve the non-convex optimization problem under the majorization-minimization framework. The Nesterov’s acceleration strategy is modified to accommodate the requirements of non-convex optimization. Simulations and experiments are conducted on composite panels to validate the effectiveness of the FOGSLp method. Results demonstrate the efficiency and robustness of the FOGSLp method to localize the impact force and reconstruct its time history simultaneously while 25 potential impact points are monitored using two sensors. Compared with L1-norm, Lp-norm, and L2,1-norm regularization methods, the proposed method performs best in both simulations and experiments.

Pose-Dependent Cutting Force Identification for Robotic Milling

AbstractCutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the modal parameters of the robotic milling system vary with the robot pose. This paper presents a novel pose-dependent method to identify the cutting force using the acceleration signal generated by robotic milling. First, the modal parameters at different machining points are employed as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are employed to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is employed to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the effectiveness of the proposed method is verified with robotic milling experiments, and the results show that the identification error and time are acceptable under the condition of variable robot pose.

Parameter adaptive sliding mode trajectory tracking strategy with initial value identification for the swing in a hydraulic construction robot

A novel trajectory tracking strategy is developed for a double actuated swing in a hydraulic construction robot. Specifically, a nonlinear hydraulic dynamics model of a double actuated swing is established, and a parameter adaptive sliding mode control strategy is designed to enhance the trajectory tracking performance. When an object is grabbed and unloaded, the moment of inertia of a swing considerably changes, and the performance of the estimation algorithm is generally inadequate. Thus, it is necessary to establish an algorithm to identify the initial value of the moment of inertia of the object. To this end, this paper proposes a novel initial value identification algorithm based on a two-DOF robot gravity force identification method combined with stereo vision information. The performance of the identification algorithm is enhanced. Simulations and experiments are performed to verify the effect of the novel control scheme.

Frequency domain identification of modal characteristics and loads from output‐only measurements

AbstractThis paper proposes a frequency domain framework for concurrently estimating the modal structural parameters and modal loads from the measured output response. A mathematical load model is theoretically developed and compared to the load determined by force identification. Unlike conventional modal identification schemes, the proposed approach can effectively accommodate the spectral shape of the external load in the frequency domain, enabling the determination of the modal parameters without explicit white noise assumption. The proposed approach is verified by applying it to a single‐degree‐of‐freedom system system representing a modal response, an aeroelastic model in an atmospheric boundary layer wind tunnel, a multi‐degree‐of‐freedom system, and the measured acceleration response of a 40‐story building. The simulation results demonstrate that the proposed technique can reliably estimate the modal parameters and load model and is robust against the noise present in the response and the order of the load model. Additionally, the proposed technique can be applied to various loading conditions such as vortex‐induced and buffeting effects since the modal parameters and the load model can be identified for any shape of the loading spectrum.

A Deep Learning-Based Unbalanced Force Identification of the Hypergravity Centrifuge.

Accurate and quantitative identification of unbalanced force during operation is of utmost importance to reduce the impact of unbalanced force on a hypergravity centrifuge, guarantee the safe operation of a unit, and improve the accuracy of a hypergravity model test. Therefore, this paper proposes a deep learning-based unbalanced force identification model, then establishes a feature fusion framework incorporating the Residual Network (ResNet) with meaningful handcrafted features in this model, followed by loss function optimization for the imbalanced dataset. Finally, after an artificially added, unbalanced mass was used to build a shaft oscillation dataset based on the ZJU-400 hypergravity centrifuge, we used this dataset to train the unbalanced force identification model. The analysis showed that the proposed identification model performed considerably better than other benchmark models based on accuracy and stability, reducing the mean absolute error (MAE) by 15% to 51% and the root mean square error (RMSE) by 22% to 55% in the test dataset. Simultaneously, the proposed method showed high accuracy and strong stability in continuous identification during the speed-up process, surpassing the current traditional method by 75% in the MAE and by 85% in the median error, which provided guidance for counterweight and guaranteed the unit's stability.

Real-Time Response Estimation of Structural Vibration with Inverse Force Identification

Virtual sensing is an effective method to identify the inaccessible state of the structural systems by compensating the limitations of the conventional physical sensing techniques. Recently, it becomes popular in structural vibration field and their relevant physical domains such as civil, mechanical, aerospace engineering, thermal dynamics, and acoustics. This study aimed to develop a virtual sensing algorithm of structural vibration for the real-time identification of unmeasured information. First, certain local point vibration responses (such as displacement and acceleration) are measured using physical sensors, and the data sets are extended using a numerical model to cover the unmeasured quantities through the entire spatial domain in the real-time computation process. A modified time integrator is then proposed to synchronize the physical sensors and the numerical model using inverse dynamics. In particular, an efficient inverse force identification method is derived using implicit time integration. The second-order ordinary differential formulation and its projection-based reduced-order modeling are used to avoid two times larger degrees of freedom within the state-space form. Then, the Tikhonov regularization noise-filtering algorithm is employed instead of Kalman filtering. The performance of the proposed method is investigated on both numerical and experimental testbeds under sinusoidal and random excitation loading conditions. In the numerical test, the system could identify the status of the motor housing structure in the speed of 16,181 S a m p l e s / s . The FDE and RMSE values are bounded under 0.1816 and 1503.2. In the case of the experimental test, the algorithm is implemented to the beam structure using a single-board computer, including inverse force identification and unmeasured response prediction. Even in the limited computational environment, the system could identify the applied forces in real time in the speed of 2,173 S a m p l e s / s . Through all experimental cases, FDE and RMSE values are bounded under 0.2925 and 0.1660. The results show that the virtual sensing algorithm can accurately identify unmeasured information, forces, and displacements throughout the vibration model in real time in a very limited computing environment.

A new aerodynamic identification technology for short-time hypersonic wind tunnels while considering inertial force interference

When aerodynamic force tests are performed in hypersonic wind tunnels, the test model in the force measurement system (FMS) will be subjected to transient impact and induced transient vibration due to the high-speed airflow generated during start-up. The output signal of the FMS will contain the real aerodynamic signal and the inertial force signal caused by model vibration. The inertial force signal completely overcomes the aerodynamic signal, strongly influencing the test accuracy of the aerodynamic force and even making the aerodynamic force signal completely unusable. Therefore, this study develops a novel intelligent aerodynamic identification method based on deep residual shrinkage network (DRSN) deep learning technology and applies this method to aerodynamic force tests in hypersonic wind tunnels. This method can achieve intelligent identification and filtering of inertial force interference signals and instrument noise signals, and improve the test accuracy of aerodynamic forces. Also, this study performed a numerical simulation experiment and a test-bed experiment; verified the results through hypersonic wind tunnel test signals; and compared the identification results of the DRSN network method with the identification results of the traditional CNN network method, the mean method and the FFT method. From comparison results, the aerodynamic force identification accuracy of the DRSN network method proposed in this paper is shown to be nearly ideal and always remains above 95%. Thus, the proposed method effectively reduces the interference of instrument noise and inertial force, markedly improving the accuracy and reliability of the hypersonic wind tunnel aerodynamic force test.

Identification of Lateral-Directional Aerodynamic Parameters for Aircraft Based on a Wind Tunnel Virtual Flight Test

In the early stages of aircraft design, a scaled model of the aircraft is installed in a wind tunnel for dynamic semi-free flight to approximate real flight, and the test data are then used to identify the aerodynamic parameters. However, the absence of the translational motion of the test model makes its flight dynamics different from those in free flight, and the effect of this difference on parameter identification needs to be investigated. To solve this problem, a 3-DOF wind tunnel virtual flight test device is built to fix the test model on a rotating mechanism, and the model is free to rotate in three axes through the deflection of the control surfaces. The flight dynamics equations for the wind tunnel virtual flight test are established and expressed as a decoupled form of the free flight force and the influence of the test support frame force on the model’s motions through linearization. The differences between wind tunnel virtual flight and free flight are analysed to develop a model for the identification of aerodynamic parameters. The selection of the lateral-directional excitation signal and the design method of its parameters are established based on the requirements for the identifiability of the aerodynamic derivatives, and a step-by-step method for the identification of aerodynamic force and moment derivatives is established. The aerodynamic parameter identification results of a blended wing body aircraft show that the identification method proposed in this paper can obtain results with high accuracy, and the response of the modified motion model is consistent with that of the free flight motion model.

Systemic problems of administrative and legal regulation of the powers of state bodies in the field of forced migration in Russia

The relevance of the research topic consist in the existing systemic problems in the field of forced migration, including the lack of a real coordinating link, unsystematic state policy in the field of social adaptation and integration of forced migrants, and identified problems in statistical activities. The main conclusions of the study are to substantiate the need for high-quality administrative reform, including the formation of a single coordinating body authorized to carry out coordination functions and determine public administration schemes in various situations, modernize statistical activities, and create a specialized body authorized to carry out functions in the field of social and cultural adaptation of forced migrants and the adoption of the corresponding federal law. The author’s special contribution is a systematic study of statistical data on forced migration and identification of systemic problems in statistical activities, analysis of the latest legislation in the field of social and cultural adaptation. The scientific novelty of the study is due to the systematic approach to the study of issues of forced migration as a special category of public administration, different from labor and voluntary migration, the synthesis of law enforcement and protective powers and powers in the field of social and cultural adaptation and integration of forced migrants. This made it possible to analyze the activities of government bodies both from the perspective of suppressing illegal migration and compliance with established administrative procedures, and from the perspective of implementing a unified state policy in the field of social and cultural adaptation and integration, legal education, promoting the prevention of crime, strengthening integration and increasing the positive economic effect from the labor activity of forced migrants.

Randomized Kaczmarz and Landweber algorithms for impact force identification on a composite panel

Randomized Kaczmarz and Landweber algorithms for impact force identification on a composite panel

A real-time unmeasured dynamic response prediction for nuclear facility pressure pipeline system

A real-time unmeasured dynamic response prediction process for the nuclear power plant pressure pipeline is proposed and its performance is tested in the test-loop system (KAERI). The aim of the process is to predict unmeasurable or unreachable dynamic responses such as acceleration, velocity, and displacement by using a limited amount of directly measured physical responses. It is achieved by combining a well-constructed finite element model and robust inverse force identification algorithm. The pressure pipeline system is described by using the displacement-pressure vibro-acoustic formulation to consider fully filled liquid effect inside the pipeline structure. A robust multiphysics modal projection technique is employed for the real-time sensor synchronized prediction. The inverse force identification method is also derived and employed by using Bathe's time integration method to identify the full-field responses of the target system from the modal domain computation. To validate the performance of the proposed process, an experimental test is extensively performed on the nuclear power plant pressure pipeline test-loop under operation conditions. The results show that the proposed identification process could well estimate the unmeasured acceleration in both frequency and time domain faster than 32,768 samples per sec.