Motion Modeling and Control of Lower Limb Exoskeleton Based on Max-Plus Algebra

Max-plus algebra is a special method to describe the discrete event system. In this paper, it is introduced to describe the motion of lower limb exoskeleton. Based on the max-plus algebra and the timed event graph, the walking process of exoskeleton is modelled. The max-plus algebra approach can describe the logical sequence and safety condition in the walking process, which cannot be achieved via other conventional modelling approaches. The autonomous control of lower limb exoskeleton system is studied via the model based on max-plus algebra. In the end, an FSM (finite state machine) controller embedded with the max-plus algebra model is proposed, and the experiments show ideal speed and gait/phase period control effect, as well as the good safety and stable performance.

Background Invariant Faster Motion Modeling for Drone Action Recognition

Visual data collected from drones has opened a new direction for surveillance applications and has recently attracted considerable attention among computer vision researchers. Due to the availability and increasing use of the drone for both public and private sectors, it is a critical futuristic technology to solve multiple surveillance problems in remote areas. One of the fundamental challenges in recognizing crowd monitoring videos’ human action is the precise modeling of an individual’s motion feature. Most state-of-the-art methods heavily rely on optical flow for motion modeling and representation, and motion modeling through optical flow is a time-consuming process. This article underlines this issue and provides a novel architecture that eliminates the dependency on optical flow. The proposed architecture uses two sub-modules, FMFM (faster motion feature modeling) and AAR (accurate action recognition), to accurately classify the aerial surveillance action. Another critical issue in aerial surveillance is a deficiency of the dataset. Out of few datasets proposed recently, most of them have multiple humans performing different actions in the same scene, such as a crowd monitoring video, and hence not suitable for directly applying to the training of action recognition models. Given this, we have proposed a novel dataset captured from top view aerial surveillance that has a good variety in terms of actors, daytime, and environment. The proposed architecture has shown the capability to be applied in different terrain as it removes the background before using the action recognition model. The proposed architecture is validated through the experiment with varying investigation levels and achieves a remarkable performance of 0.90 validation accuracy in aerial action recognition.

An Experimental Approach towards Motion Modeling and Control of a Vehicle Transiting a Non-Newtonian Environment

The present work tackles the modeling of the motion dynamics of an object submerged in a non-Newtonian environment. The mathematical model is developed starting from already known Newtonian interactions between the submersible and the fluid. The obtained model is therefore altered through optimization techniques to describe non-Newtonian interactions on the motion of the vehicle by using real-life data regarding non-Newtonian influences on submerged thrusting. For the obtained non-Newtonian fractional order process model, a fractional order control approach is employed to sway the submerged object’s position inside the viscoelastic environment. The presented modeling and control methodologies are solidified by real-life experimental data used to validate the veracity of the presented concepts. The robustness of the control strategy is experimentally validated on both Newtonian and non-Newtonian environments.

Design, Modeling, and Optimization of a Novel Asymmetrical Piezoelectrically Actuated Microgripper

Microgripper, as the end-effector of a micromanipulation robot, has been widely used in semiconductors, biomedicine, optical engineering, etc. The stack piezoelectric ceramic actuated microgripper has the advantages of fast response speed, high motion resolution, and good control performance. Traditional symmetrical microgripper has defects such as lack of positioning reference and dense modal of resonance. Therefore, it is necessary to develop an asymmetrical clamp with a large clamping stroke and good dynamic characteristics. This paper introduces the design, modeling, optimization, and simulation of a new asymmetric microgripper. The designed asymmetric gripper has the advantages of large-displacement magnification and high natural frequency. The gripper’s movable side is comprised of three-level compliant amplification mechanisms including bridge mechanism, Scott-Russell mechanism, and parallelogram mechanism. The linear relationship between the input displacement and the output displacement is obtained through motion modeling. The multi-objective genetic algorithm optimizes the major parameters of the structure which affect the clamping stroke. Finite element analysis is conducted to verify the clamping stroke, clamping force, and resonance frequency of the gripper. The simulation results show that the proposed microgripper has good working performance with a clamping stroke of 156.75 μm and a natural frequency of 952.91 Hz, which is promising for practical application.

Near-Source Ground Motions and Their Variability Derived from Dynamic Rupture Simulations Constrained by NGA-West2 GMPEs

ABSTRACT Empirical ground-motion prediction equations (GMPEs) lack a sufficient number of measurements at near-source distances. Seismologists strive to supplement the missing data by physics-based strong ground-motion modeling. Here, we build a database of ∼3000 dynamic rupture scenarios, assuming a vertical strike-slip fault of 36×20 km embedded in a 1D layered elastic medium and linear slip-weakening friction with heterogeneous parameters along the fault. The database is built by a Monte Carlo procedure to follow median and variability of Next Generation Attenuation-West2 Project GMPEs by Boore et al. (2014) at Joyner–Boore distances 10–80 km. The synthetic events span a magnitude range of 5.8–6.8 and have static stress drops between 5 and 40 MPa. These events are used to simulate ground motions at near-source stations within 5 km from the fault. The synthetic ground motions saturate at the near-source distances, and their variability increases at the near stations compared to the distant ones. In the synthetic database, the within-event and between-event variability are extracted for the near and distant stations employing a mixed-effect model. The within-event variability is lower than its empirical value, only weakly dependent on period, and generally larger for the near stations than for the distant ones. The between-event variability is by 1/4 lower than its empirical value at periods >1 s. We show that this can be reconciled by considering epistemic error in Mw when determining GMPEs, which is not present in the synthetic data.

Mice and primates use distinct strategies for visual segmentation

The rodent visual system has attracted great interest in recent years due to its experimental tractability, but the fundamental mechanisms used by the mouse to represent the visual world remain unclear. In the primate, researchers have argued from both behavioral and neural evidence that a key step in visual representation is "figure-ground segmentation," the delineation of figures as distinct from backgrounds [1-4]. To determine if mice also show behavioral and neural signatures of figure-ground segmentation, we trained mice on a figure-ground segmentation task where figures were defined by gratings and naturalistic textures moving counterphase to the background. Unlike primates, mice were severely limited in their ability to segment figure from ground using the opponent motion cue, with segmentation behavior strongly dependent on the specific carrier pattern. Remarkably, when mice were forced to localize naturalistic patterns defined by opponent motion, they adopted a strategy of brute force memorization of texture patterns. In contrast, primates, including humans, macaques, and mouse lemurs, could readily segment figures independent of carrier pattern using the opponent motion cue. Consistent with mouse behavior, neural responses to the same stimuli recorded in mouse visual areas V1, RL, and LM also did not support texture-invariant segmentation of figures using opponent motion. Modeling revealed that the texture dependence of both the mouse's behavior and neural responses could be explained by a feedforward neural network lacking explicit segmentation capabilities. These findings reveal a fundamental limitation in the ability of mice to segment visual objects compared to primates.

Selection of random vibration theory procedures for the NGA-East project and ground-motion modeling

Traditional ground-motion models (GMMs) are used to compute pseudo-spectral acceleration (PSA) from future earthquakes and are generally developed by regression of PSA using a physics-based functional form. PSA is a relatively simple metric that correlates well with the response of several engineering systems and is a metric commonly used in engineering evaluations; however, characteristics of the PSA calculation make application of scaling factors dependent on the frequency content of the input motion, complicating the development and adaptability of GMMs. By comparison, Fourier amplitude spectrum (FAS) represents ground-motion amplitudes that are completely independent from the amplitudes at other frequencies, making them an attractive alternative for GMM development. Random vibration theory (RVT) predicts the peak response of motion in the time domain based on the FAS and a duration, and thus can be used to relate FAS to PSA. Using RVT to compute the expected peak response in the time domain for given FAS therefore presents a significant advantage that is gaining traction in the GMM field. This article provides recommended RVT procedures relevant to GMM development, which were developed for the Next Generation Attenuation (NGA)-East project. In addition, an orientation-independent FAS metric—called the effective amplitude spectrum (EAS)—is developed for use in conjunction with RVT to preserve the mean power of the corresponding two horizontal components considered in traditional PSA-based modeling (i.e., RotD50). The EAS uses a standardized smoothing approach to provide a practical representation of the FAS for ground-motion modeling, while minimizing the impact on the four RVT properties ( zeroth moment, [Formula: see text]; bandwidth parameter, [Formula: see text]; frequency of zero crossings, [Formula: see text]; and frequency of extrema, [Formula: see text]). Although the recommendations were originally developed for NGA-East, they and the methodology they are based on can be adapted to become portable to other GMM and engineering problems requiring the computation of PSA from FAS.