Generating provably stable walking gaits that yield natural locomotion when executed on robotic-assistive devices is a challenging task that often requires hand-tuning by domain experts. This paper presents an alternative methodology, where we propos
e the addition of musculoskeletal models directly into the gait generation process to intuitively shape the resulting behavior. In particular, we construct a multi-domain hybrid system model that combines the system dynamics with muscle models to represent natural multicontact walking. Stable walking gaits can then be formally generated for this model via the hybrid zero dynamics method. We experimentally apply our framework towards achieving multicontact locomotion on a dual-actuated transfemoral prosthesis, AMPRO3. The results demonstrate that enforcing feasible muscle dynamics produces gaits that yield natural locomotion (as analyzed via electromyography), without the need for extensive manual tuning. Moreover, these gaits yield similar behavior to expert-tuned gaits. We conclude that the novel approach of combining robotic walking methods (specifically HZD) with muscle models successfully generates anthropomorphic robotic-assisted locomotion.
Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a users utility landscape. Learning these landscapes is challenging, as walking trajectories are def
ined by numerous gait parameters, data collection from human trials is expensive, and user safety and comfort must be ensured. This work proposes the Region of Interest Active Learning (ROIAL) framework, which actively learns each users underlying utility function over a region of interest that ensures safety and comfort. ROIAL learns from ordinal and preference feedback, which are more reliable feedback mechanisms than absolute numerical scores. The algorithms performance is evaluated both in simulation and experimentally for three non-disabled subjects walking inside of a lower-body exoskeleton. ROIAL learns Bayesian posteriors that predict each exoskeleton users utility landscape across four exoskeleton gait parameters. The algorithm discovers both commonalities and discrepancies across users gait preferences and identifies the gait parameters that most influenced user feedback. These results demonstrate the feasibility of recovering gait utility landscapes from limited human trials.
Despite the recognition of two-dimensional (2D) systems as emerging and scalable host materials of single photon emitters or spin qubits, uncontrolled and undetermined chemical nature of these quantum defects has been a roadblock to further developme
nt. Leveraging the design of extrinsic defects can circumvent these persistent issues and provide an ultimate solution. Here we established a complete theoretical framework to accurately and systematically design quantum defects in wide-bandgap 2D systems. With this approach, essential static and dynamical properties are equally considered for spin qubit discovery. In particular, many-body interactions such as defect-exciton couplings are vital for describing excited state properties of defects in ultrathin 2D systems. Meanwhile, nonradiative processes such as phonon-assisted decay and intersystem crossing rates require careful evaluation, which compete together with radiative processes. From a thorough screening of defects based on first-principles calculations, we identify promising single photon emitters such as SiVV and spin qubits such as TiVV and MoVV in hexagonal boron nitride. This work provided a complete first-principles theoretical framework for defect design in 2D materials.
The quality control of fetal sonographic (FS) images is essential for the correct biometric measurements and fetal anomaly diagnosis. However, quality control requires professional sonographers to perform and is often labor-intensive. To solve this p
roblem, we propose an automatic image quality assessment scheme based on multi-task learning to assist in FS image quality control. An essential criterion for FS image quality control is that all the essential anatomical structures in the section should appear full and remarkable with a clear boundary. Therefore, our scheme aims to identify those essential anatomical structures to judge whether an FS image is the standard image, which is achieved by three convolutional neural networks. The Feature Extraction Network aims to extract deep level features of FS images. Based on the extracted features, the Class Prediction Network determines whether the structure meets the standard and Region Proposal Network identifies its position. The scheme has been applied to three types of fetal sections, which are the head, abdominal, and heart. The experimental results show that our method can make a quality assessment of an FS image within less a second. Also, our method achieves competitive performance in both the detection and classification compared with state-of-the-art methods.
Over 54 years of hourly mean value of solar wind velocity from 27 Nov. 1963 to 31 Dec. 2017 are used to investigate characteristics of the rotation period of solar wind through auto-correlation analysis. Solar wind of high velocity is found to rotate
faster than low-velocity wind, while its rotation rate increases with velocity increasing, but in contrast for solar wind of low velocity, its rotation rate decreases with velocity increasing. Our analysis shows that solar wind of a higher velocity statistically possesses a faster rotation rate for the entire solar wind. The yearly rotation rate of solar wind velocity does not follow the Schwable cycle, but it is significantly negatively correlated to yearly sunspot number when it leads by 3 years. Physical explanations are proposed to these findings.
