Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Soft Ultrathin Electronics Innervated Adaptive Fully Soft Robots.

Abstract: Soft robots outperform the conventional hard robots on significantly enhanced safety, adaptability, and complex motions. The development of fully soft robots, especially fully from smart soft materials to mimic soft animals, is still nascent. In addition, to date, existing soft robots cannot adapt themselves to the surrounding environment, i.e., sensing and adaptive motion or response, like animals. Here, compliant ultrathin sensing and actuating electronics innervated fully soft robots that can sense the environment and perform soft bodied crawling adaptively, mimicking an inchworm, are reported. The soft robots are constructed with actuators of open-mesh shaped ultrathin deformable heaters, sensors of single-crystal Si optoelectronic photodetectors, and thermally responsive artificial muscle of carbon-black-doped liquid-crystal elastomer (LCE-CB) nanocomposite. The results demonstrate that adaptive crawling locomotion can be realized through the conjugation of sensing and actuation, where the sensors sense the environment and actuators respond correspondingly to control the locomotion autonomously through regulating the deformation of LCE-CB bimorphs and the locomotion of the robots. The strategy of innervating soft sensing and actuating electronics with artificial muscles paves the way for the development of smart autonomous soft robots.

The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms

Abstract: Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle taxis to passengers in need. To precisely balance the supply and the demand of taxis, online taxicab platforms need to predict the Unit Original Taxi Demand (UOTD), which refers to the number of taxi-calling requirements submitted per unit time (e.g., every hour) and per unit region (e.g., each POI). Predicting UOTD is non-trivial for large-scale industrial online taxicab platforms because both accuracy and flexibility are essential. Complex non-linear models such as GBRT and deep learning are generally accurate, yet require labor-intensive model redesign after scenario changes (e.g., extra constraints due to new regulations). To accurately predict UOTD while remaining flexible to scenario changes, we propose LinUOTD, a unified linear regression model with more than 200 million dimensions of features. The simple model structure eliminates the need of repeated model redesign, while the high-dimensional features contribute to accurate UOTD prediction. We further design a series of optimization techniques for efficient model training and updating. Evaluations on two large-scale datasets from an industrial online taxicab platform verify that LinUOTD outperforms popular non-linear models in accuracy. We envision our experiences to adopt simple linear models with high-dimensional features in UOTD prediction as a pilot study and can shed insights upon other industrial large-scale spatio-temporal prediction problems.

Superhydrophobic gecko feet with high adhesive forces towards water and their bio-inspired materials

Abstract: Functional integration is an inherent characteristic for multiscale structures of biological materials. In this contribution, we first investigate the liquid–solid adhesive forces between water droplets and superhydrophobic gecko feet using a high-sensitivity micro-electromechanical balance system. It was found, in addition to the well-known solid–solid adhesion, the gecko foot, with a multiscale structure, possesses both superhydrophobic functionality and a high adhesive force towards water. The origin of the high adhesive forces of gecko feet to water could be attributed to the high density nanopillars that contact the water. Inspired by this, polyimide films with gecko-like multiscale structures were constructed by using anodic aluminum oxide templates, exhibiting superhydrophobicity and a strong adhesive force towards water. The static water contact angle is larger than 150° and the adhesive force to water is about 66 μN. The resultant gecko-inspired polyimide film can be used as a “mechanical hand” to snatch micro-liter liquids. We expect this work will provide the inspiration to reveal the mechanism of the high-adhesive superhydrophobic of geckos and extend the practical applications of polyimide materials.

Driver drowsiness detection with eyelid related parameters by Support Vector Machine

Abstract: Various investigations show that drivers' drowsiness is one of the main causes of traffic accidents. Thus, countermeasure device is currently required in many fields for sleepiness related accident prevention. This paper intends to perform the drowsiness prediction by employing Support Vector Machine (SVM) with eyelid related parameters extracted from EOG data collected in a driving simulator provided by EU Project SENSATION. The dataset is firstly divided into three incremental drowsiness levels, and then a paired t-test is done to identify how the parameters are associated with drivers' sleepy condition. With all the features, a SVM drowsiness detection model is constructed. The validation results show that the drowsiness detection accuracy is quite high especially when the subjects are very sleepy.

Nanocasting SiO 2 into metal–organic frameworks imparts dual protection to high-loading Fe single-atom electrocatalysts

Abstract: Single-atom catalysts (SACs) have sparked broad interest recently while the low metal loading poses a big challenge for further applications. Herein, a dual protection strategy has been developed to give high-content SACs by nanocasting SiO2 into porphyrinic metal–organic frameworks (MOFs). The pyrolysis of SiO2@MOF composite affords single-atom Fe implanted N-doped porous carbon (FeSA–N–C) with high Fe loading (3.46 wt%). The spatial isolation of Fe atoms centered in porphyrin linkers of MOF sets the first protective barrier to inhibit the Fe agglomeration during pyrolysis. The SiO2 in MOF provides additional protection by creating thermally stable FeN4/SiO2 interfaces. Thanks to the high-density FeSA sites, FeSA–N–C demonstrates excellent oxygen reduction performance in both alkaline and acidic medias. Meanwhile, FeSA–N–C also exhibits encouraging performance in proton exchange membrane fuel cell, demonstrating great potential for practical application. More far-reaching, this work grants a general synthetic methodology toward high-content SACs (such as FeSA, CoSA, NiSA). Single-atom catalysts (SACs) with high metal loading are highly desired to improve catalytic performance. Here, the authors report a dual protection strategy by nanocasting SiO2 into metal–organic frameworks to prepare high-loading SACs with excellent catalytic performance toward oxygen reduction.