4,218 publications from this institution
The kernmantle construction, a kind of braiding structure that is characterized by the kern absorbing most of the stress and the mantle protecting the kern, is widely employed in the field of loading and rescue services, but rarely in flexible electronics. Here, a novel kernmantle electronic braid (E-braid) for high-impact sports monitoring, is proposed. The as-fabricated E-braids not only demonstrate high strength (31 Mpa), customized elasticity, and nice machine washability (>500 washes) but also exhibit excellent electrical stability (>200 000 cycles) during stretching. For demonstration, the E-braids are mounted to different parts of the trampoline for athletes' locomotor behavior monitoring. Furthermore, the E-braids are proved to act as multifarious intelligent sports gear or wearable equipment such as electronic jump rope and respiration monitoring belt. This study expands the kernmantle structure to soft flexible electronics and then accelerates the development of quantitative analysis in modern sports industry and athletes' healthcare.
With the rapid development of computer vision, the application of computer vision to precision farming in animal husbandry is currently a hot research topic. Due to the scale of goose breeding continuing to expand, there are higher requirements for the efficiency of goose farming. To achieve precision animal husbandry and to avoid human influence on breeding, real-time automated monitoring methods have been used in this area. To be specific, on the basis of instance segmentation, the activities of individual geese are accurately detected, counted, and analyzed, which is effective for achieving traceability of the condition of the flock and reducing breeding costs. We trained QueryPNet, an advanced model, which could effectively perform segmentation and extraction of geese flock. Meanwhile, we proposed a novel neck module that improved the feature pyramid structure, making feature fusion more effective for both target detection and instance individual segmentation. At the same time, the number of model parameters was reduced by a rational design. This solution was tested on 639 datasets collected and labeled on specially created free-range goose farms. With the occlusion of vegetation and litters, the accuracies of the target detection and instance segmentation reached 0.963 (mAP@0.5) and 0.963 (mAP@0.5), respectively.
