There is currently a growing demand for flexible strain sensors with high performance and water repellency for various applications such as human motion monitoring, sweat or humidity detection, and certain underwater tests. Among these strain sensors, paper-based ones have attracted increasing attention because they coincide with the future development trend of environment-friendly electronic products. However, paper-based electronics are easy to fail when they encounter water and are thus unable to be applied to humid or underwater circumstances. Herein, based on a strategy of coupling bionics inspired by lotus leaf and scorpion, which exhibit superhydrophobic characteristics and ultrasensitive vibrationsensing capacity, respectively, a paper-based strain sensor with high sensitivity and water repellency is successfully fabricated. As a result, the strain sensor exhibits a gauge factor of 263.34, a high strain resolution (0.098%), a fast response time (78 ms), excellent stability over 12,000 cycles, and a water contact angle of 164°. Owing to the bioinspired structures and function mechanisms, the paper-based strain sensor is suitable to not only serve as regular wearable electronics to monitor human motions in real-time but also to detect subtle underwater vibrations, demonstrating its great potential for numerous applications like wearable electronics, water environmental protection, and underwater robots.

No abstract

Knowledge distillation has been applied to image classification successfully. However, object detection is much more sophisticated and most knowledge distillation methods have failed on it. In this paper, we point out that in object detection, the features of the teacher and student vary greatly in different areas, especially in the foreground and background. If we distill them equally, the uneven differences between feature maps will negatively affect the distillation. Thus, we propose Focal and Global Distillation (FGD). Focal distillation separates the foreground and background, forcing the student to focus on the teacher's critical pixels and channels. Global distillation rebuilds the relation between different pixels and transfers it from teachers to students, compensating for missing global information in focal distillation. As our method only needs to calculate the loss on the feature map, FGD can be applied to various detectors. We experiment on various detectors with different backbones and the results show that the student detector achieves excellent mAP improvement. For example, ResNet-50 based RetinaNet, Faster RCNN, RepPoints and Mask RCNN with our distillation method achieve 40.7%, 42.0%, 42.0% and 42.1% mAP on COCO2017, which are 3.3, 3.6, 3.4 and 2.9 higher than the baseline, respectively. Our codes are available at https://github.com/yzd-v/FGD.

No abstract

The resistance of ordinary potato digging shovels can increase dramatically when used in a clay soil because of the adhesion between the soil and shovel. In this paper, a new type of bionic potato digging shovel was designed to decrease adhesion. The bionic structural elements, i.e., scalelike units (S-U) were applied to the potato digging shovel with inspiration from pangolin scales. The discrete element method (DEM) considered cohesion was used to simulate the drag reduction performance in clayey soil conditions. An ordinary plane shovel (O-P-S) was used for comparison. Three indicators (total force, draft force and compressive force) were used to characterize the drag reduction performance. The effect of the design variables of the bionic structures (length [l] and height [h]) and the transversal and longitudinal arrangement spacing (S1 and S2) of the structures on the drag reduction performance were analyzed. The results showed that the drag reduction performance of the bionic shovels with suitable parameters was better than that of the O-P-S. The best bionic sample labeled as a bionic prototype had a 22.26% drag reduction rate during the soil bin test and a 14.19% drag reduction rate during the field test compared to the O-P-S.