Stretchable and High-Performance Supercapacitors with Crumpled Graphene Papers

Fabrication of unconventional energy storage devices with high stretchability and performance is challenging, but critical to practical operations of fully power-independent stretchable electronics. While supercapacitors represent a promising candidate for unconventional energy-storage devices, existing stretchable supercapacitors are limited by their low stretchability, complicated fabrication process, and high cost. Here, we report a simple and low-cost method to fabricate extremely stretchable and high-performance electrodes for supercapacitors based on new crumpled-graphene papers. Electrolyte-mediated-graphene paper bonded on a compliant substrate can be crumpled into self-organized patterns by harnessing mechanical instabilities in the graphene paper. As the substrate is stretched, the crumpled patterns unfold, maintaining high reliability of the graphene paper under multiple cycles of large deformation. Supercapacitor electrodes based on the crumpled graphene papers exhibit a unique combination of high stretchability (e.g., linear strain ~300%, areal strain ~800%), high electrochemical performance (e.g., specific capacitance ~196 F g[superscript −1]), and high reliability (e.g., over 1000 stretch/relax cycles). An all-solid-state supercapacitor capable of large deformation is further fabricated to demonstrate practical applications of the crumpled-graphene-paper electrodes. Our method and design open a wide range of opportunities for manufacturing future energy-storage devices with desired deformability together with high performance.United States. Office of Naval Research (N00014-14-1-0619)National Science Foundation (U.S.) (CMMI-1253495)National Science Foundation (U.S.) (DMR-1121107)National Science Foundation (U.S.) (EECS-1344745

Orbital Variations and Impacts on Observations from SNPP, NOAA 18-20, and AQUA Sun-Synchronous Satellites

The AQUA, SNPP, and NOAA 18-20 PM sun-synchronous satellites were designed with similar local time, local solarzenith angles, and overlapping temporal coverage. Although the satellites are expected to have fixed local equator-crossing time, during the satellite lifetime, the equator-crossing times of these satellites drift. For NOAA 18-19, the driftin equator-crossing time is significant (few hours) and no correction has been done over the lifetime. For SNPP andAQUA, correction in the orbital inclination angle was periodically performed to maintain the equator-crossing timearound the designed value. The impact of systematic drift of the local observation time during the satellite life cycle canbe significant and should be accounted for when using multi-year time series of satellite products in long-termenvironmental studies. In this paper, the equator-crossing time drift of AQUA, SNPP, and NOAA 18-20, the correctionof SNPP and AQUA equator-crossing time via orbital inclination angle change, and the consequent local solar zenithangle variation are evaluated. The impact of such drift on low-latitude mean brightness temperature trend derived fromthe similar ~11 m thermal emissive channel of AQUA MODIS CH31, SNPP Visible Infrared Imaging RadiometerSuite (VIIRS) CH15 and NOAA 18-19 HIRS CH08 are analyzed. The drift in the mean brightness temperature measuredby these sensors is combined as a function of local time and analyzed using diurnal cycle analysis. The mean brightnesstemperature drift for SNPP VIIRS is reconciled within the context of much larger temperature drift of NOAA 18-19

Finite Element Model Fractional Steps Updating Strategy for Spatial Lattice Structures Based on Generalized Regression Neural Network

In order to get a more accurate finite element model of a spatial lattice structure with bolt-ball joints for health monitoring, a method of modifying the bolt-ball joint stiffness coefficient was proposed. Firstly, the beam element with adjustable stiffness was used in the joint zone in this paper to reveal the semirigid characteristic of the joint. Secondly, the value of stiffness reduction factor (ar) was limited in the range of [0.2,0.8] and the reference value (ar0) of it was suggested to be 0.5 based on referenced literatures. Finally, the finite element model fractional steps updating strategy based on neural network technique was applied and the limited measuring point information was used to form the network input parameter. A single-layer latticed cylindrical shell model with 157 joints and 414 tubes was used in a shaking TABLE test. Based on the measured modal data, the presented method was verified. The results show that this model updating technique can reflect the true dynamic characters of the shell structure better. Moreover, the neural network can be simplified considerably by using this algorithm. The method can be used for model updating of a latticed shell with bolt-ball joints and has great value in engineering practice

3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers

Multi-camera 3D object detection, a critical component for vision-only driving systems, has achieved impressive progress. Notably, transformer-based methods with 2D features augmented by 3D positional encodings (PE) have enjoyed great success. However, the mechanism and options of 3D PE have not been thoroughly explored. In this paper, we first explore, analyze and compare various 3D positional encodings. In particular, we devise 3D point PE and show its superior performance since more precise positioning may lead to superior 3D detection. In practice, we utilize monocular depth estimation to obtain the 3D point positions for multi-camera 3D object detection. The PE with estimated 3D point locations can bring significant improvements compared to the commonly used camera-ray PE. Among DETR-based strategies, our method achieves state-of-the-art 45.6 mAP and 55.1 NDS on the competitive nuScenes valuation set. It's the first time that the performance gap between the vision-only (DETR-based) and LiDAR-based methods is reduced within 5\% mAP and 6\% NDS.Comment: 10 pages, 7 figure

Dual-Context Aggregation for Universal Image Matting

Natural image matting aims to estimate the alpha matte of the foreground from a given image. Various approaches have been explored to address this problem, such as interactive matting methods that use guidance such as click or trimap, and automatic matting methods tailored to specific objects. However, existing matting methods are designed for specific objects or guidance, neglecting the common requirement of aggregating global and local contexts in image matting. As a result, these methods often encounter challenges in accurately identifying the foreground and generating precise boundaries, which limits their effectiveness in unforeseen scenarios. In this paper, we propose a simple and universal matting framework, named Dual-Context Aggregation Matting (DCAM), which enables robust image matting with arbitrary guidance or without guidance. Specifically, DCAM first adopts a semantic backbone network to extract low-level features and context features from the input image and guidance. Then, we introduce a dual-context aggregation network that incorporates global object aggregators and local appearance aggregators to iteratively refine the extracted context features. By performing both global contour segmentation and local boundary refinement, DCAM exhibits robustness to diverse types of guidance and objects. Finally, we adopt a matting decoder network to fuse the low-level features and the refined context features for alpha matte estimation. Experimental results on five matting datasets demonstrate that the proposed DCAM outperforms state-of-the-art matting methods in both automatic matting and interactive matting tasks, which highlights the strong universality and high performance of DCAM. The source code is available at \url{https://github.com/Windaway/DCAM}