A smelting-rolling strategy for ZnIn bulk phase alloy anodes.

Reversibility and stability are considered as the key indicators for Zn metal anodes in aqueous Zn-ion batteries, yet they are severely hindered by uncontrolled Zn stripping/plating and side reactions. Herein, we fabricate a bulk phase ZnIn alloy anode containing trace indium by a typical smelting-rolling process. A uniformly dispersed bulk phase of the whole Zn anode is constructed rather than only a protective layer on the surface. The Zn deposition can be regarded as instantaneous nucleation due to the adsorption of the evenly dispersed indium, and formation of the exclusion zone for further nucleation can be prevented at the same time. Owing to the bulk phase structure of ZnIn alloy, the indium not only plays a crucial role in Zn deposition, but also improves the Zn stripping. Consequently, the as-designed ZnIn alloy anode can sustain stable Zn stripping/plating for over 2500 h at 4.4 mA cm-2 with nearly 6 times smaller voltage hysteresis than that of pure Zn. Moreover, it enables a substantially stable ZnIn//NH4V4O10 battery with 96.44% capacity retention after 1000 cycles at 5 A g-1. This method of regulating the Zn nucleation by preparing a Zn-based alloy provides a potential solution to the critical problem of Zn dendrite growth and by-product generation fundamentally.

Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach.

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to "see" which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas.

Temporal-Spatial Surface Seasonal Mass Changes and Vertical Crustal Deformation in South China Block from GPS and GRACE Measurements.

The solid Earth deforms elastically in response to variations of surface atmosphere, hydrology, and ice/glacier mass loads. Continuous geodetic observations by Global Positioning System (CGPS) stations and Gravity Recovery and Climate Experiment (GRACE) record such deformations to estimate seasonal and secular mass changes. In this paper, we present the seasonal variation of the surface mass changes and the crustal vertical deformation in the South China Block (SCB) identified by GPS and GRACE observations with records spanning from 1999 to 2016. We used 33 CGPS stations to construct a time series of coordinate changes, which are decomposed by empirical orthogonal functions (EOFs) in SCB. The average weighted root-mean-square (WRMS) reduction is 38% when we subtract GRACE-modeled vertical displacements from GPS time series. The first common mode shows clear seasonal changes, indicating seasonal surface mass re-distribution in and around the South China Block. The correlation between GRACE and GPS time series is analyzed which provides a reference for further improvement of the seasonal variation of CGPS time series. The results of the GRACE observations inversion are the surface deformations caused by the surface mass change load at a rate of about -0.4 to -0.8 mm/year, which is used to improve the long-term trend of non-tectonic loads of the GPS vertical velocity field to further explain the crustal tectonic movement in the SCB and surroundings.