Preparation of NaBiO3 and the electrochemical characteristic of manganese dioxide doped with NaBiO3

NaBiO₃ crystal of high purity has been synthesized through chemical oxidization. The morphology and thermal stability of NaBiO₃ were examined with scanning electron microscope (SEM) and thermogravimetry-differential scanning calorimetry (TG-DSC). The electrochemical properties of MnO₂ electrodes with and without doping NaBiO₃ were studied through galvanostatic charge/discharge and cyclic voltammetry. The results indicate that the MnO₂ electrode doped with NaBiO₃ possesses remarkably higher discharge voltage and capacity and better reversibility than the pure MnO₂ electrode and Bi₂O₃ doping MnO₂ electrode.     

改进YOLOV3算法的视频目标检测

由于监控中的行人检测存在背景复杂,目标尺度和姿态多样性及人与周围物体互相遮挡的问题,造成YOLOV3对部分目标检测不准确,会产生误检、漏检或重复检测的情况。因此,在YOLOV3的网络基础上,利用残差结构思想,将浅层特征和深层特征进行上采样连接融合得到104×104尺度检测层,并将K-means算法聚类得到的边界框尺寸应用到各尺度网络层,增加网络对多尺度、多姿态目标的敏感度,提高检测效果。同时,利用预测框对周围其他目标的斥力损失更新YOLOV3损失函数,使预测框向正确的目标靠近,远离错误的目标,降低模型的误检率,以改善目标间互相遮挡而影响的检测效果。实验结果证明,在MOT16数据集上,相比YOLOV3算法,提出的网络模型具有更好的检测效果,证明了方法的有效性。     

A computational model of topological and geometric recovery for visual curve completion

Computational Visual Media - Visual curve completion is a fundamental problem in understanding the principles of the human visual system. This problem is usually divided into two problems: a...     

Ultraprecise 3D Printed Graphene Aerogel Microlattices on Tape for Micro Sensors and E-Skin

3D printed graphene aerogels hold promise for flexible sensing fields due to their flexibility, low density, conductivity, and piezo-resistivity. However, low printing accuracy/fidelity and stochastic porous networks have hindered both sensing performance and device miniaturization. Here, printable graphene oxide (GO) inks are formulated through modulating oxygen functional groups, which allows printing of self-standing 3D graphene oxide aerogel microlattice (GOAL) with an ultra-high printing resolution of 70 µm. The reduced GOAL (RGOAL) is then stuck onto the adhesive tape as a facile and large-scale strategy to adapt their functionalities into target applications. Benefiting from the printing resolution of 70 µm, RGOAL tape shows better performance and data readability when used as micro sensors and robot e-skin. By adjusting the molecular structure of GO, the research realizes regulation of rheological properties of GO hydrogel and the 3D printing of lightweight and ultra-precision RGOAL, improves the sensing accuracy of graphene aerogel electronic devices and realizes the device miniaturization, expanding the application of graphene aerogel devices to a broader field such as micro robots, which is beyond the reach of previous reports.     

Regulating the Water Molecular in the Solvation Structure for Stable Zinc Metal Batteries

Rechargeable aqueous zinc batteries are promising energy storage devices because of their low cost, high safety, and high energy density. However, their performance is plagued by the unsatisfied cyclability due to the dendrite growth and hydrogen evolution reaction (HER) at the Zn anode. Herein, it is demonstrated that the concentrated hybrid aqueous/non-aqueous ZnCl₂ electrolytes constitute a peculiar chemical environment for not only the Zn-ions but also water molecules. The high concentration of chloride ions substitutes the H₂O molecular in the solvation structure of Zn²⁺, while the acetonitrile further interacts with H₂O to decrease its activity. The hybrid electrolytes both inhibit the dendrite formation and HER, enabling an ultrahigh average Coulombic efficiency of 99.9% in the Zn||Cu half-cell and a highly reversible Zn plating/stripping with a low overpotential of 21 mV. Using this hybrid electrolyte, the Zn||polytriphenylamine (PTPAn) full cell deliveres a high discharge capacity of 110 mAh g⁻¹, a high power density of 9200 W kg⁻¹ at 100 °C and maintains 85% of the capacity for over 6000 cycles at 10 °C. This study provides a deep understanding between the solvation structure and columbic efficiency of Zn anode, thus inspiring the development for stable Zn batteries.     

局域能源市场多产消者P2P交易框架设计

随着局域能源市场中分布式能源的大量接入和电力系统运行方式的逐渐转变,具有电能生产/消费行为的产消者得到了广泛关注与研究。在此背景下,为提高小规模能源在局域能源市场中的就地消纳能力,设计了一种面向局域能源市场中多产消者的点对点(P2P)日前市场交易框架。在局域能源市场建立了P2P市场交易平台,并对产消者的基本特征进行了归纳分析,提出了典型的产消者组合类型;在产消者层面,采用供给函数均衡模型对产消者竞价行为进行建模,确立了含多产消者的P2P交易平台参与局域能源市场交易;采用连续双边拍卖机制对产消者之间进行P2P匹配,并确定交易过程中的拍卖价格和交易价格。最后,通过算例验证了所提P2P交易框架的有效性。     

基于LM法的换流站关键设备宽频建模方法研究

直流输电系统换流站存在多种产生或传输谐波的电气设备,建立适用于仿真研究的换流站宽频模型十分重要。传统建模方法较为复杂,且不适用于低阶等效模型。因此,基于元件物理模型和阻抗特性,利用列文伯格-马夸尔特算法求解宽频模型参数、拟合阻抗特性曲线,并简化了宽频模型。然后据此建立了晶闸管、平波电抗器和换流变压器等换流站关键设备的宽频模型,验证了该方法的正确性。最后,将元件宽频模型等效电路应用于仿真模型,仿真对比表明宽频模型相比于理想模型,5次谐波幅值减小约基波的1.7%~1.8%,并且简化宽频模型可以加快仿真速度。     

Power system transient stability assessment based on the multiple paralleled convolutional neural network and gated recurrent unit

In order to accurately evaluate power system stability in a timely manner after faults, and further improve the feature extraction ability of the model, this paper presents an improved transient stability assessment (TSA) method of CNN?+?GRU. This comprises a convolutional neural network (CNN) and gated recurrent unit (GRU). CNN has the feature extraction capability for a micro short-term time sequence, while GRU can extract characteristics contained in a macro long-term time sequence. The two are integrated to comprehensively extract the high-order features that are contained in a transient process. To overcome the difficulty of sample misclassification, a multiple parallel (MP) CNN?+?GRU, with multiple CNN?+?GRU connected in parallel, is created. Additionally, an improved focal loss (FL) function which can implement self-adaptive adjustment according to the neural network training is introduced to guide model training. Finally, the proposed methods are verified on the IEEE 39 and 145-bus systems. The simulation results indicate that the proposed methods have better TSA performance than other existing methods.     

Decomposed-distance weighted optimal transport for unsupervised domain adaptation

Applied Intelligence - Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a label-rich source domain to an unlabeled target domain with a different but related distribution....