Improved Algorithm for Solving Inverse Kinematics of Biped Robots

Mobile Networks and Applications - Inverse kinematics is an important basic theory in walking control of biped robot. This study focuses on the parameter setting using the improved algorithm in...     

Surface Reconstruction Engineering with Synergistic Effect of Mixed-Salt Passivation Treatment toward Efficient and Stable Perovskite Solar Cells

Surface passivation treatment is a widely used strategy to resolve trap-mediated nonradiative recombination toward high-efficiency metal-halide perovskite photovoltaics. However, a lack of passivation with mixture treatment has been investigated, as well as an in-depth understanding of its passivation mechanism. Here, a systematic study on a mixed-salt passivation strategy of formamidinium bromide (FABr) coupled with different F-substituted alkyl lengths of ammonium iodide is demonstrated. It is obtained better device performance with decreasing chain length of the F-substituted alkyl ammonium iodide in the presence of FABr. Moreover, they unraveled a synergistic passivation mechanism of the mixed-salt treatment through surface reconstruction engineering, where FABr dominates the reformation of the perovskite surface via reacting with the excess PbI₂. Meanwhile, ammonium iodide passivates the perovskite grain boundaries both on the surface and top perovskite bulk through penetration. This synergistic passivation engineer results in a high-quality perovskite surface with fewer defects and suppressed ion migration, leading to a champion efficiency of 23.5% with mixed-salt treatment. In addition, the introduction of the moisture resisted F-substituted groups presents a more hydrophobic perovskite surface, thus enabling the decorated devices with excellent long-term stability under a high humid atmosphere as well as operational conditions.     

P-doped CoSe2 nanoparticles embedded in 3D honeycomb-like carbon network for long cycle-life Na-ion batteries

We report for the first time a Na-ion battery anode material composed of P-doped CoSe2 nanoparticles(P-CoSe2)with the size of 5-20 nm that are uniformly embed in a 3D porous honeycomb-like carbon network.High rate capability and cycling stability are achieved simultaneously.The honeycomb-like carbon network is rationally designed to support high electrical conductivity,rapid Na-ion diffusion as well as the accommodation of the volume expansion from the active P-CoSe2 nanoparticles.In particular,heteroatom P-doping within CoSe2 introduces stronger P-Co bonds and additional P-Se bonds that signif-icantly improve the structure stability of P-CoSe2 for highly stable sodiation/desodiation over long-term cycling.P-doping also improves the electrical conductivity of the CoSe2 nanoparticles,leading to highly elevated electrochemical kinetics to deliver high specific capacities at high current densities.Benefiting from the unique nanostructure and atomic-level P-doping,the P-CoSe2(2∶1)/C anode delivers an excel-lent cycle stability with a specific capacity of 206.9 mA h g-1 achieved at 2000 mA g-1 after 1000 cycles.In addition,this material can be synthesized using a facile pyrolysis and selenization/phosphorization approach.This study provides new opportunities of heteroatom doping as an effective method to improve the cycling stability of Na-ion anode materials.     

基于热电联产的汽轮机组油膜振荡故障诊断系统设计

常规振荡故障诊断系统,采集振荡信号噪声较大,导致诊断的故障类别信任度较低。针对这一问题,提出基于热电联产的汽轮机组油膜振荡故障诊断系统。耦合热电联产生产方式,循环汽轮机组多余排汽,通过排汽热量增加系统发电量完成硬件设计;采用小波包降噪算法,消除采集振荡信号噪声,分解信号各个时刻和频带,排序频带内时域子信号能量值,构成故障信号特征向量,输入神经网络,训练后输出振荡故障诊断模式完成软件设计。采集满负荷、最小负荷、噪声条件下的油膜振荡信号,设置对比实验,结果表明,所设计系统提高了故障诊断信任度,故障诊断结果更为准确可靠。     

Optimization of post-classification processing of high-resolution satellite image: A case study

The application of remote sensing monitoring techniques plays a crucial role in evaluating and governing the vast amount of ecological construction projects in China. However, extracting information of ecological engineering target through high-resolution satellite image is arduous due to the unique topography and complicated spatial pattern on the Loess Plateau of China. As a result, enhancing classification accuracy is a huge challenge to high-resolution image processing techniques. Image processing techniques have a definitive effect on image properties and the selection of different parameters may change the final classification accuracy during post-classification processing. The common method of eliminating noise and smoothing image is majority filtering. However, the filter function may modify the original classified image and the final accuracy. The aim of this study is to develop an efficient and accurate post-processing technique for acquiring information of soil and water conservation engineering, on the Loess Plateau of China, using SPOT image with 2.5 m resolution. We argue that it is vital to optimize satellite image filtering parameters for special areas and purposes, which focus on monitoring ecological construction projects. We want to know how image filtering influences final classified results and which filtering kernel is optimum. The study design used a series of window sizes to filter the original classified image, and then assess the accuracy of each output map and image quality. We measured the relationship between filtering window size and classification accuracy, and optimized the post-processing techniques of SPOT5 satellite images. We conclude that (1) smoothing with the majority filter is sensitive to the information accuracy of soil and water conservation engineering, and (2) for SPOT5 2.5 m image, the 5×5 pixel majority filter is most suitable kernel for extracting information of ecological construction sites in the Loess Plateau of China.     

大同矿区忻州窑矿(井工矿)土地利用分类最佳波段组合研究

选择具有历史悠久的井工矿大同矿区忻州窑矿为研究区域,根据矿区的开采状况,基于多光谱Landsat系列遥感数据,选择2004和2017年2期的遥感影像,采用OIF指数法确定遥感数据的最佳波段组合.结果表明:2004年波段7-3-1是最佳波段组合,且监督分类精度较高,2017年波段7-2-3是最佳波段组合,且精度最高.     

The Effects of Random Porosities in Resonant Frequencies of Graphene Based on the Monte Carlo Stochastic Finite Element Model

With the distinguished properties in electronics, thermal conductivity, optical transparence and mechanics, graphene has a powerful potential in nanosensors, nano-resonators, supercapacitors, batteries, etc. The resonant frequency of graphene is an important factor in its application and working environment. However, the random dispersed porosities in graphene evidently change the lattice structure and destroy the integrity and geometrical periodicity. This paper focuses on the effects of random porosities in resonant frequencies of graphene. Monte Carlo simulation is applied to propagate the porosities in the finite element model of pristine graphene. The statistical results and probability density distribution of porous graphene with atomic vacancy defects are computed based on the Monte Carlo finite element model. The results of porous graphene with atomic vacancy defects are compared and discussed with the results of graphene with bond vacancy defects. The enhancement effects of atomic vacancy defects are confirmed in porous graphene. The influences of atomic vacancy defects on displacement and rotation vector sums of porous graphene are more concentrated in local places.     

NPHMC: An R-package for estimating sample size of proportional hazards mixture cure model

Due to advances in medical research, more and more diseases can be cured nowadays, which largely increases the need for an easy-to-use software in calculating sample size of clinical trials with cure fractions. Current available sample size software, such as PROC POWER in SAS, Survival Analysis module in PASS, powerSurvEpi package in R are all based on the standard proportional hazards (PH) model which is not appropriate to design a clinical trial with cure fractions. Instead of the standard PH model, the PH mixture cure model is an important tool in handling the survival data with possible cure fractions. However, there are no tools available that can help design a trial with cure fractions. Therefore, we develop an R package NPHMC to determine the sample size needed for such study design.     

NHD脱碳工段技改总结

合成氨装置脱碳工段采用NHD脱碳工艺,由于脱碳后的脱碳气中CO2含量偏高,造成H 2损耗较大,为此采用ZUPAC系列双向金属折峰式波纹填料和新型槽盘式液体分布器对脱碳塔进行改造。改造后,脱碳气中CO2体积分数从2.0%降至1.6%,年可增加经济效益1000万元左右。     

基于改进粒子群和模糊神经网络的雾霾分类研究

针对粒子群算法对初始种群敏感和易陷入局部最优解等问题,提出了佳点集理论结合多种群多策略协同进化算法改进的粒子群算法(IMPMSPSO).首先采用佳点集理论生成佳点作为初始种群,使种群分布更均匀而在一定程度上减弱其对位置的敏感性;然后利用协同进化算法,先将种群随机分成若干子种群,各子种群随机选择一种改进的进化策略并行计算,并进行最优位置的共享.经过测试,IMPMSPSO在计算精度和收敛速度上均优于其他算法.最后利用IMPMSPSO优化模糊神经网络初始权值和阈值构造分类预测模型,对雾霾污染等级进行分类预测.结果表明,与其他分类模型相比,该模型在各等级上的准确率均有提高.