Binder‐Free and Carbon‐Free 3D Porous Air Electrode for Li‐O2 Batteries with High Efficiency,High Capacity,and Long Life

Pt‐Gd alloy polycrystalline thin film is deposited on 3D nickel foam by pulsed laser deposition method serving as a whole binder/carbon‐free air electrode, showing great catalytic activity enhancement as an efficient bifunctional catalyst for the oxygen reduction and evolution reactions in lithium oxygen batteries. The porous structure can facilitate rapid O₂ and electrolyte diffusion, as well as forming a continuous conductive network throughout the whole energy conversion process. It shows a favorable cycle performance in the full discharge/charge model, owing to the high catalytic activity of the Pt‐Gd alloy composite and 3D porous nickel foam structure. Specially, excellent cycling performance under capacity limited mode is also demonstrated, in which the terminal discharge voltage is higher than 2.5 V and the terminal charge voltage is lower than 3.7 V after 100 cycles at a current density of 0.1 mA cm^?2. Therefore, this electrocatalyst is a promising bifunctional electrocatalyst for lithium oxygen batteries and this depositing high‐efficient electrocatalyst on porous substrate with polycrystalline thin film by pulsed laser deposition is also a promising technique in the future lithium oxygen batteries research.     

智能交流接触器温度补偿控制策略研究

环境温度及接触器频繁工作会导致线圈温度上升,对接触器吸合产生影响,针对此问题,基于带反馈控制的PWM(脉宽调整)型智能交流接触器系统结构、工作原理及其动态特性,提出一种基于智能交流接触器的温度补偿控制策略。智能控制系统采集实时的输入电压和线圈温度值作为反馈输出控制量,通过特性分析与实验建立动态控制表,根据电压与温度值查表计算输出最佳占空比及其强激磁时间的PWM波,控制接触器合闸。研究结果表明,通过此控制策略能使接触器在各工作温度及阈值电压范围内实现最优合闸,并保证在较高温度下可靠合闸,提高其工作性能、使用寿命和环境适用范围。     

基于GSM的双向汽车防盗系统的研究与设计

提出了一种基于GSM通信模块和无线射频模块双向通信的汽车防盗系统设计方案。该设计以GSM网络为通信媒介,将车载主控制器检测到的各种汽车状态传送至预先设定的车主手机中,实现了车主对汽车的跟踪及监控,可有效防止汽车盗窃事件的发生。     

Reliable stabilization of stochastic time-delay systems with nonlinear disturbances

This paper is concerned with the stabilization problem for a class of continuous stochastic time-delay systems with nonlinear disturbances, parameter uncertainties and possible actuator failures. Both the stability analysis and synthesis problems are considered. The purpose of the stability analysis problem is to derive easy-to-test conditions for the uncertain nonlinear time-delay systems to be stochastically, exponentially stable. The synthesis problem, on the other hand, aims to design state feedback controllers such that the closed-loop system is exponentially stable in the mean square for all admissible uncertainties, nonlinearities, time-delays and possible actuator failures. It is shown that the addressed problem can be solved in terms of the positive definite solutions to certain algebraic matrix inequalities. Numerical examples are provided to demonstrate the effectiveness of the proposed design method.     

High Order Well-Balanced WENO Scheme for the Gas Dynamics Equations Under Gravitational Fields

The gas dynamics equations, coupled with a static gravitational field, admit the hydrostatic balance where the flux produced by the pressure is exactly canceled by the gravitational source term. Many astrophysical problems involve the hydrodynamical evolution in a gravitational field, therefore it is essential to correctly capture the effect of gravitational force in the simulations. Improper treatment of the gravitational force can lead to a solution which either oscillates around the equilibrium, or deviates from the equilibrium after a long time run. In this paper we design high order well-balanced finite difference WENO schemes to this system, which can preserve the hydrostatic balance state exactly and at the same time can maintain genuine high order accuracy. Numerical tests are performed to verify high order accuracy, well-balanced property, and good resolution for smooth and discontinuous solutions. The main purpose of the well-balanced schemes designed in this paper is to well resolve small perturbations of the hydrostatic balance state on coarse meshes. The more difficult issue of convergence towards such hydrostatic balance state from an arbitrary initial condition is not addressed in this paper.     

An energy-efficient clustered distributed coding for large-scale wireless sensor networks

A wireless sensor network (WSN) usually consists of a large number of battery-powered low-cost sensors with limited data collection and processing capacity. In order to prolong the lifetime of the WSN with a target error performance, a novel clustered distributed coding framework, referred to as distributed multiple-sensor cooperative turbo coding (DMSCTC), is developed for a large-scale WSN with sensor grouped in cooperative cluster. In the proposed DMSCTC scheme, a simple forward error correction is employed at each sensor and a simple multi-sensor joint coding is adopted at the cluster head, while complicated joint iterative decoding is implemented only at the data collector. The proposed DMSCTC scheme achieves extra distributed coding gain and cooperative spatial diversity without introducing extra complexity burden on the sensors by transferring the complicated joint decoding process to the data collector. With the proposed scheme, the WSN can achieve the target error performance with less power consumption, thus prolonging its lifetime. The error performance and energy efficiency of the proposed DMSCTC scheme are analyzed, and followed by Monte Carlo simulations. Both analytical and simulation results show that the DMSCTC can substantially improve the energy efficiency of the clustered WSN.     

Three-dimensional human shape inference from silhouettes: reconstruction and validation

Silhouettes are robust image features that provide considerable evidence about the three-dimensional (3D) shape of a human body. The information they provide is, however, incomplete and prior knowledge has to be integrated to reconstruction algorithms in order to obtain realistic body models. This paper presents a method that integrates both geometric and statistical priors to reconstruct the shape of a subject assuming a standardized posture from a frontal and a lateral silhouette. The method is comprised of three successive steps. First, a non-linear function that connects the silhouette appearances and the body shapes is learnt and used to create a first approximation. Then, the body shape is deformed globally along the principal directions of the population (obtained by performing principal component analysis over 359 subjects) to follow the contours of the silhouettes. Finally, the body shape is deformed locally to ensure it fits the input silhouettes as well as possible. Experimental results showed a mean absolute 3D error of 8 mm with ideal silhouettes extraction. Furthermore, experiments on body measurements (circumferences or distances between two points on the body) resulted in a mean error of 11 mm.     

Modeling and advanced control method of PVC polymerization process

Based on data driven modeling theory, PVC polymerization process modeling and intelligent optimization control algorithm is studied. Firstly, a multi-T–S fuzzy neural networks soft-sensing model combining the principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm is proposed to predict the convention rate and velocity of Vinyle Chloride Monomer (VCM). The proposed hybrid learning algorithm utilizing the harmony search (HS) and least square method is used to adjust the model premise parameters and consequent parameters. Secondly, the generalized predictive control (GPC) algorithm of polymerizer temperature based on segmental affine is proposed. According to dynamic equation of polymerizer temperature deduced by heat balance mechanism, the segmental affine model is built by temperature and convention rate of the polymerizer. Then linear matrix inequality (LMI) method is used to design the controller. Finally, simulation results and industrial application show the validity and feasibility of the proposed control strategy.     

多文档文摘语义单元自动去噪器的监督学习方法

多文档文摘的处理对象是存在噪音的文档集.现有文摘系统一般使用由人工设定阈值的固定阈值去噪器.但通过实验可见,不同文摘算法本身的抗噪能力各有高低,最优阈值随文档集、文摘算法、文本表示方法而改变,人工设定的固定阈值无法达到较好的通用性和去噪效果.为此,提出一种用于生成自动去噪器的监督学习方法,通过从人工文摘中自动获得标注信息,为语义单元提取多个特征,训练语义单元分类器而构成自动去噪器.可通用于不同文本表示所生成的语义单元,在不同多文档文摘系统的预处理阶段为任意文档集自动去除噪音语义单元.实验表明,该监督学习方法所生成的自动去噪器在不同文档集、文摘算法和文本表示方法下具有通用性,较好的去噪性能使各文摘算法的速度及所提取文摘的质量得到不同程度的提升.