车载火箭炮行军动力学研究

车载火箭炮在行驶中,路面激励产生的动载荷会对火箭炮的零部件产生破坏作用,从而影响火箭炮的使用寿命。通过分析全炮的拓扑结构,用RECURDYN、ANSYS软件,建立了车炮在行驶状态下的刚柔耦合模型。在实际建模中,取大梁为柔体。根据相关军用标准,编制六种路面谱。对六种路况进行行军分析,获得了相关零部件的动态参数。为某新型火箭炮的总体设计及零部件设计提供依据。     

弹体姿态调控引信系统设计与仿真

为减小巡飞弹弹体姿态偏差对引战配合的不利影响,提出弹体姿态调控引信的概念。在利用弹上信息的基础上,设计一种通过弹上加装微型脉冲推冲器产生的直接力快速调控弹体滚转角的引信;建立滚转角调控量计算模型,利用相平面分析法,设计微型脉冲推冲器的启动策略,保证弹体姿态在起爆时满足最佳引战配合要求。仿真结果表明调控时间短,使用的微型脉冲推冲器数量少,利用引信进行姿态调控是可行的。     

化学共沉淀工艺制备钛酸锶钡纳米粉体

以硝酸钡、硝酸锶、草酸和钛酸丁酯为原料,采用草酸盐共沉淀法制备草酸氧钛锶钡(BSTO,Ba0.6Sr0.4TiO(C2O4)2·4H2O)前躯体粉体,将该前躯体800℃煅烧4h得到钛酸锶钡(BST,Ba0.6Sr0.4TiO3)纳米粉体。用差热分析仪研究BSTO的热分解过程,用X射线衍射仪(XRD)和扫描电镜(SEM)研究钛酸锶钡粉体的相组成和微观形貌。结果表明:BSTO前驱体的热分解过程可以分为失去结晶水、草酸氧钛跟(TiO(C2O4)22-)分解和BST结晶化3个阶段;所制备的钛酸锶钡粉体为高纯立方晶相,粉体呈类球状,平均粒径约为100nm。     

BST-BSL体系的烧结行为和介电性能

采用草酸盐共沉淀工艺制备高反应活性Ba0.6Sr0.4TiO3(BST)粉体,添加适量的B2O3-SiO2-Li2O(BSL)为烧结助剂,采用传统陶瓷制备工艺制备BST-BSL复相陶瓷。用扫描电镜(SEM)和X射线衍射(XRD)分别研究BST-BSL的微观形貌和相组成,并测试BST-BSL的介电性能参数。结果表明:添加的少量B2O3-Li2O-SiO2在烧结过程中与BST主晶相发生复杂的化学反应,使BST的烧结温度由1 350℃降低到1 000℃以下;B2O3-Li2O-SiO2的添加大幅改善BST的介电常数温度稳定性,引入的非铁电第二相对BST铁电性的‘稀释作用’使BST-BSL的可调率降低,但由于烧结助剂的添加量少,使BST-BSL在1 kV/mm外加偏场下的可调率仍在5%以上。     

Novel pyrene-based donor–acceptor organic dyes for solar cell application

Pyrene is an alternant polycyclic aromatic hydrocarbon consisting of four fused benzene rings with a large, flat aromatic system, showing high thermal stability, extensive electron delocalization and electron accepted nature. In this work, pyrene was firstly employed as π-conjugated bridge to construct electron donor–π–electron acceptor (D–π–A) organic dyes, where diarylamine or indoline was used as donor, and cyanoacrylic acid as electron acceptor. The peryne-based dyes were employed as sensitizers in dye-sensitized solar cells, and give a short circuit photocurrent density (J_sc) of 12.1 mA/cm², an open circuit voltage (V_oc) of 0.71 V, and a fill factor (FF) of 0.71, corresponding to an overall conversion efficiency (η) of 6.1% under AM 1.5 conditions.     

Enhanced carrier mobility and photon-harvesting property by introducing Au nano-particles in bulk heterojunction photovoltaic cells

In this study, polymer solar cells (PSCs) doped with Au nanoparticles (Au NPs) were successfully fabricated to maximize the photon-harvesting properties on the photoactive layer. In addition, a conductivity-enhanced hybrid buffer layer was introduced to improve the photon absorption properties and effectively separate the generated charges by adding Au NPs and dimethylsulfoxide (DMSO) to the PH 500 as a buffer layer. The PSC performance was optimized with a 88% improvement over the conventional PSCs (photoactive area: 225 mm², power conversion efficiency (PCE): 3.2%) by the introduction to the buffer layer of Au NPs and DMSO at 10 wt% and 1.0 wt%, respectively, and with 15 wt% Au NP doping in the photoactive layer. The internal resistance was decreased due to the increased photocurrent caused by the localized surface plasmon resonance (LSPR) effect of the Au NPs in the photoactive layer and by the improvement of carrier mobility induced by the DMSO doping of the buffer layer. As a result, the series resistance (R_S) deceased from 42.3 to 19.7 Ω cm² while the shunt resistance (R_SH) increased from 339 to 487 Ω cm².     

Effect of DBR Geometry on Reflectivity and Spectral Linewidth of DBR Lasers

对DBR几何参量不同的InGaAs-GaAs-AlGaAs DBR半导体激光器样品的输出线宽进行了测量和分析.样品激光器DBR光栅取不同的长度和蚀刻深度以考察其几何特性对耦合系数、反射率以及输出线宽的影响.线宽通过自差频测量系统测量得到.对实验结果与理论计算结果进行了对比.对测得的光学特性参数与几何特性参数之间的联系进行了分析.在此基础上讨论了DBR几何特性对激光器输出线宽的影响.研究结果为该类型DBR半导体激光器的制造提供了有用的信息.     

Fair and Efficient Spectrum Splitting for Unlicensed Secondary Users in Cooperative Cognitive Radio Networks

In cooperative cognitive radio networks (CCRNs), a licensed primary-user (PU) is allowed to leverage several unlicensed secondary-users (SUs) to relay its traffic. In this paper, a staged dynamic spectrum allocation (DSA) scheme is proposed for CCRNs. In the first stage, the network is uncongested. A simple pricing based DSA scheme is proposed for the PUs to lease their idled frequency bands to the SUs. And, hence, the initial quality of service (QoS) demands (in terms of the minimum rate requirements) of the PUs and the SUs are both satisfied through direct transmission on the allocated frequency bands. In the second stage, the network reaches the full-loaded situation. Therefore, a cooperative relaying based DSA scheme is proposed to stimulate the PUs to split more extra frequency bands to fulfill the increased QoS demands of the SUs, on condition that the QoS of the PUs are well maintained. By applying the cooperative bargaining game theory in the proposed cooperative relaying based DSA, on the one hand, the SUs can get fairness rate-rewards from the PUs according to the level of contribution that they can make to compensate the PUs for the rate-losses. Hence, the increased QoS demands of the SUs can be accommodated in short term. On the other hand, the PUs could retain the SUs successfully to obtain the long-term revenue, on condition that their QoS constraints are still satisfied. Finally, the analysis results of the proposed bargaining game theoretic DSA scheme (in the second stage) are testified through computer simulations.     

USB3.0数据传输协议分析及实现

USB接口以其简单通用的特点成为消费类连接协议的首选.但随着多媒体技术的快速发展,USB 2.0已无法满足现在的高速应用需求.USB3.0以其5 Gb/s的传输速率和向后兼容等特点,成为下一代高速连接标准.根据USB3.0协议架构,文中分别从物理层、链路层、协议层和架构层对USB3.0的数据传输协议进行分析,并与USB2.0协议进行比较.最后介绍了基于EZ-USB FX3芯片的USB3.0加密U盘的实现方法.     

A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.