支持跨步访问的嵌入式存储系统

提出了一种新型多素数嵌入式存储系统,能够显著改善系统跨步访问的性能。提高跨步访存的带宽,对于改善系统的整体性能有着重要的意义。但是,在嵌入式系统中,受片外结构的尺寸限制,直接应用经典的素数存储系统理论无法显著改善跨步访存性能。为此,该新型系统以素数存储系统理论为基础,引入主存访问调度策略并结合嵌入式系统的实际结构特征,构造了一种两层结构的多素数存储系统,可以用较少数量的存储模块实现,而且从逻辑地址到物理地址的映像计算简单,能够以相对较小的硬件代价实现对嵌入式存储系统跨步访问的有效支持。理论分析和实验结果均证实了该系统的正确性和有效性。     

Microstructure and Properties of Co2+: ZnAl2O4/SiO2 Nanocomposite Glasses Prepared by Sol–Gel Method

Transparent bulk Co²⁺: ZnAl₂O₄/SiO₂ nanocomposites containing nanocrystalline Co²⁺: ZnAl₂O₄ dispersed in silica glass matrix were obtained by the sol–gel method. The gels of composition 89SiO₂–6Al₂O₃–5ZnO− x CoO ( x =0.2, 0.4, 0.6, 0.8, 1.0) (mol%) were prepared at room temperature by using two different aluminum salts, aluminum nitrate and aluminum alkoxide (aluminum-iso-propoxide, Al(OPrⁱ)₃), as starting materials. The transparent gels were converted to the crystalline phase of gahnite by heating above 900°C. The microstructural evolution of gels was characterized. The effect of Co²⁺ concentration on spectroscopic properties was also discussed. Co²⁺: ZnAl₂O₄ nanocrystals dispersed in the SiO₂-based glass are formed at lower heat-treatment temperature and shorter heating time by using Al(OPrⁱ)₃ as raw material.     

组合KdV方程的Hamilton系统

本文根据KdV方程的Hamilton系统,构造并证明了组合KdV方程的Hamilton系统。     

基于通道注意力机制和U-net的医学图像分割方法

随着人工智能和医学大数据的发展,基于深度学习的医学图像分割技术因具有重要的应用价值和前景,已经成为目前的研究热点.为了增强特征图的语义信息,在U-net网络的基础上引入通道注意力机制,对U-net生成的特征逐通道进行压缩,将压缩后的特征逐通道计算权重,然后将该权重与原始特征相乘得出最终的特征.通过在两个不同器官的医学图像数据集上进行实验,Dice系数相较于原始U-net网络分别提高了2.7％和1.8％,验证了该方法的可行性和有效性.     

Augmented Online Sequential Quaternion Extreme Learning Machine

Neural Processing Letters - Online sequential extreme learning machine (OS-ELM) is one of the most popular real-time learning strategy for feedforward neural networks with single hidden layer due...     

Visualizing and understanding graph convolutional network

Multimedia Tools and Applications - The graph convolutional network (GCN), which can handle graph-structured data, is enjoying great interest in recent years. However, while GCN achieved remarkable...     

SDN南向通道的安全性研究

软件定义网络(SDN)技术分离了网络的控制平面和数据平面,有效地解决了网络安全管理和控制问题,然而SDN自身安全性也尚有诸多问题需要解决.本文在分析SDN架构及其安全性的基础上,针对南向通道安全进行了研究,并基于OTR协议给出了一种安全解决方法.该方法具有前向安全性、安全认证等特性,对于SDN环境中的安全身份认证、密钥安全保存以及SDN组件隐蔽保护等问题提供了较好的解决方案.     

煤粉源集合体对水力压裂效果的影响

煤粉颗粒在压裂液中发生"造浆"及"聚集"作用是影响煤层气藏水力压裂效果的关键.构造煤粉源集合体对压裂效果影响显著,研究结果表明:原位状态下,煤粉以构造煤粉源和原生裂缝煤粉源集合体两种形式赋存.前者体积在20~30m3之间,呈顺层和断层两种产状,其明显受煤储层小微构造的控制;后者规模很小,主要分布在内生裂隙缝间,其受煤变质程度和煤岩类型影响.煤层气完井期间,井筒附近构造煤坍塌扩径,构造煤粉源卸压膨胀,煤粉颗粒启动难度降低;压裂期间,在压裂液动压、紊流搅动作用下,煤粉颗粒均匀混合在流体中形成浆体;排采期间,回排的压裂液中煤粉含量较高,导致气井近井部位压裂裂缝及炮眼较早地堵塞,气井减产.而原生裂缝内煤粉在压裂液中通过颗粒疏水相互作用发生聚集,在压裂裂缝内形成堵塞,但堵塞程度较轻.稳定压裂液注入排量、减小流速,利用煤储层小微构造解剖技术预测构造煤粉源集合体发育部位能够降低近井部位堵塞的风险,改善水力压裂效果.     

Numerical Simulation of Energy Saving Potential in Dispersing Process of Blasted Smoke in Mines

The dispersing process of the blasted smoke in underground mines by ventilation is very important for the safety of personnel, and its energy consumption is also worthy of concern. In this study, the impact of supply air flow rate on the decay process of harmful gases and fan energy, and the role of harmful gases buried by blasted pile in the smoke dispersing process is analyzed with three-dimensional numerical simulation. According to the results, air flow facilitates quick smoke dispersion and the gas emits from the blasted pile is only significant at the beginning of ventilation. It is thus recommended that large amount of air supply is taken to disperse the smoke quickly until the concentration of harmful gases reaches relevant standards, and reduce the amount of air in the remain time with frequency conversion technology to save energy. In the given study, 12 m/s air flow rate is taken for dispersing smoke and 3 m/s air flow rate is taken for the waiting time. The energy consumption for ventilation is reduced by about 50％. In addition, due to the gases in the blasted pile remains very high concentration after the dispersing process, it is recommended to keep suitable amount of ventilation in the consequent process.     

Grid-Based Path Planner Using Multivariant Optimization Algorithm

To solve the shortest path planning problems on grid-based map efficiently, a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm (MOA) and a modified indirect encoding scheme are proposed. In MOA, the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals, who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration, a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further, the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.