日志结构文件系统技术的研究

介绍了一种新的磁盘管理技术－日志结构文件系统，把对文件的修改汇成日志条目顺序地写入磁盘，既加速了写文件的速度，又加速了崩溃恢复的速度，把整个磁盘作为日志，磁盘上包含有效的读取日志结构文件所需的索引信息，为了保持快速写所需的大的磁盘空闲快，将磁盘分成段，用一个清理工线程收集压缩分散到各个段中的有效信息，以一个日志结构文件系统的原型，即Sprite LFS为例，对日志文件系统设计和实现的各个阶段进行了分析，并与Unix的文件系统，即快速文件系统（FFS）进行了比较。     

一种新型的高性能计算机存储系统的研究与实现

文中提出了一种改善计算机存储系统写请求特别是小写请求性能的新的存储结构。采用有着高存储速度、高可靠性的固态盘和廉介的硬盘空间共同和为磁光盘的写高速缓存,并结合顺序文件存取技术,从存储体系结构角度出发,研究并实现了一种以较低成本实现快速、可靠、大容量的存储系统。试验表明新型存储系统不必修改现有文件系统,即能大幅提高存储系统性能。中用于要求高可靠性的军用环境以及高可靠性和快速大容量要求的民用系统。     

散乱点云的自适应α-shape曲面重建

针对α-shape算法不适用于散乱非均匀点集曲面重建的问题,提出了一种基于点云数据局部特征尺寸（LFS）的自适应α-shape曲面重建改进算法。首先,以采样点的k-邻近点计算出负极点逼近曲面中轴（MA）;然后,根据近似中轴计算曲面在采样点处的局部特征尺寸,并依据局部特征尺寸对原始点云进行非均匀降采样;最后,根据三角面片的外接球半径和对应的α值自适应重建出物体表面。与α-shape算法相比,所提算法可以有效合理地减少点云数据量,点云简化率达到70%左右,同时重建结果中冗余三角面片更少且基本没有孔洞。实验结果表明,所提算法能够自适应地重建出非均匀点集的表面。     

LFS智能温控器在球磨机轴承温度监测中的应用

介绍了LFS智能温度控制器的性能特点,以及在选矿厂球磨机轴承温度监测及控制中的应用。     

Customizing the swarm storage system using agents

Swarm is a scalable, modular storage system that uses agents to customize low‐level storage functions to meet the needs of high‐level services. Agents influence low‐level storage functions such as data layout, metadata management, and crash recovery. An agent is a program that is attached to data in the storage system and invoked when events occur during the data's lifetime. For example, before Swarm writes data to disk, agents attached to the data are invoked to determine a layout policy. Agents are typically persistent, remaining attached to the data they manage until the data are deleted; this allows agents to continue to affect how the data are handled long after the application or storage service that created the data has terminated. In this paper, we present Swarm's agent architecture, describe the types of agents that Swarm supports and the infrastructure used to support them, and discuss their performance overhead and security implications. We describe how several storage services and applications use agents, and the benefits they derive from doing so. Copyright © 2005 John Wiley & Sons, Ltd.     

Robust predictive dual-loop control method based on Lyapunov function stability and energy equilibrium though double-core processors for active power filter

In this paper, a robust predictive dual-loop control method based on Lyapunov function stability and energy equilibrium for active power filter (APF) is proposed to improve the anti-interference performance and self-adaptive capability of system. The proposed control method mainly includes robust predictive current control based on Lyapunov function stability (RPCC-LFS) in the inner current loop and energy equilibrium proportional-integrator (PI) control in the outer dc-link voltage loop. The RPCC-LFS is proposed to enhance self-adaptive capability when the output filter inductors vary, speed up the dynamic response, and improve the tracking accuracy when the loads fluctuate. The energy equilibrium PI controller is proposed to maintain the dc-link voltage stable and suppress the transient impulse. The stability and dynamic response of the proposed control system are analyzed in detail, and the proper control parameters are selected. A specific hardware and software design program based on double-core processors DSP + FPGA is thoroughly given out. Finally, the comparative simulations and experiments verified the validity of the proposed method.     

浅谈OBO产品在现代智能建筑中的应用

简单分析了OBO的线缆管理系统（UFS和LFS）在智能建筑中应用的可行性，并对OBO的雷电防护系统（TBS）在智能建筑中的应用进行简单说明。     

A fast algorithm for local feature selection in data classification

Typical feature selection methods select a global feature subset that is applied over all regions of the sample space. In localized feature selection (LFS), each region of the sample space is associated with its own optimized feature subset. This allows the feature subset to adapt to local variations in the sample space. Feature subsets are selected such that within a localized region, within‐class distances are minimized and between‐class distances are maximized. LFS outperforms global feature selection methods. LFS is solved using a randomized rounding approach when weights of regions are fixed. Randomized rounding is a too time‐consuming algorithm. In this paper, we show that LFS has a closed‐form solution when weights of regions are fixed. Using this closed‐form solution can decrease the runtime of solving LFS substantially. Experimental results on real datasets confirm that the classification error rate of our proposed method and the randomized rounding‐based method are the same; the runtime of our proposed method is much better than that of the randomized rounding‐based method; and the classification error rate of our proposed method and the randomized rounding‐based method outperforms the state‐of‐the‐art feature selection methods.