基于ASP.NET缓存与分页策略优化Web数据查询性能

利用ASP．NET的缓存功能，结合性能良好的存储过程，一次性地缓存数据库数页数据，以供用户分页查询，是优化Web数据查询性能的比较理想的方式。文章详细介绍了优化模型的实现，并进行了性能分析。     

基于Web Dynpro Java平台的查询技术应用分析

为了解决以"大集中"方式设计的企业管理系统的查询性能下降的问题,提出了完善的查询性能优化模型.该模型是在对通用查询性能优化模型进行分析与的对SAP系统查询性能优化方法的研究基础之上,加入了业务规则优化层,从而使软件技术与业务规则优化相结合而形成的.通过完善的查询性能优化模型在电力系统发展建设的应用实例,验证了所提模型在提高以"大集中"方式设计的企业管理系统的查询性能方面的有效性.     

基于岛屿群体模型的并行粒子群优化算法

为改善粒子群优化算法对大规模多变量求解的性能，提出了基于岛屿群体模型的并行粒子群优化算法．对粒子群优化算法机理和本质并行性进行分析，设计和实现了一种并行粒子群优化算法．实验结果表明，基于岛屿群体模型的并行粒子群优化算法不仅提高了求解效率，而且改善了早收敛现象，算法的性能比经典粒子群优化算法有了很大提高．     

Linux下基于iSCSI存储系统的实现与性能评测

本文分析了新的网络存储协议--iSCSI协议，并给出了Linux 下 iSCSI协议实现的一种软件模型，进而搭建于iSCSI的存储系统，对其进行了性能测试，并指出了优化iSCSI系统性能的途径。     

蚁群系统模型的比较及参数优化

蚁群算法是一种模拟进化算法，根据信息素更新策略的不同，蚁群系统模型分为蚁密系统、蚁量系统和蚁周系统。本文对三种模型的原理、特点进行研究，并通过仿真实验分析三种模型的性能及参数对其性能的影响，进而提出的参数优化规则，有利于蚁群算法在组合优化问题中的推广和应用。     

非结构化环境下基于外观的闭环检测研究综述

首先讨论了现有的场景外观描述方法及其应对各类非结构化因素时的性能。其次，介绍了基于纯图像检索、引入拓扑和度量信息的场景记忆模型的特性，并作为案例讨论了视觉词典的性能优化及构造方法。接着，对闭环检测的关键环节——闭环匹配、后验和优化——进行了分析。再次，概述了常用的性能评估指标和基准数据集。最后，总结了闭环检测研究现状，展望了无监督学习、语义上下文信息和模型轻量化等新技术的应用潜力。     

一种图K划分的随机算法

本文提出一个求解图Ｋ划分难题的模拟进化退火模型。该模型将模拟退火和模拟进化方法相结合，实现了多目标并行寻优策略。理论分析和实验结果表明，模拟进化退火模型的性能更优，解的优化程度更高。     

优化并行计算的性能评价

传统的并行计算的性能评价模型是加速比,文中讨论了加速比的缺点和不足,在此基础上提出了一种新的优化并行计算的性能评价模型（我们称之为优化加速比）。利用优化加速比分析了NAS基准测试程序MG和FT在IBM SP2(66mhz/wn)上的性能。     

基于改进粒子群优化算法的多用户检测器

提出了一种基于改进粒子群优化算法的多用户检测器。介绍了最佳多用户检测模型以及粒子群优化算法的基本思想。进行了理论依据和仿真性能分析。仿真结果表明：该检测器在误码率性能和抗“远近”效应上优于传统检测器和基于粒子群优化得多用户检测器，计算复杂度较低。     

多数据流上共享窗口连接查询的降载策略

数据流模型作为一种新型的模型，在许多应用中扮演着重要的角色．基于数据流模型的查询处理技术也得到了广泛的研究．为了提高查询系统的性能，现有的研究成果主要可以划分为两类：调度优化和降低负载方法．调度优化方法通过改变元组执行次序来提高查询性能．降低负载方法在负载超出系统处理能力时，通过减少输入流量来提高吞吐率．然而，同时运用这两种方法来提高查询性能的研究工作还很少．结合共享滑动窗口查询操作的调度优化方法和降低负载方法，提出了两种在burst环境下提高查询吞吐率的策略：均匀降载策略和小窗口准确降载策略．理论分析和实验结果均证明这两种策略能显著提高系统的性能．     

Design and experimental evaluation of robust controllers for a two-wheeled robot

The paper presents the design and experimental evaluation of two alternative μ-controllers for robust vertical stabilisation of a two-wheeled self-balancing robot. The controllers design is based on models derived by identification from closed-loop experimental data. In the first design, a signal-based uncertainty representation obtained directly from the identification procedure is used, which leads to a controller of order 29. In the second design the signal uncertainty is approximated by an input multiplicative uncertainty, which leads to a controller of order 50, subsequently reduced to 30. The performance of the two μ-controllers is compared with the performance of a conventional linear quadratic controller with 17th-order Kalman filter. A proportional-integral controller of the rotational motion around the vertical axis is implemented as well. The control code is generated using Simulink® controller models and is embedded in a digital signal processor. Results from the simulation of the closed-loop system as well as experimental results obtained during the real-time implementation of the designed controllers are given. The theoretical investigation and experimental results confirm that the closed-loop system achieves robust performance in respect to the uncertainties related to the identified robot model.     

Quantitative Characterization of Event Streams in Analysis of Hard Real-Time Applications

Many real-time embedded systems process event streams that are composed of a finite number of different event types. Each different event type on the stream would typically impose a different workload to the system, and thus the knowledge of possible correlations and dependencies between the different event types could be exploited to get tighter analytic performance bounds of the complete system. We propose an abstract stream model to characterize such an event stream. The model captures the needed information of all possible traces of a class of event streams. Hence, it can be used to obtain hard bounded worst-case and best-case analysis results of a system. We show how the proposed abstract stream model can be obtained from a concrete stream specification, and how it can be used for performance analysis. The applicability of our approach and its advantages over traditional worst-case performance analysis are shown in a case study of a multimedia application.Ernesto Wandeler is a Ph.D. student at the Computer Engineering and Networks Laboratory of the Swiss Federal Institute of Technology, Zurich. His research interests include models, methods and tools for system-level performance analysis of real-time embedded systems. He holds a Dipl. El.-Ing. degree from ETH Zurich. In 2003, he received the Willi Studer Price and the ETH Medal, both from the Swiss Federal Institute of Technology, Zurich. He is a student member of the IEEE and the ACM.Alexander Maxiaguine is a Ph.D. student at the Computer Engineering and Networks Laboratory of the Swiss Federal Institute of Technology, Zurich. His research interests include models and methods for system-level performance analysis and scheduling of embedded multiprocessor architectures, especially for real-time multimedia applications. Maxiaguine has an M.S. in electrical engineering from the Moscow Technical University of Communications and Informatics. He is a member of the IEEE and the ACM.Lothar Thiele is a full professor of computer engineering at the Swiss Federal Institute of Technology, Zurich. His research interests include models, methods and software tools for the design of embedded systems, embedded software and bioinspired optimization techniques. In 1986 he received the Dissertation Award of the Technical University of Munich, in 1987, the Outstanding Young Author Award of the IEEE Circuits and Systems Society, in 1988, the Browder J. Thompson Memorial Award of the IEEE, and in 2000–2001, the IBM Faculty Partnership Award. In 2004, he joined the German Academy of Natural Scientists Leopoldina.     

To Schedule or to Execute: Decision Support and Performance Implications

This paper addresses a fundamental trade-off in dynamic scheduling between the cost of scheduling and the quality of the resulting schedules. The time allocated to scheduling must be controlled explicitly, in order to obtain good-quality schedules in reasonable times. As task constraints are relaxed, the algorithms proposed in this paper increase scheduling complexity to optimize longer and obtain high-quality schedules. When task constraints are tightened, the algorithms adjust scheduling complexity to reduce the adverse effect of long scheduling times on the schedule quality. We show that taking into account the scheduling time is crucial for honoring the deadlines of scheduled tasks. We investigate the performance of our algorithms in two scheduling models: one that allows idle-time intervals to exist in the schedule and another that does not. The model with idle-time intervals has important implications for dynamic scheduling which are discussed in the paper. Experimental evaluation of the proposed algorithms shows that our algorithms outperform other candidate algorithms in several parameter configurations.     

On Using Real-Time Static Locking Protocols for Distributed Real-Time Databases

The use of Static Two Phase Locking Protocols (S2PL) for concurrency control in real-time database systems (RTDBS) has received little attention in the past. Actually, real-time S2PL (RT-S2PL) protocols do possess some desirable features making them suitable for RTDBS, especially for distributed real-time database systems (DRTDBS) in which remote locking is required and distributed deadlock is possible. In this paper, different RT-S2PL protocols are proposed. They differ in their methods of reducing the blocking time of higher priority transactions. Their performance is studied and compared with a real-time dynamic two phase locking protocol (RT-D2PL), called Hybrid Two Phase Locking (Hb2PL). The impact of different system and workload parameters, such as mean inter-arrival time of transactions, number of remote lock requests of a transaction, communication overhead for sending messages, and database size on their performance have been examined. The performance results indicate that the RT-S2PL protocols are suitable for DRTDBS in which the proportion of local locks of a transaction is small and the communication overhead for locking is high.     

中断调度模型的算法实现及性能评价

通过对一个嵌入式实时操作系统（ARTs-OS）的研究,介绍了减少中断延迟的具体方法.中断模拟是RT-Linux中采用的减少中断延迟的方法,解决中断延迟的另一种方法是使用中断嵌套,这两种方法各有优缺点.在分析RT-Linux方法和VxWorks方法优劣的基础上,提出了ARTs-OS中断调度模型.针对ARTs-OS的中断调度模型,给出了其算法实现及这种算法的性能分析.在Pentium166MHZ机上的测试数据和性能评价结果表明,ARTs-OS采用的方法是可行的.     

A new real-time suboptimum filtering and prediction scheme for general nonlinear discrete dynamic systems with Gaussian or non-Gaussian noise

A new suboptimum state filtering and prediction scheme is proposed for nonlinear discrete dynamic systems with Gaussian or non-Gaussian disturbance and observation noises. This scheme is an online estimation scheme for real-time applications. Furthermore, this scheme is very suitable for state estimation under either constraints imposed on estimates or missing observations. State and observation models can be any nonlinear functions of the states, disturbance and observation noises as long as noise samples are independent, and the density functions of noise samples and conditional density functions of the observations given the states are available. State models are used to calculate transition probabilities from gates to gates. If these transition probabilities are known or can be estimated, state models are not needed for estimation. The proposed scheme (PR) is based upon state quantisation and multiple hypothesis testing. Monte Carlo simulations have shown that the performance of the PR, sampling importance resampling (SIR) particle filter and extended Kalman (EK) filter are all model-dependent, and that the performance of the PR is better than both the SIR particle filter and EK filter for some nonlinear models, simulation results of three of which are given in this article.     

Feedback Control Real-Time Scheduling: Framework,Modeling, and Algorithms*

This paper presents a feedback control real-time scheduling (FCS) framework for adaptive real-time systems. An advantage of the FCS framework is its use of feedback control theory (rather than ad hoc solutions) as a scientific underpinning. We apply a control theory based methodology to systematically design FCS algorithms to satisfy the transient and steady state performance specifications of real-time systems. In particular, we establish dynamic models of real-time systems and develop performance analyses of FCS algorithms, which are major challenges and key steps for the design of control theory based adaptive real-time systems. We also present a FCS architecture that allows plug-ins of different real-time scheduling policies and QoS optimization algorithms. Based on our framework, we identify different categories of real-time applications where different FCS algorithms should be applied. Performance evaluation results demonstrate that our analytically tuned FCS algorithms provide robust transient and steady state performance guarantees for periodic and aperiodic tasks even when the task execution times vary by as much as 100% from the initial estimate.     

面向可重构控制器实时中间件的设计与实现

可重构控制器的出现要求系统软件模块不但要满足性能和精度要求,还应具有模块化、可集成、可重用等特性.如果将实时中间件引入到控制器的设计中,由中间件管理模块间的交互,可实现控制器的可重构.建立基于RTLinux和实时CORBA的实时中间件平台,需要将CORBA引入RTLinux实时内核空间.该文给出了RTLinux实时内核中的实时CORBA和minimum CORBA实现RTCK的设计,并基于RTLinux MBuff共享内存驱动和实时FIFO设计和实现了GIOP通信协议,最后给出了RTCK与TAO的延迟抖动测试结果并做了相关的分析.     

多任务实时声音事件检测卷积模型与复合数据扩增

现有的声音事件检测研究多为对离线音频进行分析,且模型参数量较多、计算效率低,不适用于实时检测。提出一种面向多任务实时声音事件检测的轻量化卷积神经网络模型,它将唤醒与检测任务整合成多任务学习框架,此外模型的卷积结构联合了稠密连接、Ghost模组与SE注意力机制;另外还提出了一种复合数据扩增方法,将音频变换、随机裁剪与频谱掩蔽相结合。实验结果显示,该模型在ESC-10和Urbansound8K数据集上的平均预测准确率高于当前新型的基线模型2%以上,同时模型的参数和内存更少。研究表明,多任务学习的方式节省了计算量,又因为卷积结构复用了中间层特征,模型可以快速地反馈检测结果。另外,复合数据方法相比传统方法使模型获得了更好的性能和鲁棒性。     

烟厂润叶机电控制系统改造

根据实际润叶生产工艺和控制要求，对红塔烟叶复烤线WF300型润叶机电控系统进行改造，针对原有系统控制精度低，滞后及自动监控不完善提出了新的解决方案，建立了加水前馈一串级控制模型；对加料采用批量控制进行实时修正，上述控制方案利用PLC编程实现，并用CITECT开发实时监控程序实现与PLC的数据交互，完成对生产的实时监控，运行情况表明，改造后该系统控制精度提高、可靠性增强、使用更灵活，更好地满足了润叶工艺质量要求。