Research on virtual dynamic optimization design for NC machine tools

Autodesk Nastran 2023 参考手册说明书

DATINFILE1 ........................................................................................................................................................... 9
FILESPEC ............................................................................................................................................................ 13
DISPFILE ............................................................................................................................................................. 11
File Management Directives – Output File Specifications: .............................................................................. 5
BULKDATAFILE .................................................................................................................................................... 7

动态个体反恶化混沌粒子群优化算法

法更为行之有效
化、神经网络训练、糊系统控制、产和生活中的大模生规模组合优化等问题求解
粒子群优化算法形式如下【ｌ１：
’ ｃｒ（．，１１ｐ
ｖ
１动态个体反恶化混沌粒子群优化算法
为使粒子避开恶化环境．逃离局部最优。免进化避停止．出了个体反恶化思想。提现有粒子群优化算法基
据记忆当中个体最优和整体最优逐步调整方向。除这
作者简介：苏瑞娟（８－，士，教，究方向为智能优化算法１０）硕９助研
现计机２１．ｏ代算００４０
种行为外，还有另外一种行为：子有意识地回避记忆粒
ＰＯ）是由Ｋｎｅｙ和Ｅｂｒａｔ１９Ｓｅｎｄｅｈｒ于９５年首次提出的
一
有影响的两种改进方法为：ｌｃ ①Ｃｅ引入了收缩因子Ｋ，ｒ圈
种群智能优化算法。它源于对鸟群等生物群体觅食粒子群优化算法提出之后引起了全世界学者的广
当中个体和整体最差环境．如食物匮乏、例长期干旱无
水、度极高或极低、温容易遭到捕杀等难以生存的恶劣环境本文加入了这种粒子群有意识避开恶化环境的行为．出了个体反恶化机制的粒子群优化算法．算提该法形式如公式（）（）示。公式（）用到的变量Ｕ３和４所３所和混沌映射形式都如公式（）（）示。５和６所

微虚拟化技术将虚拟机效率再提升30%

微虚拟化技术将虚拟机效率再提升30%作者：郭涛来源：《中国计算机报》2012年第15期大多数用户已经认同，使用VMware、Citrix、微软的服务器虚拟化软件，可以大幅提升服务器的效率。

如果有厂商说，它不仅可以将物理服务器的效率提升60%～80%，而且可以将虚拟环境中服务器的效率再提升30%，你相信吗？Virtustream公司高级副总裁Reuven Cohen坚定地表示，Virtustream可以做到这一点。

Virtustream是一家云计算软件和服务提供商，其产品的核心技术之一就是Reuven Cohen 津津乐道的微虚拟化技术。

Reuven Cohen将VMware、Citrix、微软等提供的虚拟化技术称为第一代虚拟化技术。

这些技术都是以虚拟机（VM）为最小单位进行部署和调整的，每个VM 配备多少CUP、内存等都是相对固定的。

Virtustream的微虚拟化技术则不同，它可以深入到VM之下，实现更细粒度的虚拟化资源调配。

比如，它可以对每个VM之下的CPU、内存进行调整、管理和优化，因此才能将虚拟环境中的服务器效率再提升30%。

Virtustream不仅可以实现更细粒度的虚拟化，而且其计费也不是以VM为单位，而是细化到I/O层面。

“微虚拟化技术在VM的基础上实现了更细粒度的虚拟化。

”Reuven Cohen表示，“微虚拟化技术与第一代虚拟化技术之间并不是相互替代的关系，而是实现了进一步优化。

”作为一个云基础架构方案提供商，Virtustream主要为世界2000强这样的大型企业服务。

因此，Virtustream的云平台除了具有高性能以外，还必须在可扩展性以及安全性上优于其他为中小企业或个人提供服务的云平台。

很多人习惯将Virtustream云平台与Amazon进行比较。

Reuven Cohen表示，Virtustream与Amazon的不同主要体现在两方面：第一，Virtustream的可扩展性更强；第二，Virtustream是为大型企业服务的，而Amazon主要是为中小企业服务的。

毕业论文外文文献翻译

学号：20090127712009012771
2013届本科生毕业论文英文参考文献翻译
Oracle虚拟机服务器软件虚拟化在一个64位
Linux环境的性能和可扩展性
（译文）
学院（系）：
信息工程
专业年级：
学生姓名：
指导教师：
合作指导教师：
完成日期：
2013年6月
Oracle虚拟机服务器软件虚拟化在一个64位Linux环境的性能和可扩展性
benefits, however, this has not been without its attendantproblems and anomalies, such as performance tuning anderratic performance metrics, unresponsive virtualized systems,crashed virtualized servers, misconfigured virtual hostingplatforms, amongst others. The focus of this research was theanalysis of the performance of the Oracle VM servervirtualization platform against that of the bare-metal serverenvironment. The scalability and its support for high volumetransactions were also analyzed using 30 and 50 active usersfor the performance evaluation. Swingbench and LMbench,two open suite benchmark tools were utilized in measuringperformance. Scalability was also measured using Swingbench.Evidential results gathered from Swingbench revealed 4% and8% overhead for 30 and 50 active users respectively in theperformance evaluation of Oracle database in a single OracleVM. Correspondingly, performance metric法

dynamic optimization 中译本

动态优化（dynamic optimization）是一种在不确定性环境下对系统进行优化的方法。

随着信息技术的发展和应用范围的扩大，动态优化在实际问题中得到了广泛的应用和研究。

1.动态优化的概念动态优化是指在不断变化的环境下，通过调整系统的参数或策略，以达到最优化的目标。

在这种情况下，传统的静态优化方法往往不再适用，因为系统不再是静态的，而是不断变化的。

动态优化的目标可以是最大化收益、最小化成本、或者在特定约束条件下达到最优的状态。

2.动态优化的应用领域动态优化的应用领域非常广泛，包括但不限于生产调度、资源分配、供应链管理、金融投资、能源管理等。

在这些领域中，由于环境的变化和不确定性因素的影响，传统的优化方法往往无法达到预期的效果，因此需要采用动态优化的方法来解决问题。

3.动态优化的方法动态优化的方法包括动态规划、强化学习、遗传算法、粒子群优化等。

这些方法通过对环境的监测和学习，不断调整系统的参数或策略，以适应环境的变化，并达到最优的目标。

其中，动态规划是一种经典的动态优化方法，通过将问题分解为子问题，并利用子问题的最优解来推导出原问题的最优解。

4.动态优化的挑战虽然动态优化在理论上是非常有吸引力的，但在实际应用中也面临着很多挑战。

其中包括环境的不确定性、系统的复杂性、数据的稀疏性等。

这些挑战使得动态优化的方法在实际应用中往往需要更多的技术和经验的积累。

5.动态优化的未来随着数据技术和人工智能的发展，动态优化的方法也在不断地得到改进和完善。

未来，动态优化的方法将更加注重对环境的感知和学习能力，以适应更为复杂和不确定的环境，并在更多的领域中得到应用。

动态优化是一种在不确定性环境下对系统进行优化的方法，它在生产调度、资源分配、供应链管理、金融投资、能源管理等领域得到了广泛的应用和研究。

在实际应用中，动态优化面临着诸多挑战，但随着数据技术和人工智能的不断发展，动态优化的方法也在不断地得到改进和完善。

未来，动态优化将在更多的领域中得到应用，并发挥越来越重要的作用。

【计算机应用】_自适应变异_期刊发文热词逐年推荐_20140727

2011年科研热词混沌序列混沌差分进化高维多模态飞行控制系统铁路空车调配遗传算法进化策略自适应蛙跳算法自适应资源分配自适应变异自适应网格维变异精英保留策略粒子群算法粒子群优化算法粒子群优化禁忌搜索混沌自适应变异混沌变异正交设计改进粒子群优化算法惯性权重并行差分演化算法局部搜索小生境识别多目标进化算法变异策略变异操作双种群危险理论动态调整动态克隆选择算法任务调度人工免疫系统交叉概率自适应 nsga-ⅱ laplace分布推荐指数 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
科研热词推荐指数遗传算法 4 多目标优化 3 变异 3 演化算法 2 非劣分层遗传算法2 1 阈值 1 门限阈值 1 量子门 1 量子进化算法 1 量子粒子群优化算法 1 遗传优化 1 通用差异演化算法 1 进化阶段 1 航路规划 1 自适应重采样 1 自适应遗传算法 1 自适应控制参数 1 自适应差分进化算法 1 自适应变异概率 1 自适应变异 1 自适应交替变异算子 1 自适应 1 聚焦距离变化率 1 群体智能 1 组合电路 1 组合测试 1 线网优化 1 约束处理 1 精英策略 1 精英克隆选择 1 粗糙集 1 粒子群算法 1 粒子群优化算法 1 粒子群(pso) 1 粒子群 1 粒子滤波 1 粒子变异操作 1 相似度 1 直方图 1 物联网 1 物种监测 1 熵 1 混沌变异 1 混沌 1 混合整数非线性规划问题 1 混合建议分布 1 武器-目标分配(wta) 1 正态云模型 1 模拟退火遗传算法 1 模式识别 1 模式替代操作 1 核属性 1

改进的二进制粒子群优化算法

改进的二进制粒子群优化算法二进制粒子群优化算法（Binary Particle Swarm Optimization, BPSO）是一种常用的启发式优化算法，它基于群体智能和仿生学理论，模拟鸟群觅食过程中的行为，并通过群体中个体之间的协作和信息共享来寻找最优解。

在传统的粒子群优化算法中，粒子的位置是连续的实数值，而在二进制粒子群优化算法中，粒子的位置和速度都被表示为二进制串，从而减少了计算的复杂性，提高了算法的效率和可靠性。

为了进一步改进二进制粒子群优化算法的性能，研究者们提出了一系列的改进方法，包括参数调整、约束处理、局部搜索策略、自适应策略等。

下面将详细介绍一些改进的二进制粒子群优化算法及其特点：1. Adaptive Binary Particle Swarm Optimization（ABPSO）：ABPSO算法引入了自适应参数调整策略，根据粒子群的搜索状态动态调整惯性权重、学习因子等参数，以提高算法的收敛速度和收敛精度。

通过适应性的参数调整，ABPSO算法能够更好地适应不同的优化问题，取得更好的优化性能。

2. Hybrid Binary Particle Swarm Optimization（HBPSO）：HBPSO算法将二进制粒子群优化算法与其他优化方法（如遗传算法、模拟退火算法、蚁群算法等）进行有效结合，形成混合优化算法，以充分利用各种算法的优势，提高优化性能。

通过灵活的混合策略，HBPSO算法能够更好地克服局部最优、收敛速度慢等问题，取得更好的优化效果。

3. Constrained Binary Particle Swarm Optimization（CBPSO）：CBPSO算法针对约束优化问题提出了专门的处理策略，通过有效的约束处理技术，使算法能够在满足约束条件的前提下搜索最优解。

CBPSO算法能够有效处理约束优化问题，提高了算法的鲁棒性和可靠性。

4. Local Search Binary Particle Swarm Optimization（LSBPSO）：LSBPSO算法在二进制粒子群优化算法中引入局部搜索策略，通过在粒子的邻域空间进行局部搜索，加速算法的收敛速度，提高优化性能。

基于扩展有限状态机测试序列生成方法研究

基于扩展有限状态机测试序列生成方法研究
蒋凡;魏蓉;郐吉丰
【期刊名称】《计算机工程与应用》
【年(卷),期】2007(043)007
【摘要】扩展有限状态机是对有限状态机的扩展,由于引入了变量、状态迁移的前置条件以及状态迁移所引起的操作,它的测试序列存在可执行性问题.讨论了基于扩展有限状态机的测试序列生成方法的主要特点及局限性,指出了有待进一步研究的若干问题.
【总页数】4页(P62-64,74)
【作者】蒋凡;魏蓉;郐吉丰
【作者单位】中国科学技术大学,计算机科学与技术系,合肥,230026;中国科学技术大学,计算机科学与技术系,合肥,230026;中国科学技术大学,计算机科学与技术系,合肥,230026
【正文语种】中文
【中图分类】TP311
【相关文献】
1.基于可扩展有限状态机规格说明的测试数据生成效率因素模型分析 [J], 江良;赵瑞莲;李征
2.基于禁忌搜索算法的可扩展有限状态机模型测试数据自动生成 [J], 任君;赵瑞莲;李征
3.基于多种群遗传算法的可扩展有限状态机测试数据自动生成 [J], 周小飞;赵瑞莲;
李征
4.基于扩展有限状态机测试用例生成方法 [J], 王蒙蒙; 罗杨
5.基于扩展有限状态机测试用例生成方法 [J], 王蒙蒙; 罗杨
因版权原因，仅展示原文概要，查看原文内容请购买。

改进的量子遗传算法用于函数极值优化

ＡｂｔａｔＡｎｏｅｉｐｒｖｄｕａｔｓｒｃ：ｖｌｍｏｅｑｎｕｍｇｅｅｉａｇｏｉｈｉｐｏｐｅｔｏｅｃｎｔｃｌｒｔｍｓｒｏｓｄｏｖｒｏｍｅｈｅｔ
ｓｏｔｏｉｇｏｈｕｎｕｇｎｔｌｏｉｍ（ｈｒｃｍｎｆｔｅｑａｔｍｅｅｉａｇｒｔｃｈＱＧＡ）ｆｒｅａｐｅｌｃｌｏｔｉａｉｎ，ｏｘｍｌ，ｏａｐｉｚｔ．ｍｏ
第３卷第３２期青岛科技大学学报（自然科学版）Ｖ１３ｏ３ｏ．２Ｎ．２１０１年６月ＪｕｎｌｏｎｄｏＵｎｖｒｉｙｏｃｅｃｎｃｎｌｇ（ｔｒｌｃｅｃｄｔｎＪｎ２１ｏｒａｆＱｉｇａｉｅｓｔｆＳｉｎｅａｄＴｅｈｏｏｙＮａｕａｉｎｅＥｉｏ）Ｓｉｕ．０１
Ｔｈｅｔｓｏｓｔｔｔｅｐｅ — ｏｍａｅｏｆｔｅｐｒｐｏｅｌｏｉｈｍｓｓｐｅｉｏｔｕｎ— ｅｔｓｈｗｈａｈｒ— ｒｎｃｈｏｓｄａｇｒｔｆｉｕｒｏｒｔｈｅｑａ — ｔｎｅｅｉｌｏｉｈｍｎｒｄｉｉｎａｎｔｃａｇｏｉｈｍ．ｕｒｇｎｔｃａｇｒｔａｄｔａｔｏｌｇｅｅｉｌｒｔＫｅｒ：ｑｎｕｍｎｔｃａｇｏｉｈｍ；ｏｉｉａｉｎｆｅｔｅａｕｎｔｏｎ；ｑｎｕｍｏ— ｙｗｏｄｓｕａｔｇｅｅｉｌｒｔｐｔｍｚｔｏｏｘｒｍｌｆｃｉｕａｔｒｔｔｏｎｃｎｒ；ｅｏｕｉｎｙｓｒｔｇｙｗｉｈｎｃｅａｉｏｒｅｖｌｔｏａｒｔａｅｔｉｈ

虚拟机动态扩缩容的策略与实现

虚拟机动态扩缩容的策略与实现虚拟机是一种通过软件模拟硬件功能的计算机系统，它具有独立的操作系统和应用程序。

随着云计算和虚拟化技术的兴起，虚拟机的使用越来越广泛。

但是，随着业务的发展和需求的变化，虚拟机的资源配置问题也逐渐凸显出来。

虚拟机动态扩缩容成为了一项重要的技术。

一、什么是虚拟机动态扩缩容虚拟机动态扩缩容是指根据负载的变化，自动调整虚拟机的资源配置。

当负载较高时，可以增加虚拟机的资源，提高性能和稳定性；当负载较低时，可以减少虚拟机的资源，节约成本和资源。

二、虚拟机动态扩缩容的策略虚拟机动态扩缩容的策略有多种，下面介绍几种常见的策略。

1. 基于阈值的策略基于阈值的策略是最常见的一种，通过设定负载的阈值，当负载超过或低于这个阈值时，自动扩展或缩容虚拟机的资源。

例如，当CPU 使用率超过80%时，自动增加虚拟机的CPU核心数；当CPU使用率低于20%时，自动减少虚拟机的CPU核心数。

2. 基于预测的策略基于预测的策略是根据过去的负载数据和趋势，预测未来的负载情况，从而调整虚拟机的资源配置。

例如，根据过去一周的负载数据，预测未来1小时的负载情况，然后相应地扩容或缩容虚拟机。

3. 基于机器学习的策略基于机器学习的策略是通过分析历史数据，使用机器学习算法来预测未来的负载情况，并根据预测结果调整虚拟机的资源配置。

这种策略可以根据实时的负载情况，不断更新和优化模型，提高预测的准确性。

三、虚拟机动态扩缩容的实现虚拟机动态扩缩容的实现依赖于云平台和虚拟化管理软件的支持。

一般来说，实现虚拟机动态扩缩容需要以下几个步骤：1. 监控负载情况首先，需要监控虚拟机的各种资源使用情况，如CPU、内存、磁盘和网络等。

可以使用监控工具或云平台提供的监控功能，实时获取虚拟机的负载情况。

2. 判断负载状态根据监控数据，判断当前负载的状态，判断是否需要进行扩容或缩容。

可以根据阈值、预测模型或机器学习模型来进行判断。

3. 扩容或缩容虚拟机如果判断需要进行扩容或缩容，就通过云平台或虚拟化管理软件的API，调用相应的命令或接口进行虚拟机的扩容或缩容操作。

一个基于NC++的可视化分布式程序设计模型

一个基于NC++的可视化分布式程序设计模型
柳颖;谢立
【期刊名称】《计算机工程》
【年(卷),期】1998(024)009
【摘要】并行、分布式系统应用日益广泛,但它的开发却很困难,可视化是其解决方法之一. 给出了一个以面向对象的分布式程序设计语言NC++为基础的可视化分布式程序设计的基本模型VGOM,将一个分布式程序的特征用图加以描述;同时在原有NC++语言基础上增加了高层的基于图结构的图操作原语,包括通信原语等,从而提供结构化的通信方式.在SUN工作站上,使用Motif与PVM实现了该模型.
【总页数】4页(P3-6)
【作者】柳颖;谢立
【作者单位】南京大学计算机科学与技术系,南京,210093;南京大学计算机软件新技术国家重点实验室,南京,210093
【正文语种】中文
【中图分类】TP3
【相关文献】
1.MVSMg:一个基于网格的计算燃烧学可视化共享模型 [J], 杨钦涌;赵文涛
2.基于NOW的对象式分布式程序设计语言NC++ [J], 顾庆;谢立;陈道蓄;吴迎红;孙钟秀
3.基于面向对象的分布式程序设计语言NC＋＋的测试系统 [J], 顾庆;陈道蓄
4.一个基于认知特性的关联规则可视化3D模型的研究 [J], 宋婷婷;刘万春
5.可视化分布式程序设计中的任务信道描述模型 [J], 柳颖;赵欣
因版权原因，仅展示原文概要，查看原文内容请购买。

Optimization and Control of Dynamic Systems

Optimization and Control of DynamicSystemsOptimization and control of dynamic systems is a crucial field in engineering that focuses on finding the best possible solution for a given problem. This field encompasses various aspects such as modeling, analysis, design, and implementation of control algorithms to achieve desired system performance. In this response, I will explore the importance of optimization and control of dynamic systems from multiple perspectives. From an engineering perspective, optimization and control of dynamic systems play a vital role in improving the performance, efficiency, and reliability of complex systems. By utilizing mathematical models and control algorithms, engineers can design and implement control strategies that ensure the system operates within desired specifications. This is particularly important in industries such as aerospace, automotive, and manufacturing, where theoptimization of systems can lead to significant cost savings, improved safety, and enhanced productivity. Moreover, optimization and control techniques areessential in addressing real-world challenges. For instance, in the field of renewable energy, the integration of renewable sources into the power gridrequires advanced control strategies to ensure stability and reliability. Optimization techniques can be used to determine the optimal placement and sizing of renewable energy sources to maximize their contribution while minimizing costs. Similarly, in autonomous vehicles, control algorithms are crucial for safe and efficient navigation, taking into account various factors such as traffic conditions, weather, and pedestrian movement. From a societal perspective, optimization and control of dynamic systems have a direct impact on our daily lives. For example, in transportation systems, traffic control algorithms optimize traffic flow, reducing congestion and travel time. This not only improves the efficiency of transportation networks but also reduces fuel consumption and greenhouse gas emissions. Similarly, in healthcare, optimization techniques can be used to improve patient scheduling, resource allocation, and treatment planning, leading to better healthcare outcomes and reduced costs. Furthermore,optimization and control of dynamic systems have significant economic implications.By optimizing processes and systems, companies can reduce operational costs, improve product quality, and enhance customer satisfaction. For instance, in manufacturing, control algorithms can be used to optimize production processes, minimizing waste and maximizing throughput. This leads to increased profitability and competitiveness in the market. Optimization techniques are also widely used in financial markets, where algorithms are employed to optimize investment portfolios and trading strategies, maximizing returns while minimizing risks. From apersonal perspective, optimization and control of dynamic systems can have a profound impact on individuals' lives. For instance, in the context of smart homes, control algorithms can be used to optimize energy consumption, adjusting heating, cooling, and lighting systems based on occupancy and weather conditions. This not only reduces energy bills but also contributes to environmental sustainability. Additionally, optimization techniques can be applied to personal finance, helping individuals make informed decisions about saving, investing, and spending, ultimately improving their financial well-being. In conclusion, optimization and control of dynamic systems are of utmost importance from various perspectives. From an engineering standpoint, these techniques enable the design and implementation of control strategies that enhance system performance andreliability. Societally, optimization and control techniques have a direct impact on transportation, healthcare, and energy sectors, leading to improved efficiency, reduced costs, and enhanced quality of life. Economically, optimization andcontrol contribute to increased profitability, competitiveness, and financialwell-being. Personally, these techniques can improve energy efficiency, financial decision-making, and overall quality of life. Thus, optimization and control of dynamic systems are essential in addressing complex problems and driving progressin various domains.。

改进适应度函数的阵列综合粒子群算法

（ＡＶＩＡｉｂｒｅＭｉｓｌａｍｙ，Ｌｕｙｎ１０９ＣｒｏｎｓｉｅＡｃｄｅｏａｇ４７０，Ｃｈｎ）ｉａ
ＡｂｔａｔＡｎｉｒｖｄｐｒｉｌｗａｍｐｉｚｔｎａｇｒｔｍａｅｎｉｒｖｄｆｎｓｕｃｉｎｃｌｕｓｒｃ：ｍｐｏｅａｔｅｓｒｏｔｍｉａｉｌｏｉｃｏｈｂｓｄｏｍｐｏｅｉｅｓｆｎｔａｃ — ｔｏｌｔｎｉｒｐｓｄｔｏｖｈｒｂｅｓｏｖｒｃｔｒｔｖｎｓｎｌｗｖｌｉｇｓｅｄｏｏｖｒｅｃｎａｉｐｏｏｅｏｓｌｅｔｅｐｏｌｍｆｏｅｍｕｈｉｅａｉｅｅｓａｄｓｏｅｏｖｎｐｅｆｃｎｅｇｎｅｉｏｓａｒｙｓｎｈｓｓＢａｅｎｔｅｃｎｅｇｎｅｔｅｄ，ｔｅｐｏｏｅｔｏｏｄｃｓｗｅｇｔｄｃｌｕａｉｎｉｎｒａｙｔｅｉ．ｓｄｏｈｏｖｒｅｃｒｎｈｒｐｓｄｍｅｈｄｃｎｕｔｉｈｅａｃｌｔｎａｏａｐｏｒａｅｒｎｅｉｈｉｅｓｆｎｔｏａｃｌｔｎｏｉｃｎｄａｔｈａｔｉｈａｆｃｈｐｅｆｐｒｐｉｔａｇｎｔｅｆｎｓｕｃｉｎｃｌｕａｉ，ｓｔａｅｌｔｏｗｉｈｔｅｆｃｓｗｈｃｆｅｔｔｅｓｅｄｏ
得到提高、得到优先处理，而降低平均计算时间。通过对线阵天线的仿真实验，果表明该方法效果明并从结显，以在满足方向图要求的前提下，大减少收敛所需迭代的次数，快收敛速度。可大加

加州大学伯克利动态优化讲义Dynamic Optimization in Continuous-Time Economic Models

Dynamic Optimization in Continuous-Time Economic Models(A Guide for the Perplexed)Maurice Obstfeld*University of California at BerkeleyFirst Draft:April1992*I thank the National Science Foundation for research support.I.IntroductionThe assumption that economic activity takes placecontinuously is a convenient abstraction in many applications.In others,such as the study of financial-market equilibrium,the assumption of continuous trading corresponds closely to reality. Regardless of motivation,continuous-time modeling allows application of a powerful mathematical tool,the theory ofoptimal dynamic control.The basic idea of optimal control theory is easy to grasp--indeed it follows from elementary principles similar to thosethat underlie standard static optimization problems.The purpose of these notes is twofold.First,I present intuitivederivations of the first-order necessary conditions that characterize the solutions of basic continuous-time optimization problems.Second,I show why very similar conditions apply in1deterministic and stochastic environments alike.A simple unified treatment of continuous-time deterministic and stochastic optimization requires some restrictions on theform that economic uncertainty takes.The stochastic models I discuss below will assume that uncertainty evolves continuously^according to a type of process known as an Ito(or Gaussian------------------------------------------------------------------------------------------------------1When the optimization is done over a finite time horizon,the usual second-order sufficient conditions generalize immediately. (These second-order conditions will be valid in all problems examined here.)When the horizon is infinite,however,some additional"terminal"conditions are needed to ensure optimality.I make only passing reference to these conditions below,even though I always assume(for simplicity)that horizons areinfinite.Detailed treatment of such technical questions can be found in some of the later references.1diffusion)process.Once mainly the province of finance^theorists,Ito processes have recently been applied tointeresting and otherwise intractable problems in other areas of economics,for example,exchange-rate dynamics,the theory of the firm,and endogenous growth theory.Below,I therefore include a brief and heuristic introduction to continuous-time stochastic processes,including the one fundamental tool needed for this^type of analysis,Ito’s chain rule for stochastic differentials.^Don’t be intimidated:the intuition behind Ito’s Lemma is nothard to grasp,and the mileage one gets out of it thereaftertruly is amazing.II.Deterministic Optimization in Continuous TimeThe basic problem to be examined takes the form:Maximize8i-----d t(1)2e U[c(t),k(t)]dtjsubject toQ(2)k(t)=G[c(t),k(t)],k(0)given,where U(c,k)is a strictly concave function and G(c,k)is concave.In practice there may be some additional inequality constraints on c and/or k;for example,if c stands for consumption,c must be nonnegative.While I will not deal in any detail with such constraints,they are straightforward to22incorporate.In general,c and k can be vectors,but I will concentrate on the notationally simpler scalar case.I call cthe control variable for the optimization problem and k the state variable.You should think of the control variable as a flow--for example,consumption per unit time--and the state variable as a stock--for example,the stock of capital,measured in units of consumption.The problem set out above has a special structure that we can exploit in describing a solution.In the above problem, planning starts at time t=0.Since no exogenous variablesenter(1)or(2),the maximized value of(1)depends only onk(0),the predetermined initial value of the state variable.In other words,the problem is stationary,i.e.,it does not change3in form with the passage of time.Let’s denote this maximized value by J[k(0)],and call J(k)the value function for the8problem.If{c*(t)}stands for the associated optimal path oft=084the control and{k*(t)}for that of the state,then byt=0definition,------------------------------------------------------------------------------------------------------2The best reference work on economic applications of optimal control is still Kenneth J.Arrow and Mordecai Kurz,Public Investment,the Rate of Return,and Optimal Fiscal Policy (Baltimore:Johns Hopkins University Press,1970).3Nonstationary problems often can be handled by methods analogous to those discussed below,but they require additional notation to keep track of the exogenous factors that are changing.4According to(2),these are related bytik*(t)=2G[c*(s),k*(s)]ds+k(0).j38i-----d tJ[k(0)]=2e U[c*(t),k*(t)]dt.jThe nice structure of this problem relates to the following property.Suppose that the optimal plan has been followed until a time T>0,so that k(T)is equal to the value k*(T)given in the last footnote.Imagine a new decision maker who maximizesthe discounted flow of utility from time t=T onward,8i---d(t---T)(3)2e U[c(t),k(t)]dt,jTsubject to(2),but with the intial value of k given by k(T)=k*(T).Then the optimal program determined by this new decision maker will coincide with the continuation,from time T onward,of the optimal program determined at time0,given k(0).You should construct a proof of this fundamental result,which is intimately related to the notion of"dynamic consistency."You should also convince yourself of a key implicationof this result,thatTi-----d t----d T(4)J[k(0)]=2e U[c*(t),k*(t)]dt+e J[k*(T)],jwhere J[k*(T)]denotes the maximized value of(3)given that k(T) =k*(T)and(2)is respected.Equation(4)implies that we can think of our original,t=0,problem as the finite-horizon problem of maximizing4Ti---d t----d Te U[c(t),k(t)]dt+e J[k(T)]jsubject to the constraint that(2)holds for0<t<T.Of course,in practice it may not be so easy to determine thecorrect functional form for J(k),as we shall see below!Nonetheless,this way of formulating our problem--which is known as Bellman’s principle of dynamic programming--leadsdirectly to a characterization of the optimum.Because this characterization is derived most conveniently by starting in discrete time,I first set up a discrete-time analogue of our basic maximization problem and then proceed to the limit of continuous time.Let’s imagine that time is carved up into discrete intervals of length h.A decision on the control variable c,which is a flow,sets c at some fixed level per unit time over an entire period of duration h.Furthemore,we assume that changes in k, rather than accruing continuously with time,are"credited"only at the very end of a period,like monthly interest on a bank account.We thus consider the problem:Maximize8s---d th(5)e U[c(t),k(t)]htt=0subject to5(6)k(t+h)---k(t)=hG[c(t),k(t)],k(0)given.Above,c(t)is the fixed rate of consumption over period t whilek(t)is the given level of k that prevails from the very end of period t-----1until the very end of t.In(5)[resp.(6)]I have multiplied U(c,k)[resp.G(c,k)]by h because the cumulative flowof utility[resp.change in k]over a period is the product of a fixed instantaneous rate of flow[resp.rate of change]and the period’s length.Bellman’s principle provides a simple approach to the preceding problem.It states that the problem’s value function is given by()----d h(7)J[k(t)]=max{U[c(t),k(t)]h+e J[k(t+h)]},c(t)90subject to(6),for any initial k(t).It implies,in particular, that optimal c*(t)must be chosen to maximize the term in braces. By taking functional relationship(7)to the limit as h L0,we5will find a way to characterize the continuous-time optimum.We will make four changes in(7)to get it into useful form. First,subtract J[k(t)]from both sides.Second,impose the----------------------------------------------------------------------------------------------------------------5All of this presupposes that a well-defined value functionexists--something which in general requires justification.(Seethe extended example in this section for a concrete case.)Ihave also not proven that the value function,when it does exist,is differentiable.We know that it will be for the type ofproblem under study here,so I’ll feel free to use the value function’s first derivative whenever I need it below.With somewhat less justification,I’ll also use its second and third derivatives.6constraint(6)by substituting for k(t+h),k(t)+hG[c(t),k(t)].-----d hThird,replace e by its power-series representation,1----d h+2233(d h)/2-----(d h)/6+....Finally,divide the whole thing by h.The result is&2(8)0=max U(c,k)---[d-----(d h/2)+...]J[k+hG(c,k)]7c*+{J[k+hG(c,k)]----J(k)}/h,8 where implicitly all variables are dated t.Notice thatJ[k+hG(c,k)]-----J(k){J[k+hG(c,k)]----J(k)}G(c,k)-----------------------------------------------------------------------------------------=------------------------------------------------------------------------------------------------------------------.h G(c,k)hIt follows that as h L0,the left-hand side above approachesJ’(k)G(c,k).Accordingly,we have proved the followingPROPOSITION II.1.At each moment,the control c*optimalfor maximizing(1)subject to(2)satisfies the Bellman equation(9)0=U(c*,k)+J’(k)G(c*,k)----d J(k)()=max{U(c,k)+J’(k)G(c,k)-----d J(k)}.c90This is a very simple and elegant formula.What is its interpretation?As an intermediate step in interpreting(9), define the Hamiltonian for this maximization problem as7(10)H(c,k)_U(c,k)+J’(k)G(c,k).In this model,the intertemporal tradeoff involves a choicebetween higher current c and higher future k.If c isconsumption and k wealth,for example,the model is one in which the utility from consuming now must continuously be traded off against the utility value of savings.The Hamiltonian H(c,k)can be thought of as a measure of the flow value,in current utility terms,of the consumption-savings combination implied by the consumption choice c,given the predetermined value of k.TheHamiltonian solves the problem of"pricing"saving in terms ofQ current utility by multiplying the flow of saving,G(c,k)=k,byJ’(k),the effect of an increment to wealth on total lifetime utility.A corollary of this observation is that J’(k)has a natural interpretation as the shadow price(or marginal current utility)of wealth.More generally,leaving our particular example aside,J’(k)is the shadow price one should associatewith the state variable k.This brings us back to the Bellman equation,equation(9). Let c*be the value of c that maximizes H(c,k),given k,which is arbitrarily predetermined and therefore might not have been6chosen optimally.Then(9)states that(11)H(c*,k)=max{H(c,k)}=d J(k).c------------------------------------------------------------------------------------------------------6It is important to understand clearly that at a given point in time t,k(t)is not an object of choice(which is why we call it a state variable).Variable c(t)can be chosen freely at time t (which is why it is called a control variable),but its level influences the change in k(t)over the next infinitesimal time interval,k(t+dt)-----k(t),not the current value k(t).8In words,the maximized Hamiltonian is a fraction d of anoptimal plan’s total lifetime value.Equivalently,the instantaneous value flow from following an optimal plan divided by the plan’s total value--i.e.,the plan’s rate of return--must equal the rate of time preference,d.Notice that if we were to measure the current payout of the plan by U(c*,k)alone,we would err by not taking proper account of the value of the current increase in k.This would be like leaving investment out of our measure of GNP!The Hamiltonian solves this accounting problem by valuing the increment to k using the shadow price J’(k).To understood the implications of(9)for optimalconsumption we must go ahead and perform the maximization that it specifies(which amounts to maximizing the Hamiltonian).As aby-product,we obtain the Pontryagin necessary conditions for optimal control.7 Maximizing the term in braces in(9)over c,we get(12)U(c*,k)=-----G(c*,k)J’(k).c cThe reason this condition is necessary is easy to grasp.At each moment the decision maker can decide to"consume"a bit more,but at some cost in terms of the value of current"savings."A unitof additional consumption yields a marginal payoff of U(c*,k),cbut at the same time,savings change by G(c*,k).The utilityc------------------------------------------------------------------------------------------------------7I assume interior solutions throughout.9value of a marginal fall in savings thus is---G(c*,k)J’(k);andcif the planner is indeed at an optimum,it must be that this marginal cost just equals the marginal current utility benefit from higher consumption.In other words,unless(12)holds,there will be an incentive to change c from c*,meaning that c* cannot be optimal.Let’s get some further insight by exploiting againthe recursive structure of the problem.It is easy to see from (12)that for any date t the optimal level of the control,c*(t),depends only on the inherited state k(t)(regardless of whether k(t)was chosen optimally in the past).Let’s write this functional relationship between optimal c and k as c* =c(k),and assume that c(k)is differentiable.(For example,if c is consumption and k total wealth,c(k)will be the household’s consumption function.)Functions like c(k) will be called optimal policy functions,or more simply,policy functions.Because c(k)is defined as the solution to(9),it automatically satisfies0=U[c(k),k]+J’(k)G[c(k),k]-----d J(k).Equation(12)informs us as to the optimal relation between c and k at a point in time.To learn about the implied optimalbehavior of consumption over time,let’s differentiate the preceding equation with respect to k:100=[U(c*,k)+J’(k)G(c*,k)]c’(k)+U(c*,k)c c k+[G(c*,k)----d]J’(k)+J"(k)G(c*,k).kThe expression above,far from being a hopeless quagmire,is actually just what we’re looking for.Notice first that the left-hand term multiplying c’(k)drops out entirely thanks to (12):another example of the envelope theorem.This leaves us with the rest,(13)U(c*,k)+J’(k)[G(c*,k)-----d]+J"(k)G(c*,k)=0.k kEven the preceding simplified expression probably isn’t totally reassuring.Do not despair,however.A familiar economic interpretation is again fortunately available.We saw earlier that J’(k)could be usefully thought of as the shadow price of the state variable k.If we think of k as an asset stock(capital,foreign bonds,whatever),this shadow price corresponds to an asset price.Furthermore,we know that under perfect foresight,asset prices adjust so as to equate the asset’s total instantaneous rate of return to some required or benchmark rate of return,which in the present context can only be the time-preference rate,d.As an aid to clear thinking, let’s introduce a new variable,l,to represent the shadow price J’(k)of the asset k:l_J’(k).11Our next step will be to rewrite(13)in terms of l.Thekey observation allowing us to do this concerns the last term on the right-hand side of(13),J"(k)G(c*,k).The chain rule of calculus implies thatdJ’(k)dk d l dk d l QJ"(k)G(c*,k)=--------------------------*---------=---------*--------=--------=l;dk dt dk dt dtand with this fact in hand,it is only a matter of substitution to express(13)in the formQU+l G+lk k(14)-----------------------------------------------=d.lThis is just the asset-pricing equation promised in thelast paragraph.Can you see why this last assertion is true?To understand it,let’s decompose the total return to holding a unit of stock k into"dividends"and"capital gains."The"dividend"is the sum of two parts,the direct effect of an extra unit of k on utility,U,and its effect on the rate of increase of k,l G.(We mustk kmultiply G by the shadow price l in order to express thekQphysical effect of k on k in the same terms as U,that is,inkterms of utility.)The"capital gain"is just the increase inQthe price of k,l.The sum of dividend and capital gain,divided by the asset price l,is just the rate of return on k,which,by12(14)must equal d along an optimal path.-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ExampleLet’s step back for a moment from this abstract setting toconsolidate what we’ve learned through an example.Consider the8----d tstandard problem of a consumer who maximizes i e U[c(t)]dtQsubject to k=f(k)-----c(where c is consumption,k capital,andf(k)the production function).Now U=0,G(c,k)=f(k)----c,Gk c=-----1,and G=f’(k).In this setting,(14)becomes thekstatement that the rate of time preference should equal the marginal product of capital plus the rate of accrual of utility capital gains,Q ld=f’(k)+-----.lCondition(12)becomes U’(c)=l.Since this last equalityQ Qimplies that l=U"(c)c,we can express the optimal dynamics of c and k as a nonlinear differential-equation system:U’(c)q eQ Q(15)c=----------------------2f’(k)-----d2,k=f(k)---- c.U"(c)z cYou can see the phase diagram for this system in figure 1. (Be sure you can derive it yourself!The diagram assumes thatlim f’(k)=0,so that a steady-state capital stock exists.) k L8The diagram makes clear that,given k,any positive initial c13initiates a path along which the two preceding differential equations for c and k are respected.But not all of these paths are optimal,since the differential equations specify conditions that are merely necessary,but not sufficient,for optimality.Indeed,only one path will be optimal in general:we can write the associated policy function as as c*=c(k)(it is graphed in figure1).For given k,paths with initialconsumption levels exceeding c(k)imply that k becomes negative after a finite time interval.Since a negative capital stock is nonsensical,such paths are not even feasible,let alone optimal. Paths with initial consumption levels below c(k)imply that kgets to be too large,in the sense that the individual couldraise lifetime utility by eating some capital and never replacing it.These"overaccumulation"paths violate a sort of terminal condition stating that the present value of the capital stock should converge to zero along an optimal path.I shall not take the time to discuss such terminal conditions here.If we take1---(1/e)c-----1U(c)=------------------------------------,f(k)=rk,1----(1/e)where e and r are positive constants.we can actually findan algebraic formula for the policy function c(k).Let’s conjecture that optimal consumption is proportional to wealth,that is,that c(k)=h k for some constant h to be14determined.If this conjecture is right,the capital stock k Qwill follow k=(r-----h)k,or,equivalently,Q k------=r---h.kThis expression gives us the key clue for finding h.If c= h k,as we’ve guessed,then alsoQ c------=r---h.cQ cBut necessary condition(15)requires that----=e(r----d),cwhich contradicts the last equation unless(16)h=(1---e)r+ed.Thus,c(k)=[(1-----e)r+ed]k is the optimal policy function.In the case of log utility(e=1),we simply have h=d.We getthe same simple result if it so happens that r and d are equal.Equation(16)has a nice interpretation.In Milton Friedman’s permanent-income model,where d=r,people consume the annuity value of wealth,so that h=r=d.This ruleresults in a level consumption path.When d$r,however,the optimal consumption path will be tilted,with consumption rising over time if r>d and falling over time if r<d.By writing15(16)ash=r-----e(r-----d)we can see these two effects at work.Why is the deviation fromthe Friedman permanent-income path proportional to e?Recallthat e,the elasticity of intertemporal substitution,measures an individual’s willingness to substitute consumption today for consumption in the future.If e is high and r>d,for example, people will be quite willing to forgo present consumption to take advantage of the relatively high rate of return to saving;andthe larger is e,certeris paribus,the lower will be h.Alert readers will have noticed a major problem with all this.If r> d and e is sufficiently large,h,and hence"optimal" consumption,will be negative.How can this be?Where has our analysis gone wrong?The answer is that when h<0,no optimum consumption plan exists!After all,nothing we’ve done demonstrates existence:our arguments merely indicate some properties that an optimum,if one exists,will need to have.No optimal consumption path exists when h<0for thefollowing reason.Because optimal consumption growth necessarily Qsatisfies c/c=e(r-----d),and e(r-d)>r in this case,optimal consumption would have to grow at a rate exceeding the rate ofQreturn on capital,r.Since capital growth obeys k/k=r----(c/k),however,and c>0,the growth rate of capital,and hence16that of output,is at most r.With consumption positive andgrowing at 3percent per year,say,but with capital growing at a lower yearly rate,consumption would eventually grow to begreater than total output--an impossibility in a closed economy.So the proposed path for consumption is not feasible.This means that no feasible path--other than the obviously suboptimal path with c(t)=0,for all t--satisfies the necessary conditions for optimality.Hence,no feasible path is optimal:no optimal path exists.Let’s take our analysis a step further to see how the value function J(k)looks.Observe first that at any time t,k(t)=(r ------h )t e (r ----d )t k(0)e =k(0)e ,where k(0)is the starting capital stock and h is given by (16).Evidently,the value function at t=0is just8q e -1()2211i -----d t 1----(1/e )J[k(0)]=22{2e [h k(t)]dt -------}1---------j 22d e 90z c 08q e -1()2211i -----d t e (r ----d )t 1----(1/e )=22{2e [h k(0)e ]dt --------}1----------j 22d e 90z c 0q e 1-----(1/e )-1()22[h k(0)]11=22{-----------------------------------------------------------------------------------}.1---------22d ----(e -----1)(r ----d )d e 90zc So the value function J(k)has the same general form as the utility function,but with k in place of c.This is not the last 17time we’ll encounter this property.Alert readers will again notice that to carry out the final step of the last calculation, I had to assume that the integral in braces above is convergent, that is,that d---(e-----1)(r-----d)>0.Notice,however,that d---(e------1)(r-----d)=r-----e(r-----d)=h,so the calculation is valid provided an optimal consumption program exists.If one doesn’t, the value function clearly doesn’t exist either:we can’t specify the maximized value of a function that doesn’t attain a maximum. This counterexample should serve as a warning against blithely assuming that all problems have well-defined solutions and value functions.-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Return now to the theoretical development.We have seen how to solve continuous-time determinstic problems using Bellman’s method of dynamic programming,which is based on the valuefunction J(k).We have also seen how to interpret the derivative of the value function,J’(k),as a sort of shadow asset price, denoted by l.The last order of business is to show that we have8 actually proved a simple form of Pontryagin’s Maximum Principle: PROPOSITION II.2.(Maximum Principle)Let c*(t)solve the problem of maximizing(1)subject to(2).Then there exist variables l(t)--called costate variables--such that the Hamiltonian------------------------------------------------------------------------------------------------------8First derived in L.S.Pontryagin et al.,The Mathematical Theory of Optimal Processes(New York and London:Interscience Publishers,1962).18H[c,k(t),l(t)]_U[c,k(t)]+l(t)G[c,k(t)]is maximized at c=c*(t)given l(t)and k(t);that is,d H(17)------------(c*,k,l)=U(c*,k)+l G(c*,k)=0c cd cat all times(assuming,as always,an interior solution).Furthermore,the costate variable obeys the differential equationd HQ(18)l=ld-------------(c*,k,l)=ld-----[U(c*,k)+l G(c*,k)]k kd kQ9for k=G(c*,k)and k(0)given.------------------------------------------------------------------------------------------------------------9You should note that if we integrate differential-equation (18),we get the general solution8d Hi-----d(s-----t)d tl(t)=2e------[c*(s),k(s),l(s)]ds+Ae,j d ktwhere A is an arbitrary constant.[To check this claim,just differentiate the foregoing expression with respect to t:if theQintegral in the expression is I(t),we find that l=d I---(d H/d k)d t+d Ae=dl---(d H/d k).]I referred in the prior example to an additional terminal condition requiring the present value of the capital stock to converge to zero along an optimal path.Since l(t)is the price of capital at time t,this terminal condition-----d tusually requires that lim e l(t)=0,or that A=0in thet L819You can verify that if we identify the costate variable with the derivative of the value function,J’(k),the Pontryagin necessary conditions are satisfied by our earlier dynamic-programming solution.In particular,(17)coincides with(12) and(18)coincides with(14).So we have shown,in a simple stationary setting,why the Maximum Principle"works."The principle is actually more broadly applicable than you might guess from the foregoing discussion--it easily handles nonstationary environments,side constraints,etc.And it has a10stochastic analogue,to which I now turn.-----------------------------------------------------------------------------------------------------------------------solution above.The particular solution that remains equates the shadow price of a unit of capital to the discounted stream of its shadow"marginal products,"where the latter are measured by partial derivatives of the flow of value,H,with respect to k. 10For more details and complications on the deterministic Maximum Principle,see Arrow and Kurz,op.cit.20。

高效学术搜索引擎算法研究与性能优化

高效学术搜索引擎算法研究与性能优化1. 引言学术搜索引擎是帮助研究人员快速获取相关学术信息的重要工具。

随着科研领域的快速发展和学术资源的日益增多，如何设计高效的学术搜索引擎算法并优化性能成为一个关键问题。

本文旨在探讨高效学术搜索引擎算法的研究和性能优化。

2. 学术搜索引擎算法研究2.1 关键词匹配算法学术搜索引擎的核心功能是实现用户关键词与学术文献的匹配。

传统的关键词匹配算法主要基于精确匹配或布尔逻辑，通过搜索引擎索引中的关键字与用户输入进行匹配。

然而，这种算法在面对长尾关键词和模糊查询时存在局限性。

近年来，学者们提出了基于机器学习和自然语言处理技术的改进算法，如基于词向量的关键词匹配算法。

这种算法能够通过将关键词和文本转换为向量形式，利用向量的相似度计算来获取更准确的匹配结果。

此外，还有基于知识图谱和语义分析的算法，可以更好地理解用户查询意图并提供更相关的搜索结果。

2.2 排名算法学术搜索引擎返回大量的搜索结果，如何根据相关性将结果进行排名是学术搜索引擎算法中的一个重要挑战。

传统的排名算法主要基于关键字的频率和位置来确定搜索结果的相关性，这种算法容易出现过度匹配和低质量结果的问题。

为了改进搜索结果的排名质量，学者们提出了基于机器学习和协同过滤的排名算法。

这些算法可以利用用户的点击行为和历史搜索记录来学习用户的偏好，并根据这些偏好生成更准确的排名结果。

此外，还有基于领域知识的算法，通过分析文献的引用关系和作者影响力等因素来确定搜索结果的权威性和质量。

3. 学术搜索引擎性能优化随着学术文献数量的快速增长，学术搜索引擎的性能优化成为一个紧迫的问题。

以下是一些常见的学术搜索引擎性能优化策略：3.1 分布式计算学术搜索引擎需要处理大量的学术文献数据，传统的单机架构难以应对高并发和海量数据的需求。

因此，分布式计算已成为提高学术搜索引擎性能的重要手段。

通过将计算任务分配给多台计算机进行并行计算，可以加快搜索引擎的响应速度和吞吐量。

轻量级嵌入式软件动态二进制插桩算法

/-----------------------N0TINFO SECURITY __________________________________________________________________________________________________/021年第4期理论研究■doi：10.3969/j.issn.1671-1122.2021.04.010轻量级嵌入式软件动态二进制插桩算法----------------------梁晓兵1,孔令达1,刘岩1,叶莘2---------------------------------(1.中国电力科学研究院有限公司计量研究所，北京100085;2.国网浙江省电力有限公司营销服务中心，杭州310007)摘要：软件二进制插桩是软件性能分析、漏洞挖掘、质量评价领域的关键技术。

在嵌入式环境下，传统动态插桩算法受到无操作系统、CPU架构复杂、内存资源紧张等局限，难以展开工作。

文章以软件动态二进制插桩算法为研究目的，通过静态特征分析和动态跟踪算法，引入图论算法对固件中的二进制进行分析，提出了嵌入式设备远程调试协议，实现了对软件运行时信息的获取。

与传统方案相比，文章所想方案解决了现有工具对源码、操作系统或CPU架构的依赖，同时显著降低了内存和运算资源的占用率，可以有效解决嵌入式设备的动态插桩问题。

关键词：软件插桩；二进制插桩；软件调试；控制流分析中图分类号：TP309文献标志码：A文章编号：1671-1122(2021)04-0089-07中文引用格式：梁晓兵，孔令达，刘岩，等.轻量级嵌入式软件动态二进制插桩算法[J].信息网络安全，2021,21(4)：89-95.英文引用格式:LIANG Xiaobing,KONG Lingda,LIU Yan,et al.Lightweight Dynamic Binary Instrumentation Algorithm for Embedded Software[J].Netinfo Security,2021,21(4):89-95.Lightweight Dynamic Binary Instrumentation Algorithm forEmbedded SoftwareLIANG Xiaobing1,KONG Lingda1,LIU Yan1,YE Xin2(1.Institute of M etrology,China Electric Power R esearch Institute Co.f Ltd.,Beijing100085,China;2.MarketingService Center,State Grid Z hejiang E lectric Power Co.,Ltd.,Hangzhou,310007,China)Abstract:Binary instrumentation is a key technology in the fields of software performance analysis,vulnerability mining,and quality evaluation.When working on the embeddedenvironment,traditional dynamic instrumentation algorithms are facing limitations like lackingoperating system,complex CPU architecture,and tight memory resources.Those limitationsmake binary instrumentation on embedding software extremely difficult.Therefore,this paperstudies the lightweight binary dynamic instrumentation technology,and realizes the acquisitionof software runtime information through static feature analysis and dynamic tracking algorithms.Graph-based algorithms and embedded-oriented remote debugging protocol are introduced aspared with the traditional solution,the solution in this article solves the dependence onsource code,operating system or CPU architecture,while significantly reducing the occupancy收稿日期：2020-12-03基金项目：国家电网有限公司总部科技项目[5600-201955458A-0-0-00]作者简介：梁晓兵（1978—）,男,河南，高级工程师，博士，主要研究方向为信息妥全；孔令达（1990—）,男，黑龙江，工程师，硕士,主要研究方向为电表质量分析、软件安全；刘岩（1982—）,女，山东，高级工程师，硕士，主要研究方向为智能量测；叶莘（1991一）,男，浙江，工程师，硕士，主要研究方向为嵌入式妥全。

Linux内核实时性能改进方案

Linux内核实时性能改进方案
来宾;蒋泽军;王丽芳
【期刊名称】《网络新媒体技术》
【年(卷),期】2007(028)003
【摘要】中断是现代计算机中一项重要的功能.对于具有多个外部设备的计算机系统而言,中断更是不可或缺.Linux内核是一种分时系统,在实时应用方面还有待改进.文章将在分析中断机制的基础上提出一种改进Linux内核实时性能的方案.
【总页数】5页(P311-315)
【作者】来宾;蒋泽军;王丽芳
【作者单位】西北工业大学,计算机科学与工程系,西安,710072;西北工业大学,计算机科学与工程系,西安,710072;西北工业大学,计算机科学与工程系,西安,710072【正文语种】中文
【中图分类】TP3
【相关文献】
1.μClinux内核研究及实时性能的实现 [J], 杨学民;徐雅斌
2.增强Linux内核实时性能的研究与实现 [J], 宋颖慧;迟关心;赵万生;侯爽
3.嵌入式Linux内核实时调度策略改进方案 [J], 张传栋
4.增强Linux内核实时任务调度性能的研究 [J], 姚君兰
5.增强Linux内核实时任务调度性能的研究 [J], 姚君兰
因版权原因，仅展示原文概要，查看原文内容请购买。

不确定多时滞线性Ito随机系统的保性能控制

ＯｐｔｍａｕａａｅｄＣｏｔＣｏｒｌｆｎｃｒａｎｒｅｃｌｎｅｍｅｉｌＧｒｎｔｅｓｎｔｏｏｒＵｅｔｉＬａｇ－ｓａｅＬｉａｒＴｉ＂ｄｅａｉｏｈａｔｃＳｙｔｍｓｌｙｎｇＳｔｃｓｉｓｅ
【关键词】不确定线性多时滞随机系统；保性能控制；线性矩阵不等式
【图分类号】Ｔ１４中Ｈ３【文献标识码】Ａ【章编号】１７— ９３（００５０１ — ３文６４４９２１）０ — １７０
［ｂｔａｔＴｅｐｏｅｆｅｉｎｎｓａｅｆｅｂｃｕｒｎｅｄｃｓｃｎｒｌａＳｔｄｅｏＡｓｒｃ】ｈｒｂｌｍｏｄｓｇｉｇｔｔｅｄａｋｇａａｔｅｏｔｏｔｏｌｗｓ（）（＋＋县Ｏ）ｆ）Ｅｖ）Ｗ（ △ ，“ ）（）ｆ △ ）（一＋￣ｔ（ｄｔ）
ｚ）ｒｔ＋Ｄｕｔ０＝Ｃ（）（）
＝
理方法，并取得了大量的研究成果【。然而，经典的反馈控１】
ｃｌａｓＳｏｆｕｎｒｔｎｌｉａｍｅｃｅａｉｎｅｒｔｉｄｅａｙＳｃｈｌｔｏａｓｔＣｓｙｅｉｓｔｍｓ．ＢｙＵＳｉｈｅｎｇｔ１ｉｅａａｔＸｎｅｕａｌｉｙａｒｏｈ，ｎｒｍｒｉｉｑｔｐｐａｃｔｘｉｓｅｎｃｎｄｉｏｎｓａｅｒｉｅｄａｎｄａａｒｔｅｏｓｔｃｏｒｏ１ｅｒｓｉＳｐｒｅｎｔｄ．Ｆｕｒｔｒｈｅｅｔｃｅｏｔｉｒｅｄｖｇｕａｎｅｄｃｎｔ］ｅｓｅｈｅｍｏｒｅ，Ｄｅｓｎｉｇａｉａｒｍｅｍｙｌｌｎｅｏｒｅｓｓｔｓａｔｅｅｅａｃｃｔｆｄｂｋｏｎｒｏｌ１ｒｕｃａｔｅｓｈｔｈａｌｓｙｅｌｓｔｍｓａｌｌｗｏｒｔｃｅａｉｃｌｓｅｏｆｈｅｕｎｒｔｎｏｄ—ｌｏｐｏｓｙｓｔｍｏｒｂｏｅｉｎｍＳｕｎｄ．ｅｄ

一种非隔离改进二次型Boost高增益DC-DC变换器

，非隔离型变换器具有小、高等优点。
非隔离型变换器 -oost、二次型-oost、-uc＞、
Buck-Boost^开关电容型、开关电感型变换器等［$*0-12 o其中传统Boost变换器凭借其拓扑结构
简单、输出增益较高的优点
应用。为获得
更高的电压增益,研究者在传统Boost电路的基础上引入各种辅助电路［$ *i ，虽然提升了输出电压,
放电。态下有：
一 -；+ ； + ；： ? 0
4 -;C 1 + ；2 + ；2C 二 0
(4)
-；CG1 + ；l2 + ；gC3 ? 0
、-；+ ； + ；l2 + ；c2 ? 0
2性能分析
2.1 Type-1变换器性能分析
2. 1. 1 Type-1变换器电压增益
根据变换器的模态
电感伏-秒平衡原
dt+[
J T
(22
sfdt
=0
DT
I(
w0
一(a
- "0 ) d<
+
T
I(-
"0 ) d<
=
0
1
J DT
(9) :(_o、(_°ff、im_o (=_°ff分别是开关闭合和关断时的电感电输入电流°
假设电感值足够大，电感电流连续;流过电感
的平均电
下式表示：
1 DT
1
T
"7 _
0 (gdt = ( 1 - D) TDT(od< ( 10)
—100 —
电札与挖剧应用2021,48(6)
电力电子变流器技术I EMCA

一种基于线性动态参数的自适应粒子群优化算法

（）５
和ｃ是ｃ所能取到的最小值和最大值，和ｃ是ｃ。ｃ
所能取到的最大值和最小值
２２ＡＰＯ－ＤＰ的算法的描述．ＳＬ
∞（）（）Ｚｆ７＝ ∞ 广（ｌ）硼一／（）４
其中，
为最大迭代次数，为初始惯性权速度；表示第
收稿日期：０１２２２１一Ｏ —２修稿日期：０１０－０２１－３１
地改变速度，６ｓ￣ｇｅ朝ｐｅｔｌｂ的位置靠近。ｌ
很快吸引了公众注意．并且实现了在各科学和工程问
题上的应用，如混合离散非线性编程、例软件开发和最
近邻居分类。粒子群优化算法是模拟鸟群的捕食行为．
这个鸟就是 “ 子 ”所有的粒子都有一个被适应函数粒．
１粒子群优化算法的基本思想
粒子群优化算法是基于群体在所给定区域运动的随机优化算法。该算法能够动态更新其速度和位置，能
够进行群体粒之间信息交流。而最终收敛于一个最优值。不像其他的进化算法［］２，－没有信息交互和交叉因子３
个粒子苟维方向坐标；、是加速系ｅ：上的ｃｃ。２数；ｓ是
到第ｔ为止所有粒子在第的全局极值点的位置；代维
ｒｄ￣ｒｎ２ａＩｄ为两个在［，范围内变化的随机数。ｎａ０１】粒子群算法的基本思想是Ｉ用随机解初始化一群４１：粒子，然后通过迭代找到最优解。在每一次迭代中，粒子通过跟踪两个 “ 极值 ” 来更新自己．第一个就是粒子历史最优解ｅｔ即个体极值；ｓ，另一个极值是整个粒子群所有粒子的最优解ｅｔ即全体极值。ｓ，每个粒子不断