Synthesis and Characterization of Micron-size Monodisperse Carboxylated Polystyrene Microspheres

文章通过分散聚合法,以苯乙烯(ST)为聚合单体,聚乙烯吡咯烷酮(PVP)为稳定剂,偶氮二异丁腈(AIbn)为引发剂,乙醇和水作为分散介质,合成微米级聚苯乙烯微球,并以此微球为种子,利用种子修饰法进一步合成羧基聚苯乙烯微球,并对合成的羧基微球单分散性、表面形貌及表面羧基密度进行表征。结果表明,在合成的聚苯乙烯微球表面成功连接上羧基基团,微球具有较高的羧基密度,并且保持良好的单分散性,适合下一步在其表面进行化学与生物活化以制备液相芯片的敏感元件。Micron-size monodispersed polystyrene microspheres were prepared by using styrene(St),poly(N-vinylpyrrolidone)(PVP),2,2'-azo-bisisobutyronitrile(AIBN),ethanol/water,as monomer,stabilizer,initiator and the media through dispersion polymerization and then modified with carboxyl group on the surface.We also characterized the surface morphology and determined the carboxyl content of the microspheres.Monodisperse Carboxylated polystyrene microspheres with the mean size of 2.2 μm diameter and the smooth surface were obtained.The prepared microspheres are of good characteristics to be applied for the sensing unit carriers of the liquid biochips.国家国家自然科学基金(20775065;20835005);教育部高校博士点基金(20070384023);国家基础科学人才培养基金(J1030415)资

三坐标测量机零件位置自动识别系统的实现

被测量零件在三坐标测量机位置的自动识别是实现三坐标测量机智能化的关键技术。将CMOS相机与激光器通过夹具固定在三坐标测量机测座上,实现与接触式测头的多传感器集成系统;采用机器视觉方法获取被测量零件的视觉坐标;用VISuAl bASIC 6.0应用程序开发工具对CAd模型接口问题做出了解决方案,实现了从CAd模型中自动提取检测特征与公差要求;根据零件三维信息的获取与图像处理,实现了零件在图像坐标系中位置和方向的自动识别;利用各坐标系之间的转换关系,实现了零件在三坐标测量机机器坐标系中位置和方向的自动识别功能

3D vision measurement based on shape from silhouette

提出了一种将SHAPE frOM SIlHOuETTE(SfS)技术应用在三坐标机上的新方法,即用摄像机获取图像,用三坐标机作移动平台,构建视觉测量系统,应用SfS技术对物体进行三维测量。该视觉测量系统扩展了坐标机功能,扩大了其应用范围。实验结果表明,该视觉测量系统具有SfS技术和坐标机两者的优点,重构物体三维模型的过程简单快捷,精度高于0.4MM,其结果可作为坐标机智能测量的基础,是一种非常实用的方法。Shape from Silhouette(SfS) is a passive non-contact 3D measurement method,which is used in 3D modeling and reverse engineering etc.Using SfS method,a simple,low cost,efficient and accurate 3D information acquisition system can be established and the request of the surface material of the object that is measured is low.Compared to laser scanning,SfS can also measure an object with hole.A new method that Shape from Silhouette is applied to measure the objects in coordinate metrology is proposed,that is the vision measurement system is made up of the camera,the coordinate metrology and SfS.Intelligent measurement is carried out on the vision measurement system to 3D reconstruction and consequently the precision is improved.The functions of coordinate metrology are expanded and the applications of coordinate metrology are enlarged by the system.The running experiment results on the vision measurement system show that this system has SfS and coordinate metrology's advantages.The approach to 3D reconstruction is very efficient and the accuracy is higher than 0.4mm.This approach is a much applied way and it can be a foundation of intelligent measurement

面向PaaS的分布式缓存服务关键技术研究

随着云计算的进一步发展，当前Web应用呈现出规模不断增大、业务逻辑更加复杂和用户群体庞大的发展趋势。针对大用户量访问、高并发请求处理，传统数据库频繁面临I/O访问瓶颈、难于扩展等挑战。在这一背景下，分布式缓存技术正逐步得到广泛应用。它将数据均匀分布到多个缓存服务节点，在内存中管理数据，对外提供统一的数据访问接口，基于冗余备份机制实现高可用支持；可有效降低后台数据库的访问压力，拉近数据与应用间的距离，是云计算环境下提升应用性能的一种重要手段。从云计算的三层服务模式(IaaS, PaaS, SaaS)角度分析，分布式缓存作为一种中间件，主要运行于PaaS层。作为云服务家族的重要成员，分布式缓存在云应用运行支撑和云状态存储方面发挥着日益突出的作用。与此同时，云平台固有的开放性、动态性以及多租户、弹性资源供给、自适应性、可用性等特点和需求，也给缓存服务的运行、维护和管理带来了新的问题与挑战，现有分布式缓存技术与云计算实际需求间仍存在一定差距。鉴于目前业界对具有云特性的缓存服务的迫切需求，本论文借鉴自治计算的思想，深入研究面向PaaS的分布式缓存服务关键技术，重点解决缓存策略自适应重配、缓存数据迁移优化和缓存服务性能隔离等方面的问题，建立具有云特性的缓存服务框架，并进行原型系统的设计与实现。本论文的主要贡献如下：&nbsp;1) 缓存策略自适应重配方法研究：不同缓存策略往往针对不同的问题场景提出，鉴于Web应用的访问量、访问模式具有时变性的特点，使用单一策略配置或静态策略配置，通常难以适应环境与需求的动态变化。针对最优缓存策略如何在线选择这一问题，本文提出一种基于机器学习的缓存策略自适应重配方法。着力解决如何持续优化离线训练得到的负载分类模型，以及如何有效降低缓存策略重配开销的问题。提出两种分类模型的增强机制；通过状态转换图、缓存对象同步算法以及策略重配优化机制来保障自适应操作的稳定、高效执行。同已有工作相比，论文方法支持负载分类模型的在线演化，自适应操作及模型优化对系统性能的影响较小。相比于静态策略配置，该方法可提升系统吞吐率15-31.5%。2) 缓存数据迁移优化方法研究：数据迁移是实现分布式缓存动态扩展与负载均衡的核心技术。如何降低数据迁移的开销是云服务提供商着力解决的问题。已有研究工作大多针对非虚拟化环境下的数据迁移问题，这些方法对于云缓存服务而言往往并不适用。针对这一问题，论文研究了虚拟化环境对缓存数据迁移的重要影响，在此基础上，提出一种基于面积的迁移开销模型，该模型一方面可以有效权衡迁移时间与性能衰减值，另一方面可以感知底层I/O操作的细节并刻画虚拟机性能干扰的程度。基于上述模型，本文给出了一种开销敏感的数据迁移算法，为迁移决策的制订和优化提供支持。本文方法在吞吐率和平均响应时间方面，分别优于已有方法11-23%、8-18%。3) 缓存服务性能隔离方法研究：对缓存服务而言，数据量大、频繁访问的租户往往会侵占其他租户资源，从而产生性能干扰。现有平台服务商未提供面向SLA保障的缓存性能隔离机制，使得租户应用的性能变得难以预测。论文围绕缓存空间与带宽资源的公平分配问题展开研究。首先，提出一种支持租户间内存配额动态调整的新的缓存空间划分机制，动态调整的目标是使每个租户的资源量与负载量相匹配，因此该机制具有较优的灵活性与扩展性；在此基础上，提出一种租户敏感的缓存替换算法，保障各租户使用的缓存空间大小接近其配额。针对网络资源竞争，提出一种租户带宽分配机制和请求调度算法。论文方法可有效消除租户间的性能干扰，平均响应时间优于已有方法7-18%，引入的性能开销低于5%。&nbsp;本论文的研究成果对自适应软件系统以及云平台资源管理等领域的研究具有一定的参考价值，可进一步应用于云缓存服务的维护和管理。综合上述研究成果，论文设计并实现了一个面向PaaS的缓存服务支撑软件系统ElastiCamel。该系统主要由缓存服务器、节点管理器、主节点和缓存客户端四部分组成。同时，基于Eclipse SWT技术实现了一个图形化的管理控制台，便于租户和管理员进行操作。With the rapid development of cloud computing, Web applications are becoming larger-scale and more complex. They are often demanded to provide highly- responsive, low latency service to a vast number of users. This brings new challenges to the traditional 3-tier architecture model. One major factor that hits developers big time is the I/O bottleneck in database. Besides, the background database is always hard to scale. This confluence of trends requires a new computing paradigm. In recent years, distributed cache is more widely used to remove bottlenecks from disk I/O. It uses a count of loosely-coupled RAM-based data caches to store objects and provides a unified data view to the applications. As one important way of boosting the performance of Web applications for the Cloud, distributed cache distributes cache data transparently and evenly to the entire cluster, and narrows the distance between data and Web applications. High availability is ensured by replication and redundancy. From the view of IaaS, PaaS and SaaS, cache belongs to a special type of middleware and mainly works on PaaS tier. As an important part of cloud services, distributed caching service plays a critical role in providing runtime support for cloud-based applications and storing their states. However, due to the open and highly dynamic nature of cloud platform and its multi-tenancy, elastic supply, self-adaptation and high availability features, the running, maintenance and management of caching service face a range of new challenges. That is to say, a wide gap still remains between the existing caching technologies and the requirements of cloud computing. Considering that there is an increasingly urgent need for the development of cloud-based caching services, in this thesis, we draw lessons from automatic computing, and conduct an in-depth study of the key technologies of PaaS-oriented distributed caching service, focusing on how to achieve self-reconfiguration of cache strategies, how to optimize the cost of data migration and how to enable per-tenant performance isolation and fairness. We also build a caching service framework and develop a prototype. The detailed research issues are described as follows:1) Self-reconfiguration of cache strategies. Different cache strategies are often designed for different scenarios. Considering that the number of concurrent users and traffic patterns of Web applications are time-varying, using single strategy or static configuration often seems difficult to adapt to the environmental dynamics and demand changes. To determine the optimal cache strategy, we propose a machine learning based approach, and focus on solving the problems of how to continuously optimize the offline classification model and how to effectively reduce the reconfiguration overhead. Two mechanisms are proposed to enhance the offline model. Reconfiguration actions can be executed stably and effectively by using state transition diagram, cache entries synchronization algorithm as well as two optimization mechanisms. Compared with the existing works, our approach supports online optimization of classification model with low overheads; the impacts brought by reconfiguration actions are quite small. Compared with static strategy configuration, our approach can increase the system throughput by 15-31.5%. 2) Optimization of cost of data migration. Data migration is the key technology to the dynamic scaling and load rebalancing of distributed cache. Cloud service providers are focused on how to reduce the cost of data migration. Existing works mainly use non-virtualized environment as the research background. Thus these approaches cannot be directly applicable to the caching service in the cloud. To solve this problem, we firstly study how virtualization poses impacts on data migration, and then build an area-based cost model for data migration. It can effectively make tradeoffs between migration time and performance impact for each migration action. It could also be aware of the underlying details and I/O interference. Based on this model, we develop a cost-aware data migration algorithm to determine an optimal set of migration actions. Compared with the existing works, our approach performs better, with 11-23% higher throughput and 8-18% lower response time. 3) Enabling per-tenant performance isolation and fairness. For caching service, frequently accessed tenants (Web applications) or tenants who own large volumes of data often tend to eat other tenants&rsquo; resources, resulting in performance interference. Existing caching service providers also donot provide performance isolation mechanism, which targets at per-tenant SLA guarantees. This makes the performance of tenants unpredictable. In this chapter, we focus on how to enforce fairness in the allocation of cache space and bandwidth resources among tenants. We firstly propose a new mechanism that supports dynamic weight adjustment to match tenants&rsquo; variable demands. The mechanism can achieve more flexibility and scalability. We then present a tenant-aware replacement algorithm. It is applied to guarantee the amount of resources consumed by each tenant is close to its allocation weight. To deal with the problem of network I/O contention, we propose a bandwidth allocation mechanism and a request scheduling algorithm. Experimental results indicate that, our approach can effectively get rid of performance interference among tenants, with 7-18% lower response time while its overhead is probably below 5%.The research achievements can provide some reference values to the fields such as adaptive software systems and resource management of cloud. They can also be applied to the maintenance and management of caching service. Based on the issues mentioned above, we have designed and developed a PaaS-oriented distributed caching system named ElastiCamel. It includes four parts, namely cache server, node agent, master node and cache client. It also provides a powerful graphical management console using Eclipse SWT to facilitate operations for administrators and tenants.</div

Gastric magnetic slow wave signal frequency detection method based on characteristic spectrum

本发明为一种基于特征谱的胃磁慢波信号频率检测方法，包括：数据预处理，读取巨磁阻抗传感器在特表测量到的原始胃磁数据，并对原始胃磁数据进行重新采样和滤波处理；特征谱计算，计算所述滤波处理后的数据的特征谱；特征谱主峰识别，对上述特征谱进行主峰识别。本发明为面向胃磁慢波信号的频率识别算法的设计，使用了频率估计的噪声子空间的特征谱方法，能精确得到胃磁慢波的频率。通过对胃磁慢波信号频率的精确识别，能精确给出胃磁慢波信号的平均特征谱图

Parallelized Algorithm for Radix-2 Fast Hadamard Transform

快速Hadamard变换被广泛应用于信号与图像处理、通信系统、数字逻辑等领域中．当问题规模非常大时，快速Hadamard变换有可能不能满足计算时间的要求；这种情况下，算法并行化是一种行之有效的手段．本文以单像素相机的压缩感知图像复原为应用背景，利用基二快速Hadamard变换与快速傅里叶变换的结构相似性，提出一种通用的基二快速Hadamard变换的任务级并行算法，并用构造方式证明了该并行算法与串行算法计算结果之间的等价性．仿真表明对于小于220向量长度的问题规模以及并行子任务数少于210的情况，该并行算法对比串行算法的数值计算结果的欧氏距离平方误差小于10-18，佐证了并行算法的正确性。在PC平台通过多核CPU上POSIX线程实现的实验表明：在该特定平台和特定配置上对于220至225向量长度的问题规模并行计算加速比为1.33~1.42，证明了文中提出方法的可行性和有效性。</p

design and realization of requirement management tool in status customization

分析了需求管理中需求项的状态属性特点,针对现有的软件需求管理工具对此处理的局限性,结合国内从事软件开发组织的实际需求,提出了在需求管理工具引入状态定制的思想,并结合数据库中元数据的概念,实现了在需求管理工具RMT中的自定义状态机制

考虑摆线轮齿廓修形的RV减速器齿轮结构多目标优化设计

针对RV减速器结构设计过程中未考虑摆线轮齿廓修形对接触力变化的影响，提出了一种考虑摆线轮齿廓修形的RV减速器结构多目标优化设计方法。将齿廓修形理论与摆线轮接触应力计算结合起来，考虑体积、效率、接触应力等因素，建立了以体积小、效率高、摆线轮接触应力小为多目标函数的优化数学模型，采用NSGA-Ⅱ算法进行求解，并与单目标优化方法进行比较。研究结果表明：相比于原始设计，RV减速器体积减小了21.24%，效率提高了2.03%，摆线轮接触应力降低了20.26%，同时，多目标优化方法相较于单目标优化方法具有更高的综合性能

蜗轮蜗杆齿轮组合机构的传动精度分析

以MCH50加工中心回转工作台为对象,考虑加工误差、偏心误差、相位角、负载与变形等因素建立了组合机构的传动精度模型。通过实例计算,验证了该综合传动误差与实验所测结果基本一致。在此基础上,分析了二级组合机构中传动比以及齿轮传动中心距等对传动精度的影响