基于Struts框架的科研资源计划系统的应用研究

Struts是基于模型视图控制体系(MVC)的Web应用框架。详细介绍了基于MVC体系的Struts工作原理，探讨应用Struts框架构建企业级应用系统的步骤及方法。给出了基于Struts的科研资源计划系(SRRPS)的设计实现，结果表明Struts框架能极大地提高系统的可扩展性、可重用性及可维护性。     

Pruning Algorithms for Rule Learning

Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.     

Non-contact handling in microassembly: Acoustical levitation

Microassembly is currently of the utmost importance in industry. Nevertheless, the classical assembly processes are no longer usable for very small components, typically ranging from 10  m to 10 mm, since usually neglected surface forces disturb the handling task by inducing adhesion between the component and the gripper. A promising alternative to tackle surface forces consists in levitating the handled component. The various advantages of this contactless handling method are reviewed here and justify the choice of this approach. Consequently, the numerous physical principles suitable for contactless handling are briefly described together with their limitations. The evaluation shows that acoustic levitation is best fitted in the case of microassembly. A classification of literature applications is presented hereafter with special focus on acoustic levitation. Finally, the most common models of acoustical levitation are inspected in a general way. The described models come within the scope of non-linear acoustics.     

起重搬运作业的安全风险分析

用安全系统的理论和方法 ,以起重作业为研究对象 ,通过危险识别 ,对造成人员损伤的危险事件发生的可能性和导致伤害后果的严重程度 ,进行了定性的风险分析 ,介绍了我国采取的针对性综合管理对策     

Assessment of factors considered to be important in handling tasks by expert handlers

Twenty-eight experienced handlers, considered the best in two transport companies, were interviewed using a questionnaire consisting of partially closed-ended questions. They had to assess 20 videotape handling sequences showing two different handling methods to determine, for 10 factors and overall, which one was the best. They were asked about the inportance of these factors in their choices of the best method. The aims of the study were to verify whether there was a consensus among experienced handlers as to what was a good handling method and to identify the factors taken into consideration by them. Results show the lack of consensus on the best methods. Handlers favor different strategies depending on the worjk context and their own priorities.     

Designing collaborative,constructionist and contextual applications for handheld devices

This paper explores current applications for handheld devices and questions which of these make full use of the unique attributes of handheld devices in order to facilitate learning in a pedagogically sensible manner. In order to do so, the paper presents a functional framework which analyses handheld application in relation to their use as well as the pedagogical underpinning, if any, that informs their development. Our framework currently consists of seven distinct categories of application, which we term: administrative, referential, interactive, microworld, data collection, location aware and collaborative. We argue that three categories, namely data collection, location aware and collaborative, are particularly suited to learning with handheld devices when they are informed by collaborative, contextual and constructionist learning theories. Furthermore, we contend that applications of the type just outlined deserve further research since they are not attempting to replicate, or even augment, existing learning scenarios but rather they try to create new learning opportunities which would not be possible without (mobile) technology. Finally, the authors introduce a number of applications and learning scenarios that they have developed for handheld devices and explain their position within the framework.     

Java异常处理

异常处理是Java程序设计中一个非常重要的机制,也是程序设计的一大难点,就异常和异常处理方式等方面进行介绍,并通过一个实例来验证异常。     

Scalable framework for blockchain-based shared manufacturing

Shared Manufacturing is a new mode of social manufacturing based on the principles of a sharing economy. This paper presents a scalable framework for blockchain-based Shared Manufacturing that preserves the transparency and immutability characteristics of transaction records, which is critical to building trust between entities in blockchain-based systems. We define a blockchain-based protocol for the service execution according to the design principles of the sharing economy. We present a scalable integration of blockchain technology into the concept of Shared Manufacturing by employing cross-chain solutions. We discuss existing cross chain technologies regarding the requirements of Shared Manufacturing and propose hybrid approach. We compare implementations of the proposed framework on two different blockchain networks: Ethereum public network and Xdai sidechain network. We conduct user-oriented test to explore the performance (cost and time) of the implementations in realistic situations in order to justify the use of the sidechain technology. Results indicate that the implementation on the sidechains provides greater scalability than the implementation on the public blockchain network.     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

领域驱动模型的WEB软件系统设计研究

首先分析了J2EE3层架构的特性及局限,说明了领域驱动设计模型中的充血模型的优势。通过运用最新JDKI．7及Tomcat的相应改造,设计出能实现以充血模型结构为基础的新型WEB系统开发框架,并通过一简单实例说明架构如何支持系统开发。