A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times

Injection molding is an ideal manufacturing process for producing high volumes of products from both thermoplastic and thermo setting materials. Nevertheless, in some cases, this type of manufacturing process decelerates the production rate as a bottleneck. Thus, layout optimization plays a crucial role in this type of problem in terms of increasing the efficiency of the production line. In this regard, a novel computer simulation–stochastic data envelopment analysis (CS-SDEA) algorithm is proposed in this paper to deal with a single row job-shop layout problem in an injection molding process. First, the system is modeled with discrete-event-simulation as a powerful tool for analyzing complex stochastic systems. Then, due to lack of information about some operational parameters, theory of uncertainty is imported to the simulation model. Finally, an output-oriented stochastic DEA model is used for ranking the outputs of simulation model. The proposed CS-SDEA algorithm is capable of modeling and optimizing non-linear, stochastic, and uncertain injection process problems. The solution quality is illustrated by an actual case study in a refrigerator manufacturing company.     

基于事件驱动方式的高速串口通信方案的设计

针对串口通信中系统资源利用不足的问题,提出了一种基于多线程技术和事件驱动方式的串口通信方案。该方案在读取串口数据、对串口通信的错误及通信事件进行监视时,用OVERLAPPED结构体中的hEvent参数指定I/O操作完成后所触发的事件对象。然后,用等待函数检查事件对象的当前状态。最后,当事件对象为有信号状态时完成后续操作。在Win7操作系统的Visual C++开发平台下,结合Windows通信API控制串口的编程方法进行仿真测试。测试结果表明该方案能有效解决采用查询方式时因数据高速涌入造成数据丢失的问题。     

Exploring the Interactions of Storylines from Informative News Events

Today＇s news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.     

基于贝叶斯网络的新媒体事件分类模型

为了对新媒体事件进行准确有效分类,提出一种将K-means算法和贝叶斯网络相结合的混合算法。该算法首先运用K-means算法将训练样本聚类,再根据聚类的结果,运用改进的贝叶斯网络对新媒体事件进行分类。其中,层次贝叶斯网络模型的构建,避免了贝叶斯网络在参数学习时陷入局部寻优;同时引入隐藏节点,更大程度满足了贝叶斯网络的条件独立假设。实验结果表明该算法效果明显。     

Performance evaluation of an importance sampling technique in a Jackson network

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.     

基于串空间认证测试的DTLS协议认证性分析

网络通信的普及和发展使得对网络协议尤其是安全协议的需求日益增长,同时安全要求及攻击方式的多样化对网络安全协议的效率和准确性提出了更高的要求。通过对各种协议安全性分析方法进行研究,采用串空间方法对DTLS协议进行形式化建模,进而采用认证测试方法进行协议认证性证明,用简明清晰的方式验证DTLS协议的认证性,保障了数据传输的安全。     

硬件中断引入方法及中断深度性能测试

讨论了Windows操作系统下硬件中断产生的方法,使用硬件DIO输出卡对设计的中断服务程序作了多种时序下试验,做了Windows系统下中断服务工作时的干扰试验,在中断线程和工作线程内部提出了多种试验方法,深入研究了Windows系统下的内核级调度执行相关代码段所需的时间,测试了多种时钟的精确度,设计了工程实用的中断服务程序。     

以写字本为例辨析Canvas绘图机制的优劣

通过比较SVG和Canvas两种绘图机制,辨析了使用Canvas在网页中绘图方案的优缺点。使用一个在线写字本案例,论述了Canvas捕获用户的鼠标事件根据用户输入来绘图的过程。最后总结了Canvas绘图的特性以及应用前景。     

历史事件时空过程描述及其可视化研究

结合基于事件的时空数据模型和面向对象的思想,提出一种面向历史事件的时空数据模型。分析基于事件、变化与状态之间的关系,描述和再现重大历史事件中的时空信息及时空变化。给出一种结合事件链表与子事件树的历史事件时态树结构,有效地将时间、事件及相关时空对象组织起来,动态追溯、反演和检索历史事件的发展过程与状态。利用事件时态树结构实现历史事件时空过程可视化,并以Adobe Flex为工具开发实现了历史事件时空过程可视化算法,取得良好效果。     

基于EFPN的模糊离散事件系统可诊断性分析

针对模糊有限自动机及模糊Petri网在模糊离散事件系统(Fuzzy Discrete Event System,FDES)行为建模中的不足,提出一种扩展模糊Petri网(Extended Fuzzy Petri Net,EFPN),并基于EFPN构造了FDES行为模型及系统故障诊断器。EFPN以一种新的事件模型为基础对FDES的系统行为进行建模,并根据事件触发矩阵计算下一系统状态中各分量的可能性分布。与模糊有限自动机及模糊Petri网的对比分析表明,在描述FDES模糊事件时,对于同步距离为0的一组事件所造成的影响,EFPN的表达能力强于经典模糊Petri网;在对含有多个状态分量的FDES建模时,EFPN模型的规模远小于模糊有限自动机。在基于EFPN构造的系统行为模型中,运用可达状态标识图分析系统状态集,可有效地构造FDES的故障诊断器,并可据此进行系统可诊断性分析。