An intelligent system for monitoring and diagnosis of the CO2 capture process

Amine-based carbon dioxide capture has been widely considered as a feasible ideal technology for reducing large-scale CO₂ emissions and mitigating global warming. The operation of amine-based CO₂ capture is a complicated task, which involves monitoring over 100 process parameters and careful manipulation of numerous valves and pumps. The current research in the field of CO₂ capture has emphasized the need for improving CO₂ capture efficiency and enhancing plant performance. In the present study, artificial intelligence techniques were applied for developing a knowledge-based expert system that aims at effectively monitoring and controlling the CO₂ capture process and thereby enhancing CO₂ capture efficiency. In developing the system, the inferential modeling technique (IMT) was applied to analyze the domain knowledge and problem-solving techniques, and a knowledge base was developed on DeltaV Simulate.The expert system helps to enhance CO₂ capture system performance and efficiency by reducing the time required for diagnosis and problem solving if abnormal conditions occur. The expert system can be used as a decision-support tool that helps inexperienced operators control the plant; it can be used also for training novice operators.     

基于JPEG2000的码率控制算法的研究

码率控制是JPEG2000编码的重要过程,其算法的优劣直接决定着整个编码效率的高低和压缩图像质量的好坏.针对JPEG2000标准建议的码率控制算法的计算复杂度高且占用存储空间大的问题,提出了对隔行扫描编码通道算法ACPI的一种改进算法,包括添加估计斜率值阶段、逆转扫描分辨率水平顺序,以及逆转同等级子带扫描的顺序.实验结果表明,该算法有效地减少了一JPEG2000的计算复杂度和存储量,提高了性能.     

强化学习方法在Web服务组合中的应用比较研究

为了提高服务组合适应动态环境的能力,将强化学习技术引入到Web服务组合.目前常用的强化学习方法有三种:蒙特卡罗、时序差分和Q-Learning,为了发现最适合于服务组合的强化学习方法,对这三种方法进行了对比研究.首先将Web服务组合建模为马尔科夫决策过程,然后介绍了这三种强化学习方法并分析了它们的异同,同时,提出了Web服务组合领域的奖赏值确定方法.最后,通过实验比较了这三种强化学习方法的学习效果,实验结果显示,在Web服务组合应用中,Q-Learning比另外两种方法收敛速度更快,因此更适合执行服务组合.     

基于μC/OS-Ⅱ的车载操作系统设计

根据OSEK/VDX规范,以及μC/OS-Ⅱ内核的特点,分析二者的差异,通过修改μC/OS-Ⅱ内核,设计出一种符合OSEK/VDX规范的车载嵌入式操作系统.并从任务管理与调度、资源管理、警报与计数器管理、事件管理和中断管理等多方面,详细地介绍了该系统的主要设计思想.     

基于SVM和小波分析的脑电信号分类方法

根据癫痫脑电信号与正常脑电信号波形和能量特征的不同,研究了两种的脑电信号分类方法,一种采用支持向量机SVM(Support Vector Machines)分类器对正常脑电和癫痫脑电进行分类;另一种使用小波分析和支持向量机相结合的方法对脑电进行分类,并比较了这两种方法对正常脑电和癫痫脑电分类的正确率。实验结果表明,小波分析和SVM结合的方法对脑电信号分类可以取得更好的效果,能有效区分癫痫脑电和正常脑电。     

一种安全高效的Java操作系统IPC机制的设计与实现

安全高效的进程间通信机制是操作系统中实现进程间协同工作的关键.针对现有Java操作系统普遍使用的共享堆和Java远程过程调用(RMI)等进程间通信机制在安全性、高效性等方面的缺陷,面向Java操作系统的结构特点和进程间通信需求,设计和实现了一种新的安全、高效的进程间通信机制MSP.MSP机制通过区分小尺寸和大尺寸消息,...     

基于ITSQEM的IT服务质量研究

在SERVQUAL模型以及软件工程产品质量模型的基础上,结合IT服务质量的内涵和范畴,提出了IT服务质量评价模型(ITSQEM);并对该模型特性及子特性进行分析,提出了模型裁剪原则以及相应指标项的度量方法,使得质量评价更具有实用性和可操作性。     

Discrete particle swarm optimization based on estimation of distribution for terminal assignment problems

Terminal assignment problem (TEAP) is to determine minimum cost links to form a network by connecting a given set of terminals to a given collection of concentrators. This paper presents a novel discrete particle swarm optimization (PSO) based on estimation of distribution (EDA), named DPSO-EDA, for TEAP. EDAs sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from personal best solutions of all particles into the PSO, and therefore each particle has comprehensive learning and search ability. In the DPSO-EDA, a modified constraint handling method based on Hopfield neural network (HNN) is also introduced to fit nicely into the framework of the PSO and thus utilize the merit of the PSO. The DPSO-EDA adopts the asynchronous updating scheme. Further, the DPSO-EDA is applied to a problem directly related to TEAP, the task assignment problem (TAAP), in order to show that the DPSO-EDA can be generalized to other related combinatorial optimization problems. Simulation results on several problem instances show that the DPSO-EDA is better than previous methods.     

An effective 3-in-1 keyword search method over heterogeneous data sources

Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogeneous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.