人工免疫记忆模型研究

免疫记忆模型对人工免疫具有重要的作用。文中详细研究了两种免疫记忆模型：记忆细胞模型和残余抗原模型。并对这两种模型进行了详细的分析和对比。最后，对残余抗原模型进行了改进．从而实现了残余抗原的动态性。     

基于残余抗原学说的动态记忆风险识别模型

以小概率事件风险识别为研究对象,提出一个基于残余抗原学说的动态记忆风险识别模型DMRIM。DMRIM针对小概率事件风险的无规则等特点,将风险的强度和频度直观地、动态地映射为残余抗原的浓度,以残余抗原刺激免疫记忆、指导抗体进化、控制识别器的生命周期,突破了传统的记忆细胞生命周期,实现了识别器分布自制,提高了小概率事件的辨识能力。仿真实验表明,DMRIM充分体现免疫记忆的动态性,有效地识别小概率事件,其可行性在实际应用中得到了验证。     

入侵检测中基于残余抗原理论的动态记忆算法

依据生物免疫的防御层次结构,分析了网络入侵的多层防御体系.引入生物学中残余抗原理论,结合入侵检测过程中存在的残余抗原,提出了一种新的基于残余抗原理论的动态记忆算法.在此基础上,分析了算法的设计思想,给出了算法的具体实现过程.实验数据表明:该算法实现了识别器记忆的动态性和持久性,并且能够提高系统资源的利用率.     

基于人工免疫网络记忆的新型分类器研究

该文首先简要介绍了自然免疫系统的免疫记忆原理,之后对aiNet和AIRS机制进行了分析,指出免疫记忆在两种算法中起关键作用。aiNet利用传统聚类技术对产生的记忆抗体进行数据聚类分析,无法得知原始抗原类别。AIRS通过训练产生记忆细胞池,利用最近邻原理对原始抗原分类。文中,aiNet的记忆抗体生成机制与KNN分类机制结合,提出基于人工免疫网络记忆的新型分类器AINMC———人工免疫网络记忆分类器。实验结果表明,所提出的新型分类器具有良好的记忆和泛化性能,准确率可以与许多传统方法相比较。     

基于免疫网络的遥感影像分类算法

基于独特型免疫网络原理,提出了一种新型的分区记忆模式人工独特型网络模型,并利用其对卫星遥感数据进行了分类。该模型在结构上将免疫网络的记忆抗体划分为特异记忆抗体区和自由记忆抗体区。前者的主要功能是记忆各类别抗原的特异特征,后者为前者提供各种类型的抗体源。记忆抗体间按照亚动力学原理进行调节,实现免疫网络的寻优过程。基于上述分区,它在初次免疫响应过程中实现网络的搭建和训练,在二次免疫响应过程中实现信息提取。最后利用该模型对ETM数据进行地物分类,并与传统分类方法进行对比。结果表明：该模型的总分类精度和Kappa系数分别是92.6%和0.91,优于传统分类方法。     

基于免疫记忆的人工免疫算法模型及其应用

本文在深入分析现有人工免疫算法模型优缺点的基础上,提出了一种基于免疫记忆机制的改进人工免疫算法模型ARTIA.该模型融合了由生物免疫系统启发而来的免疫记忆机制,包括联想记忆和迭代记忆两种,采用了多种策略以保持群体多样性,进而在数值试验的基础上对ARTIA算法模型的性能进行了分析和讨论.最后通过本质上可以归结为旅行商问题(TSP)的多目标组合优化工程实例--岩石钻孔机路径选择问题,验证了该算法的有效性.结论部分对全文作了总结并对今后研究工作进行了展望.     

基于免疫记忆的蚁群算法

充分利用前期迭代中解的信息是构造高效蚁群算法实现的关键之一.文中把免疫记忆和克隆选择的思想引入蚁群算法,提出了基于免疫记忆的蚁群算法(IMBACA).算法通过在原有蚁群模型上增加一个免疫记忆库,将记忆库中的解对应为免疫记忆细胞(及其产生的抗体),将问题对应为抗原,并借鉴克隆选择和免疫记忆的思想进行解的构造和信息素更新.算法从解的质量和时间方面与传统蚁群算法进行了比较,实验结果表明,所提出的IMBACA算法可明显提高传统蚁群算法的性能,同时也为解决其他组合优化问题提出了一个新的思路.     

模拟电路免疫记忆网络故障诊断新方法

提出了一种基于免疫记忆网络理论与$k$近邻算法的模拟电路故障诊断方法。首先,利用免疫记忆网络寻找各故障空间的最佳记忆抗体。在免疫记忆网络中根据浓度来选择记忆抗体,以促进记忆抗体在各故障空间的均匀分布。利用克隆和超级变异机制来保证抗体多样性,再利用浓度和期望值对抗体进行促进和抑制,以避免早熟现象的产生;然后,根据所得到的各故障空间的最佳记忆抗体,使用改进的阈值k近邻算法对抗原进行故障分类;最后,以带通滤波器为诊断实例,利用实际电路测试数据和仿真数据作为测试样本进行故障诊断性能评估;实验结果证明该故障诊断方法具有较高的故障诊断率。     

一种新型的模糊C均值聚类初始化方法

模糊C均值聚类(FCM)是一种广泛采用的动态聚类方法，其聚类效果往往受初始聚类中心的影响。受自适应免疫系统对入侵机体的抗原产生免疫记忆的机理启示，提出了一种新的产生初始聚类中心的方法。算法中，待分析的数据被视为入侵性抗原，产生的记忆细胞作为聚类分析的初始中心。克隆选择用来产生抗原的记忆细胞群体，免疫网络理论则用来抑制该群体规模的快速增长。实验结果表明免疫记忆机理用于FCM初始中心的选择是可行的，不仅提高了FCM算法的收敛速度，而且可以通过改变阈值的大小自动决定类别数。     

按模式记忆理论的记忆结构刻画

给出了一个按模式记忆铁记忆模型,详细讨论了它的基本构成单元－智能记忆单元ＩＭＥ的结构和操作,由ＩＭＥ构成的记忆结构是一个开放性的存储结构,可以实现记忆的层次性、语义性、时效性和灰度性,并提供在此记忆结构进行联想记忆的必要信息。     

存储模型仿真器的设计与实现

存储一致性问题和高速缓存一致性问题是共享存储并行计算机中两个最关键的问题,通过仿真器对它们进行了量化研究,设计并实现了一个存储模型仿真器MMS．基于MMS仿真了不同并行机结构模型下多种存储一致性模型的行为;针对不同类型的计算问题比较了不同的存储一致性模型,并对实验结果进行了分析;实现了几个不同的高速缓存一致性协议,并比较了它们的性能．     

Models of parallel computation :a survey and classification

In this paper, the state-of-the-art parallel computational model research is reviewed. We will introduce various models that were developed during the past decades. According to their targeting architecture features, especially memory organization, we classify these parallel computational models into three generations. These models and their characteristics are discussed based on three generations classification. We believe that with the ever increasing speed gap between the CPU and memory systems, incorporating non-uniform memory hierarchy into computational models will become unavoidable. With the emergence of multi-core CPUs, the parallelism hierarchy of current computing platforms becomes more and more complicated. Describing this complicated parallelism hierarchy in future computational models becomes more and more important. A semi-automatic toolkit that can extract model parameters and their values on real computers can reduce the model analysis complexity, thus allowing more complicated models with more parameters to be adopted. Hierarchical memory and hierarchical parallelism will be two very important features that should be considered in future model design and research.     

A Study on Modeling and Optimization of Memory Systems

Accesses Per Cycle(APC),Concurrent Average Memory Access Time(C-AMAT),and Layered Performance Matching(LPM)are three memory performance models that consider both data locality and memory assess concurrency.The APC model measures the throughput of a memory architecture and therefore reflects the quality of service(QoS)of a memory system.The C-AMAT model provides a recursive expression for the memory access delay and therefore can be used for identifying the potential bottlenecks in a memory hierarchy.The LPM method transforms a global memory system optimization into localized optimizations at each memory layer by matching the data access demands of the applications with the underlying memory system design.These three models have been proposed separately through prior efforts.This paper reexamines the three models under one coherent mathematical framework.More specifically,we present a new memory-centric view of data accesses.We divide the memory cycles at each memory layer into four distinct categories and use them to recursively define the memory access latency and concurrency along the memory hierarchy.This new perspective offers new insights with a clear formulation of the memory performance considering both locality and concurrency.Consequently,the performance model can be easily understood and applied in engineering practices.As such,the memory-centric approach helps establish a unified mathematical foundation for model-driven performance analysis and optimization of contemporary and future memory systems.     

结合自注意力和残差的BiLSTM_CNN文本分类模型

双向长短期记忆网络（BiLSTM）和卷积神经网络（CNN）很难在文本的多分类任务中提取到足够的文本信息。提出了一种基于自注意力机制（self_attention）和残差网络（ResNet）的BiLSTM_CNN复合模型。通过自注意力赋予卷积运算后信息的权重,接着将池化后的特征信息层归一化并接入残差网络,让模型学习到残差信息,从而进一步提高模型的分类性能。在模型的运算过程中,使用了更加光滑的Mish非线性激活函数代替Relu。通过与深度学习模型对比,所提出的方法在准确率以及F1值评价指标上均优于现有模型,为文本分类问题提供了新的研究思路。     

记忆神经网络的研究与发展

首先,根据记忆神经网络训练形式的不同,介绍了强监督模型和弱监督模型的结构特征和各自应用场景以及处理方式,总结了两类主要模型的优缺点;随后,对两类模型的发展和应用（包括模型创新和应用创新）进行了简要综述,总结了各类新模型在处理自然语言过程中所起的关键作用;最后梳理了记忆神经网络处理自然语言所面临的复杂性挑战,并预测了记忆神经网络未来的发展方向.     

Super－Object模型：在分布存储器多计算机上实现共享存储器程序设计模式

Ｓｕｐｅｒ－Ｏｂｊｅｃｔ模型提出了一种新的方法，在分布存储器多计算机上实现语言级虚拟共享存储器以支持共享存储器通信模式．Ｓｕｐｅｒ－Ｏｂｊｅｃｔ模型引入新的概念ｓｕｐｅｒ－ｏｂｊｅｃｔ，不同于其它模型，基于ｓｕｐｅｒ－ｏｂｊｅｃｔ，它提出了新的共享数据定位方法，全局地址标识（ｎａｍｅ，ｏｆｆ－ｓｅｔ）．Ｓｕｐｅｒ－Ｏｂｊｅｃｔ模型与Ｆｏｒｔｒａｎ７７结合，我们实现了一个运行时间系统和库调用，支持程序员使用Ｆｏｒｔｒａｎ语言编写并行程序，最后介绍了系统的实现和取得的性能．     

Comparing shared and distributed memory computers

There are two distinct types of MIMD (Multiple Instruction, Multiple Data) computers: the shared memory machine, e.g. Butterfly, and the distributed memory machine, e.g. Hypercubes, Transputer arrays. Typically these utilize different programming models: the shared memory machine has monitors, semaphores and fetch-and-add; whereas the distributed memory machine uses message passing. Moreover there are two popular types of operating systems: a multi-tasking, asynchronous operating system and a crystalline, loosely synchronous operating system.

In this paper I firstly describe the Butterfly, Hypercube and Transputer array MIMD computers, and review monitors, semaphores, fetch-and-add and message passing; then I explain the two types of operating systems and give examples of how they are implemented on these MIMD computers. Next I discuss the advantages and disadvantages of shared memory machines with monitors, semaphores and fetch-and-add, compared to distributed memory machines using message passing, answering questions such as “is one model ‘easier’ to program than the other?” and “which is ‘more efficient‘?”. One may think that a shared memory machine with monitors, semaphores and fetch-and-add is simpler to program and runs faster than a distributed memory machine using message passing but we shall see that this is not necessarily the case. Finally I briefly discuss which type of operating system to use and on which type of computer. This of course depends on the algorithm one wishes to compute.     

DaSH: A benchmark suite for hybrid dataflow and shared memory programming models

The current trend in development of parallel programming models is to combine different well established models into a single programming model in order to support efficient implementation of a wide range of real world applications. The dataflow model has particularly managed to recapture the interest of the research community due to its ability to express parallelism efficiently. Thus, a number of recently proposed hybrid parallel programming models combine dataflow and traditional shared memory models. Their findings have influenced the introduction of task dependency in the OpenMP 4.0 standard.This article presents DaSH – the first comprehensive benchmark suite for hybrid dataflow and shared memory programming models. DaSH features 11 benchmarks, each representing one of the Berkeley dwarfs that capture patterns of communication and computation common to a wide range of emerging applications. DaSH also includes sequential and shared-memory implementations based on OpenMP and Intel TBB to facilitate easy comparison between hybrid dataflow implementations and traditional shared memory implementations based on work-sharing and/or tasks. Finally, we use DaSH to evaluate three different hybrid dataflow models, identify their advantages and shortcomings, and motivate further research on their characteristics.