InfServer:一个基于分布计算环境的智能推理服务软件

介绍了一个基于分布计算环境的智能推理服务条件ＩｎｆＳｅｖｅｒ。     

浅析基于多Agent的数据挖掘技术

随着数据库应用规模在各领域的不断扩大,数据挖掘技术得到广泛应用,但传统数据挖掘存在着智能性低,准确率差,人工工作量大的缺陷。而Agent技术具有反应性、自治性、面向目标性和针对环境性等特性。本文分析了数据挖掘技术和多Agent技术,并总结归纳出一中多Agent数据挖掘系统模型。     

基于多Agent的远程协作故障诊断模型研究与设计

随着Agent技术的发展,出现了多种基于多Agent远程协作故障诊断模型,但它们不能很好地满足实时性要求较高系统的需求,因此本文以诊断Agent为基础,构建了基于多Agent的远程协作故障诊断模型,并对模型采用了层次划分,根据各层Agent的功能,采用合同网协议对Agent间任务进行分配协作,提高了系统故障诊断速度,从而为远程故障诊断的实现提供了支持。     

基于多Agent的远程协作故障诊断模型研究与设计

随着Agent技术的发展,出现了多种基于多Agent远程协作故障诊断模型,但它们不能很好地满足实时性要求较高系统的需求,因此本文以诊断Agent为基础,构建了基于多Agent的远程协作故障诊断模型,并对模型采用了层次划分,根据各层Agem的功能,采用合同网协议对Agent间任务进行分配协作,提高了系统故障诊断速度,从而为远程故障诊断的实现提供了支持.     

基于多Agent的自行火炮故障诊断系统研究

本文以自行火炮的状态监测与故障诊断过程为研究对象,通过对自行火炮故障特点的讨论,以多Agent理论为基础,提出了面向故障类型的多Agent故障诊断技术.这种方法把传统的多故障综合诊断变为面向故障类型的多Agent诊断方法,由此建立的诊断系统主要由管理Agent、功能Agent和应用Agent组成.为了提高求解能力,诊断系统将不同推理机制以异构Agent的形式统一于多Agent诊断系统的框架中3并采用真值维护系统(Truth Maintenance System)的方法实现冲突消解.     

基于多Agent混合智能实现个性化信息推荐

在基于Agent的个性化网页推荐中,目前主要有两种过程方法：基于内容的过滤和基于多Agent合作的过滤。本文分析了单独使用这两种方法存在的不足,给出了结合两种方法优点的混合智能过滤虎法,并通过网络Bookmark服务,给出了该算法的一个实际应用系统,最后对该系统的运行结果进行了定性分析。     

多对多RPC设计模型研究

提出了多对多ＲＰＣ模型，讨论了该模型各部分的结构以及服务请求对象的内容。为了防止一个Ａｇｅｎｔ－ｇｒｏｕｐ超载，我们提出了两种多Ａｇｅｎｔ－ｇｒｏｕｐ方法。     

基于多Agent的产品概念结构多方案的协同选择

基于多Agent的建模方法以及美国运筹学家Saaty的层次分析法，提出了产品概念结构设计多方案的多设计专家协同选择的形式化方法，并确定了相应的数学求解模型和算法，最后通过一个设计实例验证了这一协同选择方法的有效性。     

基于多Agent的可控网络安全系统研究

在当前我国的网络技术得到迅猛发展的过程中,网络安全问题也得到了人们的重视,以太网推动的全球信息化迅速发展使得人们对于计算机网络的依赖得到了加强,电脑技术以及互联网技术已经得到了比较广泛的应用,在企业内部网络规模的日益庞大以及网络拓扑结构的复杂化,这些都对社会的发展提出了挑战。基于多Agent的可控网络安全系统的研究就显得格外重要。本文主要就是在多Agent基础上进行对可控网络安全系统进行深入的分析探究,希望能够通过此次的研究对实际起到一定的指导作用。     

分布式多Agent仿真环境系统体系结构研究

为了提高仿真系统的灵活性和易扩展性,从面向服务的架构(SOA)的角度引入了Agent仿真技术,提出了分布式多Agent仿真系统的控制功能框架,该框架包括仿真运行支撑系统(SRSS)、主代理(MA)、领域代理(DA)及其子域代理(SA)。描述了该实体内Agent的分类和功能,同时,对确保各层次代理之间高效交互的Agent间的通信接口和通信过程进行了描述。最后,给出了一个基于上述框架和方法构建的原型系统。研究表明了代理技术的应用有助于加强分布式仿真系统的规范性,提高其重用性和协同互操作性。     

HCE：一个面向分布协同应用的超媒体合作环境

实现了一个能够运行于异构网络通信设施之上，具有实时视频会议，通用ＣＳＣＷ工具以及超媒体合作编辑，制作，表现和管理功能，并能根据特定条件进行推理计算和支持协同应用的超媒体合作环境ＨＣＥ。介绍了ＨＣＥ的总体结构，系统特点及其典型应用。     

基于粒计算的认知模型

本文从粒计算的观点对人类认知过程作了详细的研究,分析了属性与对象的充分性和必要性,并建立了严格的数学模型,将直觉和推理结合在一起得到了认知过程重要的本质结果,从而给出了认知的粒化描述和新的认知模型。该模型较为准确地描述了人类的认知过程,为研究模拟人类的高级智能、形象思维能力提供了一种新的便利工具。     

一种基于分布式LDAP的分布对象名字服务机制

针对传统的集中式分布对象名字服务机制在功能与性能两个方面存在的不足，提出了一种基于分布式LDAP的分布对象名字服务机制。这种新机制使得用户在大量分布对象中查找信息更加方便。同时，它的面向“区域自治”的分布式体系结构，使得系统的负载趋于均衡，消除了系统瓶颈，缩短了用户请求响应时间。     

A Multi-Tenant Usage Access Model for Cloud Computing

Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures. It offers tremendous advantages for enterprises and service providers. It is anticipated that this situation will evolve to foster cross-tenant collaboration supported by Authorization as a service. To realize access control in a multi-tenant cloud computing environment, this study proposes a multi-tenant cloud computing access control model based on the traditional usage access control model by building trust relations among tenants. The model consists of three submodels, which achieve trust relationships between tenants with different granularities andsatisfy the requirements of different application scenarios. With an established trustrelation in MT-UCON (Multi-tenant Usage Access Control), the trustee can precisely authorize cross-tenant accesses to the trustor’s resources consistent with constraints over the trust relation and other components designated by the trustor. In addition, the security of the model is analyzed by an information flow method. The model adapts to the characteristics of a dynamic and open multi-tenant cloud computing environment and achieves fine-grained access control within and between tenants.     

Distributed Trusted Computing for Blockchain-Based Crowdsourcing

A centralized trusted execution environment (TEE) has been extensively studied to provide secure and trusted computing. However, a TEE might become a throughput bottleneck if it is used to evaluate data quality when collecting large-scale data in a crowdsourcing system. It may also have security problems compromised by attackers. Here, we propose a scheme, named dTEE, for building a platform for providing distributed trusted computing by leveraging TEEs. The platform is used as an infrastructure of trusted computations for blockchain-based crowdsourcing systems, especially to securely evaluate data quality and manage remuneration: these operations are handled by a TEE group. First, dTEE uses a public blockchain with smart contracts to manage TEEs without reliance on any trusted third parties. Second, to update TEE registration information and rule out zombie TEEs, dTEE uses a reporting mechanism. To attract TEE owners to join in and provide service of trusted computations, it uses a fair monetary incentive mechanism. Third, to account for malicious attackers, we design a model with Byzantine fault tolerance, not limited to a crash-failure model. Finally, we conduct an extensive evaluation of our design on a local cluster. The results show that dTEE finishes evaluating 10,000 images within one minute and achieves about 65 tps throughput when evaluating Sudoku solution data with collective signatures both in a group of 120 TEEs.     

MoTransFrame: Model Transfer Framework for CNNs on Low-Resource Edge Computing Node

Deep learning technology has been widely used in computer vision, speech recognition, natural language processing, and other related fields. The deep learning algorithm has high precision and high reliability. However, the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power. In this paper, we propose MoTransFrame, a general model processing framework for deep learning models. Instead of designing a model compression algorithm with a high compression ratio, MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and accurately. By the integration method, Deep learning models can be converted into portable projects for Arduino, a typical edge device with limited resources. Our experiments show that MoTransFrame has good adaptability in edge devices with limited memories. It is more flexible than other model transplantation methods. It can keep a small loss of model accuracy when the number of parameters is compressed by tens of times. At the same time, the computational resources needed in the reasoning process are less than what the edge node could handle.     

A Distributed LRTCO Algorithm in Large-Scale DVE Multimedia Systems

In the large-scale Distributed Virtual Environment (DVE) multimedia systems, one of key challenges is to distributedly preserve causal order delivery of messages in real time. Most of the existing causal order control approaches with real-time constraints use vector time as causal control information which is closely coupled with system scales. As the scale expands, each message is attached a large amount of control information that introduces too much network transmission overhead to maintain the real-time causal order delivery. In this article, a novel Lightweight Real-Time Causal Order (LRTCO) algorithm is proposed for large-scale DVE multimedia systems. LRTCO predicts and compares the network transmission times of messages so as to select the proper causal control information of which the amount is dynamically adapted to the network latency variations and unconcerned with system scales. The control information in LRTCO is effective to preserve causal order delivery of messages and lightweight to maintain the real-time property of DVE systems. Experimental results demonstrate that LRTCO costs low transmission overhead and communication bandwidth, reduces causal order violations efficiently, and improves the scalability of DVE systems.     

开放式设计在数字化形态下的服务模型

目的探寻数据信息时代开放式设计新的特点与内涵,分析其设计形态的变化及发展趋势,以及新设计形态下的现存问题及解决对策,以开放式设计数字化为研究基础,探究适用于现今阶段发展的设计思维、方法与框架。方法通过文献综述法、比较分析法、实例研究法、系统分析法等分析开放式设计现阶段的新特点与新问题,以"数据库本体"思想结合"复杂适应系统理论"建立开放式设计服务模型。结论构建开放式设计服务模型,实则是在创造一种开发设计方法的创新工具,而设计师在此模型中的角色转换为"数据设计师",该服务模型某种意义上是一个用于平衡输入要素、工序要素、输出要素之间结构组织关系的动态调节系统,能够为设计不断探究出新的方法、流程与模型。     

Quantum Fuzzy Regression Model for Uncertain Environment

In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation. The application of the quantum fuzzy regression model to small-scale financial data proves that its accuracy is greatly improved compared with the quantum regression model. Moreover, due to the introduction of quantum computing, the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model. The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data. Thus, it is a new model for efficient and accurate big data processing in uncertain environments.