一种基底细胞概略检测方法

根据宫颈细胞病理学及2001TBS诊断系统,在液基宫颈癌细胞自动筛查过程中,基底细胞过多是一种病理异常现象,需要提示人工阅片.针对这点,提出一种快速识别异常标本的概略检测方法.该方法根据计算机多尺度视觉不变性,采用视觉尺度缩小方式,提高了计算速率,同时利用从北京协和检验洛奇临床检验所采集来的大量标本进行实验验证.结果表明:该方法速度快、计算量小、可靠性高,在工程应用中是可行的.     

晶体管模型参数的计算机辅助测试

本文介绍了一种基于VXIbus模块仪器的晶体管模型参数自动测试方法,并形成了一个实用的计算机辅助测试系统。各参数根据其在GP模型中的物理意义,测得特定工作状态下的电流、电压值,经计算处理而得到。与传统测试方法相比,该测试方案简单实用,减少了众多复杂的测量仪器,具有使用方便、自动化程度高等优点。可应用于电子线路的计算机辅助分析,尤其可用于电路的故障诊断。在模拟印制板故障诊断系统中的实际应用表明,此方法能提供电路故障诊断系统可以接受的计算精度。     

基于多神经网络多参数综合的旋转机械故障诊断系统研究

本文提出并建立了基于多神经网络多参数综合的旋转机械故障诊断系统。在多层多输出前向神经网络的算法基础上,对多个征兆域分别建立相应的诊断网络,然后利用加权方法进行综合评判,并且该诊断系统具有自学习、自适应能力,以便能够适应大型旋转机械,特别是汽轮发电机组等实际产生故障的振动原因的复杂性及诱发的振动征兆的多元性等特点,从而提高了故障诊断的可靠性和诊断精度。本系统对工程应用具有较高的实用价值。     

故障诊断神经网络在食糖储存仓中的应用

本文提出了食糖储存仓计算机监控系统的ANFDS故障诊断策略,故障诊断神经网络改进算法,多重结构神经网络。针对食糖储存仓多输入多输出的多变量系统特点,设计的ANFDS故障诊断系统,经实验结果表明,该方法切实有效。该方法在工程及应用方面有重要意义。     

发动机动力总成惯性参数的扭摆测试研究

发动机动力总成的惯性参数包括其质量、质心位置、转动惯量、惯性积和惯性主轴.本文应用"悬线法"原理与计算机三维模型分析相结合的方法测量发动机动力总成质心位置,并研究应用三线扭摆法测量发动机动力总成转动惯量、惯性积,并计算出其惯性主轴.     

用VB计算湿空气参数

针对湿空气的计算繁琐的特点,本文提出了采用计算机计算的方法,并提供了流程和设计思路.并通过实际计算证实,该程序是准确有效的.     

流程工业设备诊断系统的开发

针对流程工业设备诊断技术的开发、研究和应用现状，较全面地分析了在建立流程工业设备诊断系统中实现综合化、智能化和网络化的问题，以及通过人工神经网络应用技术的开发研究来提高设备综合诊断系统的智能水平问题和快速响应与自组织／自学习神经网络模型的选择问题。为开发研究智能型流程工业设备综合诊断系统提供了依据。     

透平膨胀机制造参数的确定

本文分析了以往透平膨胀机最佳制造参数优化方法的局限性,提出了综合参数选择法,该法适宜计算机计算,已设计并制造出高效率的透平膨胀机,具有很强的实用性。     

浅谈任务驱动在计算机基础教学中的应用

<大学计算机文化基础>是大学各个专业学生的公共基础课.作为接受计算机基础教育的入门课程,在培养大学生的计算机知识.技能、应用能力等方面起到了先导性的作用.结合教学实践谈谈利用"任务驱动法"在计算机基础课程教学中激发学生的学习兴趣,提高学生的学习效率.     

一种简便求解单一要素形状误差的最小区域评定值的方法

本文在深入研究单一要素的形状误差的特点及最小区域法评定的基础上,提出了用旋转,平移方法计算机求解单一要素的最小区域法评定值,经过理论推导和实例计算结果表明,该能成功地解决单一要素形状误差的最小区域法评定。     

故障树分析法在某型飞机火控系统故障诊断中的应用

故障树分析法是系统安全、可靠性分析研究中常用的一种方法。基于故障树分析法与专家系统相结合的某型飞机火控系统故障诊断仪,以机载火控系统不工作为顶事件,建立了故障树,并对故障树作了定性分析,本系统不但具有故障诊断能力,还具有较强的自学习的功能。结果表明,故障树分析法是机载火控系统故障诊断的一种有效方法。     

Automated experience-based learning for plug and produce assembly systems

This paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.     

大气数据测试系统的设计

大气数据系统是重要的机载电子设备，本文应用微诊断法和功能测试法，对大气数据测试系统进行了设计，实现了对大气数据系统各组件的测试，故障覆盖率高，该测试方法对其它机载计算机系统的测试具有推广价值。     

柳钢中板双机架轧机Level Ⅱ控制系统开发

针对柳钢2 800 mm 双机架中厚板轧机,自主设计开发了完整的Level Ⅱ计算机控制系统。Level Ⅱ系统由数据通讯模块、模型设定模块、模型自学习模块、轧件跟踪模块、数据管理模块等多个模块组成。数据通讯模块负责Level Ⅱ控制系统和其他系统之间的数据通讯;数据管理模块包括数据采集与数据记录,为模型计算查询模型参数,记录每块轧件的实际工艺过程参数等;轧件跟踪模块负责轧件从出炉到轧制结束全过程位置和数据的跟踪,并在不同的触发点调用其他功能模块,进行自动轧钢的轧件调度控制和轧制节奏控制;模型设定模块利用数学模型,计算轧制规程;模型自学习模块包括长期自学习和短期自学习。通过各功能模块的协作,可以实现轧机生产过程的模型自动设定和全自动轧钢控制。该系统已经成功应用于生产现场,且应用效果良好。     

热泵空调换热器水侧污垢故障的诊断

提出了换热器水侧污垢的理论模型;利用减小水流量的方法来模拟污垢故障,当故障发生时,吸排气压力会上升,这相当于热泵的制热系数降低了;神经网络由于具有模拟任何连续非线性函数的能力和利用样本学习的能力,已被用于本系统的故障诊断中;采用感知器学习算法对热泵空调换热器水侧污垢故障进行诊断.     

Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation Algorithm for Information Services

Intuitionistic fuzzy Petri net is an important class of Petri nets, which can be used to model the knowledge base system based on intuitionistic fuzzy production rules. In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems, a new Petri net modeling method is proposed by introducing BP (Error Back Propagation) algorithm in neural networks. By judging whether the transition is ignited by continuous function, the intuitionistic fuzziness of classical BP algorithm is extended to the parameter learning and training, which makes Petri network have stronger generalization ability and adaptive function, and the reasoning result is more accurate and credible, which is useful for information services. Finally, a typical example is given to verify the effectiveness and superiority of the parameter optimization method.     

Datenverwaltungssysteme zur Speicherung von Beanspruchungs- und Schwingfestigkeitsdaten

Data Management Systems for Load and Fatigue Strength Data Load and fatigue strength data, which result of tests with technical components, can be stored and managed in computer systems and then worked up to programs for different kinds of special problems. The present report shows the difference between data files and data base systems and the problems by storing and managing the experimental values. Data base systems can be divided into textual and factual data base systems. The report shows further, that factual data base are the best method to store an extensive data collecting.     

An Intelligent Two Phase Fuzzy Decision Tree Based Clustering Model for Design of Computer Aided Detection/Diagnosis (CADe/CADx) System

Automated computer aided detection/diagnosis (CADe/CADx) system plays a key role in decision making process and work as a recommender system for researchers. Nowadays, CADe/CADx systems are getting popular due to their strong ability to extract complex relations present in unprocessed data set. In this research article, we present an intelligent two phase classification model for the design of CADe/CADx system. In order to design an intelligent CAD system, the primary challenge lies in identifying important attributes. The presence of irrelevant and redundant attributes in the data can have adverse impact over classifier accuracy. An efficient dimensionality reduction technique aims at achieving lower computational cost with reduced storage requirement by choosing problem specific relevant or significant attributes. The secondary challenge is to provide unambiguous and comprehensible rule base for accurate predictions. The contribution of work can be stated twofold: first, to attain reasonably good classification accuracy with possible speed up, linear discriminant analysis and some popular correlation coefficients (Fisher, Phi and Point bi-serial) are being used to identify significant attributes. Second, to generate comprehensible and understandable rule set a fuzzy decision tree based clustering approach is used. The performance of proposed model is verified on twelve famous UCI data sets.     

Organizing distributed data bases for parallel searching

Abstract

This paper is mainly concerned with the problem of distributing a data base (i.e., a set of segments) in a computer network system so as to facilitate parallel searching. In our distributed data base model, we assume that all segments are stored in nodes. Each time a query occurs, all nodes are searched concurrently. For convenience, we define the time required to access a segment from any node as a time unit. For a network with d nodes, the response time of a query is then identical to the maximum (n ₁ , n ₂, …, n_d ), where n_i , is the number of segments that satisfies the query and is stored in node i. Unfortunately, the solution for finding an optimal way to organize a distributed data base for parallel searching is still at large. In other words, given a data base, there is no efficient polynomial time algorithm for finding an optimal arrangement of segments onto nodes. In this article, we shall present a “heuristic algorithm” based upon a multivariant analysis method in statistics to distribute a data base in a network system. Some experimental results will show that our method is indeed feasible and effective.     

基于数控加工的熔融沉积成型精度控制

目的 为了提高零件熔融成型精度,提高零件的加工质量.方法 提出基于数控加工的熔融沉积成型精度控制方法,根据零件图纸利用计算机实现加工代码的自动编制,通过熔融沉积成型精度自学习,准确生成熔融沉积成型微调值,进而实现熔融沉积成型精度控制.结果 在相同的测试时间内,设计控制方法的控制偏度系数比对照组的低.结论 设计的控制方法能够确保熔融沉积成型精度.