面向艺术生Arduino硬件接口的可用性设计决策

文章探究Arduino硬件接口的可用性设计决策方法以提高目标用户的使用效率。以艺术生为目标用户,以ISO及尼尔森可用性定义为基础,研究Arduino硬件接口的可用性因素。分析该硬件接口的主要构成,归纳出目标用户与硬件的主要交互方式,以求解最优交互方式为目标,建立层次分析法模型。通过用户访谈得到专家用户对于可用性因素的权重评分及新手用户对每种交互方式在可用性因素上的评分,层次运算得到各交互方式的综合得分。透过数据分析用户动因,以最优方案推理设计原则并重构Arduino硬件接口。通过验证,该设计决策方法能综合多层次用户意见,以结合定性分析和定量分析方式改进其可用性。     

国内软件组织可用性能力成熟度评估

可用性工程是开发高可用性交互式软件和IT产品的有效途径，这要求根据开发组织的可用性成熟度状况，把以用户为中心的设计方法结合到产品生命周期过程中，为指导可用性工程在国内的引进和推广，我们按照可用性能力成熟度模型UMM，选择代表性的软件企业进行了一次可用性能力成熟度评估。本文介绍这次评估依据的UMM模型，所采用的评估方法和评估结果，重点讨论所发现的阻碍企业应用可用性工程的问题，并提出过程改进的对策。     

基于加权FPN的聚驱综合调整系统决策分析

模糊Petri网(Fuzzy Petri Nets, FPN)是一种适合于描述异步并发事件的计算机系统模型,可以有效地对并行和并发系统进行形式化验证和决策分析.针对聚驱综合调整系统知识具有不确定性和模糊性的特点,给出了基于加权模糊产生式规则的加权FPN决策模型.在此模型的基础上,给出了决策推理过程的形式化推理算法.算法考虑了推理过程中的众多约束条件,将复杂的推理过程采用矩阵运算来实现,充分利用了FPN的并行处理能力,使决策推理过程更加简单和快速.并以压裂方式调整为例,说明了该模型具有直观、表达能力强和易于推理等优点,具有较强的实用价值.     

时间序列下模式挖掘模型设计

在模式挖掘应用于智能化方法过程中,为了提高数据变化模式的准确性和可用性,以FC闭包模型为基础,对专家界定的领域影响因子进行逻辑转化,采用距离均方差算法以时间序列为基础处理原始数据,并利用激巨判定函数摒弃无效元素,降低数据维度,完成数据准备。选定恰当可行的数学模型进行时序数据拟合,借鉴分类分析法的思想,引入CCM-ECM模型表达最终挖掘结果,完成时序下模式挖掘模型（TODM）设计,同时为该模型的置信度计算和自适应调整提出一套较为科学的计算方法,以此达到深度挖掘数据内部潜在规律,提高数据变化模式的高精细化描述程度的目的。最后结合油井施工作业过程,利用TODM模型实现了油井施工作业后模式挖掘系统的设计。     

基于T-S推理网络的油田开发指标预测方法*

针对油田开发指标预测问题,提出一种T-S推理元模型,该模型包括输入层、模糊化层和推理层。每个推理元对应一条模糊逻辑规则,由若干T-S推理元可构成T-S推理网络。网络可调参数包括模糊集参数和模糊规则参数。提出了基于改进量子粒子群优化的参数确定方法。以油田开发指标中含水率和采油量预测为例,结果表明,该方法是有效且可行的,从而表明模糊逻辑与智能优化算法的融合对于解决指标预测问题具有一定潜力。     

一种基于Markov模型的自适应软件可用性监测模型

软件可用性工程是当前软件开发的重要研究领域,研究者试图通过对软件可用性特征的分析提高使用者使用效率,改善软件质量。本文针对软件使用过程中的用户操作特征,采用Markov模型对用户软件操作过程进行聚类和建模。并在此基础上根据用户模型的概率描述指标,提出了相应的在线模型更新算法和帮助系统设计方法。在实际软件设计过程中设计实现了以此为理论基础的试验系统,取得了较好的应用效果。     

支持短时交通流量预测的概率图模型构建与推理

短时交通流量预测,是交通系统信息化和智能化交通运输管理技术领域研究的关键问题.目前的方法对历史数据具有较高的依赖程度,或者具有较高的计算成本,或者不能有效反映实际中较复杂的交通网络及各结点之间的相互关系、以及依赖的不确定性,或者多种模型的组合使得预测方法较复杂.贝叶斯网是一种重要的概率图模型,本文以交通网络结构为基础,利用概率图模型在不确定性知识表示和推理方面的良好性质,考虑路口交通流量及其预测的时序依赖特征,构建了带有时序条件依赖关系的交通贝叶斯网.进而针对短时交通流量预测的实时性和高效性要求,提出了基于Gibbs采样的交通贝叶斯网近似概率推理算法,并进行交通流量的短时预测.实验结果表明,本文提出的交通贝叶斯网构建、近似推理以及相应的短时交通流量的预测方法,具有高效性、准确性和可用性.     

空间推理中方向关系模型的研究

空间方向关系的基本模型在研究空间推理上起着至关重要的作用,直接影响空间推理中合成或反方向合成的准确性和效果。文章阐述了现有空间推理中所采用的几种基本模型,对比和分析了现有模型的优点、缺点以及其适用性等问题,在现有主流的MBR框架基础上提出了一种改良后的新模型。经过对比和分析,得到结果表明此新模型在适应度、灵活度、准确度上都有一定提高并能很好的匹配人们的认知习惯。为今后的空间推理寻找到了一种新的思路和新的方法。     

基于机器学习的临床决策支持系统的设计

临床决策支持系统对提高医生决策能力具有重要意义,本文设计了临床决策系统的功能及组成,并利用粗糙集理论及神经网络的学习能力来获取疾病知识,提高了知识获取的准确性和全面性;并采用模糊神经网络的高效推理机制,构建了临床决策系统模型,为引导医生逐步诊断出疾病,提供了参考的治疗方案。     

范例推理在智能决策系统中的应用研究

范例推理是人工智能中重要的推理方法和机器学习技术,它也是智能系统中实用的技术之一。基于范例的决策是决策者认知心理的决策过程的一个合理描述,它提供了一种实现智能系统及决策的现实环境和技术方法。本文提出了基于范例推理的智能决策技术,给出应用模型,并进行了深入讨论。     

基于产生式探井决策专家系统的研究与应用①

基于产生式的探井决策专家系统以人工智能为理论,采用产生式知识表示方法,以油井钻探数据为依据,将获取的探井专家经验知识存入知识库,通过高效的推理,给出合理的决策方案,提高探井工程效率。     

基于产生式探井决策专家系统的研究与应用①

基于产生式的探井决策专家系统以人工智能为理论,采用产生式知识表示方法,以油井钻探数据为依据,将获取的探井专家经验知识存入知识库,通过高效的推理,给出合理的决策方案,提高探井工程效率。     

Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning

The acquisition of data through remote sensing has become of great importance in precision agriculture, as it covers large geographical areas faster and cheaper than ground inspections. The challenge is to develop technical solutions that can benefit from both huge amounts of raw data extracted from satellite images, but also from the robust amount of knowledge refined during centuries of agricultural practice. Aiming to accurately classify crops from satellite images, we developed a hybrid intelligent system that can exploit both agricultural expert knowledge and machine learning algorithms. As the crop raw data is characterized by heterogeneity, we drive our attention to ensemble learners, while expert knowledge is encapsulated within a rule-based system. Vote-based methods for solving conflicts between ensemble’s base learners have difficulties in classifying exceptional cases correctly and also to give the rationale behind their decision. The conceptual research question is on conflict resolution in ensemble learning. To deal with debatable cases in ensemble learning and to increase transparency in such debatable decisions, our hypothesis is that argumentation could be more effective than voting-based methods. The main contribution is that voting system in ensemble learning is substituted by an argumentation-base conflict resolutor. Prospective decisions of base classifiers are presented to an argumentative system based on defeasible logic that performs dialectical reasoning on pros and cons against a classification decision. The system computes a recommendation considering both the rules extracted from base learners and the available expert knowledge. The investigated case study deals with crop classification into four classes: corn, soybean, cotton, and rice. The test site used for the experiment is an area of 20 square kilometers in the New Madrid County, southeast of the Missouri State, USA. The results show that our approach increases classification accuracy compared to the voting-based method for conflict resolution in an ensemble learner comprising of three base classifiers: a decision tree, a neural network, and a support vector machine algorithm. We also argue that combining ensemble learning and argumentation fits the decision patterns of human agents, who first collect various opinions and then perform dialectical reasoning on these opinions. We think that the people who can benefit from the conceptual instrumentation presented in this work are decision makers in domains characterized by high data availability, robust expert knowledge, and a need for justifying the rationale behind decisions.     

多引擎动物疾病诊断专家系统

传统疾病诊断专家系统通过单次推理过程进行知识运用,其知识利用率低,结论准确度低且不具备对比度。该文以山羊为例,运用面向对象的知识表示方法对疾病诊断领域知识库进行建模,结合专家训练过程与诊断模型,提出知识库多引擎判决思想,多次牵引知识库运用知识,构造面向对象加权不确定判决和样本匹配判决算法。实验结果表明,多引擎诊断模型提高了知识库数据资源的利用率,改善了诊断准确度,增加了对比度。     

临近空间成像资源配置专家决策系统研究

针对临近空间成像资源配置的多目标组合优化特性,为了降低该问题的求解难度,迅速求解合理可行的临近空间成像资源配置方案,研究了融合决策支持模型的专家决策系统,建立了专家知识库、决策推理机以及规则管理模块,基于专家知识、决策支持模型,运用多级推理机制,其多级推理机制主要包括约束过滤、冲突过滤与综合评价决策,求解临近空间资源配置方案,实际运行结果表明,该系统能较好地解决临近空间资源配置问题.     

Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices — The case of sugarcane harvest on Reunion Island

Time series of optical satellite images acquired at high spatial resolution is a potentially useful source of information for monitoring agricultural practices. However, the information extracted from this source is often hampered by missing acquisitions or uncertain radiometric values. This paper presents a novel approach that addresses this issue by combining time series of satellite images with information from crop growth modeling and expert knowledge. In a fuzzy framework, a decision support system that combines multi-source information was designed to automatically detect the sugarcane harvest at field scale. The formalism that we used deals with the imprecision of the data and the approximation of expert reasoning. System performances were analyzed using a time series of SPOT-5 images. Results obtained were in substantial agreement with ground truth data: overall accuracy reached 97.80% with stability values exceeding 89.21% for all decisions. The contribution of fuzzy sets to overall accuracy reached 15.08%. The approach outlined in this paper is very promising and could be very useful for other agricultural applications.     

A Strategy of Dynamic Reasoning in Knowledge-Based System with Fuzzy Production Rules

Fuzzy production rules have been successfully applied to represent uncertainty in a knowledge-based system. The knowledge organized as a knowledge base is static. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a strategy to reflect the dynamic nature of a system when we make reasoning with a knowledge-based system.This paper proposes a strategy of dynamic reasoning that can be used to takes account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy rules. A degree of match (DM) between actual input information and antecedent of a rule is represented by a value in interval [0, 1]. Weights of relative importance of attributes in a rule are obtained by the AHP (Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected to reasoning in knowledge-based system with fuzzy rules.With the proposed reasoning procedure, a decision maker can take his judgment on the given decision environment into a static knowledge base with fuzzy rules when he makes decision with the knowledge base. This procedure can be automated as a pre-processing system for fuzzy expert systems. Thereby the quality of decisions could be enhanced.     

How to take into account general and contextual knowledge for interactive aiding design: Towards the coupling of CSP and CBR approaches

The goal of this paper is to show how it is possible to support design decisions with two different tools relying on two kinds of knowledge: case-based reasoning operating with contextual knowledge embodied in past cases and constraint filtering that operates with general knowledge formalized using constraints. Our goals are, firstly to make an overview of existing works that analyses the various ways to associate these two kinds of aiding tools essentially in a sequential way. Secondly, we propose an approach that allows us to use them simultaneously in order to assist design decisions with these two kinds of knowledge. The paper is organized as follows. In the first section, we define the goal of the paper and recall the background of case-based reasoning and constraint filtering. In the second section, the industrial problem which led us to consider these two kinds of knowledge is presented. In the third section, an overview of the various possibilities of using these two aiding decision tools in a sequential way is drawn up. In the fourth section, we propose an approach that allows us to use both aiding decision tools in a simultaneous and iterative way according to the availability of knowledge. An example dealing with helicopter maintenance illustrates our proposals.     

Decision support analysis for safety control in complex project environments based on Bayesian Networks

This paper presents a novel and systemic decision support model based on Bayesian Networks (BN) for safety control in dynamic complex project environments, which should go through the following three sections. At first, priori expert knowledge is integrated with training data in model design, aiming to improve the adaptability and practicability of model outcome. Then two indicators, Model Bias and Model Accuracy, are proposed to assess the effectiveness of BN in model validation, ensuring the model predictions are not significantly different from the actual observations. Finally we extend the safety control process to the entire life cycle of risk-prone events in model application, rather than restricted to pre-accident control, but during-construction continuous and post-accident control are included. Adapting its reasoning features, including forward reasoning, importance analysis and background reasoning, decision makers are provided with systematic and effective support for safety control in the overall work process. A frequent safety problem, ground settlement during Wuhan Changjiang Metro Shield Tunnel Construction (WCMSTC), is taken as a case study. Results demonstrate the feasibility of BN model, as well as its application potential. The proposed model can be used by practitioners in the industry as a decision support tool to increase the likelihood of a successful project in complex environments.     

基于专家系统的多角色烧结配矿决策支持系统

针对烧结配矿中传统试错法难以求出最优解的问题,综合考虑品位、成本、库存、国外矿比例等多种影响因素,建立其优化配比的线性规划模型,用带罚函数的一阶梯度法完成模型求解.并在构建知识库和模型库的基础上,引入专家系统的解释器和推理机制,建立一套基于专家系统的多角色决策支持系统.实验结果表明,该系统在满足烧结配矿工艺要求的基础上...