一种基于决策树和词义相似度的N1+N2结构语法关系判定方法

设计了一种基于决策树算法的N1+N2结构语法关系判定方法.首先建立了该结构的语料库,对每条语料都标注构建特征集所需的词性、《同义词词林》语义编码、语法关系和词义相似度等信息;然后为证明相似度在判断语法关系中的合理性,根据语言学原理研究了N1+N2结构两个名词间语义相似度与语法关系之间的内在规律:①从语法关系的角度比较两个名词间的语义相似度结果为:并列关系>复指关系>定中关系>主谓关系;②从语言功能焦点的角度比较两个名词间的语义相似度结果为:并焦型短语>后焦型短语.最后以此为基础构建了特征集,运用决策树C4.5算法建立了自动判定N1+N2结构语法关系的方法.运用该算法在自建语料库的测试集中进行测试,正确率为89.39％.     

基于深度学习和三支决策的DDoS攻击检测算法

针对软件定义网络(Software Defined Ntwork,SDN)中的分布式拒绝服务(Distribute Denial of Service,DDoS)攻击检测的方法少、现存方法入侵检测率低的问题,提出了一种基于深度学习和三支决策的入侵检测算法.首先使用深度信念网络对SDN的流表项进行特征提取,然后利用基于三支决策理论的入侵检测模型进行DDoS攻击的入侵检测,对于正域和负域的数据直接进行分类,对于边界域中的数据使用K近邻算法重新进行分类.仿真实验结果表明,与其他入侵检测模型相比,所提算法的入侵检测效率更高.     

A systematic literature review on intelligent automation: Aligning concepts from theory,practice, and future perspectives

With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA’s benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.     

A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network

This paper focuses on consensus reaching process (CRP) under social network in which the trust relationship expressed by linguistic information. A new feedback mechanism in social network group decision making (SN-GDM) is proposed, which mainly consists of the following two aspects: (1) The propagation of distributed linguistic trust is investigated to study trust relation among experts; (2) A maximum self-esteem degree based feedback mechanism is developed to produce personalized advice for reaching higher group consensus. To do so, a novel linguistic trust propagation method is proposed to obtain the complete trust relationship among group. The self-esteem degree is used to define the extent that an individual makes concessions. Then, a maximum self-esteem degree based optimal feedback mechanism is built to produce personalized advice to help inconsistent experts make change of their opinion. Its novelty lies in the establishment of an optimization model with the nonlinear group self-esteem degree function as the objective function while group consensus threshold as the restrictions. Therefore, the inconsistent experts will reach a group consensus with the minimum loss of self-esteem degree, and then, it achieves the optimal balance between individual self-esteem and group consensus. Finally, a ranking process is applied to derive the appropriate consensus solution.     

优化强化学习路径特征分类的脉象识别法

脉象识别是中医诊断的重要手段之一。长期以来,依据个人经验进行的脉诊制约了中医的推广与发展。因此,利用传感设备进行脉象识别的研究正在逐步展开。针对神经网络识别脉象的相关研究中,存在需要大量训练数据集,以及存在处理“黑箱”和时间花销较大等问题,在强化学习的框架下,提出了一种采用马尔可夫决策和蒙特卡罗搜索的脉象图分析法。首先依据中医理论对特定的脉象进行路径分类,然后在此基础上为不同的路径选择代表性特征,最终通过对代表性特征的阈值对比完成对脉象的识别。实验结果表明,所提方法可缩减训练时间和所需资源,并可保留完整的经验轨迹;且在提高脉象识别的准确率的同时,还可解决数据处理过程中的“黑箱”问题。     

基于OE-概念格的形式背景属性约简

在三支概念格的属性约简框架下,借助布尔矩阵理论,研究保持OE-对象粒矩阵不变的属性约简问题。给出OE-对象粒矩阵的概念以及OEG粒协调集的定义,在此基础之上讨论属性之间的相似性,并且刻画属性的内外重要度。针对对象导出三支概念格的形式背景设计基于矩阵理论的启发式属性约简算法。将上述理论结果应用于对象导出三支概念格的决策形式背景,提出三支协调决策形式背景及OEG粒协调集的概念,并且从规则提取的角度说明约简集对应的OE-概念格的决策形式背景的三支规则集比原背景的三支规则集更加简洁。通过数值实验阐明该理论的可行性和合理性。     

Variable sorting based on fault tree structure adjustment for multi-valued decision diagrams

The dynamic fault tree does not have the ability of fault calculation when analyzing the reliability of the system, so it needs to be modeled and analyzed by means of multi-valued decision diagrams and so on. The dynamic fault tree is translated into a multi-valued decision diagram according to the ordering of variables of basic events which affects the size of the generated multi-valued decision diagram and the cost of traversal to get cut sequences. An adjustment method of the dynamic fault tree structure is proposed based on the importance degree of repeated events, dynamic gates and the locations of events of the dynamic fault tree. By taking the fact that the basic events at the same level are equally important into account, an improved dynamic variable ordering method and ordering generation algorithm for the multi-valued decision diagram are presented in this paper. The result analysis of an example shows that the improved dynamic variable ordering method can reduce the size of the multi-valued decision diagram and space and time complexity of the reliability calculation compared to the similar methods.     

博斯腾湖流域水资源管理决策支持系统设计与实现

为了提高博斯腾湖流域水资源管理水平,设计开发了针对该流域的水资源管理决策支持系统。系统基于Microsoft.NET平台,采用Visual Studio、SQL Server、ArcGIS工具集开发,在流域“地-云-空”一体化监测体系和系统架构的基础上,构建了地图要素管理、基本水文信息、流域生态流量等主要模块,实现了流域空间可视化查询、实时流量动态模拟、流域生态基流计算以及水资源管理配置等功能。结合博斯腾湖流域1956—2019年水文资料及目前数据获取情况,系统可为不同发展模式下水资源管理提供多方面参考信息和技术支撑,能有效提高水资源管理部门决策效率。     

不完备系统多准则决策的云PDR方法

针对多准则决策问题的意义以及经典粗糙集方法在不完备系统方案排序问题中的不足,在前人基于概率优势关系(PDR)排序方法的基础上,结合云理论提出了基于云概率优势关系的不完备系统多准则决策方法,即云PDR排序法。将期望、熵、超熵等参数引入PDR方法,充分考虑了决策的模糊性、波动性和随机性,评价过程和结果更加客观全面。以煤炭资源型城市为例进行应用研究,获得其中各城市发展水平及其波动情况的排序,并通过与其他方法结果的比较验证了本文方法的优势与有效性。