聚类算法在高脂血症辨证分型研究中的应用

高脂血症的辨证分型标准化研究是中医基础研究的热点与难点,利用计算机挖掘技术中的聚类算法对高脂血症临床中医症状数据进行分析,得到具有统计学意义的分类结果,同时寻找其与中医证候相对应的关系具有重要研究价值.通过编写适用于中医证候分类的聚类算法并提供设置相似度阈值等交互界面,实现了大规模样本的自适应分类,从而使聚类结果更加科学客观.将得到的每个类别中所对应样本的病症特征与所属中医的证候类别联系起来,为辨证分型标准化奠定了基础.     

贝叶斯分类算法在冠心病中医临床证型诊断中的应用

在中医药临床个体化诊疗信息平台的基础上,使用中医证型的辨证相关因素,利用信息增益算法进行辨证属性选择,并分别采用朴素贝叶斯和强属性集贝叶斯网络算法建立了中医冠心病临床证型诊断模型。实验结果表明该分类算法在中医冠心病临床诊断模型中具有良好的分类性能。     

基于逆数据包络分析法的中医方药评价与药量计算研究

针对中医组方的药量推算建模问题,提出了基于证候上下文分析的逆数据包络分析（IDEA）算法。该算法以证侯的典型方剂为依据,以数据包络分析为评价工具,建立起反映证候演化的方药评价模型;依据代表证候状态的评价值,给相应的证型症状信息赋值,以完成“词计算”过程。最后,把具体的辨证过程植入到证侯发展的上下文环境中,以典型证型的“词计算”为依据,给出具体案例的证侯状态评价,再采用IDEA算法推算出药量数据。该模型的推算结果表明,它能够反映中医的一般用药规律,这将对辨证论治组方环节的数值化、客观化研究提供一种可能的思路。     

中医八纲辨证神经网络构造技术研究

由于一些常用中医辨证智能化方法不能符合中医八纲辨证特点,因此文章在传统人工神经网络模型的基础上,根据中医八纲辨证的特点,设计了一种用于中医八纲辨证的结构独特的新型人工神经网络模型——中医八纲辨证神经网络。它具有记忆标准样本病例的能力,并能对临床病例作出较准确的八纲辨证分型。     

中医舌诊神经网络的优化遗传算法

从舌象的特点和基于舌象的八纲辨证中医知识的参数形式,并结合各证型的主证分析因子,利用改进遗传算法对构建的前馈式神经网络进行优化,是对目前中医诊断神经网络方法运用的一种新发展.该遗传算法结合了实数编码、数学交叉、PSO等思想,对于形成一种中医专家系统的软计算构成技术奠定了基本的方法.仿真实验结果表明,该方法具有较为理想的效果.     

基于Caché数据库的中医诊断数据挖掘应用研究

概述了基于Caché数据库的中医诊断数据挖掘应用研究的基本思路与方法,表达了以"证候-证素-证型辨证统一体系"为切入点开展中医诊断研究的可达到的目标.通过建立通用病历信息文件结构,整合数据挖掘工具,搭建了一个用于开展中医诊断研究的数据挖掘科研平台,为科学深入地开展中医诊断研究提供了一种途径.     

基于Caché数据库的中医诊断数据挖掘应用研究

概述了基于Caché数据库的中医诊断数据挖掘应用研究的基本思路与方法,表达了以“证候-证素-证型辨证统一体系”为切入点开展中医诊断研究的可达到的目标。通过建立通用病历信息文件结构,整合数据挖掘工具,搭建了一个用于开展中医诊断研究的数据挖掘科研平台,为科学深入地开展中医诊断研究提供了一种途径。     

基于遗传算法的古代闭经方剂聚类方法研究

辨证分型是中医诊断治疗中的一个重要环节，首先要诊断患者疾病，提出相应的辨证方法，并进行证候分型以指导治疗，同时证型可以从古代方剂中归纳和总结。文章采用遗传算法原理对古代闭经方剂进行聚类，得到的方剂聚类簇即可作为证型总结和归纳的依据，为当代中医诊断智能化提供思路。     

基于多层前馈型人工神经网络的抑郁症分类系统研究

多层前馈型人工神经网络(MLPANN)是应用广泛的一种人工神经网络。该文研究了用于抑郁症中医证候分类的一类MLPANN,设计了一种基于自定义网络结构及其他参数的BP训练算法的分类系统,并首次应用在抑郁症的中医证候分类研究中。该系统利用实际病症样本数据进行了训练和分类,结果表明系统具有很好的分类效果,可以用于指导抑郁症诊断和治疗。     

两层级神经网络及在中医智能诊断中的应用 *

通过分析中医临床数据的特性 ,将临床数据分为低层级数据和高层级数据 ,每个层级数据又分为全局输入参数和局部输入参数。基于这些概念 ,建立了一种两层级神经网络 ,低层级子神经网络局部处理低层级数据,高层级子神经网络综合处理高层级数据和低层级子神经网络的输出结果。这样的结构不仅能有效地刻画中医辨证问题 ,而且简化了计算 ,提高了学习收敛速度。实验结果表明 ,这种两级神经网络可以较好地应用于具有复杂数据关系的中医辨证智能计算。     

Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Syndrome differentiation-based treatment is one of the key characteristics of Traditional Chinese Medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity and vagueness. Analyzing syndrome principles from historical records of TCM using data mining (DM) technology has been of high interest in recent years. Nevertheless, in most relevant studies, existing DM algorithms have been simply developed for TCM mining, while the combination of TCM theories or its characteristics with DM algorithms has rarely been reported. This paper presents a novel Symptom-Syndrome Topic Model (SSTM), which is a supervised probabilistic topic model with three-tier Bayesian structure. In the SSTM, syndromes are considered as observed topic labels to distinguish certain symptoms from possible symptoms according to their different positions. The generation of our model is in full compliance with the syndrome differentiation theory of TCM. Experimental results show that the SSTM is more effective than other models for syndrome differentiating.     

基于贝叶斯方法的中医“症-证”分析

中医“症-证”分析在中医诊断学和中医证候分析中非常重要。该文以数据挖掘技术为手段对选取的古方进行“症-证”研究,对古方的主治症状进行规范,挖掘“症-证”之间的关系,从而判定方剂的主治证、兼治证。为了挖掘中医“症-证”之间的关系,提出了基于KNN的挖掘算法和基于贝叶斯的挖掘算法。对比实验证明,基于贝叶斯方法正确率达到65.76%,高于KNN的62.50%。     

Measuring and reaching consensus in group decision making with the linguistic computing model based on discrete fuzzy numbers

The linguistic computing model based on discrete fuzzy numbers has some good properties compared with other existing models and should be further studied, which has been proved by some researchers. However, the research of group consensus with this linguistic model is insufficient, given that group consensus is an important issue in group decision making. Therefore, this paper would concentrate on this subject. It includes two main issues: research on consensus measure and research on the method for improving group consensus in group decision making based on this linguistic computing model. For research on the consensus measure, this paper first studies on the aggregation method for discrete fuzzy numbers. Then, the index of measuring group consensus is determined. For research on improving the group consensus, considering the characteristics of discrete fuzzy numbers, we present an algorithm to improve group consensus. In addition, an illustrative example of a decision-making problem about investment is stated to show the whole solving process. It also illustrates the feasibility, rationality and validity of all the proposed methods. Finally, the comparisons between some proposals and existing studies are made, which helps point out the advantages of the proposed methods.     

Early experiences with a program designed for the diagnosis of thoracic disorders

Studies of the reasoning processes of clinicians have led to proposals to use computers to and clinical decision making. The aims of such aids being to improve accuracy, consistency and cost-effectiveness of clinical decisions.The program described in this paper deals with the diagnosis of thoracic disorders in patients whose chest X-ray is substantially normal and who are complaining of one or more of the symptoms cough, shortness of breath, coughing up blood or chest pain. Standard observations such as the patient's age, height and weight and the responses to a few more detailed questions about the patient's presenting symptoms are recorded. The program provides a probability-based system dealing with 21 different diagnoses or a combination of two diagnoses. The probabilities of the occurrence of the different symptoms for the various diseases were initially allocated subjectively by an expert and have subsequently been modified by more objective experience. The prior probabilities of the different diseases were obtained from previous clinic records. The program uses a method of logical inference based on Bayes' theorem to calculate the possible diagnoses.The program serves two purposes. Firstly as an educational system for students, guiding them in taking a history and showing them a method of thinking more logically about diagnosis. Secondly the system is used as a diagnostic tool for new patients in the chest clinic, recommending simple tests to help confirm or deny the suggested diagnoses.     

低空目标的雷达/可见光协同监视跟踪方法研究

近年来,随着通用航空和旋翼无人机的飞速发展,对相关空域的安全性监视与管理提出了迫切要求。由于雷达在低空空域存在探测盲区,极易受到杂波干扰,无法准确获取目标信息,而ADS-B系统大规模布设存在局限性,低空空域的有效监视与管理成为研究的热点。研究了一种低空目标的雷达/可见光协同监视跟踪方法,该方法基于跟踪-学习-检测（TLD）架构,将雷达作为主跟踪器,可见光传感器作为检测器,通过交互多模型算法和学习器实现量测模型切换和数据在线更新,从而获取更准确的目标状态信息,实现低空空域更精确的监视和目标跟踪,数据仿真说明了该方法的有效性。     

基于关联假设的定性组合建模算法

组合建模是基于建模假设和模型片断库，构造定性仿真模型的自动化方法。关联假设是一类模型假设，它既可以用来定义模型片断本身，也可以定义有关模型片断使用的决策信息，将模型库与建模任务联系起来。在知识分类的基础上，该文提出了基于关联假设的模型组合算法，解决了定性仿真模型的构造问题，主要内容包括关联性的表示、关联推理算法及其具体实现，最后给出一个实例。     

采用U盘制作xPC目标启动盘

xPC目标是MATLAB提供的一种对硬件进行操作的工具,它采用了宿主机-目标机的技术途径,需用特殊的启动盘启动目标机。传统的方法是用软盘制作xPC目标启动盘,但目前软盘已有被淘汰的趋势。鉴于此,文章介绍了一种用U盘代替软盘制作xPC目标启动盘的方法,实际制作表明本方法是比较简易和有效的。     

Academic engagement: Differences between intention to adopt Social Networking Sites and other online technologies

This article studies firstly academic willingness to use online technologies in order to engage with their peers and secondly, whether there are any differences between academics using Social Networking Sites (SNS) and other online technologies. We synthesised the Decomposed Theory of Planned Behaviour and the Uses and Gratifications Theory, proposing a conceptual model that is evaluated twice using Structural Equation Modelling. Differences were observed between the model of SNS and the model of online technologies. Academics consider SNS more suitable for networking and presenting a professional image and the rest of online technologies for making new acquaintances in their research area and seeking academic information. Our findings have important implications as we were able to demonstrate the ecological validity of the joint model in two different cases and provide information about how academics approach online engagement. The need for providing training in utilising online technologies is evident, especially in the case of SNS, as self-efficacy is the main factor that affects perceived behavioural control, which in turn affects behavioural intention. In addition, the non-significant effect that social norms have on intention in the case of SNS indicates that universities may have to use different promotional techniques internally.     

一种基于前景理论的三参数区间灰数型群体灰靶决策方法

考虑决策者关于指标满意域和风险态度对群体决策的影响, 提出基于前景理论的三参数区间灰数型群体灰靶决策方法. 该方法利用“奖优罚劣”的三参数区间灰数线性变换算子对原始决策信息进行规范化处理, 能够充分反映决策值是否中靶. 在此基础上, 以规范化区间的零点作为参考点, 定义前景价值函数. 依据群体一致性和极大熵原理构建决策者权重模型, 根据综合前景值的正负判断方案是否中靶. 最后, 通过实例表明了所提出方法的有效性和优越性.     

Demand-side response model to avoid spike of electricity price

The aim of this work is to develop a demand-side-response model, which assists electricity consumers exposed to the market price to independently and proactively manage air-conditioning peak electricity demand. The main contribution of this research is to show how consumers can optimize the energy cost caused by the air conditioning load considering to several cases e.g. normal price, spike price, and the probability of a price spike case. This model also investigated how air-conditioning applies a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve financial benefits for consumers and target the best economic performance for electrical generation distribution and transmission. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics regarding hot days from 2011 to 2012.