洪水预报智能模型在中国半干旱半湿润区的应用对比

为探究不同洪水预报智能模型在我国半干旱半湿润区的应用效果,选用决策树、多层感知器、随机森林和支持向量机4种模型在陕西省3个典型流域进行逐时洪水预报;选择相关系数、纳什效率系数、均方根误差、平均绝对误差和相对误差等评价指标,比较不同预见期下4种模型在半干旱半湿润典型流域洪水预报的适用性。结果表明:在短预见期预报中,4种模型在半湿润区典型流域均可获得较高的预报结果,在半干旱典型流域模拟精度相对偏低,仅支持向量机模型满足预报要求;随着预见期延长,不同模型性能变化差异大,支持向量机模型整体稳定,在小流域实时洪水预报中具有明显优势;随机森林模型与决策树模型精度随预见期延长而缓慢下降,前者适应性更好;多层感知器模型精度随预见期延长而骤减,模型稳定性较差。     

Probabilistic heuristic estimates

Though they constitute the major knowledge source in problem-solving systems, no unified theory of heuristics has emerged. Pearl [15] defines heuristics as criteria, methods, or principles for deciding which among several alternative courses of action promises to be the most effective in order to achieve some goal. The absence of a more precise definition has impeded our efforts to understand, utilize, and discover heuristics. Another consequence is that problem-solving techniques which rely on heuristic knowledge cannot be relied upon to act rationally — in the sense of the normative theory of rationality.To provide a sound basis for BPS, the Bayesian Problem-Solver, we have developed a simple formal theory of heuristics, which is general enough to subsume traditional heuristic functions as well as other forms of problem-solving knowledge, and to straddle disparate problem domains. Probabilistic heuristic estimates represent a probabilistic association of sensations with prior experience — specifically, a mapping from observations directly to subjective probabilities which enables the use of theoretically principled mechanisms for coherent inference and decision making during problem-solving. This paper discusses some of the implications of this theory, and describes its successful application in BPS.This research was made possible by support from Heuristicrats, the National Aeronautics and Space Administration, and the Rand Corporation.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

基于SPOT-5多光谱影像的矿区塌塘水体提取方法研究

针对基于遥感影像的水体提取方法存在水体提取不完整和误提的现象,提出了一种基于SPOT-5多光谱影像的矿区塌塘水体提取方法。在利用波段合成增加一个可用波段的基础上对已有的水体提取方法进行适当的改进,并基于决策树分类器和改进后的方法进行矿区水体的四级提取,保证了水体提取的完整性,同时减少了误提率;最后利用实测数据对水体提取的精度进行了评定。试验结果表明,基于决策树分类器的水体提取方法具有较高的精度,能满足矿区实际应用的需要。     

煤矿应急通信保障系统的设计

提出了一种煤矿应急通信保障系统的设计方案。该系统的核心即应急救援软件平台通过中继网关、专用转换器等设备整合井下扩播电话机、小灵通、WiFi手机、调度电话机、井下广播系统,从而实现各通信系统间的互联互通;该系统通过应急救援软件平台实现与矿井人员定位系统的互联互通、与矿井工业以太网的综合接入。实际测试结果表明,该系统具有线路管理、音频播放、一键通、广播及应急通信救援等功能,提高了应急救援工作的效率。     

煤炭企业决策支持信息系统

以龙口矿业集团有限公司的决策支持信息系统为例,详细介绍了煤炭企业决策支持信息系统的总体架构、系统数据来源及处理、关键指标信息、系统功能。该系统通过DTS、中间件等技术获取数据;采用面向主题的数据仓库多维表结构和与业务系统对应的ODS层表结构存储数据;通过分析各种数据指标的类型及属性,建立起一套共包含45个关键指标的企业关键指标体系结构;并采用同比分析、环比分析、趋势分析等数学模型对数据进行分析和展现,从而实现辅助决策。     

矿用救生舱环境监控系统软件的研究

给出了矿用救生舱环境监控系统的组成,主要介绍了该系统软件的功能设计、总体架构及界面设计方案。该系统软件可实时显示各传感器的运行状态和检测数据、舱内气体余量、电源电量、照明灯状态、空气净化状态、自动控制状态、舱门状态等,人机交互界面友好,操作简便。该软件经长期连续运行实验,工作正常,达到了设计要求。     

矿用隔爆兼本质安全型不间断直流稳压电源的设计

基于本质安全型电源的原理及特点,提出了一种矿用隔爆兼本质安全型不间断直流稳压电源的设计方案,详细介绍了该电源主要电路的设计。该电源采用LM317可调稳压芯片实现稳压功能,具有使用方便、电路简单、变换效率高、成本低等优点。测试结果表明,该电源达到了本安标准,满足了安全型电气设备的需求。     

基于集成神经网络的多故障诊断方法

铝电解过程是一个非线性、多耦合、时变和大时滞过程,受强电场、强磁场、强热场交互干扰,形成了复杂多变的槽况特征,故障种类繁多,发生频繁,有效地故障预报和诊断,对电解系列平稳供电,节约电能、提高铝的产量和质量有重要意义。根据铝电解过程故障特点,提出了基于主成分分析的集成神经网络铝电解多故障诊断方法,建立分层故障诊断模型结构,包括子神经网络层和决策融合神经网络层,子神经网络模块采用了改进型的Elman神经网络,强化信息的记忆功能,并通过主成分分析优化了神经网络结构;决策融合神经网络通过各子网络传递的相关信息,进一步验证对子神经网络诊断结果和复合故障进行综合决策。仿真结果表明,具有良好的诊断效果,验证了该故障诊断方法的可行性和有效性。