邻域等价关系诱导的改进ID3决策树算法

经典ID3决策树算法适用于离散型数据分类,但用于连续处理时需要数据离散化容易导致信息损失。提出邻域等价关系从而诱导邻域ID3(NID3)决策树算法,NID3算法改进了ID3决策树算法,能够直接实施连续预测并获取更好的分类效果。在邻域决策系统中,挖掘一种邻域等价关系;基于邻域等价粒化,构建邻域信息度量;基于邻域信息增益,设计NID3决策树算法。实例分析与数据实验均表明,NID3算法具有连续数据分类预测有效性,在分类机器学习中优于ID3算法。     

堆石混凝土单轴受压应力-应变全曲线试验方法研究

为了研究堆石混凝土应力-应变全曲线,采用碟形弹簧与钢柱组合成附加刚性元件增强压力机的刚性,研究了碟形弹簧并联与串联联结装置的变形量和刚度以及实现单轴受压端部减摩措施,提出了一种堆石混凝土单轴受压应力-应变全曲线的试验方法。在混凝土压力试验机上进行了堆石混凝土立方体试件单轴受压试验,测得了堆石混凝土受压应力-应变全曲线。试件破坏形态表明,采用的端部减摩措施有效,测得的应力-应变曲线下降段验证了碟形弹簧与钢柱组合装置能够吸收试件破坏时混凝土压力试验机存储的变形能。研究结果为在混凝土压力试验机上进行堆石混凝土单轴受压应力-应变全曲线提供了可行、简便、经济的测试方法。     

基于极限学习机的堤防工程多元风险指标评价方法

为提高堤防工程风险评价的准确性,提出了基于极限学习机的堤防工程多元风险指标评价方法。首先,综合考虑影响堤防风险的28个评价指标,利用层次分析法从预警系统、堤防工程系统、环境系统和社会经济系统这4个方面建立了堤防工程多元风险评价指标体系。接着,基于极限学习机算法对28个指标进行标准化处理及分级标准构建,以风险指标作为输入量,分级隶属度作为输出量,划分风险等级,量化评价指标,估计多元风险评价指标值和判断风险的严重程度。最后,依托鄱阳湖重点堤防——康山大堤,构建多元风险评价指标体系,运用极限学习机算法计算多元风险评价指标值。评价结果表明:康山大堤目前处于基本安全水平,符合康山大堤经过两次加固后的工程实际情况,并与其他方法进行对比,验证了提出方法的可靠性和有效性。该方法可拓展应用到其他重要水工结构工程风险评估中。     

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。     

Digital Twin-driven machining process for thin-walled part manufacturing

Thin-walled parts are widely used in the aerospace, shipbuilding, and automotive industry, but due to its unique structure and high accuracy requirements, which leads to an increase in scrapped parts, high cost in production, and a more extended period in the trial machining process. However, to adapt to fast production cycles and increase the efficiency of thin-walled parts machining, this paper presents a Digital Twin-driven thin-walled part manufacturing framework to allow the machine operator to manage the product changes, make the start-up phases faster and more accurate. The framework has three parts: preparation, machining, and measurement, driven by Digital Twin technologies in detail. By establishing and updating the workpiece Digital Twin under a different status, various manufacturing information and data can be integrated and available to machine operators and other Digital Twins. It can serve as a guideline for establishing the machine tool and workpiece Digital Twin and integrating them into the machining process. It provides the machine operator opportunities to interact with both the physical manufacturing process and its digital data in real-time. The digital representation of the physical process can support them to manage the trial machining from different aspects. In addition, a demonstrative case study is presented to explain the implementation of this framework in a real manufacturing environment.     

隐极无刷双馈电动机的研究

针对无刷双馈电动机的转子磁场转换效率决定了其工作效率的问题,提出了一种隐极无刷双馈电动机隐极转子结构的优化设计方案。对作用较小的隐极转子进行优化设计,即将它们的槽型更改为半开口槽,不断改变它们的长宽比;利用有限元软件ANSYS分析不同长宽比转子结构的隐极无刷双馈电动机,获得其功率绕组励磁与控制绕组励磁分布;利用Matlab软件分解转子磁动势,获得转子磁动势的谐波含量,从而确定最有效谐波含量。实验结果表明,在设定隐极转子的长宽比分别为10、13、15、17、20的情况下,隐极无刷电动机在15倍长宽比转子结构时的能量转换效果最好。     

ACS运动控制器运动实时暂停功能的实现方法

提出了一种ACS运动控制器运动实时暂停功能的实现方法。该方法通过将大的程序段按时间细分成细小段轨迹运动程序,然后在各细小段轨迹运动程序之间加入条件控制语句,使各细小轨迹运动程序段逐条载入到运动命令队列中,从而有效控制整个程序的运行,实现运动实时暂停功能。该方法已在基于ACS运动控制器的卷簧机数控系统中得到应用,取得了理想的运动实时暂停效果,且未出现明显的累积误差。     

一种六辊矫直机电气控制系统设计

根据六辊矫直机的工程实例,结合矫直机的控制工艺以及控制特点,介绍了六辊矫直机控制系统的设计方法以及自动控制系统的实现方法;使用西门子S7-300PLC进行自动控制,对西门子MM440变频器进行PROFIBUS组态,以及通过西门子DI模块对绝对值型脉冲编码器的数据进行读取;解决了矫直机设备的自动运行与矫直速度的实时监控,以及上下辊间距的精确测量的问题;实验结果表明,控制系统运行稳定,辊间距测量精确无误,增加了矫直控制的精确性和稳定性,实现棒料矫直的自动化生产。     

基于CPLD的系统硬件看门狗设计

基于以DSP芯片TMS320F2812为核心的数字伺服控制器,以国微电子公司的SM1032国产CPLD(兼容Lattice公司的ispLSI 1032)为载体,设计了专用的系统硬件看门狗模块,具备识别DSP软件初始化时序、自由定制看门狗时序等功能。通过对伺服控制器上电及工作运行时序的分析、仿真和实验验证,确定了硬件看门狗功能模块的设计方案,并给出了实验结果。     

Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.