洪水基流分割非参数检验方法优选

基于江西省萍乡市五丰河流域2016—2018年实测场次洪水数据和直线斜割法所得基流流量,对比分析累积距平法、Pettitt法、滑动T检验法和Mann-Kendall检验法应用于基流分割的适用性和准确性,选择集对分析法为优选非参数检验方法。结果表明:海绵城市建设后(2018年)相对建设前(2016年)标准退水曲线基流流量增加65.2%,地下径流量增加47.3%,地下径流量占径流总量比例增加53%。Pettitt法与直线斜割法所得基流流量相对误差最小,年均相对误差为1.7%;变差系数年均相对误差最小,为5%;集对分析联系度最大,为0.81。Pettitt方法是检验海绵城市直接地表和地下二元径流成分转化的最适非参数检验法。     

立轴冲击式水轮发电机组技术供水系统优化

水电站的技术供水系统要根据水电站的基本技术参数及设备要求的技术供水参数进行详细设计和论证,并结合电站所在区域的实际情况,使系统满足机组在各种工况下的正常安全稳定运行。主要阐述米角河一级水电站立轴冲击式水轮发电机组技术供水系统的优化设计,采用一次冷却供水和冷却水二次循环供水相结合的设计方案,避免供水水质变化时停机造成的经济损失,提高了供水系统的运行可靠性,减少了检修维护量,为电站"无人值班,少人值守"创造了必要条件。     

DETECTOR: A knowledge-based system for injection molding diagnostics

A knowledge-based system (KBS) for diagnosis of multiple defects in injection molding is presented. The general scheme for knowledge representation based on fuzzy set theory has been shown useful in representing inexact and incomplete information for developing the KBS. An optimality criterion is created for selecting a simple and best cover to explain the given problem. An efficient search algorithm for finding such cover is also discussed.     

基于YOLOv3的安全帽佩戴检测方法研究

在大型锅炉、钢铁和其他制造业工厂中,正确佩戴安全帽是进入现场的前提。但是在现实场景中,由于各种原因,工人伤亡事故中没有正确佩戴安全帽而导致的事故占比一直较高。为了减少该类事故的发生,对深度学习、人工智能展开了研究,采用了卷积神经网络的方法。首先,对相关数据集标定,将数据集总体分为三种类别,针对标定好的数据,使用迁移学习的训练方式训练YOLOv3网络。接着,使用该参数和模型对监控视频数据检测人形和安全帽的类别和位置。最后,对检测的三种类别计算相关交并比,并以此判断工人是否正确佩戴安全帽。该算法创新性地将YOLOv3网络模型和交并比算法结合,对网络输出的类别和位置信息再细化处理,提高了检出的准确性,降低了误识别率。试验结果表明,该算法可以满足安全帽佩戴检测中的实时性要求,同时能够准确检出未佩戴安全帽的人员并通知有关人员。     

复杂航空电子装备故障诊断规则提取

针对复杂航空电子装备故障样本不足的问题,为简化测试过程、提高故障诊断效率,提出了一种基于粗糙集的故障诊断规则提取方法;该方法构建了故障诊断决策信息系统,通过构造故障诊断决策信息系统的决策辨识矩阵和决策辨识函数,计算测试参数集的所有约简,从而提取故障诊断规则;以某型雷达系统为例,采用上述方法进行规则提取,将故障诊断所需测试参数从9个约简为3个;研究结果表明,该方法能在故障样本较少的条件下有效约简测试参数集,提取故障诊断规则,有利于简化测试过程,提高诊断效率.     

基本型一般访问结构视觉密码的两种算法

视觉密码是一种完善安全的密码体制。近几年,视觉密码学的研究受到广泛关注。本文提出了一般访问结构的两种算法,通过Ateniese-Blundo-Santis-Stinson模型和累计数组的构造方法,对一般访问结构的基本型视觉密码进行讨论,获得了一个一般访问结构的基本型视觉密码方案。     

433MHz物联网开发平台的设计与开发

物联网是当前信息领域中研究的热点。本文以CC1110芯片为核心,利用温湿度、烟雾、亮度等传感器结合自主开发的WIA-PA协议栈软件,研制了一种基于433MHz频段的物联网开发平台。利用该平台提供的开发套件,可方便地开发智能家居、环境监测等应用系统。目前,重庆伟岸测器利用本平台已成功开发了无线热能表系统。     

A balanced solution of a fuzzy soft set based decision making problem in medical science

The purpose of this paper is two folded. Firstly, the concept of mean potentiality approach (MPA) has been developed and an algorithm based on this new approach has been proposed to get a balanced solution of a fuzzy soft set based decision making problem. Secondly, a parameter reduction procedure based on relational algebra with the help of the balanced algorithm of mean potentiality approach has been used to reduce the choice parameter set in the parlance of fuzzy soft set theory and it is justified to the problems of diagnosis of a disease from the myriad of symptoms from medical science. Moreover the feasibility of this proposed method is demonstrated by comparing with Analytical Hierarchy Process (AHP), Naive Bayes classification method and Feng's method.     

基于变精度粗糙集的煤与瓦斯突出预测

煤与瓦斯突出是受诸多因素影响的复杂问题,为了提高其预测的准确性,本文以变精度粗糙集理论中的决策表为主要工具,首先将影响煤与瓦斯突出的检测信号作为预测的条件属性集,煤与瓦斯突出量作为对预测的决策属性,建立决策表,然后利用小生境遗传算法适合于进行多峰值函数优化的特点,提出了一种基于小生境遗传算法的粗糙集属性约简方法,用于求解决策表的多个约简,进而进行值约简后抽取出预测规则。算例结果说明了本算法的正确性和可行性。     

Continuous process control using neural networks

We present some adaptive control strategies based on neural networks that can be used for designing controllers for continuous process control problems. Specifically, a learning algorithm has been formulated based on reinforcement learning, a weakly supervised learning technique, to solve set-point control and control scheduling for continuous processes where the process cannot be modeled easily. It is shown how reinforcement learning can be used to learn the control strategy adaptively based on exploration of the control space without making assumptions about the process model. A new learning scheme, handicapped learning, was developed to learn a control schedule that specifies a schedule of set points. Applications studied include the control of a nonisothermal continuously stirred tank reactor at its unstable state and the learning of the daily time-temperature schedule for an environment controller. Experimental results demonstrate good learning performance, indicating that the learning algorithm can be used for solving transient startup and boundary value control problems.