Memory-based dynamic event-triggered control for networked interval type-2 fuzzy systems subject to DoS attacks

In this article, the memory-based dynamic event-triggered controller design issue is investigated for networked interval type-2 (IT2) fuzzy systems under non-periodic denial-of-service (DoS) attacks. For saving limited network bandwidth, a novel memory-based dynamic event-triggered mechanism (DETM) is proposed to schedule data communication. Unlike existing event-triggered generators, the developed memory-based DETM can utilize a series of newly released signals and further save network resources by introducing interval dynamic variables. Moreover, to improve design flexibility, an IT2 fuzzy controller with freely selectable fuzzy rule number and premise membership functions (MFs) is synthesized. Then, a new switched time-delay system with imperfectly matched MFs is established under the consideration of memory-based DETM and DoS attacks simultaneously. Besides, based on the property of MFs, the boundary information of membership grades and slack matrices are introduced in the stability analysis. Furthermore, by using a piecewise Lyapunov–Krasovskii method, membership-functions-dependent criteria are deduced to ensure the asymptotic stability of built fuzzy switched systems. Finally, the effectiveness of proposed control strategies is demonstrated by simulation examples.     

Growth mechanism and gas-sensing characteristics of organic-additive-free zinc oxide of various shapes

Zinc oxide is widely used in gas sensors, solar cells, and photocatalysts because of its wide bandgap and exciton binding energy of 60 meV in various metal oxides. To use ZnO as a gas sensor, it is necessary to synthesize it with surface defects and a large specific surface area. In this study, hydrothermal synthesis without surfactants was employed to obtain organic-additive-free ZnO. For morphology control, we varied the ratio of the hydroxide ion concentration to the zinc ion concentration. To confirm the growth mechanism of ZnO, we performed X-ray diffraction, scanning electron microscopy, and transmission electron microscopy analyses. Raman spectroscopy and photoluminescence measurements were performed to analyze the surface properties. The Brunauer–Emmett–Teller method and probe stations were used to measure the specific surface area and sensitivity of the gas sensor, respectively. The results confirmed that flower-shaped ZnO is the most suitable gas-sensing material.     

基于英飞凌TC275的整车控制器CCP协议底层驱动开发

针对以英飞凌TC275单片机为平台的整车控制器,进行控制器局域网络标定协议(controller area network calibration protocol,CCP)底层驱动程序的开发。依据CCP的需求开发了英飞凌TC275的控制器局域网络(controller area network,CAN)模块、系统定时器(system timer,STM)模块和FLASH模块的底层驱动程序,实现CCP标定系统的通信连接、数据上传、数据下载、数据存储等功能。为验证系统的性能,进行整车控制器的实际测试,测试结果表明:所开发的基于TC275单片机的CCP底层驱动程序能够实现CCP的功能要求。     

Emergency decision support modeling for COVID-19 based on spherical fuzzy information

Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision-makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID-19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision-making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.     

基于CAN总线的多通道开度阀控制系统的设计

针对现有电动阀门控制系统可嵌入性差的问题，设计了一种基于CAN总线的网络化、多通道、开度型阀门嵌入式控制系统。其中，上位机主要用于下达各阀门开度等控制指令并同时监测其工作状态，而下位机采用双MCU架构，主MCU用于控制各阀门的开度大小，从MCU负责采集开度反馈电流以实现闭环控制。下位机采用了模糊PID算法进行控制参数的动态整定，可满足阀门在不同工况下的控制精度要求，并通过OLED屏显示各通道阀门的实际开度值。实验表明,该控制系统运行稳定，能实现对多通道开度阀的精准控制，控制精度在0.6%以内，并支持远程上位机控制和现地控制等多种工作模式。     

基于模糊综合评判方法的水文地质类型评价

以开元矿井为例,在水文地质资料基础上,选定合适的指标,根据不同指标的重要程度确定各个指标权重,运用模糊综合评判理论建立矿井水文地质类型划分模型,运用该模型进行水文地质类型评价。     

区域控制器的安全需求建模与自动验证

轨道交通区域控制器（ZC）是我国轨道交通信号系统选型的主流制式——基于通信的列车控制系统（CBTC）的核心子系统，其突出的安全性使得安全需求的形式化验证成为一个非常重要的问题.但是ZC自身的复杂性以及领域知识的繁杂难以掌握，使得形式化方法很难应用到安全需求的验证中去.针对这些问题，提出一种安全需求的自动验证方法，使用半形式化的问题框架方法（PF）来建模和分解安全需求，根据需求模型自动生成安全需求的验证模型和验证性质，在此基础上自动生成验证模型的Scade语言实现，并通过Design Verifier验证器对需求进行组合验证.最后，使用了某个实际案例ZC的一个子问题CAL_EOA进行了研究，实验结果证明了该方法的可行性与有效性，它能自动地将安全需求模型进行组合验证，改善了验证的效率.     

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q_L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.     

基于模糊评价理论对层燃工业锅炉综合性能的研究与分析

为有效地平衡锅炉效率与排放之间的关系,通过分析锅炉的能效指标,建立了结合效率因素、燃料管理因素和环境因素等的能效评价指标体系。采用组合赋权法确定权重,建立了锅炉系统模糊综合评价模型,并利用该模型对山西大同某层燃蒸汽锅炉进行了综合能效评价与分析。结果表明:锅炉的综合评级得分为0.59,燃料设备因素指标得分仅为0.19。该锅炉热效率虽然达到了设计要求,但锅炉飞灰含碳量偏大,同时过量空气系数为2.34,远大于设计标准1.65。综合分析其原因是漏风过大,该分析结果与现场热力测试实验数据相符合。     

Bitumen-water interfacial tension modeling by using subtractive clustering method

Calculation of interfacial tension during bitumen production is a crucial issue in heavy crude oil history. Upon variation in pressure, temperature and phases composition, interfacial tension between bitumen and water change. In this work a sophisticated method called subtractive clustering was utilized to predict dynamic interfacial tension between bitumen and water. The subtractive clustering method is composed of optimized fuzzy logic algorithm. A data bank which is collected from open-source literature, is used to create a reliable model. Then the prediction accuracy of the measured dynamic interfacial tension using subtractive clustering have been examined. Results state that the comparison of measured interfacial tension and predicted interfacial tension indicate acceptable accuracy of proposed model. Also more than 90 percent of data points have less than 3 percent absolute error.