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.     

基于多种评价方法的昭君村次级滑坡稳定性评价

为提高昭君村次级滑坡稳定性评价的精确性,同时运用模糊数学法和极限平衡法中的传递系数法对昭君村次级滑坡的稳定性进行评价,并与实际测量情况进行对比。结果表明,两种方法得到的昭君村次级滑坡稳定性评价结果与现场地质调查结果基本相符,滑坡整体处于欠稳定状态,且结合两种方法进行评价使得评价结果更为合理。研究成果可为类似滑坡稳定性评价提供参考。     

农产品质量安全舆情风险综合指数研究

在调研当前食品农产品质量安全问题舆情风险评估研究现状基础上,面向我国农产品质量安全监管工作,从媒体关注度、网民关注度、内容敏感度和负面情绪聚集度4个角度提出了农产品质量安全舆情风险综合评估体系,并通过科学方法确立的指标体系的权重分布和不同指标数值的隶属度子集情况,推导得出一定时期内某一农产品质量安全问题事件的舆情风险综合指数公式。该指数的提出在国内具有首创性,对我国农产品质量安全舆情监管及相关研究工作具有一定的借鉴和指导意义。     

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。     

The structural variability of quarters and residential areas in the historical texture of the city of Yazd based on Islamic rules and jurisprudence: a case study of Golchinan quarter

According to many researchers and scholars, religious beliefs can be considered as the most influential factor in forming Muslim urban spaces and their architectural elements. In fact, historical cities reflect the culture and beliefs of their residences. Among all historical places in Iranian cities, the historical texture of the city of Yazd, especially the quarters since the age of Qajar dynasty, are well preserved. The present study aims at identifying the Islamic principles and values influencing the formation of architectural spaces and the extent of their applications in one of the historical quarters known as Golchinan. The results of the conformation of the theoretical findings with the data acquired from field studies revealed that almost 90% of the urban and architectural spaces (e.g. residential areas, mosques, bazaars, and pathways) in Yazd are directly influenced by Islamic principles. In other words, there is a strong conformity between the accepted Islamic values of the society and what actually was applied to urban constructions during Qajar dynasty.     

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.