正则化图形模糊聚类及鲁棒分割算法

针对现有图形模糊聚类算法合理性差和抗噪能力弱的问题，提出嵌入对称正则项的图形模糊聚类鲁棒算法。将样本聚类所对应的中立度与拒分度相结合构造对称正则项，嵌入现有图形模糊聚类所对应的目标函数；同时，利用像素邻域所对应的均值信息辅助当前像素聚类并构造了空间信息约束正则项，采用拉格朗日乘子法获得正则化图形模糊聚类鲁棒分割算法。不同噪声干扰图像分割结果表明，所建议的分割算法是有效的，相比现有的鲁棒模糊聚类分割算法具有更强的抑制噪声能力。     

基于PSO-ELM模型的潜油柱塞泵冲次优化方法

为改进油井后期泵效低下、能耗高等缺陷，提出了一种基于PSO-ELM模型的潜油柱塞泵冲次优化方法.采用粒子群（PSO）算法与极限学习机（ELM）相结合的方式来实现动液面软测量建模；根据动液面及潜油柱塞泵工作电流变化，以油井运行经济性最优为目的建立目标函数得到潜油柱塞泵冲次，解决了在油井生产时不能准确调节抽油机冲次问题；最后以目标函数关系建立模糊控制器模型，根据输入参数调整潜油柱塞泵冲次.实验结果表明，建立的软测量模型预测动液面精度高，模糊控制器能够更加合理地调整抽油机冲次，最终达到智能调整冲次大小、提高油井采油率及节能的目的.     

Eliciting and measuring hesitance in decision-making

Hesitance is an innate human psychological phenomenon pervasive in our daily life. Although it is very complex and still not fully understood, there have been many studies related to this interesting topic. In this study, we proposed two fundamental problems about hesitance: the first is the generating problem, which is related to how to elicit hesitance information from human thinking; the second is the measuring problem, which is related to how to devise effective and reasonable methods to measure hesitance degrees from given hesitance information. With these two fundamental questions, we first discussed and analyzed several examples in real life involving hesitance, showing that it can indeed be recorded and represented. Then we roughly classified the hesitance information into two major classes: static hesitance and dynamic hesitance. Some simple and interesting methods were proposed to elicit hesitance, and more reasonable methods were proposed to measure the hesitance degree from the two classes of hesitance information. All methods that have been discussed in this study try to avoid complexity in every aspect while reserving strictness and reasonability; therefore, the present study provides suitable information for practitioners with different backgrounds.     

基于模糊综合评判和层次分析法的中子管故障风险评估

中子管是一种用途广泛的关键部件，然而目前国产中子管的可靠性水平与国外相比还有一定差距，难以满足各行业的使用需求。本文分析了中子管的主要故障模式，采用模糊综合评判和层次分析法对中子管故障进行了多级模糊综合评判，并对中子管故障风险进行了评估。结果表明，中子管总体故障风险水平中等，故障风险前3位的部件为靶、氘氚储存器和加速极，在设计和使用中应重点关注靶释放氦气，靶膜氧化，氘氚储存器吸气剂局部破损，绝缘瓷管破损，加速极离子加速、聚焦不稳定等故障。     

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.     

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.     

Adaptive fuzzy control for nontriangular form systems with time-varying full-state constraints

This article investigates an adaptive fuzzy tracking control problem for a class of nontriangular form systems with asymmetric time-varying full state constraints. Unknown functions are approximated by the fuzzy logic systems. A domination approach is employed to tackle the nontriangular form structure. Time-varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure full-state constraints satisfaction. Based on the backstepping technique and time-varying ABLFs, an adaptive controller is proposed and guarantees that all the signals in the closed-loop system are ultimately bounded and the time-varying full state constraints are met. Simulation examples are presented to further demonstrate the effectiveness of the proposed approach.     

Type II fuzzy set-based data analytics to explore amino acid associations in protein sequences of Swine Influenza Virus

The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset.