Technology evaluation through the use of interval type-2 fuzzy sets and systems

Even though fuzzy logic is one of the most common methodologies for matching different kind of data sources, there is no study which uses this methodology for matching publication and patent data within a technology evaluation framework according to the authors’ best knowledge. In order to fill this gap and to demonstrate the usefulness of fuzzy logic in technology evaluation, this study proposes a novel technology evaluation framework based on an advanced/improved version of fuzzy logic, namely; interval type-2 fuzzy sets and systems (IT2FSSs). This framework uses patent data obtained from the European Patent Office (EPO) and publication data obtained from Web of Science/Knowledge (WoS/K) to evaluate technology groups with respect to their trendiness. Since it has been decided to target technology groups, patent and publication data sources are matched through the use IT2FSSs. The proposed framework enables us to make a strategic evaluation which directs considerations to use-inspired basic researches, hence achieving science-based technological improvements which are more beneficial for society. A European Classification System (ECLA) class – H01-Basic Electric Elements – is evaluated by means of the proposed framework in order to demonstrate how it works. The influence of the use of IT2FSSs is investigated by comparison with the results of its type-1 counterpart. This method shows that the use of type-2 fuzzy sets, i.e. handling more uncertainty, improves technology evaluation outcomes.     

L2–L∞ fuzzy control for Markov jump systems with neutral time-delays

The L₂–L_∞ fuzzy control problem is considered for nonlinear stochastic Markov jump systems with neutral time-delays. By means of Takagi–Sugeno fuzzy models, the fuzzy controller systems and the overall closed-loop fuzzy dynamics are constructed. A sufficient condition is firstly established on the stochastic stability using stochastic Lyapunov–Krasovskii functional. Then in terms of linear matrix inequalities techniques, the sufficient conditions on the existence of mode-dependent state feedback L₂–L_∞ fuzzy controller are presented and proved respectively for constant and time varying case. Finally, the design problems are formulated as optimization algorithms. Simulation results are exploited to illustrate the effectiveness of the developed techniques.     

多级旋转变压器模糊控制逆变电源的应用研究

介绍了多级旋转变压器模糊控制逆变电源.该电源为单输入、多输出高精度电压源.控制器部分采用多赋值模糊式精确控制方式,模糊控制器以输出电压偏差、电流偏差及逆变器导通和关断时间的占空比作为输入量,将输入量进行模糊化处理、判决,得到控制量的精确输出值,从而获得多个等级的标准电压,为多级旋转变压器提供了高精度、多等级电压源.多级旋转变压器可以同时驱动多个被控负载,已达到单输入控制多输出的目的.     

Fuzzy uncertainty modelling for project planning: application to helicopter maintenance

Maintenance is an activity of growing interest, especially for critical systems. In particular, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance centre is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans in the short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multi-resource, multi-project planning (respectively Rough Cut Capacity Planning and the Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on a tactical level considering uncertainties in macro-task work content, and modelling the continuous workload on the operational level considering uncertainties in task duration. We model uncertainties using a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy Rough Cut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster that aims at building a centre for civil helicopter maintenance.     

Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C‐means algorithm

This paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C‐means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM‐based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.     

矿坝输送带实时图像探伤方法

长期工作在艰苦的环境下,钢丝绳芯输送带易发生断裂故障,对运输生产造成影响。提出一种基于模糊聚类算法的图像识别方法对输送带的损伤与接头进行检测,据背景差分法与阈值分割,分离出输送带前景、背景视频图像得到目标。利用Sobel算子检测得到损伤与接头处边缘关系,消除冗余信息,最后用模糊聚类算法对图像分类,从而对输送带是否有损伤作出判断。实验结果用以验证算法的有效性。     

高速公路车辆行驶路径模糊匹配分析

高速公路车辆行驶路径在省界站处被自然分隔为两段,不能真实反映行驶车辆在高速公路上的路径信息.本文选取收费系统中车牌号、出入口时间、轴型、车货总重等字段信息,利用贝叶斯方法对分隔路径进行模糊匹配,从而得出行驶车辆在高速公路的全路径信息.