基于未确知集和模糊TOPSIS决策的故障模式风险评估

为了解决列车控制系统无线闭塞中心设备故障模式与影响分析的风险评估过程中专家评判的认知模糊性和未知不确定性问题,本文提出基于未确知集和模糊TOPSIS决策的故障模式风险评估方法。该方法采用三角模糊数对发生度、严重度、可检测度、维修度4项风险评价指标分别进行模糊化处理,运用未确知数理论来处理各评价指标权重的不确定性以表现其相对重要度;通过使用模糊TOPSIS多准则决策理论对故障模式进行排序最终得到风险评估结果。最后,应用实例对提出的评估方法有效性和可行性进行了验证,结果表明,该方法能有效减少专家主观性对指标权重量化的影响,适用于一些依赖于专家评估经验的故障模式风险评估领域。     

A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique

This paper presents a novel intelligent diagnosis method based on multiple domain features, modified distance discrimination technique and improved fuzzy ARTMAP (IFAM). The method consists of three steps. To begin with, time-domain, frequency-domain and wavelet grey moments are extracted from the raw vibration signals to demonstrate the fault-related information. Then through the modified distance discrimination technique some salient features are selected from the original feature set. Finally, the optimal feature set is input into the IFAM incorporated with similarity based on the Yu’s norm in the classification phase to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearing, and the test results show that the IFAM identify the fault categories of rolling element bearing more accurately and has a better diagnosis performance compared to the FAM. Furthermore, by the application of the bootstrap method to the diagnosis results it can testify that the IFAM has more capacity of reliability and robustness.     

Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment

This paper develops an evaluation approach based on the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), to help the Air Force Academy in Taiwan choose optimal initial training aircraft in a fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterised by triangular fuzzy numbers. This study applies the fuzzy multi-criteria decision-making (MCDM) method to determine the importance weights of evaluation criteria and to synthesize the ratings of candidate aircraft. Aggregated the evaluators’ attitude toward preference; then TOPSIS is employed to obtain a crisp overall performance value for each alternative to make a final decision. This approach is demonstrated with a real case study involving 16 evaluation criteria, seven initial propeller-driven training aircraft assessed by 15 evaluators from the Taiwan Air Force Academy.     

Design and performance evaluation of a fuzzy based traffic conditioner for differentiated services

The current Internet can only provide the best-effort services. By growing the Internet and its applications, new services with different requirements are developed. The differentiated services (Diffserv) [S. Blake, M. Carlson, E. Davies, Z. Wang, W. Weiss, An Architecture for Differentiated Services, IETF RFC 2475, 1998; K. Nichols, V. Jacobson, L. Zhang, A Two-bit Differentiated Services Architecture for the Internet, IETF RFC 2638, July 1999; T. Li, Y. Rekhter, A Provider Architecture for Differentiated Services and Traffic Engineering (PASTE), IETF RFC 2430, October 1998] is a scalable model for providing end-to-end quality of services. A Diffserv router consists of different components including classifier, meter, marker, dropper, shaper and scheduler. In this paper, we use the benefits of the fuzzy logic controller to design a fuzzy based traffic conditioner for Internet differentiated services. Our proposed model consists of three major parts: a fuzzy meter/marker (FM), a fuzzy active queue management (FAQM) algorithm and a fuzzy scheduler (FS). The proposed FM used to measure and mark the input traffic is based on the single rate three colors meter (srTCM) mechanism [J. Heinane, et al., A Single Rate Three Color Marker, RFC 2697, September 1999]. The FM meters an IP packet stream and marks its packets GREEN, YELLOW, or RED. The proposed FAQM is a fuzzy implementation of the well-known BLUE active queue management (AQM) algorithm [W. Feng, D. Kandlur, D. Saha, K. Shin, BLUE: A New Class of Active Queue Management Algorithms U. Michigan CSE-TR-387-99, April 1999]. To achieve a good throughput and reasonable packet loss ratio, the proposed FAQM mechanism employs a fuzzy logic controller. The third part of the proposed fuzzy traffic conditioner is FS. It is based on the traditional weighted fair queue (WFQ) mechanism [A. Demers, S. Keshav, S. Shenker, Analysis and simulation of a fair queueing algorithm, in: SIGCOMM Symposium on Communications Architectures and Protocols, Austin, Texas, September 1989, pp. 1–12] that tunes the weight of each queue by a fuzzy logic controller. To evaluate the performance of the proposed model, we developed a software in C++ environment to simulate it. Different network topologies with different traffic parameters were performed. The simulation results easily helped us to judge the merit of the proposed FM, FAQM and FS because of their remarkable performance over the traditional non-fuzzy mechanisms.     

基于D-S证据理论的齿轮箱故障诊断

在分析模糊识别和证据理论应用于故障诊断特点的基础上.提出了一种应用D-S证据理论融合神经网络和模糊识别的方法,应用于某坦克发动机齿轮箱故障诊断,通过多次实验及仿真,证实这种方法能明显提高故障的正确诊断率,具有良好的实际应用价值.     

基于模糊理论的可信计算信任评估方法研究

信任链是可信计算的关键技术之一,如何对其表达和评估是研究热点。针对可信计算环境中影响信任评价的各种复杂因素以及信任关系的不确定性和动态性,提出一种基于模糊理论的可信计算信任评估方法。引入历史度量记录和时间衰减因子以构建直接信任度,建立粒度为5的信任等级空间,并给出间接信任度的模糊评判方法。运用改进的Einstein算子定义相似度函数,在此基础上给出信任链上实体的信任模糊推理和评估过程。方法把模糊推理和信任传递相结合,可以全面地评估信任链上实体的信任度。模拟实验表明,与同类方法相比较,所提方法对恶意评估具有较好的抵抗能力,且评估结果的可信性和可靠性更好,是一种可信计算信任评估的新方法。     

The effect of a telepointer on student performance and preference

While the telepointer has been widely accepted in the Computer Supported Collaborative Work community, little work has been done to quantify its effect on performance and perception. We present preliminary results quantifying the telepointer’s effect on knowledge retention and satisfaction in an online collaboration. In experiments, a remote expert communicated with small student groups to explain an online scanning probe microscope (SPM) interface. The expert used two-way audio–video plus a telepointer to describe the interface to half of the participants, and only two-way audio–video (no telepointer) with the other half. The data show that use of a telepointer improved task completion time tenfold and long-term knowledge test performance by 30–40% on specific concepts. The telepointer group was also more likely to rate the online SPM as a substitute for a local SPM and felt the expert was significantly less distant than did the non-telepointer group.     

Vague集模糊熵的约束条件及其度量方法分析*

为了体现vague集模糊熵既有vague集的未知性又有fuzzy集的不确定性,首先给出了反例说明已有方法在某些情况得出的结果与实际结论不一致情况下,提出了一个真正考虑了两种因素的改进的vague集模糊熵定义,然后给出一种vague集模糊熵的新度量方法,最后通过定理证明和实例分析,指出它确实同时考虑到vague集的未知性和不确定性,从而证明所给出vague集模糊熵的定义是更加合理的。     

Improvement of risk assessment in the FMEA using nonlinear model,revised fuzzy TOPSIS,and support vector machine

In every organization, performing accurate risk assessment along with consideration of increasing accidents is a necessary tool to prevent and reduce the fatal and non-fatal consequences of their occurrence. One of the most popular methods of risk assessment is Failure Mode and Effects Analysis, which evaluate failure modes in a system by using risk priority numbers (RPNs). These methods have been criticized for including several deficiencies such as the effect of personal ’opinions, the same importance of the factors and risk rating. The present work utilizes a hybrid approach based on support vector machine and fuzzy inference system to decrease the effect of personal's opinions in determining the factors of the severity and occurrence. Also, Logarithmic Fuzzy Preference Programming is used to determine the crisp weight of dependent factor of FMEA and revised fuzzy TOPSIS used for more accurate ranking of risks. One main feature of the proposed model is that it can be used to evaluate safety risks in all organizations. To investigate the suitability of this approach, the proposed model was presented in the Copper leaching factory, Kerman, Iran. The results showed that this model has the ability to predict severity and occurrence refers to occupational accidents which occurred in a 5-year period (2012–2017) with accuracy of 87% and 95%, respectively. Also, based on the results, it was found that the weights of severity, occurrence, and detection were 0.479, 0.335, and 0.186, respectively. Results of the ranking process showed that the risk of fall from height and stucking between the objects had the highest and the lowest priority, respectively.     

Connection number of set pair analysis based TOPSIS method on intuitionistic fuzzy sets and their application to decision making

Intuitionistic fuzzy set plays a significant role to handle the uncertainties in the data during the decision-making process. Keeping the advantage of it in mind, an attempt has been made in the present article for rating the different preferences of the object based on the set pair analysis (SPA). For this, a major component of SPA, known as a connection number, has been constructed based on the preference values and the comprehensive ideal values of the object. An extension of TOPSIS method is further developed, based on the proposed connection number of SPA, to calculate relative-closeness of sets of alternatives which are used to generate the ranking order of the alternatives. A real example is taken to demonstrate the applicability and validity of the proposed methodology.     

Research on assessment method of network quality performance for a private data network

Microsystem Technologies - These authors focuses on an assessment method of network quality performance research for a private data network which can be made up of either leased lines or IP-Virtual...     

Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits.     

比例模糊控制在压电陶瓷执行器中的应用

针对压电陶瓷的逆压电效应,提出了一种以压电陶瓷驱动精密流量阀为执行器,采用压电陶瓷驱动流量阀阀芯,控制其开口大小,从而达到精确控制流过阀的液体流量的目的.在减压出口处安装了金属橡胶材料,以达到消除流体通过节流口后压力剧烈变化所产生的纹波现象,并就其驱动电压一输出位移一输出流量之间的数学关系建立模型.由于压电陶瓷的非线性、迟滞、蠕变等特性影响控制精度且控制复杂,提出了一种比例模糊控制方法,取得了较通常采用的控制方法更为理想的效果.     

Bidirectional project method for dual hesitant Pythagorean fuzzy multiple attribute decision-making and their application to performance assessment of new rural construction

The dual hesitant Pythagorean fuzzy set (DHPFS) consists of two parts, that is, the membership hesitancy function and the nonmembership hesitancy function, supporting a more exemplary and flexible access to assign values for each element in the domain. It is very suitable to handle the situation that there are various possible values in membership and nonmembership degrees to depict the true circumstance. The bidirectional project method of DHPFS calculates method considered not only the bidirectional projection magnitudes and the distance but also includes angle between objects evaluated. Therefore, this paper proposes a bidirectional project method of DHPFS to handle the multiple attribute decision-making (MADM) problem under the dual hesitant Pythagorean fuzzy environment. Through the measure between each alternative decision matrix and the positive and negative ideal alternative matrix, the ranking order all alternatives can be used to select the best alternative. Furthermore, a model for MADM has been given. Finally, a numerical example for performance assessment of new rural construction has been given to demonstrate the application of bidirectional project method of DHPFS, and we used the dual hesitant Pythagorean weighted Bonferroni mean to compare its reasonable and effectiveness.     

一种土壤湿度测定方法在Zig Bee无线传感器网络中的应用

农业无线传感器网络(WSNs)的终端节点存在着低功耗和低成本的要求,而现有的土壤湿度传感器难以满足这种要求.提出一种将土壤电阻传感器和土壤温度传感器的数据进行融合的方法,并利用多元线性回归和BP神经网络建立了土壤湿度的测定模型.该方法在实际的Zig Bee农业无线传感器网络中得到应用.实验结果表明:在同一种类型的土壤中,该方法测定土壤湿度的精度可达±2.5 ％RH,可用于实际农业大田的土壤湿度测定.     

模糊数据融合算法在煤矿安全系统中的应用

采用多传感器对煤矿井内的温度和通风量进行监测,基于模糊系统理论,提出一种应用多传感器模糊数据融合的融合检测方法,将多个传感器所获取的信息模糊化,经过融合中心的合成运算和决策规则,获取矿井温度和通风量状态的精确估计。     

Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method

Condition assessment is one of the most important techniques to realize the equipment's health management and condition based maintenance (CBM). This paper introduces a preprocessing model of the bearing using wavelet packet–empirical mode decomposition (WP-EMD) for feature extraction. Then it uses self-organization mapping (SOM) for the condition assessment of the performance degradation. To verify the superiority of the proposed method, it is compared with some traditional features, such as RMS, kurtosis, crest factor and entropy. Meanwhile, seventeen datasets from the bearing run-to-failure test are used to validate the proposed method. The analysis results from the bearing's signals with multiple faults show that the proposed assessment model can effectively indicate the degradation state and help us to estimate remaining useful life (RUL) of the bearings.     

MetricM: a modeling method in support of the reflective design and use of performance measurement systems

Performance indicators play a key role in management practice. The existence of a coherent and consistent set of performance indicators is widely regarded as a prerequisite to making informed decisions in line with set objectives of the firm. Designing such a system of performance indicators requires a profound understanding of the relations between financial and non-financial metrics, organizational goals, aspired decision scenarios, and the relevant organizational context—including subtleties resulting from implicit assumptions and hidden agendas potentially leading to dysfunctional consequences connected with the ill-informed use of performance indicators. In this paper, we investigate whether a domain-specific modeling method can address requirements essential to the reflective design of performance measurement systems, and which structural and procedural features such a method entails. The research follows a design research process in which we describe a research artifact, and evaluate it to assess whether it meets intended goals and domain requirements. In the paper, we specify design goals, requirements and assumptions underlying the method construction, discuss the structural specification of the method and its design rationale, and provide an initial method evaluation. The results indicate that the modeling method satisfies the requirements of the performance measurement domain, and that such a method contributes to the reflective definition and interpretation of performance measurement systems.     

Unsupervised change detection in SAR images based on frequency difference and a modified fuzzy c-means clustering

Change detection for synthetic aperture radar (SAR) images is a key process in many applications exploiting remote-sensing images. It is a challenging task due to the presence of speckle noise in SAR imaging. This article investigates the problem of change detection in multitemporal SAR images. Our motivation is to avoid using only one detector to measure the change level of different features which is usually considered by classical methods. In this article, we propose an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c-means (FCM) clustering algorithm. First, the proposed method extracts high-frequency and low-frequency components using wavelet transform, and then constructs high-frequency and low-frequency difference images using different detectors. Finally, inverse wavelet transform is carried out to obtain the final difference image. In addition, inspired by manifold structure constraint, we incorporate weighted local information into the FCM to reduce the influence of speckle noise. Experimental results performed on simulated and real SAR images show the effectiveness of the proposed method, in terms of detection performance, compared with the state-of-the-art methods.     

Application of fuzzy Naive Bayes and a real-valued genetic algorithm in identification of fuzzy model

We present a method to identify a fuzzy model from data by using the fuzzy Naive Bayes and a real-valued genetic algorithm. The identification of a fuzzy model is comprised of the extraction of “if–then” rules that is followed by the estimation of their parameters. The involved parameters include those which determine the membership function of fuzzy sets and the certainty factors of fuzzy if–then rules. In our method, as long as the fuzzy partition in the input–output space is given, the certainty factor of each rule is computed with the fuzzy conditional probability of the consequent conditioned on the antecedent by using the fuzzy Naive Bayes, which is a generalization of Naive Bayes. The fuzzy model involves the rules characterized by the highest values of certainty factors. The certainty factor of each rule is the fuzzy conditional probability, and it reflects the inner relationship between the antecedent and the consequent. In order to improve the accuracy of the fuzzy model, the real-valued genetic algorithm is incorporated into our identification process. This process concerns the optimization of the membership functions occurring in the rules. We just involve the parameters of membership function of the fuzzy sets into the real-valued genetic algorithm, since the certainty factor of each rule can be computed automatically. The performance of the model is shown for the backing-truck problem and the prediction of Mackey–Glass time series.