Road context evaluated by means of fuzzy interval

In this article, an application of the Hollnagel’s CREAM model, known for its determination of human reliability within complex systems, is proposed and opportunely adapted to road safety and enhanced with a control mechanism of uncertain variables based on the fuzzy interval. The aim of the research is to use an analytical technique that could allow the management in a temporarily and economically convenient way of a great number of variables without losing the information on the uncertainty of the final result. In order to do this, the author has handled the outputs of the fuzzy model with techniques of interval analysis, thus preserving the uncertain content in the following stages of the analysis. The results obtained, expressed with a simple application, permit the determination of the influence that the environmental context exercises towards driver behaviour. Such a model, once in place, allows the rapid examination of further scenarios and in this way is able to constitute an important instrument in the preliminary stages of comparison between other design solutions. Therefore, the methodology is useful not only to quantify the reliability of the route covered by the driver but also to address the strategies of the maintenance towards the improvement of those components of the environmental context that can be critical with regard to road safety.     

An improved weighted fuzzy CREAM model for quantifying human reliability in subway construction: Modeling,validation, and application

On construction sites, there are many catastrophic accidents induced by human error. The fuzzy cognitive reliability and error analysis method (CREAM) is an effective method for assessing human error hazards in such a context. However, deficiencies regarding the aspects of reasonably considering the input weight, evaluation of common performance conditions (CPCs), and the rule‐based approach for fuzzy illation are frequently encountered. This paper provides an improved weighted fuzzy CREAM model. First, the reasonable weight of the CPCs is obtained by using multiple correlation analysis and evidence theory. Second, the specific evaluation rules for CPCs are established based on Chinese codes, and the membership function for each CPC level is obtained by a statistical method. The discrete basic diagram for the control mode is then transformed into a continuous fuzzy rule‐based model. On that basis, the rationality and reliability of the model are verified by four axioms. The method that introduces the weights into the calculation of the expected effects of CPCs and the reasoning of human error probability (HEP) can help evaluate the effectiveness of CPCs on human performance. The model is sensitive to the minor alterations of CPC scores and weights; the sufficiency of the data utilization and the solution domain are also verified. An exemplified application shows the rationality and validity of the new weighted fuzzy CREAM for shield tunneling human reliability analysis. This model also provides essential HEP data that can be used in hazard analysis in other engineering fields.     

Modeling optimal release policy under fuzzy paradigm in imperfect debugging environment

ContextIn this study, a software optimal release time with cost-reliability criteria has been discussed in an imperfect debugging environment.ObjectiveThe motive of this study is to model uncertainty involved in estimated parameters of the software reliability growth model (SRGM).MethodInitially the reliability parameters of SRGM are estimated using least square estimation (LSE). Considering the uncertainty involved in the estimated parameters due to human behavior being subjective in nature and the dynamism of the testing environment, the concept of fuzzy set theory is applicable in developing SRGM. Finally, using arithmetic operations on fuzzy numbers, the reliability and total software cost are calculated.ResultsVarious reliability measures have been computed at different levels of uncertainties, and a comparison has been made with the existing results reported in the literature.ConclusionIt is evident from the results that a better prediction of reliability measures, namely, software reliability and total software cost can be made under the fuzzy paradigm.     

A non-linear fuzzy regression for estimating reliability in a degradation process

Since some assumptions such as the function ϕ(·) needs to be completely specified and the relationship between μ and ϕ(s) must have linear behavior in the model μ = a + bϕ(S) used in the accelerated life testing analysis, generally do not hold; the estimation of stress level contains uncertainty. In this paper, we propose to use a non-linear fuzzy regression model for performing the extrapolation process and adapting the fuzzy probability theory to the classical reliability including uncertainty and process experience for obtaining fuzzy reliability of a component. Results show, that the proposed model has the ability to estimate reliability when the mentioned assumptions are violated and uncertainty is implicit; so that the classical models are unreliable.     

Fuzzy reliability analysis of nanocomposite ZnO beams using hybrid analytical-intelligent method

A hybrid analytical-intelligent approach is proposed for fuzzy reliability analysis of the composite beams reinforced by zinc oxide (ZnO) nanoparticle. The fuzzy reliability index corresponding to buckling failure mode of nanocomposite beam under thickness-direction external voltage is computed based on three-levels: (1) fuzzy analysis, (2) reliability analysis and (3) analytical buckling analysis. In fuzzy analysis level, an improved gravitational search algorithm has been applied to determine uncertainty interval for membership levels of reliability index. The adaptive formulation with a dynamical self-adjusting process is used for reliability analysis level based on conjugate first-order reliability method (FORM). The self-adjusting term in conjugate sensitivity vector is used to satisfy the sufficient descent condition for controlling instability of FORM formula while the proposed conjugate scalar factor is computed less than the original conjugate FORM, thus it may be provided with the efficient results for the convex problem. The new and previous sensitivity vectors obtained by conjugate and steepest descent vectors dynamically adjusted the proposed conjugate factor. In the buckling analysis level, an exponential theory in conjunction with the method of energy is utilized. Fuzzy random variables including applied voltage, the volume fraction of ZnO, thickness of beam, spring constant and shear constant of the foundation are considered in studied nanocomposite beam. Survey results indicated that the proposed method can provide stable and acceptable fuzzy membership functions for parametric study. Moreover, the ratio of length to thickness and spring constant of foundation are the more sensitive parameters which affect fuzzy reliability index significantly.

     

Hybrid intelligent method for fuzzy reliability analysis of corroded X100 steel pipelines

Epistemic uncertainties are critical for reliable design of corroded pipes made of high-strength grade steel. In this work, corrosion defects geometries and operating pressure are provided as the epistemic uncertainties in reliability analysis. A framework of an iterative approach-based bi-loop is presented for fuzzy reliability analysis (FRA) of corroded pipelines to evaluate the fuzzy reliability index-based various fuzzy-random variables (FRVs). In the inner loop, the conjugate first-order reliability method using adaptive finite-step size is applied for carried out the reliability analysis. The outer loop is structured based on the fuzzy analysis corresponding to a modified particle swarm optimization as an intelligent tool. The adaptive conjugate fine step size is dynamically computed to adjust the conjugate sensitivity vector in the reliability loop. The sufficient descent condition is satisfied based on three-term conjugate first-order reliability method. The performance function of corroded pipelines is defined based on average shear stress yield-based plastic flow theory, remaining strength factor, and operating pressure. Two applicable examples as corroded pipelines made from X100 high-strength steel are given to illustrate the effects of epistemic uncertainties under corrosion defects. Investigation the results has shown that modeling of epistemic uncertainty in the reliability analysis of high-grade steel pipelines could result more reasonable reliability indexes. In addition, results indicate that FRVs have significant influence on fuzzy reliability index calculations, especially corrosion defect depth and operating pressure (as FRVs). The sensitivity measure of FRA demonstrated that fuzzy reliability index of corroded X100 steel pipelines is more sensitive to the FRVs means.

     

Applying weakest t-norm based approximate intuitionistic fuzzy arithmetic operations on different types of intuitionistic fuzzy numbers to evaluate reliability of PCBA fault

This research addresses system reliability analysis using weakest t-norm based approximate intuitionistic fuzzy arithmetic operations, where failure probabilities of all components are represented by different types of intuitionistic fuzzy numbers. Due to the incomplete, imprecise, vague and conflicting information about the component of system, the present study evaluates the reliability of system in terms of membership function and non-membership function by using weakest t-norm (T_w) based approximate intuitionistic fuzzy arithmetic operations on different types of intuitionistic fuzzy numbers. In general, interval arithmetic (α-cut arithmetic) operations have been used to analyze the fuzzy system reliability. In complicated systems, interval arithmetic operations may occur the accumulating phenomenon of fuzziness. In order to overcome the accumulating phenomenon of fuzziness, this research adopts approximate intuitionistic fuzzy arithmetic operations under the weakest t-norm arithmetic operations (T_w) to analyze fuzzy system reliability. The approximate intuitionistic fuzzy arithmetic operations employ principle of interval arithmetic under the weakest t-norm arithmetic operations. The proposed novel fuzzy arithmetic operations may obtain fitter decision values, which have smaller fuzziness accumulating and successfully analyze the system reliability. Also weakest t-norm arithmetic operations provide more exact fuzzy results and effectively reduce fuzzy spreads (fuzzy intervals). Using proposed approach, fuzzy reliability of series system and parallel system are also constructed. For numerical verification of proposed approach, a malfunction of printed circuit board assembly (PCBA) is presented as a numerical example. The result of the proposed method is compared with the listing approaches of reliability analysis methods.     

A NEW FUZZY-DATA MINING METHOD FOR PATTERN CLASSIFICATION BY PRINCIPAL COMPONENT ANALYSIS

ABSTRACT

Data mining techniques can be used to discover useful information by exploring and analyzing data. The aim of this article is to propose a new fuzzy-data mining method to find a compact set consisting of fuzzy if-then classification rules with high classification capability using the genetic algorithm. Furthermore, for not reducing the usefulness of the proposed method for classification problems with high dimensional feature space, the curse dimensionality resulting from the grid partition is overcome in the proposed method by employing the principal component analysis to reduce the dimensions. Through computer simulations, it can be seen that the proposed method is comparable to the other fuzzy classification methods on the well-known iris data, the appendicitis data, and the cancer data.     

USING META-PROGRAMMING,CONSTRAINT LOGIC PROGRAMMING AND APPROXIMATE REASONING FOR EXPERT SYSTEMS DESIGN

Abstract

This paper presents an enhancement of the CARESS system—A Constraint Approximative Reasoning System Support—introduced in (Popescu and Roventa, 1994). CARESS is an experimental system with primarily two objectives:

(1)knowledge representation and manipulation techniques and to implement them in PROLOG III, and

(2) to develop a knowledge programming environment for building expert systems. We discuss here the use of meta-programming, constraint logic programming and approximate reasoning for the design of expert systems

It has already been proven that meta-programming and logic programming are powerful techniques for expert system design. Fuzzy logic can be used to model one kind of uncertainty. Constraint logic programming is useful for dealing with the constraints given by operations using fuzzy sets.     

An adaptive discrete-time ILC strategy using fuzzy systems for iteration-varying reference trajectory tracking

In this article, a novel fuzzy systems based on adaptive Iterative Learning Control (ILC) strategy is presented to deal with a class of non-parametric nonlinear discrete-time systems which perform iteration-varying reference trajectory tracking. Using the technique of fuzzy systems to compensate for the non-parametric uncertainty of the discrete-time system dynamics, the proposed adaptive ILC scheme can well track the iteration-varying reference trajectory beyond the initial time points. The convergence of the fuzzy systems based adaptive ILC algorithm is guaranteed by theoretical analysis, and a numerical example is given to illustrate the effectiveness of the adaptive ILC scheme.

     

Multivariate analysis of human behavior data using fuzzy windowing: Example with driver–car–environment system

In most human component system studies performed in simulators, several factors (or independent variables) (at least two, i.e., individual and time) and many variables (or dependent variables) are present. Large and complex databases have to be analyzed. Instead of using rather automatic procedures, this article suggest that, for a very first analysis at least, the human being must be present and he/she must choose a method being adapted to the data, which is different to run a method supposing that the data fit such or such model. This article suggests starting the analysis while keeping both the multifactorial (MF) and multivariate (MV) aspects. To achieve this aim, with the possibility to show nonlinear relationships, a MFMV exploration of the experimental database is performed using the pair (fuzzy space windowing, Multiple Correspondence Analysis). Then may come an inference analysis. This long (due to multiple large graphical views) but rich procedure is illustrated and discussed using a car driving study example.     

Two techniques of sensitivity and uncertainty analysis of fuzzy expert systems

Problems characterized by qualitative uncertainty described by expert judgments can be addressed by the fuzzy logic modeling paradigm, structured within a so-called fuzzy expert system (FES) to handle and propagate the qualitative, linguistic assessments by the experts. Once constructed, the FES model should be verified to make sure that it represents correctly the experts’ knowledge. For FES verification, typically there is not enough data to support and compare directly the expert- and FES-inferred solutions. Thus, there is the necessity to develop indirect methods for determining whether the expert system model provides a proper representation of the expert knowledge. A possible way to proceed is to examine the importance of the different input factors in determining the output of the FES model and to verify whether it is in agreement with the expert conceptualization of the model. In this view, two sensitivity and uncertainty analysis techniques applicable to generic FES models are proposed in this paper with the objective of providing appropriate tools of verification in support of the experts in the FES design phase. To analyze the insights gained by using the proposed techniques, a case study concerning a FES developed in the field of human reliability analysis has been considered.     

Representation and Reasoning Under Uncertainty in Deception Detection: A Neuro-Fuzzy Approach

An analysis of the process and human cognitive model of deception detection (DD) shows that DD is infused with uncertainty, especially in high-stake situations. There is a recent trend toward automating DD in computer-mediated communication. However, extant approaches to automatic DD overlook the importance of representation and reasoning under uncertainty in DD. They represent uncertain cues as crisp values and can only infer whether deception occurs, but not to what extent deception occurs. Based on uncertainty theories and the analyses of uncertainty in DD, we propose a model to represent cues and to reason for DD under uncertainty, and address the uncertainty due to imprecision and vagueness in DD using fuzzy sets and fuzzy logic. Neuro-fuzzy models were developed to discover knowledge for DD. The evaluation results on five data sets showed that the neuro-fuzzy method not only was a good alternative to traditional machine-learning techniques but also offered superior interpretability and reliability. Moreover, the gains of neuro-fuzzy systems over traditional systems became larger as the level of uncertainty associated with DD increased. The findings of this paper have theoretical, methodological, and practical implications to DD and fuzzy systems research.     

Dual hesitant Z-number (DHZN), correlated distance,and risk quantification

Information reliability and uncertainty are two important characteristics that need to be taken care of in any kind of decision-making process. The hesitancy of the human mind is one of the major factors that introduce uncertainty in decision-making. To include the reliability and to resolve the issue of hesitancy in a single framework, in this study, two novel concepts, namely, dual hesitant Z-number (DHZN) and correlated distance measure between two DHZNs (CD-DHZN) are proposed. DHZN = (A^h, B^h), is a judicious integration of hesitant fuzzy sets and Z-number. Here, A^h is the expert's opinion and B^h is the reliability of the expert's opinion. Further, the randomness of the information brings uncertainty to the decision-making process. To circumvent this issue, DHZN is quantified with Cloud model theory and fuzzy envelope. To enhance the information utilization and to reduce the information loss problem while quantifying CD-DHZN, the correlation and the underlying hidden probabilistic relationship between the two parts of a DHZN are captured using the Pearson correlation coefficient, the maximum entropy principle, and Hellinger distance. Then, the extended failure mode and effect analysis (E-FMEA) is proposed with the help of DHZN, CD-DHZN, and VlseKriterijuska Optimizacija IKomoromisno Resenje technique for prioritization of risks. Similarly, extended bow-tie (E-BT) is also proposed for the quantification of basic events (BEs), top event, and accident scenarios. Two case studies with systematic experimental investigations are presented and results are compared with other existing techniques. The results show that the proposed models are able to effectively prioritize the failure modes in E-FMEA and quantify the BEs in E-BT. The results confirm the feasibility and applicability of the proposed models. Sensitivity analysis is also performed to ensure the plausibility and robustness of the proposed model.     

The Need for Fuzzy AI

Artificial intelligence (AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by uncertainty. Fuzzy systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is permitted. This paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including variability. In conclusion, this paper argues for the need for " fuzzy AI” in two senses: (i) the need for fuzzy methodologies (in the technical sense of Zadeh’s fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and (ii) the need for fuzziness (in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.      

Change detection techniques

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

Abbreviations used in this paper

6S second simulation of the satellite signal in the solar spectrum

ANN artificial neural networks

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

AVHRR Advanced Very High Resolution Radiometer

AVIRIS Airborne Visible/Infrared Imaging Spectrometer

CVA change vector analysis

EM expectation–maximization algorithm

ERS-1 Earth Resource Satellite-1

ETM+ Enhanced Thematic Mapper Plus, Landsat 7 satellite image

GIS Geographical Information System

GS Gramm–Schmidt transformation

J-M distance Jeffries–Matusita distance

KT Kauth–Thomas transformation or tasselled cap transformation

LSMA linear spectral mixture analysis

LULC land use and land cover

MODIS Moderate Resolution Imaging Spectroradiometer

MSAVI Modified Soil Adjusted Vegetation Index

MSS Landsat Multi-Spectral Scanner image

NDMI Normalized Difference Moisture Index

NDVI Normalized Difference Vegetation Index

NOAA National Oceanic and Atmospheric Administration

PCA principal component analysis

RGB red, green and blue colour composite

RTB ratio of tree biomass to total aboveground biomass

SAR synthetic aperture radar

SAVI Soil Adjusted Vegetation Index

SPOT HRV Satellite Probatoire d'Observation de la Terre (SPOT) high resolution visible image

TM Thematic Mapper

VI Vegetation Index     

基于模糊技术的彩色图像分割方法

由于彩色图像提供了比灰度图像更为丰富的信息,因此彩色图像处理正受到人们越来越多的关注。彩色图像分割是彩色图像处理的重要问题,目前对彩色图像的分割已提出了许多种算法,在这些算法中由于模糊技术能很好地表达和处理不确定性问题,因此在彩色图像分割领域会有更广阔的应用前景。本文主要介绍了基于模糊技术的模糊阈值分割法、模糊聚类分割法和模糊连接度分割法。     

Analysis of Organisation-Committed Human Error by Extended CREAM

This report analyses the results of the human error classification scheme of CREAM applied to organisation-committed human errors related to six departments. These human errors were not caused by the tasks spotlighted by CREAM, but were concentrated on managerial or administrative tasks, so that the authors have corrected and analysed the definitions and links of cause–effect relations related to the large organisation by means of an extended method of CREAM. Considering human errors on the basis of these analyses, findings have allowed the authors to come up with effective relations between organisation-related causal factors and person-related ones. This has demonstrated that organisation-caused human errors are sufficiently analysable by means of the extended method of CREAM.     

Analysing network uncertainty for industrial product-service delivery: A hybrid fuzzy approach

More and more manufacturers are transitioning from product-focused operations towards global service-oriented operations through approaches such as an industrial product-service system (IPS²). These systems deliver a blend of goods, equipment and services for improved value/revenue streams and there is a need to leverage the knowledge of domain experts in evaluating the uncertainties of IPS² adoption.Along these lines, this article proposes a hybrid fuzzy methodology that leverages the knowledge of domain experts for evaluating the uncertainty of service networks that deliver an IPS². The proposed methodology conceptualises a framework of network uncertainty metrics and applies a set of fuzzy-based techniques (fuzzy Delphi, fuzzy Analytical Hierarchy Process (fuzzy AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS)) to evaluate levels of fuzziness for transitions from traditional product-focused operations towards service-oriented operations. The applicability of the proposed methodology is demonstrated through a case study of a stainless steel manufacturer and the limitations and generalisation potentials of the research are used to highlight future research challenges for service-oriented uncertainty evaluation.     

Application of non-normal p-norm trapezoidal fuzzy number in reliability evaluation of electrical substations

This paper presented a non-normal p-norm trapezoidal fuzzy number–based fault tree technique to obtain the reliability analysis for substations system. Due to uncertainty in the collected data, all the failure probabilities are represented by non-normal p-norm trapezoidal fuzzy number. In this paper, the fault tree incorporated with the non-normal p-norm trapezoidal fuzzy number and minimal cut sets approach are used for reliability assessment of substations. An example of 66/11 kV substation is given to demonstrate the method. Further, fuzzy risk analysis problems are described to find out the probability of failure of each components of the system using linguistic variables, which could be used for managerial decision making and future system maintenance strategy.