Sugeno Fuzzy Integral Research Articles

Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines

Induction machines are widely used in the industry as one of the major actuators, such as water pumps, air compressors, and fans. It is necessary to monitor and diagnose these induction motors to prevent any sudden shut downs caused by premature failures. Numerous fault detection and isolation techniques for the diagnosis of induction machines have been proposed over the past few decades. Among these techniques, motor current signature analysis (MCSA) and vibration analysis are two of the most common signal‐based condition monitoring methods. They are often adopted independently, but each method has its strengths and weaknesses. This research proposed a systemic method to integrate the information received from the vibration and current measurements. We applied the wavelet packet decomposition to extract the time‐frequency features of the vibration and current measurements and used the support vector machines as classifiers for the initial decision‐making. The significant features were identified, and the performances of several classifiers were compared. As a result, the decision‐level sensor fusion based on the Sugeno fuzzy integral was proposed to integrate the vibration and current information to improve the accuracy of the diagnosis.

Fuzzy classifier fusion: an application to reservoir facies identification

An application of classifier fusion technique is presented to improve the performance of automated reservoir facies identification system. The algorithm presented in this study uses three well-known classifiers, namely Bayesian, k-nearest neighbor (kNN), and support vector machine (SVM) to automatically identify four defined facies of Asmari Formation from log-derived amplitude versus offset (AVO) attributes. Fuzzy Sugeno integral (FSI) method is then employed to combine the outputs of three investigated classifiers and increase the consistency of reservoir facies identification process. The experimental results obtained from applying the presented algorithm on data related to three wells drilled in Asmari Formation provide evidence of the effectiveness of the proposed algorithm regarding true positive (TP), false positive (FP), and classification accuracy criteria.

Improving pore type identification from thin section images using an integrated fuzzy fusion of multiple classifiers

A semi-automatic algorithm for identification of five pore types in carbonate rocks of a gas field, including inter-particle, intra-particle, oomoldic, biomoldic, and vuggy, from thin section images is presented. The proposed algorithm involves four main steps. Firstly, a color-based image segmentation procedure is carried out using K-means clustering algorithm to separate regions corresponding to the considered pores. Secondly, six geometrical shape parameters of 384 pores are extracted from each segmented region to obtain the distinctive features for each pore type. Three classifiers (i.e. kNN, RBF, and SVM) are considered for classification to identify the type and percentage of interested porosities. Experimental results show that SVM with polynomial kernel function yields the highest accuracy and can effectively identify the pores with average accuracy of 94.4% whereas the kNN gives the lowest accuracy. The final step include combination of outputs of three employed classifiers that are trained separately on same dataset to recognize each pore space using Fuzzy Sugeno Integral (FSI) method. As expected, the fuzzy fusion of single classifiers improves the results of classification up to 9.4%, which is a considerable improvement in the classification from the stand point of petroleum geology.

Emotional inference by means of Choquet integral and λ-fuzzy measurement in consideration of ambiguity of human mentality

Research on human emotions has started to address psychological aspects of human nature and has advanced to the point of designing various models that represent them quantitatively and systematically. Based on the findings, a method is suggested for emotional space formation and emotional inference that enhance the quality and maximize the reality of emotion-based personalized services. In consideration of the subjective tendencies of individuals, AHP was adopted for the quantitative evaluation of human emotions, based on which an emotional space remodeling method is suggested in reference to the emotional model of Thayer and Plutchik, which takes into account personal emotions. In addition, Sugeno fuzzy inference, fuzzy measures, and Choquet integral were adopted for emotional inference in the remodeled personalized emotional space model. Its performance was evaluated through an experiment. Fourteen cases were analyzed with 4.0 and higher evaluation value of emotions inferred, for the evaluation of emotional similarity, through the case studies of 17 kinds of emotional inference methods. Matching results per inference method in ten cases accounting for 71% are confirmed. It is also found that the remaining two cases are inferred as adjoining emotion in the same section. In this manner, the similarity of inference results is verified.

Robust soft-biometrics prediction from off-line handwriting analysis

Currently, writer's soft-biometrics prediction is gaining an important role in various domains related to forensics and anonymous writing identification. The purpose of this work is to develop a robust prediction of the writer's gender, age range and handedness. First, three prediction systems using SVM classifier and different features, that are pixel density, pixel distribution and gradient local binary patterns, are proposed. Since each system performs differently to the others, a combination method that aggregates a robust prediction from individual systems, is proposed. This combination uses Fuzzy MIN and MAX rules to combine membership degrees derived from predictor outputs according to their performances, which are modeled by Fuzzy measures. Experiments are conducted on two Arabic and English public handwriting datasets. The comparison of individual predictors with the state of the art highlights the relevance of proposed features. Besides, the proposed Fuzzy MIN-MAX combination comfortably outperforms individual systems and classical combination rules. Relatively to Sugeno's Fuzzy Integral, it has similar computational complexity while performing better in most cases.

The Sugeno fuzzy integral of log-convex functions

In this paper, we give an upper bound for the Sugeno fuzzy integral of log-convex functions using the classical Hadamard integral inequality. We present a geometric interpretation and some examples in the framework of the Lebesgue measure to illustrate the results.

Data-Informed Fuzzy Measures for Fuzzy Integration of Intervals and Fuzzy Numbers

The fuzzy integral (FI) with respect to a fuzzy measure (FM) is a powerful means of aggregating information. The most popular FIs are the Choquet and Sugeno, and most research focuses on these two variants. The arena of the FM is much more populated, including numerically derived FMs such as the Sugeno $\lambda$ -measure and decomposable measure, expert-defined FMs, and data-informed FMs. The drawback of numerically derived and expert-defined FMs is that one must know something about the relative values of the input sources. However, there are many problems where this information is unavailable, such as crowdsourcing. This paper focuses on data-informed FMs, or those FMs that are computed by an algorithm that analyzes some property of the input data itself, gleaning the importance of each input source by the data they provide. The original instantiation of a data-informed FM is the agreement FM, which assigns high confidence to combinations of sources that numerically agree with one another. This paper extends upon our previous work in data-informed FMs by proposing the uniqueness measure and additive measure of agreement for interval-valued evidence. We then extend data-informed FMs to fuzzy number (FN)-valued inputs. We demonstrate the proposed FMs by aggregating interval and FN evidence with the Choquet and Sugeno FIs for both synthetic and real-world data.

A multi-criteria decision-making formulation of trust using fuzzy analytic hierarchy process

Trust models have widespread applications in different areas. In this paper, we formulate trust as a multi-criteria decision-making (MCDM) problem. By applying this new formulation, one can treat the process of trust establishment as a decision-making process based on some set of trustworthiness criteria. By looking at trust as a whole, we may miss some important factors. Only by dividing this concept into its building blocks and weighting those building blocks based on the proposed perception of importance in a context and toward a specific goal, we can achieve a better decision. As a structural way for making trust decisions, the proposed method uses fuzzy analytic hierarchy process (AHP). Fuzzy AHP provides both the advantages of fuzziness, which is compatible with the uncertain, vague, and subjective nature of trust and AHP as a systematic approach to make decisions in complex MCDM problems. In order to aggregate the evaluations of each recommender about every trustee candidate, the Sugeno fuzzy integral is used to build the final trust score. At last, the method calculates the final recommendation score based on the trustor's subjective belief. We present an illustrative example plus an empirical experiment in the context of Web service selection to demonstrate the application of the proposed formulation of trust and the evaluation of trust scores.

Design a New Image Encryption using Fuzzy Integral Permutation with Coupled Chaotic Maps

This article introduces a novel image encryption algorithm based on DNA addition combining and coupled two-dimensional piecewise nonlinear chaotic map. This algorithm consists of two parts. In the first part of the algorithm, a DNA sequence matrix is obtained by encoding each color component, and is divided into some equal blocks and then the generated sequence of Sugeno integral fuzzy and the DNA sequence addition operation is used to add these blocks. Next, the DNA sequence matrix from the previous step is decoded and the complement operation to the result of the added matrix is performed by using Sugeno fuzzy integral. In the second part of the algorithm, the three modified color components are encrypted in a coupling fashion in such a way to strengthen the cryptosystem security. It is observed that the histogram, the correlation and avalanche criterion, can satisfy security and performance requirements (Avalanche criterion > 0.49916283). The experimental results obtained for the CVG-UGR image databases reveal the fact that the proposed algorithm is suitable for practical use to protect the security of digital image information over the Internet. .

AN IMPROVED DECISION FUSION TECHNIQUE TO INCREASE THE PERFORMANCE LEVEL OF HRR ATR SYSTEMS

According to literature, a signiflcant and up to date research direction to increase the performance level of automatic target recognition (ATR) systems is focused on the use of information coming from an appropriate set of EM sensors and high-quality decision fusion techniques, respectively. Consequently, in this paper a genetic optimized version of Sugeno's fuzzy integral is discussed. In addition, using a real database belonging to the high-resolution radar (HRR) imagery, the superiority of the proposed decision fusion technique related to its standard version and other well-known decision fusion methods is also demonstrated.

Decision Model of the Soft Ground Improvement Based on Improved Sugeno Fuzzy Integral

In order to completely consider affects of various subjective and objective factors for decision of soft ground improvement, according to quantitative index and qualitative index influencing soft ground improvement, a fuzzy integral decision model about definitive set was presented, based on improved Sugeno fuzzy integral on the basis of fuzzy measure. A computer program for applying the model to solve multi-objective decision making problem was given. The feasibility of its model was verified through the optimal choice for decision of soft ground improvement in practical engineering. The given example shows this model can be successfully used to make decision about soft ground improvement method of the expressway. At the same time, the decisional model above is useful and effective for evaluating the other complicated systems.

Development of Modular Neural Networks with Fuzzy Logic Response Integration for Signature Recognition

This paper describes a modular neural network (MNN) for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community. For instance, many attempts have been made in order to automate the process of identifying a person's handwritten signature, however this problem has proven to be a very difficult task. In this work, we propose an MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined by using a Sugeno fuzzy integral. The experimental results obtained by using a database of 30 individual's shows that the modular architecture can achieve a very high 98% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system.

Estimation of adult skeletal age‐at‐death using the Sugeno fuzzy integral

Age-at-death estimation of an individual skeleton is important to forensic and biological anthropologists for identification and demographic analysis, but it has been shown that the current aging methods are often unreliable because of skeletal variation and taphonomic factors. Multifactorial methods have been shown to produce better results when determining age-at-death than single indicator methods. However, multifactorial methods are difficult to apply to single or poorly preserved skeletons, and they rarely provide the investigator with information about the reliability of the estimate. The goal of this research is to examine the validity of the Sugeno fuzzy integral as a multifactorial method for modeling age-at-death of an individual skeleton. This approach is novel because it produces an informed decision of age-at-death utilizing multiple age indicators while also taking into consideration the accuracies of the methods and the condition of the bone being examined. Additionally, the Sugeno fuzzy integral does not require the use of a population and it qualitatively produces easily interpreted graphical results. Examples are presented applying three commonly used aging methods on a known-age skeletal sample from the Terry Anatomical Collection. This method produces results that are more accurate and with smaller intervals than single indicator methods.

A selection approach for scalable fuzzy integral combination

We consider the problem of collective decision-making from an arbitrary set of classifiers under the Sugeno fuzzy integral (SFI). We assume that classifiers are given, i.e., they cannot be modified towards their effective combination. Under this baseline, we propose a selection-combination strategy, which separates the whole process into two stages: the classifiers selection, to discover a subset of cooperative classifiers under SFI, and the typical SFI combination of selected classifiers. The proposed selection is based on a greedy algorithm which through a heuristic allows an efficient search.

Extensions of fuzzy measures and Sugeno integral for possibilistic truth values

The Sugeno integral has been identified and used as an aggregation operator many times in the past. In this paper, an extension of the Sugeno integral for the framework of possibilistic truth values is presented, resulting in a powerful domain-specific aggregation operator. Next, it is shown how the presented integral can be plugged into a reasoning framework for identification of coreferent objects, which are entity descriptions that refer to the same entity in a different way. The concept of hierarchical fuzzy measures, linked to an object structure is introduced, offering a new conditional possibilistic reasoning framework for object matching. © 2008 Wiley Periodicals, Inc.

AGGREGATES COMPUTED OVER FUZZY SETS AND THEIR INTEGRATION INTO SQLf

The SQLf query language is an extension of SQL to flexible querying of regular relational databases using fuzzy set theory. In this context, atomic predicates (called vague or fuzzy predicates) are expressing preferences and are defined by fuzzy sets. The main purpose of flexible querying is to provide the user with a set of discriminated answers (in the form of a fuzzy set). Until now, the use of aggregates (such as maximum or average) in SQLf queries is limited to the particular case where the aggregate applies to a crisp set. The objective of this paper is to propose an approach to define SQLf queries where the aggregate applies to a fuzzy set of items. The approach proposed here can indeed be seen as a generalization of Sugeno's fuzzy integral.

Fingerprint recognition using the fuzzy Sugeno integral for response integration in modular neural networks

We describe in this paper a new approach for pattern recognition using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral. Response integration is required to combine the outputs of all the modules in the modular network. We have applied the new approach for fingerprint recognition with a real database of fingerprints from students of our institution.

Application of Fuzzy-Sets Integral in Expert Systems

In more situations for decision-making the methods based on expert estimations are used, that is, human intellect is being used as a measuring instrument. The decision risk level dependent on expert’s facilities (defined with the consistency and competence criterions) must be known in the decision-making process. The methodology of experts decisions risk level estimation’s is discussed. The methodology is based on search and use of experts’ consistency and competence rates, what can be get applying excess of the experts offered information. The application of Sugeno and Choquet fuzzy integrals for processing group expert estimations are offered. Ill. 1, bibl. 4 (in English; summaries in English, Russian and Lithuanian).

기질신뢰, 일반신뢰, 상황신뢰, 명성을 고려한 퍼지 신뢰모델

신뢰는 다른 상대가 특정한 행동을 할 것이라고 믿는 정도를 나타내는 주관적인 확률값으로 정의할 수 있다. 본 논문에서는 신뢰값을 결정하는 데서 고려되는 기질신뢰, 일반신뢰, 상황신뢰를 모두 고려하고, 또한 상대에 대한 명성정보를 이용하는 종합적인 퍼지 신뢰모델을 제안한다. 제안한 모델은 거래에 대한 평가결과들에 대해서 만족하는 정도를 퍼지집합으로 나타내고 평가 항목에 대한 중요도를 <TEX>${\lambda}$</TEX>-퍼지측도의 성질을 만족하도록 설정하고, 평가항목별 만족정도를 Sugeno 퍼지적분을 이용하여 결합하는 방법을 사용한다. Trust can be defined as the level of subjective probability with which an agent will perform a particular action. This paper proposes a comprehensive fuzzy trust model which incorporates dispositional trust, general trust, and situational trust and reputation information. In the model, the preference degrees for the interaction outcomes with respect to the evaluation criteria are expressed in a fuzzy set, and Sugeno's fuzzy integral is employed to aggregate the satisfaction degrees with respect to the importance of evaluation criteria which can be assigned in a way to preserve the properties of the <TEX>${\lambda}-fuzzy$</TEX> measure.

Image Coding Quality Assessment Using Fuzzy Integrals With a Three-Component Image Model

Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.