Fuzzy Decision Fusion Research Articles

Twin data multimode collaborative transfer learning for bearing failure diagnosis

To tackle the challenges of gathering and labeling data in practical engineering applications, a multimode collaborative transfer learning method is proposed to bridge the reality gap between labeled twin fault data and unlabeled real-world data. A bearing fault digital twin model is built to produce failure twin data of the test bearing under different health conditions. Three simulation source domains are constructed in accordance with the twin data's transferable modes. The iterative joint geometric-statistical alignment is used to perform the collaborative transfer of twin data's modes, which suppresses the negative transfer brought on by insufficient transferable information and the large inter-domain discrepancy. The fuzzy integral decision fusion is optimized and used to automatically label the real-world samples, which increases the cross-domain category alignment feasibility. The experimental results of extensive diagnosis tasks verified that the proposed method significantly outperforms the state-of-the-art domain adaption methods and can achieve an average classification accuracy of more than 91% in the bearing fault diagnosis with the support of fault twin data alone, without the supervision and guidance of historical labeling data.

A noninvasive blood glucose detection method with strong time adaptability based on fuzzy operator decision fusion and dynamic spectroscopy characteristics of PPG signals

A noninvasive blood glucose detection method with strong time adaptability based on fuzzy operator decision fusion and dynamic spectroscopy characteristics of PPG signals

Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images

Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature uncertainty, spectral feature change is generated by three independent methods, and spatial change information is obtained by spatial feature set construction and the optimal feature selection strategy. Secondly, the saliency change map of bi-temporal images is obtained with the co-saliency detection method to complement the insufficiency of image features. Then, the image objects are acquired by multi-scale segmentation based on the staking images. Finally, different pixel-level image change information and the segmentation result are fused using the fuzzy integral decision theory to determine the object change probability. Three high-resolution remote sensing image datasets and three comparative experiments were carried out to evaluate the performance of the proposed algorithm. Spectral–spatial–saliency change information was found to play a major role in the change detection of high-resolution remote sensing images, and the fuzzy integral decision strategy was found to effectively obtain reliable changed objects to improve the accuracy and robustness of change detection.

Analytic Hierarchy Process Based Fuzzy Decision Fusion System for Model Prioritization and Process Monitoring Application

Many fault detection and identification methods have been developed in recent years; each method works under its own assumption, which means a method that works well under one condition may not provide a satisfactory performance under another condition. In this paper, several data-based process monitoring methods are used in order to provide an effective monitoring scheme for processes under various conditions, and then the analytic hierarchy process approach is introduced for model prioritization. Compared with conventional ensemble systems, the proposed method is able to provide different priorities for different models in monitoring different process faults. Furthermore, a new fuzzy decision fusion system is designed for the purpose of online process monitoring. The effectiveness of the developed method is verified through using the Tennessee Eastman benchmark process.

A Dempster Shafer-based fuzzy multisensor fusion system using airborne lidar and hyperspectral imagery

ABSTRACTNowadays, accurate spectral reflectance information is provided by hyperspectral (HS) data while light detection and ranging (lidar) data provides precise information about the height and geometrical properties of the surfaces. In the most research papers, data fusion of disparate sensors significantly improves object classification performance compared to that of just an individual sensor. Previous researches on fusion of these two sensors had problems such as crisp classifiers or simple fuzzy decision-making systems. This article tries to overcome these weaknesses by accurate support vector machine (SVM) and Fuzzy SVM as classifiers in crisp and fuzzy decision fusion system and fusion of two sensors by two different methods based on precise theories of Bayesian and Shafer. Also, the proposed method tries to compare the results of fusion of both data using decision fusion system with stacked features strategy. This study focuses on HS and lidar fusion through three main phases. The first phase is based on the using of Noise Weighted Harsanyi-Farrand-Chang method and principal component analysis to overcome the high dimensionality problem of HS data. The second phase is based on the feature extraction and selection strategy on lidar data. Finally, fuzzy SVM and Dempster Shafer methods are applied as fuzzy classification and fuzzy decision fusion strategies on the feature spaces. A co-registered HS and lidar data set from Houston of U.S.A. by 15 classes was available to examine the effectiveness of the proposed method. The results of this study highlight that the combination of HS and lidar data enable reliable mapping of land cover.

Interval type‐2 fuzzy‐logic‐based decision fusion system for air‐lane monitoring

Eliminating mishaps due to human error is a primary focus of the avionics industry. Air-lane monitoring is critical to avert occurrences of such mishaps and is achieved using intelligence imparting techniques. Fuzzy logic is one such technique, which incorporates human knowledge for decision making with ease. Type-1 fuzzy logic for decision fusion is established to be advantageous for air-lane monitoring considering sensor input data. Decision making capabilities of type-1 systems are inconsistent when uncertainties or noise is present in the input data. To overcome this issue, this study discusses on interval type-2 fuzzy logic-based decision fusion software (IT2FLDS) for air-lane monitoring. IT2FLDS is realised using an interval type-2 Mamdani model. Experimental results presented prove that IT2FLDS exhibits better decision making capabilities when compared with type-1 fuzzy logic systems considering uncertainties in input sensor data. IT2FLDS is further extended to include flight level parameters for air-lane monitoring. Results presented prove that IT2FLDS works better than its type-1 counterpart when flight level examples are considered. Using type-2 fuzzy logic systems for avionics problems related to air-lane discipline is advocated.

Research on Cultivated Land Change Based on GIS and High Resolution Remote Sensing Images

This paper study an automatic monitoring method for land change based on high resolution remote sensing images and GIS data, and we use three classification methods to classify and fuse the research area. Secondly, the paper calculates the corresponding map class components and compares them with their historical attributes; it can automatically monitor land use change. The experimental results show that the fuzzy decision fusion classification can significantly improve the classification effect, and it can accurately determine the change area accurately and automatically. However, there are some partial errors in the region.

Fuzzy decision fusion system for fault classification with analytic hierarchy process approach

Performance of the most existing fault detection and classification methods can only be guaranteed when each of their own assumptions are met. In other words, a method works well in one condition may not perform well in another. In this paper, a new analytic hierarchy process (AHP) based fuzzy decision fusion system is proposed to tackle the fault classification problem. The AHP approach is introduced to determine the priorities of different classifiers, which are further utilized as the weights in ensemble system. Comparing to conventional equal weighted fusion system, the proposed fuzzy fusion system is able to provide more rational and convincing fault classification result. Effectiveness of the proposed fuzzy fusion system with model evaluation is verified through the Tennessee Eastman (TE) benchmark process.

Fuzzy decision fusion approach for loss-given-default modeling

In this paper, fuzzy decision fusion techniques are applied to predict loss-given-default of corporate bonds. In our model, we add the principal components derived from more than 100 macroeconomic variables as explanatory variables. However, in order to improve the performance of the model, the Box–Cox transformation of macroeconomic variables is applied prior to loss-given-default modeling. A differential evolution algorithm is used to create an optimized fuzzy rule-based model that fuses the outputs of several base models. We compare the predictions from fuzzy decision fusion techniques with support vector regression techniques, regression trees and OLS regressions. Our findings show that fuzzy decision fusion techniques increase prediction accuracy of loss-given-default modeling and transformations of macroeconomic factors do not affect prediction accuracy of fuzzy models.

High resolution multisensor fusion of SAR, optical and LiDAR data based on crisp vs. fuzzy and feature vs. decision ensemble systems

Synthetic Aperture Radar (SAR) data are of high interest for different applications in remote sensing specially land cover classification. SAR imaging is independent of solar illumination and weather conditions. It can even penetrate some of the Earth’s surface materials to return information about subsurface features. However, the response of radar is more a function of geometry and structure than a surface reflection occurs in optical images. In addition, the backscatter of objects in the microwave range depends on the frequency of the band used, and the grey values in SAR images are different from the usual assumption of the spectral reflectance of the Earth’s surface. Consequently, SAR imaging is often used as a complementary technique to traditional optical remote sensing. This study presents different ensemble systems for multisensor fusion of SAR, multispectral and LiDAR data. First, in decision ensemble system, after extraction and selection of proper features from each data, crisp SVM (Support Vector Machine) and Fuzzy KNN (K Nearest Neighbor) are utilized on each feature space. Finally Bayesian Theory is applied to fuse SVMs when Decision Template (DT) and Dempster Shafer (DS) are applied as fuzzy decision fusion methods on KNNs. Second, in feature ensemble system, features from all data are applied on a cube. Then classifications were performed by SVM and FKNN as crisp and fuzzy decision making system respectively. A co-registered TerrraSAR-X, WorldView-2 and LiDAR data set form San Francisco of USA was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with different sensor improves classification results for most of the classes.

Fuzzy decision fusion of complementary experts based on evolutionary cepstral coefficients for phoneme recognition

Optimal representation of acoustic features is an ongoing challenge in automatic speech recognition research. As an initial step toward this purpose, optimization of filterbanks for the cepstral coefficient using evolutionary optimization methods is proposed in some approaches. However, the large number of optimization parameters required by a filterbank makes it difficult to guarantee that an individual optimized filterbank can provide the best representation for phoneme classification. Moreover, in many cases, a number of potential solutions are obtained. Each solution presents discrimination between specific groups of phonemes. In other words, each filterbank has its own particular advantage. Therefore, the aggregation of the discriminative information provided by filterbanks is demanding challenging task. In this study, the optimization of a number of complementary filterbanks is considered to provide a different representation of speech signals for phoneme classification using the hidden Markov model (HMM). Fuzzy information fusion is used to aggregate the decisions provided by HMMs. Fuzzy theory can effectively handle the uncertainties of classifiers trained with different representations of speech data. In this study, the output of the HMM classifiers of each expert is fused using a fuzzy decision fusion scheme. The decision fusion employed a global and local confidence measurement to formulate the reliability of each classifier based on both the global and local context when making overall decisions. Experiments were conducted based on clean and noisy phonetic samples. The proposed method outperformed conventional Mel frequency cepstral coefficients under both conditions in terms of overall phoneme classification accuracy. The fuzzy fusion scheme was shown to be capable of the aggregation of complementary information provided by each filterbank.

Applying evolutionary methods for early prediction of sleep onset

Driving safety can be achieved by predicting imminent falling asleep at the wheel. Several methods of early detection have been investigated by continuous monitoring of physiological and behavioral parameters. Requirements for noninvasive, unattended, personal adaptation need to be met, along with the effectiveness of the detection method, in order to perform reliably when applied. Because wakefulness and sleep are reflected in several human physiological conditions, such as cardiac activity, breathing, movement, and galvanic skin conductance, captured bioelectric signal features were extracted. A fuzzy decision-fusion logic was tuned to make inferences about oncoming driver fatigue and drowsiness. The evolving fuzzy neural network paradigm was applied to the previous developed framework to improve reliability while keeping target system complexity low.

Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system

Regarding to the limitations and benefits of remote sensing sensors, fusion of remote sensing data from multiple sensors such as hyperspectral and LIDAR (light detection and ranging) is effective at land cover classification. Hyperspectral images (HSI) provide a detailed description of the spectral signatures of classes, whereas LIDAR data give height detailed information. However, because of the more complexities and mixed information in LIDAR and HSI, traditional crisp classification methods could not be more efficient. In this situation, fuzzy classifiers could deliver more satisfactory results than crisp classification approaches. Also, referring to the limitation of single classifiers, multiple classifier system (MCS) may exhibit better performance in the field of multi-sensor fusion. This paper presents a fuzzy multiple classifier system for fusions of HSI and LIDAR data based on decision template (DT). After feature extraction and feature selection on each data, all selected features of both data are applied on a cube. Then classifications were performed by fuzzy k-nearest neighbour (FKNN) and fuzzy maximum likelihood (FML) on cube of features. Finally, a fuzzy decision fusion method is utilized to fuse the results of fuzzy classifiers. In order to assess fuzzy MCS proposed method, a crisp MCS based on support vector machine (SVM), KNN and maximum likelihood (ML) as crisp classifiers and naive Bayes (NB) as crisp classifier fusion method is applied on selected cube feature. A co-registered HSI and LIDAR data set from Houston of USA was available to examine the effect of proposed MCSs. Fuzzy MCS on HSI and LIDAR data provide interesting conclusions on the effectiveness and potentialities of the joint use of these two data.

Feature grouping-based multiple fuzzy classifier system for fusion of hyperspectral and LIDAR data

Interest in the joint use of different data from multiple sensors has been increased for classification applications. This is because the fusion of different information can produce a better understanding of the observed site. In this field of study, the fusion of light detection and ranging (LIDAR) and passive optical remote sensing data for classification of land cover has attracted much attention. This paper addressed the use of a combination of hyperspectral (HS) and LIDAR data for land cover classification. HS images provide a detailed description of the spectral signatures of classes, whereas LIDAR data give detailed information about the height but no information for the spectral signatures. This paper presents a multiple fuzzy classifier system for fusion of HS and LIDAR data. The system is based on the fuzzy K-nearest neighbor (KNN) classification of two data sets after application of feature grouping on them. Then a fuzzy decision fusion method is applied to fuse the results of fuzzy KNN classifiers. An experiment was carried out on the classification of HS and LIDAR data from Houston, USA. The proposed fuzzy classifier ensemble system for HS and LIDAR data provide interesting conclusions on the effectiveness and potentials of the joint use of these two data. Fuzzy classifier fusion on these two data sets improves the classification results when compared with independent single fuzzy classifiers on each data set. The fuzzy proposed method represented the best accuracy with a gain in overall accuracy of 93%.

Application and Research of Land Changes Based on GIS and RS

Research of a high-resolution remote sensing images and automatic monitoring of land changes based on GIS data. Were used three kinds of fuzzy classification and data fusion classification of the study area. combined with the historical period GIS thematic data and classification results based on highly accurate classification results, calculated the corresponding polygons category ingredients and comparative analysis of its historical properties, and automatically monitor change of land use. The experimental results show that the fuzzy decision fusion classification can significantly improve the classification results, to obtain more accurate results than a single classifier. Decision classification results combined with GIS data changes in regional decision experimental results show that there is a big change in the region able to accurately automatically determine change area, area less part of the change part of misjudgment phenomenon.

Modeling Fuzzy Decision Fusion Based on Vague Set

As a further generalization of fuzzy set theory, the vague set theory can overcome the shortcomings of fuzzy set by describing the membership from two sides of both TRUE and FALSE, rather than only by a single membership value. Since vague sets can provide more information than fuzzy sets, it is superior in mathematical analysis of system with uncertainty. Thus, vague set is more powerful in the describing and processing of uncertain, inaccurate, even conflicting information. In this paper, a new method vague set-based is proposed to deal with fuzzy decision fusion problem. Compared with traditional fuzzy decision fusion method- such as fuzzy comprehensive evaluation, the new method is more efficient and powerful to fulfill decision fusion with uncertain and inaccurate information. Generally, the new method is the same with group decision fusion and soft fusion.

Obstacle Detection and Classification in Dynamical Background

In Obstacle detection is based on inverse perspective mapping and homography. Obstacle classification is based on fuzzy neural network. The estimation of the vanishing point relies on feature extraction strategy. The method exploits the geometrical relations between the elements in the scene so that obstacle can be detected. The estimated homography of the road plane between successive images is used for image alignment. A new fuzzy decision fusion method with fuzzy attribution for obstacle detection and classification application is described The fuzzy decision function modifies parameters with auto-adapted algorithm to get better classification probability It is shown that the method can achieve better classification result