1,656 research outputs found
An evidence-based approach to damage location on an aircraft structure
International audienceThis paper discusses the use of evidence-based classifiers for the identification of damage. In particular, a neural network approach to Dempster-Shafer theory is demonstrated on the damage location problem for an aircraft wing. The results are compared with a probabilistic classifier based on a multi-layer perceptron neural network and shown to give similar results. The question of fusing classifiers is considered and it is shown that a combination of the Dempster-Shafer and MLP classifiers gives a significant improvement over the use of individual classifiers for the aircraft wing data
Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification
To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps(ts)
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
Adaptive imputation of missing values for incomplete pattern classification
In classification of incomplete pattern, the missing values can either play a
crucial role in the class determination, or have only little influence (or
eventually none) on the classification results according to the context. We
propose a credal classification method for incomplete pattern with adaptive
imputation of missing values based on belief function theory. At first, we try
to classify the object (incomplete pattern) based only on the available
attribute values. As underlying principle, we assume that the missing
information is not crucial for the classification if a specific class for the
object can be found using only the available information. In this case, the
object is committed to this particular class. However, if the object cannot be
classified without ambiguity, it means that the missing values play a main role
for achieving an accurate classification. In this case, the missing values will
be imputed based on the K-nearest neighbor (K-NN) and self-organizing map (SOM)
techniques, and the edited pattern with the imputation is then classified. The
(original or edited) pattern is respectively classified according to each
training class, and the classification results represented by basic belief
assignments are fused with proper combination rules for making the credal
classification. The object is allowed to belong with different masses of belief
to the specific classes and meta-classes (which are particular disjunctions of
several single classes). The credal classification captures well the
uncertainty and imprecision of classification, and reduces effectively the rate
of misclassifications thanks to the introduction of meta-classes. The
effectiveness of the proposed method with respect to other classical methods is
demonstrated based on several experiments using artificial and real data sets
Vibration condition monitoring of planetary gears based on decision level data fusion using Dempster-Shafer theory of evidence
In recent years, due to increasing requirement for reliability of industrial machines, fault diagnosis using data fusion methods has become widely applied. To recognize crucial faults of mechanical systems with high confidence, indubitably decision level fusion techniques are the foremost procedure among other data fusion methods. Therefore, in this paper in order to improve the fault diagnosis accuracy of planetary gearbox, we proposed a representative data fusion approach which exploits Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers and Dempster-Shafer (D-S) evidence theory for classifier fusion. We assumed the SVM and ANN classifiers as fault diagnosis subsystems as well. Then output values of the subsystems were regarded as input values of decision fusion level module. First, vibration signals of a planetary gearbox were captured for four different conditions of gear. Obtained signals were transmitted from time domain to time-frequency domain using wavelet transform. In next step, some statistical features of time-frequency domain signals were extracted which were used as classifiers input. The gained results of every fault diagnosis subsystem were considered as basic probability assignment (BPA) of D-S evidence theory. Classification accuracy for the SVM and ANN subsystems was determined as 80.5 % and 74.6 % respectively. Then, by using the D-S theory rules for classifier fusion, ultimate fault diagnosis accuracy was gained as 94.8 %. Results show that proposed method for vibration condition monitoring of planetary gearbox based on D-S theory provided a much better accuracy. Furthermore, an increase of more than 14 % accuracy demonstrates the strength of D-S theory method in decision fusion level fault diagnosis
- …