Different Decision Fusion Methods for Modular Common Vector Approach

The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.

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Using decision fusion methods to improve outbreak detection in disease surveillance

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0774-3 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 4

Author(s):

Gaëtan Texier ◽

Rodrigue S. Allodji ◽

Loty Diop ◽

Jean-Baptiste Meynard ◽

Liliane Pellegrin ◽

...

Keyword(s):

Disease Surveillance ◽

Decision Fusion ◽

Outbreak Detection ◽

Fusion Methods

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Protein Structure Prediction by Fusion,Bayesian Methods

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch195 ◽

2011 ◽

pp. 1330-1336

Author(s):

Somasheker Akkaladevi ◽

Ajay K. Katangur ◽

Xin Luo

Keyword(s):

Neural Network ◽

Amino Acids ◽

Secondary Structure ◽

Structure Prediction ◽

Prediction Accuracy ◽

Protein Secondary Structure ◽

Decision Fusion ◽

Amino Acid Residues ◽

Fusion Methods ◽

Bayesian Inference Method

Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging and difficult task, and the problem has been approached from several angles. A protein is a sequence of amino acid residues and can thus be considered as a one dimensional chain of ‘beads’ where each bead correspond to one of the 20 different amino acid residues known to occur in proteins. The length of most protein sequence ranges from 50 residues to about 1000 residues but longer proteins are also known, e.g. myosin, the major protein of muscle fibers, consists of 1800 residues (Altschul et al. 1997). Many techniques were used many researchers to predict the protein secondary structure, but the most commonly used technique for protein secondary structure prediction is the neural network (Qian et al. 1988). This chapter discusses a new method combining profile-based neural networks (Rost et al. 1993b), Simulated Annealing (SA) (Akkaladevi et al. 2005; Simons et al. 1997), Genetic algorithm (GA) (Akkaladevi et al. 2005) and the decision fusion algorithms (Akkaladevi et al. 2005). Researchers used the neural network (Hopfield 1982) combined with GA and SA algorithms, and then applied the two decision fusion methods; committee method and the correlation methods and obtained improved results on the prediction accuracy (Akkaladevi et al. 2005). Sequence profiles of amino acids are fed as input to the profile-based neural network. The two decision fusion methods improved the prediction accuracy, but noticeably one method worked better in some cases and the other method for some other sequence profiles of amino acids as input (Akkaladevi et al. 2005). Instead of compromising on some of the good solutions that could have generated from either approach, a combination of these two approaches is used for obtaining better prediction accuracy. This criterion is the basis for the Bayesian inference method (Anandalingam et al. 1989; Schmidler et al. 2000; Simons et al. 1997). The results obtained show that the prediction accuracy improves by more than 2% using the combination of the decision fusion approach and the Bayesian inference method.

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Decision Fusion Methods in a Dispersed Decision System - A Comparison on Medical Data

Computational Collective Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-319-67077-5_14 ◽

2017 ◽

pp. 139-149 ◽

Cited By ~ 1

Author(s):

Małgorzata Przybyła-Kasperek ◽

Agnieszka Nowak-Brzezińska ◽

Roman Simiński

Keyword(s):

Decision Fusion ◽

Medical Data ◽

Decision System ◽

System A ◽

Fusion Methods

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A NEW APPROACH FOR MULTI-SYSTEM/SENSOR DECISION FUSION BASED ON JOINT MEASURES

International Journal of Information Acquisition ◽

10.1142/s0219878904000148 ◽

2004 ◽

Vol 01 (02) ◽

pp. 109-120 ◽

Cited By ~ 6

Author(s):

ALI J. RASHIDI ◽

M. HASSAN GHASSEMIAN

Keyword(s):

High Performance ◽

Decision Fusion ◽

Sensor Data ◽

Commission Errors ◽

Multiple Sensors ◽

Spectral Bands ◽

Mathematical Properties ◽

Fusion Methods ◽

Local Results ◽

Fusion Scheme

This article describes the joint measures method as a new powerful method for the development of a high performance multi-sensor data/image fusion scheme at the decision level. The images are received from distributed multiple sensors, which sense the targets in different spectral bands including visible, infrared, thermal and microwave. At first, we study the decision fusion methods, including voting schemes, rank based algorithm, Bayesian inference, and the Dempster-Shafer method. Then, we extract the mathematical properties of multi-sensor local classification results and use them for modeling of the classifier performances by the two new measures, i.e. the plausibility and correctness. Then we establish the plausibility and correctness distribution vectors and matrices for introducing the two improvements of the Dempster-Shafer method, i.e. the DS (CM) and DS (PM) methods. After that we introduce the joint measures decision fusion method based on using these two measures jointly. The Joint Measures Method (JMM) can deal with any decision fusion problem in the case of uncertain local classifiers results as well as clear local classifiers results. Finally, we deploy the new and previous methods for the fusion of the two different sets of multispectral image classification local results and we also compare their reliabilities, the commission errors and the omission errors. The results obviously show that the DS (PM), DS (CM) and JMM methods which use the special properties of the local classifiers and classes, have much better accuracies and reliabilities than other methods. In addition, we show that the reliability of the JMM is at least 3% higher than all other methods.

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