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An engine-fault-diagnosis system based on sound intensity analysis and wavelet packet pre-processing neural network
Dergi Makalesi

Dergi Makalesi
Kaynak :
Science Direct
Dergi:
Engineering Applications of Artificial Intelligence
Yayıncı:
Elsevier
Yazar(lar) :
Y.S. Wang , N.N. Liu , H. Guo , X.L. Wang
Yıl :
2020
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Based on the techniques of sound intensity analysis, incomplete wavelet packet analysis (WPA) and artificial neural network (ANN), a WPA pre-processing method for noise-based engine fault diagnosis (EFD), so-called WPA–ANN model, is presented in this paper. The noises of an EFI gasoline engine under normal and fault states are measured and their contours of sound intensity level (SIL) are calculated by interpolation approach to initially investigate the possibility of a SIL-based EFD. Furthermore, an incomplete WPA model, which consists of a five-level discrete wavelet transform (DWT) and a four-level WPA, is developed and applied to the measured noise signals for extracting fault features of the engine, as is a multi-layered ANN model for engine failure classification by using the extracted features of the noises. To verify the proposed approach, the WPA–ANN model is extended to recognize other noise-related faults of the engine. The results suggest that the noise-based WPA–ANN models are effective for engine fault diagnosis. Due to its time–frequency characteristics and pattern recognition capacity, the WPA–ANN can be used to process both the stationary and nonstationary signals. In view of the applications, the proposed WPA–ANN model can be directly used in vehicle EFDs, and may be extended to other sound-related fields for failure diagnosis in engineering.
DOI : 10.1016/j.engappai.2020.103765
Anahtar Kelimeler : Engine fault diagnosis, Sound intensity analysis, Noise-based fault recognition, Wavelet package analysis (WPA), Artificial neural network (ANN)
ISSN: 0952-1976
Cilt: 94
Sayfa: 103765
Sınıflandırma :
2
Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques
Dergi Makalesi

Dergi Makalesi
Kaynak :
Science Direct
Dergi:
Cognitive Systems Research
Yayıncı:
Elsevier
Yazar(lar) :
Amita Das , U. Rajendra Acharya , Soumya S. Panda , Sukanta Sabut
Yıl :
2019
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Liver cancer is one of the leading cause of death in all over the world. Detecting the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided diagnosis (CAD) is used in decision making process for accurate detection for appropriate therapy. Therefore the main objective of this work is to detect the liver cancer accurately using automated method. Methods In this work, we have proposed a new system called as watershed Gaussian based deep learning (WGDL) technique for effective delineate the cancer lesion in computed tomography (CT) images of the liver. A total of 225 images were used in this work to develop the proposed model. Initially, the liver was separated using marker controlled watershed segmentation process and finally the cancer affected lesion was segmented using the Gaussian mixture model (GMM) algorithm. After tumor segmentation, various texture features were extracted from the segmented region. These segmented features were fed to deep neural network (DNN) classifier for automated classification of three types of liver cancer i.e. hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET). Results We have achieved a classification accuracy of 99.38%, Jaccard index of 98.18%, at 200 epochs using DNN classifier with a negligible validation loss of 0.062 during the classification process. Conclusions Our developed system is ready to be tested with huge database and can aid the radiologist in detecting the liver cancer using CT images.
DOI : 10.1016/j.cogsys.2018.12.009
Anahtar Kelimeler : Liver cancer, CT scan, Watershed transform, Gaussian mixture model, DNN model
ISSN: 1389-0417
Cilt: 54
Sayfa: 165-175
Sınıflandırma :
3
Solutions for biomedical grid computing—Case studies from the D-Grid project Services@MediGRID
Dergi Makalesi

Dergi Makalesi
Kaynak :
Science Direct
Dergi:
Journal of Computational Science
Yayıncı:
Elsevier
Yazar(lar) :
Frank Dickmann , Jürgen Falkner , Wilfried Gunia , Jochen Hampe , Michael Hausmann , Alexander Herrmann , Nick Kepper , Tobias A. Knoch , Svenja Lauterbach , Jörg Lippert , Kathrin Peter , Eberhard Schmitt , Ulrich Schwardmann , Juri Solodenko , Dietmar Sommerfeld , Thomas Steinke , Anette Weisbecker , Ulrich Sax
Yıl :
2012
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
The project [email protected] consortium established a tool set of grid-based biomedical services since 2008. The services are related to genetic analysis, genome data visualization, and pharmacokinetic modeling. Furthermore, business concepts for these services have been examined which are supported by an accounting and billing service. While the tools cover a whole service chain for biomedicine, the business concepts are rather heterogeneous. However, the overall addressed target market areas show promising potential. In addition, a structured coaching process reduces friction in the technology transfer from grid computing to biomedicine. This should be considered for similar future endeavors.
DOI : 10.1016/j.jocs.2011.06.006
Anahtar Kelimeler : email protected, MediGRID, Healthgrids, Grid-based tool set for genetics, Grid business concepts
ISSN: 1877-7503
Sayı: 5
Cilt: 3
Sayfa: 280-297
Sınıflandırma :
4
A dominance intuitionistic fuzzy-rough set approach and its applications
Dergi Makalesi

Dergi Makalesi
Kaynak :
Science Direct
Dergi:
Applied Mathematical Modelling
Yayıncı:
Elsevier
Yazar(lar) :
Bing Huang , Yu-liang Zhuang , Hua-xiong Li , Da-kuan Wei
Yıl :
2013
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Although the rough set and intuitionistic fuzzy set both capture the same notion, imprecision, studies on the combination of these two theories are rare. Rule extraction is an important task in a type of decision systems where condition attributes are taken as intuitionistic fuzzy values and those of decision attribute are crisp ones. To address this issue, this paper makes a contribution of the following aspects. First, a ranking method is introduced to construct the neighborhood of every object that is determined by intuitionistic fuzzy values of condition attributes. Moreover, an original notion, dominance intuitionistic fuzzy decision tables (DIFDT), is proposed in this paper. Second, a lower/upper approximation set of an object and crisp classes that are confirmed by decision attributes is ascertained by comparing the relation between them. Third, making use of the discernibility matrix and discernibility function, a lower and upper approximation reduction and rule extraction algorithm is devised to acquire knowledge from existing dominance intuitionistic fuzzy decision tables. Finally, the presented model and algorithms are applied to audit risk judgment on information system security auditing risk judgement for CISA, candidate global supplier selection in a manufacturing company, and cars classification.
DOI : 10.1016/j.apm.2012.12.009
Anahtar Kelimeler : Intuitionistic fuzzy set, Rough set model, Dominance relation, Reduction, Rule extraction
ISSN: 0307-904X
Sayı: 12-13
Cilt: 37
Sayfa: 7128-7141
Sınıflandırma :

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