scholarly journals Performance Degradation Prediction Based on a Gaussian Mixture Model and Optimized Support Vector Regression for an Aviation Piston Pump

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3854 ◽  
Author(s):  
Chuanqi Lu ◽  
Shaoping Wang
Keyword(s):  
Support Vector Regression ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Performance Degradation ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Information Quantity ◽  
Prediction Ability ◽  
Outlet Pressure

Performance degradation prediction plays a key role in realizing aviation pump health management and condition-based maintenance. Thus, this paper proposes a new approach that combines a Gaussian mixture model (GMM) and optimized support vector regression (SVR) to predict aviation pumps’ degradation processes based on the pump outlet pressure signals. Different from other feature extraction methods in which the information of intrinsic mode functions (IMFs) is not fully utilized, some useful IMF components are firstly chosen, and the corresponding multi-domain features are extracted from each selected component. Considering that it is not the case that all features are equally sensitive to degradation assessment, PCA is used to select more sensitive degradation features. Since the distribution of these extracted features is a stochastic process in feature space, meanwhile, self-information quantity can describe the uncertainty of system by measuring the average information quantity contained in the probability distribution, self-information quantity based on GMM is defined as degradation index (DI) to describe the degradation degree of the pump quantitatively. Finally, an SVR model is constructed to predict the degradation status of the pump. To achieve higher prediction accuracy, phase space reconstruction theory is first employed to determine the number of the inputs of the SVR model, then a new method combining particle swarm optimization (PSO) with grid search (GS) is developed to optimize the parameters of the SVR model. Finally, both the online data and historical data are utilized for the construction of the SVR model, respectively. The effectiveness of the proposed approach is validated by full life cycle data collected from an aviation pump test rig. The results demonstrate that the DI extracted from pump outlet pressure signals can effectively identify and track the current deterioration stage, and the established SVR model has better prediction ability when compared with previously published methods.

Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdullah Alharbi ◽  
Wajdi Alhakami ◽  
Sami Bourouis ◽  
Fatma Najar ◽  
Nizar Bouguila
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection Method ◽  
Gaussian Mixture ◽  
Generative Models ◽  
Support Vector ◽  
Learning Framework ◽  
Probabilistic Support ◽  
Discriminative Approach ◽  
Vector Machines

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

scholarly journals Indonesian Stock Prediction using Support Vector Machine (SVM)

2018 ◽  
Vol 164 ◽  
pp. 01031 ◽  
Author(s):  
Murtiyanto Santoso ◽  
Raymond Sutjiadi ◽  
Resmana Lim
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Prediction Method ◽  
Gaussian Mixture ◽  
Machine Intelligence ◽  
Support Vector ◽  
Predicted Model ◽  
Python Programming ◽  
Model Support

This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence) or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK) stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data) and the remainder (20 %) was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

scholarly journals Forecasting risk of crop disease with anomaly detection algorithms

2020 ◽  
Author(s):  
Peter Skelsey
Keyword(s):  
Anomaly Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Added Value ◽  
Support Vector ◽  
Density Estimator ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Crop Disease

Information from crop disease surveillance programs and outbreak investigations provide real-world data about the drivers of epidemics. In many cases, however, only information on outbreaks is collected and data from surrounding healthy crops is omitted. Use of such data to develop models that can forecast risk/no-risk of disease is therefore problematic, as information relating to the no-risk status of healthy crops is missing. This study explored a novel application of anomaly detection techniques to derive models for forecasting risk of crop disease from data comprised of outbreaks only. This was done in two steps. In the training phase the algorithms were used to learn the envelope of weather conditions most associated with historic crop disease outbreaks. In the testing phase the algorithms were used for hindcasting of historic outbreak events. Five different anomaly-detection algorithms were compared according to their accuracy in forecasting outbreaks: robust covariance, one-class k-means, Gaussian mixture model, kernel density estimator, and one-class support vector machine. A case study of potato late blight survey data from across Great Britain was used for proof-of-concept. The results showed that Gaussian mixture model had the highest forecast accuracy at 97.0%, followed by one-class k-means at 96.9%. There was added value in combining the algorithms in an ensemble to provide a more accurate and robust forecasting tool that can be tailored to produce region-specific alerts. The techniques used here can easily be applied to outbreak data from other crop pathosystems to derive tools for agricultural decision support.

scholarly journals Video Summarization Based on Gaussian Mixture Model and Kernel Support Vector Machine for Forest Fire Detection

2019 ◽  
Vol 9 (1) ◽  
pp. 1827-1831
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Forest Fire ◽  
Video Summarization ◽  
Fire Detection ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Kernel Support Vector Machine ◽  
Forest Fire Detection

Exponential growth in the generation of multimedia data especially videos resulted to the development of video summarization concept. The summary of the videos offers a collection of frames which precisely define the video content in a considerably compacted form. Video summarization models find its applicability in various domains especially surveillance. This paper intends to develop a video summarization technique for the application of forest fire detection. The proposed method involves a set of processes namely convert frames, key frame extraction, feature extraction and classification. Here, a Merged Gaussian Mixture Model (MGMM) is applied for the process of extracting key frames and kernel support vector machine (KSVM) is employed for classifying a frame into normal frame and forest fire frame. The simulation analysis is performed on the forest fire video files from FIRESENSE database and the results are assessed under several dimensions. The final outcome proves the efficiency of the presented MGMM-KSVM model in a considerable way.

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