scholarly journals A Method for Aero-Engine Gas Path Anomaly Detection Based on Markov Transition Field and Multi-LSTM

Aerospace ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 374
Author(s):  
Langfu Cui ◽  
Chaoqi Zhang ◽  
Qingzhen Zhang ◽  
Junle Wang ◽  
Yixuan Wang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
High Accuracy ◽  
High Dimensional ◽  
True Value ◽  
Markov Transition ◽  
Aero Engine ◽  
Transition Field

There are some problems such as uncertain thresholds, high dimension of monitoring parameters and unclear parameter relationships in the anomaly detection of aero-engine gas path. These problems make it difficult for the high accuracy of anomaly detection. In order to improve the accuracy of aero-engine gas path anomaly detection, a method based on Markov Transition Field and LSTM is proposed in this paper. The correlation among high-dimensional QAR data is obtained based on Markov Transition Field and hierarchical clustering. According to the correlation analysis of high-dimensional QAR data, a multi-input and multi-output LSTM network is constructed to realize one-step rolling prediction. A Gaussian mixture model of the residuals between predicted value and true value is constructed. The three-sigma rule is applied to detect outliers based on the Gaussian mixture model of the residuals. The experimental results show that the proposed method has high accuracy for aero-engine gas path anomaly detection.

scholarly journals The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Hsiuying Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Conventional Method ◽  
High Dimensional Data ◽  
Audio Signal ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Signal Recognition ◽  
Bayes Method ◽  
Generalized Bayes

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.

scholarly journals High-dimensional Probabilistic Fingerprinting in Wireless Sensor Networks based on a Multivariate Gaussian Mixture Model

2018 ◽  
Author(s):  
Yan Li ◽  
Simon Williams ◽  
Bill Moran ◽  
Allison Kealy ◽  
Guenther Retscher
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Low Cost ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Mobile Users ◽  
Prototype Model ◽  
Location Awareness ◽  
Multivariate Gaussian

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment, where three typical problems arise. First, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Second, heterogeneous devices record different received signal strength (RSS) levels due to the variations in chip-set and antenna attenuation. Third, APs are not necessarily visible in every scanning cycle leading to missing data. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. Considering the spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) is fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP has been investigated in this research, and demonstrated the efficiency of differentiation between cells. The proposed system is able to achieve comparable localization performance. The filed test results present a reliable 97% localization room level accuracy of multiple mobile users in a real university campus WiFi network without any prior knowledge of the environment.

scholarly journals Definition of Speech Intelligibility of the Uzbek Language

2020 ◽  
Vol 9 (3) ◽  
pp. 2863-2865
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Speech Intelligibility ◽  
Voice Recognition ◽  
Gaussian Mixture ◽  
High Accuracy ◽  
Definition Of ◽  
Personality Recognition

This article proposes an algorithm for automating the process of personality recognition based on voice, provides an analysis of existing methods used to solve the problem that needs to be solved. A method was implemented based on the Gaussian mixture model, which distinguishes a person’s voice with high accuracy. The components of this model allow you to simulate sound characteristics that are unique to each person. The results of the proposed algorithm and the use of voice recognition based on the results of the proposed algorithm are presented.

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.

Regularized Gaussian Mixture Model for High-Dimensional Clustering

2019 ◽  
Vol 49 (10) ◽  
pp. 3677-3688 ◽  
Author(s):  
Yang Zhao ◽  
Abhishek K. Shrivastava ◽  
Kwok Leung Tsui
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
High Dimensional Clustering
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