scholarly journals A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1062
Author(s):  
Yuqing Li ◽  
Mingjia Lei ◽  
Pengpeng Liu ◽  
Rixin Wang ◽  
Minqiang Xu
Keyword(s):  
Anomaly Detection ◽  
Detection Rate ◽  
Recognition Accuracy ◽  
Simulation Models ◽  
Physical Models ◽  
Telemetry Data ◽  
Data Types ◽  
Multi Scale ◽  
Svm Model ◽  
The Time Domain

The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

2021 ◽  
pp. 1748006X2198964
Author(s):  
Juan Luis Pérez-Ruiz ◽  
Igor Loboda ◽  
Iván González-Castillo ◽  
Víctor Manuel Pineda-Molina ◽  
Karen Anaid Rendón-Cortés ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Recognition Accuracy ◽  
Data Driven ◽  
Fault Identification ◽  
Fault Recognition ◽  
Data Driven Approach ◽  
Diagnostic Approaches ◽  
Artificial Neural Network Ann ◽  
Two Stages

The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of estimated fault parameter. Different case studies for both anomaly detection and fault identification are proposed to evaluate the diagnostic spaces. They are formed by varying the classification, type of diagnostic analysis, and deviation noise scheme. In the second stage, the original GPA is reconstructed replacing the ANN with a tolerance-based rule to make diagnostic decisions. Here, two aspects are under analysis: the comparison of GPA classification rules and whole approaches. The results reveal that for simple classifications both spaces are equally accurate for anomaly detection and fault identification. However, for complex scenarios, the data-driven approach provides on average slightly better results for fault identification. The use of a hybrid GPA with ANN for a full classification instead of an original GPA with tolerance-based rule causes an increase of 12.49% in recognition accuracy for fault identification and up to 54.39% for anomaly detection. As for the whole approach comparison, the application of a data-driven approach instead of the original GPA can lead to an improvement of 12.14% and 53.26% in recognition accuracy for fault identification and anomaly detection, respectively.

scholarly journals Network Anomaly Detection System with Optimized DS Evidence Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Liu ◽  
Xiaofeng Wang ◽  
Kaiyu Liu
Keyword(s):  
Anomaly Detection ◽  
Detection Rate ◽  
Computer Network ◽  
Detection System ◽  
Evidence Theory ◽  
Optimization Methods ◽  
High Detection Rate ◽  
Detection Model ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

scholarly journals Anomaly detection in server metrics with use of one-sided median algorithm

2017 ◽  
Vol 9 (1) ◽  
pp. 5-22
Author(s):  
Szymon Zacher ◽  
Przemysław Ryba
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Detection Rate ◽  
Short Description ◽  
Algorithm Performance ◽  
Mail Service ◽  
Over Time ◽  
Simple Production ◽  
Good Detection

AbstractIn this paper we consider the problem of anomaly detection over time series metrics data took from one of corporate grade mail service cluster. We propose the algorithm based on one-sided median concept and present some results of experiments showing impact of parameters settings on algorithm performance. In addition we present short description of classes of anomalies discovered in monitored system. Proposed one-sided median based algorithm shows great robustness and good detection rate and can be considered as possible simple production ready solution.

scholarly journals ABWiSE v1.0: Toward and Agent-Based Approach to Simulating Wildfire Spread

2021 ◽  
Author(s):  
Jeffrey Katan ◽  
Liliana Perez
Keyword(s):  
Human Life ◽  
Simulation Models ◽  
Empirical Models ◽  
Physical Models ◽  
Fire Behaviour ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Fire Front ◽  
Wildfire Simulation ◽  
Behaviour Simulation

Abstract. Wildfires are a complex phenomenon emerging from interactions between air, heat, and vegetation, and while they are an important component of many ecosystems’ dynamics, they pose great danger to those ecosystems, and human life and property. Wildfire simulation models are an important research tool that help further our understanding of fire behaviour and can allow experimentation without recourse to live fires. Current fire simulation models fit into two general categories: empirical models and physical models. We present a new modelling approach that uses agent-based modelling to combine the complexity found in physical models with the ease of computation of empirical models. Our model represents the fire front as a set of moving agents that respond to, and interact with, vegetation, wind, and terrain. We calibrate the model using two simulated fires and one real fire, and validate the model against another real fire and the interim behaviour of the real calibration fire. Our model successfully replicates these fires, with a Figure of Merit on par with simulations by the Prometheus simulation model. Our model is a stepping-stone in using agent-based modelling for fire behaviour simulation, as we demonstrate the ability of agent-based modelling to replicate fire behaviour through emergence alone.

A Novel Network Traffic Anomaly Detection Based on Multi-Scale Fusion

2011 ◽  
Vol 48-49 ◽  
pp. 102-105
Author(s):  
Guo Zhen Cheng ◽  
Dong Nian Cheng ◽  
He Lei
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Network Traffic ◽  
Self Similarity ◽  
Data Set ◽  
Multi Scale ◽  
Traffic Anomaly ◽  
Detection Evaluation ◽  
Better Than

Detecting network traffic anomaly is very important for network security. But it has high false alarm rate, low detect rate and that can’t perform real-time detection in the backbone very well due to its nonlinearity, nonstationarity and self-similarity. Therefore we propose a novel detection method—EMD-DS, and prove that it can reduce mean error rate of anomaly detection efficiently after EMD. On the KDD CUP 1999 intrusion detection evaluation data set, this detector detects 85.1% attacks at low false alarm rate which is better than some other systems.

Multi-Scale, Multi-Dimensional Modelling of Future Energy Systems

2015 ◽  
pp. 113-135 ◽  
Author(s):  
Catalina Spataru ◽  
Andreas Koch ◽  
Pierrick Bouffaron
Keyword(s):  
Material Science ◽  
Control Strategies ◽  
Energy Systems ◽  
Simulation Models ◽  
Energy System ◽  
System Modelling ◽  
Multi Scale ◽  
Future Challenges ◽  
Urban Energy ◽  
Power System Engineering

This chapter provides a discussion of current multi-scale energy systems expressed by a multitude of data and simulation models, and how these modelling approaches can be (re)designed or combined to improve the representation of such system. It aims to address the knowledge gap in energy system modelling in order to better understand its existing and future challenges. The frontiers between operational algorithms embedded in hardware and modelling control strategies are becoming fuzzier: therefore the paradigm of modelling intelligent urban energy systems for the future has to be constantly evolving. The chapter concludes on the need to build a holistic, multi-dimensional and multi-scale framework in order to address tomorrow's urban energy challenges. Advances in multi-scale methods applied to material science, chemistry, fluid dynamics, and biology have not been transferred to the full extend to power system engineering. New tools are therefore necessary to describe dynamics of coupled energy systems with optimal control.

A Comparative Review of Various Machine Learning Approaches for Improving the Performance of Stego Anomaly Detection

2018 ◽  
pp. 351-371 ◽  
Author(s):  
Hemalatha Jeyaprakash ◽  
KavithaDevi M. K. ◽  
Geetha S.
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Rate ◽  
High Dimensional ◽  
Learning Approaches ◽  
Learning Tools ◽  
High Detection Rate ◽  
Comparative Review ◽  
High Detection

In recent years, steganalyzers are intelligently detecting the stego images with high detection rate using high dimensional cover representation. And so the steganographers are working towards this issue to protect the cover element dependency and to protect the detection of hiding secret messages. Any steganalysis algorithm may achieve its success in two ways: 1) extracting the most sensitive features to expose the footprints of message hiding; 2) designing or building an effective classifier engine to favorably detect the stego images through learning all the stego sensitive features. In this chapter, the authors improve the stego anomaly detection using the second approach. This chapter presents a comparative review of application of the machine learning tools for steganalysis problem and recommends the best classifier that produces a superior detection rate.

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