Event storm detection and identification in communication systems

Albaghdadi, Mouayad; Briley, Bruce E.; Evens, Martha

doi:10.1016/j.ress.2005.05.001

Cited by 16 publications

(6 citation statements)

References 12 publications

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“…In recent years, adaptive Infinite impulse response (IIR) filter has become an emerging field of research and has been widely utilized in the disciplines of control systems [1] , communication [2] , signal processing [3] , and image processing [4] . Systems or plants can be better modeled through IIR models because they emulate physical plants more accurately than their equivalent Finite impulse response (FIR) model.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer with Ranking‐Based Mutation Operator for IIR Model Identification

Zhang

Zhou

2018

Chin. j. electron.

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Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer with Ranking‐Based Mutation Operator for IIR Model Identification

Zhang

Zhou

2018

Chin. j. electron.

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“…The design of Digital IIR filter, as a subject of pinnacle priorities in the science and engineering fields, has recently aroused wider concern in the circle since it can usually incur less computational cost and presents a much better performance than its equivalent Finite Impulse Response (FIR) filter [1]. To our knowledge, it has many important applications in the disciplines of communication [2], image processing [3], control systems [4], signal processing [5], and so on.…”

Section: Introductionmentioning

confidence: 99%

Artificial Raindrop Algorithm for Optimal Parameter Preference in Digital IIR Filters

Huang¹,

Qiao²

2017

AMA_C

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The system identification of digital Infinite Impulse Response (IIR) filter, as a key knowledge domain, is an important research subject in the automatic control field. However, the error surface of digital IIR filter is usually nonlinear and multimodal, which makes the cost function rather difficult to minimize. Whilst some global optimization techniques such as metaheuristic algorithms are essential for avoiding local minima encountered in conventional IIR modeling mechanisms. In this paper, Artificial Raindrop Algorithm (ARA), a metaheuristic approach recently developed as a member of the family of nonlinear optimization, is applied to identify the unknown parameters in the design of digital IIR filter. The ARA is inspired by the phenomenon of natural rainfall, whose components include the generation of raindrop, the descent of raindrop, the collision of raindrop, the flowing of raindrop and the updating of vapor. The paper studies algorithm's performance by a comparative law, aiming at eight primal intelligence optimization algorithms, as some state-ofthe-art models, and eight improved metaheuristic algorithms. The experimental results show that ARA can more accurately identify the parameters as most of chosen and widely used cases and may become a promising candidate for digital IIR filter.

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“…Efficient detection of such outliers can help, in a timely way, to rectify faulty behavior of a system and, consequently, to avoid losses. In view of this, anomaly detection techniques have been applied to various fields, including industrial fault detection, network intrusion detection and so forth [8][9][10]. High dimensionality complicates anomaly detection tasks because the degree of data abnormality in relevant dimensions can be obscured or even masked by irrelevant dimensions [5,11,12].…”

Section: Introductionmentioning

confidence: 99%

An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

Zhang

Lin

Karim

2015

Reliability Engineering & System Safety

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a b s t r a c tThe accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces.

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Event storm detection and identification in communication systems

Cited by 16 publications

References 12 publications

Grey Wolf Optimizer with Ranking‐Based Mutation Operator for IIR Model Identification

Grey Wolf Optimizer with Ranking‐Based Mutation Operator for IIR Model Identification

Artificial Raindrop Algorithm for Optimal Parameter Preference in Digital IIR Filters

An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

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