The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics

Yeganeh, Ali; Johannssen, Arne; Chukhrova, Nataliya

doi:10.1016/j.engappai.2023.107282

Engineering Applications of Artificial Intelligence

2024

DOI: 10.1016/j.engappai.2023.107282

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The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics

Ali Yeganeh,

Arne Johannssen,

Nataliya Chukhrova

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2024

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Cited by 3 publications

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Security strategies for AI systems in Industry 4.0

Bairaktaris,

Johannssen,

Tran

2024

Quality & Reliability Eng

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The recent emergence and widespread adoption of Artificial Intelligence (AI) across various industries have not only increased efficiency and innovation but also introduced complex security challenges. Addressing these security challenges is an ever‐evolving area of research that seeks to mitigate issues affecting a wide range of individuals and sectors. Understanding the threats and vulnerabilities posed by these systems, and how to effectively defend against them, has become more crucial than ever. This paper aims to provide an overview of the most common attack vectors and their respective defense strategies, focusing particularly on those relevant to Industry 4.0. A major area of interest is adversarial machine learning, a relatively new field that focuses on corrupting, confusing, and manipulating AI models by intervening in different phases of their life cycle. The key findings indicate that FLAME offers the best protection against data poisoning attacks for neural network image detection models in a federated learning environment. Additionally, due to the specific threat model in Industry 4.0, defensive distillation emerges as the most promising defense strategy against evasion attacks.

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Security strategies for AI systems in Industry 4.0

Bairaktaris,

Johannssen,

Tran

2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

Control charting methods for monitoring high dimensional data streams: A conceptual classification scheme

Jalilibal,

Karavigh,

Maleki

et al. 2024

Computers & Industrial Engineering

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High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines

Mahmood,

Ahmed,

Riaz

et al. 2024

Production & Manufacturing Research

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The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics

Cited by 3 publications

References 56 publications

Security strategies for AI systems in Industry 4.0

Security strategies for AI systems in Industry 4.0

Control charting methods for monitoring high dimensional data streams: A conceptual classification scheme

High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines

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