Engineering Intelligent Systems

Brown, Barclay

doi:10.1002/9781119665649

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the technical debt of DMAIL algorithms is also emerging and is attributed to lack of operational considerations (Sculley et al 2015). Barclay Brown in his recent book, Engineering Intelligent Systems, further highlight this problem as "the green school bus problem" where a hypothetical AI system trained on military vehicles dataset (that are typically green in color) will likely classify a green school bus as a military vehicle unless the dataset included green school buses (which are uncommon but possible) or the decision making constructs of the underlying AI algorithm were known/guaranteed (Brown 2022).…”

Section: A Intelligent Engineered Systems and The Challenges For Syst...mentioning

confidence: 99%

Explainable AI and Counterfactuals for Test and Evaluation of Intelligent Engineered Systems

Raz,

Miller,

Chang

et al. 2023

INCOSE International Symp

View full text Add to dashboard Cite

Systems Engineering (SE) based test and evaluation (T&E) approaches have proven crucial for successful realization of most modern‐day complex systems and system‐of‐systems solutions. The design, test, and evaluation of engineered systems with many technologically advanced and complex components hinges on well‐structured integration and life cycle evolution process models, clearly defined requirements, along with controllability, observability, and stability (COS) of components that transcend to the system‐level. However, integration of machine learning (ML), deep learning (DL) and artificially intelligent (AI) components invalidates some SE foundations as the DL algorithms are primarily data‐driven with opaque decision‐making constructs. As a result, the current SE T&E approaches, although necessary, have become insufficient to evaluate the adoption of ML/DL/AI methods in engineered systems. This paper proposes two new approaches—explainable AI (xAI) and counterfactual testing and evaluation (cT&E)—as addition to the SE tool set for T&E of intelligent engineered systems. An example of SE T&E considerations is provided based on a conceptual aircraft control system implementation comparing as a classical controller and a DL‐based reinforcement learning controller.

show abstract

Section: A Intelligent Engineered Systems and The Challenges For Syst...mentioning

confidence: 99%

Explainable AI and Counterfactuals for Test and Evaluation of Intelligent Engineered Systems

Raz,

Miller,

Chang

et al. 2023

INCOSE International Symp

View full text Add to dashboard Cite

show abstract

A Conceptual Framework for the SE of AI‐Intensive Systems (SE4AI) – Considering Data Through the Life‐Cycle

Bhalla,

Cook,

Harvey

2023

INCOSE International Symp

View full text Add to dashboard Cite

This paper considers the challenge of the Systems Engineering of AI‐Intensive Systems (SE4AI) with a particular focus on their high reliance on data for model design verification, model training and model validation. The paper opens by describing the contextual background and reviewing contemporary AI/ML development approaches, identifying these to be largely software based “bottom‐up” driven by technological advancements, lacking a holistic top‐down “end‐to‐end” systems‐based perspective. Concepts and principles from systems thinking, modelling and simulation, and situation awareness are then briefly described in part two, as exaptive enabling building blocks, for integration into best‐practice evolutionary SE life‐cycle processes. A proposed tailored evolutionary SE life‐cycle process, with a focus on model design verification, model training and model validation data is then described in part three, through an example life‐cycle iteration, through each phase, in terms of the phase objective, the proposed refinement to the phase activities to cater for ML systems and the proposed phase outcomes. Related initiatives of direct relevance to this research are identified in part four, followed by a summary and conclusion.

show abstract

Combustion Control of Ship’s Oil-Fired Boilers Based on Prediction of Flame Images

Lee

2024

JMSE

View full text Add to dashboard Cite

This study proposes and validates a novel combustion control system for oil-fired boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs and applies internal model control (IMC)-based proportional-integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC filter constant tuning through experimentation is essential for achieving effective control performance. Experimental results showed that optimal control performance was achieved when the filter constant λ was set to 1.5. In this scenario, the peak overshoot Mp was reduced to 0.19245, and the Integral of Squared Error (ISE) was minimized to 10.1159, ensuring a stable response with minimal oscillation and maintaining a fast response speed. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

show abstract

Engineering Intelligent Systems

Cited by 4 publications

References 0 publications

Explainable AI and Counterfactuals for Test and Evaluation of Intelligent Engineered Systems

Explainable AI and Counterfactuals for Test and Evaluation of Intelligent Engineered Systems

A Conceptual Framework for the SE of AI‐Intensive Systems (SE4AI) – Considering Data Through the Life‐Cycle

Combustion Control of Ship’s Oil-Fired Boilers Based on Prediction of Flame Images

Contact Info

Product

Resources

About