Trust and security of electric vehicle-to-grid systems and hardware supply chains

Moghadasi, Negin; Collier, Zachary A.; Koch, Andrew; Slutzky, David; Polmateer, Thomas L.; Manasco, Mark C.; Lambert, James H.

doi:10.1016/j.ress.2022.108565

Cited by 29 publications

(13 citation statements)

References 31 publications

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“…Ongoing work in the field of engineering systems is focused on disrupted order rather than disrupted performance. The multi-criteria preference method (Karvetski et al, 2010;Moghadasi, Collier, et al, 2022;Montibeller et al, 2006;VanYe et al, 2021) can be used to identify priorities, the most and least disruptive scenarios, emergent conditions, criteria, and initiatives (Moghadasi, Luu, et al, 2022). Examples of priorities are the importance orders of endangered species, radiological, chemical, or biological contaminants, critical infrastructure assets, future projects or investments, policies, key personnel or organizations, geographic locations, and others.…”

Section: Discussionmentioning

confidence: 99%

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On Evaluating System Resilience by the Degree of Order Disruption

Moghadasi,

Lambert

2023

INCOSE International Symp

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Resilience curves describe the diminished performance of a system over time following a shock, with the area integral above the curve representing the persistence of the lost performance. For example, in a power system, the lost power generation is measured in megawatts, and the persistence of the disruption is in megawatt hours; in a communications system, the performance in gigabits per second can be integrated over time as gigabits. The persistence metrics from the examples are not comparable. Ongoing engineering systems work is focused on disrupted order rather than disrupted performance. The Kendall's tau statistic is used to compare an initial, pre‐shock set of priorities with the priorities after the shock. This paper will describe complementing system performance with system order in the estimation and integration of resilience curves, thus enabling a new quantification of resilience as a disruption of system order can be used across application domains of risk analysis.

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Section: Discussionmentioning

confidence: 99%

“…Emergent conditions are disruptive future events, trends, and other uncertainties that can affect a project, system, schedule, and budget. Scenarios consist of one or more emergent conditions (Loose et al, 2022;Moghadasi, Collier, et al, 2022). Kendall's tau.…”

Section: Resilience Curvementioning

confidence: 99%

On Evaluating System Resilience by the Degree of Order Disruption

Moghadasi,

Lambert

2023

INCOSE International Symp

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“…However, this remains a preliminary study, which requires validation using larger datasets and further risk assessment. Risk assessment can be conducted using a multicriteria decision analysis (MCDA) [ 35 , 36 ].…”

Section: Limitationsmentioning

confidence: 99%

A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function

et al. 2022

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Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (±7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics.

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Section: Introductionmentioning

confidence: 99%

“…The modern economy, telecommunications, infrastructure and transportation systems, healthcare, and almost every aspect of our daily lives are enabled and enhanced by digital connectivity. 1 This connectivity is made possible by microelectronics. Due to the critical nature of these components and their applications, assurance of the security and trust of these components, and of the global supply chain, is a national priority.…”

Section: Introductionmentioning

confidence: 99%

On metrics and prioritization of investments in hardware security

Collier

Briglia

Finkelston

et al. 2023

Systems Engineering

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The security risks posed by electronics are numerous. There are typically a variety of risk‐reducing countermeasures for a given system or across an enterprise. Each countermeasure is associated with both a level of risk reduction and its lifecycle costs. Given budgetary constraints, risk managers and systems engineers must determine what combinations of countermeasures cost‐effectively maximize risk reduction, and what metrics best guide the investment process. In this paper, we seek to answer these questions through exploration of risk reduction metrics from the field of security economics, including the benefit/cost ratio, return on security investment (ROSI), expected benefit of information security (EBIS), and expected net benefit of information security (ENBIS). The results suggest that ratio‐based metrics are not strongly correlated with risk reduction, while EBIS is equivalent to risk reduction and ENBIS is equal to risk reduction minus cost.

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Trust and security of electric vehicle-to-grid systems and hardware supply chains

Cited by 29 publications

References 31 publications

On Evaluating System Resilience by the Degree of Order Disruption

On Evaluating System Resilience by the Degree of Order Disruption

A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function

On metrics and prioritization of investments in hardware security

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