Minding the Cyber-Physical Gap: Model-Based Analysis and Mitigation of Systemic Perception-Induced Failure

Mordecai, Yaniv; Dori, Dov

doi:10.3390/s17071644

Cited by 15 publications

(9 citation statements)

References 47 publications

(51 reference statements)

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“…The source of this gap could be the technological, cognitive, or engineering limitations. 34 The unawareness or insufficient awareness of these limitations by systems and engineers results in not mitigating the gap, hence putting the system exposed to the CPG risk. This risk can "potentially result in systemic misconceptions, disrupted functionality and performance, system failure, severe damage, and potential detrimental impacts on the system and its environment."…”

Section: Discussionmentioning

confidence: 99%

“…Hence, a CPG aware modeling is required to mitigate the risk as described by Dori and colleagues. 34 Here, in the current study, we assume a CPG-aware system design process is followed together with the use of CPG risk mitigation techniques. It is recommended to start the safety analysis process after the confirmation of the CPG-aware design.…”

Section: Discussionmentioning

confidence: 99%

“…Cyber‐Physical Gap : The difference between the “real” state of the world and the way system or sub‐systems perceives it can be described as the Cyber‐physical Gap 34 …”

Section: Ontologymentioning

confidence: 99%

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Integration of systems design and risk management through model‐based systems development

Uludag

Evin

Gürbüz³

2022

Systems Engineering

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Model‐based systems engineering is a powerful methodology to develop safety‐critical systems. The use of the system model as a single source of truth for risk and dependability analysis results in a consistent and complete assessment. Besides, representation and logging of the assessment within the model result in a complete and up‐to‐date single source of information that can be used during the device certification as well. This paper aims to provide a comprehensive risk management SysML profile that includes interconnected safety analysis [functional hazard assessment (FHA), fault tree, and failure mode and effect analysis (FTA, FMEA)], control measure, and evaluation model elements in compliance with the medical standards. Model‐based risk assessment of a point‐of‐care diagnostic device for sepsis has been shown as a case study to show the implementation of the profile. This device is a standalone unit and the test results obtained directly affect the patient. Therefore, both the top‐down (FHA and FTA) and bottom‐up (FMEA) safety assessment methods have been used. Another objective of the study is to define a systematic and holistic method to perform fault tree analysis, not only from the system architecture models but also from the functional, activity, and sequence diagrams of the system model.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Integration of systems design and risk management through model‐based systems development

Uludag

Evin

Gürbüz³

2022

Systems Engineering

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“…Though knowledge-state 68 may be used to solve this problem, it would be challenging to implement it in real-life scenarios without the types of knowledge. Though the semantic networks of linked data and knowledge (ie, knowledge graphs or concept maps) integrate the relevant data, information, and knowledge to solve problems in engineered systems, [76][77][78] the maps are created without making any distinctions among knowledge, data, and information. As a result, when the so-called knowledge state 68 changes, its influence randomly propagates to the whole network.…”

Section: Revised Definition Of Knowledgementioning

confidence: 99%

What is knowledge in Industry 4.0?

Ullah

2020

Engineering Reports

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Industry 4.0 relevant systems (eg, cyber‐physical systems, digital twins, and alike) need digitized knowledge to function. Before digitizing knowledge, a fundamental question arises: What is knowledge? In order to answer this question, this study first reviews the definitions of knowledge found in the extant literature of epistemology, engineering design, manufacturing, organization science, information science, and education science. Since the definitions reported so far are not succinct and suffer circularity, this study overcomes this by introducing a three‐element‐based definition of knowledge—a piece knowledge consists of three elements defined as claim, provenance, and inference. This results in four types of knowledge defined as definitional, deductive, inductive, and creative knowledge, and each type of knowledge is again divided into some categories. Some real‐life scenarios relevant to engineering design and manufacturing are used to clarify the proposed knowledge types/categories; the relevant pieces of knowledge are represented by knowledge graphs (concept maps) for the sake of digitization. The myriad proximal and distal relationships between knowledge and other relevant entities (human/machine learning, logical inferences, experimental data, analytical results, creative thinking, and cognitive reflections) become succinct and transparent due to the proposed definition of knowledge. Consequently, this study establishes the fundamentals of developing sophisticated methods and tools for the advancement of Industry 4.0.

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“…Zupančič et al [42] defines a conceptual framework that considers human participation in mobile crowd detection systems and takes into account that users provide their opinions and other subjective data in addition to unprocessed detection data generated by your smart devices. Mordecai et al [43] proposes a conceptual approach based on models to capture, explain and mitigate CPG, which improves the systems engineer's ability to cope with CPGs, mitigate them by design and avoid decisions and wrong actions. González et al [44] presents a new architecture based on OPC to implement automation systems dedicated to R&D and educational activities.…”

mentioning

confidence: 99%

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Sendra

Heras

Ávila-Gutiérrez

et al. 2020

Sensors

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This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.

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Minding the Cyber-Physical Gap: Model-Based Analysis and Mitigation of Systemic Perception-Induced Failure

Cited by 15 publications

References 47 publications

Integration of systems design and risk management through model‐based systems development

Integration of systems design and risk management through model‐based systems development

What is knowledge in Industry 4.0?

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

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