Enhancement of Classification of Small Data Sets Using Self-awareness — An Iris Flower Case-Study

Kholerdi, Hedyeh A.; TaheriNejad, Nima; Jantsch, Axel

doi:10.1109/iscas.2018.8350992

Cited by 15 publications

(6 citation statements)

References 19 publications

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“…The control and awareness module is designed to implement an effective compensation process deploying ML. However, in manufacturing systems, it is usually the case that not all data that may affect the controlled target are available, and furthermore, the autocorrelation and patterns within the monitored target can change dynamically, which can significantly reduce the performance of machine-learning algorithms with regard to anomaly detection (Kholerdi et al 2018). In other words, these models can behave accurately during training, or during a specific monitoring period, as long as the data or patterns do not change dramatically.…”

Section: Process-diagnosis Modulementioning

confidence: 99%

A smart process controller framework for Industry 4.0 settings

Cohen

Singer

2021

J Intell Manuf

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This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module-performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process-diagnosis module-performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module-performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform-an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework's applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

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Section: Process-diagnosis Modulementioning

confidence: 99%

A smart process controller framework for Industry 4.0 settings

Cohen

Singer

2021

J Intell Manuf

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“…While a selfawareness property has some fundamental characteristic, a corresponding functionality may be implemented in different ways. For example, while confidence is a measure of how trustworthy the work of a task, part of the system, or the entire system is (Section VI-C), it may be calculated in many different ways [100] (see for example [31], [55], [101]). What interface to use for a self-awareness property depends on the actual usage.…”

Section: A Modeling Self-awarenessmentioning

confidence: 99%

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

et al. 2020

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The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness.

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“…This process has been modeled different ways by various groups, among which some of the more well-known ones are Observe-Decide-Act (ODA) [24] and Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K) [25]. Several works have been done in order to implement self-awareness in various systems, and take advantage of its properties [12,23,24,[26][27][28]. However, most of these works are more focused on the smart decision-making process, while paying little 2 The level of consciousness is excluded because it is not applicable in out-of-hospital monitoring.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In 2016, TaheriNejad et al published a paper [29] which highlighted this aspect and elaborated on different elements of observation and their potential effect on self-awareness and the overall performance of the system. Since then, several publications have appeared in the literature which demonstrated this effect in various applications [13,[26][27][28][30][31][32][33].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The importance of confidence is in its ability to improve the decision-making processes [12] and allow a self-aware system to question certain abstracted data it has processed, and make more reliable decisions based on the reliability of its sub-processes and sub-algorithms. An important application of this concept for the decision-making unit is to enable it to switch between different algorithms based on their confidence, the usefulness of which has been shown in [28].…”

Section: Confidencementioning

confidence: 99%

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Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

et al. 2019

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Cardiovascular diseases are one of the world's major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability-even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that-under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions)-our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.

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Enhancement of Classification of Small Data Sets Using Self-awareness — An Iris Flower Case-Study

Cited by 15 publications

References 19 publications

A smart process controller framework for Industry 4.0 settings

A smart process controller framework for Industry 4.0 settings

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

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