Anomaly Detection in Digital Twin Model

Wang, Yutong; Cao, Yansong; Wang, Fei‐Yue

doi:10.1109/dtpi52967.2021.9540116

Cited by 16 publications

(11 citation statements)

References 17 publications

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“… 67 Besides, domain-specific components vary across various complex systems. For instance, artificial hospital systems include subcomponents such as artificial diagnosis, treatment, and nursing subsystems, 52 while artificial population systems comprise elements such as artificial cognition and human subsystems. 67 In the realm of agriculture, artificial systems are made up of components such as artificial genotypes, environments, crop growth, and farmer behavior subsystems.…”

Section: Parallel Systems Method: An Interdisciplinary Solutionmentioning

confidence: 99%

“… 14 Nowadays, CPSS has become a foundational paradigm for studying systems that encompass both social and engineering complexity in areas such as transportation, 45 agriculture, 17 healthcare, 46 and other fields, 47 involving activities such as sensing, 3 , 48 , 49 , 50 computing, 51 producing, 15 and operating. 52 …”

Section: Paradigm Shift For Understanding Complex Systemsmentioning

confidence: 99%

“…To address the social, humanistic, and scientific challenges in healthcare, a new infrastructure known as parallel healthcare has emerged based on the ACP approach. 52 , 148 The two main areas of application lie in the operations of healthcare systems and hospital systems. Smart healthcare systems have played a crucial role in advancing modern medicine; however, their practical implementation still faces challenges, including inadequate cross-border collaboration among experts and insufficient personalized diagnoses and treatments.…”

Section: Parallel Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Toward parallel intelligence: An interdisciplinary solution for complex systems

Zhao,

Zhu,

Chen

et al. 2023

The Innovation

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Section: Parallel Systems Method: An Interdisciplinary Solutionmentioning

confidence: 99%

Section: Paradigm Shift For Understanding Complex Systemsmentioning

confidence: 99%

Section: Parallel Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Toward parallel intelligence: An interdisciplinary solution for complex systems

Zhao,

Zhu,

Chen

et al. 2023

The Innovation

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“…Digital twins are low-cost and provide rapid improvement in operational performance and business processes by performing iterative simulations using digitized data to optimize real world problems. Digital twins are used to predict product manufacturing results and to control the manufacturing environment [1]- [4]. Preplanned maintenance and spare parts replenishment by digital twins minimize service times and avoid costly resource failures.…”

Section: Introductionmentioning

confidence: 99%

Runtime Tracking-Based Replication of On-Chip Embedded Software Using Transfer Function Learning for Dust Particle Sensing Systems

2023

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A digital twin is a widely used method that uses digitized simulations of the real-world characteristics because it is effective in predicting results at a low cost. In digital twin analysis, the transfer function between the input and output data is an important research subject. In this study, we intend to investigate the application of the digital twin method to dust particle sensing. A high-performance multichannel reference dust particle sensor provides particle count as well as particulate matter information, whereas a lightweight embedded test device only provides a particle count. The particulate matter acquisition algorithm for a reference device is unknown and complex. Instead of that, we propose a simple method to calculate the transfer function using singular-value decomposition. In the experimental results, using singular-value decomposition, the predicted particulate matter of the test device was similar to that of the reference device. The obtained transfer function shows similar measurement results of the two dust particle sensor devices, confirming that particulate matter environmental information can be digitized even with low-power and lightweight sensor-embedded devices. In addition, the power consumption of the test device was approximately ten times lower than that of the reference device. INDEX TERMS Digital twin, Particle sensing, Particulate matter, Singular-value decomposition.

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“…Introduction: A digital twin involves modelling physical objects or processes from the real world and translating them into virtual world data. By analyzing and simulating the behaviour and state of virtual objects obtained through a digital twin, it becomes possible to use them for monitoring, analyzing, and optimizing the conditions or processes of realworld objects in the physical world [1][2][3][4].…”

mentioning

confidence: 99%

LSTM‐based post‐processing for noise reduction in SVD‐based particulate matter digital twinning on lightweight embedded devices

Lee,

Park

2024

Electronics Letters

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Among various applications of digital twins, measuring particulate matter in the context of air pollution has become increasingly important due to growing interest in atmospheric environments. By obtaining particle count values from analogue‐to‐digital converter raw data through photodiode and calibrating them, particulate matter values can be acquired. The singular‐value–decomposition is efficient transfer function learning method. However, due to the linear nature of the transfer function of singular‐value–decomposition, it also conveys noise information, necessitating post‐processing to suppress noise. This study proposes a method that utilizes long short‐term memory neural networks to effectively stabilize noise based on the output of continuous dust sensor particulate matter distributions. Using the singular value decomposition based particulate matter shows relative root‐mean‐square error (4.4761, 100%) as a reference, the proposed long short‐term memory post‐processing demonstrates an improved result (2.9328, 65.52%) compared to other post‐processing method: the mean filter (3.6704, 82.00%), low‐pass filter (3.7719, 84.27%) and Kalman filter (3.5550, 79.42%). Furthermore, to address the limitation of initial delay in long short‐term memory to achieve stable data output, a method of iteratively training the initial input data sample is proposed. For the initial input data, the non‐initialization approach exhibited a relative error of 87.57% and it took around 5 samples to predict stable output. In contrast, the proposed iterative training method, applied five times to the initial data sample, achieved a relative error of 9.51% and immediately obtained stable data.

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Anomaly Detection in Digital Twin Model

Cited by 16 publications

References 17 publications

Toward parallel intelligence: An interdisciplinary solution for complex systems

Toward parallel intelligence: An interdisciplinary solution for complex systems

Runtime Tracking-Based Replication of On-Chip Embedded Software Using Transfer Function Learning for Dust Particle Sensing Systems

LSTM‐based post‐processing for noise reduction in SVD‐based particulate matter digital twinning on lightweight embedded devices

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