Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

Sands, Timothy

doi:10.3390/s21134603

Cited by 30 publications

(26 citation statements)

References 30 publications

(54 reference statements)

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“…equations ( 35) -( 38)) expressed in terms of desired trajectories [24] which must be analytic expressions to facilitate autonomous calculation. The desired trajectories are currently formulated using single sinusoidal trajectories [24] as described in section 2.11, while the current novel developments seek to utilize constrained optimal trajectories [60] The modification of the governing differential equations of motion by replacing the motion states with desired states necessitates autonomous desired state trajectory generation, and this partially motivates the application of single-sinusoidal frequency-based state trajectories. Development of such trajectories in this manuscript make possible future sequel research applying deterministic artificial intelligence (as currently instantiated) to the optimal control of highly flexible space robotics.…”

Section: Self-awareness Statementsmentioning

confidence: 99%

Flattening the Curve of Flexible Space Robotics

Sands¹

2022

Preprint

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Infrastructure monitoring, inspection, repair, and replacement in space is crucial for continued usage and safety, yet it is expensive, time-consuming, and technical very challenging. New robotics technologies and artificial intelligence algorithms are potentially novel approaches that may alleviate such demanding operations using existing or novel sensing technologies. Space structures must necessarily be very light weight due to high costs of placing robots in space. Several methods are proposed and compared to control highly flexible space robotics, where a key challenge is the presence of flexible resonant modes at frequencies so low as to reside inside typical feedback controller bandwidths. Such conditions imply the very action of sending control signals to the ultra-light weight robotics will cause structural resonance. Implementations of incrementally increasing order are offered, achieving over ninety percent performance improvement in trajectory tracking errors, while improvement using unshaped methods merely achieve twenty-four percent improvement in direct comparison (where the only modification is the proposed control methodology). Based on superior performance, single-sinusoidal trajectory shaping is recommended with a corollary benefit of preparing future research into applying deterministic artificial intelligence whose current instantiation relies on single-sinusoidal, autonomous trajectory generation.

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Section: Self-awareness Statementsmentioning

confidence: 99%

Flattening the Curve of Flexible Space Robotics

Sands¹

2022

Preprint

Self Cite

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“…In the literature, virtual sensing has been used for physical sensors replacement [54], as remote sensing for unobserved spectra [55], for estimating nonmeasurable quantities [56], and for the calibration of low-cost air quality sensors [57].…”

Section: Synthetic Data Augmentationmentioning

confidence: 99%

Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines

2021

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As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy.

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“…Tse et al [ 32 ] used a low-cost sensing system based on edge computing to monitor indoor environment of cultural heritage buildings. Recently, real-time optimization methods [ 33 ] and artificial intelligence methods [ 34 ] provide the ability for better control of the system. In this paper, cloud computing and edge computing are integrated to provide the capability for real-time analysis.…”

Section: Introductionmentioning

confidence: 99%

A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings

Liu

Karlsson

et al. 2021

Sensors

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Monitoring the indoor environment of historic buildings helps to identify potential risks, provide guidelines for improving regular maintenance, and preserve cultural artifacts. However, most of the existing monitoring systems proposed for historic buildings are not for general digitization purposes that provide data for smart services employing, e.g., artificial intelligence with machine learning. In addition, considering that preserving historic buildings is a long-term process that demands preventive maintenance, a monitoring system requires stable and scalable storage and computing resources. In this paper, a digitalization framework is proposed for smart preservation of historic buildings. A sensing system following the architecture of this framework is implemented by integrating various advanced digitalization techniques, such as Internet of Things, Edge computing, and Cloud computing. The sensing system realizes remote data collection, enables viewing real-time and historical data, and provides the capability for performing real-time analysis to achieve preventive maintenance of historic buildings in future research. Field testing results show that the implemented sensing system has a 2% end-to-end loss rate for collecting data samples and the loss rate can be decreased to 0.3%. The low loss rate indicates that the proposed sensing system has high stability and meets the requirements for long-term monitoring of historic buildings.

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Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

Cited by 30 publications

References 30 publications

Flattening the Curve of Flexible Space Robotics

Flattening the Curve of Flexible Space Robotics

Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines

A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings

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