| 1. |
- Adin, Veysi, et al.
(author)
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Tiny Machine Learning for Damage Classification in Concrete Using Acoustic Emission Signals
- 2023
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In: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
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Conference paper (peer-reviewed)abstract
- Acoustic emission (AE) is a widely used non-destructive test method in structural health monitoring applications to identify the damage type in the material. Usually, the analysis of the AE signal is done by using traditional parameter-based methods. Recently, machine learning methods showed promising results for the analysis of AE signals. However, these machine learning models are complex, slow, and consume significant amounts of energy. To address these limitations and to explore the trade-off between model complexity and the classification accuracy, this paper presents a lightweight artificial neural network model to classify damage types in concrete material using raw acoustic emission signals. The model consists of one hidden layer with four neurons and is trained on a public acoustic emission signal dataset. The created model is deployed to several microcontrollers and the performance of the model is evaluated and compared with a state-of-the-art machine learning model. The model achieves 98.4% accuracy on the test data with only 4019 parameters. In terms of evaluation metrics, the proposed tiny machine learning model outperforms previously proposed models 10 to 1000 times. The proposed model thus enables machine learning in real-time structural health monitoring applications.
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| 2. |
- Adin, Veysi, et al.
(author)
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Tiny Machine Learning for Real-Time Postural Stability Analysis
- 2023
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In: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings. - 9798350323078
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Conference paper (peer-reviewed)abstract
- Postural sway is a critical measure for evaluating postural control, and its analysis plays a vital role in preventing falls among the elderly. Typically, physiotherapists assess an individual's postural control using tests such as the Berg Balance Scale, Tinetti Test, and time up-and-go test. Sensor-based analysis is available based on devices such as force plates or inertial measurement units. Recently, machine learning methods have demonstrated promising results in the sensor-based analysis of postural control. However, these models are often complex, slow, and energy-intensive. To address these limitations, this study explores the design space of lightweight machine learning models deployable to microcontrollers to assess postural stability. We developed an artificial neural network (ANN) model and compare its performance to that of random forests, gaussian naive bayes, and extra tree classifiers. The models are trained using a sway dataset with varying input sizes and signal-to-noise ratios. The dataset comprises two feature vectors extracted from raw accelerometer data. The developed models are deployed to an ARM Cortex M4-based microcontroller, and their performance is evaluated and compared. We show that the ANN model has 99.03% accuracy, higher noise immunity, and the model performs better with a window size of one second with 590.96 us inference time.
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| 3. |
- Kwon, Seongil, et al.
(author)
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Camera sheath with transformable head for minimally invasive surgical instruments
- 2024
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In: MITAT. Minimally invasive therapy & allied technologies. - : Informa UK Limited. - 1364-5706 .- 1365-2931.
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Journal article (peer-reviewed)abstract
- IntroductionThis paper presents a camera sheath that can be assembled to various minimally invasive surgical instruments and provide the localized view of the instrument tip.Material and methodsThe advanced transformable head structure (ATHS) that overcomes the trade-off between the camera resolution and the instrument size is designed for the sheath. Design solutions to maintain the alignment between the camera's line of sight and the instrument tip direction during the transformation of the ATHS are derived and applied to the prototype of the sheath.ResultsThe design solution ensured proper alignment between the line of sight and the tip direction. The prototype was used with the curved micro-debrider blades in simulated functional endoscopic sinus surgery (FESS). Deep regions of the sinus that were not observable with the conventional endoscopes was accessed and observed using the prototype.ConclusionsThe presented camera sheath allows the delivery of the instrument and camera to the surgical site with minimal increase in port size. It may be applied to various surgeries to reduce invasiveness and provide additional visual information to the surgeons.
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| 4. |
- Martinez Rau, Luciano, et al.
(author)
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Real-Time Acoustic Monitoring of Foraging Behavior of Grazing Cattle Using Low-Power Embedded Devices
- 2023
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In: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings. - 9798350323078
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Conference paper (peer-reviewed)abstract
- Precision livestock farming allows farmers to optimize herd management while significantly reducing labor needs. Individualized monitoring of cattle feeding behavior offers valuable data to assess animal performance and provides valuable insights into animal welfare. Current acoustic foraging activity recognizers achieve high recognition rates operating on computers. However, their implementations on portable embedded systems (for use on farms) need further investigation. This work presents two embedded deployments of a state-of-the-art foraging activity recognizer on a low-power ARM Cortex-M0+ microcontroller. The parameters of the algorithm were optimized to reduce power consumption. The embedded algorithm processes masticatory sounds in real-time and uses machine-learning techniques to identify grazing, rumination and other activities. The overall classification performance of the two embedded deployments achieves an 84% and 89% balanced accuracy with a mean power consumption of 1.8 mW and 12.7 mW, respectively. These results will allow this deployment to be integrated into a self-powered acoustic sensor with wireless communication to operate autonomously on cattle.
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| 5. |
- Zhang, Yuxuan, et al.
(author)
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Leveraging Acoustic Emission and Machine Learning for Concrete Materials Damage Classification on Embedded Devices
- 2023
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In: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 72
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Journal article (peer-reviewed)abstract
- For the field of structural health monitoring (SHM), acoustic emission (AE) technology is important as a damage identification technique that does not cause secondary damage to concrete. Nowadays, applications of non-destructive concrete damage identification are mostly limited to commercial software or identification algorithms running on desktop computers. It has so far not been deployed in low-power embedded devices. In this study, a lightweight convolutional neural network (CNN) model for online non-destructive damage type recognition of concrete materials is presented and deployed on a resource-constrained microcontroller unit as a tiny machine learning (TinyML) application. The CNN model uses raw acoustic emission signals as input and damage recognition types as output. 15,000 acoustic emission signals are used as data sets divided into training, validation, and test sets in the ratio of 8:1:1. The experimental results show that an accuracy of 99.6% is achieved on the nRF52840 microcontroller (ARM Cortex M4) with only 166.822 ms and 0.555mJ for a single inference using only 20K parameters and 30.5KB model size. This work demonstrates the effectiveness and feasibility of the proposed model, which achieves a trade-off between high classification accuracy and deployability on resource-constrained MCUs. Consequently, it provides strong support for online continuous non-destructive structural health monitoring.
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