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Publikasjoner (10 av 68) Visa alla publikasjoner
Phan, T., Xu, Y., Kanoun, O., Oelmann, B. & Bader, S. (2024). Automated Ortho- Planar Spring Design for Vibration Energy Harvesters. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Åpne denne publikasjonen i ny fane eller vindu >>Automated Ortho- Planar Spring Design for Vibration Energy Harvesters
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2024 (engelsk)Inngår i: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Energy harvesting has been proposed to support or replace battery-based energy supplies in low-power loT systems. However, manual design and optimization is currently required to implement energy harvesters. This implies significant time and cost requirements, reducing the competitiveness of energy harvesting solutions in many applications. In this paper, we propose and study a method for the automated design of ortho-planar springs in vibration energy harvesters. The proposed method uses a Python tool that has been developed to translate parametric descriptions of the spring into 2D and 3D spring CAD models. The tool is combined with a black-box optimization algorithm in order to identify optimized spring parameters. An evaluation of the proposed method on a case study of an electromagnetic vibration energy harvester demonstrates that the approach successfully results in spring designs that perform well in the energy harvesting application. Consequently, the requirement of expert competence to adjust an energy harvester to new application constraints is significantly reduced, contributing to the availability and competitiveness of energy harvesting solutions in real world applications. 

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2024
Emneord
design automation, low-power sensing systems, ortho-planar springs, vibration energy harvesting
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-52590 (URN)10.1109/SAS60918.2024.10636429 (DOI)001304520300028 ()2-s2.0-85203713371 (Scopus ID)9798350369250 (ISBN)
Konferanse
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Tilgjengelig fra: 2024-09-24 Laget: 2024-09-24 Sist oppdatert: 2024-11-25bibliografisk kontrollert
Nguyen Phan, T., Xu, Y., Oelmann, B. & Bader, S. (2024). Electromagnetic Vibration Energy Harvester with Replaceable Ortho-planar Springs. In: 2024 IEEE SENSORS: . Paper presented at Proceedings of IEEE Sensors. IEEE conference proceedings
Åpne denne publikasjonen i ny fane eller vindu >>Electromagnetic Vibration Energy Harvester with Replaceable Ortho-planar Springs
2024 (engelsk)Inngår i: 2024 IEEE SENSORS, IEEE conference proceedings, 2024Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper proposes and investigates a novel electro-magnetic vibration energy harvester using ortho-planar springs. Addressing the limited output power of previously reported designs, the proposed harvester targets milliwatt level power outputs to be relevant for practical sensing and IoT applications. The proposed design employs an optimized pickup unit with Halbach array configuration and an optimized ortho-planar spring, which are integrated in a 3D-printed housing. During experimental evaluations, the harvester demonstrates an output power of up to 5.26mW and a normalized power density of 133.05 μW/(cm3∗g2), exceeding the performance of previously reported harvesters with ortho-planar springs. Moreover, exploiting the versatility of ortho-planar spring designs, it is shown that the harvester's frequency response can easily be adjusted to different application conditions. 

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2024
Emneord
electromagnetic transduction, energy harvesting, ortho-planar springs, vibration energy harvester
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-53682 (URN)10.1109/SENSORS60989.2024.10785000 (DOI)2-s2.0-85215272025 (Scopus ID)9798350363517 (ISBN)
Konferanse
Proceedings of IEEE Sensors
Tilgjengelig fra: 2025-01-28 Laget: 2025-01-28 Sist oppdatert: 2025-01-28bibliografisk kontrollert
Zhang, Y., Martinez Rau, L., Oelmann, B. & Bader, S. (2024). Enabling Autonomous Structural Inspections with Tiny Machine Learning on UAVs. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Åpne denne publikasjonen i ny fane eller vindu >>Enabling Autonomous Structural Inspections with Tiny Machine Learning on UAVs
2024 (engelsk)Inngår i: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Visual structural inspections in Structural Health Monitoring (SHM) are an important method to ensure the safety and long lifetime of infrastructures. Unmanned Aerial Vehicles (UAVs) with Deep Learning (DL) have gained in popularity to automate these inspections. Yet, the vast majority of research focuses on algorithmic innovations that neglect the availability of reliable generalized DL models, as well as the effect that the model's energy consumption would have on the UAV flight time. This paper highlights the performance of 14 popular CNN models with less than six million parameters for crack detection in concrete structures. Seven of these models were successfully deployed to a low-power, resource-constrained mi-crocontroller using Tiny Machine Learning (TinyML). Among the deployed models, MobileNetV1-x0.25 achieves the highest test accuracy (75.83%) and F1-Score (0.76), the second-lowest flash memory usage (273.5 kB), the second-lowest RAM usage (317.1kB), the fourth-fastest single-trial inference time (15.8ms), and the fourth-lowest number of Multiply-Accumulate operations (MACC) (42126514). Lastly, a hypothetical study of the DJI Mini 4 Pro UAV demonstrated that the TinyML model's energy consumption has a negligible impact on the UAV flight time (34 minutes vs. 33.98 minutes). Consequently, this feasibility study paves the way for future developments towards more efficient, autonomous unmanned structural health inspections. 

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2024
Emneord
convolutional neural networks, damage classification, embedded systems, structure health monitoring, Tiny machine learning, unmanned aerial vehicles
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-52591 (URN)10.1109/SAS60918.2024.10636583 (DOI)001304520300085 ()2-s2.0-85203704393 (Scopus ID)9798350369250 (ISBN)
Konferanse
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Tilgjengelig fra: 2024-09-24 Laget: 2024-09-24 Sist oppdatert: 2024-11-25bibliografisk kontrollert
Jansen, K., Shallari, I., Mourad, S., Werheit, P. & Bader, S. (2024). Image-Based Condition Monitoring of Air-Spinning Machines with Deep Neural Networks. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Åpne denne publikasjonen i ny fane eller vindu >>Image-Based Condition Monitoring of Air-Spinning Machines with Deep Neural Networks
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2024 (engelsk)Inngår i: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Industrial condition monitoring has benefited significantly from developments in machine learning and deep learning. However, textile machines, to a large extent, still use simple sensor systems, requiring additional manual quality inspections. This paper focuses on applying deep neural networks (DNNs) in image-based condition monitoring of air-spinning machines. It specifically focuses on the spinning pressure parameter, which is strongly related to the quality of the produced yarn. The study aims to develop a method to detect structural defects in yarns and assign them to specific machine conditions. DNNs are used to analyze images of yarns generated at different spinning pressures within the spinning box to create a rich dataset for training deep learning models. The study then evaluates the effectiveness of the DNN-based approach in detecting and classifying structural defects in yarns and determining the corresponding machine conditions. The results demonstrate that the developed model can distinguish good yarn from bad yarn, which is used to analyze the proportion of good yarn segments in a longer yarn section. A decreasing proportion with decreasing spinning pressure can thus be used to identify trends in degrading machine conditions. The outcomes of the presented research could potentially help textile enterprises improve the quality and efficiency of their yarn manufacturing processes. 

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2024
Emneord
AI, air-spinning, artificial intelligence, condition monitoring, Deep learning, textile machines
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-52592 (URN)10.1109/SAS60918.2024.10636697 (DOI)001304520300119 ()2-s2.0-85203698814 (Scopus ID)9798350369250 (ISBN)
Konferanse
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Tilgjengelig fra: 2024-09-24 Laget: 2024-09-24 Sist oppdatert: 2024-11-25bibliografisk kontrollert
Bader, S. & Oelmann, B. (2024). Instrumentation and Measurement Systems: The Challenge of Designing Energy Harvesting Sensor Systems. IEEE Instrumentation & Measurement Magazine, 27(4), 22-28
Åpne denne publikasjonen i ny fane eller vindu >>Instrumentation and Measurement Systems: The Challenge of Designing Energy Harvesting Sensor Systems
2024 (engelsk)Inngår i: IEEE Instrumentation & Measurement Magazine, ISSN 1094-6969, E-ISSN 1941-0123, Vol. 27, nr 4, s. 22-28Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

With the advent of low-cost and low-power computation, communication and sensor devices, novel instrumentation and measurement applications have been enabled, such as real-time industrial condition monitoring and fine-grained environmental monitoring. In these application scenarios, a lack of available infrastructures for communication and power supply is a common problem. In industrial applications, for example, the machine to be monitored and the monitoring system itself have significantly different technology lifespans, which requires that the monitoring system be retrofitted to machines that are already in use. In environmental monitoring, measurement systems are deployed as standalone devices in potentially remote areas. Consequently, the more autonomous the sensor system can be in terms of required infrastructure, the better it can match application and business needs.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2024
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-51461 (URN)10.1109/MIM.2024.10540407 (DOI)001237211600004 ()2-s2.0-85195049128 (Scopus ID)
Tilgjengelig fra: 2024-06-11 Laget: 2024-06-11 Sist oppdatert: 2024-06-14bibliografisk kontrollert
Martinez Rau, L., Chelotti, J. O., Giovanini, L. L., Adin, V., Oelmann, B. & Bader, S. (2024). On-Device Feeding Behavior Analysis of Grazing Cattle. IEEE Transactions on Instrumentation and Measurement, 73, Article ID 2512113.
Åpne denne publikasjonen i ny fane eller vindu >>On-Device Feeding Behavior Analysis of Grazing Cattle
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2024 (engelsk)Inngår i: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 73, artikkel-id 2512113Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Precision livestock farming (PLF) leverages cutting-edge technologies and data-driven solutions to enhance the efficiency of livestock production, its associated management, and its welfare. Continuous monitoring of the masticatory sound of cattle allows the estimation of dry-matter intake, classification of jaw movements (JMs), and recognition of grazing and rumination bouts. Over the past two decades, algorithms for analyzing feeding sounds have seen improvements in performance and computational requirements. Nevertheless, in some cases, these algorithms have been implemented on resource-constrained electronic devices, limiting their functionality to one specific task: either classifying JMs or recognizing feeding activities (such as grazing and rumination). In this work, we present an acoustic monitoring system that comprehensively analyzes grazing cattle's feeding behavior at multiple scales. This embedded system classifies different types of JMs, identifies feeding activities, and provides predictor variables for estimating dry-matter intake. Results are transmitted remotely to a base station using long-range communication (LoRa). Two variants of the system have been deployed on a Raspberry Pi Pico board, based on a low-power ARM Cortex-M0+ microcontroller. Both firmware versions make use of direct access memory, sleep mode, and clock-gating techniques to minimize energy consumption. In laboratory experiments, the first deployment consumes 20.1 mW and achieves an F1-score of 87.3% for the classification of JMs and 87.0% for feeding activities. The second deployment consumes 19.1 mW and reaches an F1-score of 84.1% for JMs and 83.5% for feeding activities. The modular design of the proposed embedded monitoring system facilitates integration with energy-harvesting power sources for autonomous operation in field conditions.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2024
Emneord
Monitoring, Cows, Animals, Acoustics, Microphones, Agriculture, Classification algorithms, Edge computing, embedded machine learning, feeding behavior, microcontroller, on-device processing, precision livestock farming (PLF)
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-52041 (URN)10.1109/TIM.2024.3376013 (DOI)001193312100043 ()2-s2.0-85188001389 (Scopus ID)
Tilgjengelig fra: 2024-08-07 Laget: 2024-08-07 Sist oppdatert: 2024-08-07bibliografisk kontrollert
Martinez Rau, L., Zhang, Y., Oelmann, B. & Bader, S. (2024). TinyML Anomaly Detection for Industrial Machines with Periodic Duty Cycles. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Åpne denne publikasjonen i ny fane eller vindu >>TinyML Anomaly Detection for Industrial Machines with Periodic Duty Cycles
2024 (engelsk)Inngår i: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Electro-mechanical systems operating in periodic cycles are pivotal in the Industry 4.0, enabling automated processes that enhance efficiency and productivity. Early detection of failures and anomalies in duty cycles of these machines is crucial to ensure uninterrupted operation and prevent costly downtimes. Although the wear and damage of machines have been extensively studied, a significant proportion of these problems can be traced back to operator errors, underlining the importance of continuously monitoring the machine activity to ensure optimal performance. This work presents an automatic algorithm designed to identify improper duty cycles of industrial machines, exemplified on a mining conveyor belt. To enable the identification of duty cycles, the operational states of the machine are first categorized using machine learning (ML). The study compares six tiny ML techniques on two resource-constrained microcontrollers, reporting an f1-score of 87.6% for identifying normal and abnormal duty cycles and 96.8% for the internal states of the conveyor belt system. Deployed on both low-power microcontrollers, the algorithm processes input data in less than 106 μs, consuming less than 1.16 μJ. These findings promise to facilitate integration into more comprehensive preventive maintenance algorithms. 

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2024
Emneord
anomaly detection, conveyor belt, industry 4.0, low-power microcontroller, machine learning, maintenance, tinyML
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-52585 (URN)10.1109/SAS60918.2024.10636584 (DOI)001304520300086 ()2-s2.0-85203721689 (Scopus ID)9798350369250 (ISBN)
Konferanse
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Tilgjengelig fra: 2024-09-24 Laget: 2024-09-24 Sist oppdatert: 2024-11-25bibliografisk kontrollert
Trpcheska, A., Zevnik, F. & Bader, S. (2024). Towards Real-Time Vision-Based Sign Language Recognition on Edge Devices. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Åpne denne publikasjonen i ny fane eller vindu >>Towards Real-Time Vision-Based Sign Language Recognition on Edge Devices
2024 (engelsk)Inngår i: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper presents a comparative study focused on the classification of American Sign Language (ASL) gestures and the challenges involved in interpreting the signs for effective communication. Using transfer learning this study evaluates three variants of MobileNet, a machine-learning model optimized for low-resource environments, on a vision-based dataset. The models are deployed on an STM32F746G microcontroller with a Cortex-M7 core. Two frameworks are compared, namely TensorFlow Lite for Microcontrollers and STM32Cube.AI. An ArduCam Mini camera with a maximum image resolution of 5 megapixels is utilized to capture the hand gestures. The study concludes that STM32Cube.AI is the preferred implementation due to its lower model ROM and RAM requirements. Among the three tested models, MobileNetV1 is the most suitable for the task, achieving the highest F1-score of 0.865, the smallest memory footprint of 290.96 kB of ROM and 85.59 kB of RAM, and the shortest inference time of 103 ms. Despite these promising results, the models encountered some difficulties distinguishing between similar signs, highlighting the challenges involved in real-time sign language recognition and the need for further research. 

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2024
Emneord
American sign language, computer vision, embedded machine learning, embedded systems, TinyML, transfer learning
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-52587 (URN)10.1109/SAS60918.2024.10636604 (DOI)001304520300093 ()2-s2.0-85203713331 (Scopus ID)9798350369250 (ISBN)
Konferanse
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Tilgjengelig fra: 2024-09-24 Laget: 2024-09-24 Sist oppdatert: 2024-11-25bibliografisk kontrollert
Zhang, Y., Wang, W., Wu, X., Lei, Y., Cao, J., Bowen, C., . . . Yang, B. (2023). A comprehensive review on self-powered smart bearings. Renewable & sustainable energy reviews, 183, Article ID 113446.
Åpne denne publikasjonen i ny fane eller vindu >>A comprehensive review on self-powered smart bearings
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2023 (engelsk)Inngår i: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 183, artikkel-id 113446Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

Recently, with the development of industrial informatization and intellectualization, the development of smart bearings has attracted a lot of significant attention in an attempt to reduce maintenance costs and increase reliability by providing online health condition monitoring. As a result of advances in miniaturization and low power consumption of wireless sensor nodes (WSNs), self-powered technologies have been considered as a promising method to achieve autonomous WSNs in smart bearings. Although the self-powered technology has received considerable achievements, there are less reviews covering the development of self-powered structures towards smart bearing and providing potential guidelines for the future development. To bridge the gap, this paper presents a comprehensive state-of-the-art review and guidelines on self-powered methods to create smart bearings, including outlining the underlying theory, modeling methods, methodologies and technologies. The topology of a self-powered smart bearing is clarified, and the mechanisms and benefits of piezoelectricity, electromagnetism, triboelectricity, thermoelectricity and wireless power transfer for powering WSNs in smart bearing are discussed. To improve the applicability of self-powered smart bearing in a range of working conditions, the design methodologies and technologies of a variety of transducers are reviewed to provide guidelines for performance enhancement. Finally, the future challenges and perspectives are proposed for outlining potential research directions and opportunities in future self-powered smart bearing systems, including the impact on bearing performance, engineering implementation, reliability, power management and storage. 

sted, utgiver, år, opplag, sider
Elsevier, 2023
Emneord
Condition monitoring, Energy harvesting, Rotational motion, Self-powered, Smart bearing
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-48604 (URN)10.1016/j.rser.2023.113446 (DOI)001030402300001 ()2-s2.0-85161637144 (Scopus ID)
Tilgjengelig fra: 2023-06-27 Laget: 2023-06-27 Sist oppdatert: 2023-08-16bibliografisk kontrollert
Tran, T., Bader, S. & Lundgren, J. (2023). Denoising Induction Motor Sounds Using an Autoencoder. In: 2023 IEEE Sensors Applications Symposium (SAS): . Paper presented at 2023 IEEE Sensors Applications Symposium (IEEESAS), Ottawa, Canada, 18-20 July, 2023 (pp. 01-06). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Denoising Induction Motor Sounds Using an Autoencoder
2023 (engelsk)Inngår i: 2023 IEEE Sensors Applications Symposium (SAS), IEEE, 2023, s. 01-06Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Denoising sound is essential for improving signal quality in various applications such as speech processing, sound event classification, and machine failure detection systems. This paper proposes an autoencoder method to remove two types of noise, Gaussian white noise, and environmental noise from water flow, from induction motor sounds. The method is trained and evaluated on a dataset of 246 sounds from the Machinery Fault Database (MAFAULDA). The denoising effectiveness is measured using the mean square error (MSE), which indicates that both noise types can be significantly reduced with the proposed method. The MSE is below or equal to 0.15 for normal operation sounds and misalignment sounds. This improvement in signal quality can facilitate further processing, such as induction motor operation classification. Overall, this work presents a promising approach for denoising machine sounds using an autoencoder, with potential for application in other industrial settings.

sted, utgiver, år, opplag, sider
IEEE, 2023
Emneord
Autoencoder, Denoise sound
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-49392 (URN)10.1109/sas58821.2023.10254150 (DOI)001086399500082 ()2-s2.0-85174071313 (Scopus ID)
Konferanse
2023 IEEE Sensors Applications Symposium (IEEESAS), Ottawa, Canada, 18-20 July, 2023
Tilgjengelig fra: 2023-09-27 Laget: 2023-09-27 Sist oppdatert: 2023-11-10bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-8382-0359