Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir
Open accessAbstract
The focus of this work is to design a deeply quantized anomaly detector of oil leaks that may happen at the junction between the wind turbine high-speed shaft and the external bracket of the power generator. We propose a block-based binary shallow echo state network (BBS-ESN) architecture belonging to the reservoir computing (RC) category and, as we believe, it also extends the extreme learning machines (ELM) domain. Furthermore, BBS-ESN performs binary block-based online training using fixed and minimal computational complexity to achieve low power consumption and deployability on an off-the-shelf micro-controller (MCU). This has been achieved through binarization of the images and 1-bit quantization of the network weights and activations. 3D rendering has been used to generate a novel publicly available dataset of photo-realistic images similar to those potentially acquired by image sensors on the field while monitoring the junction, without and with oil leaks. Extensive experimentation has been conducted using a STM32H743ZI2 MCU running at 480 MHz and the results achieved show an accurate identification of anomalies, with a reduced computational cost per image and memory occupancy. Based on the obtained results, we conclude that BBS-ESN is feasible on off-the-shelf 32 bit MCUs. Moreover, the solution is also scalable in the number of image cameras to be deployed and to achieve accurate and fast oil leak detections from different viewpoints.
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Authors in [138] created a TinyML model deployed on ESP-WROOM-32 MCU device to detect anomalies in thermal images; it sends data only when it detects an anomaly on a machine to the server using MQTT.
“The authors in [138] created a TinyML model deployed on ESP-WROOM-32 MCU device to detect anomalies in thermal images; it sends data only when it detects an anomaly on a machine to the server using Message Queuing Telemetry Transport(MQTT) protocol.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [138]
The Abstract describes an MCU-based model for detecting oil-leak anomalies in images. However, Section 8 names an STM32H743ZI2 MCU, and Section 4.1 describes RGB images, not thermal images. The cited passages do not support the claim that data are sent only upon detecting an anomaly, or that MQTT is used. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe results in [138] show around 94% accuracy and 92% f1-score.
“The results show around 94% accuracy and 92% f1-score.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [138]
Section 7 describes the accuracy tests in terms of false-positive and false-negative rates, and the Conclusions report those rates under different noise conditions. Searches for “94% accuracy,” “92% f1-score,” and “F1-score” found no passage reporting the claimed figures. The reported rates alone do not establish those two metrics. (LLM, conf=0.78)
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,748 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285 · see citing works
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687 · see citing works
scikit-image: image processing in Python
(2014) · doi:10.7717/peerj.453 · Cited by 6,794 · see citing works
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
Matsumoto, M., & Nishimura, T. (1998) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/272991.272995 · Cited by 5,780 · see citing works
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,564 · see citing works
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596 · see citing works
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956 · see citing works
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837 · see citing works
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
Huang, G.-B., Chen, L., & Siew, C. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875977 · Cited by 2,614 · see citing works
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1602.02830 · Cited by 2,106 · see citing works
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., et al. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.48550/arxiv.2007.02500 · Cited by 1,718 · see citing works
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Kubát, M., Holte, R. C., & Matwin, S. (1998) · Machine Learning · doi:10.1023/a:1007452223027 · Cited by 1,255 · see citing works
Wind-Turbine and Wind-Farm Flows: A Review
Porté‐Agel, F., Bastankhah, M., & Shamsoddin, S. (2019) · Boundary-Layer Meteorology · doi:10.1007/s10546-019-00473-0 · Cited by 1,124 · see citing works
Oil spill detection by satellite remote sensing
Brekke, C., & Solberg, A. H. S. (2005) · Remote Sensing of Environment · doi:10.1016/j.rse.2004.11.015 · Cited by 1,098 · see citing works
Outlier Detection for Temporal Data: A Survey
Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.184 · Cited by 997 · see citing works
An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels
Huang, G.-B. (2014) · Cognitive Computation · doi:10.1007/s12559-014-9255-2 · Cited by 958 · see citing works
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
Jiang, G., He, H., Yan, J., & Xie, P. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2844805 · Cited by 896 · see citing works
A survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864 · see citing works
Machine learning methods for wind turbine condition monitoring: A review
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J. A., & Nenadić, G. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.10.047 · Cited by 839 · see citing works
Review of the current status, technology and future trends of offshore wind farms
Díaz, H., & Soares, C. G. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107381 · Cited by 701 · see citing works
Wang, Z., Liu, M., Yan, W., Song, H., Zhou, Z., Yuen, K., Wei, Q., & Cheng, S. S. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3123218 · Cited by 678 · see citing works
Binary Neural Networks: A Survey
Qin, H., Gong, R., Liu, X., Bai, X., Song, J., & Sebe, N. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107281 · Cited by 546 · see citing works
Deep reservoir computing: A critical experimental analysis
Gallicchio, C., Micheli, A., & Pedrelli, L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.089 · Cited by 513 · see citing works
Latent Space Autoregression for Novelty Detection
Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00057 · Cited by 508 · see citing works
Survey of modelling methods for wind turbine wakes and wind farms
Crespo, A., Hernández, J., & Frandsen, S. T. (1999) · Wind Energy · doi:10.1002/(sici)1099-1824(199901/03)2:1 · Cited by 477 · see citing works
Huang, G.-B. (2015) · Cognitive Computation · doi:10.1007/s12559-015-9333-0 · Cited by 472 · see citing works
Autoencoder-based network anomaly detection
Chen, Z., Yeo, C. K., Lee, B., & Lau, C. T. (2018) · Wireless Telecommunications Symposium · doi:10.1109/wts.2018.8363930 · Cited by 470 · see citing works
Automatic detection of oil spills in ERS SAR images
Solberg, A. H. S., Storvik, G. O., Solberg, R., & Volden, E. (1999) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.774704 · Cited by 410 · see citing works
Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm
Barthelmie, R. J., & Jensen, L. E. (2010) · Wind Energy · doi:10.1002/we.408 · Cited by 364 · see citing works
Anomaly detection and fault analysis of wind turbine components based on deep learning network
Zhao, H., Liu, H., Hu, W., & Yan, X. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.05.024 · Cited by 301 · see citing works
LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT
Wu, D., Jiang, Z., Xie, X., Wei, X., Yu, W., & Li, R. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2952917 · Cited by 290 · see citing works
Architectural and Markovian factors of echo state networks
Gallicchio, C., & Micheli, A. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.02.002 · Cited by 198 · see citing works
An Integrated Framework for Privacy-Preserving Based Anomaly Detection for Cyber-Physical Systems
Keshk, M., Sitnikova, E., Moustafa, N., Hu, J., & Khalil, I. (2019) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2019.2906657 · Cited by 165 · see citing works
CoDetect: Financial Fraud Detection With Anomaly Feature Detection
Huang, D., Mu, D., Yang, L., & Cai, X. (2018) · IEEE Access · doi:10.1109/access.2018.2816564 · Cited by 149 · see citing works
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images
Zeng, K., & Wang, Y. (2020) · Remote Sensing · doi:10.3390/rs12061015 · Cited by 147 · see citing works
Ridge, a computer program for calculating ridge regression estimates /
Hilt, D., & Seegrist, D. W. (1977) · doi:10.5962/bhl.title.68934 · Cited by 146 · see citing works
Chang, Y., Chen, J., Qu, C., & Pan, T. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.02.004 · Cited by 134 · see citing works
Cantorna, D., Dafonte, C., Iglesias, A., & Arcay, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105716 · Cited by 100 · see citing works
Deep Echo State Network (DeepESN): A Brief SurveyPreprint
Gallicchio, C., & Micheli, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.04323 · Cited by 91 · see citing works
Detecting Anomalies in Time Series Data via a Meta-Feature Based Approach
Hu, M. C., Ji, Z., Yan, K., Guo, Y. C., Feng, X., Gong, J., Zhao, X., & Dong, L. (2018) · IEEE Access · doi:10.1109/access.2018.2840086 · Cited by 85 · see citing works
Multiple-Instance Learning for Anomaly Detection in Digital Mammography
Quellec, G., Lamard, M., Cozic, M., Coatrieux, G., & Cazuguel, G. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2521442 · Cited by 84 · see citing works
Block-based compressed sensing of images via deep learning
Adler, A., Boublil, D., & Zibulevsky, M. (2017) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2017.8122281 · Cited by 79 · see citing works
Echo-state conditional variational autoencoder for anomaly detection
Suh, S., Chae, D. H., Kang, H.-G., & Choi, S. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727309 · Cited by 78 · see citing works
Industrial Anomaly Detection: A Comparison of Unsupervised Neural Network Architectures
Siegel, B. (2020) · IEEE Sensors Letters · doi:10.1109/lsens.2020.3007880 · Cited by 71 · see citing works
Feedback Control by Online Learning an Inverse Model
Waegeman, T., wyffels, F., & Schrauwen, B. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2208655 · Cited by 67 · see citing works
Gallicchio, C., & Micheli, A. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489464 · Cited by 67 · see citing works
Data Improving in Time Series Using ARX and ANN Models
Akouemo, H. N., & Povinelli, R. J. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2656939 · Cited by 64 · see citing works
SCueU-Net: Efficient Damage Detection Method for Railway Rail
Lu, J., Liang, B., Lei, Q., Li, X., Liu, J., Ji, L., Xu, J., & Wang, W. (2020) · IEEE Access · doi:10.1109/access.2020.3007603 · Cited by 63 · see citing works
A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences
Rasheed, F., & Alhajj, R. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcc.2013.2261984 · Cited by 59 · see citing works
Image Recapture Detection with Convolutional and Recurrent Neural Networks
Li, H., Wang, S., & Kot, A. C. (2017) · Electronic Imaging · doi:10.2352/issn.2470-1173.2017.7.mwsf-329 · Cited by 59 · see citing works
An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset
Lu, H., Liu, Y., Fei, Z., & Guan, C. (2018) · IEEE Access · doi:10.1109/access.2018.2870151 · Cited by 54 · see citing works
A Machine Vision-based Realtime Anomaly Detection Method for Industrial Products Using Deep Learning
Jiang, Y. L., Wang, W., & Zhao, C. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8997079 · Cited by 54 · see citing works
Black, I. M., Richmond, M., & Kolios, A. (2021) · International Journal of Sustainable Energy · doi:10.1080/14786451.2021.1890736 · Cited by 54 · see citing works
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware
Kleyko, D. V., Frady, E. P., Kheffache, M., & Osipov, E. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3043309 · Cited by 47 · see citing works
A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network
Qian, P., Tian, X., Kanfoud, J., Lee, J., & Gan, T. (2019) · Energies · doi:10.3390/en12183411 · Cited by 46 · see citing works
Karadayı, Y., Aydın, M. N., & Öğrencı, A. S. (2020) · Applied Sciences · doi:10.3390/app10155191 · Cited by 43 · see citing works
Change detection tests using the ICI rule
Alippi, C., Boracchi, G., & Roveri, M. (2010) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596537 · Cited by 41 · see citing works
A Deep Learning Approach to Block-based Compressed Sensing of ImagesPreprint
Adler, A., Boublil, D., Elad, M., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01519 · Cited by 39 · see citing works
Utilizing online learning based on echo-state networks for the control of a hydraulic excavator
Park, J., Cho, D., Kim, S.-H., Kim, Y. B., Kim, P. Y., & Kim, H. J. (2014) · Mechatronics · doi:10.1016/j.mechatronics.2014.10.004 · Cited by 38 · see citing works
Block-Matching Convolutional Neural Network for Image DenoisingPreprint
Ahn, B., & Cho, N. I. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.00524 · Cited by 37 · see citing works
Imbalanced dataset-based echo state networks for anomaly detection
Chen, Q., Zhang, A., Huang, T., He, Q., & Song, Y. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3747-z · Cited by 33 · see citing works
Wang, Y., Liu, Z., Wang, D., Li, Y., & Yan, J. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2704584 · Cited by 31 · see citing works
Using Echo State Networks for Anomaly Detection in Underground Coal Mines
Obst, O., Wang, X. R., & Prokopenko, M. (2008) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2008.35 · Cited by 29 · see citing works
Online learning control with Echo State Networks of an oil production platform
Jordanou, J. P., Antonelo, E. A., & Camponogara, E. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.06.011 · Cited by 28 · see citing works
Anomaly Monitoring Framework in Lane Detection With a Generative Adversarial Network
Kim, H., Park, J., Min, K., & Huh, K. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2973398 · Cited by 26 · see citing works
Automated Product Boundary Defect Detection Based on Image Moment Feature Anomaly
Peng, Y., Ruan, S., Cao, G., Huang, S., Kwok, N. M., & Zhou, S. (2019) · IEEE Access · doi:10.1109/access.2019.2911358 · Cited by 24 · see citing works
BlockCNN: A Deep Network for Artifact Removal and Image CompressionPreprint
Maleki, D., Nadalian, S., Derakhshani, M. M., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11091 · Cited by 23 · see citing works
Rasheed, W., & Tang, T. B. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2948798 · Cited by 22 · see citing works
Hyperspectral Anomaly Detection via Image Super-Resolution Processing and Spatial Correlation
Li, Z., & Zhang, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3005924 · Cited by 22 · see citing works
A Model for Non-Stationary Time Series and its Applications in Filtering and Anomaly Detection
Wang, S., Li, C., & Lim, A. E. B. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3059321 · Cited by 21 · see citing works
Yu, W., Huang, S., & Weihong, X. (2018) · Energies · doi:10.3390/en11102561 · Cited by 20 · see citing works
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
Ye, F., Zheng, H., Huang, C., & Zhang, Y. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506079 · Cited by 15 · see citing works
Analysis of the Gearbox Oil Maintenance Procedures in Wind Energy
del Álamo, J. R., Duran, M. J., & Muñoz, F. (2020) · Energies · doi:10.3390/en13133414 · Cited by 14 · see citing works
Removing JPEG blocking artifacts using machine learning
Quijas, J., & Fuentes, O. (2014) · doi:10.1109/ssiai.2014.6806033 · Cited by 11 · see citing works
How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise
Ehret, T., Davy, A., Delbracio, M., & Morel, J. (2019) · Image Processing On Line · doi:10.5201/ipol.2019.263 · Cited by 10 · see citing works
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkPreprint
Heim, N., & Avery, J. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01709 · Cited by 5 · see citing works
Deep Anomaly Detection by Residual AdaptationPreprint
Deecke, L., Ruff, L., Vandermeulen, R. A., & Bilen, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02310 · Cited by 5 · see citing works
Package Leak Detection Based on Aperiodic Anomaly Extraction
Muramatsu, H., Kakimi, Y., Takeuchi, I., & Katsura, S. (2021) · IEEE Journal of Emerging and Selected Topics in Industrial Electronics · doi:10.1109/jestie.2021.3056692 · Cited by 4 · see citing works
Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
Cardoni, M., Pau, D. P., Falaschetti, L., Turchetti, C., & Lattuada, M. (2021) · Data in Brief · doi:10.1016/j.dib.2021.107538 · Cited by 3 · see citing works
Self-citationThe Influence of Gearbox Oil-Leak to the Bolt Joint in Wind Turbine Tower
Gao, Y. Y., Zhang, W. Z., & Wang, H. (2013) · Advanced materials research · doi:10.4028/www.scientific.net/amr.744.87 · Cited by 2 · see citing works
Wang, S., Li, C., & Lim, A. E. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09245 · Cited by 2 · see citing works
Deep Unsupervised Image Anomaly Detection: An Information Theoretic FrameworkPreprint
Ye, F., Zheng, H., Huang, C., & Zhang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04837
Flagged first, then by citations
Research works printed but not linked: 2
- Basseville, M.; Nikiforov, I.V. Detection of Abrupt Changes: Theory and Application; Prentice Hall Englewood Cliffs: Hoboken, NJ, USA, 1993; Volume 104.
- Fadnavis, S. Image Interpolation Techniques in Digital Image Processing: An Overview. Int. J. Eng. Res. Appl. 2014, 4, 2248-2270.
Cited by
Sorted by citations
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
“The authors in [138] proposed a Block based Binary”
BackgroundFrom Tiny Machine Learning to Tiny Deep Learning: A Survey
Somvanshi, S., Islam, M. M., Chhetri, G., Chakraborty, R., Mimi, M. S., Shuvo, S. A., Islam, K. S., Javed, S. A., Rafat, S. A., Dutta, A. K., et al. (2025) · ACM Computing Surveys · doi:10.1145/3776588 · Cited by 70 · see citing works
Benbouhenni, H., Boudjema, Z., Bizon, N., Thounthong, P., & Takorabet, N. (2022) · Energies · doi:10.3390/en15103689 · Cited by 40 · see citing works
Turnbull, A., McKinnon, C., Carroll, J., & McDonald, A. (2022) · Energies · doi:10.3390/en15093180 · Cited by 19 · see citing works
A Quantitative Review of Automated Neural Search and On-Device Learning for Tiny Devices
Pau, D. P., Ambrose, P. K., & Aymone, F. M. (2023) · Chips · doi:10.3390/chips2020008 · Cited by 11 · see citing works
Self-citationTiny Machine Learning Business Intelligence in the Semiconductor Industry: A Case Study
Casiroli, M., & Pau, D. P. (2023) · IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) · doi:10.1109/gcaiot61060.2023.10385110 · Cited by 11 · see citing works
Self-citationAutomated Neural and On-Device Learning for Micro Controllers
Pau, D. P., & Ambrose, P. K. (2022) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon53508.2022.9843050 · Cited by 10 · see citing works
Self-citationTinyRCE: Forward Learning Under Tiny Constraints
Pau, D. P., Ambrose, P. K., Pisani, A., & Aymone, F. M. (2023) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine58569.2023.10405784 · Cited by 5 · see citing works
Self-citationTransEdge: a lightweight transformer-based anomaly detection framework for edge devices
Shi, Q., Yang, Z., & Shao, S. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01043-w · Cited by 3 · see citing works
Detecting unregistered users through semi-supervised anomaly detection with similarity datasets
Heo, D. H., Park, S. H., & Kang, S. J. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00791-8 · Cited by 2 · see citing works
Albaiz, A., & Amsaad, F. (2026) · IEEE International Conference on Secure IoT, Assured and Trusted Computing (SATC) · doi:10.1109/satc69565.2026.11542250 · Cited by 1 · see citing works
Emerging application use cases and future directions
Ghorpade, S. N., & Chougule, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00024-5 · Cited by 1 · see citing works
Exploring Local and Cloud-Based Training Use Cases for Embedded Machine Learning
Andalib, N., & Selimi, M. (2024) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco62516.2024.10577837 · Cited by 1 · see citing works
A Transfer Learning-Based New User Recognition for Minimizing Retraining Time in Edge Deep Learning
Heo, D. H., & Kang, S. J. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00031
Bakhtiyarov, B., Jabiyeva, A. J., & Khudaverdiyeva, M. (2026) · Applied Computer Science · doi:10.35784/acs_8104
Albaiz, A., & Amsaad, F. (2026) · IEEE International Conference on Secure IoT, Assured and Trusted Computing (SATC) · doi:10.1109/satc69565.2026.11542487
Das, P., & Biswas, J. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109675
- Authors
- Cardoni, M., Pau, D. P., Falaschetti, L., Turchetti, C., & Lattuada, M.
- Affiliations
- Marche Polytechnic University (IT)
- STMicroelectronics (Italy) (IT)
- Corresponding author
- Pau, Danilo Pietro
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2021-11-18
- Volume
- 10
- Issue
- 22
- Pages
- 2836
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3214107103
- Cited by
- 17
- References
- 76
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 2 · 2023: 6 · 2022: 3
- FWCI
- 0.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- oil leak detection, wind turbines, anomaly detection, reservoir computing, 1-bit quantization, binarization, extreme learning machine, microcontroller deployment, low-power machine learning, multi-camera surveillance
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