{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:59:06Z","timestamp":1782968346044,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:00:00Z","timestamp":1657843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["Nos. 61602381"],"award-info":[{"award-number":["Nos. 61602381"]}]},{"name":"National Natural Science Foundation of China","award":["Nos. 2020KW-004"],"award-info":[{"award-number":["Nos. 2020KW-004"]}]},{"name":"National Natural Science Foundation of China","award":["Nos. 2018TD-026"],"award-info":[{"award-number":["Nos. 2018TD-026"]}]},{"name":"International Cooperation Project of Shaanxi Province","award":["Nos. 61602381"],"award-info":[{"award-number":["Nos. 61602381"]}]},{"name":"International Cooperation Project of Shaanxi Province","award":["Nos. 2020KW-004"],"award-info":[{"award-number":["Nos. 2020KW-004"]}]},{"name":"International Cooperation Project of Shaanxi Province","award":["Nos. 2018TD-026"],"award-info":[{"award-number":["Nos. 2018TD-026"]}]},{"name":"Shaanxi Science and Technology Innovation Team Support Project","award":["Nos. 61602381"],"award-info":[{"award-number":["Nos. 61602381"]}]},{"name":"Shaanxi Science and Technology Innovation Team Support Project","award":["Nos. 2020KW-004"],"award-info":[{"award-number":["Nos. 2020KW-004"]}]},{"name":"Shaanxi Science and Technology Innovation Team Support Project","award":["Nos. 2018TD-026"],"award-info":[{"award-number":["Nos. 2018TD-026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provide a real-time response due to transmission packet loss and latency. However, the limited computing power of edge devices and the existing methods for feature extraction pose a significant challenge to performing diagnosis on the edge. In this paper, we propose a fast Lightweight Fault Diagnosis method for power transformers, referred to as LightFD, which integrates several technical components. Firstly, before feature extraction, we design an asymmetric Hamming-cosine window function to reduce signal spectrum leakage and ensure data integrity. Secondly, we design a multidimensional spatio-temporal feature extraction method to extract acoustic features. Finally, we design a parallel dual-layer, dual-channel lightweight neural network to realize the classification of different fault types on edge devices with limited computing power. Extensive simulation and experimental results show that the diagnostic precision and recall of LightFD reach 94.64% and 95.33%, which represent an improvement of 4% and 1.6% over the traditional SVM method, respectively.<\/jats:p>","DOI":"10.3390\/s22145296","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T01:53:22Z","timestamp":1658109202000},"page":"5296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4039-6869","authenticated-orcid":false,"given":"Xinhua","family":"Fu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kejun","family":"Yang","sequence":"additional","affiliation":[{"name":"Anhui Nanrui Jiyuan Electricity Grid Technical Co., Ltd., Hefei 230088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[{"name":"Anhui Zhongke Haoyin Intelligent Technology Co., Ltd., Hefei 230000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianzhang","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8218-1209","authenticated-orcid":false,"given":"Chase","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.enpol.2012.07.039","article-title":"Economic development and the demand for energy: A historical perspective on the next 20 years","volume":"50","author":"Appleby","year":"2012","journal-title":"Energy Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e12704","DOI":"10.1002\/2050-7038.12704","article-title":"Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience","volume":"31","author":"Aziz","year":"2021","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1109\/TPWRD.2019.2913777","article-title":"Estimation of individual failure rates for power system components based on risk functions","volume":"34","author":"Hilber","year":"2019","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8499","DOI":"10.3390\/s150408499","article-title":"Monitoring of low levels of furfural in power transformer oil with a sensor system based on a POF-MIP platform","volume":"15","author":"Cennamo","year":"2015","journal-title":"Sensors"},{"key":"ref_5","first-page":"2555","article-title":"Research of vibration characteristics of power transformer winding based on magnetic-mechanical coupling field theory","volume":"36","author":"Wang","year":"2016","journal-title":"Proc. CSEE"},{"key":"ref_6","first-page":"3990","article-title":"Study on the Vibration of Grain-oriented Transformer Core Based on the Magnetos-trictive Intrinsic Characteristics","volume":"36","author":"Zhang","year":"2016","journal-title":"Proc. CSEE"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Boles, A., and Rad, P. (2017, January 18\u201321). Voice biometrics: Deep learning-based voiceprint authentication system. Proceedings of the 12th System of Systems Engineering Conference (SoSE), Waikoloa, HI, USA.","DOI":"10.1109\/SYSOSE.2017.7994971"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/MPRV.2012.54","article-title":"Mobile biometrics (mobio): Joint face and voice verification for a mobile platform","volume":"12","author":"Tresadern","year":"2013","journal-title":"IEEE Pervasive Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neunet.2020.06.015","article-title":"Heart sound classification based on improved MFCC features and convolutional recurrent neural networks","volume":"130","author":"Deng","year":"2020","journal-title":"Neural Netw."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Campos Souza, P.V., and Lughofer, E. (2020). Identification of heart sounds with an interpretable evolving fuzzy neural network. Sensors, 20.","DOI":"10.3390\/s20226477"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kulkarni, A., Kalburgi, D., and Ghuli, P. (2017, January 21\u201323). Design of Predictive Model for Healthcare Assistance Using Voice Recognition. Proceedings of the 2017 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Bengaluru, India.","DOI":"10.1109\/CSITSS.2017.8447814"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1109\/TIA.2017.2661250","article-title":"Deep learning based approach for bearing fault diagnosis","volume":"53","author":"He","year":"2017","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106226","DOI":"10.1016\/j.ymssp.2019.07.007","article-title":"Acoustic fault analysis of three commutator motors","volume":"133","author":"Glowacz","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.apacoust.2018.03.010","article-title":"Acoustic based fault diagnosis of three-phase induction motor","volume":"137","author":"Glowacz","year":"2018","journal-title":"Appl. Acoust."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107568","DOI":"10.1016\/j.apacoust.2020.107568","article-title":"Late fusion framework for Acoustic Scene Classification using LPCC, SCMC, and log-Mel band energies with Deep Neural Networks","volume":"172","author":"Paseddula","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gupta, H., and Gupta, D. (2016, January 14\u201315). LPC and LPCC method of feature extraction in Speech Recognition System. Proceedings of the 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), Noida, India.","DOI":"10.1109\/CONFLUENCE.2016.7508171"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wan, X., Song, H., Luo, L., Li, Z., Sheng, G., and Jiang, X. (2018, January 23\u201326). Pattern recognition of partial discharge image based on one-dimensional convolutional neural network. Proceedings of the Condition Monitoring and Diagnosis (CMD), Perth, WA, Australia.","DOI":"10.1109\/CMD.2018.8535761"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lawson, A., Vabishchevich, P., Huggins, M., Ardis, P., Battles, B., and Stauffer, A. (2011, January 22\u201327). Survey and evaluation of acoustic features for speaker recognition. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947590"},{"key":"ref_19","first-page":"1","article-title":"Transformer failure analysis: Reasons and methods","volume":"4","author":"Singh","year":"2016","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11239","DOI":"10.1109\/TVT.2019.2939179","article-title":"A synergetic trust model based on SVM in underwater acoustic sensor networks","volume":"68","author":"Han","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8860841","DOI":"10.1155\/2020\/8860841","article-title":"Modified support vector machine for detecting stress level using EEG signals","volume":"2020","author":"Gupta","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TDEI.2018.007191","article-title":"Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis","volume":"25","author":"Faiz","year":"2018","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1016\/j.procs.2017.05.398","article-title":"Research on insulator fault diagnosis and remote monitoring system based on infrared images","volume":"109","author":"Zhou","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mohammadi, F., Nazri, G.A., and Saif, M. (2019, January 26\u201327). A fast fault detection and identification approach in power distribution systems. Proceedings of the 2019 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET), Istanbul, Turkey.","DOI":"10.1109\/PGSRET.2019.8882676"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sykora, M., and Schlosser, M. (2016, January 6\u20138). Noise measurement of power transformer. Proceedings of the Conference on Diagnostics in Electrical Engineering (Diagnostika), Pilsen, Czech Republic.","DOI":"10.1109\/DIAGNOSTIKA.2016.7736493"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1109\/61.97688","article-title":"Validation of acoustic intensity measurements for power transformers","volume":"6","author":"Kendig","year":"1991","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yan, B., Qian, G., Wang, F., and Chen, S. (2016, January 3\u20135). Noise recognition of power transformers based on improved MFCC and VQ. Proceedings of the 2016 IEEE\/PES Transmission and Distribution Conference and Exposition (T&D), Dallas, TX, USA.","DOI":"10.1109\/TDC.2016.7519923"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.specom.2014.12.008","article-title":"Improved mispronunciation detection with deep neural network trained acoustic models and transfer learning based logistic regression classifiers","volume":"67","author":"Hu","year":"2015","journal-title":"Speech Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2018.03.025","article-title":"Random forest classification based acoustic event detection utilizing contextual-information and bottleneck features","volume":"81","author":"Xia","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"025108","DOI":"10.1063\/5.0024611","article-title":"Multi-variable classification model for valve internal leakage based on acoustic emission time\u2013frequency domain characteristics and random forest","volume":"92","author":"Ye","year":"2021","journal-title":"Rev. Sci. Instrum."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1016\/j.renene.2020.07.062","article-title":"Acoustical damage detection of wind turbine yaw system using Bayesian network","volume":"160","author":"Chen","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Grandhi, R.T., and Krishna Prakash, N. (2021). Machine-Learning Based Fault Diagnosis of Electrical Motors Using Acoustic Signals. Data Intelligence and Cognitive Informatics, Springer.","DOI":"10.1007\/978-981-15-8530-2_52"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.sysarc.2018.05.010","article-title":"Efficient FPGA-based architecture of an automatic wheeze detector using a combination of MFCC and SVM algorithms","volume":"88","author":"Boujelben","year":"2018","journal-title":"J. Syst. Archit."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Akpudo, U.E., and Hur, J.W. (2021). A cost-efficient MFCC-based fault detection and isolation technology for electromagnetic pumps. Electronics, 10.","DOI":"10.3390\/electronics10040439"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8760","DOI":"10.1109\/TIE.2018.2833045","article-title":"Real-time fault detection and identification for MMC using 1-D convolutional neural networks","volume":"66","author":"Kiranyaz","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Park, D., Kim, S., An, Y., and Jung, J.Y. (2018). LiReD: A light-weight real-time fault detection system for edge computing using LSTM recurrent neural networks. Sensors, 18.","DOI":"10.3390\/s18072110"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.future.2020.03.008","article-title":"Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning - ScienceDirect","volume":"108","author":"Cza","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"207377","DOI":"10.1109\/ACCESS.2020.3038386","article-title":"Convolutional-neural-network-based partial discharge diagnosis for power transformer using UHF sensor","volume":"8","author":"Do","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dang, X., Wang, F., and Ma, W. (2020, January 6\u201310). Fault Diagnosis of Power Transformer by Acoustic Signals with Deep Learning. Proceedings of the 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Beijing, China.","DOI":"10.1109\/ICHVE49031.2020.9279751"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1109\/TR.2010.2083231","article-title":"Health condition prediction of gears using a recurrent neural network approach","volume":"59","author":"Tian","year":"2010","journal-title":"IEEE Trans. Reliab."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","article-title":"Short-term residential load forecasting based on LSTM recurrent neural network","volume":"10","author":"Kong","year":"2017","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103498","DOI":"10.1016\/j.compind.2021.103498","article-title":"A survey on anomaly detection for technical systems using LSTM networks","volume":"131","author":"Lindemann","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/S0263-2241(98)00074-8","article-title":"Against spectral leakage","volume":"25","author":"Breitenbach","year":"1999","journal-title":"Measurement"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shan, Y., and Zhu, Q. (2014, January 7\u20139). Speaker identification under the changed sound environment. Proceedings of the 2014 International Conference on Audio, Language and Image Processing, Shanghai, China.","DOI":"10.1109\/ICALIP.2014.7009816"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"349","DOI":"10.14257\/ijmue.2016.11.10.33","article-title":"Pre-emphasis, Windowing and Spectral Estimation of Silent Speech Signals Using Embedded Systems","volume":"11","author":"Amaya","year":"2016","journal-title":"Int. J. Multimed. Ubiquitous Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1006\/jsvi.1996.0072","article-title":"The analysis of non-stationary signals using time-frequency methods","volume":"190","author":"Hammond","year":"1996","journal-title":"J. Sound Vib."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Astuti, W., Sediono, W., Aibinu, A., Akmeliawati, R., and Salami, M.J.E. (2012, January 23\u201326). Adaptive Short Time Fourier Transform (STFT) Analysis of seismic electric signal (SES): A comparison of Hamming and rectangular window. Proceedings of the IEEE Symposium on Industrial Electronics and Applications, Bandung, Indonesia.","DOI":"10.1109\/ISIEA.2012.6496663"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.cpc.2007.11.005","article-title":"Eliminating the picket fence effect of the fast Fourier transform","volume":"178","author":"Li","year":"2008","journal-title":"Comput. Phys. Commun."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.21437\/Eurospeech.2003-611","article-title":"Usefulness of Phase Spectrum in Human Speech Perception","volume":"3","author":"Paliwal","year":"2003","journal-title":"Eurospeech Geneva"},{"key":"ref_51","first-page":"445","article-title":"Robust MFCC feature extraction algorithm using efficient additive and convolutional noise reduction procedures","volume":"2","author":"Kotnik","year":"2002","journal-title":"Proc. ICSLP"},{"key":"ref_52","unstructured":"Chazan, D., Hoory, R., Cohen, G., and Zibulski, M. (2000, January 5\u20139). Speech reconstruction from mel frequency cepstral coefficients and pitch frequency. Proceedings of the IEEE International Conference on Acoustics, Speech, and  Signal Processing. Proceedings (Cat. No.00CH37100), Istanbul, Turkey."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/89.928915","article-title":"Noise power spectral density estimation based on optimal smoothing and minimum statistics","volume":"9","author":"Martin","year":"2001","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hossan, M.A., Memon, S., and Gregory, M.A. (2010, January 13\u201315). A novel approach for MFCC feature extraction. Proceedings of the 2010 4th International Conference on Signal Processing and Communication System, Gold Coast, QLD, Australia.","DOI":"10.1109\/ICSPCS.2010.5709752"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/TNN.2002.1031953","article-title":"RBF neural network center selection based on Fisher ratio class separability measure","volume":"13","author":"Mao","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1109\/TNNLS.2016.2601084","article-title":"A unified Fisher\u2019s ratio learning method for spatial filter optimization","volume":"28","author":"Li","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_57","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 14\u201319). Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., and Li, L. (2016, January 11\u201313). Using LSTM and GRU neural network methods for traffic flow prediction. Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China.","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Dey, R., and Salem, F.M. (2017, January 6\u20139). Gate-variants of gated recurrent unit (GRU) neural networks. Proceedings of the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA.","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"ref_64","unstructured":"Yun, W., Leonardo, N., and Florian, M. (2016, January 20\u201325). Audio-based multimedia event detection using deep recurrent neural networks. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Parascandolo, G., Huttunen, H., and Virtanen, T. (2016, January 20\u201325). Recurrent neural networks for polyphonic sound event detection in real life recordings. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472917"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1111\/2041-210X.13652","article-title":"Broad-scale applications of the Raspberry Pi: A review and guide for biologists","volume":"12","author":"Jolles","year":"2021","journal-title":"Methods Ecol. Evol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5296\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:51:10Z","timestamp":1760140270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5296"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,15]]},"references-count":66,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145296"],"URL":"https:\/\/doi.org\/10.3390\/s22145296","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,15]]}}}