{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:28:34Z","timestamp":1783009714955,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,16]],"date-time":"2020-02-16T00:00:00Z","timestamp":1581811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.<\/jats:p>","DOI":"10.3390\/s20041070","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"1070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network"],"prefix":"10.3390","volume":"20","author":[{"given":"Yibeltal Chanie","family":"Manie","sequence":"first","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jyun-Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng-Chun","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Run-Kai","family":"Shiu","sequence":"additional","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan-Ta","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6906","DOI":"10.1364\/AO.57.006906","article-title":"Augmentation of sensitivity of FBG strain sensor for biomedical operation","volume":"57","author":"Ghosh","year":"2018","journal-title":"Appl. Opt."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JPHOT.2019.2912266","article-title":"Highly sensitive FBG strain sensor with enhanced measurement range based on higher order FWM","volume":"12","author":"Ghosh","year":"2019","journal-title":"IEEE Photonics J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yao, K., Lin, Q., Jiang, Z., Zhao, N., Peng, G.D., Tian, B., Jia, W., and Yang, P. (2019). Design and analysis of a combined strain\u2013vibration\u2013temperature sensor with two fiber bragg gratings and a trapezoidal beam. Sensors, 19.","DOI":"10.3390\/s19163571"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhang, M., Lan, Y., Zhao, Y., and Dai, W. (2020). Detection of crack locations in aluminum alloy structures using FBG sensors. Sensors, 20.","DOI":"10.3390\/s20020347"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Haseda, Y., Bonefacino, J., Tam, H.Y., Chino, S., Koyama, S., and Ishizawa, H. (2019). Measurement of pulse wave signals and blood pressure by a plastic optical fiber FBG sensor. Sensors, 19.","DOI":"10.3390\/s19235088"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11605","DOI":"10.3390\/s111211605","article-title":"Dynamic sensing performance of a point-wise fiber bragg grating displacement measurement system integrated in an active structural control system","volume":"11","author":"Chuang","year":"2011","journal-title":"Sensors"},{"key":"ref_7","unstructured":"Shi, Z., and Liu, H. (2016, January 24\u201327). STM32F4 Based Real-Time Peak Detection of FBG. Proceedings of the 15th International Conference on Optical Communications and Networks (ICOCN), Hangzhou, China."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4670","DOI":"10.1364\/AO.55.004670","article-title":"Using maximum spectrum of continuous wavelet transform for demodulation of an overlapped spectrum in a fiber bragg grating sensor network","volume":"55","author":"Hu","year":"2016","journal-title":"Appl. Opt."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"035026","DOI":"10.1088\/1361-665X\/aa5747","article-title":"Capacity and capability enhancements of FBG sensor system by utilizing intensity and WDM detection technique","volume":"26","author":"Yeh","year":"2017","journal-title":"Smart Mater. Struct."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/LPT.2003.818916","article-title":"Intensity and wavelength-division multiplexing FBG sensor system using a tunable multiport fiber ring laser","volume":"16","author":"Peng","year":"2004","journal-title":"IEEE Photon. Techn. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1109\/68.806872","article-title":"Enhanced FBG strain sensing multiplexing capacity using combination of intensity and wavelength dual-coding technique","volume":"11","author":"Zhang","year":"1999","journal-title":"IEEE Photon. Techn. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TDEI.2017.006407","article-title":"Identification and localization of partial discharge in transformer insulation adopting cross recurrence plot analysis of acoustic signals detected using fiber Bragg gratings","volume":"24","author":"Kanakambaran","year":"2017","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"28318","DOI":"10.1109\/ACCESS.2018.2819647","article-title":"Method of fiber bragg grating sensing signal De-noise based on compressive sensing","volume":"6","author":"Chen","year":"2018","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/LPT.2006.875525","article-title":"Wavelength detection in FBG sensor network using tree search DMS-PSO","volume":"18","author":"Liang","year":"2006","journal-title":"IEEE Photon. Technol. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0924-4247(03)00323-6","article-title":"Improving the performance of a FBG sensor network using a genetic algorithm","volume":"107","author":"Shi","year":"2003","journal-title":"Sens. Actuators"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liang, J.J., Chan, C.C., Huang, V.L., and Suganthan, P.N. (2005). Improving the performance of a FBG sensor network using a novel dynamic multi-swarm particle swarm optimizer. Sensors for Harsh Environments II, Photo-Optical Instrumentation Engineers (SPIE).","DOI":"10.1117\/12.630037"},{"key":"ref_17","first-page":"323","article-title":"Optimization and comparison of the peak-detection algorithms for the reflection spectrum of fiber bragg grating","volume":"5","author":"Chen","year":"2015","journal-title":"Acta Photon. Sin."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3466","DOI":"10.3390\/s110403466","article-title":"Benchmark for peak detection algorithms in fiber bragg grating interrogation and a new neural network for its performance improvement","volume":"11","author":"Negri","year":"2011","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Manie, Y.C., Shiu, R.K., Peng, P.C., Guo, B.Y., Bitew, M.A., Tang, W.C., and Lu, H.K. (2018). Intensity and wavelength division multiplexing FBG sensor system using a raman amplifier and extreme learning machine. J. Sensors.","DOI":"10.1155\/2018\/7323149"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Negri, L.H., Kalinowski, H.J., and Paterno, A.S. (2011, January 17). Benchmark for Standard and Computationally Intelligent Peak Detection Algorithms for Fiber Bragg Grating Sensors. Proceedings of the 21st International Conference on Optical Fibre Sensors (OFS21), Ottawa, ON, Canada.","DOI":"10.1117\/12.885964"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ins.2016.01.039","article-title":"A survey of randomized algorithms for training neural networks","volume":"364","author":"Zhang","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TASLP.2016.2520371","article-title":"Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval","volume":"24","author":"Palangi","year":"2016","journal-title":"IEEE-ACM Trans. Audio Speech Lang. Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Lai, Y., Zhang, J., Zhao, H., and Mao, Z. (2019). Multi-factor operating condition recognition using 1D convolutional long short-term network. Sensors, 19.","DOI":"10.3390\/s19245488"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ding, S., Wang, Z., Peng, Y., Yang, H., Song, G., and Peng, X. (2017, January 19\u201321). Dynamic Prediction of the Silicon Content in the Blast Furnace Using LSTM-RNN Based Models. Proceedings of the 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), Dalian, China.","DOI":"10.1109\/ICCTEC.2017.00056"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qian, F., and Chen, X. (2019, January 12\u201315). Stock Prediction Based on LSTM under Different Stability. Proceedings of the IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China.","DOI":"10.1109\/ICCCBDA.2019.8725709"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, S., Liao, G., and Ding, Y. (June, January 31). Stock Transaction Prediction Modeling and Analysis Based on LSTM. Proceedings of the 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), Wuhan, China.","DOI":"10.1109\/ICIEA.2018.8398183"},{"key":"ref_30","first-page":"8","article-title":"LSTM with wavelet transform based data preprocessing for stock price prediction","volume":"2019","author":"Liang","year":"2019","journal-title":"Mathe. Probl. Eng."},{"key":"ref_31","first-page":"807320","article-title":"Noise reduction of FBG sensor signal by using a wavelet transform","volume":"8073","author":"Cho","year":"2011","journal-title":"Opt. Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Vani, S., and Madhusudhana Rao, T.V. (2019, January 23\u201325). An Experimental Approach towards the Performance Assessment of Various Optimizers on Convolutional Neural Network. Proceedings of the 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.","DOI":"10.1109\/ICOEI.2019.8862686"},{"key":"ref_33","unstructured":"Gupta, D.S. (2020, January 23). Fundamentals of Deep Learning-activation Functions and When to Use Them?. Available online: https:\/\/www.analyticsvidhya.com."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1070\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:58:15Z","timestamp":1760173095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1070"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,16]]},"references-count":33,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20041070"],"URL":"https:\/\/doi.org\/10.3390\/s20041070","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,16]]}}}