Machine Learning Based Outlier Detection Algorithm for Distributed Flexible Sensing Module with Non-stationary Multi-Parametric Data

[1]Costa, J. C., Spina, F., Lugoda, P., Garcia-Garcia, L., Roggen, D., & Münzenrieder, N. (2019). Flexible sensors—from materials to applications. Technologies, 7(2), 35. CostaJ. C.SpinaF.LugodaP.Garcia-GarciaL.RoggenD. & MünzenriederN. (2019). Flexible sensors—from materials to applications. Technologies, 7(2), 35.Search in Google Scholar

[2]Luo, Y., Abidian, M. R., Ahn, J. H., Akinwande, D., Andrews, A. M., Antonietti, M., ... & Chen, X. (2023). Technology roadmap for flexible sensors. ACS nano, 17(6), 5211-5295. LuoY.AbidianM. R.AhnJ. H.AkinwandeD.AndrewsA. M.AntoniettiM. ... & ChenX. (2023). Technology roadmap for flexible sensors. ACS nano, 17(6), 5211-5295.Search in Google Scholar

[3]Wang, Y., Yang, B., Hua, Z., Zhang, J., Guo, P., Hao, D., ... & Huang, J. (2021). Recent advancements in flexible and wearable sensors for biomedical and healthcare applications. Journal of Physics D: Applied Physics, 55(13), 134001. WangY.YangB.HuaZ.ZhangJ.GuoP.HaoD. ... & HuangJ. (2021). Recent advancements in flexible and wearable sensors for biomedical and healthcare applications. Journal of Physics D: Applied Physics, 55(13), 134001.Search in Google Scholar

[4]Heo, J. S., Shishavan, H. H., Soleymanpour, R., Kim, J., & Kim, I. (2019). Textile-based stretchable and flexible glove sensor for monitoring upper extremity prosthesis functions. IEEE Sensors Journal, 20(4), 1754-1760. HeoJ. S.ShishavanH. H.SoleymanpourR.KimJ. & KimI. (2019). Textile-based stretchable and flexible glove sensor for monitoring upper extremity prosthesis functions. IEEE Sensors Journal, 20(4), 1754-1760.Search in Google Scholar

[5]Wang, J., Suo, J., Song, Z., Li, W. J., & Wang, Z. (2023). Nanomaterial-based flexible sensors for metaverse and virtual reality applications. International Journal of Extreme Manufacturing, 5(3), 032013. WangJ.SuoJ.SongZ.LiW. J. & WangZ. (2023). Nanomaterial-based flexible sensors for metaverse and virtual reality applications. International Journal of Extreme Manufacturing, 5(3), 032013.Search in Google Scholar

[6]Li, Y., Wu, G., Song, G., Lu, S. H., Wang, Z., Sun, H., ... & Wang, X. (2022). Soft, pressure-tolerant, flexible electronic sensors for sensing under harsh environments. ACS sensors, 7(8), 2400-2409. LiY.WuG.SongG.LuS. H.WangZ.SunH. ... & WangX. (2022). Soft, pressure-tolerant, flexible electronic sensors for sensing under harsh environments. ACS sensors, 7(8), 2400-2409.Search in Google Scholar

[7]Afzal, U., Afzal, F., Maryam, K., & Aslam, M. (2022). Fabrication of flexible temperature sensors to explore indeterministic data analysis for robots as an application of Internet of Things. RSC advances, 12(27), 17138-17145. AfzalU.AfzalF.MaryamK. & AslamM. (2022). Fabrication of flexible temperature sensors to explore indeterministic data analysis for robots as an application of Internet of Things. RSC advances, 12(27), 17138-17145.Search in Google Scholar

[8]Elgeneidy, K., Lohse, N., & Jackson, M. (2018). Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors–a data-driven approach. Mechatronics, 50, 234-247. ElgeneidyK.LohseN. & JacksonM. (2018). Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors–a data-driven approach. Mechatronics, 50, 234-247.Search in Google Scholar

[9]Servati, A., Zou, L., Wang, Z. J., Ko, F., & Servati, P. (2017). Novel flexible wearable sensor materials and signal processing for vital sign and human activity monitoring. Sensors, 17(7), 1622. ServatiA.ZouL.WangZ. J.KoF. & ServatiP. (2017). Novel flexible wearable sensor materials and signal processing for vital sign and human activity monitoring. Sensors, 17(7), 1622.Search in Google Scholar

[10]Yoo, D., Won, D. J., Cho, W., Lim, J., & Kim, J. (2021). Double side electromagnetic interference-shielded bending-insensitive capacitive-type flexible touch sensor with linear response over a wide detection range. Advanced Materials Technologies, 6(11), 2100358. YooD.WonD. J.ChoW.LimJ. & KimJ. (2021). Double side electromagnetic interference-shielded bending-insensitive capacitive-type flexible touch sensor with linear response over a wide detection range. Advanced Materials Technologies, 6(11), 2100358.Search in Google Scholar

[11]Wang, X., Mu, B., Zhang, L., & Zhang, X. (2022). Drift characteristic analysis of additive manufactured Ag NPs-PEDOT: PSS flexible temperature sensor. Results in Engineering, 13, 100384. WangX.MuB.ZhangL. & ZhangX. (2022). Drift characteristic analysis of additive manufactured Ag NPs-PEDOT: PSS flexible temperature sensor. Results in Engineering, 13, 100384.Search in Google Scholar

[12]Fontana, C. L., Donati, M., Cester, D., Fanucci, L., Iovene, A., Swiderski, L., ... & Lunardon, M. (2017). A distributed data acquisition system for the sensor network of the TAWARA_RTM project. Physics Procedia, 90, 271-278. FontanaC. L.DonatiM.CesterD.FanucciL.IoveneA.SwiderskiL. ... & LunardonM. (2017). A distributed data acquisition system for the sensor network of the TAWARA_RTM project. Physics Procedia, 90, 271-278.Search in Google Scholar

[13]Jha, S., Katz, D. S., Luckow, A., Chue Hong, N., Rana, O., & Simmhan, Y. (2017). Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure. Concurrency and Computation: Practice and Experience, 29(8), e4032. JhaS.KatzD. S.LuckowA.Chue HongN.RanaO. & SimmhanY. (2017). Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure. Concurrency and Computation: Practice and Experience, 29(8), e4032.Search in Google Scholar

[14]Pontarolli, R. P., Bigheti, J. A., Domingues, F. O., de Sá, L. B., & Godoy, E. P. (2022). Distributed I/O as a service: A data acquisition solution to Industry 4.0. HardwareX, 12, e00355. PontarolliR. P.BighetiJ. A.DominguesF. O.de SáL. B. & GodoyE. P. (2022). Distributed I/O as a service: A data acquisition solution to Industry 4.0. HardwareX, 12, e00355.Search in Google Scholar

[15]Ayadi, A., Ghorbel, O., Obeid, A. M., & Abid, M. (2017). Outlier detection approaches for wireless sensor networks: A survey. Computer Networks, 129, 319-333. AyadiA.GhorbelO.ObeidA. M. & AbidM. (2017). Outlier detection approaches for wireless sensor networks: A survey. Computer Networks, 129, 319-333.Search in Google Scholar

[16]Wang, H., Bah, M. J., & Hammad, M. (2019). Progress in outlier detection techniques: A survey. Ieee Access, 7, 107964-108000. WangH.BahM. J. & HammadM. (2019). Progress in outlier detection techniques: A survey. Ieee Access, 7, 107964-108000.Search in Google Scholar

[17]Liu, H., Li, J., Wu, Y., & Fu, Y. (2019). Clustering with outlier removal. IEEE transactions on knowledge and data engineering, 33(6), 2369-2379. LiuH.LiJ.WuY. & FuY. (2019). Clustering with outlier removal. IEEE transactions on knowledge and data engineering, 33(6), 2369-2379.Search in Google Scholar

[18]Muhr, D., & Affenzeller, M. (2022). Little data is often enough for distance-based outlier detection. Procedia Computer Science, 200, 984-992. MuhrD. & AffenzellerM. (2022). Little data is often enough for distance-based outlier detection. Procedia Computer Science, 200, 984-992.Search in Google Scholar

[19]Zimek, A., & Filzmoser, P. (2018). There and back again: Outlier detection between statistical reasoning and data mining algorithms. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(6), e1280. ZimekA. & FilzmoserP. (2018). There and back again: Outlier detection between statistical reasoning and data mining algorithms. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(6), e1280.Search in Google Scholar

[20]Ur Rehman, A., & Belhaouari, S. B. (2021). Unsupervised outlier detection in multidimensional data. Journal of Big Data, 8(1), 80. Ur RehmanA. & BelhaouariS. B. (2021). Unsupervised outlier detection in multidimensional data. Journal of Big Data, 8(1), 80.Search in Google Scholar

[21]Lu, H., Liu, Y., Fei, Z., & Guan, C. (2018). An outlier detection algorithm based on cross-correlation analysis for time series dataset. Ieee Access, 6, 53593-53610. LuH.LiuY.FeiZ. & GuanC. (2018). An outlier detection algorithm based on cross-correlation analysis for time series dataset. Ieee Access, 6, 53593-53610.Search in Google Scholar

[22]Nikolova, N., Rodríguez, R. M., Symes, M., Toneva, D., Kolev, K., & Tenekedjiev, K. (2021). Outlier detection algorithms over fuzzy data with weighted least squares. International Journal of Fuzzy Systems, 23(5), 1234-1256. NikolovaN.RodríguezR. M.SymesM.TonevaD.KolevK. & TenekedjievK. (2021). Outlier detection algorithms over fuzzy data with weighted least squares. International Journal of Fuzzy Systems, 23(5), 1234-1256.Search in Google Scholar

[23]Singh, A. K., & Lalitha, S. (2018). A novel spatial outlier detection technique. Communications in Statistics-Theory and Methods, 47(1), 247-257. SinghA. K. & LalithaS. (2018). A novel spatial outlier detection technique. Communications in Statistics-Theory and Methods, 47(1), 247-257.Search in Google Scholar

[24]Tang, B., & He, H. (2017). A local density-based approach for outlier detection. Neurocomputing, 241, 171-180. TangB. & HeH. (2017). A local density-based approach for outlier detection. Neurocomputing, 241, 171-180.Search in Google Scholar

[25]Huang, J., Cheng, D., & Zhang, S. (2023). A novel outlier detecting algorithm based on the outlier turning points. Expert Systems with Applications, 231, 120799. HuangJ.ChengD. & ZhangS. (2023). A novel outlier detecting algorithm based on the outlier turning points. Expert Systems with Applications, 231, 120799.Search in Google Scholar

[26]Tran, L., Mun, M. Y., & Shahabi, C. (2020). Real-time distance-based outlier detection in data streams. Proceedings of the VLDB Endowment, 14(2), 141-153. TranL.MunM. Y. & ShahabiC. (2020). Real-time distance-based outlier detection in data streams. Proceedings of the VLDB Endowment, 14(2), 141-153.Search in Google Scholar

[27]Ray, B., Ghosh, S., Ahmed, S., Sarkar, R., & Nasipuri, M. (2022). Outlier detection using an ensemble of clustering algorithms. Multimedia Tools and Applications, 81(2), 2681-2709. RayB.GhoshS.AhmedS.SarkarR. & NasipuriM. (2022). Outlier detection using an ensemble of clustering algorithms. Multimedia Tools and Applications, 81(2), 2681-2709.Search in Google Scholar

[28]Wang, Y. F., Jiong, Y., Su, G. P., & Qian, Y. R. (2019). A new outlier detection method based on OPTICS. Sustainable cities and society, 45, 197-212. WangY. F.JiongY.SuG. P. & QianY. R. (2019). A new outlier detection method based on OPTICS. Sustainable cities and society, 45, 197-212.Search in Google Scholar

[29]Hassan, A. F., Barakat, S., & Rezk, A. (2022). Towards a deep learning-based outlier detection approach in the context of streaming data. Journal of Big Data, 9(1), 120. HassanA. F.BarakatS. & RezkA. (2022). Towards a deep learning-based outlier detection approach in the context of streaming data. Journal of Big Data, 9(1), 120.Search in Google Scholar

[30]Abhaya, A., & Patra, B. K. (2023). An efficient method for autoencoder based outlier detection. Expert Systems with Applications, 213, 118904. AbhayaA. & PatraB. K. (2023). An efficient method for autoencoder based outlier detection. Expert Systems with Applications, 213, 118904.Search in Google Scholar

[31]Tingrui Pei, Zhiwen Hou, Jun Zhou, Chixin Xiao & Juan Zou. (2024). Blockchain-based anonymous authentication and data aggregation for advanced metering infrastructure in smart grid. International Journal of Critical Infrastructure Protection 100702-100702. PeiTingruiHouZhiwenZhouJunXiaoChixin & ZouJuan. (2024). Blockchain-based anonymous authentication and data aggregation for advanced metering infrastructure in smart grid. International Journal of Critical Infrastructure Protection 100702-100702.Search in Google Scholar

[32]Xiangliang Meng, Zhe Zhang, Xiao Liao, Wei Cui & Shilin Wang. (2025). Power quality monitoring and analysis method of distribution station area based on edge computing. Journal of Physics: Conference Series(1), 012004-012004. MengXiangliangZhangZheLiaoXiaoCuiWei & WangShilin. (2025). Power quality monitoring and analysis method of distribution station area based on edge computing. Journal of Physics: Conference Series(1), 012004-012004.Search in Google Scholar

[33]Liang Yanhe, Li Qi, Gong You, Wang Xiaoyu & Li Xinggang. (2023). Fault Diagnosis Method for High Voltage Power Metering System Based on the PCA-ELM Algorithm. Journal of Physics: Conference Series(1). YanheLiangQiLiYouGongXiaoyuWang & XinggangLi. (2023). Fault Diagnosis Method for High Voltage Power Metering System Based on the PCA-ELM Algorithm. Journal of Physics: Conference Series(1).Search in Google Scholar

[34]Nian Zhou, Ping Jiang, Shiliang Jiang, Leshi Shu, Xiaoxian Ni & Linjun Zhong. (2024). An Identification and Localization Method for 3D Workpiece Welds Based on the DBSCAN Point Cloud Clustering Algorithm. Journal of Manufacturing and Materials Processing(6), 287-287. ZhouNianJiangPingJiangShiliangShuLeshiNiXiaoxian & ZhongLinjun. (2024). An Identification and Localization Method for 3D Workpiece Welds Based on the DBSCAN Point Cloud Clustering Algorithm. Journal of Manufacturing and Materials Processing(6), 287-287.Search in Google Scholar

[35]Zhuo Lv, Li Di, Cen Chen, Bo Zhang & Nuannuan Li. (2023). A Fast Density Peak Clustering Method for Power Data Security Detection Based on Local Outlier Factors. Processes(7). LvZhuoDiLiChenCenZhangBo & LiNuannuan. (2023). A Fast Density Peak Clustering Method for Power Data Security Detection Based on Local Outlier Factors. Processes(7).Search in Google Scholar