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F generating processes. Two alternative dissimilarities had been taken into account for comparison purposes [5,6]. In all cases, the 3 proposed algorithms outperformed the competitors. 3. Application to actual information The three procedures proposed in Section 2 were applied to carry out clustering in a genuine MTS database. Specifically, we deemed everyday stock returns and trading volume in the prime 20 providers from the S P 500 index, therefore acquiring 20 bivariate MTS. Table 1 shows the membership degrees on the series concerning the trimmed strategy.Table 1. Membership degrees for the prime 20 firms in the S P 500 index by contemplating the trimmed strategy in addition to a 6-cluster partition. Firm AAPL MSFT AMZN GOOGL GOOG FB TSLA BRK.B V JNJ WMT JPM MA PG UNH DIS NVDA HD PYPL BAC C1 0.083 0.107 0.865 0.682 0.902 0.002 0.023 0.004 0.004 0.002 0.005 0.015 0.006 0.020 0.025 0.155 0.076 C2 0.146 0.049 0.017 0.032 0.010 0.983 0.012 0.014 0.015 0.001 0.006 0.012 0.924 0.038 0.020 0.301 0.086 C3 0.299 0.213 0.051 0.092 0.031 0.006 0.056 0.015 0.019 0.003 0.968 0.028 0.026 0.772 0.085 0.297 0.225 C4 0.365 0.356 0.032 0.128 0.028 0.004 0.885 0.017 0.013 0.003 0.010 0.016 0.013 0.099 0.804 0.115 0.067 C5 0.066 0.099 0.010 0.025 0.008 0.003 0.013 0.941 0.937 0.002 0.005 0.019 0.022 0.042 0.043 0.057 0.060 C6 0.041 0.176 0.025 0.040 0.022 0.002 0.010 0.009 0.013 0.989 0.006 0.909 0.008 0.030 0.024 0.075 0.The Tacalcitol Technical Information symbols in bold correspond to the companies which were trimmed away, Berkshire Hathaway (BRK.B), Walmart (WMT) and Home Depot (HD). Similar clustering options were obtained with the remaining two approaches. 4. Conclusions This work proposes three robust methods to perform fuzzy clustering of MTS. They are depending on the so-called exponential, noise and trimmed ideas. Each and every method attains robustness to outlying series in a different way. The three procedures have been presented and assessed through a wide simulation study, substantially outperforming alternative approaches. A actual data application has been also carried out to be able to show the usefulness of your presented procedures.Acknowledgments: This analysis has been supported by MINECO (MTM2017-82724-R and PID2020113578RB-100), the Xunta de Galicia (ED431C-2020-14), and “CITIC” (ED431G 2019/01).
Proceeding PaperDedicated Wearable Sensitive Strain Sensor, Depending on Carbon Nanotubes, for Monitoring the Rat Respiration RateTieying Xu 1, , , Mohamad Yehya two, , Abhishek Singh Dahiya 1 , Thierry Gil 3 , Patrice Bideaux two , Jerome Thireau 2 , Alain Lacampagne two , Benoit Charlot 1 and Aida Todri-SanialIES, Universitde Spectinomycin dihydrochloride MedChemExpress Montpellier, CNRS, 34090 Montpellier, France; [email protected] (A.S.D.); [email protected] (B.C.) PhyMedExp, Universitde Montpellier, CNRS, INSERM, 34090 Montpellier, France; [email protected] (M.Y.); [email protected] (P.B.); [email protected] (J.T.); [email protected] (A.L.) LIRMM, Universitde Montpellier, CNRS, 34095 Montpellier, France; [email protected] (T.G.); [email protected] (A.T.-S.) Correspondence: [email protected]; Tel.: +33-7829-78228 Presented at 8th International Electronic Conference on Sensors and Applications, 15 November 2021; Accessible on the internet: These authors contributed equally to this work.Citation: Xu, T.; Yehya, M.; Dahiya, A.S.; Gil, T.; Bideaux, P.; Thireau, J.; Lacampagne, A.; Charlot, B.; Todri-Sanial, A. Dedicated Wearable Sensitive Strain Sensor, Based on Carbon Nanotubes, for Mo.

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