A New Approach to Signal Filtering Method Using K-Means Clustering and Distance-Based Kalman Filtering

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL A New Approach to Signal Filtering Method Using K-Means Clustering and Distance-Based Kalman Filtering 25 Pages Posted: 3 Aug 2022 See all articles by M. Syauqi Hanif ArdaniM. Syauqi Hanif ArdaniInstitut Teknologi Sepuluh NopemberRiyanarto SarnoInstitut Teknologi Sepuluh NopemberMalikhah MalikhahInstitut Teknologi Sepuluh NopemberDoni Putra PurbawaInstitut Teknologi Sepuluh NopemberShoffi Izza SabillaInstitut Teknologi Sepuluh NopemberKelly Rossa SungkonoInstitut Teknologi Sepuluh NopemberChastine FatichahInstitut Teknologi Sepuluh NopemberDwi SunaryonoInstitut Teknologi Sepuluh NopemberRahadian Indarto Susiloaffiliation not provided to SSRN Abstract Human axillary odours taken by an electronic nose (e-nose) device that uses a Metal Oxide Semiconductor (MOS) sensor not only contains a gas signal from the pure source of the axillary odour but also has the potential to contain other substances such as perfume and deodorant. This situation requires noise reduction so that dirty data can be cleaned and produce better predictions without wasting a lot of data. The approach taken in this study is to detect data clusters and data centroids from each reference data. Dimensional reduction using Linear Discriminant Analysis (LDA) on the reference data is carried out, then look for the centroid of each data using K-Means Clustering and use it to be a good signal estimate and process using Kalman Filtering so that it can be used to process axillary odour data containing deodorant. The proposed method was tested by a stacked Deep Neural Network (DNN) approach and can increase accuracy by 18.95% and balanced accuracy by 11.87% compared to original invalid data before filtering. The proposed method is also tested by other classification methods and able to produce the highest accuracy with 79.29% in Support Vector Classifier (SVC) and Multi-Layer Perception (MLP), while other filtering methods only get the highest accuracy with 69.03% also in SVC and MLP. We also analysed the execution time of each tested methods. Keywords: Electronic Nose, K-Means Clustering, Kalman Filtering, Noise Reduction, Signal Processing Suggested Citation: Suggested Citation Ardani, M. Syauqi Hanif and Sarno, Riyanarto and Malikhah, Malikhah and Purbawa, Doni Putra and Sabilla, Shoffi Izza and Sungkono, Kelly Rossa and Fatichah, Chastine and Sunaryono, Dwi and Susilo, Rahadian Indarto, A New Approach to Signal Filtering Method Using K-Means Clustering and Distance-Based Kalman Filtering. Available at SSRN: https://ssrn.com/abstract=4180036 M. Syauqi Hanif Ardani Institut Teknologi Sepuluh Nopember ( email ) Surabaya, 60111Indonesia Riyanarto Sarno (Contact Author) Institut Teknologi Sepuluh Nopember ( email ) Surabaya, 60111Indonesia Malikhah Malikhah Institut Teknologi Sepuluh Nopember ( email ) Surabaya, 60111Indonesia Doni Putra Purbawa Institut Teknologi Sepuluh Nopember ( email ) Surabaya, 60111Indonesia Shoffi Izza Sabilla Institut Teknologi Sepuluh Nopember ( email ) Surabaya, 60111Indonesia Kelly Rossa Sungkono Institut Teknologi Sepuluh Nopember ( email ) Surabaya, 60111Indonesia Chastine Fatichah Institut Teknologi Sepuluh Nopember ( email ) Surabaya, 60111Indonesia Dwi Sunaryono Institut Teknologi Sepuluh Nopember ( email ) Surabaya, 60111Indonesia Rahadian Indarto Susilo affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 0 Abstract Views 2 PlumX Metrics Related eJournals Biotechnology eJournal Follow Biotechnology eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 163 PAPERS 2,974 Computational & Theoretical Chemistry eJournal Follow Computational & Theoretical Chemistry eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 70 PAPERS 850 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.282 seconds