Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms
Yazarlar (2)
Doç. Dr. Serkan KESER Kırşehir Ahi Evran Üniversitesi, Türkiye
Şekip Esat Hayber Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Sensors and Actuators A Physical (Q2) | ||
| Dergi ISSN | 0924-4247 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2021 |
| Cilt / Sayı / Sayfa | 332 / 1 / 113071–0 | DOI | 10.1016/j.sna.2021.113071 |
| Makale Linki | http://dx.doi.org/10.1016/j.sna.2021.113071 | ||
| Özet |
| In this study, a sensor tip with a metallic hemispherical nozzle tip (MHNT) design based on the Fabry-Perot interferometer was developed for surface roughness recognition (SRR). Sandpaper samples with ten different arithmetical mean deviations of the surface (Sa) values were used as surfaces to be recognized. The feature vectors were found by applying the discrete wavelet transform (DWT) to the analog signals obtained from the sandpaper samples. Machine learning (ML) algorithms K-nearest neighbor (KNN) and support vector machine (SVM) were used for classification. An in-depth recognition process was carried out using the classifiers’ different length criteria and kernel types. In the test process, each category consists of two sub-categories as testing within the training dataset (TWITD) and testing without the training dataset (TWOTD). The experiments were carried out in a controlled manner with the … |
| Anahtar Kelimeler |
| DWT | Fiber optic tactile sensor | Interferometry | KNN | Surface roughness recognition | SVM |
Science Direct
Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 50 |
| Scopus | 40 |
| Web of Science | 33 |