Odor of Tropical Timber 2
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The data is used for timber species classification and verification from their aromas. Timber samples were collected from wood deposits, making data collection closer to practical situations. The measurements of the volatile compounds were made by using an array of 16 chemical sensors, whose curves are the inputs to a pattern recognition based system. Support vector machine is used for Classification; and, Gaussian mixture modeling with Universal Background Model is used for timber verification from the odor. Sensors: SENSOR(j) BRAND REF 1 HANWEI MQ-2 2 HANWEI MQ-3 3 HANWEI MQ-4 4 HANWEI MQ-6 5 HANWEI MQ-7 6 HANWEI MQ-8 7 HANWEI MQ-135 8 HANWEI MQ-9 9 FIGARO TGS-832 10 HANWEI MQ-6 11 FIGARO TGS-823 12 FIGARO TGS-816 13 FIGARO TGS-822 14 FIGARO TGS-813 15 FIGARO TGS-826 16 HANWEI MQ-3 These are de features: * Ginicialj (G0) : initial conductance value, mean of the first 100 samples of the total response. * Gfinalj (Gf) : final conductance value, mean of the last 50 samples of the phase 2 of the total response. * Gmaxj (Gmax) : maximum conductance value. * Gminj (Gmin) : minimum conductance value. * Poloj (A) : pole location, corresponding to an adjusted first-order auto-regressive model [1]: * Gananciaj(B) : gain coefficient of the same adjusted first-order auto-regressive model [1]; [1] Mantilla-Ramirez, Naren A.; Ortega-Boada, H.; Paja-Sarria, M. and Sepúlveda-Sepúlveda, A. (2020). A Low Cost Electronic Nose with a GMM-UBM Approach for Wood Species Verification.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, ISSN 2184-4313, pages 333-341. DOI: 10.5220/0008978003330341
本数据集用于基于气味的木材种类分类与验证。木材样本采集自木材仓储场景，使得数据采集更贴近实际应用场景。挥发性化合物的检测采用由16个化学传感器组成的阵列完成，传感器的响应曲线作为基于模式识别系统的输入数据。分类任务采用支持向量机（Support Vector Machine, SVM）实现；而基于气味的木材验证任务则结合高斯混合模型（Gaussian Mixture Model, GMM）与通用背景模型（Universal Background Model, UBM）完成。 传感器详情如下： 1. 汉威（HANWEI）MQ-2 2. 汉威（HANWEI）MQ-3 3. 汉威（HANWEI）MQ-4 4. 汉威（HANWEI）MQ-6 5. 汉威（HANWEI）MQ-7 6. 汉威（HANWEI）MQ-8 7. 汉威（HANWEI）MQ-135 8. 汉威（HANWEI）MQ-9 9. 费加罗（FIGARO）TGS-832 10. 汉威（HANWEI）MQ-6 11. 费加罗（FIGARO）TGS-823 12. 费加罗（FIGARO）TGS-816 13. 费加罗（FIGARO）TGS-822 14. 费加罗（FIGARO）TGS-813 15. 费加罗（FIGARO）TGS-826 16. 汉威（HANWEI）MQ-3 数据集包含以下特征： * 初始电导值（G_initialj, G0）：总响应前100个采样点的均值。 * 最终电导值（G_finalj, Gf）：总响应第二阶段最后50个采样点的均值。 * 最大电导值（G_maxj, Gmax）：响应过程中的最大电导数值。 * 最小电导值（G_minj, Gmin）：响应过程中的最小电导数值。 * 极点位置（Poloj, A）：经一阶自回归模型拟合得到的极点位置[1]。 * 增益系数（Gananciaj, B）：上述一阶自回归拟合模型的增益系数[1]。 [1] Mantilla-Ramirez, Naren A.; Ortega-Boada, H.; Paja-Sarria, M. 与 Sepúlveda-Sepúlveda, A. (2020). 一种基于GMM-UBM方法的低成本电子鼻（Electronic Nose）用于木材种类验证. 收录于第9届国际模式识别应用与方法会议论文集（卷1：ICPRAM），ISBN 978-989-758-397-1，ISSN 2184-4313，页码333-341. DOI: 10.5220/0008978003330341
