کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7223843 | 1470563 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Research on ultraviolet-visible absorption spectrum preprocessing for water quality contamination detection
ترجمه فارسی عنوان
تحقیق در زمینه پیش پردازش طیف جذب نور ماوراء بنفش برای تشخیص آلودگی با کیفیت آب
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی (عمومی)
چکیده انگلیسی
The contaminant detection in water is important to secure public health against potentially harmful substances. As a noninvasive detection technique, ultraviolet-visible (UV-Vis) spectroscopy is studied and widely applied in detecting water contamination. However, current methods for contamination detection reveal the defects of noise sensitivity and data redundancy. In this study, de-noising and dimensionality reduction of UV-Vis absorption spectrum are focused on. With regards to de-noising, the influence of various discrete wavelet transforms families, decomposition levels and threshold methods on UV-Vis absorption spectrum de-noising are analyzed and discussed in detail. Concerning dimensionality reduction, the principal component analysis (PCA) is utilized. The performance of de-noising and dimensionality reduction is assessed by signal-to-noise ratio (SNR) and normalized reconstruction error (εnorm), respectively. Results from experiments and analysis reveal the decomposition level and wavelet transforms families are the most significant parameters influencing the de-noising algorithm efficiency. Moreover, using sym5 at five level decomposition with hard threshold method had the best effect for UV-Vis absorption spectrum de-noising. Besides, PCA can project UV-Vis absorption spectrum to a much lower dimension but representative data space.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optik - Volume 164, July 2018, Pages 277-288
Journal: Optik - Volume 164, July 2018, Pages 277-288
نویسندگان
Li Guan, Yifei Tong, Jingwei Li, Dongbo Li, Shaofeng Wu,