Article
Automation & Control Systems
Carolin Loerchner, Carsten Fauhl-Hassek, Marcus A. Glomb, Vincent Baeten, Juan A. Fernandez Pierna, Susanne Esslinger
Summary: The comparability of spectra acquired by different spectroscopic instruments using the same measurement principle is essential for the application of a common spectral database in laboratories. Different mathematical correction approaches can be used to optimize the comparability of spectral data from different instruments.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Automation & Control Systems
Lakshmi Alagappan, Jia En Chu, Joanna Huixin Chua, Jia Wen Ding, Ronghui Xiao, Zhe Yu, Kun Pan, Untzizu Elejalde, Kevin Junliang Lim, Limsoon Wong
Summary: In this study, a new classification and correction technique called CSCAC is proposed, which can simultaneously correct batch effects, perform multi-class classification, and detect new classes. The effectiveness of the method is demonstrated through classification experiments on different types of oils.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Baisong Jiang, Chunxia Zhang, Nanqi Zhao, Hongguang Li, Liang Yuan, Juan Chen, Haowen Bai, Le Wang
Summary: In this study, a mathematical model of wavelength calibration was established for a scanning, double-layer, secondary diffraction, linear-array CCD spectrometer based on the grating diffraction equation. A robust, full-screen wavelength calibration algorithm was proposed, which demonstrated excellent calibration tool for conveniently and accurately calibrating the wavelengths.
APPLIED SCIENCES-BASEL
(2023)
Review
Environmental Sciences
Thi Lan Anh Dinh, Filipe Aires
Summary: Climate models are widely used in studying climate change impacts, but their direct use is often limited due to inherent limitations. Bias correction methods have been proposed to improve the simulations. This study presents an up-to-date review of these methods, comparing six representative quantile-based approaches for temperature and precipitation data in Europe. New diagnostic tools are recommended to measure the impact of the adjustment on the model's ability to reproduce observations and capture climate change signals.
Article
Biochemistry & Molecular Biology
Hui Zhang, Haining Tan, Boran Lin, Xiangchun Yang, Zhongyu Sun, Liang Zhong, Lele Gao, Lian Li, Qin Dong, Lei Nie, Hengchang Zang
Summary: Given the labor-consuming nature of model establishment, model transfer has become a considerable topic in the study of near-infrared (NIR) spectroscopy. To expand model applicability, a new methodology based on improved principal component analysis (IPCA) is proposed for calibration transfer between different types of spectrometers. The proposed method enables improvements in prediction ability rather than the degradation of the models built with original micro spectra.
Article
Computer Science, Artificial Intelligence
Ning Cheng, Chunzheng Cao, Jianwei Yang, Zhichao Zhang, Yunjie Chen
Summary: In this paper, a novel spatially constrained finite skew student's-t mixture model is proposed for accurate segmentation of brain magnetic resonance images. By combining prior and posterior probabilities to reduce the impact of noise and fitting the intensity distribution of observation data with a special distribution, the model achieves better results than other methods in experiments.
PATTERN RECOGNITION
(2022)
Article
Soil Science
Muhammad Abdul Munnaf, Abdul Mounem Mouazen
Summary: Classifying soil texture is crucial for studying soil processes and environmental management. This study examined the influence of soil moisture content on texture classification using Vis-NIRS spectroscopy and proposed a method to eliminate the moisture content effect. The results showed that the proposed method improved the accuracy of texture classification.
SOIL & TILLAGE RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Lingxue Liu, Li Zhou, Maksym Gusyev, Yufeng Ren
Summary: In this study, a novel bias-correction system equipped with the proposed Piecewise Random Forest (P-RF) model was developed to improve the potential of the global-scale river discharge reanalysis product GloFAS-ERA5 (GloFAS) as a calibration benchmark for building hydrological models in ungauged basins. The system was tested in three ungauged scenarios in China and Japan, and the results showed better performance on the temporal scale, significant impact of sample integrity and adequacy on spatial and spatiotemporal bias-corrections, and a reduction of 25%-50% in statistical metric differences through the bias-correction.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Optics
Xiaoxue Chen, Shangyuan Li, Xiaoxiao Xue, Xiaoping Zheng
Summary: This paper proposes a correction scheme to address the problem of modulation nonlinearity and optical switch crosstalk in multi-site optical converged networks. The experimental results show that the proposed scheme can significantly improve the signal-to-noise ratio without introducing additional noise, making it suitable for large-scale and complex optical networks.
Article
Agronomy
Lianjie Li, Wenqian Huang, Zheli Wang, Sanqing Liu, Xin He, Shuxiang Fan
Summary: Calibration transfer between two portable Vis/NIR devices for predicting SSC of apples was studied. PLS calibration models based on the spectra of the devices showed high prediction performance. Correction of spectral dimensions using a Hg (Ar) lamp improved transfer performance. PDS method showed better performance in transfer calibration compared with SST and CTCCA.
POSTHARVEST BIOLOGY AND TECHNOLOGY
(2022)
Article
Robotics
Yesheng Zhang, Xu Zhao, Dahong Qian
Summary: This paper focuses on the importance of camera calibration in robotic systems and proposes an improved calibration framework to enhance precision and robustness. Distortion correction and sub-pixel feature location are achieved through learning-based algorithms, and parameter estimation is done using RANSAC algorithm. Experimental results show that the proposed framework outperforms existing methods in terms of robustness and precision.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Meteorology & Atmospheric Sciences
Qin Zhang, Yaoyao Gan, Liping Zhang, Dunxian She, Gangsheng Wang, Shuxia Wang
Summary: Bias correction is a vital technique for improving the accuracy of climate model outputs in regional studies. This study introduces a novel bias correction method, piecewise-quantile mapping (PQM), that combines piecewise mapping with quantile mapping to correct both extreme and non-extreme data. The results show that PQM performs better than other commonly used methods in correcting precipitation percentiles and extreme indices. It also captures the spatial distributions of extreme indices well. PQM provides more accurate bias correction of climate model outputs, reducing uncertainty in subsequent analyses, especially in extreme conditions.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2022)
Article
Health Care Sciences & Services
Antonia K. Korre, Vassilis G. S. Vasdekis
Summary: In this study, we investigate correlated binary data and propose an adjustment to the fitting process using the regression calibration method. We correct for bias in random effects estimates and improve the properties of fixed effects estimates. Experimental results support the effectiveness of our approach.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Jihao Liu, Xihai Li, Ying Zhang, Aimin Du, Xiaoniu Zeng, Yong Yang
Summary: In this work, an improved calibration method robust to gyro bias based on relative attitude information from the gyro is proposed, which can estimate the calibration parameters more accurately and stably than the traditional method when the gyro bias exists. Experimental results demonstrate that the proposed method can effectively improve the estimation accuracy of the calibration parameters, as the influence of the gyro bias on the traditional method is 2-3 orders of magnitude higher than that on the proposed method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Chemistry, Analytical
Dengshan Li, Lina Li
Summary: This study proposes a novel hybrid calibration transfer method, named DS-AE, which combines direct standardization (DS) and autoencoder (AE) for nonlinear dimensionality reduction, to correct the spectral differences measured on different near-infrared (NIR) instruments. DS is used to eliminate the preliminary spectral difference between the master and slave instruments, and AE is used to extract spectral features for constructing the partial least squares (PLS) calibration model. Compared with linear dimensionality reduction methods, AE learns more latent features that can better reflect the chemical information of the samples.
ANALYTICAL LETTERS
(2023)
