Signal Correction Method Research Articles

Optimal antioxidant enzyme activity estimation in melon plant leaves based on microhyperspectral imaging technique

BackgroundIn order to select the most suitable type of light for plant growth, we have developed a quantitative model for the detection of antioxidant enzyme activities on melon leaves, which allows us to monitor plant growth in a timely and accurate manner. Leaf of melon ‘M5147’ and ‘M5346’ were used as test material. Six treatments have been established with different illuminance ratios. By the measurement of leaf superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) activities, the varying response of melons leaf to different grades of light have been studied using microscopic hyperspectral imaging at the plant factory. ResultThe original spectrum has been pre-processed and optimized. And then the characteristic wavelengths were extracted by ant colony optimization (ACO), iterative retained information variable (IRIV), un-information variable elimination (UVE) and genetic partial least squares (GAPLS). Partial Least Squares Regression (PLSR), least squares support vector machine (LSSVM) and convolutional neural network (CNN) are built based on the optimal feature wavelengths. T4 treatment (Light ratio: 7R/3B/5W/1UVa, Flux: μmol (m2-s), photoperi 12 h) was found to be optimum. In the pre-processing of the raw spectral data, orthogonal signal correction (OSC) methods were selected for SOD, POD and CAT. The best performance of SOD prediction model was constructed based on GAPLS-CNN (Rc = 0.751, Rp = 0.638); The best performance of POD prediction model was constructed based on UVE-CNN (Rc = 0.753, Rp = 0.530); The best performance of CAT prediction model constructed based on ACO-CNN (Rc = 0.742, Rp = 0.504). SignificanceThe aim of this study was to provide technical support for the dynamic monitoring of antioxidant enzyme activity in the leaves of other plants by establishing a quantitative detection model for antioxidant enzyme activity in melon leaves by combining chemical methods with microscopic hyperspectral imaging technology. This work provides data and a theoretical basis for screening light ratios in other plants and offers technical guidance for dynamically monitoring antioxidant enzyme activities in their leaves.

Study of interferometric signal correction methods in ultra-precision displacement measurement

The measurement of critical dimensions in the field of integrated circuits has moved from 7 nm to 5 nm. The existing chromium atomic lithography grating has a pitch period of 4700 l mm−1 and uniformity of picometer, and the interferometric signal period based on the above grating is as small as 106.4 nm, which brings new problems and challenges to the accurate processing of the signal. This paper investigates the error characteristics of ultra-high precision grating interferometric signals, establishes a Heydemann correction mathematical model for high inscribed line density grating interferometric signals, corrects the grating interferometer signals based on the random sample consensus (RANSAC), and verifies the effectiveness of the algorithm through simulation. By comparing the repeatability and linearity of the original algorithm and the self-traceable grating interferometric displacement measurement data processed by RANSAC, the conclusion that the standard deviation of the self-traceable grating interferometer repeat measurement after RANSAC is 1.60 nm in a 10 000 nm travel is obtained, and the purpose of improving the stability and uniformity of the signal solution with the algorithm of this paper is achieved, which is important for the study of laser interferometer and grating interferometer The results show that the stability and uniformity of the signal solution can be improved by the algorithm of this paper, which is of great significance for the study of the displacement solution of laser and grating interferometers.

Self-Checking Combinational Devices Synthesis Based on the Boolean Signal Correction Method Using Bose—Lin Codes

An organizing concurrent error-detection (CED) circuit new method for automation and computer technology combinational device is considered. The method based on the signal's Boolean correction method. The author proposed to use separable codes in the CED circuit synthesis when converting values from all operational outputs of the diagnosing object. The proposed approach allows to consider many possible transformations for each input combination. In addition, it will allow to choose the transformations to provide the conditions for building a completely self-checking device. In comparison with the methods previously discussed in the literature, the proposed approach makes it possible to synthesize a large number of control logic devices in the CED circuit and choose the most appropriate option. For example, with the technical implementation complexity lowest indicators. An algorithm for synthesizing the control logic block is given on the example of using a separable Bose — Lin code with module M = 4 by the signals Boolean correction method when organizing an CED circuit. The proposed method advantages and disadvantages for the CED circuit synthesis are described.

Determination of polysaccharide content in shiitake mushroom beverage by NIR spectroscopy combined with machine learning: A comparative analysis

A comprehensive comparative analysis of the performance of near-infrared (NIR) spectroscopy combined with various chemometrics and machine learning methods was performed to quantitatively determine polysaccharide content in shiitake mushroom beverages. First, Savitzky-Golay (SG) and orthogonal signal correction (OSC) methods were used for spectral pretreatment. Then, to simplify the model and enhance the generalization performance, variable selection (VS) methods including selectivity ratio (sRatio), variable importance in projection (VIP), recursive partial least square (rPLS), synergy interval partial least square (si-PLS), genetic algorithm (GA), and successive projection algorithm (SPA) were used to select the feature wavelengths. Finally, multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR), back propagation neural network (BPNN) and extreme gradient boosting (XGBoost) regression algorithms were used for the multivariate quantitative analysis of NIR data. The results show that different combinations of pretreatment, VS and modelling methods lead to different prediction performances. Except for XGBoost, almost all linear and non-linear models showed satisfying prediction performance with their Rp2 > 0.95. Of these, SG-VS-BPNN models exhibited the best performance with their Rp2 ≥ 0.98. Therefore, NIR combined with machine learning can provide an intelligent, nondestructive and rapid quantitation of polysaccharide content in beverages. Data will be made available on request.

Non-destructive monitoring the freshness of sea bass fillets using Raman spectroscopy with orthogonal signal correction and multivariate analysis

TVB-N is a commonly used chemical indicator for assessing the freshness of aquatic products during storage. In this study, the combination of Raman spectroscopy with multivariate analysis for monitoring the freshness of sea bass (Lateolabrax japonicus) fillets was investigated. Orthogonal signal correction (OSC) method was applied to pre-process the Raman spectra of sea bass fillets with different freshness levels. Results suggested that PLSR models outperformed PCR and SVMR models in monitoring TVB-N values of sea bass fillets. In comparison, the OSC-PLSR model with full-band Raman spectra showed the best performance for predicting TVB-N contents, with R2C of 0.981, RMSEC of 1.162 mgN/100 g, R2CV of 0.804, RMSECV of 3.756 mgN/100 g, R2P of 0.898, RMSEP of 2.199 mgN/100 g, and RPD of 3.824. In addition, sea bass fillets with three different freshness grades (first-grade, second-grade, and spoilage) could be distinguished from the PCA score plot. The PLS-DA model showed an average classification accuracy of 91.11% for different freshness grades. The combination of Raman spectroscopy and chemometrics provided a new tool for the non-destructive evaluation of fish quality.

Acquisition and correction for early surface nuclear magnetic resonance signal based on multi-turn small air-core coil sensors

Surface nuclear magnetic resonance (SNMR) with multi-turn small air-core coil sensor (ACS) has considerable potential for the detection of shallow soil water or oil. However, loss of the early free induced decay signal and the distortion of the recorded signal arising from the ACS, make it difficult to quantitatively evaluate hydrological information. To address this problem, we propose an early signal acquisition and correction method. A fast-switching unit is applied to acquire the early signal. And a correction method based on the mathematical model of the ACS is proposed. Finally, the performance of the proposed method is evaluated by simulation experiments. Results show that the proposed method overcomes the signal attenuation caused by the ACS, while the fitting error of key parameters decreases from about 15% to 2%. Comparative test results of the existing method in an electromagnetically shielded laboratory further demonstrate the effectiveness of the proposed method.

Quantitative Study of Sensor-Pipe Distance in Noncontact Magnetic Detection of Ferromagnetic Pipelines

Non-contact magnetic detection technology has great application potential in buried oil and gas pipelines due to the non-contact online detection of pipeline damage. However, the non-contact magnetic detecting signal is affected by the distance between the magnetometer and the ferromagnetic pipeline, leading to missed detection of pipe defects. It is essential to calibrate the detection height for the non-contact magnetic detecting signal. In this paper, the spatial analytical model of non-contact magnetic signals above ferromagnetic pipelines is established. Then, a quantitative variation law of non-contact magnetic detecting signals is obtained under different pipe diameters and internal pressures. Next, a signal calculation model based on the detection height h is established. A detection height correction method is proposed to eliminate the influence of detection heights on the magnetic detecting signal. An experimental investigation is carried out to validate the accuracy of the numerical calculation results and correction method. The results indicate that the magnetic detecting signal has a second-order exponential relationship with the detection height h within 0.1 m - 2 m. The average error of the signal correction model is 6.37%. The modified magnetic detecting signal via the signal correction method can reflect the damage status of ferromagnetic pipelines.

Phase-Discontinuity Correction Method With an Overlapped Signal Structure in Stepped-Carrier OFDM Radar With Dual Transmitters

A phase-discontinuity correction method for a stepped-carrier orthogonal frequency-division multiplexing (SC-OFDM) radar is proposed. Correction is accomplished using an overlapped signal structure in both the time and frequency domains, made possible by implementing a radar system with dual transmitters (DTs). The overlap in the frequency domain is used to correct phase discontinuities in the frequency steps, and the overlap in the time domain allows lowering the effective measurement time loss due to guard intervals. The subband phase discontinuity of DTs is extracted by cross-correlating a received subsymbol with the frequency-overlapped subsymbol from the next frequency step. After the gain and phase values at the maximum correlation output are obtained, subband and subsymbol correction processes are applied alternately for each frequency step in a chained manner. Simulated range-Doppler maps are presented without and with phase-discontinuity corrections using the proposed signal structure. Also, an SC-OFDM radar system with DTs is implemented to prove the concept. The results show that phase discontinuities among the frequency steps in a block can be readily corrected using the proposed signal structure and correction method without precalibration using a reference target.

Light-GBM based signal correction method for surface myoelectropotential measured by multi-channel band-type EMG sensor

The aim of this research is to develop a user interface using myoelectrical signals, a type of biological signal. A multi-channel band-type EMG sensor is used to measure this signal. The goal is to make it easier to attach and detach the EMG sensor, while still obtaining sufficiently accurate measurements even if the sensor is placed slightly off the standard position. To achieve this, a Light-GBM based method is proposed to estimate the degree of deviation of the EMG sensor from the standard position. Once the deviation is estimated, a filter is constructed to compensate for the effect of the deviation on the myoelectrical signal. Experimental results using this method show that the sensor misalignment can be estimated with over 90% accuracy, and that correcting the myoelectrical signal based on this information improves joint angle estimation by approximately 13% in terms of root mean square error.

Damping Subsynchronous Oscillations Using a High Voltage Direct Current-Based Multi-Modal Damping Controller

Conventional series capacitors in transmission lines are of paramount importance for enhancing power transfer capability. However, the major drawback of series capacitors is the induced subsynchronous resonance phenomenon in power systems. This phenomenon affects nearby turbogenerator shafts and severely limits their reliability. Also, the controls of High Voltage Direct Current links are considered as a potential source of subsynchronous oscillations. This paper investigates the performance of a multimodal damping controller to stabilize unstable torsional modes in combination with a Power System Stabilizer for inertial mode damping. This research uses time-domain simulation-based multimodal damping controller design methods, namely the test signal and phase correction methods. These two methods involve injecting a test signal at a frequency of interest into the rectifier current control loop and measuring its phase difference with the electromagnetic torque, thus providing corresponding compensators to minimize the resulting angle. This work uses Power System Computer-Aided Design for time-domain simulation and Fast Fourier Transforms Analysis to conduct the phase correction method and verify controller performance.

Comparison of iCOR and Rayleigh atmospheric correction methods on Sentinel-3 OLCI images for a shallow eutrophic reservoir.

Remote sensing of inland waters is challenging, but also important, due to the need to monitor the ever-increasing harmful algal blooms (HABs), which have serious effects on water quality. The Ocean and Land Color Instrument (OLCI) of the Sentinel-3 satellites program is capable of providing images for the monitoring of such waters. Atmospheric correction is a necessary process in order to retrieve the desired surface-leaving radiance signal and several atmospheric correction methods have been developed through the years. However, many of these correction methods require programming language skills, or function as commercial software plugins, limiting their possibility of use by end users. Accordingly, in this study, the free SNAP software provided by the European Space Agency (ESA) was used to evaluate the possible differences between a partial atmospheric correction method accounting for Rayleigh scattering and a full atmospheric correction method (iCOR), applied on Sentinel-3 OLCI images of a shallow, highly eutrophic water reservoir. For the complete evaluation of the two methods, in addition to the comparison of the band reflectance values, chlorophyll (CHL) and cyanobacteria (CI) indices were also calculated and their values were intercompared. The results showed, that although the absolute values between the two correction methods did not coincide, there was a very good correlation between the two methods for both bands' reflectance (r>0.73) and the CHL and CI indices values (r>0.95). Therefore, since iCOR correction image processing time is 25 times longer than Rayleigh correction, it is proposed that the Rayleigh partial correction method may be alternatively used for seasonal water monitoring, especially in cases of long time-series, enhancing time and resources use efficiency. Further comparisons of the two methods in other inland water bodies and evaluation with in situ chlorophyll and cyanobacteria measurements will enhance the applicability of the methodology.

Implication of phenol red in quantification of cultured cancerous cells using near-infrared spectroscopy and aquaphotomics

This study aims to evaluate the ability of absorbance spectra in the near-infrared (NIR) region to predict the number of cells for different cell lines. The cancerous cell lines were human cervix adenocarcinoma (HeLa) and human prostate carcinoma (DU145), and L929 was a normal mouse skin fibroblast. The number of cells varied from 50,000 to 275,000 with an interval of 25,000 for each cell line. Vis-NIR absorbance spectra (400–1100 nm) at each number of cells (50,000–275,000) for L929, DU145, and HeLa cell lines cultured in media with and without phenol red were recorded. Multiple linear regression (MLR) and partial least squares regression (PLSR) models were developed in the NIR region (680–1050 nm) to quantify the number of cells for the three cell lines. The outcomes showed that the quantification analysis of the number of cells using MLR and PLSR models produced high prediction accuracy with R2≥ 93%. The best results were obtained using PLSR for the preprocessed spectra using an orthogonal signal correction method. It was found that the presence of phenol red in the culture medium improved the prediction accuracy in the case of HeLa (SECV = 271 cells) and DU145 (SECV = 250 cells) cell lines, but the accuracy was higher when phenol red was not present for the L929 cell line (SECV = 24 cells). In addition, the existence of phenol red boosted the accuracy when the global PLSR model was built, irrespective of the cell type (SECV = 7,754 cells). The effect of phenol red was explained in the terms of its impact on the water molecular structure of the cells’ culture medium which influences the light scattering.

Deep learning–based turbidity compensation for ultraviolet-visible spectrum correction in monitoring water parameters

Ultraviolet-visible spectroscopy is an effective tool for reagent-free qualitative analysis and quantitative detection of water parameters. Suspended particles in water cause turbidity that interferes with the ultraviolet-visible spectrum and ultimately affects the accuracy of water parameter calculations. This paper proposes a deep learning method to compensate for turbidity interference and obtain water parameters using a partial least squares regression approach. Compared with orthogonal signal correction and extended multiplicative signal correction methods, the deep learning method specifically utilizes an accurate one-dimensional U-shape neural network (1D U-Net) and represents the first method enabling turbidity compensation in sampling real river water of agricultural catchments. After turbidity compensation, the R2 between the predicted and true values increased from 0.918 to 0.965, and the RMSE (Root Mean Square Error) value decreased from 0.526 to 0.343 mg. Experimental analyses showed that the 1D U-Net is suitable for turbidity compensation and provides accurate results.

Improved shock tube method for dynamic calibration of the sensitivity characteristic of piezoresistive pressure sensors

The dynamic calibration of pressure sensors with shock tube system are inevitably affected by the non-ideal shock wave characteristics and complex noise interferences, which limits the achievable accuracy of the dynamic pressure measurements. This paper proposes an improved shock tube (IST) method for identifying the sensitivity characteristic of piezoresistive pressure sensors. The incident shock wave (ISW) velocity attenuation model is first established by a distributed ISW velocity measurement combined with least squares fitting to compensate the shock wave pressure in shock tube. Besides, a signal correction method based on the hybrid adaptive mode decomposition is presented to eliminate the effects of complex noise on the response signals of pressure sensor. An accurate dynamic calibration result of the sensitivity characteristic is finally achieved with the compensated shock wave pressure and corrected response signal. A series of dynamic calibration experiments for a piezoresistive pressure sensor are carried out to verify the performance of the IST method. Results show that the IST method is able to effectively reduce the effects of the non-ideal shock wave and complex noises on the sensitivity calibration results. The mean relative error of sensitivity calibration results with the IST method is only 0.55%, which is about 1/7 times of the results obtained by the usual shock tube method. Furthermore, the sensitivity calibration uncertainty is evaluated and the smaller uncertainty results further verify the superiority of the IST method in high-accuracy dynamic calibration of piezoresistive pressure sensors.

Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy

Highly toxic pesticides are forbidden because of threatening to human lives, property, and the environment. However, some of them are still used illegally by added into normal pesticides to enhance the effectiveness and lower the cost. Therefore, it is very important to detect the illegally added ingredients in pesticides, such as Isofenphos-methyl in Tolfenpyrad. In this research, a rapid and accurate method for determining the concentration of Isofenphos-methyl in Tolfenpyrad is studied by near infrared spectroscopy with only nine characteristic wavelengths. Different pre-treating methods of direct orthogonal signal correction and first-order derivative on raw spectra and analysis methods of back propagation neural network and partial least squares are investigated to establish quantitative analysis models for comparison. Among them, the back propagation neural network model pre-treated with the direct orthogonal signal correction method has good prediction accuracy and prevents the data from being over-corrected. The correlation coefficients of the model calibration set and prediction set are as high as 0.999 and 0.989, respectively. Root Mean Square Error of Prediction and Ratio of Prediction to Deviation are 0.27% and 9.17, respectively. The prediction concentration limit can be as low as 0.5%. The results show that the direct orthogonal signal correction pre-treating method combined with back propagation neural network can be used for precisely quantitative determination of pesticide doping concentration. It provides an effective approach for rapid and accurate determination of prohibited components in pesticides.

Fault-Tolerant Cooperative Control of Large-Scale Wind Farms and Wind Farm Clusters

Large-scale wind farms and wind farm clusters with many installed wind turbines are increasingly built around the world, and especially in offshore regions. The reliability and availability of these assets are critically important for cost-effective wind power generation. This requires effective solutions for online fault detection, diagnosis and fault accommodation to improve the overall reliability and availability of wind turbines and entire wind farms. To meet this requirement, this paper proposes a novel active fault-tolerant cooperative control (FTCC) scheme for large-scale wind farms and wind farm clusters (WFCs). The proposed scheme is based on a signal correction method at wind turbine level that is augmented with two innovative “control reallocation” mechanisms at wind farm and network operator levels. Applied to a WFC, this scheme detects, identifies and accommodates the effects of both mild and severe power-loss faults in wind turbines. Various simulation studies on an advanced WFC benchmark indicate the high efficiency and effectiveness of the proposed solutions.

Fast determination of lipid and protein content in green coffee beans from different origins using NIR spectroscopy and chemometrics

The chemical compounds including lipid and protein in green coffee beans are important indicators of the final quality of the coffee products, which are usually determined by time-consuming and destructive chemical methods. Therefore, a fast and reliable method was attempted to exploit by near-infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for the determination of lipid and protein in green coffee beans from different origins. Orthogonal signal correction (OSC) and several traditional spectral pretreatment methods were compared during the PLS regression model building process. Important variables selection was further achieved based on the regression coefficients (β). The results showed that the 1st and 2nd derivative reduced the model quality, while OSC, MSC, and SNV pretreatment enhanced the model quality. The quality of PLS models was significantly improved after important variable selection. Especially, OSC-PLS models were the most robust for protein and lipid prediction with the best performance indicators (R2p>0.982, RPD > 7.55, RMSEcv <0.101, RMSEP < 0.106). The excellent performance showed that the NIR technique together with PLS regression could be applied as a substitute way to determine the protein and lipid content in green coffee beans.

Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy

Several orthogonal signal correction (OSC) methods coupled with partial least-squares (PLS) were applied to near-infrared spectra in a longitudinal study for the prediction of glucose, urea, and triacetin in liquid samples. The OSC methods included Wold’s OSC, Fearn’s OSC, orthogonal projection to latent structures (OPLS), and direct orthogonal signal correction (DOSC). These preprocessing methods are designed to simplify the spectra and remove information that is orthogonal to the analyte. Samples consisted of various concentrations of glucose, urea, and triacetin in the range from 1 to 19 mM. Calibration and prediction data were collected over 1.7 years to test how well each of the OSC methods helped to maintain analyte prediction performance over time. When comparing the OSC methods, it was found that all of the OSC methods improved the predictions in most data sets throughout the study but that DOSC clearly outperformed the others. On the basis of 18 prediction sets collected across 617 days after the collection of the calibration data, the average percent improvement in the standard error of prediction (SEP) when applying DOSC was 4.6%, 83.1%, and 43.0%, respectively, for glucose, urea, and triacetin. These comparisons were made relative to the SEP values obtained with unprocessed spectra.

Measurement of endothelial function using photoplethysmography with continuous correction approach: a pilot study

Background: Photoplethysmography flow-mediated dilatation (PPG-FMD) is an endothelial function measurement tool, and its use is increasing as it is easy to apply and relatively inexpensive. The analysis protocol of PPG-FMD has not yet been standardized. This study aimed to define the continuous correction method in PPG-FMD analysis and interpret the results obtained from a healthy population.Methods: The study population consisted of 30 healthy persons. Endothelial function measurement was done with the PPG-FMD method. In the correction process of the PPG signal, a continuous (element-wise) correction approach was defined and applied. PPG-dilation indexes (PPG-DI) were calculated for each subject, and its correlation with demographic and anthropometric data were analyzed. Finally, the study group was divided into subgroups, and endothelial function results were compared.Results: The median PPG-DI (IQR) value of the whole study group was 112% (60). The Pearson correlation coefficients (r) obtained in the analysis were as follows; r=-0.6278 for age, r=-0.4635 for BMI, r=-0.4628 for waist circumference, r=-0.4207 for systolic blood pressure, r=-0.2804 for diastolic blood pressure. In the subgroup analysis, groups with older age, high BMI, male sex, smoker, and high waist circumference had lower PPG-DI results.Conclusions: PPG-FMD could be a good alternative in the endothelial function measurement, with its operator-independent and easily applicable nature. Although the measurement protocol is similar to USG-FMD, PPG-FMD contains much more technical details during the analysis process. We introduced a novel signal correction method, and we believe that this will be a basis for future studies on this subject.

Probabilistic Characteristics of Non-Test Adaptive Signal Correction Methods

The article presents a description of a model and expressions derived for evaluating probabilistic characteristics of non-test methods for adaptive signal correction, namely, the probability of stable operation of a communication system for a predetermined time and the average time of stable operation. The dependences of probability of finding a signal segment and calculating newly found (updated) coefficients of the filter under correction, as well as failure probability of a non-test adaptive correction procedure, are shown here. The obtained analysis demonstrates the efficiency of non-test methods for adaptive signal correction when used in channels with intersymbol interference (ISI) at relatively low values of signal-to-noise ratio, 8‒15 dB.