Continuous Wavelet Transform Methods Research Articles

Hyperspectral Estimation of Chlorophyll Content in Wheat under CO2 Stress Based on Fractional Order Differentiation and Continuous Wavelet Transforms

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.

Comparison of Citrus Leaf Water Content Estimations Based on the Continuous Wavelet Transform and Fractional Derivative Methods

Citrus tangerines are famous fruits worldwide, and monitoring the water content of citrus leaves is highly important for citrus production. However, there are still challenges in quantitatively estimating the water content of citrus leaves using hyperspectral technology, and the random noise generated during spectral acquisition and the overlapping peaks in the sensitive band of the citrus leaf water content will affect estimation accuracy. To solve these problems and further explore the roles of the continuous wavelet transform (CWT) and fractional-order derivative (FOD) in the estimation of citrus leaf water content, this study intends to use of CWT and FOD to decompose the original spectrum, and then compare the correlation between the original spectrum and leaf water content to explore whether the decomposition treatment has improved the correlation between spectrum and leaf moisture content. Then, the successive projections algorithm (SPA) was used to select feature bands and combine spectral vegetation indices. Partial least squares regression (PLSR) was used to construct water-content inversion models for citrus leaves, and the inversion accuracies of two commonly used spectral preprocessing methods were compared. The results indicate that (1) the CWT can improve the sensitivity of the spectrum to the citrus leaf water content to a certain extent, and the inversion accuracy of the CWT is approximately 5% greater than that of the FOD. (2) On the basis of the CWT and FOD methods, the inversion accuracy of the citrus leaf water content based on SPA screening increased by 9.61% and 9.29%, respectively, compared with the original spectrum. (3) Under CWT decomposition, Scale4 of the Gaus1 wavelet was screened by the SPA, and the inversion model of citrus leaf water content was constructed by combining the spectral vegetation index NDVI with the best results. The R-squared (R2) and root mean square error (RMSE) values were 0.7491 and 0.0284, respectively, which were both 0.0138 greater than those of the best inversion model for the FOD R2. In conclusion, the CWT-SPA combined with the spectral vegetation index can improve the sensitivity of the spectrum to the citrus leaf water content, eliminate a large amount of redundant data, and enhance the prediction ability and stability of the citrus leaf water content.

Stratified diagnosis of cotton canopy spectral characteristics based on CWT-SPA and its relationship with moisture, nitrogen, and SPAD values

ABSTRACT Cotton is an economically valuable crop worldwide, and accurate and rapid leaf moisture content, nitrogen content, and soil plant analysis development (SPAD) value estimations are crucial for cotton growth. In this study, high-resolution spectral data were collected for the Tahe 2 cotton variety in Alar City, Xinjiang. The raw spectra were preprocessed using four methods: first derivative, standard normal variate transformation, second derivative, and multiplicative scatter correction. Wavelet coefficients at multiple scales were generated from the raw spectra using a continuous wavelet transform (CWT) and CWT with first derivative (CWT-FD). A quantitative detection model combining CWT and CWT-FD with successive projection algorithm – partial least squares (SPA-PLS) was developed to estimate the leaf moisture content, nitrogen content, and SPAD values. The performances of the different scales and methods for estimating these parameters were compared. Six stratification methods were employed to determine the optimal modelling results and decomposition scales for the parameters under various stratification schemes. The results demonstrated that the CWT and CWT-FD methods significantly improved the accuracy of estimating cotton canopy spectral characteristics related to the parameters, surpassing that of conventional spectral processing methods. The mexh, db5, and db5 wavelet basis functions, along with scale 3, were identified as the optimal modelling choices for moisture content, nitrogen content, and SPAD values throughout the cotton plant growth period. Among the six vertical stratification structures of cotton, the CWT and CWT-FD methods, along with the three wavelet basis functions, achieved the best results for the three parameters in the upper layer. The optimal relative percent deviation values for the three parameters of typical leaves ranged from 1.603 to 2.016, indicating satisfactory modelling performance. These findings provide a theoretical foundation for accurately estimating leaf moisture content, nitrogen content, and SPAD values using spectral technology, enabling the simultaneous extraction and monitoring of multiple physiological indicators.

Comparison of leaf chlorophyll content retrieval performance of citrus using FOD and CWT methods with field-based full-spectrum hyperspectral reflectance data

Citrus is one of the most economically valuable fruit trees in the world, for which leaf chlorophyll content (LCC) serves as a crucial indicator for evaluating its growth and health status. However, the quantitative estimation of LCC using remote-sensing techniques is still challenging owing to unclear sensitive spectral ranges, baseline drift and overlapping spectrum peaks. To resolve these issues, we clarified the spectral response characteristics of citrus LCC using fractional-order derivatives (FOD) and continuous wavelet transform (CWT) methods to determine its sensitive spectral range with in situ full-spectrum leaf hyperspectral data. We proposed a novel method for estimating the LCC of citrus by combining an ensemble learning regression model based on Hyperopt optimization (H-ELR) with partial least squares regression (PLSR) using the ultra-high dimensional feature variables produced by dual- and tri-band combination strategies. We evaluated the retrieval performance of LCC between the FOD- and CWT-based optimal spectral feature variables. Besides, we further examined the feasibility of improving the estimation accuracy of LCC by the combination of their optimal feature variables. Finally, we evaluated the effect of the spectral curve trend changes and different dimensional spectral features on LCC estimation. The results showed that: (1) The FOD- and CWT-based methods improved the correlation between original spectral reflectance and LCC, with the correlation coefficient increasing by 0.046 and 0.054, respectively. We confirmed that 425–740 nm is the optimal spectral range for LCC estimation. (2) We found that the 0.9 order derivative Tri-band index (TBI2 (R571, R1697, R740)) constructed by combining the sensitive spectral bands of leaf water content achieved a high-precision LCC estimation (R2 = 0.876). In addition, the MERIS terrestrial chlorophyll index (MTCI) constructed based on the scale6 of CWT also gained good retrieval accuracy (R2 = 0.806). (3) We demonstrated that a combination of FOD-based and CWT-based sensitive spectral features improves estimation accuracies (R2 = 0.891) of LCC and revealed that the reflectance peaks and slope peaks at 550 and 750 nm are essential variables for predicting the citrus LCC. (4) The combination of PLSR and H-ELR model provided a good retrieval performance of citrus LCC with the kurtosis (γ = 3.2) and skewness (Sk = 0.066) of the residual prediction values. Our proposed method can provide a scientific basis for estimating LCC and other physiological parameters of citrus and other crop types, which is also important to optimize agricultural management practices and improve crop yield.

Continuous wavelet transform for solving the problem of minor components in quantitation of pharmaceuticals: a case study on the mixture of ibuprofen and phenylephrine with its degradation products

The presence of minor components represents a challenging problem in spectrophotometric analysis of pharmaceuticals. If one component has a low absorptivity or present in a low concentration compared to the other components, this will hinder its quantitation by spectrophotometric methods. Continuous Wavelet Transform (CWT) as a signal processing technique was utilized to figure out a solution to such a problem. A comparative study was established between traditional derivative spectrophotometry (Numerical Differentiation, ND) and CWT to indicate the advantages and limitations of each technique and possibility of solving the problem of minor components. A mixture of ibuprofen (IBU) and phenylephrine (PHE) with its degradation products forming a ternary mixture was used for comparing the two techniques. The two techniques were applied on raw spectral data and on ratio spectra data resulting in four methods, namely ND, CWT, Derivative Ratio-Zero Crossing (DRZC) and Continuous Wavelet Transform Ratio-Zero Crossing (CWTR-ZC) methods. By comparing the results in laboratory prepared mixtures, CWT technique showed advantages in analysis of mixtures with minor components than ND. The proposed methods were validated according to the ICH guideline Q2(R1), where their linearity was established with correlation coefficient ranging from 0.9995 to 0.9999. The linearity was in the range 3–40 μg/mL for PHE in all methods, while for IBU it was 20–180 and 30–180 μg/mL in CWT and ND methods, respectively. The CWT methods were applied for quantitative determination of the drugs in their dosage form showing the ability of the methods to quantitate minor components in pharmaceutical formulations.

Frequency characteristics analysis of pipe-to-soil potential under metro stray current interference using continuous wavelet transform method

This study aims to investigate the impact of stray current interference from metro systems on the pipe-to-soil potentials of metallic pipelines, which is closely associated with stray current corrosion. The pipe-to-soil potentials were assessed using coupon testing, while in-situ measurement was conducted to measure the metro leakage currents. The collected field data from the pipe-to-soil potentials and metro leakage current measurements were subjected to a statistical analysis using Continuous Wavelet Transform (CWT) and linear correlation methods. The analysis revealed consistent dominant frequencies of interference at the same test point for both on-potentials and off-potentials conditions, as well as within different time periods. However, variations in dominant frequencies of interference were observed among test points along different sections of the pipeline. Notably, three primary test points exhibited frequencies of 8.3, 8.9, and 7.8 mHz, corresponding to the main interstation travel intervals of neighboring metro lines. Furthermore, the dominant period ranges of interference of metro stray current interference were found to align with the interstation travel interval ranges of the metro lines. When the OVPD acts, the leakage current of the metro station grounding system near the test point has a significant contribution to the amplitude of pipe-to-soil potential fluctuation. These findings highlight the significant role of interstation travel intervals in the periodicity of metro stray current interference. The identified dominant interference frequency ranges can be utilized to determine interference sources and evaluate the extent of metro stray current corrosion.

A Simple UV Spectrophotometric Method Based on Continuous Wavelet Transform and Multivariate Calibration Model for the Concurrent Analysis of Three Water-Soluble Vitamins in Fertility Supplements.

Owing to the presence of overlapping spectra in pharmaceutical components, classical spectrophotometry is hard for concurrent determination. The advance of chemometrics along with UV-Vis spectrophotometry has contributed to solving this problem. In this study, a fast, easy, precise, accurate, low-cost, and eco-friendly spectrophotometric technique was introduced and validated for the simultaneous analysis of vitamin B6, vitamin B12, and vitamin C in fertility supplements for men and women using continuous wavelet transform (CWT) and partial least squares (PLS) techniques without using time-consuming extraction process and organic solvents. In the CWT method, the zero-crossing technique was applied to obtain the optimum points for plotting calibration curves for each component. The validation of both methods was evaluated by analyzing several mixtures with different concentrations. The efficiency of the proposed methods was also surveyed on commercial capsules. Wavelet families, including Symlet (sym2) at 230, Biorthogonal (bior1.3) at 378 nm, and Daubechies (db2) at 261, were considered for vitamins B6, B12, and C, respectively. The linear range was found to be 8-20, 8-20, and 10-25 μg/mL with the coefficient of determination (R2) equal to 0.9982, 0.9978, and 0.9701 for B6, B12, and C, respectively. Low limit of detection (LOD) (<0.09 μg/mL) and limit of quantification (LOQ) <0.9 μg/mL were achieved. The mean recovery values in synthetic mixtures were from 98.38 to 98.89% and from 99.83 to 99.99%, where root-mean-square error (RMSE) of not more than 0.4 and 0.05 using the CWT and PLS methods, respectively. The obtained results from the commercial capsules, applying the suggested techniques, were compared to those yielded by the high-performance liquid chromatography (HPLC) method using the analysis of variance (ANOVA) test. According to the results, there are no significant differences, and they were in good agreement. According to all the mentioned cases, the proposed approaches can replace the time-consuming and costly HPLC method in quality control laboratories. Green spectrophotometry coupling chemometrics methods were proposed. Simultaneous determination of three water-soluble vitamins in fertility supplements was done using these approaches. Rapidity, simplicity, low cost, and accuracy are the benefits of the proposed methods. A HPLC technique was used as a reference method to compare with the chemometrics methods.

Intelligent Fault Diagnosis Using Deep Learning for a Microgrid with High Penetration of Renewable Energy Sources

—Fault diagnosis is essential to attain the microgrid’s stable, reliable, and economical operation. However, a microgrid’s fault current varies drastically based on the connected operating mode and connected renewable energy sources. This paper develops an intelligent fault detection and identification scheme for microgrids with high renewable energy penetration by combining Fourier-based Continuous Wavelet Transform (FTCWT) and Deep Learning (DL) methods. Several time-domain case studies representing different microgrid operating conditions were obtained and pre-processed in MATLAB/SIMULINK. The time-series data at the relay location are processed using an FTCWT to get time-frequency representation, and the sequence components waveforms were computed. The time-frequency representation and sequence components waveform contain helpful information, including fault conditions and healthy operating conditions for grid-connected and islanded microgrids that act as input datasets to the DL models. The proposed DL models perform fault location identification, fault type identification, and fault phase-detection for different operating modes. The development of the DL model for the fault detection/classification unit and fault location identification unit is carried out in the PyTorch framework. The experimental results show that the proposed model is more accurate when compared to the state-of-the-art methods.

COVID-19 and human development: An approach for classification of HDI with deep CNN

The measures taken during the pandemic have had lasting effects on people's lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people's behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Convolutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).

FPGA-Based Hardware Accelerator on Portable Equipment for EEG Signal Patterns Recognition

Electroencephalogram (EEG) is a recording of comprehensive reflection of physiological brain activities. Because of many reasons, however, including noises of heartbeat artifacts and muscular movements, there are complex challenges for efficient EEG signal classification. The Convolutional Neural Networks (CNN) is considered a promising tool for extracting data features. A deep neural network can detect the deeper-level features with a multilayer through nonlinear mapping. However, there are few viable deep learning algorithms applied to BCI systems. This study proposes a more effective acquisition and processing HW-SW method for EEG biosignal. First, we use a consumer-grade EEG acquisition device to record EEG signals. Short-time Fourier transform (STFT) and Continuous Wavelet Transform (CWT) methods will be used for data preprocessing. Compared with other algorithms, the CWT-CNN algorithm shows a better classification accuracy. The research result shows that the best classification accuracy of the CWT-CNN algorithm is 91.65%. On the other side, CNN inference requires many convolution operations. We further propose a lightweight CNN inference hardware accelerator framework to speed up inference calculation, and we verify and evaluate its performance. The proposed framework performs network tasks quickly and precisely while using less logical resources on the PYNQ-Z2 FPGA development board.

A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods

Bearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today?s machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time savings and high success rates. Deep transfer learning models also improve diagnosis accuracy under certain conditions by greatly reducing human intervention. Diagnosis at the right time is very important for the sustainability and efficiency of industrial production. A technique based on continuous wavelet transform (CWT) and two dimensional (2D) convolutional neural networks (CNN) is presented in this paper to detect fault size from vibration data of various bearing failure types. Time-frequency (TF) color scalogram images for bearing vibration signals were obtained using the CWT method. Using AlexNet, GoogleNet, Resnet, VGG16, and VGG19 deep transfer learning methods with scalogram images, fault size prediction from vibration signals was performed. Five different transfer deep learning models were used for three different data sets. It was observed that the success rates obtained varied between 96.67% and 100%.

Fast custom wavelet analysis technique for single molecule detection and identification

Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments.

Lithium industry and the U.S. crude oil prices. A fractional cointegration VAR and a Continuous Wavelet Transform analysis

This paper analyzes the dynamics of U.S. lithium mining companies, the lithium industry and West Texas Intermediate (WTI) crude oil prices using a Fractional Cointegration Vector AutoRegressive model (FCVAR model) and a Continuous Wavelet Transform (CWT) for its resolution. The results indicate evidence of a negative relationship between FMC Corp with Albermale and SQM stock prices. These results are similar if we analyze the risk based on the beta term structure of each company. Analyzing the fractional differencing parameter for the stock prices and their logs, we observe that they are very persistent, and there are no long-term deviations in the stock prices. The same happens when analyzing the beta term structure. Based on Continuous Wavelet Transform (CWT) methods, our results show that lithium mining companies and the lithium industry are weakly correlated with WTI crude oil prices at higher frequencies (short-run) and persist through the sample period. At lower frequencies (long-term) the time series reached a high level of dependence between late 2012 to mid 2016, concluding that the lithium mining companies and the lithium industry reflect and foreshadow the responsiveness of the WTI crude oil prices during the period mentioned above.

Rapid simultaneous spectrophotometric determination of acetaminophen, phenylephrine, and guaifenesin in a cold syrup formulation based on continuous wavelet transform and first derivative transform methods

In this study, simultaneous determination of acetaminophen (ACT), phenylephrine (PHE), and guaifenesin (GUA) in synthetic mixtures and syrup formulation was performed using spectrophotometry technique along with continuous wavelet transform (CWT) and derivative transform (DT). Among various wavelet families, Daubechies (Db2), Symlet (Sym4), and Coiflet (Coif2), were selected to analyze ACT, PHE, and GUA, respectively. Mean recovery and root mean square error (RMSE) of CWT method were found 100.19 %, 98.96 %, 95.35 % and 0.00023, 0.0026, 0.0031 for ACT, PHE, and GUA, respectively. For DT method, the first derivative absorption spectra were chosen for determining ACT, PHE, and GUA at 237, 224, and 219 nm, respectively. Mean recovery values were achieved between 97.15 % and 100.35 %. Also, limit of detection (LOD) and limit of quantification (LOQ) were obtained between 0.0138−0.1507, 0.0012−0.0123 μg ml−1 and 0.0695−0.7786, 0.0025−0.0767 μg ml−1 for CWT and DT methods, respectively. These proposed procedures were compared with the high-performance liquid chromatography (HPLC) as a reference technique via analysis of variance (ANOVA). The results did not show a significant difference between the methods. The suggested methods are simple and cost-effective for the quantitative assessment of three-component mixture.

Improved spectral resolution for the rapid simultaneous spectrophotometric determination of sofosbuvir and daclatasvir as anti hepatitis C virus drugs in pharmaceutical formulation and biological fluid using continuous wavelet and derivative transform

In this study, the simultaneous spectrophotometric estimation of Sofosbuvir (SOF) and Daclatasvir (DAC) in synthetic mixtures and tablet formulation in the presence of overlapping spectra was performed based on continuous wavelet transform (CWT) and derivative spectrophotometry (DS) methods without any separation process. The Coiflet (Coif2) and Daubechies (Db3) wavelet families with wavelength of 256 nm and 218 nm were obtained as the best families for the simultaneous determination of SOF and DAC, respectively. Also, the first derivative absorption spectra revealed the best results corresponding to the analysis of SOF and DAC at 237 nm and 291 nm, respectively. The ranges of limit of detection (LOD) and limit of quantitation (LOQ) related to the CWT and DS methods were 2.45 × 10-3 to 0.5054 and 6.91 × 10-3 to 0.6027, respectively. Mean recovery values of SOF and DAC in synthetic mixtures for CWT approach were 98.55%, 98.09% and in DS method were 98.78% and 95.83%, respectively. Real samples, including Sovodak tablet and urine was used for accurate simultaneous determination of the mentioned components. Analyzing Sovodak tablet was implemented using high-performance liquid chromatography (HPLC) as a reference method that the results were near to the CWT and DS methods. In order to investigate the existence of significant differences between the methods, analysis of variance (ANOVA) test at the 95% confidence level was performed but no significant differences were observed. In addition, the amounts of SOF and DAC in the complex matrix of biological sample were well predicted by the proposed methods.

Application of UV/vis spectrophotometry based on using least squares support vector machine and continuous wavelet transform methods for the simultaneous analysis of antibiotics drugs in tablet formulation: Comparison with HPLC method

In this study, chemotremic methods with the spectrophotometric method was used for simultaneous determination of Trimethoprim (TMP) and Sulfamethoxazole (SMZ) in Co-trimoxazole tablet without the need separation steps. The proposed methods included the least squares support vector machine (LS-SVM) and continuous wavelet transform (CWT). Modeling of suggested methods was tested by synthetic mixtures containing different concentrations of TMP and SMZ. Root mean square error (RMSE) of the LS-SVM model was 0.471 and 0.147 for TMP and SMZ, respectively. On the other hand, in the CWT method, different families of wavelets were studied. The Daubechies (Db2) and Biortogonal (rbio2.2) wavelet families were selected to analyze TMP and SMZ, respectively. In this method, the mean recovery values related to the synthetic mixtures were 99.69 % and 98.15 % for TMP and SMZ, respectively. The obtained results from analyzing the real sample (Co-trimoxazole) by the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One-way ANOVA test at the 95 % confidence level was applied to the results. The data showed that there were no significant differences between the proposed and the reference methods.

Transient simulation on closure of wicket gates in a high-head Francis-type reversible turbine operating in pump mode

To solve the problem of grid instabilities, regulation in the Pumped Hydro Energy Storage (PHES) plants should quickly respond to the variation of electricity produced by unpredictable renewable energy. In this paper, a power reduction scenario applied to a pump turbine of a PHES is simulated considering the transient closure process of wicket gate. A novel dynamic mesh technique is applied to simulate the rotation of wicket gate vanes from best efficiency point to shutdown condition. Detached Eddy Simulation (DES) turbulence model is utilized to capture complex unsteady flow and the water weak compressibility effect is considered in the transient simulation. Flow rate, torque, power and pressure are analysed by the Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) methods. The results illustrate the delay between the performance parameters flow rate and power and the wicket gate opening angle. The closure of wicket gates affects the flow characteristics downstream the wicket gates greatly, causing intensive pressure fluctuations. The magnitude of pressure fluctuations downstream the wicket gate becomes the highest with the wicket gate closure of about 60%. Aside the blade passage frequency, a low frequency occurs, with the appearance of unsteady flow in pump turbine. Moreover, strong torque pulsations occur on the pin of the wicket vane when the percentage of closure is between 60% and 80%, with peaks much higher than that at the best efficiency point. The transient results can provide meaningful reference to the regulation law of wicket gate for safe operation of the pump turbine.

Land subsidence lagging quantification in the main exploration aquifer layers in Beijing plain, China

Land subsidence is rapidly developing across the Beijing Plain, China. Long-term intense overexploitation of groundwater is the main reason for land subsidence in Beijing. In this study, an optimized Small Baseline Subset (SBAS) interferometry method was developed to process 46 RADATSAT-2 images from 2011 to 2015 to investigate the spatial and temporal dynamics of land subsidence in the Beijing Plain. The lag time between land subsidence and groundwater exploitation was first analyzed by the Continuous Wavelet Transform (CWT) and Cross Wavelet Transform (XWT) methods Our study found that the maximum subsidence rate reached 141 mm per year. The analysis of the areas and volumes of the annual subsidence rates indicated that the overall deformation trend slowed down from 2011 to 2015. Our results indicate that the subsidence center is always located in the southeast of Chaoyang District from 2011 to 2015. The lag time between the observed subsidence and the groundwater level drops in the main exploration aquifer layers was 0.57–1.76 months. This information is helpful to reveal the mechanism of land subsidence and build hydrogeological model.

Hydrological Processes in the Huaihe River Basin, China: Seasonal Variations, Causes and Implications

Understanding streamflow changes in terms of trends and periodicities and relevant causes is the first step into scientific management of water resources in a changing environment. In this study, monthly streamflow variations were analyzed using Modified Mann-Kendall (MM-K) trend test and Continuous Wavelet Transform (CWT) methods at 9 hydrological stations in the Huaihe River Basin. It was found that: 1) streamflow mainly occurs during May to September, accounting for 70.4% of the annual total streamflowamount with Cv values between 0.16–0.85 and extremum ratio values between 1.70–23.90; 2) decreased streamflow can be observed in the Huaihe River Basin and significant decreased streamflow can be detected during April and May, which should be the results of precipitation change and increased irrigation demand; 3) significant periods of 2–4 yr were detected during the 1960s, the 1980s and the 2000s. Different periods were found at stations concentrated within certain regions implying periods of streamflow were caused by different influencing factors for specific regions; 4) Pacific Decadal Oscillation (PDO) has the most significant impacts on monthly streamflow mainly during June. Besides, Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO) and the Nino3.4 Sea Surface Temperature (Nino3.4) have impacts on monthly streamflow with three months lags, and was less significant in time lag of six months. Identification of critical climatic factors having impacts on streamflow changes can help to predict monthly streamflow changes using climatic factors as explanatory variables. These findings were well corroborated by results concerning impacts of El Nino-Southern Oscillation (ENSO) regimes on precipitation events across the Huaihe River Basin. The results of this study can provide theoretical background for basin-scale management of water resources and agricultural irrigation.

Application of wavelet and Fuorier transforms as powerful alternatives for derivative spectrophotometry in analysis of binary mixtures: A comparative study

Two signal processing methods, namely, Continuous Wavelet Transform (CWT) and the second was Discrete Fourier Transform (DFT) were introduced as alternatives to the classical Derivative Spectrophotometry (DS) in analysis of binary mixtures. To show the advantages of these methods, a comparative study was performed on a binary mixture of Naltrexone (NTX) and Bupropion (BUP). The methods were compared by analyzing laboratory prepared mixtures of the two drugs. By comparing performance of the three methods, it was proved that CWT and DFT methods are more efficient and advantageous in analysis of mixtures with overlapped spectra than DS. The three signal processing methods were adopted for the quantification of NTX and BUP in pure and tablet forms. The adopted methods were validated according to the ICH guideline where accuracy, precision and specificity were found to be within appropriate limits.