Maximum Correlation Research Articles

Revisiting the Evaluation for Chinese Grammatical Error Correction

English grammar error correction (GEC) has been a popular topic over the past decade. The appropriateness of automatic evaluations, e.g., the combination of metrics and reference types, has been thoroughly studied for English GEC. Yet, such systematic investigations on the Chinese GEC are still insufficient. Specifically, we noticed that two representative Chinese GEC evaluation datasets, namely YACLC and MuCGEC, adopt fluency edits-based references with the automatic evaluation metric, which was designed for minimal edits-based references and differs from the convention of English GEC. However, it is unclear whether such evaluation settings are appropriate. Furthermore, we explored other dimensions of Chinese GEC evaluation, such as the number of references and tokenization granularity, and found that the two datasets exhibit significant differences. We hypothesize that these differences are crucial for Chinese GEC automatic evaluation. Thus, we publish the first human-annotated rankings on Chinese GEC system outputs and conducted an analytical meta-evaluation which discovered that 1) automatic evaluation metrics should match the types of reference; 2) the evaluation performance grows with the number of references, a consistent finding with English GEC, while four is the smallest reference number that empirically shows maximum correlation with human annotators; and 3) the granularity of tokenization has a minor impact, which is however a necessary preprocessing step for Chinese texts. We have made the proposed dataset publicly accessible at https://github.com/wang136906578/RevisitCGEC.

Projection to latent structures regression and its application to Mach–Zehnder interferometer optical fiber sensors for acetone detection

The techniques of multivariate analysis methods as prediction models open a new vision to the analysis of applied data. The method used in this work was projection to latent structures regression, which is a regression method that uses the latent variables generated by calculating the linear relationship between the dependent and independent variables giving them equal importance and thus obtaining the maximum variance and correlation of these variables. This method was applied to optical fiber sensors for acetone detection. It is important to detect acetone because it is a biomarker of diabetes mellitus. The sensor was fabricated with two cascaded long-period fiber gratings (LPFG) with a 515 µm grating period and separated 1 cm to form a Mach–Zehnder interferometer (MZI). Clinical studies for the diagnosis of diabetes are usually invasive, the development of these sensors proposes a new non-invasive option through the detection of acetone in human breath whose concentrations are in the order of 1.25–2.5 ppm. To study the response of the sensors to acetone, different sensing films, such as polydimethylsiloxane (PDMS), polymethyl methacrylate, Apiezon L and Apiezon T were used, which have a good affinity to this compound. Spectral changes in the transmission spectrum of the MZI were obtained due to the modes interference together with an increment of the sensitivity, since the interaction between the acetone concentration and the sensing film provokes a change in the effective index of the cladding, which in turn is amplified by the LPFGs separation. The analysis showed that the best results were obtained for the sensor with PDMS as sensing film, with the lowest limit of detection, 1.7 ppm using 4 latent structures.

Adjusting the significance of daily climate responses in tree-ring proxies

Correlating tree-ring parameters with daily resolved climate data is becoming increasingly common for understanding the complex relationships between tree growth and the surrounding environment. However, with an increased number of calculated correlations, there is an inherent risk of spurious significance. In this study, we present an analysis using synthetic weather and tree-ring data mimicking the statistical properties of ten real-world sites across Europe to quantify the extent to which numerous comparisons may inflate maximum correlations. Comparisons of different tree-ring proxies, considering varying overlapping period lengths and seasons, revealed 95th percentile correlation differences reaching 0.25 by chance. Using synthetic tree-ring chronologies with an assigned non-signal (r = 0.00), spurious correlations can reach statistical significance in over 60% of tests. Correlation inflation is greater when: (1) the climate-proxy relationship is weaker; (2) comparison periods are shorter; and (3) the length of seasonal windows is longer. Autocorrelation in the proxy records does not appear to have a major effect. These findings indicate that caution should be exercised when computing high numbers of correlations with limited observations. We provide tables listing correlation inflations for precipitation- and temperature-sensitive tree-ring chronologies that can inform interpretations of significance.

Capturing spontaneous interactivity: a multi-measure approach to analyzing the dynamics of interpersonal coordination in dance improvisation.

Interpersonal coordination is widely acknowledged as critical to relating with, connecting to, and understanding others, but the underlying mechanisms of this phenomenon are poorly understood. Dance-particularly improvised dance-offers a valuable paradigm for investigating the dynamics of interpersonal coordination due to its inherent ability to connect us. However, conventional approaches to studying coordination often fail to capture the co-creative spontaneity that is intrinsic to such interactions. This study combined multiple measures of interpersonal coordination to detect moments of high coordination between two freely improvising dancers. We applied maximum correlation vectors, normalized Symbolic Transfer Entropy (NSTE), and surveys to analyze the time-varying dynamics of similarity in movement speeds, directed influence, and subjective perception of dancers engaged in an improvisation task. This multi-measure approach offers a means of capturing the interplay between different dimensions of interpersonal coordination. This approach may be used to understand the underlying mechanisms of co-creative social interactions in improvised dance and other forms of spontaneous interactivity.

Can Offset Analgesia Magnitude Provide Additional Information About Endogenous Pain Modulation in People With Knee Osteoarthritis? An Experimental Study.

To investigate the relationship between offset analgesia magnitude and the responsiveness to conditioned pain modulation (CPM), temporal summation of (second) pain (TSP), and clinical pain severity in people with knee osteoarthritis (KOA). Electrical stimuli were applied to 88 participants with KOA to measure offset analgesia at the volar forearm of the dominant hand, and CPM and TSP at the most symptomatic knee and ipsilateral volar wrist. Clinical pain severity was assessed using the pain subscale of the Knee injury and Osteoarthritis Outcome Score (KOOSPAIN). Linear mixed effects models evaluated pain modulatory effects across all tests, and Spearman's partial correlations assessed associations between offset analgesia, CPM, TSP, and KOOSPAIN while accounting for covariates of interest. Participants unable to validly finish all psychophysical tests were excluded from effect and correlation analyses but were evaluated for predictors of non-valid completion using bivariate Stochastic Search Variable Selection. Significant pain modulation was observed across all psychophysical tests (P < 0.05) and no meaningful predictors of non-valid test completion were found. Offset analgesia magnitude did not significantly correlate with CPM, TSP, or KOOSPAIN (p ≥ 0.05), with a maximum partial correlation coefficient of ρ = 0.21. Offset analgesia was not associated with CPM, TSP, or KOOSPAIN in people with KOA. Despite the lack of case-control studies comparing offset analgesia between people with KOA and healthy controls, these findings suggest that offset analgesia may provide information about endogenous pain modulation beyond CPM and TSP, though its clinical translation remains uncertain.

Drought Characteristics and Drought-Induced Effects on Vegetation in Sri Lanka

Understanding the spatiotemporal characteristics of drought and its impacts on vegetation is a timely prerequisite to ensuring agricultural, environmental, and socioeconomic sustainability in Sri Lanka. We investigated the drought characteristics (duration, severity, frequency, and intensity) from 1990 to 2020 by using the Standardized Precipitation Evapotranspiration Index (SPEI) at various timescales and the cumulative and lagged effects on vegetation between 2000 and 2020 across the climatic zones of Sri Lanka (Dry, Wet, and Intermediate). SPEI indexes at 1-, 3-, 6-, 12-, and 24-month scales were used to analyze the drought characteristics. Frequent droughts (~13%) were common in all zones, with a concentration in the Dry zone during the last decade. Drought occurrences mostly ranged from moderate to severe in all zones, with extreme events more common in the Dry zone. This research used SPEI and the Standardized Normalized Difference Vegetation Index (SNDVI) at 0 to 24-month scales to analyze the cumulative and lagged effects of drought on vegetation. Cumulated drought effects and vegetation had maximum correlation coefficient values concentrated in the −0.41–0.98 range in Sri Lanka. Cumulated drought effects affected 40% of Dry and 16% of Intermediate zone vegetation within 1–4 months. The maximum correlation between the lagged drought effect and vegetation SNDVI showed coefficient values from −0.31–0.94 across all zones, and the high correlation areas were primarily distributed in Dry and Intermediate zones. Over 60% of the Dry and Intermediate zones had a lagged drought impact within 0 to 1 month, while 52% of the Wet zone experienced it over 11 months. The resulting dominant shorter timescale responses indicate a higher sensitivity of vegetation to drought in Sri Lanka. The findings of this study provide important insights into possible spatiotemporal changes of droughts and their possible impact on vegetation across climate zones.

Investigation of Lamb wave modes recognition and acoustic emission source localization for steel plate based on golden jackal optimization VMD parameters and CWT

Accurate identification of Lamb wave modes in acoustic emission(AE) signals propagating on steel plates is crucial for precise source localization. In this paper, we propose a novel method that optimizes variational mode decomposition(VMD) parameters using golden jackal optimization(GJO) and identifies Lamb wave modes on steel plates through continuous wavelet transform(CWT). The Hsu-Nielsen source(HNS) is employed as the AE source. The minimum permutation entropy of the AE signal is used as the optimization objective, with GJO adaptively determining the optimal mode number (K) and penalty factor (α) for VMD. The maximum correlation coefficient method is applied to reconstruct the AE waveform, and both A0 and S0 Lamb wave modes are identified in the wavelet time–frequency domain using CWT. Furthermore, an AE source localization algorithm based on the time difference of arrival and the geometric relationship between two sensors is developed, utilizing the group velocity of the S0 mode. The proposed method effectively identifies acoustic wave modes, achieving an average relative localization error of approximately 0.48% for HNS.

Six-degrees-of-freedom pelvic bone monitoring on 2D kV intrafraction images to enable multi-target tracking for locally advanced prostate cancer.

Patients with locally advanced prostate cancer require the prostate and pelvic lymph nodes to be irradiated simultaneously during radiation therapy treatment. However, relative motion between treatment targets decreases dosimetric conformity. Current treatment methods mitigate this error by having large treatment margins and often prioritize the prostate at patient setup at the cost of lymph node coverage. Treatment accuracy can be improved through real-time multi-target adaptation which requires simultaneous motion monitoring of both the prostate and lymph node targets. This study developed and evaluated an intrafraction pelvic bone motion monitoring method as a surrogate for pelvic lymph node displacement to be combined with prostate motion monitoring to enable multi-target six-degrees-of-freedom (6DoF) tracking using 2D kV projections acquired during treatment. A method to monitor pelvic bone translation and rotation was developed and retrospectively applied to images from 20 patients treated in the TROG 15.01 Stereotactic Prostate Ablative Radiotherapy with Kilovoltage Intrafraction Monitoring (KIM) trial. The pelvic motion monitoring method performed template matching to calculate the 6DoF position of the pelvis from 2D kV images. The method first generated a library of digitally reconstructed radiographs (DRRs) for a range of imaging angles and pelvic rotations. The normalized 2D cross-correlations were then calculated for each incoming kV image and a subset of DRRs and the DRR with the maximum correlation coefficient was used to estimate the pelvis translation and rotation. Translation of the pelvis in the unresolved direction was calculated using a 3D Gaussian probability estimation method. Prostate motion was measured using the KIM marker tracking method. The pelvic motion monitoring method was compared to the ground truth obtained from a 6DoF rigid registration of the CBCT and CT. The geometric errors of the pelvic motion monitoring method demonstrated sub-mm and sub-degree accuracy and precision in the translational directions ( , , ) and rotational directions ( , , ). The 3D relative displacement between the prostate and pelvic bones exceeded 2, 3, 5, and 7mm for approximately 66%, 44%, 12%, and 7% of the images. Accurate intrafraction pelvic bone motion monitoring in 6DoF was demonstrated on 2D kV images, providing a necessary tool for real-time multi-target motion-adapted treatment.

Maximal point-polyserial correlation for non-normal random distributions.

We consider the problem of determining the maximum value of the point-polyserial correlation between a random variable with an assigned continuous distribution and an ordinal random variable with categories, which are assigned the first natural values , and arbitrary probabilities . For different parametric distributions, we derive a closed-form formula for the maximal point-polyserial correlation as a function of the and of the distribution's parameters; we devise an algorithm for obtaining its maximum value numerically for any given . These maximum values and the features of the corresponding -point discrete random variables are discussed with respect to the underlying continuous distribution. Furthermore, we prove that if we do not assign the values of the ordinal random variable a priori but instead include them in the optimization problem, this latter approach is equivalent to the optimal quantization problem. In some circumstances, it leads to a significant increase in the maximum value of the point-polyserial correlation. An application to real data exemplifies the main findings. A comparison between the discretization leading to the maximum point-polyserial correlation and those obtained from optimal quantization and moment matching is sketched.

The Impact of Climate Change on Cropping Patterns in the Jurang Sate Hulu Irrigation Area

For many years, the active management of the Jurang Sate Hulu Irrigation Area has provided significant benefits to cropping patterns. However, the impacts of climate change on water availability and temperature variations present critical challenges to crop productivity. Therefore, an analysis of the effects of climate change is essential. The methods employed in the analysis of climate change include correlation and regression methods. This study uses nine variables of global climate change. The correlation method is applied to determine the extent of the influence of global climate variables, represented by Global Circulation Model (GCM) data, on rainfall and climate in the Jurang Sate Hulu Irrigation Area. The regression method is used to validate the prediction model for rainfall from 2024 to 2100. In the cropping pattern analysis, the Penman-Monteith method is used to calculate evapotranspiration, while the F.J. Mock method is used to calculate flow discharge. Based on the analysis, the global climate variables that most significantly influence rainfall in the Jurang Sate Hulu Irrigation Area are air temperature, wind speed, eastward wind, and surface temperature, with maximum correlation values of 0.551, 0.540, 0.589, and 0.625, respectively. In contrast, local climate variables have less influence, as they yield correlation values below 0.500. The regression analysis showed an average validation result for the selected model using two goodness-of-fit parameters: Root Mean Square Error (RMSE) of 124.371 and Nash-Sutcliffe Efficiency (NSE) of 0.403. The highest irrigation water requirement occurs during the third planting period, from 2051 to 2075, at 0.465 m³/s. The planned cropping pattern is paddy-paddy-second crops, with planting areas of 3,807 hectares for the period from 2024 to 2050, 3,595 hectares for the period from 2051 to 2075, and 3,383 hectares for the period from 2076 to 2100, with a total planting intensity of 300% during each period.

Site Selection Decision-Making for Offshore Wind-to-Hydrogen Production Bases Based on the Two-Dimensional Linguistic Cloud Model

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics.

A point cloud segmentation algorithm based on multi-feature training and weighted random forest

Abstract Point cloud segmentation is the process of dividing point cloud data into a series of coherent subsets according to its attributes. It has been widely used in target recognition, digital protection of cultural relics, medical research and other fields. To improve the classification accuracy of point cloud and achieve accurate segmentation of objects or scenes, a point cloud segmentation algorithm based on multi–features training and weighted random forest (RF) is proposed. Firstly, the feature vector composed of 3D coordinate value, RGB value, echo intensity, point cloud density, normal direction and average curvature is used to train the SVM classifier, and the ‘one–to–one’ strategy is adopted to achieve the initial multivariate rough segmentation of point cloud. Then, the maximum information coefficient and sample correlation coefficient (SCC) are used to evaluate the correlation of the decision tree, and the decision tree is weighted accordingly to build a weak correlation weighted RF, so as to achieve further accurate segmentation of the point cloud. The experiment verifies the effectiveness of the proposed algorithm by segmenting the outdoor scene point cloud data model. The results show that the segmentation algorithm based on multi–features training and weighted RF can achieve accurate point cloud segmentation, and is an effective point cloud segmentation method.

Assessment of superior vena cava diameter and collapsibility index in liver transplantation: a prospective observational study

BackgroundSuperior Vena Cava (SVC) diameter and collapsibility index, dynamic measures of fluid responsiveness, have been successfully utilized as echocardiographic indices for fluid responsiveness in ventilated septic patients. Whether these measurements are correlated with Central Venous Pressure (CVP) measurements in liver transplant patients is unknown. We sought to assess the correlation of maximum and minimum SVC diameter and SVC collapsibility index measurements obtained intraoperatively by Transesophageal Echocardiography (TEE) with those of simultaneously recorded CVP measurements obtained through a right atrial port of a pulmonary artery catheter. The secondary aim of the study was to assess the correlation between SVC measurements and simultaneously obtained thermodilution cardiac index measurements. MethodsSingle center prospective observational trial of patients with end stage liver disease undergoing liver transplantation in an academic tertiary care center. ResultsThe minimum SVC exhibited a mild significant correlation with CVP as did the maximum SVC. The correlation between the SVC collapsibility index and CVP was not significantly different from zero. In our secondary analysis, the correlation between minimum SVC diameter and cardiac index was determined to be weak but non-zero as was the correlation between the maximum SVC diameter and cardiac index. The correlation between SVC collapsibility index and cardiac index was not different from zero. ConclusionWhile statistically significant, the weak clinical correlation of intraoperative SVC measurements obtained by TEE make them unsuitable as a replacement for central venous pressure or thermodilution cardiac index measurements in liver transplant recipients.

K-fold matching model for crowd behavioral anomaly detection from discontinuous inputs

Image and associated features detect behavioural anomalies in crowd scenes through matching. Existingmethods use continuous data inputs to track crowd behaviour. As a result, the systemmistakenly assumes normal behaviour during data gaps, leading to false positives or missing detections of small abnormalities. The design of precision-focused matching requires simplified yet robust validation for any number of crowd elements. This article presents a K-fold Matching Model (KFMM) with edge-to-edge correlation. This proposed model identifies the region edges of an input crowd image sequentially. The difference in edges due to crowd misbehaviour activities is validated using random and continuous image features in K number of steps. Considering the complexity of multiple K-steps, the confinement is performed using a neural sigmoid function where the K × K edge matching is reduced. This reduction is based on activity classification i.e. minimum and maximum between two consecutive input sequences. Therefore, as the classification increases, the K is halted with the maximum correlation; contrarily for a low number of classifications, the manifold matching is pursued until the maximum is reached. This correlation count is defined using the neural sigmoid with a linear function for the edges. For the maximum features, this proposed model achieves an 11.21 % high matching ratio, 8.15 % high classifications, 9.71 % high precision, 9.61 % less complexity, and 8.9 % less linear error.

The Correlation analysis between ambient noise level at low-frequency range and wind speed in shallow water

The ambient noise level in the ocean is an important consideration in the enhancement of passive sonar detection performance as well as undersea surveillance in a harbor area. The wind affects considerably the ambient noise level in the ocean. For this reason, many researchers have analyzed the wind dependency of ambient noise levels in the ocean in the last few decades. However, this analysis is mainly limited to more than the frequency of 100Hz, because the ambient noise level below 100 Hz has been affected by sea current. In this study, we have investigated ambient noise levels in the frequency range 10 Hz - 100Hz as the wind speed increases in shallow water. A drifting small vessel is used to exclude the effect of fluid noise generated on the surface of a hydrophone due to the sea current. The correlation between the ambient noise level and wind speed in frequency range 10 - 25 Hz showed from 0.77 to 0.95. Whereas, it was from 0.11 to 0.69 in the frequency range 32 -100 Hz. The maximum correlation appeared at 10 and 16 Hz. This was much higher than those measured in the frequency range 1kHz - 25 kHz.

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

A WRF-UCM-SOLWEIG framework for mapping thermal comfort and quantifying urban climate drivers: Advancing spatial and temporal resolutions at city scale

The capability to evaluate city-scale outdoor thermal comfort is crucial for understanding the public's experience of heat stress, especially during heat waves. Nevertheless, there remains a methodological gap in accurately mapping thermal comfort with high spatial and temporal resolutions in complex urban environment and in quantifying the contributions of various urban climate drivers, such as sky view factors and tree canopy fraction. To bridge this gap, we propose an innovative WRF-UCM-SOLWEIG framework that couples a mesoscale model with an urban microclimate model. Specifically, it integrates the Weather Research and Forecasting model coupled with the urban canopy model (WRF-UCM) and the Solar and Longwave Environmental Irradiance Geometry model (SOLWEIG). This framework is then applied to a densely populated tropical city in China, to quantify the impact of urban climate drivers on the Universal Thermal Climate Index (UTCI). The results reveal a significant diurnal variation in the impact of urban morphology. Notably, the most significant drivers affecting daytime UTCI are the impervious surface fraction and the tree canopy fraction, with maximum Pearson's correlation coefficients of 0.80 and -0.70, respectively. These findings suggest that urban heat mitigation strategies should prioritize on reducing impervious surfaces and enhancing shade management, alongside expanding urban forestry measures.

Application of Diverse Silicon Sources to Soil for Reducing Wheat Blast Disease

Despite the economic potential of wheat for food security, blast disease is one of the major constraints to wheat production in warm and humid areas. Silicon (Si) increases crop yields and resistance to both biotic and abiotic stresses. The effect of various Si sources, including Na2SiO3, MgSiO3, CaSiO3, and rice hull ash (RHA), on suppressing wheat blast (WB) incidence was studied at Bangladesh Institute of Nuclear Agriculture field from November 2019 to March 2020 using a complete randomized design (CRD) with three replications. Total 42 plastic pots were filled with the recommended doses of fertilizers and Si. On 17 days after inoculation (DAI), the lowest WB incidence (83.16%) and severity matrix (80.20%) were found with the treatment of CaSiO3 @ 100 kg ha-1. At 15 DAI, the same treatment reduced the WB incidence and severity matrix by 21.10% and 41.73%, respectively. Most yield indicators were significantly improved by the Si fertilizers, except for grain spike-1 quantity, weight, and thousand grain weight (g). However, pots that were fertilized with Si distinguished out from both the control and absolute control groups. While CaSiO3 at 75 kg ha-1, RHA at 6.0 t ha-1, and MgSiO3 at 100 kg ha-1 produced respectable yields, the maximum was achieved by CaSiO3 at 100 kg ha-1. There was a significant positive relationship between the leaf concentrations of K and Si, with the highest concentration found in plants treated with CaSiO3. A large number of yield metrics showed moderate to strong positive associations with leaf K and Si concentrations, whereas the disease severity matrix revealed the maximum significant negative correlation with these elements. The best outcomes were obtained by applying Si as CaSiO3 @100 kg ha-1, which has potential as an environmentally acceptable source for controlling WB disease and increasing wheat yields. J Bangladesh Agril Univ 22(3): 295–309, 2024

Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

Background: Gear is the key part of the transmission part of mechanical equipment, which has a high failure rate under complex working conditions or long-term operation, directly affecting the reliability of mechanical equipment. To realize online monitoring of gears and predict gear faults, whether the diagnosis of typical gear faults is accurate and timely is a key link, so it is necessary to study the diagnosis of typical gear faults. Objective: This paper aims to propose a method that can effectively diagnose typical faults of gears and is easy to program, which is beneficial to realize online monitoring of rotating machinery equipment faults. Method: A typical gear fault diagnosis method based on GHM multiwavelet transform and maximum correlation kurtosis deconvolution algorithm (MCKD) is proposed in this paper. The measured vibration signal is decomposed into components with multiple scale spaces and frequency bands through the GHM multiwavelet transform. Then, the MCKD algorithm is run to suppress the noise of GHM multiwavelet transform coefficients, and the periodic components containing fault information are retained and reconstructed. The typical faults such as gear pitting and broken teeth are tested. Results: GHM multiwavelet transform combined with MCKD algorithm can accurately locate the frequency band containing fault information from the vibration signal with multiple modulations, double sideband, and multi-frequency interference and effectively extract the fault frequency and its frequency multiplication. The root means a square error of the extracted fault frequency is 0.08. This method has a good noise suppression effect and high accuracy in extracting fault frequency. Conclusion: The method proposed in this paper is helpful in realizing automatic programming of gear fault diagnosis and is of great significance in realizing real-time monitoring and online diagnosis of rotating machinery equipment faults. More related patents will appear in the future.

Spatiotemporal analysis of different vegetation indices and relation to meteorological parameters over a tropical urban location and its surroundings

The paper investigates the long-term spatiotemporal characteristics of various satellite-derived vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), as well as Gross Primary Productivity (GPP) and Sun-induced Chlorophyll Fluorescence (SIF) over the Kolkata conurbation and its surrounding areas from 2003 to 2016. Additionally, it analyzes the correlation between these vegetation indices and atmospheric parameters like rainfall, soil moisture (SM), evapotranspiration (ET), and land surface temperature (LST). Monthly variations of these parameters are observed, and inter-annual variability is examined using linear regression techniques. The study also observes the time average spatial correlation between vegetation indices and weather parameters. Moreover, it investigates the time-lag effect (0, 1, 2, and 3 months) using Pearson correlation coefficient analysis between VI and other meteorological parameters. NDVI and EVI exhibit maximum correlation with rainfall, SM, ET, and LST within specific lag periods. NDVI and EVI show a slow response rate to rainfall, and their sensitivity depends on SM and ET. A positive correlation is observed between NDVI and ET, indicating that NDVI increases with vaporized water in the atmosphere. A negative correlation is noted between NDVI and LST in the region studied. The study’s insights are valuable for predicting future vegetation index characteristics based on meteorological parameters in tropical urban areas like Kolkata and its surroundings. This predictive capability can aid in mitigating adverse weather effects on vegetation.