Morlet Wavelet Research Articles

A novel kernel ridge grey system model with generalized Morlet wavelet and its application in forecasting natural gas production and consumption

Natural gas production (NGP) and consumption (NGC) always exhibit high nonlinearity, posing challenges for accurate small-sample forecasting. In this work, a novel kernel ridge grey system model with an extended parametric Morlet wavelet (GMW-KRGM) is proposed by integrating the kernel ridge regularization and grey system modelling within a partially linear regression framework and trained by the conjugate gradient method to mitigate the ill-posed problem. Besides, a weighted multi-objective optimization strategy is designed for model hyperparameter optimization and solved by the grey wolf optimizer (GWO). Six real-world NGP and NGC forecasting cases are carried out and empirical results demonstrate that the proposed GMW-KRGM model with optimal hyperparameters solved by GWO always yields superior forecasting performance than the other 2 machine learning models and 7 conventional grey system benchmarks with out-of-sample mean average percentage error (MAPE) improved in 7.4245%–91.8392% and 14.7303%–42.67% on average, respectively and yields more precise forecasting accuracy with fast and stable convergence than the other 5 optimization algorithms with improved MAPE range from 9.5608% to 48.2584%, indicating that the proposed model holds the capability to effectively deal with the nonlinear complex system and has great potential in nonlinear small sample forecasting.

Spanwise correlation of unsteady lift due to vortex shedding on the square cylinder: Perspectives provided by a time–frequency analysis

Spanwise correlation of unsteady lift due to vortex shedding is of particular interest for slender structures. In this paper, Hilbert transform and Wavelet transform were applied as time–frequency analysis tools to the sectional lift of a square cylinder. For Wavelet transform, the complex Morlet wavelet and the generalized Morse wavelet were considered as the mother wavelets, and the synchrosqueezing technique was used for post-processing. At high Reynolds number and in smooth flow, Hilbert transform and Wavelet transform mutually confirm that vortex shedding is characterized by an instantaneous shedding frequency fluctuating in real-time. As an example, at Reynolds number 3.9×104, the results of Wavelet transform suggest a standard deviation of the instantaneous shedding frequency of at least 4% about the conventional Strouhal frequency. The extracted instantaneous frequency is found stochastic, showing reasonably good Gaussian properties for its probability density function. The instantaneous frequencies simultaneously extracted at two cross sections of a spanwise separation are correlated, being the correlation coefficient lower than that calculated with fluctuating lifts. The shape of the coherence of the instantaneous frequency is found quite similar to that of turbulence, and the magnitude overall decreases with the increase of spanwise separation. The fluctuation of the instantaneous vortex shedding frequency, especially the low-frequency part (lower than about one-half of the Strouhal frequency), is believed critical in determining the spanwise correlation of unsteady lift, which was confirmed reconstructable via the extracted instantaneous shedding frequency. Finally, attention in this paper has also been paid to a reliable extraction and quantification of the concerning time–frequency characteristics. For instance, tests of the used time–frequency tools first on the purposely prepared artificial signal suggest, that the high-frequency fluctuation in the instantaneous vortex shedding frequency (higher than the Strouhal frequency) can actually not be reliably extracted.

A machine learning approach combined with wavelet analysis for automatic detection of Pc5 geomagnetic pulsations observed at geostationary orbits

Pc5 geomagnetic pulsations are ultra-low frequency (ULF) waves whose period lies within 150–600 s. Their detection is crucial for accurate space weather modelling, monitoring, and analysis. Identification of these pulsations using manual approaches is difficult and time-consuming because of the smaller magnitudes and the limited durations within which they occur. To overcome this challenge, we propose a robust Cascade Forward Neural Network (CFNN) model combined with the wavelet technique for automatically detecting Pc5 events from satellite datasets observed at geostationary orbits. The dataset used in this study is the magnetic field vector measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) from 2000 to 2009. Pc5 geomagnetic pulsations were extracted from the toroidal component of the field perturbation using a bandpass Butterworth filter. Continuous wavelet transform (CWT) analysis using the mother Morlet wavelet was utilized to validate the integrity of the extracted signal in the time–frequency domain. The extracted Pc5 signal was decomposed into details and approximations using Daubechies wavelet transform to separate the intelligible signal from the incoherent noise that left their trace on the magnetic field time series. The detail signal was subjected to denoising using the heuristic Stein Unbiased Risk Estimate (SURE) approach with soft thresholding to obtain the denoised Pc5 events. The denoised Pc5 events were utilized as the target in the machine learning whereas the inputs were obtained from 5-dimensional space transformation of the toroidal component of the time series. The developed algorithm performed well and demonstrated a detection accuracy of 80 % and a Root Mean Square Error (RMSE) of 0.13 nT indicating a high performance in detecting Pc5 events. For effective validation of the model, Pc5 events detected by our model were correlated with the Kp index and the amplitude of the Pc5 events observed at geostationary orbit. A good correlation was obtained in both cases, making our model a practical and preferred choice for Pc5 pulsation detection in contrast to conventional frequency analysis tools which take much time for signal processing on large data sets.

Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model

In this study, a design of Morlet wavelet neural networks (MWNNs) is presented to solve the prediction differential model (PDM) by applying the global approximation capability of a genetic algorithm (GA) and local quick interior-point algorithm scheme (IPAS), i.e., MWNN-GAIPAS. The famous and historical PDM is known as a variant of the functional differential system that works as theopposite of the delay differential models. A fitness function is constructed by using the mean square error and optimized through the GA-IPAS for solving the PDM. Three PDM examples have been presented numerically to check the authenticity of the MWNN-GAIPAS. For the perfection of the designed MWNN-GAIPAS, the comparability of the obtained outputs and exact results is performed. Moreover, the neuron analysis is performed by taking 3, 10, and 20 neurons. The statistical observations have been performed to authenticate the reliability of the MWNN-GAIPAS for solving the PDM.

Exploring the performance of implicit neural representations for brain image registration

Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, we explore the efficacy of implicit neural representations (INRs) in improving the performance of brain image registration in magnetic resonance imaging. In this setting, INRs serve as a continuous and coordinate based approximation of the deformation field obtained through a multi-layer perceptron. Previous research has demonstrated that sinusoidal representation networks (SIRENs) surpass ReLU models in performance. In this study, we first broaden the range of activation functions to further investigate the registration performance of implicit networks equipped with activation functions that exhibit diverse oscillatory properties. Specifically, in addition to the SIRENs and ReLU, we evaluate activation functions based on snake, sine+, chirp and Morlet wavelet functions. Second, we conduct experiments to relate the hyper-parameters of the models to registration performance. Third, we propose and assess various techniques, including cycle consistency loss, ensembles and cascades of implicit networks, as well as a combined image fusion and registration objective, to enhance the performance of implicit registration networks beyond the standard approach. The investigated implicit methods are compared to the VoxelMorph convolutional neural network and to the symmetric image normalization (SyN) registration algorithm from the Advanced Normalization Tools (ANTs). Our findings not only highlight the remarkable capabilities of implicit networks in addressing pairwise image registration challenges, but also showcase their potential as a powerful and versatile off-the-shelf tool in the fields of neuroscience and radiology.

A novel deep learning based intrusion detection system for the IoT-Cloud platform with blockchain and data encryption mechanisms

The Internet of Things (IoT) integrated Cloud (IoT-Cloud) has gotten much attention in the past decade. This technology’s rapid growth makes it even more critical. As a result, it has become critical to protect data from attackers to maintain its integrity, confidentiality, protection, privacy, and the procedures required to handle it. Existing methods for detecting network anomalies are typically based on traditional machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and so on. Although these methods can produce some outstanding results, they have low accuracy and rely heavily on manual traffic feature design, which has become obsolete in the age of big data. To overcome such drawbacks in intrusion detection (ID), this paper proposes a new deep learning (DL) model namely Morlet Wavelet Kernel Function included Long Short-Term Memory (MWKF-LSTM), to recognize the intrusions in the IoT-Cloud environment. Initially, to maintain a user’s privacy in the network, the SHA-512 hashing mechanism incorporated a blockchain authentication (SHABA) model is developed that checks the authenticity of every device/user in the network for data uploading in the cloud. After successful authentication, the data is transmitted to the cloud through various gateways. Then the intrusion detection system (IDS) using MWKF-LSTM is implemented to identify the type of intrusions present in the received IoT data. The MWKF-LSTM classifier comes up with the Differential Evaluation based Dragonfly Algorithm (DEDFA) optimal feature selection (FS) model for increasing the performance of the classification. After ID, the non-attacked data is encrypted and stored in the cloud securely utilizing Enhanced Elliptical Curve Cryptography (E2CC) mechanism. Finally, in the data retrieval phase, the user’s authentication is again checked to ensure user privacy and prevent the encrypted data in the cloud from intruders. Simulations and statistical analysis are performed, and the outcomes prove the superior performance of the presented approach over existing models.

Immature event-related alpha dynamics in children compared with the young adults during inhibition shown by day-night stroop task.

Inhibitory control develops gradually from infancy to childhood and improves further during adolescence as the brain matures. Related previous studies showed the indispensable role of task-related alpha power during inhibition both in children and young adults. Nonetheless, none of the studies have been able to investigate the direct differences in brain responses between children and young adults when confronted with a stimulus that should be inhibited. Because, unlike event-related designs, task-related designs involve continuous tasks over a certain period, which precludes the possibility of making such a comparison. Accordingly, by employing event-related design, the present study first time in the literature, aimed to analyze the event-related alpha phase locking and event-related alpha synchronization/ desynchronization to differentiate the inhibitory processes in children compared to young adults. Twenty children between the ages of 6 to 7 years and 20 healthy young adult subjects between the ages of 18 to 30 years were included in the study. Day-night Stroop task was applied to all subjects during 18-channel EEG recordings. Event-related time-frequency analysis was performed with the complex Morlet Wavelet Transform for the alpha frequency band (8-13 Hz). Event related spectral perturbation (ERSP) in three different time windows (0-200 ms, 200-400 ms, 400-600 ms) and Event-related phase locking in the early time window (0-400 ms) was calculated. The children had increased alpha power in early and late time windows but decreased alpha phase locking in the early time windows compared to young adults. There were also topological differences between groups; while young adults had increased alpha phase-locking in frontal and parietal electrode sites, children had increased occipital alpha power and phase locking. The shift in event-related alpha power observed from posterior to anterior regions with age may suggest a progressive maturation of the frontal areas involved in inhibitory processes from childhood to adulthood. The results of the present study showed that children and young adults had different EEG oscillatory dynamics during inhibitory processes at alpha frequency range.

Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy

The worm gearbox is a power transmission system that has various applications in industries. Being vital element of machinery, it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis. The optimal selection of hyperparameters (HPs) of deep learning models plays a significant role in stable performance. Existing methods mainly focused on manual tunning of these parameters, which is a troublesome process and sometimes leads to inaccurate results. Thus, exploring more sophisticated methods to optimize the HPs automatically is important. In this work, a novel optimization, i.e. amended gorilla troop optimization (AGTO), has been proposed to make the convolutional neural network (CNN) adaptive for extracting the features to identify the worm gearbox defects. Initially, the vibration and acoustic signals are converted into 2D images by the Morlet wavelet function. Then, the initial model of CNN is developed by setting hyperparameters. The search space of each HP is identified and optimized by the developed AGTO algorithm. The classification accuracy has been evaluated by AGTO-CNN, which is further validated by the confusion matrix. The performance of the developed model has also been compared with other models. It has been observed that the proposed AGTO not only achieved the highest degree of recognition accuracy i.e. 98.95 % but also achieved the least standard of deviation of 0.2145 than that of other classifiers. The AGTO algorithm is examined on twenty-three classical benchmark functions and the Wilcoxon test which demonstrates the effectiveness and dominance of the developed optimization algorithm. The results obtained suggested that the AGTO-CNN has the highest diagnostic accuracy, more stable while diagnosing the worm gearbox.

Physics-constrained Data-Driven Variational method for discrepancy modeling

A data-driven discrepancy modeling method is presented that variationally embeds measured data in the modeling and analysis framework. The proposed method exploits the variationally derived loss function that is comprised of the residual between the first-principles theory and sensor-based measurements to augment the physics-based model. The method was first developed in the context of linear elasticity (Masud and Goraya, J. Appl. Mech. 89 (11) 2022) wherein the relation between the discrepancy model and the loss terms was derived to show that the data embedding terms behave like residual-based least-squares regression functions. An interpretation of the stabilization tensor as a kernel function was formally established and its role in assimilating a-priori knowledge of the problem in the modeling method was highlighted. The present paper employs linear elastodynamics as a model problem where Data-Driven Variational (DDV) method incorporates high-fidelity data into forward calculations. This results in driving the problem with not only the boundary and initial conditions, but also with the sensor data that is available at only a small subset of the total domain. The effect of the loss function on the time-dependent response of the system is investigated under a variety of loading conditions and model discrepancies. The energy and Morlet wavelet analyses reveal that the problem with embedded data recovers the energy and the fundamental frequency band of the target system. Time histories of strain energy and kinetic energy of a cantilever beam undergoing damped oscillations are recovered by including known data in an undamped model. This highlights the discrepancy modeling attributes of the method under combined effects of parameter and model discrepancy.

Multi-Temporal Variabilities of Extreme Precipitation over Central Asia and Associated Planetary-Scale Climate Modes

Arid- and semi-arid Central Asia is particularly sensitive to climate change. The changes in extreme precipitation in Central Asia stemming from climate warming are the subject of intense debate within the scientific community. This study employed a Morlet wavelet analysis to examine the annual occurrence number of extreme precipitation in Central Asia from May to September during the period of 1951–2005. Their modulating planetary-scale climate modes were identified by using linear regression analysis. Two major scales of the temporal variability were derived: 2–3.9 years and 4–6 years. The dominant variability was a 2–3.9-year scale and was associated with the negative phase of the Polar/Eurasia (POL) pattern. The 4–6-year scale provided a secondary contribution and was closely linked to the negative phase of the North Atlantic Oscillation (NAO). These planetary climate modes acted as precursors of extreme precipitation over Central Asia. The negative phase of POL directly contributed to a negative height anomaly over Central Asia, which was intimately related to extreme precipitation. In contrast, the negative NAO phase possibly manifested as a Rossby wave source, which was subsequently exported to Central Asia through a negative–positive–negative Rossby wave train.

Study of the effectiveness of wavelet genetic programming model for water quality analysis in the Uttar Pradesh region.

Water constitutes an essential part of the earth as it helps in making the environment greener and support life. But water quality and availability are drastically affected by rising water pollution and its poor sanitation. Water gets contaminated due to the excessive use of chemicals by the industries, fertilizers, and pesticides by the farmers. Not only the surface water, groundwater and river water are also getting contaminated. Several published work in Indian context have used different models for the prediction of water quality. Some of them performed poorly due to the presence of irrelevant and missing data in the training samples. Moreover, these studies have assessed water quality on the basis of biochemical oxygen demand (BOD) and coliform and chemical oxygen demand (COD), whereas dissolved oxygen(DO) is one of the most important parameters in terms of water quality assessment as it is considered a key determinant of pollution. Thus, there is a strong need to categorically identify and visualize the DO as one of the key components responsible for deteriorating the quality of water in Indian context. The main objective of this work is to build a wavelet genetic programming (WGP)-based workflow model for the assessment of water quality in 13 rivers of Uttar Pradesh region. WGP model has a unique feature of discarding the redundant and irrelevant data values from the source data. The proposed WGP model has given promising results which can be attributed to two factors: firstly, the novel use of Morlet wavelet in place of the widely popular Db wavelet, as the mother wavelet, and secondly, the use of MICE technique for missing value imputation in the pre-processing stage. The proposed model not only cleans the data but also demonstrates the feasibility of using DO values as one of the prime factors to assess the water quality.

A Study of Drought and Flood Cycles in Xinyang, China, Using the Wavelet Transform and M-K Test

Accurately identifying and predicting droughts can provide local managers with a basis for decision-making. The Xinyang region is prone to droughts and floods, which have a large impact on local agriculture and socio-economics. This paper employs precipitation data from the Xinyang region to provide a scientific basis for drought and flood control measures in this region. The data are first treated with standardized precipitation indices (SPIs) on three-month, six-month, and nine-month time scales. Subsequently, a Morlet wavelet analysis is performed for each of the three time scales analyzed for the SPI. The results show multiple time scales of drought and flood disasters in the Xinyang region. The cycles of drought and flood disasters in the Xinyang region show different fluctuations on different SPI scales. The SPI time series reflect a strong fluctuation period of 17a for drought and flood disasters in the Xinyang region. An analysis of the variance of the wavelet coefficients showed that the first main cycle of drought and flood disasters in the Xinyang region is 7a, and the second and third sub-cycles are 4a and 13a, respectively. We conclude that floods are more frequent than droughts in Xinyang and are more likely to occur from 2017 to 2021, with a subsequent shift to droughts. Local managers should put drought prevention measures in place to deal with droughts after 2021.

Spatiotemporal Characteristics of Drought in Northwest China Based on SPEI Analysis

Drought has a direct impact on regional agricultural production, ecological environment, and economic development. The northwest region of China is an important agricultural production area, but it is also one of the most serious areas of water shortage due to drought and little rain. It is of great significance to make full use of agricultural resources to clarify the temporal and spatial distribution characteristics of the drought regime in Northwest China. Based on the Standardized Precipitation Evapotranspiration Index (SPEI), this paper used the methods of Mann–Kendall non-parameter trend, mutation test, and Morlet wavelet analysis to explore the drought characteristics in Northwest China from 1961 to 2017. The results showed that the spatial distribution of SPEI on annual and seasonal scales differed slightly in different regions, but from northwest to southeast, the distribution was generally wetter to drier. The drought intensity (Sij) had a step-like distribution with a range of 1.14–1.98. Based on Sij analysis, the frequency of drought in Northwest China was moderate, followed by extreme drought, severe drought, and light drought. The inter-annual drought station proportion (Pj) ranged from 7.4% to 84.1%. A total of 25, 18, 7, and 5 years of pan-regional drought, regional drought, partial region drought, and local drought occurred, respectively, based on Pj analysis. Moreover, from the whole study period, the regional drought changes tended to cause humidification to different degrees. The results of Morlet wavelet analysis showed that there were multiple time scales of 33–52, 11–19, and 4–7 years of SPEI in the entire time domain, and dry and wet trends occurred. The results of the present research can provide a reference for the efficient utilization of water resources, drought monitoring and early warning, drought prevention, and drought relief in Northwest China.

Evolution Characteristics of Rainfall and Runoff in the Upper Reaches of Zhang River Basin

It is of great significance to study and analyze the surface water resources and their change trend in the groundwater overexploitation area of the North China Plain, which is of great significance to solve the shortage of water resources in the groundwater overexploitation area of the North China Plain, promote the exploitation of groundwater, and realize the sustainable development of water resources. This paper takes Minyou Irrigation District of Handan City, a typical overexploitation area in the North China Plain, as an example. Based on the measured rainfall and runoff data from 1957 to 2020, the Mann–Kendall trend test, cumulative anomaly method, double cumulative curve method, and Morlet wavelet transform were used to analyze and predict the trend of water resources in the irrigation area and the individual contribution of climate change and human activities to runoff change. The results show that the annual rainfall and annual runoff in the irrigation area have a significant downward trend and significant cyclical changes throughout the study period. In 1977, the annual runoff showed a sudden change, and the average contribution rates of climate change and human activities to its change were 40.55% and 59.46%, respectively. In the future (2020–2035), runoff will remain stable and rainfall will show an increasing trend. The research results can provide scientific reference for the development, utilization, and rational allocation of surface water resources in the groundwater overexploitation area of the North China Plain.

Experimental Study of the Transient Behavior of a Wind Turbine Wake Following Yaw Actuation

Wind tunnel experiments were performed to investigate the response of a wind turbine model immersed in a replicated atmospheric boundary layer to dynamic changes in the yaw angle. Both the flow field in the wake and the operating properties of the turbine, namely its thrust force, torque, and angular velocity, were monitored during repeated yaw maneuvers for a variety of yaw angles. It was observed that the characteristic time scale of the transient experienced by the turbine scalar quantities was one order of magnitude larger than that of the yaw actuation and depended primarily on the inertia of the rotor and the generator. Furthermore, a Morlet wavelet analysis of the thrust signal showed a strong peak at the rotation frequency of the turbine, with the transient emergence of high activity at a lower frequency during the yaw maneuver. The insights provided by the proper orthogonal decomposition analysis performed on the wake velocity data enabled the development of a simple reduced-order model for the transient in the flow field based on the stationary states before and after the yaw maneuver. This model was then further improved to require only the final state, extending its applicability to any arbitrary wind farm as a dynamical surrogate of the farm behavior.

Research and application of wavelet neural network in electrical resistivity imaging inversion

In order to obtain high-quality electrical resistivity imaging (ERI) inversion results, this paper proposes a wavelet-based neural network inversion method. In addition, it is applied to 2D electrical resistivity tomography imaging. Firstly, a sample set suitable for wavelet neural network training is designed to optimize neural network parameters and improve the accuracy of electrical resistivity inversion. Secondly, a hybrid multi-layer wavelet neural network is designed, which uses Mexican-hat and Morlet wavelets as activation functions for different hidden layers, to improve the generalization ability and stability of the network. Finally, the superiority of the WNN is verified through two experiments and applied for inversion in field examples. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results.

Synthetic Light Curve Design for Pulsating Binary Stars to Compare the Efficiency in the Detection of Periodicities

B supergiant stars pulsate in regular and quasi-regular oscillations resulting in intricate light variations that might conceal their binary nature. To discuss possible observational bias in a light curve, we performed a simulation design of a binary star affected by sinusoidal functions emulating pulsation phenomena. The Period04 tool and the WaveletComp package of R were used for this purpose. Thirty-two models were analysed based on a combination of two values on each of the k = 6 variables, such as multiple pulsations, the amplitude of the pulsation, the pulsation frequency, the beating phenomenon, the light-time effect, and regular or quasi-regular periods. These synthetic models, unlike others, consider an ARMA (1, 1) statistical noise, irregular sampling, and a gap of about 4 days. Comparing Morlet wavelet with Fourier methods, we observed that the orbital period and its harmonics were well detected in most cases. Although the Fourier method provided more accurate period detection, the wavelet analysis found it more times. Periods seen with the wavelet method have a shift due to the slightly irregular time scale used. The pulsation period hitting rate depends on the wave amplitude and frequency with respect to eclipse depth and orbital period. None of the methods was able to distinguish accurate periods leading to a beating phenomenon when they were longer than the orbital period, resulting, in both cases, in an intermediate value. When the beating period was shorter, the Fourier analysis found it in all cases except for unsolved quasi-regular periods. Overall, the Morlet wavelet analysis performance was lower than the Fourier analysis. Considering the strengths and disadvantages found in these methods, we recommend using at least two diagnosis tools for a detailed time series data analysis to obtain confident results. Moreover, a fine-tuning of trial periods by applying phase diagrams would be helpful for recovering accurate values. The combined analysis could reduce observational bias in searching binaries using photometric techniques.

Spatiotemporal Variation Characteristics of Reference Evapotranspiration and Relative Moisture Index in Heilongjiang Investigated through Remote Sensing Tools

Reference evapotranspiration (ET0) is one of the significant parameters in agricultural irrigation, especially in Heilongjiang, a big agricultural province in China. In this research, the spatiotemporal variation characteristics of evapotranspiration (ET), relative moisture index (MI) and influencing factors of ET0 in Heilongjiang, which was divided into six ecology districts according to landforms, were analyzed with meteorological data observed over 40 years from 1980 and MOD16 products from 2000 to 2017 using Morlet wavelet analysis and partial correlation analysis. The results indicated that (1) the spatial distribution of ET and PET in Heilongjiang in humid, normal and arid years showed a distribution of being higher in the southwest and lower in the northwest, and higher in the south and lower in the north. The PET was higher than ET from 2002 to 2017, and the difference was small, indicating that the overall moisture in Heilongjiang was sufficient in these years. (2) In the last 40 years, the ET0 increased while the annual MI decreased. The annual minimum of MI in the six regions of Heilongjiang was −0.25, showing that all six regions were drought free. (3) The importance of the meteorological factors affecting ET0 was ranked as average relative humidity > average wind speed > sunshine duration. This research provides scientific guidance for the study of using remote sensing to reverse ET.

The contribution of gamma bursting to spontaneous gamma activity in schizophrenia.

Increased spontaneous gamma (30-100 Hz) activity (SGA) has been reported in the auditory cortex in schizophrenia. This phenomenon has been correlated with psychotic symptoms such as auditory hallucinations and could reflect the dysfunction of NMDA receptors on parvalbumin-expressing inhibitory interneurons. Previous findings are from time-averaged spectra, so it is unknown whether increased spontaneous gamma occurs at a constant level, or rather in bursts. To better understand the dynamical nature of spontaneous gamma activity in schizophrenia, here we examined the contribution of gamma bursting and the slope of the EEG spectrum to this phenomenon. The main results from this data set were previously reported. Participants were 24 healthy control participants (HC) and 24 matched participants with schizophrenia (SZ). The data were from EEG recordings during auditory steady-state stimulation, which were localized to bilateral pairs of dipoles in auditory cortex. Time-frequency analysis was performed using Morlet wavelets. Oscillation bursts in the gamma range were defined as periods during which power exceeded 2 standard deviations above the trial-wide average value for at least one cycle. We extracted the burst parameters power, count, and area, as well as non-burst trial power and spectral slope. Gamma burst power and non-burst trial power were greater in SZ than HC, but burst count and area did not differ. Spectral slope was less negative in SZ than HC. Regression modeling found that gamma burst power alone best predicted SGA for both HC and SZ (> = 90% of variance), while spectral slope made a small contribution and non-burst trial power did not influence SGA. Increased SGA in the auditory cortex in schizophrenia is accounted for by increased power within gamma bursts, rather than a tonic increase in gamma-range activity, or a shift in spectral slope. Further research will be necessary to determine if these measures reflect different network mechanisms. We propose that increased gamma burst power is the main component of increased SGA in SZ and could reflect abnormally increased plasticity in cortical circuits due to enhanced plasticity of synapses on parvalbumin-expressing inhibitory interneurons. Thus, increased gamma burst power may be involved in producing psychotic symptoms and cognitive dysfunction.

Application of two dimensional Morlet wavelet transform in damage detection for composite laminates

Wavelet transform as a novel signal processing method for singularity detection has been introduced into vibration-based nondestructive testing (NDT) methods. However, for plate-like structures, the mode-shape-based NDT methods depend on the powerful two-dimensional (2D) singularity detection method. The 2D continuous wavelet transform (CWT) based on 2D Morlet wavelet function has high sensitivity to singularity in certain direction, but its anisotropic behavior strongly limits its application in damage detection for plate-like structures. In this paper, a modified 2D Morlet wavelet function is proposed to make the 2D CWT behave like a quasi-isotropic transform and show high sensitivity to damage in all directions. After a series of numerical simulations are conducted to investigate the performance of the modified 2D Morlet wavelet function and verify its effectivity in damage detection, a damage identification procedure for composite laminates is experimentally conducted and damage index (DI) is also determined by the modified wavelet coefficient. Results indicate that the 2D CWT based on the modified 2D Morlet wavelet can provide a baseline-free NDT method based on MS, and its effectivity and robustness are experimentally confirmed.