Double Gaussian Function Research Articles

Different manifestations of a loop-like transient brightening in solar atmospheres

Small-scale transient brightenings that are the consequence of magnetic reconnection play pivotal roles in the heating process of solar atmospheres. These phenomena contain key information about the dynamic evolution of the solar magnetic field. The fine-scale structures triggered by instabilities in these brightenings are intimately connected with the release of magnetic energy. To better understand the conversion and release of magnetic energy in small-scale heating events, we investigated the thermal-dynamical behaviors of a loop-like transient brightening (LTB) with plasma blobs. We used the spectroscopic and slit-jaw imaging observations taken from the Interface Region Imaging Spectrograph and the extreme-ultraviolet images taken from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory to analyze the plasma properties of an LTB that occurred on February 28, 2014. The space-time maps were created to present the spatial evolution of the LTB, and the light curves were calculated to illustrate the heating process. Additionally, we employed the differential emission measure (DEM) method to compute the temperature and emission measure of the LTB. In order to investigate the plasma motion along the line-of-sight direction, a double-Gaussian function was used to fit the Si IV spectral profiles. The spectrum and DEM analysis indicate that the LTB was constituted by multithermal plasma with temperatures reaching up to $5.4 $ K. The space-time maps of the emission and the Gaussian-fitting results of the Si IV line demonstrate that the LTB not only exhibited bidirectional flows, but was also twisted. Several plasma blobs were identified in the spine of the LTB, suggesting the potential presence of a tearing-mode instability. The low-temperature bands peaked approximately one minute prior to the high-temperature bands, suggesting the occurrence of a heating process driven by magnetic reconnection. The appearance of plasma blobs closely coincided with the sudden increase in the velocity and the quick rise of light curves, providing evidence that plasma blobs facilitate the release of magnetic energy during solar activity. Based on these findings, we speculate that the LTB was a complex structure that occurred in the upper chromosphere-transition region. These results clearly demonstrate that plasma blobs are important for the conversion and release processes of magnetic energy.

Formation of submicron conical morphology of the surface of nanometer-thick films of the Al–Fe alloy under various conditions of ion-assisted deposition on glass

The morphology, topography and wetting with distilled water of Al–1.5 at. % Fe alloy films with a thickness 25–90 nm, formed on glass by ion-assisted deposition using a resonance ion source of vacuum electric arc plasma, have been studied. Using scanning probe microscopy, it is shown that, depending on the mode and the time of deposition, the modification of films is accompanied by changes in longitudinal and transverse roughness parameters, as well as parameters – dimensionless complexes, the measurement of which makes it possible to quantitatively describe the processes of coning in the Al–Fe alloy/glass system. Thus, the arithmetic mean roughness of the films increases with deposition time in the range 20–40 nm. Under self-irradiation conditions, a transition from island-like film growth to layer-by-layer film growth was detected. The influence of the substrate topography on the longitudinal step parameters of the film topography has been established. The size and surface density of the microdroplet fraction particles are studied by scanning electron microscopy. The frequency distributions of the microdroplet fraction by size are satisfactorily approximated by the lognormal distribution. It is found that in the mode of irradiation with intrinsic ions, 60–70% of the microparticles have a size of up to 0.8 μm. For the first time, a double Gaussian function was used to approximate the distribution histograms of local maxima and minima of the film relief, which made it possible to increase the accuracy of the description compared to the normal law. The effectiveness of this approach in analyzing the structure formation of nanometer films at various stages of growth has been demonstrated. Using the bi-Gaussian surface model, the role of topographic characteristics in controlling the wetting of modified coatings is revealed. The mechanism of heterogeneous wetting of hydrophilic films in the Cassie mode with contact angles ranging 50°–80° is discussed. It is found that in the potential mode, with an increase in the deposition duration to 10 h, the distribution of the film relief is close to the normal law, and the formation of a developed submicron conical morphology on the surface leads to blended wetting.

Oversampling method via adaptive double weights and Gaussian kernel function for the transformation of unbalanced data in risk assessment of cardiovascular disease

In risk assessment of cardiovascular disease (CVD), the classification error caused by unbalanced data is a significant challenge, which has sparked widespread concern and research upsurge in the field of data mining. Therefore, in view of the imbalance of CVD data sets, an oversampling method via adaptive double weights and Gaussian kernel function (ADWGKFO) is proposed, which converts the unbalanced data sets into balanced data sets. Firstly, clustering algorithm is utilized to cluster minority samples, boundary samples are identified by Borderline-Synthetic Minority Over-sampling Technique (Borderline-SMOTE), K nearest neighbor and support vector machine algorithms, and the number of samples synthesized in each group is calculated based on the double weights of boundary points and majority distribution. Secondly, in order to clearly define the classification boundary, the mutual class potential of new samples in each cluster is calculated by Gaussian kernel function, and new samples are filtered according to the mutual class potential until the data set is balanced. Finally, taking the data sets from Kaggle platform as the research samples, the proposed method is empirically analyzed. In order to validate the efficacy and universality of the proposed method, this paper selects CatBoost that is a new integrated algorithm to test the effect of the ADWGKFO method, and compares it with different sampling methods and different classifiers using performance evaluation indexes such as accuracy, F1-score and area under the curve (AUC). Compared with the combinations of other methods, the accuracy, F1-score and AUC are significantly improved. It is concluded that the ADWGKFO method described in this paper can successfully improve the data quality, and increases the reliability of CVD risk assessment.

Study on radiological environmental impact assessment of nuclear fuel cycle facilities

The radiological assessment model REIA 1.0 is established to assess the radio-logical environmental impact of radioactive airborne effluents released from nuclear fuel cycle facilities. It is based on the double Gaussian probability distribution function model and considers the wind pendulum effect of the long-term diffusion factor. The simulation results of the REIA 1.0 model are compared with those of the AERMOD-VIEW model and radiation environmental monitoring results of nuclear fuel cycle facilities. The results are: using the REIA 1.0 and AERMOD-VIEW models, the relative deviations of simulation results are less than 33.33% in 192 subregions. Except for three points, the relative deviations are less than 20%, the simulated results are consistent with the radiation environmental monitoring results in seven sites using the REIA 1.0 model, and the REIA 1.0 model can characterize the incremental contribution of radionuclide concentration in the air caused by the radioactive airborne effluents from nuclear fuel cycle facilities. It can effectively assess the radiological environmental impact of nuclear fuel cycle facilities.

Target edge extraction for array single-photon lidar based on echo waveform characteristics

The target edge contains the shape and structure information of the target. Accurate extraction of target edges is the basis of target recognition and location. The number of pixels and signal-to-noise ratio severely limit the edge extraction capability of single-photon lidar. In this work, a target edge extraction algorithm for array single-photon lidar is proposed based on the distribution characteristics of the target edge echo waveform. The algorithm does not need to image the target or rely on the fixed direction edge detection template. The target edge is divided into the inside and outside edges based on the established target edge echo signal model of array single-photon lidar. Single and double Gaussian function fitting is used to extract the outside edge whose echo waveform is a double pulse distribution. The feature vector composed of skewness, FWHM, and total detection probability of the echo waveform is weighted K-means clustered to extract the inside edge whose echo waveform is a single pulse distribution. Screening misjudged pixels based on the Jensen-Shannon divergence between echo waveforms of each pixel to improve the edge extraction accuracy. Simulation and experimental results demonstrate that the proposed algorithm can extract target edges more completely and accurately than traditional edge extraction methods. The minimum inside edge depth of the target that the proposed algorithm can extract is cτ/2, where τ is the standard deviation of the emitted Gaussian laser pulse. This research has the potential to improve the target detection and recognition capabilities of array single-photon lidar.

Аппроксимация временных рядов индексов вегетации (NDVI и EVI) для мониторинга сельхозкультур (посевов) Хабаровского края

Approximation of the series of the seasonal vegetation index time series is the basis for monitoring agricultural crops, their identification and cropland classification. For cropland of the Khabarovsk Territory in the period from May to October 2021, NDVI and EVI time series were constructed using Sentinel-2A (20 m) multispectral images using a cloud mask. Five functions were used to approximate time series: Gaussian function; double Gaussian; double sine wave; Fourier series; double logistic. Characteristics of extremums for approximated time series for different types of arable land were built and calculated: buckwheat, perennial grasses, soybeans, fallow and ley. It was shown that each type requires a characteristic species. It was found (p<0.05) that Fourier approximation showed the highest accuracy for NDVI and EVI series (average error, respectively, 8.5% and 16.0%). Approximation of the NDVI series using a double sine, double Gaussian and double logistic function resulted in an error increase of 8.9-10.6%. Approximation of EVI series based on double Gaussian and double sine wave causes an increase in average errors up to 18.3-18.5%. The conducted a posteriori analysis using the Tukey criterion showed that for soybean, fallow and ley lands, it is better to use the Fourier series, double Gaussian or double sine wave to approximate vegetation indices, for buckwheat it is advisable to use the Fourier series or double Gaussian. In general, the average approximation error of the NDVI seasonal time series is 1.5-4 times less than the approximation error of the EVI series.

Denoising and Accuracy Evaluation of ICESat-2/ATLAS Photon Data for Nearshore Waters Based on Improved Local Distance Statistics

The second-generation spaceborne LiDAR-Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which can penetrate a certain depth of water, and is one of the important means to obtain the water depth information of nearshore water. However, due to the influence of the atmospheric environment, water quality and color, the system itself and other factors, the photon point cloud introduces survey noise, which restricts the survey accuracy and reliability of nearshore water depth. Therefore, in this study, we presented a photon denoising algorithm for layered processing of submarine surface. Firstly, rough denoising of the original photon data was completed by smoothing filtering. Then, elevation histogram statistics were carried out on the photon data, two peaks of the histogram were fitted by a double Gaussian function, and the intersection of two curves was then taken to separate the water surface and underwater photons. The surface photons were denoised by the DBSCAN clustering algorithm. Then according to the distribution characteristics of underwater signal photons, a single-photon point cloud filtering bathymetric method was proposed based on improved local distance statistics (LDSBM), which was used for fine denoising of underwater point cloud data. Finally, the Gaussian function was used to fit the frequency histogram, and the signal photons were screened to extract the water depth information. In this study, 13 groups of the ATL03 dataset from the Xisha Islands, the St. Thomas and the Acklins Island were used for denoising. The denoising results were compared with the signal photons manually marked and the signal photons extracted by the official built-in method (OM). The experimental results showed that, compared with the official method results of ATL03, the LDSBM had a higher F value (comprehensive evaluation index), with an average of more than 96.70%. In conclusion, the proposed underwater single-photon point cloud filtering bathymetric method was superior to the traditional algorithm and could recover terrain information accurately.

Separating selenium species by diffusion in Brazilian bentonite: a mathematical modeling approach

Bentonite was applied in diffusive studies for selenium, an emerging contaminant. The planar source method was used to determine the apparent and effective diffusion coefficients and assess the mobility of the selenium species. A double Gaussian function described the results. Different diffusion coefficients were associated with different mobilities, and consequently, to the coexistence of two selenium species: selenite and selenate. Apparent diffusion coefficients were higher for selenate, around 10- 10 m2 s- 1, than for selenite, around 10- 12 m2 s- 1. Results from sequential extraction and distribution coefficient justified selenate's greater mobility than selenite. Since the increase in redox potential from 448 to 511 mV may be associated with selenite oxidation in an interconversion process, the diffusion in bentonite demonstrates that applications in geological barriers deserve attention regarding the mobilization of selenium species. Interconversions can mobilize selenium, as reduced species can shift to more oxidized and mobile species, enhancing environmental contamination.

Reconstruction of Vegetation Index Time Series Based on Self-Weighting Function Fitting from Curve Features

Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used effective noise reduction technique for VI time series, but it is vulnerable to noise. Thus, ancillary data about VI quality are utilized to alleviate the interference of noise. However, this approach is limited by the availability, accuracy, and application rules of ancillary data. In this paper, we aimed to develop a new reconstruction method that does not require ancillary data. Based on the assumptions that VI time series follow the gradual growth and decline pattern of vegetation dynamics, and that clouds or poor atmospheric conditions usually depress VI values, we proposed a reconstruction method for VI time series based on self-weighting function fitting from curve features (SWCF). SWCF consists of two major procedures: (1) determining a fitting weight for each VI point based on the curve features of the VI time series and (2) implementing the weighted function fitting to reconstruct the VI time series. The double logistic function, double Gaussian function, and polynomial function were tested in SWCF based on a simulated dataset. The results indicate that the weighted function fitting with SWCF outperformed the corresponding unweighted function fitting with the root-mean-square error (RMSE) significantly reduced by 26.82–52.44% (p < 0.05), and it also outperformed the Savitzky–Golay filtering with the RMSE significantly reduced by 13.98–54.04% (p < 0.05) for 270 sample points selected in mid-high latitudes of the Northern Hemisphere. Moreover, SWCF showed excellent robustness and applicability in regional applications.

Stable Aqueous Colloidal Solutions of Nd3+: LaF3 Nanoparticles, Promising for Luminescent Bioimaging in the Near-Infrared Spectral Range.

Two series of stable aqueous colloidal solutions of Nd3+: LaF3 single-phase well-crystallized nanoparticles (NPs), possessing a fluorcerite structure with different activator concentrations in each series, were synthesized. A hydrothermal method involving microwave-assisted heating (HTMW) in two Berghof speedwave devices equipped with one magnetron (type I) or two magnetrons (type II) was used. The average sizes of NPs are 15.4 ± 6 nm (type I) and 21 ± 7 nm (type II). Both types of NPs have a size distribution that is well described by a double Gaussian function. The fluorescence kinetics of the 4F3/2 level of the Nd3+ ion for NPs of both types, in contrast to a similar bulk crystal, demonstrates a luminescence quenching associated not only with Nd–Nd self-quenching, but also with an additional Nd–OH quenching. A method has been developed for determining the spontaneous radiative lifetime of the excited state of a dopant ion, with the significant contribution of the luminescence quenching caused by the presence of the impurity OH– acceptors located in the bulk of NPs. The relative quantum yield of fluorescence and the fluorescence brightness of an aqueous colloidal solution of type II NPs with an optimal concentration of Nd3+ are only 2.5 times lower than those of analogous Nd3+: LaF3 single crystals.

Recent advancements in the use of remote sensing observations to inform ground level pollen dynamics

BACKGROUND AND AIM: Pollen information (e.g., pollen seasons and concentrations) is highly needed for allergy disease studies. However, in-situ pollen data from stations are still limited. Satellite data with repeated observations and global coverage provide the possibility to inform ground pollen dynamics over large areas. This study aims to improve the understanding of the relationship between vegetation phenology and pollen season, for its potential in developing fine spatiotemporal pollen maps over large areas. METHODS: We developed a framework to explore the relationship between the onset of vegetation phenology and pollen season. First, we derived pollen indicators using a double-Gaussian function and the collected pollen concentration data at five in-situ pollen stations. Next, we estimated vegetation phenology indicators and their interannual variability using satellite derived vegetation index. Finally, we explored the relationship between the indicators of pollen and phenology, and evaluated the scale effect of the relationship at the five stations during 2001–2015. RESULTS:Although indicators of pollen season (i.e., start of pollen season, SPS) and the vegetation phenology (i.e., start of season, SOS) vary spatially and temporally over stations, they exhibit consistent temporal trends during 2001-2015. SOS derived from finer Landsat satellite observations outperforms that from MODIS observations with a coarse spatial resolution. Our results indicate that satellite-derived SOS can serve as a proxy for pollen seasons in birch dominated areas. CONCLUSIONS:In this study, we explored the relationship between satellite-derived vegetation phenology and station-based pollen season at stations. Our results indicate that satellite-derived phenology can be used as an indicator for pollen dynamics over large areas, and therefore provides the possibility to assess the risk of respiratory allergies, especially in highly heterogeneous urban areas with high spatiotemporal satellite observations. KEYWORDS: Satellite remote sensing, Phenology, Start of Season, Pollen

Widespread occurrence of high-velocity upflows in solar active regions

Aims. We performed a systematic study of 12 active regions (ARs) with a broad range of areas, magnetic fluxes, and associated solar activity in order to determine whether there are upflows present at the AR boundaries and, if these upflows exist, whether there is a high-speed asymmetric blue wing component present in them. Methods. To identify the presence and locations of the AR upflows, we derive relative Doppler velocity maps by fitting a Gaussian function to Hinode/EIS Fe XII 192.394 Å line profiles. To determine whether there is a high-speed asymmetric component present in the AR upflows, we fit a double Gaussian function to the Fe XII 192.394 Å mean spectrum that is computed in a region of interest situated in the AR upflows. Results. Upflows are observed at both the eastern and western boundaries of all ARs in our sample, with average upflow velocities ranging between −5 and −26 km s−1. A blue wing asymmetry is present in every line profile. The intensity ratio between the minor high-speed asymmetric Gaussian component compared to the main component is relatively small for the majority of regions; however, in a minority of cases (8/30) the ratios are large and range between 20 and 56 %. Conclusions. These results suggest that upflows and the high-speed asymmetric blue wing component are a common feature of all ARs.

Observational constraint on the dark energy scalar field * *H. Li is Supported by the Sino US Cooperation Project of Ministry of Science and Technology (2016YFE0104700), Youth Innovation Promotion Association Project of CAS. The research is also Supported in part by NSFC (11653001) and Pilot B Project of CAS (XDB23020000)

In this paper, we study three scalar fields, namely the quintessence, phantom, and tachyon fields, to explore the source of dark energy via the Gaussian processes method from the background and perturbation growth rate data. The corresponding reconstructions suggest that the dark energy should be dynamical. Moreover, the quintom field, which is a combination of the quintessence and phantom fields, is powerfully favored by the reconstruction. The mean values indicate that the potential in the quintessence field is a double exponential function, whereas in the phantom field is a double Gaussian function. This reconstruction can provide an important reference for the scalar field study. The two types of data employed reveal that the tachyon field is disadvantageous for describing the cosmic acceleration.

Liquid jet breakup and spray formation with annular swirl air

Spray formation is important for liquid fuel combustion from the perspective of emissions and sustaining combustion. Employing swirl flow increases turbulent intensity, which leads to a much-desired outcome for sprays in a reduction of the jet breakup length, better air-fuel mixing and vaporization characteristics, and stabilizes the combustion operation. The present investigation endeavors to characterize the near field spray developing region of a swirl airblast atomizer. The high-speed digital imaging system is employed to record the unsteady dynamics when the liquid jet breaks to form mono-disperse or poly-disperse drop size distribution depending upon the atomizing air to liquid jet momentum ratio. The coherent spatial nature of the atomization process is identified using Proper Orthogonal Decomposition (POD) of acquired high-speed images of the spray in the near field. The simultaneous droplet size and velocities (axial, radial, and tangential) are measured through Phase Doppler Particle Analyzer (PDPA) technique to quantify the effect of the flow field on spray characteristics in the near field of an atomizer. Joint probability distributions of drop size and velocity along with Weber number distribution provide insight into the spray developing near field region. The spatial variations in the drop size distribution are found to be a function of turbulent Weber number (Wet) and shear Weber number (Wes) distribution. Double Gaussian function representing superimposed distributions for larger and smaller droplets provides the highest confidence fit for both velocity and dropsize distributions throughout the flow field. The volume-averaged statistics are proposed for a better description of the actual spray quality.

Effect of electrode coat's surface resistivity of Resistive Plate Chamber on the space dispersion of induced charge

Resistive Plate Chamber (RPC) is a parallel plate gaseous detector built using electrodes of high volume resistivity. Operation and performance of the RPCs crucially depends upon the conductive coating on the outer side of the electrodes. Therefore, it is imperative to study in detail the effect of the surface resistivity of the electrodes' coating on RPC characteristics and performance. In this report, the effects of surface resistivity of conductive coating on the spatial dispersion of the induced charge by the three fabricated prototype RPCs with surface resistivities of 1 MΩ/ □, 100 kΩ/ □, 40 kΩ/ □ were studied experimentally. A double-Gaussian function was used to fit the dispersion of the induced charge of the RPC. It is shown that the Gaussian distribution with the narrower width is due to the spread of the avalanche charge in the gas gap of the RPC and that of larger width is due to diffusion of charge as it passes through the resistive coat film. Effect of resistivity on other RPC detector parameters such as efficiency, noise rate and strip-multiplicity is also reported.

IRIS Si iv Line Profiles at Flare Ribbons as Indications of Chromospheric Condensation

Abstract We present temporal variations of the Si iv line profiles at the flare ribbons in three solar flares observed by the Interface Region Imaging Spectrograph. In the M1.1 flare on 2014 September 6 and the X1.6 flare on 2014 September 10, the Si iv line profiles evolve from wholly redshifted to red-wing enhanced with the flare development. However, in the B1.8 flare on 2016 December 2, the Si iv line profiles are wholly redshifted throughout the flare evolution. We fit the wholly redshifted line profiles with a single-Gaussian function, but fit the red-asymmetric ones with a double-Gaussian function to deduce the corresponding Doppler velocities. In addition, we find that hard X-ray emission above 25 keV shows up in the two large flares, implying a nonthermal electron beam heating. In the microflare, there only appears weak hard X-ray emission up to 12 keV, indicative of a mostly thermal heating. We interpret the redshifts or red asymmetries of the Si iv line at the ribbons in the three flares as spectral manifestations of chromospheric condensation. We propose that whether the line appears to be wholly redshifted or red-asymmetric depends on the heating mechanisms, as well as on the propagation of the condensation.

Spontaneous symmetry breaking in coupled ring resonators with linear gain and nonlinear loss

We present the study of the dynamics of a two-ring waveguidestructure with space-dependent coupling, constant linear gain and nonlinear absorption. This system can be implemented in various physical situations such as optical waveguides, atomic Bose-Einstein condensates, polarization condensates, etc. It is described by two coupled nonlinear Schrödinger equations. For numerical simulations we take local Gaussian coupling (single-Gaussian and double-Gaussian). We find that, depending on the values of involved parameters, we can obtain several interesting nonlinear phenomena, which include spontaneous symmetry breaking, modulational instability leading to generation of stable circular flows with various vorticities, stable inhomogeneous states with interesting structure of currents flowing between rings, as well as dynamical regimes having signatures of chaotic behavior. In this paper, we only focused on consider phenomenon of spontaneous symmetry breaking in the case of space dependent coupling. The results show that in the case of a coupling between the two rings is a function of single-Gaussian symmetry breaking only between rings. In contrast, in the case of a coupling between them as a double-Gaussian function, the symmetry breaking occurs only in each ring, breaking the symmetry of the space dependent coupling.

A regularization method for backward parabolic equations with time-dependent coefficients

We present the study of the dynamics of a two-ring waveguidestructure with space-dependent coupling, constant linear gain and nonlinear absorption. This system can be implemented in various physical situations such as optical waveguides, atomic Bose-Einstein condensates, polarization condensates, etc. It is described by two coupled nonlinear Schrödinger equations. For numerical simulations we take local Gaussian coupling (single-Gaussian and double-Gaussian). We find that, depending on the values of involved parameters, we can obtain several interesting nonlinear phenomena, which include spontaneous symmetry breaking, modulational instability leading to generation of stable circular flows with various vorticities, stable inhomogeneous states with interesting structure of currents flowing between rings, as well as dynamical regimes having signatures of chaotic behavior. In this paper, we only focused on consider phenomenon of spontaneous symmetry breaking in the case of space dependent coupling. The results show that in the case of a coupling between the two rings is a function of single-Gaussian symmetry breaking only between rings. In contrast, in the case of a coupling between them as a double-Gaussian function, the symmetry breaking occurs only in each ring, breaking the symmetry of the space dependent coupling.

The galaxy luminosity function in the LAMOST Complete Spectroscopic Survey of Pointing Area at the Southern Galactic Cap

We present optical luminosity functions (LFs) of galaxies in the 0.1g, 0.1r, 0.1i bands, calculated using data in sky area of the LAMOST Complete Spectroscopic Survey of Pointing Area (LaCoSSPAr) in the Southern Galactic Cap. Redshifts for galaxies brighter than r = 18.1 were obtained mainly with LAMOST. In each band, LFs derived using both parametric and non-parametric maximum likelihood methods agree well with each other. In the 0.1r band, our fitting parameters of the Schechter function are , and , which agree with previous studies. Separate LFs are also derived for emission line galaxies and absorption line galaxies. The LFs of absorption line galaxies show a dip at 0.1r** 18.5 and can be fitted well by a double-Gaussian function, suggesting a bimodality in passive galaxies.

A hybrid deep learning-based neural network for 24-h ahead wind power forecasting

Wind power generation is always associated with uncertainties as a result of fluctuations of wind speed. Accurate predictions of wind power generation are important for the efficient operation of power systems. This paper presents a hybrid deep learning neural network for 24 h-ahead wind power generation forecasting. This novel method is based on a Convolutional Neural Network (CNN) that is cascaded with a Radial Basis Function Neural Network (RBFNN) with a double Gaussian function (DGF) as its activation function. The CNN is utilized to extract wind power characteristics by convolution, kernel and pooling operations. The supervised RBFNN, incorporating a DGF, deals with uncertain characteristics. Realistic wind power generations, measured on a wind farm, were used in simulations. The proposed method is implemented using TensorFlow and Keras Library. Comparative studies of different approaches are shown. Simulation results reveal that the proposed method is more accurate than traditional methods for 24 h-ahead wind power forecasting.