Asymmetric Gaussian Research Articles

Characteristics of the Spectral Response Function of Geostationary Environment Monitoring Spectrometer Analyzed by Ground and In-Orbit Measurements

The Geostationary Environment Monitoring Spectrometer (GEMS), an ultraviolet and visible imaging spectrometer, provides air-quality information over a large area of the Asia Pacific region with a high spatiotemporal resolution. To assure the reliability of trace gas retrieval, accurate knowledge of the spectral response function (SRF) is critical for spectral calibration as well as retrieval algorithms. Here, we characterize the GEMS SRF using prelaunch SRFs obtained with the monochromatic laser measurements during the ground test and inflight SRFs retrieved using the solar irradiance measurements after the launch. The prelaunch SRFs are analyzed in terms of shape (skewness and kurtosis), width, and under-sampling and show that the full-width at half-maximum is smaller than 0.6 nm with a maximum of 0.589 nm. The variations along both the spectral and spatial directions are smooth and within 3.65%, indicating a highly homogenous and stable optical system of GEMS. To characterize the prelaunch SRFs and monitor the behavior of inflight SRFs, we applied several analytical functions including asymmetric super Gaussian (ASG) and hybrid Gaussians to the prelaunch SRFs. The spectral fitting of the measured GEMS irradiance with a reference spectrum shows that the ASG to be the best representative of the GEMS SRFs. The inflight SRFs, retrieved with the GEMS irradiances and the ASG, agree well with the prelaunch SRFs, suggesting that the inflight spectral performance and characteristics of GEMS are similar to those investigated from the on-ground characterization.

Wind Power Curve Modeling With Hybrid Copula and Grey Wolf Optimization

Wind power curve modeling has important applications in many fields, such as wind turbine condition monitoring and wind power forecasting. There are various studies on wind power curve construction though, the relation between wind speed and errors is not taken into account when constructing wind power curves. To fill this gap, a hybrid copula-based wind power curve model (HCCM) is proposed in this paper. The independent distribution of wind speed is modeled by a Weibull distribution, and the independent distribution of errors is modeled by a mixture of asymmetric gaussian models. Then a hybrid copula model, including four copula functions, is designed to construct the joint distribution. Finally, a grey wolf optimization algorithm is applied to optimize the regression parameters of wind power curves by taking the log-likelihood function of the joint distribution as the fitness function. The proposed model is compared with ten benchmark models on four wind farms and two wind turbines. Experiments show that in terms of RMSE the HCCM achieves 99.3131%, 99.1197%, 99.1715% and 98.9339% on four wind farms, respectively, and around 80% improvement in RMSE on two wind turbines.

Shack-Hartmann Wavefront Sensing of Ultrashort Optical Vortices.

Light beams carrying Orbital Angular Momentum (OAM), also known as optical vortices (OV), have led to fascinating new developments in fields ranging from quantum communication to novel light–matter interaction aspects. Even though several techniques have emerged to synthesize these structured-beams, their detection, in particular, single-shot amplitude, wavefront, and modal content characterization, remains a challenging task. Here, we report the single-shot amplitude, wavefront, and modal content characterization of ultrashort OV using a Shack-Hartmann wavefront sensor. These vortex beams are obtained using spiral phase plates (SPPs) that are frequently used for high-intensity applications. The reconstructed wavefronts display a helical structure compatible with the topological charge induced by the SPPs. We affirm the accuracy of the optical field reconstruction by the wavefront sensor through an excellent agreement between the numerically backpropagated and experimentally obtained intensity distribution at the waist. Consequently, through Laguerre–Gauss (LG) decomposition of the reconstructed fields, we reveal the radial and azimuthal mode composition of vortex beams under different conditions. The potential of our method is further illustrated by characterizing asymmetric Gaussian vortices carrying fractional average OAM, and a realtime topological charge measurement at a 10Hz repetition rate. These results can promote Shack-Hartmann wavefront sensing as a single-shot OV characterization tool.

Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.

Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading

In the present paper, we extend the current literature in algorithmic trading with Markov-switching models with generalized autoregressive conditional heteroskedastic (MS-GARCH) models. We performed this by using asymmetric log-likelihood functions (LLF) and variance models. From 2 January 2004 to 19 March 2021, we simulated 36 institutional investor’s portfolios. These used homogenous (either symmetric or asymmetric) Gaussian, Student’s t-distribution, or generalized error distribution (GED) and (symmetric or asymmetric) GARCH variance models. By including the impact of stock trading fees and taxes, we found that an institutional investor could outperform the S&P 500 stock index (SP500) if they used the suggested trading algorithm with symmetric homogeneous GED LLF and an asymmetric E-GARCH variance model. The trading algorithm had a simple rule, that is, to invest in the SP500 if the forecast probability of being in a calm or normal regime at t + 1 is higher than 50%. With this configuration in the MS-GARCH model, the simulated portfolios achieved a 324.43% accumulated return, of which the algorithm generated 168.48%. Our results contribute to the discussion on using MS-GARCH models in algorithmic trading with a combination of either symmetric or asymmetric pdfs and variance models.

The effect of parabolic potential on ground state energy and vibration frequency of magnetopolaron in asymmetric Gaussian potential quantum wells

Anisotropy parabolic potential (APP) effects on ground state (GS) energy [Formula: see text] and the vibration frequency (VF) [Formula: see text] of weak-coupled magnetopolaron (MP) in asymmetric Gaussian quantum wells (AGQWs) were investigated using the linear combination operator and unitary transformation method. The obtained results showed that [Formula: see text] and [Formula: see text] were increased by increasing the barrier height [Formula: see text] of AGQWs as well as transverse and longitudinal confined strengths [Formula: see text] and [Formula: see text] of APP and decreased with increase in the asymmetric Gaussian confinement potential (AGCP) range [Formula: see text] and transverse and longitudinal effective confined lengths [Formula: see text] and [Formula: see text] of APP. Thus, the GS energy and VF of MP could be changed by adjusting the confinement parameters of the APP and AGCP. The study of quantum wells’ semiconductor materials has broad potential applications in semiconductor lasers, optoelectronic devices and quantum information.

A methodology to reconstruct LAI time series data based on generative adversarial network and improved Savitzky-Golay filter

High-quality leaf area index (LAI) data is essential for regional and global ecology, climate and environment research. However, there are still many quality problems in the continuity of current LAI time series products. Here we developed a new comprehensive three-step reconstruction method (GANSG) for satellite-retrieved LAI time series data based on generative adversarial network, improving the Savitzky-Golay (S-G) filter and median absolute deviation filter. We applied GANSG to the reconstruction of MODIS LAI data in China from 2001 to 2019. The reconstruction results show that the new method based on the unsupervised deep learning framework has an advantage in interpolating low-quality LAI with high precision. The new method can better retain high-quality pixel information to smoothen the interpolated LAI time series by improving the traditional S-G filter. Compared with the five other methods, including the adaptive S-G filter, double logistic, asymmetric Gaussian, modified temporal spatial filter and spatial temporal S-G filter, qualitative analysis showed the new method has a more resilient ability to handle the continuous loss of high-quality pixels and identify the phenological features of biomes. Quantitative analysis based on station observation showed that the new method performs best among the other three methods, with the optimal correlation coefficient of 0.84 relative to station observation and the lowest root mean square error of 0.71 m2/m2.

Robustness and Accuracy of the Particle Swarm Optimisation Method in the Solution of Inverse Heat Transfer Problems with Phase Change

Some engineering heat transfer problems cannot be solved directly as certain parameters influencing heat transfer are unknown. This is where the solution of inverse heat transfer problems might be utilized. The paper explores application of the Particle Swarm Optimisation (PSO) method to heat transfer problems with phase change. In order to assess the robustness and accuracy of the method, the study was conducted on simulation results. The explored case is an air-PCM heat exchanger. In the first step, the simulations with various values of parameters influencing heat transfer were performed. All cases were simulated as transient with time-dependent boundary conditions. In the second step, some of these parameters (phase change temperature, density, thermal conductivity, etc.) were assumed to be unknown and they were obtained from the simulation results with the use of the PSO method. The cost function was defined in the form of a root mean square error between the simulation results (the outlet air temperature from the heat exchanger) and the results of the optimized scenario. The number of unknown parameters varied from one to seven (in case of the description of the relationship of effective heat capacity on temperature, which was parameterized in the form of an asymmetric Gaussian function). The results have shown that the PSO is a robust and accurate tool with only up to 2 % difference in the enthalpy of fusion and no significant discrepancies during the higher dimension optimisation.

Scan-path dependence of surface ripples on Si thin films induced by laser irradiation with an asymmetric Gaussian beam

A Nd:YAG linearly polarized nanosecond laser emitting at a wavelength of 355 nm was used to irradiate amorphous-Si thin film (50 nm thickness) on a glass substrate at a repetition rate of 14 kHz and a pulse duration of 5.5 ns. By scanning the laser beam with an asymmetric Gaussian shape in the lateral and vertical directions with various scan pitches at maximum fluences of 55 mJ/cm2 and 90 mJ/cm2, uniform surface textures were obtained when the laser was scanned in the vertical direction at a scan pitch of ∼35–40 μm along the long beam axis. The surface textures were composed of nanoparticle arrays embedded in the Si layer, whose height gradually increased up to 100–150 nm as the number of pulses increased. However, when over-irradiated, the nanoparticles with a rounded shape were transformed to an irregular shape, and the surface roughness also increased. These results would enable application of a 355-nm nanosecond laser to achieve uniform surfaces as a result of the laser crystallization process for organic light-emitting diode displays or photovoltaic devices.

Fusion dynamics of 12C with 92Zr and 194,198Pt targets at energies lying in the neighbourhood of Coulomb barrier

This work theoretically analyzed the role played by intrinsic degrees of freedom of the colliding partners and/or nucleon transfer channel in the fusion dynamics of [Formula: see text]C + [Formula: see text]Zr and [Formula: see text]C + [Formula: see text]Pt reactions. The theoretical calculations for the chosen reactions have been done by using symmetric–asymmetric Gaussian barrier distribution (SAGBD) model, coupled channel code CCFULL and one-dimensional Wong formula. The fusion excitation functions for these reactions are enhanced at energies lying in the sub-barrier regions with respect to calculations based on one-dimensional Wong formula. Such enhancement can be ascribed to the involvement of the low-lying inelastic surface excitations of the colliding systems and some other favored channels. The CCFULL calculations consider the couplings to low lying vibrational states of target isotopes and hence intimate the dominance of the collective excitations associated with the fusing partners. For all the studied reactions, the SAGBD model adequately addresses the sub-barrier fusion dynamics and thus the impacts of nuclear structure degrees of freedom associated with the collision partners are intrinsically incorporated via single Gaussian type of weight function. In the SAGBD approach, the nuclear structure effects are analyzed in terms of channel coupling parameter [Formula: see text] and percentage reduction of effective fusion barrier [Formula: see text] with reference to the Coulomb barrier. The larger values for [Formula: see text] and [Formula: see text] imply that the channel couplings due to internal structure of participants are significant at below barrier energies. The CCFULL calculations and SAGBD predictions consistently explain the fusion dynamics of chosen systems and hence reveal that both models have almost similar predictive power with respect to reported fusion data.

Convolutional Sparse Coding Using Pathfinder Algorithm-Optimized Orthogonal Matching Pursuit With Asymmetric Gaussian Chirplet Model in Bearing Fault Detection

Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse representation algorithm need to be improved. In order to accurately detect the fault impulse in the original signal, a convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model (CSC-OAGCM) is proposed in this paper. A new time-frequency atom prototype, AGCM, is used to match the fault impulse waveform. The specific application steps of the proposed algorithm are as follows: Firstly, a convolution dictionary is constructed with atoms generated by AGCM. Subsequently, based on the convolution dictionary, a pathfinder algorithm-optimized orthogonal matching pursuit algorithm is used to solve the sparse representation and optimize the atomic parameters to achieve the best approximation of the original signal. In other words, the proposed method detects the convolutional sparse patterns in the signal. A simulation signal, two sets of mixed signals of experimental data collected from the experimental platform and an axle box vibration signal collected from the actual operating train are used to verify the effectiveness of proposed method. Additionally, the spectral kurtosis and empirical wavelet transform are also used to process these signals, and their processing results are compared with those obtained by the proposed method to demonstrate the superiority of the proposed method.

MASG-GAN: A multi-view attention superpixel-guided generative adversarial network for efficient and simultaneous histopathology image segmentation and classification

Efficient analysis of Haematoxylin and Eosin stained histopathology images has become a challenge in digital pathology work-flow. We propose a Multi-view Attention Superpixel-guided Generative Adversarial Network (MASG-GAN) to achieve the multi-task learning for nuclei segmentation and benign-malignant tissue classification. Firstly, a novel superpixel segmentation approach driven by Bounded Asymmetric Gaussian Mixture Model (BAGMM) is presented for generating superpixel-prior probability map with high-level semantics. Then, we develop a generator network that integrates student-branch and teacher-branch. Concretely, the teacher-branch takes superpixel-prior probability map as input and guides the student-branch for accurate segmentation and classification. Then in generator, we build a light-weight U-shaped block (LUB) that consists of depthwise separable convolutions with mini encoding-decoding structure to reduce network computational cost. Finally, a Multi-view Attention Module (MVAM) is designed for further enhance the segmentation quality of nuclei with small area and unclear boundary. Extensive experiments on five benchmark datasets demonstrate that our pipeline outperforms some state-of-the-art methods, especially in terms of efficiency.

Chiral-dissipation-assisted generation of entanglement and asymmetric Gaussian steering in a driven cascaded quantum network

We study the dissipative dynamics and the formation of entangled states in a driven cascaded quantum network, where a cascaded double-cavity optomechanical system is coupled to a common unidirectional optical fiber. In terms of two coherent laser fields simultaneously driving the chiral coupling system, the entanglement can be effectively transferred from the light to two nanomechanical resonators (NMRs) through cavity modes based on the completely destructive interference of quantum noise. By adjusting the relative strength ratio of red- and blue-detuned pump lasers, it is found that the two NMRs can be driven into a mixed two-mode Gaussian entangled mechanical state rather than a non-Gaussian entangled dark state or a pure entangled dark state. If the frequency of the NMR is tuned to be much larger than the chiral coupled-cavity-chain damping rate, it is demonstrated that the chiral dissipative cascaded system can always realize one-way Einstein-Podolsky-Rosen (EPR) steering from one mechanical oscillator to the other, while the reverse one-way EPR steering is impossible to obtain, and the reason for achieving asymmetric Gaussian steering is analyzed. Furthermore, a simple one-way EPR steering criterion based on two-photon correlation can be achieved for an arbitrary bipartite Gaussian state under the standard form of EPR steering, and the effect of mechanical thermal noise on the steering is also discussed. Moreover, the present chiral system can be realized with currently available experimental technology; therefore, we hope that it can be very helpful for potential applications in quantum computation and quantum communication.

Using Multi-Temporal Satellite Data to Analyse Phenological Responses of Rubber (Hevea brasiliensis) to Climatic Variations in South Sumatra, Indonesia

Land surface phenology derived from satellite data provides insights into vegetation responses to climate change. This method has overcome laborious and time-consuming manual ground observation methods. In this study, we assessed the influence of climate on phenological metrics of rubber (Hevea brasiliensis) in South Sumatra, Indonesia, between 2010 and 2019. We modelled rubber growth through the normalised difference vegetation index (NDVI), using eight-day surface reflectance images at 250 m spatial resolution, sourced from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites. The asymmetric Gaussian (AG) smoothing function was applied on the model in TIMESAT to extract three phenological metrics for each growing season: start of season (SOS), end of season (EOS), and length of season (LOS). We then analysed the effect of rainfall and temperature, which revealed that fluctuations in SOS and EOS are highly related to disturbances such as extreme rainfall and elevated temperature. Additionally, we observed inter-annual variations of SOS and EOS associated with rubber tree age and clonal variability within plantations. The 10-year monthly climate data showed a significant downward and upward trend for rainfall and temperature data, respectively. Temperature was identified as a significant factor modulating rubber phenology, where an increase in temperature of 1 °C advanced SOS by ~25 days and EOS by ~14 days. These results demonstrate the capability of remote sensing observations to monitor the effects of climate change on rubber phenology. This information can be used to improve rubber management by helping to identify critical timing for implementation of agronomic interventions.

Fault Calculation Method of Distribution Network Based on Deep Learning

Under the low voltage ride through (LVRT) control strategy, the inverter interfaced distributed generation (IIDG) needs to change the output mode of the inverter according to the voltage of the connected nodes. The short-circuit current is related to the system rated capacity, network short-circuit impedance, and distributed power output. So, based on the deep learning algorithm, a predicting method of the voltage drop is proposed. By predicting the voltage of connected nodes, the output mode of IIDG can be determined based on the LVRT control. Thus, the fault calculation model of IIDG is accurately established. Compared with the three-phase asymmetric Gaussian fault calculation method, the proposed method can achieve fault calculation accurately. Finally, a case study is built to verify the effectiveness of the proposed method. The results indicate that the proposed method can make accurate voltage prediction and improve the computation speed of the fault calculation.

A spatially constrained asymmetric Gaussian mixture model for image segmentation

The finite Gaussian mixture model (GMM) is a flexible and powerful tool for addressing many computer vision and pattern recognition problems. The Gaussian distribution is a probability distribution that is symmetric with respect to the mean. However, in many segmentation applications, the observed data obey an asymmetric distribution. Furthermore, the GMM is sensitive to imaging noise. To alleviate these issues, a new finite anisotropic asymmetric normal mixture model is presented in this paper. Note that GMM is a degraded case of our proposed model. First, the proposed model employs anisotropic spatial information to reduce the effect of imaging noise while preserving the details, such as edges, corners and slim structure objects. Second, the anisotropic spatial information is coupled into the skew normal distribution to fit the observed data obeying an asymmetric distribution. Then the modeling and estimation of the object intensity probability density function are proposed by using the anisotropic skew normal mixture model. The proposed model not only has the capability to fit the observed data obeying a non-symmetric distribution, but also can reduce the effect of noise while preserving the objects details. Finally, expectation maximization (EM) algorithm is adopted to estimate the model parameters in order to maximize the log-likelihood function. The experiment results on synthetic images and natural grayscale images demonstrate the superior performance of the proposed model compared with other state-of-the-art segmentation methods.

ВИКОРИСТАННЯ ФУНКЦІЙ ГАУСА ДЛЯ АПРОКСИМАЦІЇ БІОЛОГІЧНИХ СИГНАЛІВ

Purpose. Currently, the method of mathematical analysis of various biological signals is increasingly used in medicine. Most of them are random and quasi-periodic processes, which greatly complicates the possibility of using an automated data processing process. Signals of this type include respiratory noises, phonocardiographic and electrocardiographic signals. The shape of the waves of the ECG signal is quite accurately described by asymmetric Gaussian pulses. It is possible to achieve the required approximation accuracy by introducing additional pulses to describe the diagnostically important inhomogeneities of the signal. In the previous work of the authors the simple algorithm of coefficients selection of asymmetric functions in the form of the Labview virtual device is realized. But for the cases of superposition of waves of different polarity and non-standard pulse shapes, the result of applying this algorithm gives a significant error. Methodology. In the analysis of waves of negative polarity, the presence of a positive burst, even a small value, leads to a complete disregard for the negative wave. To eliminate this shortcoming, it is necessary to determine the maximum and minimum values of the amplitudes of the samples in this area and compare their absolute values. A larger value will determine the polarity of the wave coming to the virtual instrument for analysis. The next complication is the presence of a wide flat wave top with an offset maximum value by extreme samples. This is a significant shift in the symmetry of the wave and, as a consequence, incorrect determination of parameters for a wide part of the pulse. To eliminate this shortcoming, it is proposed to determine all the influential extremes, and shift the center of symmetry to the middle of the flat top of the wave. In the case where the wave has a significant violation of symmetry, it is possible that the virtual device determines the midpoints and amplitudes of the left to right parts with a significant deviation from the actual peak value. To prevent the vertices of the Gaussian functions from shifting relative to the real maximum it was proposed to mirror the data sets corresponding to the left and right parts of the wave. Results. In this article, the algorithm of the virtual device operation for automatic determination of the parameters of the approximate asymmetric Gaussian curves in the Labview software environment is improved. It is planned to use approximation by Gaussian impulses to synthesize standard leads from reduced sets of ECG signal leads. According to preliminary studies, a surface of approximation coefficients has been created, which allows synthesizing a cardiac signal for an arbitrary derivation. Originality. Algorithm for approximating biological signals by asymmetric Gaussian functions is improved. In this case, a more confident approximation of waves was obtained in the presence of: bursts of opposite polarity; wide flat top of the wave when the maximum value is shifted to extreme samples; significant violation of the symmetry of the waves; narrow fragments of waves. Practical value. A virtual device was created to approximate signals (including ECG signal leads, respiratory noises and phonocardiosignals) using asymmetric Gaussian functions. The analysis of certain coefficients will make it possible to use them as average criteria for the diagnosis of the corresponding diseases, which requires further research. References 10, figures 11.

Evaluating the impacts of models, data density and irregularity on reconstructing and forecasting dense Landsat time series

We evaluated the performance of a variety of time series models, that include the harmonic (HR) model, autoregressive (AR) model, linear Gaussian state-space (LGSS) model, cubic spline (SP) model, double logistic (DL) model, and asymmetric Gaussian (AG) model, for reconstructing (all six models) and forecasting (HR, AR, and LGSS models) dense Landsats 5–7 time series based on 4562 samples. To remove the impact of land change and human interventions on data reconstruction and forecasting, this evaluation excluded croplands and samples changed between 2000 and 2011. Results show that the widely used HR model is not a good model for data reconstruction but outperforms other models in data forecasting. The DL and AG models have the best performance in data reconstruction but cannot forecast Landsat observations. The AR and LGSS models shared similar performance in reconstructing Landsat data but are less ideal for data forecasting, particularly for the LGSS model. Integrating the HR (for forecasting) and DL or AG (for reconstruction) is recommended to improve land change detection and land cover classification results. We also evaluated the impact of data density and irregularity on reconstructing and forecasting Landsat observations. When the data density is low (<7 clear observations per year), the increase of data density can substantially improve the performance of data reconstruction and forecasting, but when the data density is higher than 7 and less than 17, the noise in the data dominates the results, and slightly lower reconstruction and forecasting accuracy is observed. When the data density is higher than 17, model performance improves with the increase of data density again. Therefore, we recommend analyzing Landsat time series for places with data density higher than 7 clear observations per year if possible. On the other hand, data irregularity has a moderate impact on data reconstruction and forecasting. When the irregularity is less than 1, the smaller the irregularity the better the performance in both data reconstruction and forecasting, and when the irregularity is higher than 2.5, it will have a more substantial negative impact. Therefore, time series analysis using Landsat data with an irregularity less than 1 is generally recommended, and time series with an irregularity larger than 2.5 should be avoided if possible.

Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System

The present study aimed to use the proposed system to measure and analyze brain waves of users to allow intelligent upper limb rehabilitation and to optimize the system using a genetic algorithm. The study used EPOC Neuroheadset for Emotiv with EEG electrodes attached as a non-invasive method for measuring brain waves. The brain waves were measured according to the EEG 10-20 standard electrode layout, which allows measurement of signals from each spot where electrodes are attached based on EEG characteristics. The measured data were added in a database. In the intelligent neuro-fuzzy model, wave transform was used for extracting brain wave characteristics according to user intentions and to eliminate noise from the signals in an effort to increase reliability. Moreover, to construct the option rules of the neuro-fuzzy system, FCM technique and optimal cluster evaluation method were used. Furthermore, the asymmetric Gaussian membership function was used to improve performance, whereas SD and WF divided into left and right sides were used to express the chromosomes. Optimal EEG electrode locations were found, and comparative analysis was performed on the differences based on membership function, number of clusters, and number of learning generations, learning algorithm, and wavelet settings. The performance evaluation results showed that the optimal EEG electrode locations were F7, F8, FC5, and FC6, whereas the accuracy of learning and test data of user-intention recognition was found to be 94.2% and 92.3%, respectively, which suggests that the proposed system can be used to recognize user intention for specific behavior. The system proposed in the present study can allow continued rehabilitation exercise in everyday living according to user intentions, which is expected to help improve the user's willingness to participate in rehabilitation and his or her quality of life.

The effect of a parabolic potential on the properties of a strongly coupled polaron in an asymmetric Gaussian quantum well

The effect of a parabolic potential on the properties of a strongly coupled polaron in an asymmetric Gaussian quantum well