•EEG based channel selection using multi-objective Jaya optimization is proposed.•Classification accuracy and channel subset size are used in fitness function.•The experiment is validated on three ...public EEG-based BCI datasets.•High classification accuracy with less channels is achieved.
BCI systems use motor imagery to allow users to control external devices through their brain activity. They extract neural signals from the brain using a large number of EEG channels. However, high-dimensional data from multichannel BCI systems increases the computational burden, leading to slower processing and higher costs. In this study, we proposed a Logistic S-shaped Binary Jaya Optimization Algorithm (LS-BJOA), which combines a logistic map with the Jaya optimization algorithm to alleviate the computational burden caused by many channels. The logistic map introduces stochasticity to better simulate the chaotic behavior of brain signals and improve predictive accuracy. Our method initializes a set of three electrodes as a candidate solution and subsequently determines the most relevant channels iteratively. We used a bi-objective fitness function to evaluate the significance of the selected channels, which involves maximizing classification accuracy and minimizing the length of the channel subset. Initially, a fifth-order bandpass filter with Independent Component Analysis (ICA) was applied to filter MI signals and artifacts reduction, respectively. The Regularized Common Spatial Pattern (RCSP) was used to extract spatiotemporal features from the selected channels. Finally, the three classifiers: (1) Support Vector Machine (SVM), (2) Naïve Bayes (NB), and (3) Linear Discriminant Analysis (LDA) were used to determine maximum classification accuracy. The experiment is validated on three public EEG datasets (BCI Competition IV- 2008 – IIA, BCI Competition IV- dataset 1, BCI competition III – dataset IVa). Our method achieved superior classification accuracy (83.59%, 82.09%, and 89.02% on datasets 1, 2, and 3 respectively) with fewer channels than baseline methods. Additionally, computation time was significantly reduced without compromising accuracy. Topographical mapping revealed frontal lobe involvement in MI tasks during physical activities. Topographical mapping of the selected channels showed that the frontal lobe executes various MI tasks during physical activities.
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Modeling of wind turbine power curve which shows the relationship between wind speed and its power output can be used as an important tool in monitoring and forecasting wind energy. A data-driven ...approach to find most probable probability distribution function (PDF) for wind speed and turbine power is presented in this study. Equations for the scale and shape parameters in the Weibull wind speed distribution and equations for the four parameters in the logistic function were obtained explicitly by maximum likelihood estimation (MLE) method. With help of a selected data set from the wind speed and the corresponding power output data which was collected over a period of a year, the values of the parameters were obtained by solving the equations by iteration procedures. The predicted powers by the obtained logistic function closely follow the measured turbine powers averaged at 5-min or 10-min. Monitoring turbine power output by the logistic function was also tested for the measured powers in other time duration.
•Modeling of power curve for wind turbine was constructed by data-driven approach.•Equations for the four parameters of a logistic function were obtained by maximum likelihood estimation (MLE) method.•Most probable probability distribution function (PDF) for wind turbine power was obtained by solving the equations.•The predicted power by logistic function closely follows the measured power averaged at 5-min or 10-min.
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Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more ...accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy.
To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms.
The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.
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•Several logistic functions were tested in order to assess its applicability to wind turbine power curves.•The 6PLE function is the best option to model a wind turbine power curve due to its ...performance.•The 3PLE and 5PLE functions are strongly recommended considering the approximation given and the number of parameters.•Functions as Gompertz, 3PL, 4PL, 5PL and 6PL have to be discarded.
In recent years logistic functions have been used to model wind turbine power curves. Generally speaking, it can be said that the results provided by the logistic functions are good enough to choose them over other options considering its continuity and adaptability. However, there are some logistic functions that have never been used to model wind turbine power curves although their use can be adequate. Comparing all logistic functions can help definitely to decide which are the best options.
In this paper, the most known logistic functions are presented and tested to model wind turbine power curves, included those already used. Moreover, a comparison is made among them, after which two logistic functions are eventually recommended and some other are definitively discarded.
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Detection of source horizontal boundaries is a common feature in the interpretation of magnetic and gravity data. A wide range of derivative- and phase-based methods are available to solve this ...problem. Here, we compare the effectiveness of the commonly used methods, and introduce a method based on the logistic function and the horizontal gradient amplitude, which shows improved performance as a boundary detection filter. The effectiveness of the proposed filter is demonstrated by evaluating synthetic examples and a real example from the Central Puget Lowland (United States). The main advantage of this method is that it provides high-resolution results, and can avoid producing spurious boundaries in the output maps.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The comparative relevance of each geospatial component of mineralization differs from one geological terrane to the other because various sought-after mineral deposit-types synonymously differ in ...different geological terranes. Hence, the possibility of employing a conceptual model to obtain a relationship or a quantitative function between various geospatial features (evidential layers) with respect to the mineral being sought is laudable, though these features may not necessarily have a generically related effect with the mineral being sought. As a consequence, there is the need to employ a technique that has the capacity to recognize the efficient and inefficient geospatial indicators of the mineral deposit-type being sought. In view of this, this study employed the logistic function, concentration-area fractal model and the prediction-area (P-A) plot to transform and discretize the continuous value of each evidential layer as well as generating intersection points of prediction rate indicators that are essential in obtaining the normalized densities, which were subsequently employed in generating the objective weight for each evidential layer in a data-driven way. The P-A and the normalized density techniques employed were vital in recognizing the indicator and non-indicator criteria. The results obtained acknowledged the potassium concentration layer as a non-indicator of gold mineralization within the study area and subsequently recognized the hydroxyl bearing mineral concentration layer as the most plausible indicator criteria among the six evidential layers (lineament density, iron concentration, hydroxyl concentration, gravity anomaly, magnetic anomaly and potassium concentration) employed in this study. These five indicator criteria were integrated to generate a mineral prospectivity map (MPM) over the study area based on the data-driven multi-index overlay approach adopted. The prediction rate for each of the 6 evidential layers (5 of which were the indicator criteria) as well as the MPM produced indicates that, the generation of objective weights in a data-driven manner via normalized density enhances the predicting ability of the MPM produced in comparison with the individual evidential layers.
•Geospatial anomaly normalization using the logistic function. .•Evidential layer discretization using the concentration-area (C-A) fractal model.•Prediction rate assessment of each evidential layer using the prediction-area (P-A) plot.•Generation of objective weights for each evidential layer based on the normalized density technique.•Generation of mineral prospectivity map based on the data-driven multi-index overlay technique.
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The challenges of the most multi-objective particle swarm optimization (MOPSO) algorithms are to improve the selection pressure, equilibrate the convergence and diversity when tackling large-scale ...many-objective problems. To overcome these challenges, this paper proposes a novel PSO-based large-scale many-objective algorithm, named as LMPSO. In LMPSO, the Alpha-stable mutation is performed to enhance the diversity of swarm for avoiding premature convergence. And the parameters of PSO and Alpha-stable mutation are dynamically set following the Logistic function, which emphasize different convergence and diversity at different optimization stages. Moreover, LMPSO adopts a fitness to maintain the external archive, and the calculation of fitness is based on binary additive epsilon indicator. The binary indicator is also used to update the personal best of particles to avoid wrongly selecting dominance resistance solutions (DRSs). Aims for improving the selection pressure, the proposed algorithm employs a concept of dominance resistance error to identify the DRSs. To verify this idea, the DTLZ, ZDT, and LSMOP test suites with up to 1000 decision variables and 10-objective are used to qualify the performance of LMPSO. The simulations reveal the fact that the LMPSO significantly outruns the several chosen state-of-the-art algorithms when solving large-scale many-objective test instances.
•A large-scale many-objective optimizer based on PSO is framed and named as LMPSO.•A new alpha-stable mutation together Logistic Function is presented.•The binary additive epsilon indicator is used to update the personal best.•Put forwards a new concept of dominance resistance error to identify the DRSs.
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•During the growing season, common reed above-ground biomass is primarily controlled by water level.•Common reed suitable habitat area is primarily controlled by intern-annual water level ...fluctuation.•High water level can destroy the stable state of common reed habitat area and total biomass.•To maintain a suitable habitat area, the monthly mean lowest ecological water level of Baiyangdian Lake is at 6.7 m.
Simulating of suitable habitat for the common reed (Phragmites australis) can provide theoretical support for water-resources managing of shallow lakes. Earlier research has focused on statistical models of shallow lakes and process-based dynamic models for coastal wetlands. However, process-based dynamic modeling for shallow lakes remains relatively incomplete, and the loss of biomass in unsuitable situations is seldom considered. This study established an occupied habitat model by coupling a cellular automaton with a modified logistic function. The cellular automaton was used to simulate the spatial diffusion of the reed. The logistic function was used to simulate the accumulation of biomass over time, and modified with the loss of biomass in unsuitable habitats. Both reed distribution and dynamic variations in reed growth can be simulated by this model. Water level was considered the most important factor in reed growth. In Baiyangdian Lake, China, the growing season is from April to October. The suitable habitat area (SHA) and aboveground biomass (AGB) of reeds were simulated using data from actual hydrological processes and hypothetical scenarios. Element contents and water consumption by transpiration were estimated on the basis of the AGB data. The results are as follows. (1) Water-level changes affected the habitat of the common reed. The SHA increased slightly (from 6.0 m) and then decreased with rising water level, reaching its maximum value at 6.7 m of the 90 % guarantee rate water level. SHA declined rapidly when the water level was higher than 7.5 m. (2) When the water level was maintained at 6.3–7.3 m, the water consumption for transpiration per unit AGB was lower than that for other water levels. (3) To maintain a higher water level for a suitable reed habitat area and to reduce water consumption per unit biomass transpiration, a reference value of 6.7 m can be regarded as the lowest ecological water level in Baiyangdian Lake.
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Analysis of dose-response data is an important step in many scientific disciplines, including but not limited to pharmacology, toxicology, and epidemiology. The R package drda is designed to ...facilitate the analysis of dose-response data by implementing efficient and accurate functions with a familiar interface. With drda it is possible to fit models by the method of least squares, perform goodness-of-fit tests, and conduct model selection. Compared to other similar packages, drda provides in general more accurate estimates in the least-squares sense. This result is achieved by a smart choice of the starting point in the optimization algorithm and by implementing the Newton method with a trust region with analytical gradients and Hessian matrices. In this article, drda is presented through the description of its methodological components and examples of its user-friendly functions. Performance is evaluated using both synthetic data and a real, large-scale drug sensitivity screening dataset.
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