•The microstructures of the real C/SiC are restructured and transformed into grids.•An efficient method for predicting the ETC of the C/SiC composite is established.•The effect of TCR between yarn ...and matrix on heat conduction is analyzed.•The correlation between the ETC and TCR is proposed.
The plain woven C/SiC composites with high thermal shock resistance are widely used in aerospace applications. For this composite, thermal contact resistance (TCR) has an unneglectable contribution to the thermophysical property. In this work, the influence of the TCR on the thermophysical property of a real 2D plain woven C/SiC composite is studied. X-ray CT scanning is used to obtain the microstructures of the C/SiC composite, and the complex structures are restructured and transformed into standard cube grids based on the theory of the intersection of rays and triangles. Combining with finite difference method (FDM) and multiple GPUs acceleration, an efficient approach for numerically predicting the thermophysical property of the real C/SiC composite is established. Based on the Laser Flash Method (LFM) and Differential Scanning Calorimeter (DSC) method, the effective thermal conductivities (ETCs) of the C/SiC composite at different temperatures are obtained experimentally. The results show that ETCs predicted by our numerical method are closer to the measured ones when TCR is considered. Through the numerical simulation and theoretical analysis, the correlation between the ETC and TCR is obtained, which is proved to be a typical Logistic function, and the physical meanings of various constants in the correlation are determined and discussed. Besides, the thermal response time increases with the TCR increasing, simultaneously, the distributions of isotherms and heat-flux trajectories more approach the distributions of fiber yarns in the material.
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•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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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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•WEEE generated in Vietnam during the period 2015–2050 was estimated.•Three scenarios in terms of lifespan were analyzed.•The relationship between lifespan and GDP per capita was considered.•The ...uncertainties related to the lifespan were covered to some extent.
During recent decades, rapid urbanization, an increase in population, and a change in lifestyle have generated a significant amount of waste electrical and electronic equipment (WEEE). To develop sustainable and efficient WEEE management policies, particularly in developing countries such as Vietnam, it is of paramount importance to forecast the amount of WEEE generation expected in the future. In the input-output approach as one of the WEEE estimation models, the determination of EEE lifespan is a major concern because it can be dynamic with time and vary with socioeconomic situations. Therefore, three scenarios were considered on the basis of the association of lifespan with gross domestic product per capita. By considering the lifespan transition, this study estimated the potential amount of WEEE, particularly large home appliances including refrigerators, washing machines, air conditioners, and televisions generated in Vietnam and the relative volume of obsolete metals contained in the WEEE during the period 2015–2050. For this, it employed a population balance model, logistic function, and Weibull distribution. It was found that the estimated weight of WEEE discarded in Vietnam during 2050 is mitigated 50% under the case where the lifespan transition is considered, compared to the case where the lifespan transition is not considered. By considering the relationship between lifespan and GDP per capita in estimating potential WEEE, this study to some extent covered the uncertainties related to the lifespan and can consequently contribute to the enhancement of e-waste estimation.
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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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•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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The wind turbine power curve (WTPC) is a mathematical model built to capture the input-output relationship between the generated electrical power and the wind speed. An adequately fitted WTPC aids in ...wind energy assessment and prediction since the actual power curve will differ from that provided by the manufacturer due to a variety of reasons, such as the topography of the wind farm, equipment aging, and multiple system faults. As such, this paper introduces a novel approach for WTPC modeling that combines Gaussian process (GP) regression, a class of probabilistic kernel-based machine learning models, and standard logistic functions. This semi-parametric approach follows a Bayesian reasoning, in the sense of maximizing the marginal likelihood to learn the parameters and hyperparameters through a variational sparse approximation to the GP model. Using real-world operational data, the proposed approach is compared with the state-of-the-art in WTPC modeling and with an alternative probabilistic approach based on generalized linear models and logistic functions. Finally, we evaluate the proposed model in its extrapolation ability for unmodelled data.
•Logistic function parametrization derived from Wind Turbine operational ranges.•Analytical procedure of parameter initialization in logistic function models.•Novel Semi-Parametric model capable of adaptation to training data and extrapolation.
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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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