An AMME approach based in neural network

The atomic and bond dipole moment derivative properties associated with the total infrared absorption intensity of the methylidyne, methylene, and methyl free radicals, and of methane, have been calculated by means of the ab initio STO-3G algorithm. These properties include the coordinate-dependent atomic polar tensors and the following invariant quantities: the dipole derivatives for a bond stretch, mean atomic dipole moment derivatives, atomic anisotropies, and the effective atomic charges. Three sets of calculations were conducted for each species by combining the experimental or theoretically determined equilibrium molecular geometry with the standard or optimum orbital exponent scaling factors. The relationship between each of these dipole derivative properties and, in turn, the equilibrium geometry, the carbon and hydrogen scaling factors, and the number of bonded hydrogen atoms is analyzed. According to any calculation, it is found that the calculated dipole derivative value for a C–H stretch in methane is negative, which agrees with the experimentally deduced results, but is positive for all the radicals, except the doublet state of CH. Furthermore, it is shown that the optimum scaling factors must be used in the calculations in order to consistently obtain good agreement between theory and experiment. Based on both the theoretical equilibrium geometry and the optimum scaling factors of a species, the following trends are exhibited by this homologous series of hydrocarbons: (1) The dipole derivative magnitude for a C–H bond stretch is a monotonic increasing function of the product of the carbon and hydrogen scaling factors. (2) The effective charge of carbon increases as the optimum size of the carbon valence shell increases, and decreases as additional hydrogens are bonded to the carbon. (3) The hydrogen effective charge generally decreases as the number of hydrogens increases. (4) The mean dipole derivative of hydrogen is a monotonic decreasing function of the number of C–H bonds, and is negative only for methane. (5) Calculations on the doublet and quartet electronic states of CH yield different values for the dipole moment, the effective charge, and the anisotropy, the dipole derivatives for a bond stretch have opposite directions.

Today, single image haze removal has attracted much attention in the community of image restoration. A popular type of approach is based on dark channel prior (DCP) because of its simplicity and effectiveness. One of the problems in the original DCP scheme is the fixed scaling factors for atmospheric light and transmittance. This paper attempts to replace them with optimal scaling factors obtained by the whale optimization algorithm (WOA). However, the WOA requires a reference to find the optimal scaling factors. To avoid using full reference in the optimization process, we adopt a half-reference that requires no ground-truth image. The proposed approach is justified by several examples and compared with the DCP scheme and a modified DCP scheme. The proposed approach shows its superiority over the comparison schemes in most of the examples, but has an overexposure or less dehazed problems in the rest of the examples. More research should be conducted on the proposed approach for better performance.

In conventional adaptive variable step size (VSS) maximum power point tracking (MPPT) algorithms, a scaling factor is utilized to determine the required perturbation step. However, the performance of the adaptive VSS MPPT algorithm is essentially decided by the choice of scaling factor. In this paper, a neural network assisted variable step size (VSS) incremental conductance (IncCond) MPPT method is proposed. The proposed method utilizes a neural network to obtain an optimal scaling factor that should be used in current irradiance level for the VSS IncCond MPPT method. Only two operating points on the characteristic curve are needed to acquire the optimal scaling factor. Hence, expensive irradiance and temperature sensors are not required. By adopting a proper scaling factor, the performance of the conventional VSS IncCond method can be improved, especially under rapid varying irradiance conditions. To validate the studied algorithm, a 400 W prototyping circuit is built and experiments are carried out accordingly. Comparing with perturb and observe (P&O), α-P&O, golden section and conventional VSS IncCond MPPT methods, the proposed method can improve the tracking loss by 95.58%, 42.51%, 93.66%, and 66.14% under EN50530 testing condition, respectively.

In order to improve the low spatial resolution of remote sensed LST, two methods based on multiple scale factors are proposed. Considering the optimal scale factor is usually not unique under environment of different land cover types, a stratified linear regression model is built, which shows a higher accuracy than global linear regression with one scale factor. In view of the relationship between scale factor and LST showing fluctuant under different conditions, such as season, size of scale and land cover type, where the errors of linear regression model often come from, a method of downscaling based on BP neural network is proposed. Multiple scale factors as input, this method shows as almost precise as the first method globally, especially for mountainous areas and mixed areas, which shows a stronger robustness.

This paper establishes, in the case of CMOS buffer chains, that along with an optimum scaling factor there exists an equivalent optimum P-to-N transistor ratio which minimises latency. An optimum triple-point of buffer number, P-to-N ratio and scaling factor is identified which provides a process-specific absolute minimum latency. It is further determined that for any number of buffers, the corresponding optimum P-to-N transistor ratio and optimum buffer scaling factor can be calculated simultaneously, through numerical approximation of the points of intersection of two transcendental equations.

Data from expression arrays must be comparable before it can be analyzed rigorously on a large scale. Accurate normalization improves the comparability of expression data because it seeks to account for sources of variation obscuring the underlying variation of interest. Undesirable variation in reported expression levels originates in the preparation and hybridization of the sample as well as in the manufacture of the array itself, and may differ depending on the array technology being employed. Published research to date has not characterized the degree of variation associated with these sources, and results are often reported without tight statistical bounds on their significance. We analyze the distributions of reported levels of exogenous control species spiked into samples applied to 1280 Affymetrix arrays. We develop a model for explaining reported expression levels under an assumption of primarily multiplicative variation. To compute the scaling factors needed for normalization, we derive maximum likelihood and maximum a posteriori estimates for the parameters characterizing the multiplicative variation in reported spiked control expression levels. We conclude that the optimal scaling factors in this context are weighted geometric means and determine the appropriate weights. The optimal scaling factor estimates so computed can be used for subsequent array normalization.

The need for securing multimedia content from being intercepted is a prominent research issue. This work proposes an optimised video steganography model that improves imperceptibility and robustness by extracting keyframes and calculating the optimal scaling factor. The squirrel search algorithm (SSA) is used to extract keyframes since it ensures distinct position updation processes through Levy flying and predator features, whilst the grasshopper optimisation and ant lion optimisation algorithms are hybridised to compute the optimal value of the scaling factor. In terms of imperceptibility and robustness, the simulation results suggest that the proposed approach outperforms existing data-hiding models. It also discovers the optimal scaling factor in under ten iterations, indicating that the fastest convergence rate is possible.

Iterative methods with variable preconditioners of additive type are proposed. The scaling factors of each summand in the additive preconditioners are optimized within each iteration step. It is proved that the presented methods converge at least as fast as the Richardson's iterative method with the corresponding additive preconditioner with optimal scaling factors. In the presented numerical experiments the suggested methods need nearly the same number of iterations as the usual preconditioned conjugate gradient method with the corresponding additive preconditioner with numerically determined fixed optimal scaling factors.

Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study.

In this proposed work, a dual image watermarking algorithm is used to protect the data against copyright violations. In this work, the DICOM image is used as a host image. Two watermark images used are the MNNIT logo and the personal data of the patient. This method utilizes the advantages of Schur decomposition, lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scaling factor is a vital parameter of watermarking technique. The firefly optimization technique is used to get the optimized scaling factor. The Speeded-up robust features (SURF) are used for watermarking authentication. To evaluate the performance of the proposed algorithm, peak signal-to-noise ratio (PSNR), normalized correlation coefficient (NCC), and structural similarity index measurement (SSIM) are used. The proposed method is tested against various attacks such as Salt and Pepper noise, Gaussian noise, Gaussian low pass filter, Average filter, Median filter, Histogram equalization, Sharpening, Rotation and Region of interest filtering. The proposed algorithm shows a high level of robustness and imperceptibility. It is found that the features of the input host image and the watermarked image are matching correctly on applying the SURF technique.

This chapter focuses on the use of genetic algorithms (GAs) in the design of FLC. An approach of adopting genetic algorithm search is adopted to determine optimal FLC scaling factors. The approach is then extended by adoption of neural network learning of the scaling factors leading to a neuro-fuzzy control method. This is further combined with genetic algorithm for optimisation of shape of activation function of the neural network. Case study experimental investigation exercises are presented demonstrating the performances of the developed paradigms in the control of a single-link flexible manipulator system.

We present a "scale-and-stretch" warping method that allows resizing images into arbitrary aspect ratios while preserving visually prominent features. The method operates by iteratively computing optimal local scaling factors for each local region and updating a warped image that matches these scaling factors as closely as possible. The amount of deformation of the image content is guided by a significance map that characterizes the visual attractiveness of each pixel; this significance map is computed automatically using a novel combination of gradient and salience-based measures. Our technique allows diverting the distortion due to resizing to image regions with homogeneous content, such that the impact on perceptually important features is minimized. Unlike previous approaches, our method distributes the distortion in all spatial directions, even when the resizing operation is only applied horizontally or vertically, thus fully utilizing the available homogeneous regions to absorb the distortion. We develop an efficient formulation for the nonlinear optimization involved in the warping function computation, allowing interactive image resizing.

Nonlinear phase noise, often called the Gordon-Mollenauer effect, can be compensated electronically by subtracting from the received phase a correction proportional to the received intensity. The optimal scaling factor is derived analytically and found to be approximately equal to half of the ratio of mean nonlinear phase noise and the mean received intensity. Using optimal compensation, the standard deviation of residual phase noise is halved, doubling the transmission distance in systems limited by nonlinear phase noise.

Norm optimization techniques for improved structural reanalysis

The purpose was to determine the physiological correlates to cycling performance within a competitive paratriathlon. Five wheelchair user and ten ambulant paratriathletes undertook laboratory-based testing to determine their: peak rate of oxygen uptake; blood lactate- and ventilatory-derived physiological thresholds; and, their maximal aerobic power. These variables were subsequently expressed in absolute (l∙min -1 or W), relative (ml∙kg-1∙min -1 or W∙kg -1) and scaled relative (or ml∙kg - 0.82 ∙min -1, ml∙kg - 0.32 ∙min -1 or W∙kg -0.32) terms. All athletes undertook a paratriathlon race with 20 km cycle. Pearson's correlation test and linear regression analyses were produced between laboratory-derived variables and cycle performance to generate correlation coefficients (r), standard error of estimates and 95% confidence intervals. For wheelchair users, performance was most strongly correlated to relative aerobic lactate threshold (W∙kg -1) (r=-0.99; confidence intervals: -0.99 to -0.99; standard error of estimate=22 s). For ambulant paratriathletes, the greatest correlation was with maximal aerobic power (W∙kg -0.32) (r=-0.91; -0.99 to -0.69; standard error of estimate=88 s). Race-category-specificity exits regarding physiological correlates to cycling performance in a paratriathlon race with further differences between optimal scaling factors between paratriathletes. This suggests aerobic lactate threshold and maximal aerobic power are the pertinent variables to infer cycling performance for wheelchair users and ambulant paratriathletes, respectively.