Optimal Internal Parameters Research Articles

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer.

This study investigates the application of a coupled multi-layer perceptrons (MLP) model with Archimedes optimizer (AO) to predict characteristics of dissimilar lap joints made of polymethyl methacrylate (PMMA) and polycarbonate (PC). The joints were welded using the laser transmission welding (LTW) technique equipped with a beam wobbling feature. The inputs of the models were laser power, welding speed, pulse frequency, wobble frequency, and wobble width; whereas, the outputs were seam width and shear strength of the joint. The Archimedes optimizer was employed to obtain the optimal internal parameters of the multi-layer perceptrons. In addition to the Archimedes optimizer, the conventional gradient descent technique, as well as the particle swarm optimizer (PSO), was employed as internal optimizers of the multi-layer perceptrons model. The prediction accuracy of the three models was compared using different error measures. The AO-MLP outperformed the other two models. The computed root mean square errors of the MLP, PSO-MLP, and AO-MLP models are (39.798, 19.909, and 2.283) and (0.153, 0.084, and 0.0321) for shear strength and seam width, respectively.

Assessing the Neurotoxicity of a Sub-Optimal Dose of Rotenone in Zebrafish (Danio rerio) and the Possible Neuroactive Potential of Valproic Acid, Combination of Levodopa and Carbidopa, and Lactic Acid Bacteria Strains.

Parkinson’s disease (PD) is an enigmatic neurodegenerative disorder that is currently the subject of extensive research approaches aiming at deepening the understanding of its etiopathophysiology. Recent data suggest that distinct compounds used either as anticonvulsants or agents usually used as dopaminergic agonists or supplements consisting of live active lactic acid bacteria strains might alleviate and improve PD-related phenotypes. This is why we aimed to elucidate how the administration of rotenone (ROT) disrupts homeostasis and the possible neuroactive potential of valproic acid (VPA), antiparkinsonian agents (levodopa and carbidopa – LEV+CARB), and a mixture of six Lactobacillus and three Bifidobacterium species (PROBIO) might re-establish the optimal internal parameters. ROT causes significant changes in the central nervous system (CNS), notably reduced neurogenesis and angiogenesis, by triggering apoptosis, reflected by the increased expression of PARKIN and PINK1 gene(s), low brain dopamine (DA) levels, and as opposed to LRRK2 and SNCA compared with healthy zebrafish. VPA, LEV/CARB, and PROBIO sustain neurogenesis and angiogenesis, manifesting a neuroprotective role in diminishing the effect of ROT in zebrafish. Interestingly, none of the tested compounds influenced oxidative stress (OS), as reflected by the level of malondialdehyde (MDA) level and superoxide dismutase (SOD) enzymatic activity revealed in non-ROT-exposed zebrafish. Overall, the selected concentrations were enough to trigger particular behavioral patterns as reflected by our parameters of interest (swimming distance (mm), velocity (mm/s), and freezing episodes (s)), but sequential testing is mandatory to decipher whether they exert an inhibitory role following ROT exposure. In this way, we further offer data into how ROT may trigger a PD-related phenotype and the possible beneficial role of VPA, LEV+CARB, and PROBIO in re-establishing homeostasis in Danio rerio.

Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer

This paper aims at developing an artificial intelligence model to forecast the water yield of a modified solar distiller integrated with evacuated tubes and an external condenser. The model consists of a hybrid long short-term memory (LSTM) model optimized by a moth-flame optimizer (MFO) used as a subroutine to obtain the optimal internal parameters of the LSTM model that maximize the forecasting accuracy. The model performance was compared with that of the standalone LSTM model. Both developed models were trained and tested using experimental data of the modified distiller and a conventional distiller. The thermal performance of both distillers is also compared in this article. The maximum daily distillate output achieved for the modified distiller was 3920 l/m2. The forecasted data of both models were compared using several statistical measures. For all measurements, LSTM-MFO outperformed standalone LSTM. The determination coefficient of the forecasted data using LSTM-MFO reached a high value of 0.999 for both solar distillers.

High-accurate camera calibration in three-dimensional visual displacement measurements based on ordinary planar pattern and analytical distortion model

Camera calibration is crucial for geometric vision-based three-dimensional (3D) deformation measurement tasks. Among existing calibration techniques, the one based on planar targets has attracted much attention in the community due to its flexibility and reliability. Our study proposes a calibration technique to obtain high-accuracy internal and external parameters based on low-cost ordinary planar patterns. The proposed method determines the optimal internal parameters for each camera by refining 3D coordinates of planar control points, where an analytic model of optics distortion is presented to enable lens distortion to be corrected directly in subsequent external calibration and underlying 3D reconstruction. External parameters are estimated by minimizing a bundle adjustment framework, which is carefully designed based on the proposed distortion correction model and depth parameterization. In contrast to the existing techniques, the proposed method is capable of obtaining a high-accuracy calibration with ordinary targets rather than the well-designed and fabricated ones. We experimented the proposed method with a calibration performance analysis and a displacement measurement; both results demonstrated the accuracy and robustness.

Design and Analysis of Magnetic Coils for Optimizing the Coupling Coefficient in an Electric Vehicle Wireless Power Transfer System

Although the wireless power transfer (WPT) system for electric vehicles (EVs) provides numerous advantages, there is still a low coupling coefficient and the misalignment between the primary coil and the secondary coil needs to be solved. In this paper, the transmission efficiency and transmitted power were calculated based on Series-Series (SS) compensation topology. The coupling coefficient is related to the coil parameters and misalignments. A simulation study was carried out to explore the variation in the coupling coefficient for different coil configurations under different air gaps and coil misalignments. Moreover, the influence of the internal parameters of the square coil on the coupling coefficient was further studied. Finally, this paper discusses the influence of ferrite cores with a square coil on the coupling coefficient. The results of this paper show that designing the optimal internal parameters of the square coil and the ferrite core can increase the coupling coefficient between the coils, which can also provide guidelines for the design and optimization of the magnetic coupling coils for a wireless charging system for electric vehicles.

GLDPC-Staircase AL-FEC codes: a fundamental study and new results

This paper provides fundamentals in the design and analysis of Generalized Low-Density Parity Check (GLDPC)-Staircase codes over the erasure channel. These codes are constructed by extending an LDPC-Staircase code (base code) using Reed-Solomon (RS) codes (outer codes) in order to benefit from more powerful decoders. The GLDPC-Staircase coding scheme adds, in addition to the LDPC-Staircase repair symbols, extra-repair symbols that can be produced on demand and in large quantities, which provides small rate capabilities. Therefore, these codes are extremely flexible as they can be tuned to behave either like predefined rate LDPC-Staircase codes at one extreme, or like a single RS code at another extreme, or like small rate codes. Concerning the code design, we show that RS codes with “quasi” Hankel matrix-based construction fulfill the desired structure properties, and that a hybrid (IT/RS/ML) decoding is feasible that achieves maximum likelihood (ML) correction capabilities at a lower complexity. Concerning performance analysis, we detail an asymptotic analysis method based on density evolution (DE), extrinsic information transfer (EXIT), and the area theorem. Based on several asymptotic and finite length results, after selecting the optimal internal parameters, we demonstrate that GLDPC-Staircase codes feature excellent erasure recovery capabilities, close to that of ideal codes, both with large and very small objects. From this point of view, they outperform LDPC-Staircase and Raptor codes and achieve correction capabilities close to those of RaptorQ codes. Therefore, all these results make GLDPC-Staircase codes a universal Application-Layer FEC (AL-FEC) solution for many situations that require erasure protection such as media streaming or file multicast transmission.

Non-rigid registration and non-local principle component analysis to improve electron microscopy spectrum images

Image registration and non-local Poisson principal component analysis (PCA) denoising improve the quality of characteristic x-ray (EDS) spectrum imaging of Ca-stabilized Nd2/3TiO3 acquired at atomic resolution in a scanning transmission electron microscope. Image registration based on the simultaneously acquired high angle annular dark field image significantly outperforms acquisition with a long pixel dwell time or drift correction using a reference image. Non-local Poisson PCA denoising reduces noise more strongly than conventional weighted PCA while preserving atomic structure more faithfully. The reliability of and optimal internal parameters for non-local Poisson PCA denoising of EDS spectrum images is assessed using tests on phantom data.

Parametric synthesis of technical systems based on the linear approximation of the operational capability range

A method for determining the optimal internal parameters of technical systems, based on the analytical description of the operational capability range, which is defined as a set of linear constraints, is considered. An expression for the objective function, which allows one to use any known search method for optimizing the system by the criterion of the operational capability reserve, is derived.

Optimal design of control valves in magnetorheological fluid dampers using a nondimensional analytical method

The magnetorheological control valve is a key element in magnetorheological dampers to achieve controllable damping characteristics in practice. The optimal design of magnetorheological control valves with an annular flow structure in two configurations of coil wire placements is investigated using a nondimensional analytical method. The achievable performances of the magnetorheological control valve are formulated in terms of several important nondimensional design parameters, which are defined based on the analytical models considering both mechanical flow characteristics and magnetic flux conservation in magnetorheological fluids and valve materials with a clear understanding and convenient specification in optimization. The design method first identifies a few optimal internal parameters through maximizing a single-objective function with predefined constraints. This can avoid empirical difficulty or uncertainty in weight selection in conventional multiobjective optimization methods and guarantee the worst-case performance. Then, the inherent sensitivity of the achievable performance with respect to external parameters is analyzed to provide practical instructions for appropriate design of the magnetorheological control valve. Finally, the analytical optimal results are verified by a finite element analysis, and a comparison is conducted to illustrate the excellent performance of a vibration isolation system employing the optimally designed magnetorheological control valve.

Musical-Noise-Free Speech Enhancement Based on Optimized Iterative Spectral Subtraction

In this paper, we provide a theoretical analysis of the amount of musical noise in iterative spectral subtraction, and its optimization method for the least musical noise generation. To achieve high-quality noise reduction with low musical noise, iterative spectral subtraction, i.e., iteratively applied weak nonlinear signal processing, has been proposed. Although the effectiveness of the method has been reported experimentally, there have been no theoretical studies. Therefore, in this paper, we formulate the generation process of musical noise by tracing the change in kurtosis of noise spectra, and conduct a comparison of the amount of musical noise for different parameter settings but the same achieved level of noise attenuation. Furthermore, we theoretically derive the optimal internal parameters that generate no musical noise. It is clarified that to find a fixed point in kurtosis yields the no-musical-noise property. Comparative experiments with commonly used noise reduction methods show the proposed method's efficacy.

Parametric Sensitivity and Search-Space Characterization Studies of Genetic Algorithms for Computer-Aided Polymer Design

Genetic Algorithms (GAs) and evolutionary methods have been demonstrated to be flexible and efficient optimization techniques with potential for locating global optima under general conditions for computer-aided molecular design (CAMD). However, they often need customization requiring detailed study of parametric sensitivity and search-space character before optimal internal parameters are determined. This paper describes such a parametric sensitivity study for GA-based polymer design. The objective of the study was to study the influence of the internal parameters of the GA on its performance on a large scale polymer design problem. The study yielded qualitative trends that clearly identified the structure of the target sought or the nature of the search-space to be the key factor determining the GA's performance. The study was then focused toward studying the structure to fitness correlation in the underlying search-space. The results of this study indicate that the performance of the GA could be enhanc...