Optimal Parameter Values Research Articles

Bayesian approach for radial kernel parameter tuning

In this paper we present a new fast and accurate method for Radial Basis Function (RBF) approximation, including interpolation as a special case, which enables us to effectively find the optimal value of the RBF shape parameter. In particular, we propose a statistical technique, called Bayesian optimization, that consists in modeling the error function with a Gaussian process, by which, through an iterative process, the optimal shape parameter is selected. The process is step by step self-updated resulting in a relevant decrease in search time with respect to the classical leave one out cross validation technique. Numerical results deriving from some test examples and an application to real data show the performance of the proposed method.

Design Optimization of Feeding Networks with a Special Size for Self-Interference Cancellation

This letter introduces an effective self-interference cancellation (SIC) for improving isolation between transmit (Tx) ports and receive (Rx) ports of an in-band full-duplex antenna with feeding networks (FNs). A significant isolation improvement is realized by replacing the traditional FNs of the antenna with SICFNs. The coupling between Tx and Rx ports is canceled in the FNs due to the specific power ratios and phase difference. Though a Python optimization procedure, the optimal values of the key parameters of the SIC FNs can be determined. To validate the proposed SIC technique, the prototype consisting of two four-element antenna arrays and four SICFNs designed with the technique is fabricated. The results of measurements performed in an anechoic chamber show an isolation improvement of 15 dB at 3.6 GHz, while only 0.3 dB gain loss for the antenna array as a sacrifice is observed.

Prediksi Jumlah Perceraian Menggunakan Metode Support Vector Regression (SVR)

The increasing number of divorces poses an increasingly significant social challenge in Indonesia, including in the city of Pekanbaru. The impact of these divorces on the adolescent population can have negative effects on their emotional and psychological well-being, as well as their ability to interact socially and engage in the learning process. This study utilizes monthly divorce data from 2015 to April 2023 to conduct time series analysis and applies the Support Vector Regression (SVR) method to predict the number of divorces in the city of Pekanbaru. Three types of SVR kernels, namely linear, polynomial, and radial basis function (RBF), are evaluated and compared to find the kernel with the best Mean Squared Error (MSE) results. Through grid search analysis, optimal parameter values for each kernel are determined. The test results indicate that the SVR model with a polynomial kernel provides more accurate predictions with an MSE of 0.010228, compared to the linear kernel (MSE = 0.012767) and the RBF kernel (MSE = 0.010812).

Knee Point‐Based Multiobjective Optimization for the Numerical Weather Prediction Model in the Greater Beijing Area

AbstractDetermination of the optimal parameter values in numerical weather prediction (NWP) models has a significant impact on predictions. Here, we propose a knee point‐based multiobjective optimization (KMO) method to find an optimal solution of the NWP model parameters. We apply it to optimize the Weather Research and Forecasting (WRF) model's summer precipitation and temperature simulations for the Greater Beijing area. The results showed that it required fewer than 125 samples (i.e., 25 times the number of dimensions of the parameter space) to obtain the WRF model's optimal parameter values. The optimal parameters determined by KMO outperform the default parameters in WRF simulations for summer precipitation and temperature prediction in the Greater Beijing area, across all periods (calibration, validation, and testing). Additionally, clear physical interpretations are provided to explain why the optimal parameters lead to improved precipitation and temperature forecasting. Overall, the proposed method is effective and efficient to improve NWP.

I-CPA: An Improved Carnivorous Plant Algorithm for Solar Photovoltaic Parameter Identification Problem.

The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance between the exploration and exploitation capabilities of CPA, minimize getting stuck in local minima, and produce more stable results. The improved CPA is called the I-CPA. To test the performance of the proposed I-CPA, it was applied to CEC2017 functions. In addition, the proposed I-CPA was applied to the problem of identifying the optimum parameter values of various solar photovoltaic modules, which is one of the real-world optimization problems. According to the experimental results, the best value of the root mean square error (RMSE) ratio between the standard data and simulation data was obtained with the I-CPA method. The Friedman mean rank statistical analyses were also performed for both problems. As a result of the analyses, it was observed that the I-CPA produced statistically significant results compared to some classical and modern metaheuristics. Thus, it can be said that the proposed I-CPA achieves successful and competitive results in identifying the parameters of solar photovoltaic modules.

Integrated Assessment of MIG Welding Parameters on Carbon Steel using RSM Optimisation

This study examined how parameter optimisation in MIG welding affects material properties, weld quality, flexural strength, and weld bead geometry using an experimental design. Structural testing reveals that weld pool imperfections, especially in welded joints, often lead to metal failure. Incorrect selection of welding parameters, material weldability, and metal forming contribute to defects and necessitate post-treatment. The study aims to minimize weld defects and maximize material strength in welded parts using a quadratic second-order regression model to predict optimal process parameters. The chosen JIS G3131 hot-rolled carbon steel material is commonly used in automotive parts, is cost-effective, and can be easily welded. The welding process is MIG buttweld according to AWS 1.1 standards. The tests match the RSM design's 3-by-3 orthogonal array. The process parameters are welding current, arc voltage, and welding speed. The responses are bead height, bead width, penetration, and flexural strength. NDT X-ray radiography detects flaws in weld pools. The welded connection's durability is determined with a three-point bending test. Experiments are conducted to find the best input parameters with 95% confidence. This study also uses an experimental design based on an orthogonal array and an analysis of variance (ANOVA). Contribution to factors and regression analysis. The predicted flexural strength was 903.1 MPa, with a 0.66% error compared to the experimental value of 897 MPa. Predicted values for weld penetration, bead width, and bead height are 3.01, 9.45, and 2.04. In the RT inspection test, the test sample with the optimum parameters had no defects in the welded joint. The optimal parameter values for the regression analysis of the RSM combination are 115, 20, and 18 for current, speed, and voltage. Confirmation tests for DT and NDT were performed to validate the optimal parameter settings. This research shows that optimizing process parameters for welded products improves weld geometry and flexural strength. This method is widely used and valuable in metal fabrication.

PMSM Torque Ripple Suppression Method Based on SMA-Optimized ILC

Periodic torque ripple often occurs in permanent magnet synchronous motors due to cogging torque and flux harmonic distortion, leading to motor speed fluctuations and further causing mechanical vibration and noise, which seriously affects the performance of the motor vector control system. In response to the above problems, a PMSM torque ripple suppression method based on SMA-optimized ILC is proposed, which does not rely on prior knowledge of the system and motor parameters. That is, an SMA is used to determine the optimal values of the key parameters of the ILC in the target motor control system, and then the real-time torque deviation value calculated by iterative learning is compensated to the system control current set end. By reducing the influence of higher harmonics in the control current, the torque ripple is suppressed. Research results show that this method has high efficiency and accuracy in parameter optimization, further improving the ILC performance, effectively reducing the impact of higher harmonics, and suppressing the torque ripple amplitude.

Experimental Study for the Enhancement of Thermal Efficiency and Development of Nusselt Number Correlation for the Roughened Collector of Solar Air Heater

Abstract The solar air heater’s thermal efficiency is relatively poor owing to the flat collector surface. This article’s primary objective is to increase the collectors’ thermal efficiency of rectangular ducts of solar air heater by adopting a novel V-shaped twisted rib element with staggering orientation. Experimentations are performed for various flow Reynolds numbers ranging from 3 k to 21 k, roughness pitch-to-rib height ratio ranging from 7 to 11, and staggering distance-to-rib height ratio between 2 and 6. Dispersion of Nusselt number over the collector surface is achieved through the liquid crystal thermography technique. Among the varied rib and flow constraints, it is observed that a maximum thermal performance enhancement index of 2.69 is observed, with the optimum value of the roughness parameter at a rib pitch-to-height ratio of 9 and a staggering distance-to-height ratio of 4. Mathematical correlation has also been developed using a regression model to estimate the Nusselt number in terms of non-dimensional roughness parameters. The percentage deviation between the Nusselt number attained from established relationships and the investigational results is found to be giving very satisfactory outcomes. The thermal efficiency of the smooth surface is recognized at 41.64% which increases for the roughened surface of twisted V-ribs to 73.63%. Hence employing twisted V-ribs as an artificial roughness element no doubt increases the Nusselt number, thermohydraulic performance enhancement index, and thermal efficiency, but it also exerts less frictional power of solar air heater.

Mathematical modeling and optimization of pyramidal still performance using response surface method and Ag-nanoparticle enhanced phase change material

This study aims to develop a robust mathematical model using the Response Surface Methodology (RSM) to predict the thermal efficiency of a pyramidal solar still (PSD). The model takes into account various environmental conditions and different concentrations of silver nanoparticles (Ag) combined with paraffin wax as a thermal storage material (PCM). The applicability of this model extends to various solar stills operating under diverse weather conditions and employing different nanoparticle concentrations. The study focuses on three crucial weather-related variables: solar irradiation, air temperature, and wind speed, all of which significantly influence the solar distillation process. By utilizing RSM with a central composite design featuring four factors and five levels, regression models are constructed to forecast the responses of various performance parameters. The optimal values for process parameters are determined using RSM, and the models are employed to predict the ideal conditions for maximizing the performance of the solar distillation system. These optimized conditions have the potential to enhance both productivity and efficiency. The investigation considers five different Ag concentrations (wt%): 0, 1, 2, 3, and 4 %. The optimized parameter settings are as follows: Ag-Nano concentration (C) at 2.78 %, ambient temperature set to 40 °C, wind velocity maintained at 0.5 m/s, and irradiation at 720 W/m2. The results show a high level of accuracy and consistency between the predicted values generated by the RSM models and the experimental data. The errors between the actual and coded values for the performance parameters (P, Tw, and Tg) fall within a narrow range, with errors of 5.2 %, 4.9 %, and 6.5 %, respectively. These findings underscore the effectiveness and reliability of the proposed mathematical modeling approach.

Investigation of structural parameters for inclined weir-type solar stills

The solar still, in which saline or brackish water is converted into freshwater using renewable solar energy, can be an effective solution to water scarcity. Inclined solar stills are well-known due to their high production yields compared to other types of solar stills. In this study, an inclined weir-type solar still was developed as a modified inclined solar still with an emphasis on the effect of structural parameters, such as weir height, distance between weirs, and distance between the absorber plate and the condenser cover, on distilled water production. The optimal values of structural parameters for the maximum freshwater production were specified by the response surface methodology and a quadratic model derived from a Box-Behnken design model. The results showed that the operational variables of the inclined weir-type solar still system significantly impacted the water production volume. Using a weir height of 2 cm, a distance of 3.5 cm between weirs, and a distance of 15 cm between the absorber plate and the condenser cover, the maximum water production volume was achieved at 6.474 kg m−2.day−1. The selected model had R2, Radj2 and Rpred2 values of 0.9978, 0.9939, and 0.9756, respectively. The maximum and minimum volumes of the produced water were obtained as 6.64125 and 1.79792 kg m−2. day1, based on the multiple response prediction of the variables. According to the validation tests, the model’s accuracy in prediction of the maximum amount of freshwater production was notable (97.49%).

Biodiesel production from used cooking oil and cost appraisal for the purpose of reduction in dependence on petroleum oil in Laos

To mitigate dependence on fossil fuels, the government of Lao PDR has promoted the development of alternative fuel production in the country. Biodiesel is one of the most feasible and attractive fuels. This paper presents a lab-scale biodiesel production from used cooking oil (UCO) and its cost appraisal. This biodiesel production is based on the base-catalyzed transesterification reaction. The optimum parameter value in terms of catalyst concentration and amount of methanol is examined to achieve the best biodiesel yield. The results of this study revealed that the optimized condition for producing biodiesel from 1 liter of UCO is obtained using 12 g of KOH and 20 % of MeOH under the operating temperature of 60 °C and mixing time of 30 min. With this condition, the maximum yield of biodiesel is obtained at 91.3%. The total cost of production for 1 liter of biodiesel produced is approximately 17782 LAK. Production quantity at break-even point is about 41989 liters, achieving a payback period of 2.67 years.

Image denoising and segmentation model construction based on IWOA-PCNN

The research suggests a method to improve the present pulse coupled neural network (PCNN), which has a complex structure and unsatisfactory performance in image denoising and image segmentation. Then, a multi strategy collaborative improvement whale optimization algorithm (WOA) is proposed, and an improved whale optimization algorithm (IWOA) is constructed. IWOA is used to find the optimal parameter values of PCNN to optimize PCNN. By combining the aforementioned components, the IWOA-PCNN model had the best image denoising performance, and the produced images were crisper and preserve more information. IWOA-PCNN processed pictures have an average PSNR of 35.87 and an average MSE of 0.24. The average processing time for photos with noise is typically 24.80 s, which is 7.30 s and 7.76 s faster than the WTGAN and IGA-NLM models, respectively. Additionally, the average NU value measures 0.947, and the average D value exceeds 1000. The aforementioned findings demonstrate that the suggested method can successfully enhance the PCNN, improving its capability for image denoising and image segmentation. This can, in part, encourage the use and advancement of the PCNN.

The Relevance of Process Parameter Optimization and Geometric Figure for Direct Laser Deposition of Inconel 738 Alloy and Its Theoretical Modeling

In order to minimize the gaps between the direct laser deposition channels and improve the quality and performance of the formed parts, the process of direct laser deposition is utilized in laser additive manufacturing to create sequential, single- and double-channel deposition layers on 304 stainless steel plates. Under the premise of keeping the layer rate and defocusing amount unchanged, this study investigates the effects of laser power, scanning speed, and powder feeding rate on the morphology and inclusions of single- and double-channel deposited layers. The aim is to determine the optimal process parameter values for direct laser deposition of single-layer, single-channel Inconel 738. The effects of the three process parameters on the response values were investigated using a multi-factor, multi-level experimental design. The evaluation indexes for the analysis included the deposited layer wetting angle and aspect ratio. The analysis involved one-way extreme analysis and ANOVA analysis. The optimal process parameters are a laser power of 550~750 W, a scanning speed of 7~13 mm/s, and the powder feeding rate was 2.1~4.33 g/min. At the same time, the relationship between surface tension and gravity was integrated with the spherical coronal model and Young’s equation to develop a mathematical model of the direct laser deposition process at a theoretical level. The mathematical model of the direct laser deposition process was utilized to analyze the correlation between the geometric parameters of the cross-section of the deposited layer. This analysis provides a valuable data reference for future Inconel 738 direct laser deposition.

Extension of optimal auxiliary function method to non-linear fifth order lax and Swada-Kotera problem

In this article, a semi-analytical approach known as the optimal auxiliary function method is extended to the approximate solution of non-linear partial differential equations. The fifth order lax and swada-kotera equations are taken as test examples. Utilizing the well-known least squares method, the optimal convergence control parameter values in the auxiliary function have been determined. The outcomes of the proposed method are contrasted with those of a new iterative approach and a homotopy perturbation method. It has been demonstrated that the suggested method for solving non-linear partial differential equations is straightforward and rapidly convergent. The numerical outcomes demonstrate the effectiveness and reliability of the suggested approach. Additionally, using higher order approximations can increase the suggested method's accuracy.

Pseudo and anisotropic MFS for Laplace equation and optimal sources using maximal projection method with a substitution function

The paper creates two new families of fundamental solutions for the 3D Laplace equation, presented into two parts. For the first part in terms of a planar line as a new coordinate the derived 2D like fundamental solution has a logarithmic singularity, which results in a method of pseudo fundamental solutions. We propose two methods to determine the optimal values of the offset parameter used to locate the source points. In the second part, an anisotropic distance function rg in terms of a symmetric non-negative anisotropic metric tensor is introduced, which satisfies a certain quadratic matrix equation, and then lnrg is proved to be a new fundamental solution. Using a unit orientation vector we can derive the metric tensor in closed-form, and prove that it is a singular projection operator. Given the unit orientation vector satisfying a cone condition, a method of anisotropic fundamental solutions is developed. They are distinct from the traditional 3D MFS. Owing to a weaker singularity than that of 1/r appeared in the 3D MFS, the method of pseudo fundamental solutions and the method of anisotropic fundamental solutions outperform the 3D MFS. Some numerical experiments explore the performance of these two novel methods.

Boosting Biodiesel Production from Dairy-Washed Scum Oil Using Beetle Antennae Search Algorithm and Fuzzy Modelling

The major goal of this study was to develop a robust fuzzy model to mimic the generation of biodiesel from the transesterification of dairy-washed milk scum (DWMS) oil. Four process parameters were considered: the molar ratio of methanol to oil, the concentration of KOH, the reaction temperature, and the reaction time. The proposed technique was divided into two steps: fuzzy modelling and optimum parameter identification. The capability of fuzzy tools to capture and make use of linguistic variables and fuzzy sets is one of their main benefits. This means that fuzzy logic allows for the representation and manipulation of values that fall across a continuum rather than merely relying on crisp values or binary categories. When dealing with non-linear relationships, this is especially helpful since it gives a more accurate and nuanced depiction of the underlying data. As a result, an accurate fuzzy model was initially built based on collected data to simulate the biodiesel production in terms of the molar ratio of methanol to oil, the concentration of KOH, the temperature of the reaction, and the reaction duration. In the second phase, the beetle antennae search (BAS) algorithm was applied to identify the optimal values of the process parameters to boost the production of biodiesel. The BAS algorithm draws inspiration from beetle behavior, particularly how they navigate using their antennae. It employs a swarm-intelligence method by deploying virtual beetles that swarm over the problem area in search of the best solution. One of its main features is the BAS algorithm’s capacity to balance exploration and exploitation. This is accomplished through the algorithm’s adaptable step-size mechanism during the search phase. As a result, the algorithm can first investigate a large portion of the problem space before gradually moving closer to the ideal answer. Compared with ANOVA, and thanks to fuzzy, the RMSE decreased from 7 using ANOVA to 0.73 using fuzzy (a decrease of 89%). The predicted R2 increased from 0.8934 using ANOVA to 0.9614 using fuzzy (an increase of 7.6). Also, the optimisation results confirmed the superiority of the BAS algorithm. Biodiesel production increased from 92% to 98.16%.

Tribological Properties of Carbon Tool Steel after Plasma Electrolytic Nitrocarburizing

The effect of plasma electrolytic nitrocarburizing on the wear resistance of carbon tool steel in friction couples with hardened steel and lead-tin bronze is considered in order to study the mechanism and type of wear, as well as the influence of structural and morphological characteristics of the surface on them. The microgeometry of friction tracks and its change with an increasing duration of friction tests are analyzed. The equilibrium roughness is determined, which is optimal for the friction couple and ensures minimal wear. The optimal values of the plasma electrolytic nitrocarburizing parameters, which provide the lowest values of the friction coefficient and wear rate, have been determined. The phase and elemental composition of the surface layer was studied using X-ray diffraction analysis and EDX analysis. The relationship of the microstructure of the nitrocarburized layer of tool steel with the friction coefficient and weight wear is established.

Frequency Control in PV-Integrated Microgrid Using Hybrid Optimization-Based ANFIS and Deep Learning Network

Frequency variation in the network and severe impact of linked frequency-sensitive loads are the main concerns of integrating RESs with traditional power system networks. Because RESs are separated from the traditional grid by power electronic converters, they have zero or very little inertia, which is the primary source of frequency variation. If different electrical motors are connected with PV, the rotor speed frequency and the pulse of the PV panel are assorted. Therefore, this paper suggests an adaptive Neuro-fuzzy inference system (ANFIS) and Deep Neural Network (DNN) based controller for improving the performance of the power system. The developed approach’s primary goal is to regulate the output waveform, hence reducing the error among the control and reference signals. Moreover, the performance model is further enhanced by optimizing the ANFIS controller using the novel hybrid optimization algorithm, named Honey Badger-based grey wolf Optimization (HB-GWO) Algorithm. The performance of the implemented scheme is done in MATLAB and the results over various controllers concerning switching frequency and time analysis. Accordingly, the optimal parameter values obtained using the HB-GWO Algorithm are, the initial step size is 0.31258, the decrease rate of step size is 0.47177 and the increase rate of step size is 1.3754.

A Method for Precise Tracking Control of Pneumatic Artificial-Muscle-Driven Exoskeletal Robot

Exoskeletal robots are of critical importance in the domain of mechanical boosting. The pneumatic artificial muscle (PAM) is commonly used as a flexible actuator in exoskeletal robots designed for upper limbs due to its high power-to-weight ratio, conformability, and safety. This study establishes a new model based on the existing model to improve its control precision by implementing elastic and frictional forces and empirical coefficients, battling against the time-variant hysteresis that PAM’s output force exhibits. In the meantime, a BP neural network is employed in reverse modeling, followed by the adoption of the least-square-based particle swarm optimization algorithm in order to determine the optimized parameter values. PAM provides the Upper Limb Exoskeletal Robot with appropriate auxiliary power, which can be adjusted to accommodate variations in posture change during the lifting process. PAM is also capable of handling variable loads based on the principle of torque balance, constructing a control system according to the inverse dynamics of exoskeletal robots accompanied by an inverse model of PAM’s output force, and finally, rendering tracking control of the elbow angle during the auxiliary process possible. Finally, the tracking error results are calculated and shown; the maximum angular error in the tracking process is 0.0175 rad, MAE value is 0.0038 rad, RMSE value is 0.0048 rad, and IEAT value is 4.6426 rad. This control method is able to improve the precision of tracking control of the elbow angle of the upper limb–exoskeleton coupled system during the process of lifting goods.

Fitting high-resolution electron density maps from atomic models to solution scattering data

Solution scattering techniques, such as small- and wide-angle X-ray scattering (SWAXS), provide valuable insights into the structure and dynamics of biological macromolecules in solution. In this study, we present an approach to accurately predict solution X-ray scattering profiles at wide angles from atomic models by generating high-resolution electron density maps. Our method accounts for the excluded volume of bulk solvent by calculating unique adjusted atomic volumes directly from the atomic coordinates. This approach eliminates the need for one of the free fitting parameters commonly used in existing algorithms, resulting in improved accuracy of the calculated SWAXS profile. An implicit model of the hydration shell is generated that uses the form factor of water. Two parameters, namely the bulk solvent density and the mean hydration shell contrast, are adjusted to best fit the data. Results using eight publicly available SWAXS profiles show high-quality fits to the data. In each case, the optimized parameter values show small adjustments demonstrating that the default values are close to the true solution. Disabling parameter optimization produces significantly more accurate predicted scattering profiles compared to the leading software. The algorithm is computationally efficient, comparable to the leading software and up to 10 times faster for large molecules. The algorithm is encoded in a command line script called denss.pdb2mrc.py and is available open source as part of the DENSS v1.7.0 software package. In addition to improving the ability to compare atomic models to experimental SWAXS data, these developments pave the way for increasing the accuracy of modeling algorithms using SWAXS data and decreasing the risk of overfitting.