Selection Of Optimal Parameters Research Articles

Fatigue loads compressed editing by discrete wavelet transform and optimal wavelet parameters selection algorithm

Discrete wavelet transform (DWT) can be used to edit the vehicle road load signals for accelerating durability analyses. Nevertheless, it should be noted that one of inherent deficiencies of DWT is the obvious energy leakage. This paper proposes an improved compressed editing method for vehicle road load signals, which is to achieve more accurate results, based on the energy leakage of DWT to select the optimal wavelet parameters. A wavelet parameters optimal method is proposed by investigating the severity of parameters on energy leakage. Daubechies wavelet functions with different order are performed on signals under the same decomposition level. Then, the intensity of energy leakage in wavelet components in each level is calculated and compared with adjacent levels. Thus, the optimal wavelet parameters will be extracted taking into account the time–frequency resolutions. The effectiveness of this method has been validated by numerical signal, and the optimal method is applied to a series of load signal to achieve compressed compilation.

Spectral analysis of seismic data based on a combined generalized S transform

The S transform is widely used to delineate seismic spectral anomalies associated with hydrocarbons during reservoir characterization. To overcome the relatively poor time-frequency resolution due to the fixed varying pattern of the window used in the S transform, we present a combined generalized S transform (CGST) for the spectral analysis of seismic data. First, the CGST decomposes the signal into a series of intrinsic mode functions (IMFs) using a complete ensemble empirical mode decomposition (CEEMD). An objective function is defined to quantitatively determine the spectral energy concentration criteria for optimal parameter selection of the improved Gaussian windows. Then, the time-frequency spectra for all the IMFs within narrower frequency bands are calculated using the respective Gaussian windows with normalized energy and individual optimal controlling parameters. We applied the CGST to spectral analyses of both synthetic signals and seismic field data. The results indicate that the CGST exhibits good performance with a higher time-frequency resolution. Field examples illustrate that the CGST can be used to characterize the spectral behavior of thin beds and indicate low-frequency shadows associated with hydrocarbon reservoirs.

Mechanism Analysis and Optimization Design of Exoskeleton Robot with Non-Circular Gear–Pentabar Mechanism

To address the complex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi−rod mechanisms, an exoskeleton knee robot with a differential non−circular gear–pentarod mechanism is designed based on non−circular gears with arbitrary transmission ratios to constrain the degrees of freedom of the R-para-rod mechanism. In this study, the kinematic model of a non-circular gear–five−rod mechanism is established based on motion mapping theory by obtaining the normal motion positions of the human lower limb. An optimization design software for the non-circular gear–five−rod mechanism is developed using the MATLAB 2018b visualization platform, with the non−circular active gear as the sole input variable. A set of ideal parameters is obtained through parameter adjustment and optimal parameter selection, and the corresponding trajectories are compared with human trajectories. The three−dimensional model of the mechanism is established according to the obtained parameters, and the motion simulation of the non−circular gear–five−bar mechanism demonstrates that the mechanism can better reproduce the expected human knee joint motion posture, meeting the working requirements of an exoskeleton knee robot.

Feasibility and optimization of19F MRI on a clinical 3T with a large field-of-view torso coil.

The purpose of this work is to: 1) demonstrate fluorine-19 (19F) MRI on a 3T clinical system with a large field-of-view (FOV) multi-channel torso coil 2) demonstrate an example parameter selection optimization for a 19F agent to maximize the SNR-efficiency for SPGR, bSSFP, and phase-cycled bSSFP (bSSFP-C), and 3) validate detection feasibility in ex vivo tissues.&#xD;Methods: Measurements were conducted on a 3.0T Discovery MR750w MRI (GE Healthcare, USA) with an 8-channel 1H/19F torso coil (MRI Tools, Germany). Numerical simulations were conducted for perfluoropolyether (PFPE) to determine the theoretical parameters to maximize SNR-efficiency for the sequences. Theoretical parameters were experimentally verified, and the sensitivity of the sequences was compared with a 10-minute acquisition time with a 3.125x3.125x3mm3 in-plane resolution. Feasibility of a bSSFP-C was also demonstrated in phantom and ex vivo tissues.&#xD;Results: Flip angles (FA) of 12 and 64 degrees maximized the signal for SPGR and bSSFP, and validation of optimal FA and receiver bandwidth showed close agreement with numerical simulations. Sensitivities of 2.47, 5.81, and 4.44 ms^(-0.5) mM^(-1) and empirical detection limits of 20.3, 1.5, and 6.2 mM were achieved for SPGR, bSSFP, and bSSFP-C, respectively. bSSFP and bSSFP-C achieved 1.8-fold greater sensitivity over SPGR (p<0.01).&#xD;Conclusion: bSSFP-C was able to improve sensitivity relative to simple SPGR and reduce both bSSFP banding effects and imaging time. The sequence was used to demonstrate the feasibility of 19F MRI at clinical FOVs and field strengths within ex-vivo tissues.

Development of a Model to Support the Management of the Resistance Welding Process

The work was carried out as part of a project to improve the corrosion resistance of welded joints. The optimal selection of input parameters reduces the consumption of welded material as well as the negative impact on the environment. The samples were welded by electrical resistance welding, more precisely by cross-wire welding. Electric resistance welding is a process for welding with electricity in which of high electrical resistance is used to generate heat at the contact point of the welded parts. Steel wire S235 with a diameter of 4 and 3 mm was used. The article shows the creation of the model to predict the percentage of setdown and weld strength as a function of the input parameters, welding current and welding time. The first task was to create a Design of experiments in which the process parameters or the range of input parameters are determined. The Design of the experiments was created for both wire diameters. This covers the range of parameters with which a welded joint of wires can be realised. A statistical analysis of the process follows, where it was found that for 4 and 3 mm diameter wires the percentage setdown increases with increasing welding current and time. An increase in strength as a function of welding current and welding time is also observed, but the results overlap in most cases and the range of their values is greater than that of setdown. The appearance of the weld was analysed for each sample. The visual inspection revealed seven categories of welds, which were divided into two groups, i.e. good and poor welds. All results were listed in a table with the percentage of expectation for a particular category of weld appearance. Based on the analysis, a model for determining the welding result parameters depending on the specified input parameters was developed, which can be used in practise to support the management of the electric resistance welding process.

Multi-parameter terahertz metamaterial sensors based on single-layer quarter ring patterns

A terahertz metamaterial sensor based on a single-layer quarter ring pattern is proposed to realize the simultaneous measurement of temperature, refractive index, and other parameters through a metasurface consisting of a patterned quarter ring gold layer and a silicon substrate. Field finite element numerical simulations were performed to analyse the transmission and reflection spectra. A two-parameter sensor combined with electric field distribution was realized using multiple peaks, thus revealing the physical mechanisms of the interaction in metamaterial structures. In addition, the effect of each geometrical parameter on the sensor performance is also simultaneously analysed to verify the optimal selection of structural parameters. On this basis, the sensors are theoretically analysed at different incidence and polarization angles, and the results show that the transmittance is not sensitive to the polarization angle. The simulation results show that the transmittance at the resonant frequencies of 0.48 THz, 0.64 THz, 0.79 THz and 1.04 THz are 93.7 %, 79.1 %, 38.4 % and 50.8 %, respectively, and the refractive index sensitivities at the resonant frequencies are 6.84 GHz/RIU, 7.42 GHz/RIU, 8.22 GHz/RIU and 32.11 GHz/RIU, and quality factor Q of 6.76, 9.63, 15.51 and 26.28, respectively. The devices proposed in this study are simple in structure, easy to fabricate, easy to miniaturize and can be integrated for sensitivity, multi-parameter sensors. They can be functionally designed for chemical and biological sensing and other important applications.

Stability analysis of multi-insert rotating boring bar with stiffness variation

Manufacturing faces challenges in boring processes, critical for hole enlargement and finishing, due to low stiffness leading to static deflection and regenerative chatter. Stiffness variation has emerged as a promising solution for effectively suppressing chatter, while multi-insert rotating boring bars offer a viable approach to addressing static deflection in long and slender boring bars, enabling better tolerance with a large operational length-to-diameter ratio. However, integrating technological innovations to suppress chatter and prevent static deflection complicates the problem from a modeling and stability analysis perspective, making it challenging to find a comprehensive solution in the existing literature. This study explores the stability of multi-insert rotating boring bars with stiffness variation, providing insights into selecting optimal stiffness variation parameters. It introduces a novel extension of the multi-dimensional cutting force model to rotating boring tools with multiple inserts, employing the zero-order harmonic solution to analyze the stability of boring processes with time-varying dynamics. Experimental validation demonstrates the effectiveness of the proposed methodology, achieving 5 out of 6 matches in stability lobe diagrams for multi-insert rotating boring bars without stiffness variation. The model’s comprehensiveness is further demonstrated by comparing the results with those of existing studies in the literature for single-insert stationary boring bars. In the case without stiffness variation, the model successfully reproduces 4 out of 5 operating points, and with stiffness variation, it accurately predicts all 5 operating points. Sensitivity analyses guide the selection of optimal stiffness variation parameters for effective chatter suppression, favoring moderate frequencies and up to a 30% amplitude ratio.

Explaining the improving effect of dough crumb-sheet composite rolling on fresh noodle quality: From microstructure and moisture distribution perspective.

A new technique known as dough crumb-sheet composite rolling (DC-SCR) was used to improve the quality of fresh noodles. However, there is a dearth of theoretical investigations into the optimal selection of specific parameters for this technology, and the underlying mechanisms are not fully understood. Therefore, the effects of dough crumb addition times in DC-SCR on the texture, cooking, and eating quality of fresh noodles were first studied. Then, the underlying regulation mechanism of DC-SCR technology on fresh noodles was analyzed in terms of moisture distribution and microstructure. The study demonstrated that the most significant enhancement in the quality of fresh noodles was achieved by adding dough crumbs six times. Compared with fresh noodles made without the addition of dough crumbs, the initial hardness and chewiness of fresh noodles made by adding six times of dough crumbs increased by 25.32% and 46.82%, respectively. In contrast, the cooking time and cooking loss were reduced by 28.45% and 29.69%, respectively. This quality improvement in fresh noodles made by DC-SCR came from the microstructural differences of the gluten network between the inner and outer layers of the dough sheet. A dense structure on the outside and a loose structure on the inside could endow the fresh noodles made by DC-SCR with higher hardness, a shortened cooking time, and less cooking loss. This study would provide a theoretical and experimental basis for creating high-quality fresh noodles.

Multiple Disturbance Suppression of IPMSM Drives Based on Embedded Discrete-Time Repetitive ADRC With Optimized Parameter Selection

Multiple Disturbance Suppression of IPMSM Drives Based on Embedded Discrete-Time Repetitive ADRC With Optimized Parameter Selection

Geometric error modeling and decoupling identification of rotary axis of five-axis machine tool based on spatial trajectory planning

This paper proposes a method for identification of geometric error and the improvement of measuring accuracy of rotary axes. The primary objective of this work concentrates on the decoupling identify of geometric errors of rotary axis, and the optimal selection of measurement parameters (velocity and measuring points), which can ensure measurement completeness and improve accuracy simultaneously. The proposed approach involves the construction of spatial trajectory driven four-axis motion synchronously. Subsequently, measurement parameters are optimal selected with identification matrix. In the next step, PDGEs are represented using NURBS rapidly solved. The investigations reveal that error decoupling can further enhance compensation accuracy. It was also shown that simply increasing the number of measurements does not improve identification accuracy. The result of experiment indicates that the average compensation rate of trajectory reaches 59.73 %. Therefore, the proposed method can be employed in the reliable calibration of geometric errors particular to five-axis machine tool.

Audio analysis speeding detection techniques based on metaheuristic-optimized machine learning models

Law enforcement plays an important role in road safety across the world. Speed enforcement merits special attention as the correlation between exceeding speed limits and the risk of fatalities and serious injuries is well-known. Methods based on radar and light detection and ranging techniques have established themselves as effective methods for measuring vehicle speeds in real time on roads. However, the price of instruments used for these measurements limits their widespread applicability. This work explored the potential of coupling artificial intelligence and audio analysis for vehicle speed assessment. The potential of deep neural networks and extreme gradient boosting is explored on a recently proposed real-world vehicle speed dataset. Frequency analysis techniques are also applied to help reduce the computational demands of experimentation. Additionally, a modified optimization algorithm is introduced to help select optimal control parameters and improve the performance of the suggested method. Five experiments were executed to demonstrate the potential for detecting the exact vehicle speed using regression techniques as well as utilizing classifications to detect vehicles breaking the speed limit. The proposed methods demonstrated promising potential when applied to this pressing challenge.

Using a One-Dimensional Convolutional Neural Network with Taguchi Parametric Optimization for a Permanent-Magnet Synchronous Motor Fault-Diagnosis System

Hyperparameter tuning requires trial and error, which is time consuming. This study employed a one-dimensional convolutional neural network (1D CNN) and Design of Experiments (DOE) using the Taguchi method for optimal parameter selection, in order to improve the accuracy of a fault-diagnosis system for a permanent-magnet synchronous motor (PMSM). An orthogonal array was used for the DOE. One control factor with two levels and six control factors with three levels were proposed as the parameter architecture of the 1D CNN. The identification accuracy and loss function were set to evaluate the fault-diagnosis system in the optimization design. Analysis of variance (ANOVA) was conducted to design multi-objective optimization and resolve conflicts. Motor fault signals measured by a vibration spectrum analyzer were used for fault diagnosis. The results show that the identification accuracy of the proposed optimization method reached 99.91%, which is higher than the identification accuracy of 96.75% of the original design parameters before optimization. With the proposed method, the parameters can be optimized with a good DOE and the minimum number of experiments. Besides reducing time and the use of resources, the proposed method can speed up the construction of a motor fault-diagnosis system with excellent recognition.

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively—figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Bayesian optimization for stable properties amid processing fluctuations in sputter deposition

We introduce a Bayesian optimization approach to guide the sputter deposition of molybdenum thin films, aiming to achieve desired residual stress and sheet resistance while minimizing susceptibility to stochastic fluctuations during deposition. Thin films are pivotal in numerous technologies, including semiconductors and optical devices, where their properties are critical. Sputter deposition parameters, such as deposition power, vacuum chamber pressure, and working distance, influence physical properties like residual stress and resistance. Excessive stress and high resistance can impair device performance, necessitating the selection of optimal process parameters. Furthermore, these parameters should ensure the consistency and reliability of thin film properties, assisting in the reproducibility of the devices. However, exploring the multidimensional design space for process optimization is expensive. Bayesian optimization is ideal for optimizing inputs/parameters of general black-box functions without reliance on gradient information. We utilize Bayesian optimization to optimize deposition power and pressure using a custom-built objective function incorporating observed stress and resistance data. Additionally, we integrate prior knowledge of stress variation with pressure into the objective function to prioritize films least affected by stochastic variations. Our findings demonstrate that Bayesian optimization effectively explores the design space and identifies optimal parameter combinations meeting desired stress and resistance specifications.

Optimizing lyophilization primary drying: A vaccine case study with experimental and modeling techniques

In this study, we present the lyophilization process development efforts for a vaccine formulation aimed at optimizing the primary drying time (hence, the total cycle length) through comprehensive evaluation of its thermal characteristics, temperature profile, and critical quality attributes (CQAs). Differential scanning calorimetry (DSC) and freeze-drying microscopy (FDM) were used to experimentally determine the product-critical temperatures, viz., the glass transition temperature (Tg’) and the collapse temperature (Tc). Initial lyophilization studies indicated that the conventional approach of targeting product temperature (Tp) below the Tc (determined from FDM) resulted in long and sub-optimal drying times. Interestingly, aggressive drying conditions where the product temperature reached the total collapse temperature did not result in macroscopic collapse but, instead, reduced the drying time by ∼ 45 % while maintaining product quality requirements. This observation suggests the need for a more reliable measurement of the macroscopic collapse temperature for product in vials. The temperature profiles from different lyophilization runs showed a drop in product temperature following the primary drying ramp, of which the magnitude was correlated to the degree of macroscopic collapse. The batch-average product resistance, Rp, determined using the manometric temperature measurement (MTM), decreased with increasing dried layer thickness for aggressive primary drying conditions. A quantitative analysis of the product temperature and resistance profiles combined with qualitative assessment of cake appearance attributes was used to determine a more representative macro-collapse temperature, Tcm, for this vaccine product. A primary drying design space was generated using first principles modeling of heat and mass transfer to enable selection of optimum process parameters and reduce the number of exploratory lyophilization runs. Overall, the study highlights the importance of accurate determination of macroscopic collapse in vials, pursuing aggressive drying based on individual product characteristics, and leveraging experimental and modeling techniques for process optimization.

Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

Investigation into hydraulic fracture propagation behavior during temporary plugging and diverting fracturing in deep coal seam

In this study, we conducted indoor temporary plugging and diverting fracturing (TPDF) experiments on samples of natural deep and shallow coal seams using a small-scale true triaxial fracturing simulation system. By integrating high-precision CT scanning technology and crack reconstruction techniques, and relying on the quantitative characterization of the crack complexity coefficient, we investigated the differences in internal structures between deep and shallow coal seam samples and the variations in the expansion of TPDF cracks. Furthermore, the study primarily focused on exploring the influence of temporary plugging agent (TPA) parameters (quantity and particle size) on the expansion patterns of TPDF cracks in deep coal seam coal-rock samples. The experimental results reveal that in shallow coal samples, artificially induced fractures are notably longer and extend to various surfaces of the samples. Conversely, in deep coal samples, the expansion of artificially induced fractures is influenced by well-developed cleavage and cleats. The crack complexity coefficient of artificial fractures in deep coal samples is 1.54 higher than that in shallow coal samples, with an increased crack width of 98 μm. However, the expansion distance of the fractures is shorter and does not extend to the S3 and S6 surfaces. Increasing the dosage of the TPA is advantageous for inducing frequent redirection of fractures and communication with previously untouched areas. When the dosage of the TPA is increased from 30 g/L to 60 g/L, the expansion distance of artificial fractures significantly increases, extending to various surfaces of the samples. The crack complexity coefficient increases by 1.1, and the crack width enlarges by 84 μm. Appropriate particle sizes of the TPA can effectively form seals. However, small particle sizes (200/400 mesh) struggle to seal initial fractures, while large particle sizes (10/20 mesh) can lead to excessive wellhead blockage, causing rapid shut-in pressure, destructive fracturing in deep coal samples, and the generation of a large amount of coal fines. This study holds significant guidance for the optimal selection of process parameters in the temporary plugging hydraulic fracturing of deep coal rock.

PV-based Performance Evaluation of Zeta and Sepic Topologies for EV Applications

In this paper, a comparative performance analysis was proposed for PV fed EV charging system for ZETA and SEPIC DC-DC converter topologies. The utilization of Electric vehicles has gained significance in recent years due to societal awareness and increasing concern for environmental sustainability. EV charging time plays a vital role in modern-day vehicle commuting with energy-efficient performance. The selection of an appropriate DC-DC converter topology plays a significant role for fast charging with improved efficiency. The paper presents an optimized parameter selection, design, simulation methodology &amp; relative performance analysis between the ZETA and SEPIC converter topologies with and without MPPT algorithm for EV charging applications. The MPPT algorithm based on P&amp;O and IC techniques investigates for switching time of the ZETA and SEPIC converters under standard test conditions of solar PV panel i.e., at irradiance of 1000 W/m2 and temperature of 25oC. In this article MATLAB/Simulink was developed to explore the performance of both topologies with and without MPPT approaches for battery SoC, battery voltage and charging current. The study shows that the battery charged from an SoC of 50% to 50.035% for SEPIC converter while the ZETA converter charges from 50% to 50.025% with a similar simulation time of ten seconds with Solar power input. The results demonstrate the DC-DC SEPIC converter is the best choice for EV charging applications through the PV system.

Cavitation assisted intensification of biogas production: A review

AbstractIntensified cavitation‐assisted biogas production from sustainable feedstock has been discussed describing the working principles and governing mechanisms for intensification. Various methods of biogas production discussed in the work include activated sludge processes, membrane bioreactor (MBR), and processes involving methanogenic and sulfate‐reducing microorganisms. Design aspects of cavitational reactors (sonochemical and hydrodynamic cavitation) have been presented with detailed understanding into effect of several operational parameters, such as the biomass‐to‐water ratio, operating pressure, treatment duration, operating temperature, power dissipation, and so on. Selection of optimum parameters is crucial to improve the performance and observed intensification from such processes. The possible benefits in terms of applicability to various types of biomass, efficiency, higher yields, and energy‐saving as compared to the conventional production processes have been demonstrated. Overall, cavitation‐assisted techniques are very effective in increasing biogas production and have significant potential for commercial applications, which would result in significant cost savings.

Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network

Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model's predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system.