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.

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 & 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&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.

Detection and Recognition of Voice Commands by a Distributed Acoustic Sensor Based on Phase-Sensitive OTDR in the Smart Home Concept.

In recent years, attention to the realization of a distributed fiber-optic microphone for the detection and recognition of the human voice has increased, whereby the most popular schemes are based on φ-OTDR. Many issues related to the selection of optimal system parameters and the recognition of registered signals, however, are still unresolved. In this research, we conducted theoretical studies of these issues based on the φ-OTDR mathematical model and verified them with experiments. We designed an algorithm for fiber sensor signal processing, applied a testing kit, and designed a method for the quantitative evaluation of our obtained results. We also proposed a new setup model for lab tests of φ-OTDR single coordinate sensors, which allows for the quick variation of their parameters. As a result, it was possible to define requirements for the best quality of speech recognition; estimation using the percentage of recognized words yielded a value of 96.3%, and estimation with Levenshtein distance provided a value of 15.

Influence of nanocolumnar electrode geometry on electrochemical sensor performance

The concentration of glucose in sweat recently has been measured with high sensitivity, selectivity, and reproducibility by nanostructured NiO electrodes manufactured by the glancing angle deposition (GLAD) technique. The GLAD technique allows electrode morphological properties such as porosity and film thickness to be tightly controlled, providing ample opportunity to enhance the sensor performance. Currently, the selection of optimal GLAD parameters is determined experimentally by trial and error, at high costs in time and resources. Numerical simulation allows the effects of various parameters on sensor performance to be investigated at a much lower cost compared to experimental studies. In this work, a 2D reaction–diffusion model for the surface-catalyzed reactions in the nanostructured GLAD electrodes is developed, which are then solved using the finite element method. Parametric studies on the nanocolumn thickness and nanocolumn separation of the GLAD structures are then conducted to optimize GLAD-based electrode structures with different adsorption and catalytic rates. This research offers new guidance for rapidly designing highly effective sensors with higher sensitivity and lower limits of detection.

Метод совершенствования планирования и управления технологическими процессами воздухообеспечения угольных шахт

Introduction. Management methods in complex technical systems of the mining and processing complex are currently considered as the main tool for improving the planning and management of technological processes in coal mines. In this regard, there is a need to develop an apparatus that allows research and design of methods on a unified basis. Methodology and research methods. During theoretical studies, methods of mathematical statistics and graph theory were used. The apparatus of mathematical logic, set theory and Gomori algorithms and their modifications were used. Discussion of research results. The article presents the results of research to develop a method for improving the planning and control of technological processes of air supply in coal mines. It has been established that the assessment and analysis of methods for improving the planning and control of technological processes of air supply (ATS) in coal mines are becoming increasingly relevant in connection with the modernization of mining production in the Russian Federation. Conclusion. Based on the research carried out, theoretical and practical problems were set and solved, an analysis of the patterns of TPV was carried out to improve the safety and prevent emergency situations of TPV in coal mines. Methods for calculating and analyzing safety for the variable structure of the system and its multifunctional states have been improved, allowing for the selection of optimal TPV parameters and performing calculations to ensure trouble-free operation of the TPV. The research results can be used to set the structure and control technological processes of air supply in coal mines. Conclusions on the article. The article presents the results of TPV studies conducted in coal mines. A method has been developed for analyzing and assessing components for controlling the air supply system of hot water supply, where its use allows for a comprehensive analysis and assessment of emergency situations of hot water supply. The research results can be useful when carrying out mining operations at all enterprises where air supply is necessary.

Preventive-Security-Constrained Optimal Power Flow Model Considering IPFC Control Modes

The interline power flow controller (IPFC) is one of the most versatile integrated flexible alternating current transmission systems (FACTS) controllers and can realize power flow control for multiple transmission lines in modern power systems. However, control characteristics are ignored in conventional IPFC models, in which unreasonable assumptions about injected voltages may lead to security problems in realistic operation. Besides, preventive security constraints considering IPFC control modes are not included in optimal power flow (OPF) control of the system with IPFC, squandering IPFC control potential. To solve these problems, a preventive-security-constrained optimal power flow (PSCOPF) model considering IPFC control modes is proposed in this paper. IPFC control characteristics under different control modes are analyzed and employed as constraints of the optimization model. The iterative updates of converter output voltages for different control modes are derived respectively for power flow calculation, and the power and voltages required in the objective function and constraints of the proposed model can then be obtained. Through optimal selection of IPFC control modes and control parameters, the proposed model can better reconcile the economical and secure operation of the system. Numerical results demonstrate the efficient performance and superiority of the PSCOPF model considering IPFC control modes.

Probabilistic gear fatigue life prediction based on physics-informed transformer

Gear fatigue testing is a complex process that is both time-consuming and labor-intensive, and often produces dispersed results. Traditional statistical analysis methods for probability-stress-life (P-S-N) and fatigue limit require large sample sizes. This study proposes a novel probabilistic gear fatigue life prediction method based on physics-informed Transformer. This method seeks to overcome the limitations of traditional statistical analysis methods and data-driven models by integrating stress-life physical knowledge with the data-driven model. Specifically, based on the obtained gear fatigue limit, this method predicts the probabilistic fatigue life at each stress level by using only a set of probability distribution characteristics of fatigue life under constant amplitude load. The proposed method was validated through multiple cases of gear bending fatigue tests with varying materials, treatments, and geometries, and prediction errors were within an error factor of 2. Compared with other traditional machine learning methods, the accuracy of life prediction was improved by 19.74% to 29.44%. Additionally, feature analysis was conducted to identify key parameters influencing the accuracy of probabilistic fatigue life prediction and provide a range of optimal parameter selection, which is expected to significantly promote the efficiency of fatigue basic data construction in the gear industry.

Optimal Temperature Calculation for Multicriteria Optimization of the Hydrogenation of Polycyclic Aromatic Hydrocarbons by NSGA-II Method

Introduction. Multicriteria optimization, taking into account contradicting criteria, is used to improve production efficiency, reduce costs, improve product quality and environmental safety of processes. The literature describes the application of multicriteria optimization for production purposes, including the selection of reaction conditions and improvement of technological processes. In the presented paper, the object of research is the process of hydrogenation of polycyclic aromatic hydrocarbons (PAH) in the production of high-density fuels. To determine the optimal conditions of the process, the problem of multicriteria optimization based on the kinetic model is solved. The criteria include maximizing the yield of targeted naphthenes and conversion of feedstock. The research objective is to create a program implementing the multicriteria optimization non-dominated sorting genetic algorithm-II (NSGA-II). Due to this, it is possible to calculate the optimal temperature for the PAH hydrogenation process on the basis of the kinetic model.Materials and Methods. The NSGA-II genetic algorithm was used to solve the multicriteria optimization problem. Modified parental and survival selection within the Pareto front was also used. If it was necessary to divide the front, solutions based on the Manhattan distance between them were selected. The program was implemented in Python.Results. In the system of ordinary nonlinear differential equations of chemical kinetics, the concentration was designated yi, the conditional contact time of the reaction mixture with the catalyst — τ. The system was solved for the hydrogenation reaction of polycyclic aromatic hydrocarbons. The calculations showed that at τ = 0 y1(0) = 0.025; y2(0) = 0.9; y6(0) = 0.067; y9(0) = 0.008; yi(0) = 0, i = 3–5, 7, 8, 10–20; Q(0) = 1. The process temperature was considered as a control parameter according to two optimality criteria: maximizing the yield of target naphthenes (f1) at the end of the reaction, and maximizing the conversion of feedstock (f2). Values f1 were in the range of 0.43–0.79; conversion — 0.01–0.03; temperature — 200–300 K. The growth of temperature was accompanied by an increase in the yield of target naphthenes and a decrease in the conversion of feedstock. Each solution obtained was not an unimprovable one. When modeling the process of hydrogenation of PAH, an algorithm was launched with a population size of 100 and a number of generations of 100. A program implementing the NSGA-II algorithm was developed. The optimal set of values of the PAH hydrogenation reaction temperature was calculated, which made it possible to obtain unimprovable values of the optimality criteria — maximizing the yield of target naphthenes and conversion of feedstock.Discussion and Conclusion. The NSGA-II algorithm is effective for solving the problem of non-dominance, and deriving the optimal solution for all criteria. Future research should be devoted to the selection of optimal algorithm parameters to increase the speed of the solution. Based on the obtained theoretical optimal conditions of the PAH hydrogenation reaction, it is possible to implement the process in industry

FDM process parameter selection by hybrid MCDM approach for flexural and compression strength maximization

Fused deposition modelling (FDM) is one of the mostly used additive technologies, due to its ability to produce complex parts with good mechanical properties. The selection of FDM process parameters is crucial to achieve good mechanical properties of the manufactured parts. Therefore, in this paper, a hybrid multi-criteria decision-making (MCDM) approach based on Preference Selection Index (PSI) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed for the selection of optimal process parameters in FDM printing of polylactic acid (PLA) parts. Printing temperature, layer thickness and raster angle were considered as input process parameters. In order to prove the effectiveness of the proposed hybrid PSI – TOPSIS method, the obtained results were compared with the results obtained with different MCDM methods. The obtained best option of process parameters was confirmed by other MCDM methods. The optimal combination of process parameters to achieve the maximal flexural strength, maximal flexural modulus and maximal compressive strength is selected using the hybrid PSI-TOPSIS method. The results show that the hybrid PSI-TOPSIS approach could be used for optimisation process parameters for any machining process.

Graph embedding orthogonal decomposition: A synchronous feature selection technique based on collaborative particle swarm optimization

In unsupervised feature selection, the clustering label matrix has the ability to distinguish between projection clusters. However, the latent geometric structure of the clustering labels is often ignored. In addition, the optimal sub-feature selection performance of feature selection techniques relies greatly on the choice of balanced parameters, and the selection range of most technical parameters is limited and fixed. To solve the above-mentioned problems, this paper proposes a synchronous feature selection technique based on graph-embedded cluster label orthogonal decomposition and collaborative particle swarm optimization (GOD-cPSO). First, GOD-cPSO extends the feature selection framework of clustering label orthogonal decomposition by graph embedding to retain the latent geometric structure of clustering labels, thus maintaining the correlation between clustered sample labels. Then, the l2,1-2-norm with strong global convergence is extended to the graph embedding clustering label orthogonal decomposition framework. By imposing this non-convex constraint, GOD-cPSO can achieve low-dimensional sparse and low-redundant sub-features. In addition, the local structure preserving of low-dimensional manifolds is integrated into the graph-embedded clustering label orthogonal decomposition framework to obtain good cluster separation and effectively maintain the latent local structure of the data. Finally, to ensure the adaptive parameter selection over a large range, GOD-cPSO synchronously guides the graph-embedding clustering labeling orthogonal decomposition framework for feature selection through collaborative particle swarm optimization. GOD-cPSO has synchronous parameter optimization and feature selection and picks parameters in a larger range. Comprehensive numerical experiments are performed on nine datasets to test the validity of the GOD-cPSO. The experimental results demonstrate that the sub-features selected by the GOD-cPSO have stronger discriminative power and are superior to other techniques in the clustering assignments.

Контроль содержания металлов в составе биологических структур

The authors propose a monitoring method for defining metal content in the biological structures, such as plant leaves, tissue samples of animal origin, human skin, etc. The authors used dielectric barrier discharge (DBD) in air at atmospheric pressure as a diagnostic medium. According to the research, at the optimal selection of gas discharge parameters it will not have destructive effect on tissues of biological structure. Indeed, generation of chemically active particles in the plasma will be minimal one. The dielectric barrier separates the investigated sample from the electrode of the discharge system. DBD activation proceeds at frequencies close to the sound range (not more than 15 kHz). It was due to the requirement of ionic component emission only from cells on the surfaces of the structures under study. The conditions of low thermal effect of atmospheric pressure plasma on plant and animal sample provide the choice 
 of DBD frequency range.