Selection Of Input Parameters Research Articles

AUTOMATIC GENERATION OF PARAMETERS IN DENSITY-BASED SPATIAL CLUSTERING

As a result of emerging new techniques for scientific way of collecting data, we are able to accumulate data in large scale pertaining to various fields. One such method of data mining is Cluster analysis. Of all clustering algorithms, density-based clustering is better in terms of clustering quality and the way the data are handled. Density based clustering is advantageous over other clustering algorithms in the following ways – arbitrary shaped clusters are formed; number of clusters need not be known and noise is handled. However, there are two main points that are critical in density-based clustering. Firstly, it is not effective while handling datasets of varied density. Secondly, the selection of input parameters e and MinPts play a critical role in the quality of clustering. This paper proposes a model – Automatic Generation of Parameters in Density-Based Spatial Clustering (AGP-DBSCAN) that aims at improving the density-based clustering by generating different candidate parameters. With these candidates, we will be able to handle both uniform density and varied density datasets. The results of experiments also look promising for different clustering datasets.

Investigating the effect of cryogenic treatment of workpieces and tools on electrical discharge machining performance

<abstract> <p>In this competitive world, manufacturers must embrace new technology in order to differentiate their products and capture market leadership. This can be achieved using advanced materials; however, these materials are difficult to machine by using traditional machining processes. A very viable and practical unconventional machining process is electrical discharge machining (EDM). EDM processes need proper selection of input parameters to get optimum productivity aspects, namely, the material removal rate and tool wear rate. Thus, the present study aims at investigating the effect of cryogenically treated work pieces and tools, gap currents, gap voltages, pulse on time and pulse off time on the material removal rate and tool wear rate during EDM of Nitinol (NiTi) alloy, Monel (NiCu) alloy and beryllium copper (BeCu) alloy. The experimental results showed that cryogenic treatment significantly improved the electrical conductivity of the workpieces and tool electrodes, which resulted in an enhanced material removal rate and reduced tool wear rate.</p> </abstract>

Advanced fatigue assessment - the future of wind turbine towers

One of the sustainable development goals of the Green Deal established by the European Union is the demand of affordable and clean energy. Among the renewable energies, the wind energy is already crucial in reaching the ambitious goals and will become even more important in the future. Since economic aspects will gain more importance, an efficient use of materials and resources is essential, especially those related to the fatigue design and service life prediction of wind turbine towers made of steel.This paper introduces an advanced fatigue assessment method, using the strain-life approach and the crack propagation method, named Two Stage Model. This model combines the two phenomenological aspects of crack initiation and propagation for a more reliable fatigue service life prognosis compared to commonly applied and simplified methods. By investigating both parts of the Two Stage Model separately, the paper shows the effect of selected input parameters on the service life for constant stress amplitudes. A Monte-Carlo Simulation is applied to validate the Two Stage Model as a vulnerable and reliable fatigue assessment concept.

Optimization of WEDM Process Parameters in Al2024‐Li‐Si3N4 MMC

The present study focuses on optimization of operating parameters in wire electric discharge machining of AA2024 aluminium alloy reinforced with lithium and silicon nitride particles. Aluminium composite was produced through the two‐step stir casting route with the combination of 2% lithium and 10% silicon nitride reinforcements. Experiments were performed using the Taguchi design of experiments to optimize the selected input parameters such as pulse on time, pulse off time, current and wire feed for the response parameter, material removal rate, and surface roughness. An ANOVA‐based regression equation with genetic algorithm was used to optimize the input variables. The gray relational grade was also performed to optimize multiple performance characteristics. Taguchi‐based optimization analysis results in wire feed as the domination factor for material removal rate and surface roughness. Increased wire feed increases the material removal rate with good surface finish as confirmed from gray relational grade analysis. Regression equation generated results with minimum error (<2%) proving the accuracy of the investigation. A genetic algorithm‐based study also confirms the analysis of Taguchi and gray relational grade. The wire feed rate at 3 m/min and pulse on time of 120 microseconds were found to be similar for material removal rate and surface finish. Current at 50 A increases the material removal rate and current at 30 A results in good surface finish.

Features Optimization of Gray Level Co-Occurrence Matrix by Artificial Bee Colony Algorithm for Texture Classification

Gray Level Co-occurrence Matrix (GLCM) is one of the most popular texture analysis methods. The fundamental issue of GLCM is the suitable selection of input parameters, where many researchers depended on trial and observation approach for selecting the best combination of GLCM parameters to improve the texture classification, which is tedious and time-consuming. This paper proposes a new optimization method for the GLCM parameters using Artificial Bee Colony Algorithm (ABC) to improve the binary texture classification. For the testing, 13 Haralick features were extracted from the UMD database, which has been used with the multi-layer perceptron neural network classifier. The experimental results proved that, the proposed method has been succeeded to finding the best combination of GLCM parameters that leads to the best binary texture classification accuracy performance.

Research on the method of travel area clustering of urban public transport based on Sage-Husa adaptive filter and improved DBSCAN algorithm.

The travel trajectory data of mobile intelligent terminal users are characterized by clutter, incompleteness, noise, fuzzy randomness. The accuracy of original data is an essential prerequisite for better results of trajectory data mining. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most effective trajectory data mining methods, but the selection of input parameters often limits it. The Sage-Husa adaptive filtering algorithm effectively controls the error range of mobile phone GPS data, which can meet the positioning accuracy requirements for DBSCAN spatial clustering having the advantages of low cost and convenient use. Then, a novel cluster validity index was proposed based on the internal and external duty cycle to balance the influence of the distance within-cluster, the distance between clusters, and the number of coordinate points in the process of clustering. The index can automatically choose input parameters of density clustering, and the effective clustering can be formed on different data sets. The optimized clustering method can be applied to the in-depth analysis and mining of traveler behavior trajectories. Experiments show that the Sage -Husa adaptive filtering algorithm proposed further improves the positioning accuracy of GPS, which is 17.34% and 15.24% higher eastward and northward, 14.25%, and 18.17% higher in 2D and 3D dimensions, respectively. The number of noise points is significantly reduced. At the same time, compared with the traditional validity index, the evaluation index based on the duty cycle proposed can optimize the input parameters and obtain better clustering results of traveler location information.

Applying intensified design of experiments to mammalian cell culture processes.

The analysis of data collected using design of experiments (DoE) is the current gold standard to determine the influence of input parameters and their interactions on process performance and product quality. In early development, knowledge on the bioprocess of a new product is limited. Many input parameters need to be investigated for a thorough investigation. For eukaryotic cell cultures, intensified DoE (iDoE) has been proposed as efficient tool, requiring fewer bioreactor runs by introducing setpoint changes during the bioprocess. We report the first successful application of iDoE to mammalian cell culture, performing sequential setpoint changes in the growth phase for the selected input parameters temperature and dissolved oxygen. The process performance data were analyzed using ordinary least squares regression. Our results indicate iDoE to be applicable to mammalian bioprocesses and to be a cost-efficient option to inform modeling early on during process development. Even though only half the number of bioreactor runs were used in comparison to a classical DoE approach, the resulting models revealed comparable input-output relations. Being able to examine several setpoint levels within one bioreactor run, we confirm iDoE to be a promising tool to speed up biopharmaceutical process development.

Hygrothermal simulations of timber-framed walls with air leakages

Air leakages can create substantial excess moisture loads into envelope structures and degrade their hygrothermal performance. Multiple previous research projects have studied the behaviour and modelling of air leakages in building physics applications, but it is still quite rare to see air leakages being considered in practical building design simulations. The purpose of this paper is to present the selection of input parameters for air leakage simulations, utilisation of a factorial design to manage simulation cases and the results for a timber-frame wall with and without air leakages. According to the results, the air permeability of mineral wool and the air pressure difference over the envelope were the two most important factors for the dry air mass flow through the structure, as opposed to gap width and leakage route. An ideally airtight structure had a better hygrothermal performance compared to leaky structure. However, when leakages were present, the exact yearly average air flow rate in the range 70…420 dm3/(m2h) did not have a strong correlation to the performance indicators. For the other studied variables, the existence of a 50 mm thick mineral wool insulation on the exterior side of the gypsum board wind barrier and the impacts from climate change had the biggest effect on the moisture performance of the structure.

Determining the Effect of Varying Magmatic Volatile Content on Lunar Magma Ascent Dynamics

AbstractThe Moon is not volcanically active at present, therefore, we rely on data from lunar samples, remote sensing, and numerical modeling to understand past lunar volcanism. The role of different volatile species in propelling lunar magma ascent and eruption remains unclear. We adapt a terrestrial magma ascent model for lunar magma ascent, considering different compositions of picritic magmas and various abundances of H2, H2O, and CO (measured and estimated) for these magmas. We also conduct a sensitivity analysis to investigate the relationship between selected input parameters (pre‐eruptive pressure, temperature, conduit radius, and volatile content) and given outputs (exit gas volume fraction, velocity, pressure, and mass eruption rate). We find that, for the model simulations containing H2O and CO, CO was more significant than H2O in driving lunar magma ascent, for the range of volatile contents considered here. For the simulations containing H2 and CO, H2 had a similar or slightly greater control than CO on magma ascent dynamics. Our results showed that initial H2 and CO content has a strong control on exit velocity and pressure, two factors that strongly influence the formation of an eruption plume, pyroclast ejection, and overall deposit morphology. Our results highlight the importance of (a) quantifying and determining the origin of CO, and (b) understanding the abundance of different H‐species present within the lunar mantle. Quantifying the role of volatiles in driving lunar volcanism provides an important link between the interior volatile content of the Moon and the formation of volcanic deposits on the lunar surface.

Nanoindentation as a Method for Determining the Mechanical Properties of Cold Spray Coatings

The paper presents the methodology behind the statistical selection of input parameters using the example of spraying two cold-sprayed coatings. The Ti and Cr3C2-25(Ni20Cr)-Gr coatings were tested. Despite the large difference in the structure of these coatings, nanoindentation studies were carried out focusing on the nano hardness H and elastic modulus E. Based on the four input parameters and two output parameters, a 2-level factorial 2(k-p) experimental design was performed. The conducted analysis showed the significant influence of the spray distance on the H and E values in the case of the Ti coating. The input parameters of the spray distance and the type of carrier gas used turned out to be statistically significant in the case of the cermet coating. Taking into account the statistical analysis, the coatings were sprayed with modified values of the input parameters.

Experimental Investigations and Pareto Optimization of Fiber Laser Cutting Process of Ti6Al4V

In the current study, laser cutting of Ti6Al4V was accomplished using Taguchi’s L9 orthogonal array (OA). Laser power, cutting speed, and gas pressure were selected as input process parameters, whereas surface roughness (SR), kerf width, dross height, and material removal rate (MRR) were considered as output variables. The effects of input variables were analyzed through the analysis of variance (ANOVA), main effect plots, residual plots, and contour plots. A heat transfer search algorithm was used to optimize the parameters for the single objective function including higher MRR, minimum SR, minimum dross, and minimum kerf. A multi-objective heat transfer search algorithm was used to create non-dominant optimal Pareto points, giving unique optimal solutions with the corresponding input parameters. For better understanding and ease of selection of input parameters in industry and by scientists, a Pareto graph (2D and 3D graph) is generated from the Pareto points.

Analysis of core casting thin section under different etching time

The Missan oil field is located in southeastern Iraq and is an important part of Iraq’s oil economy. Carbonate reservoirs in the Missan Oilfield have the characteristics of multiple mineral types, large changes in reservoir physical properties, and strong heterogeneity. It is difficult to develop reservoirs reasonably and effectively. Therefore, subsequent acidification and acid fracturing of the reservoir Reconstruction is necessary. To provide a basis for the selection of input parameters of the subsequent productivity prediction model and improve the effectiveness of acidified acid fracturing reservoir reconstruction, this paper prepares cast thin sections and uses an electron microscope to observe the microscopic pore structure of the thin sections before and after acid etching. The research results show that acid etching will significantly increase the storage space, and the dissolution rate and dissolved pore morphology of different types and shapes of minerals after exposure to acid have obvious differences. The accuracy has certain guiding significance.

Hemodynamics and stresses in numerical simulations of the thoracic aorta: Stochastic sensitivity analysis to inlet flow-rate waveform

Numerical simulations of the blood flow inside a patient-specific thoracic aorta in presence of an aneurysm are considered. We focus on the impact on the numerical predictions of the inlet flow-rate waveform. First, the results obtained by using an idealized and a MRI-measured flow-rate waveform are compared. The measured boundary condition produces significantly higher wall shear stresses than those obtained in the idealized case. Discrepancies are reduced but they are still present even if the idealized inlet waveform is rescaled in order to match the stroke volume.This motivates a systematic sensitivity analysis of numerical predictions to the shape of the inlet flow-rate waveform that is carried out in the second part of the paper. Two parameters are selected to describe the inlet waveform: the stroke volume and the period of the cardiac cycle. A stochastic approach based on the generalized Polynomial Chaos (gPC) approach, in which continuous response surfaces of the quantities of interest in the parameter space can be obtained from a limited number of simulations, is used. For both selected uncertain parameters, we use beta PDFs reproducing clinical data. The two selected input parameters appear to have a significant influence on wall shear stresses as well as on the velocity distribution in vessel regions characterized by large curvature. This confirms the need of using patient-specific inlet conditions to obtain reliable hemodynamic predictions.

Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks

Unsaturated lateritic soils are complex soils to work with due to moisture effects. So, the determination of its properties requires lots of time, labor and equipment. For this reason, the application of evolutionary learning techniques has been adopted to overcome these complexities. Lateritic soil under unsaturated condition classified as poorly graded and A-7–6 group was subjected to treatment by using hybrid cement and nanostructured quarry fines in a stabilization method. The clay activity, clay content and frictional angle were determined through multiple experiments at different proportions of the additives. 121 datasets were collected through the multiple testing of treated specimens and 70% and 30% of the datasets were used in the model training and testing, respectively to predict the coefficients of curvature and uniformity (Cc and Cu) of the unsaturated lateritic soil. Fist, the multi-linear regression (MLR) model showed that the selected input parameters correlated well with the output parameters. The model performance evaluation and validation selected indicators; R2, RMSE and MAE showed that ANFIS with 0.9999, 0.0021 and 0.0015 respectively, for the training and 0.9994, 0.0077 and 0.0059 respectively outclassed all its hybrid techniques and MLR in both training and testing. However, ANFIS-PSO with performance indicators 0.9996, 0.0062 and 0.0050 respectively (training) and 0.9989, 0.0095 and 0.0073 respectively (testing); followed by ANFIS-GA; 0.9991, 0.0094, and 0.0065 respectively (training) and 0.0089, 0.0099, and 0.0079 (testing) outclassed the other learning techniques for the Cc prediction model while ANFIS-GA; 0.9949, 0.1000, and 0.0798 respectively (training) and 0.9954, 0.0983, and 0.0807 respectively, followed by ANFIS-PSO; 0.9893, 0.1347, and 0.1011 respectively (training) and 0.9951, 0.1127, and 0.0924 respectively outclassed the other techniques for the Cu prediction model. Finally, ANFIS and its evolutionary hybrid techniques have shown their usefulness and flexibility in predicting stabilized unsaturated soil properties for sustainable earthwork design, construction and foundation performance monitoring.

Influence of smart meters on the accuracy of methods for forecasting natural gas consumption

In 2019, natural gas accounted for 25.4% of gross inland consumption in the European Union (EU), making it one of the most important energy sources in the EU. The importance of natural gas, together with the ongoing liberalization of the gas market, has made the natural gas sector significantly commercially sensitive. To reduce the risk of financial losses, balance group managers often need to have an accurate forecast of natural gas consumption. An accurate forecast will ensure small deviations between actual gas consumption and reserved gas volumes and transmission system capacity resulting in less balancing energy required, which is sold at a higher price in the final balancing process.This paper researches the optimal number of smart meters and best fitted consumption data distribution in order to achieve satisfactory results in terms of the accuracy by using simple forecasting methods. Beside mentioned, this paper provides accuracy overview of various already available forecasting methods, as well as the selection of input parameters for forecasting short term natural gas consumption. Using the calculated linear temperature dependence together with the lognormal distribution, the consumption of natural gas was simulated for 12 different cases. The simulation showed that, if more than 10 000 smart meters were installed, deviation between average estimated natural gas consumption and the real data would be less than ±2.96 %. In case of 100 000 smart meters installed, deviation would be less than ±1.20 %, but the “large” partly temperature independent consumers must be disregarded.

Characterization and Parametric Optimization of Performance Parameters of DLC-Coated Tungsten Carbide (WC) Tool Using TOPSIS

In this work, we have deposited the diamond-like carbon (DLC) coating on the tungsten carbide (WC) tool insert using the thermal chemical vapor deposition (CVD) method. For the growth of DLC coating, sugarcane bagasse was used as a carbon precursor. Raman spectroscopy, a field emission scanning electron microscope (FESEM), and X-ray diffraction (XRD) were used to confirm the presence of DLC coating on the tungsten carbide tool inserts. The hardness tests were also performed for inspecting the microhardness induced by the self-developed DLC coating on the tungsten carbide (WC) tool insert. To determine the optimum process parameters for the turning operation on an aluminum (6061) workpiece using a self-developed DLC-coated tungsten carbide (WC) tool insert, we have applied the technique for order preference by similarity to ideal solution (TOPSIS) methods. The process parameters considered for the optimization were feed rate, cutting speed, and depth of cut. Whereas chosen response variables were flank wear, temperature in the cutting zone, and surface roughness. TOPSIS is utilized to analyze the effects of selected input parameters on the selected output parameters. This study in this paper revealed that it was advantageous to develop the DLC coating on the tungsten carbide tool inserts for the machining applications. The results also revealed that a 0.635 mm depth of cut, feed rate of 0.2 mm/rev, and cutting speed of 480 m/min were the optimum combination of process parameters.

Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia.

Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.

Stagnation point bionanofluid slip flow model: Sensitivity analysis

The paper studied on thermodynamics properties of bionanofluid flow over a stagnation point on a shrinking surface. Natural properties of the stagnation point have facilitated numerous applications in the field of chemistry and biology. A Buongiorno mathematical model describing steady two-dimensional bionanofluid stagnation point flow over a shrinking surface, merged with velocity and thermal slips condition is constructed. Similarity transformation is used to reduce governing partial differential equations into a system of ordinary differential equations. Numerical analysis by shooting technique followed by sensitivity analysis is configured to determine the correlation between input parameters and output response. Relationships or sensitivities of the four parameters of interest namely skin friction coefficient, local Nusselt number, local Sherwood number and local density of motile microorganism with respective velocity, temperature, nanoparticle concentration and microorganism density distributions to the selected input parameters are being focused. Skin friction coefficient promotes with suction signifying positive sensitivity yet demotes with shrinking parameter signifying negative sensitivity. Local Sherwood number is most sensitive to Lewis number whereas local Nusselt number is conditionally sensitive to Lewis number. The sensitivity results would be the comprehensive elementary protocol for the future biomedical device fabrication such as biosensors. There are extensive numerical studies on boundary layer problem however this is the first study discussing on sensitivity of parameters of interest to the input thermophysical parameters using sensitivity analysis under the principle of RSM.

Expert solution for effects of input parameters on multiphase flow correlations

Oil and gas production represents an essential source of energy. Optimization of oil and gas production systems requires accurate calculation of pressure drop in tubing and flowlines. Many empirical correlations and mechanistic models exist to calculate pressure drop in tubing and flowlines. Previous work has shown that some correlations provide more accurate results under certain flow conditions, PVT data, and well configurations than others. However, the effects of errors in input data on the selection of which correlations to use have not been investigated. This paper studies different multiphase flow correlations to determine the effects of their input parameters on (1) the accuracy of calculated pressure drop and (2) the selection of best correlation. A database consisting of 33 oil wells and 32 gas wells was selected, and a commercial software was used to build different well models. A total of 715 well models were constructed and used to investigate the effects of errors in correlations inputs on both the calculated bottomhole pressure and the selection of best correlation(s). The methodology was based on perturbing the values of the selected input parameters and calculating the new predicted bottomhole flowing pressure. Then, the effects of error in input parameters on how the calculated bottomhole pressure was different from observed data were quantified. The effect of this error in input parameters was also checked against the algorithm that selects the best correlation(s). It was found that errors in input GOR have the greatest effects for oil wells, while gas specific gravity and the tubing roughness are the most effective parameters for gas wells. The results were integrated into a rule-based expert system. A new set of data, consisting of 220 cases from 10 new oil wells and 10 new gas wells, was used to validate the expert system. The expert system was found to predict the best correlation(s) with a success rate of 80%, and it also identifies the input parameters whose error would affect the value of calculated bottomhole pressure significantly. Finally, the rules of the expert system were programmed into a VBA-Code to ease its use.

Application of ANN for prediction of chloride penetration resistance and concrete compressive strength

Self-consolidating concrete is considered as one of the greatest developments in concrete industry. It has several advantages over conventional concrete such as minimized voids, ease of pour into congested formworks and complex shapes and absence of mechanical vibrations. One of the major problems with concrete structures is the corrosion of steel reinforcement due to penetration of chemicals like chloride. Most of the standard tests to determine chloride penetration levels are performed at 28-days. It is desired that some rapid means of determination of chloride ion diffusion in concrete are available for design and quality assurance purposes. In present study Artificial Neural Networks (ANNs) are developed for prediction of chloride penetration level and compressive strength of SCC mixes. ANN developed for prediction of chloride penetration used 294 data points for training and validation. This trained ANN was used to verify chloride penetration level of twenty experimentally evaluated SCC specimens of varied composition. Similarly, ANN developed for prediction of compressive strength used 1031 data points for training and validation of network. Compressive strength values predicted by ANN were compared with experimentally evaluated compressive strengths of thirty-three SCC specimens of varied composition. Both ANNs use relevant input parameters for training and validation of networks. A comparison between experimentally evaluated and ANN predicted values is presented in the paper for different combinations of learning rate and momentum of network. The trained ANNs were able to predict chloride iron penetration levels and compressive strength based on selected input parameters to an acceptable extent.