User-defined Input Parameters Research Articles

Extended multivariate comparison of 68 cluster validity indices. A review

Clustering is an unsupervised machine learning methodology widely used in several sciences to find groups of similar patterns in complex data. The results generated by clustering algorithms generally depend on user-defined input parameters such as the number of expected clusters, which can have a great impact on the homogeneity of the identified clusters.Clustering validity indices (CVIs) are an effective method for determining the optimal number of clusters that best fit the natural partition of a dataset. They do not require any underlying assumption nor a priori knowledge about the true dataset structure. Since 1965, many cluster validity indices have been proposed in the literature and used in several different applications.In this paper, the performance of 68 cluster validity indices was evaluated on 21 real-life research and simulated datasets. CVIs were compared on the same partition for each dataset, which was searched for by the k-means clustering algorithm. Multivariate chemometric methods were applied to disclose mutual relationships among the indices and to select those that are more effective in terms of accuracy and reliability.

A method to maximise the information obtained from low signal-to-noise acceleration data by optimising SSI-COV input parameters

Structural Health Monitoring (SHM) has mainly been undertaken on larger bridges and on a case-by-case basis. This is due to a range of factors, such as the high installation costs and the effort required to install and commission the monitoring systems. One way in which SHM systems can become more feasible for widespread adoption at a network level is to reduce the number and cost of sensors used. However, this comes with a trade-off as low-cost sensors will typically have a worse signal-to-noise ratio and the reduced number of sensors requires careful placement to maximise the amount of information acquired. One of the simplest/cheapest methods of bridge SHM is long-term tracking of the bridge frequency to identify a change in stiffness. Using data collected from five in-service bridges, this research shows that the user-defined SSI-COV input parameters can significantly impact the quality of extracted natural frequencies. Consequently, a novel method is developed to aid in choosing the inputs used in the SSI-COV method. The method developed also showed that the determined inputs resulted in the extraction of accurate natural frequencies with minimal apparent outliers on all tested bridges. The developed method allows the extraction of quality natural frequencies from low signal-to-noise acceleration data which are vital when undertaking frequency-based SHM.

J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models

Abstract. New-particle formation from condensable gases is a common atmospheric process that has significant but uncertain effects on aerosol particle number concentrations and aerosol–cloud–climate interactions. Assessing the formation rates of nanometer-sized particles from different vapors is an active field of research within atmospheric sciences, with new data being constantly produced by molecular modeling and experimental studies. Such data can be used in large-scale climate and air quality models through parameterizations or lookup tables. Molecular cluster dynamics modeling, ideally benchmarked against measurements when available for the given precursor vapors, provides a straightforward means to calculate high-resolution formation rate data over wide ranges of atmospheric conditions. Ideally, the incorporation of such data should be easy, efficient and flexible in the sense that same tools can be conveniently applied for different data sets in which the formation rate depends on different parameters. In this work, we present a tool to generate and interpolate lookup tables of formation rates for user-defined input parameters. The table generator primarily applies cluster dynamics modeling to calculate formation rates from an input quantum chemistry data set defined by the user, but the interpolator may also be used for tables generated by other models or data sources. The interpolation routine uses a multivariate interpolation algorithm, which is applicable to different numbers of independent parameters, and gives fast and accurate results with typical interpolation errors of up to a few percent. These routines facilitate the implementation and testing of different aerosol formation rate predictions in large-scale models, allowing the straightforward inclusion of new or updated data without the need to apply separate parameterizations or routines for different data sets that involve different chemical compounds or other parameters.

Synthetic population of interstellar objects in the Solar System

The discovery of the first two macroscopic interstellar objects (ISOs) passing through the Solar System has opened entirely new perspectives in planetary science. The exploration of these objects offers a qualitatively new insight into the processes related to the origin, structure and evolution of planetary systems throughout the Galaxy. Knowledge about these phenomena will greatly advance if current and future sky surveys discover more ISOs. On the other hand, the surveys require better characterization of this population in order to improve their discovery algorithms. However, despite their scientific significance, there is still no comprehensive orbital model of ISOs in the Solar System and computationally efficient algorithm for generating their synthetic representations that would respond to these increasing needs. Currently available method for generating synthetic populations cannot fully take into account important phenomena, such as gravitational focusing and the shielding effect of the Sun. On the other hand, this method is also computationally far too demanding to be used for systematic exploration of the ISO population. This paper presents an analytical method for determining the distributions of the orbital elements of ISOs, as well as computationally efficient algorithm for generating their synthetic populations, based on the multivariate inverse transform sampling method. The developed method is several orders of magnitudes more efficient than the available method, depending on the size of the synthetic population. A Python implementation of the method is freely available and can be used to generate synthetic populations of ISOs with user-defined input parameters.

The Nonlinear Thermoacoustic Eigenvalue Problem and Its Rational Approximations: Assessment of Solution Strategies

Abstract Nonlinear eigenvalue problems (NLEVPs) arise in thermoacoustics by considering the temporal evolution of small perturbations in the relevant governing equations. In this work, two solution strategies are compared: (i) a contour-integration-based method that guarantees to provide all eigenvalues in a given domain and (ii) a method that approximates the NLEVP by a rational eigenvalue problem (REVP), which is generally easier to solve. The focus lies on numerical speed, the completeness of the computed spectrum, and the appearance of spurious modes, i.e., modes that are not part of the original spectrum but appear as a result of the approximation. To this end, two prototypical thermoacoustic systems are considered: a single-flame Rijke tube and an annular model combustor. The comparison of both methods is preceded by a detailed analysis of the user-defined input parameters in the contour-integration-based method. Our results show that both methods can resolve all types of considered eigenvalues with sufficient accuracy for applications. However, the recast linear problem is overall faster to solve and allows a priori precision estimates—unlike the contour-integration-based method. Spurious modes as a by-product of the NLEVP approximation are found to play a minor role, and recommendations are given on how to eliminate them.

Finite element analysis of face milling of Ti-6Al-4 V alloy considering cutting forces and cutting temperatures

Ti-6Al-4 V alloy is the most widely used titanium alloy due to its non-corrosive nature, excellent strength to weight ratio and strength retention at relatively high temperatures. However, poor machinability of this alloy, for acquiring desired shapes and finishing levels, is a challenge which needs to be addressed. This is due to high hardness and chemical reactivity with the cutting tool at elevated temperatures. Thus, there is a need to develop certain effective measures for overcoming these limitations in Ti-6Al-4 V machining. These include optimization of input machining parameters and use of advanced cutting tools, machine tools as well as heat dissipation techniques from the cutting zone. The optimization of input parameters during machining requires precise measurement and analysis of various output parameters viz. tool wear, surface roughness, cutting forces, cutting temperatures, tool vibration etc. Of these, cutting forces and cutting temperature are difficult to measure experimentally, thus often various theoretical modelling techniques are employed to determine these parameters at the cutting zone. Finite Element Analysis of the machining simulation process employing user defined input parameters is an effective technique to determine certain output responses, which are otherwise difficult to determine.

Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube

In the present study, the heat transfer and thermal performance of a helical corrugation with perforated circular disc solar air-heater tubes are predicted using a machine learning regression technique. This paper describes a statistical analysis of heat transfer by developing an artificial neural network-based machine learning model. The effects of variation in the corrugation angle (θ), perforation ratio (k), corrugation pitch ratio (y), perforated disc pitch ratio (s), and Reynolds number have been analyzed. An artificial neural network model is used for regression analysis to predict the heat transfer in terms of Nusselt number and thermohydraulic efficiency, and the results showed high prediction accuracies. The artificial neural network model is robust and precise, and can be used by thermal system design engineers for predicting output variables. Two different models are trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The models are evaluated to have an accuracy of 97.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce.

A procedure for developing uncertainty-consistent Vs profiles from inversion of surface wave dispersion data

Non-invasive surface wave methods have become a popular alternative to traditional invasive forms of site-characterization for inferring a site's subsurface shear wave velocity (Vs) structure. The advantage of surface wave methods over traditional forms of site characterization is that measurements made solely at the ground surface can be used routinely and economically to infer the subsurface structure of a site to depths of engineering interest (20–50 m), and much greater depths (>1 km) in some special cases. However, the quantification and propagation of uncertainties from surface wave measurements into the Vs profiles used in subsequent engineering analyses remains challenging. While this has been the focus of much work in recent years, and while considerable progress has been made, no approach for doing so has been widely accepted, leading analysts to either address the propagation of uncertainties in their own specialized manner or, worse, to ignore these uncertainties entirely. In response, this paper presents a new, effective, and verifiable method for developing uncertainty-consistent Vs profiles from inversion of surface wave dispersion data. We begin by examining four approaches presented in the literature for developing suites of Vs profiles meant to account for uncertainty present in the measured dispersion data. These methods are shown to be deficient in three specific ways. First, all approaches are shown to be highly sensitive to their many user-defined inversion input parameters, making it difficult/impossible for them to be performed repeatedly by different analysts. Second, the suites of inverted Vs profiles, when viewed in terms of their implied theoretical dispersion curves, are shown to significantly underestimate the uncertainty present in the experimental dispersion data, though some may appear satisfactory when viewed purely qualitatively. Third, if the uncertainties in the implied theoretical dispersion curves were to be examined quantitatively, which has not been done previously, there is no obvious remedy available for the analyst to resolve any inconsistency between the measured and inverted dispersion uncertainty. Therefore, a new approach is proposed that seeks to remedy these shortcomings. First, beyond appropriate considerations that must be given to all inversions, the new approach is governed by only one user-defined input parameter, to which it is not overly sensitive. Second, the new approach is shown to produce suites of Vs profiles whose theoretical dispersion curves quantitatively reproduce the uncertainties in the experimental dispersion data. Third, the final step of the procedure requires the analyst to compare the measured and inverted dispersion uncertainties quantitatively, and should the analyst find the results of the new approach to be lacking, clear guidance is provided on the additional actions necessary to produce Vs profiles whose theoretical dispersion curves better account for the experimental uncertainty. Using two synthetic tests and a real-world example, the procedure is shown to produce suites of Vs profiles that accurately capture the site's Vs structure, while rigorously propagating the dispersion data's uncertainty through the inversion process.

Turbulent Flow Heat Transfer through a Circular Tube with Novel Hybrid Grooved Tape Inserts: Thermohydraulic Analysis and Prediction by Applying Machine Learning Model

The present experimental work is performed to investigate the convection heat transfer (HT), pressure drop (PD), irreversibility, exergy efficiency and thermal performance for turbulent flow inside a uniformly heated circular channel fitted with novel geometry of hybrid tape. Air is taken as the working fluid and the Reynolds number is varied from 10,000 to 80,000. Hybrid tape is made up of a combination of grooved spring tape and wavy tape. The results obtained with the novel hybrid tape show significantly better performance over individual tapes. A correlation has been developed for predicting the friction factor (f) and Nusselt number (Nu) with novel hybrid tape. The results of this investigation can be used in designing heat exchangers. This paper also presented a statistical analysis of the heat transfer and fluid flow by developing an artificial neural network (ANN)-based machine learning (ML) model. The model is trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The model is evaluated to have an accuracy of 98.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce.

First-Order Peaks Determination for Direction-Finding High-Frequency Radar

Direction-finding (DF) high-frequency radar (HFR) is preferred among the HFR family and is widely used around the world due to its compact structure. The correct determination of first-order peaks (FOPs) from Doppler spectra recorded by radar is a critical step toward attaining accurate mappings of surface currents. The commonly used FOPs determination method is generally sufficient for most situations. However, it needs six user-defined input parameters. These parameters result in complex procedures of optimizing the values of these six user-defined parameters. To simplify the FOPs determination for DF HFR, we propose an alternative method which only needs one user-defined parameter. To validate the reliability of the proposed method, we compare the FOPs determination results derived from the proposed method with those from the commonly used method on a data set covering a period of 256 days. The results indicate that the proposed method yields a similar FOPs determination result to the commonly used method. This proposed input-parameter-reduced method can greatly simplify the use of the HFR for users who are unprofessional in the HFR and promote the popularization and application of HFR.

On the formation of nano-sized precipitates during cooling of NiAl- strengthened ferritic alloys

We present an experimental study on NiAl- type precipitates in ferritic superalloys that aim for application as structural materials at high temperature. The focus of this study is on the characterization of hyperfine precipitates that form during sample cooling after the aging treatment and which have a strong influence on the alloys' room temperature hardness. Size and crystallographic information of the precipitates were obtained by electron microscopy. The chemical nature of different phases was analyzed by atom probe tomography (APT). In order to identify the hyperfine precipitates in APT reconstructions, a modified version of the maximum separation method for cluster selection is proposed. Instead of a separate erosion step, the proposed method makes use of a Delaunay tessellation which does not require any user defined input parameters and further, gives a direct access to the morphology of the clusters. The modified algorithm was tested on simulated datasets and then successfully applied to experimental datasets containing precipitates with radii down to one nanometer. The result of the chemical analysis shows that the formation of precipitates during sample cooling is a kinetically controlled process which is dominated by two different mechanisms in dependence on the temperature.

ICARE: An R package to build, validate and apply absolute risk models.

This report describes an R package, called the Individualized Coherent Absolute Risk Estimator (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individual’s risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: a model for relative risk, an age-specific disease incidence rate and the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Advanced Lake Shoreline Extraction Approach by Integration of SAR Image and LIDAR Data

Noise and an abnormal distributed-image histogram is the main challenge of using SAR data. From this point of view, this study’s authors motivated the non-use of user-defined input parameters. To achieve this purpose, a fuzzy approach was proposed to extract shoreline from SENTINEL-1A data. The parameters in the processing of the SENTINEL-1A image were generated automatically with LIDAR-intensity-derived object-based segmentation results. The LIDAR-intensity image was segmented with the Mean-shift method. The corresponding result was used to estimate the input parameters for fuzzy clustering of the SENTINEL-1A image. Fuzzy segmentation was proposed, due to the expected large number of values regarding water and land classes except for the pixels along the shoreline. The memberships for land and water classes were separately computed. In the proposed approach, the results from LIDAR and SENTINEL-1A dataset are promising, with differences below 1 pixel (10 m) by evaluation with the used reference vector data.

Development of a new algorithm for implementing the edge effect offset for subsidence calculations

The surface deformation prediction system (SDPS) program has been developed as an engineering tool for the calculation of subsidence deformation indices through the implementation of various prediction methods. From basic user-defined input parameters, SDPS can determine subsidence indices, such as mining induced displacements, strains, tilt, etc., at any elevation between the seam and the horizontal or varying surface topography. A fundamental parameter in obtaining reliable ground deformation results is the determination of the edge effect offset. The value assigned to the edge effect offset corresponds to a virtual offsetting of boundary lines delineating the extracted panel to allow for roof cantilevering over the mined-out area. The objective of this paper is to describe the methods implemented in updating the edge effect offset algorithm within SDPS. Using proven geometric equations, the newly developed algorithm provides a more robust calculation of the offset boundary of the extracted panel for simplistic as well as more complex mining geometries. Given that an extracted panel is represented by a closed polyline, the new edge offset algorithm calculates a polyline offset into the extracted panel with respect to the user defined edge effect offset distance. Surface deformations are then calculated using this adjusted panel geometry. The Matlab program was utilized for development and testing of the new edge effect offset algorithm. After completing rigorous testing regimes, the new offset algorithm will be integrated into SDPS further increasing the speed and reliability of the program resulting in a retrospective increase in capability and flexibility.

A Comparative Study Between Spin-Transfer-Torque and Spin-Hall-Effect Switching Mechanisms in PMTJ Using SPICE

The spin transfer torque magnetoresistive random access memory (STT-MRAM) is the leading candidate for spin-based memories. Nevertheless, the high write energy and read disturbance of the STT-MRAM motivated researchers to find other solutions. The spin Hall effect (SHE)-based MRAM is an alternative for the STT-MRAM, which also provides nonvolatility, zero leakage, and competitive area per bit, but with a lower write current. This paper focuses on a systematic performance analysis of these two proposed memory solutions. The SHE requires an external field to deterministically switch perpendicular magnetic anisotropy magnetic tunnel junction (MTJ). A previous experiment showed that the SHE can switch composite MTJ containing an in-plane layer without any field. In this paper, both traditional and composite MTJ structures are modeled in SPICE, which can reproduce realistic MTJ characteristics with user-defined input parameters. This self-contained model is used to compare the write energy and delay of the STT-MRAM and the SHE magnetoresistive random access memory (SHE-MRAM) for various write schemes including thermal fluctuation. Our simulations show, compared with the STT-MRAM, that the SHE-MRAM improves the write delay and the energy by eight times and seven times, respectively. Based on our extensive analysis incorporating the latest advances in magnetic materials and device technology, we predict that the SHE-MRAM is a feasible low-energy memory solution for future computing systems.

A sensitivity study of the LIdar-Radiometer Inversion Code (LIRIC) using selected cases from Thessaloniki, Greece database

ABSTRACTWe investigate the uncertainty introduced to the optical and microphysical properties estimated with the lidar radiometer inversion code (LIRIC) by user-defined input parameters based on measurements carried out with a multi-wavelength Raman lidar and a sun photometer located at Thessaloniki, Greece (40.6° N, 22.9° E, 60 m above sea level). The sensitivity study involves three tests. We first evaluate the selection of the regularization parameters needed for the algorithm to initialize the iteration process. The latter two tests consider the impact of the boundary limits at the top/bottom (upper/lower limit) of the signal to the derived concentration profiles. The aforementioned tests were applied to two different cases, a Saharan dust event and a continental pollution case. We concluded that the largest uncertainties are introduced when varying the lower limit (more than 35%) regardless of the aerosol type or mode (fine/coarse). Varying the regularization parameters resulted in an uncertainty of 20%, and the selection of upper limit led to discrepancies of less than 3%. In conclusion, this sensitivity study indicates that future LIRIC users should apply an overlap function to the lidar signals before applying the methodology for minimizing the uncertainties in the near range.

A sensor fault detection strategy for air handling units using cluster analysis

Abstract Sensors are an essential component in the control systems of air handling units (AHUs). A biased sensor reading could result in inappropriate control and thereby increased energy consumption or unsatisfied indoor thermal comfort. This paper presents an unsupervised learning based strategy using cluster analysis for AHU sensor fault detection. The historical data recorded from sensors is first pre-processed to reduce the dimensions using principal component analysis (PCA). The clustering algorithm Ordering Points to Identify the Clustering Structure (OPTICS) is then employed to identify the spatial separated data groups (i.e. clusters), which possibly indicate the occurrence of sensor faults. The data points in different clusters are then checked for temporal separation in order to confirm the occurrence of sensor faults. The proposed sensor fault detection strategy is tested and evaluated with the data collected from a simulation system. The results showed that this strategy can detect single and non-simultaneously occurred multiple sensor faults in AHUs. The fault detection results were not strongly affected by the selection of the user defined input parameters required in OPTICS.

An algorithm for automatic crystal identification in pixelated scintillation detectors using thin plate splines and Gaussian mixture models

A typical positron emission tomography detector is comprised of a scintillator crystal array coupled to a photodetector array or other position sensitive detector. Such detectors using light sharing to read out crystal elements require the creation of a crystal lookup table (CLUT) that maps the detector response to the crystal of interaction based on the x–y position of the event calculated through Anger-type logic. It is vital for system performance that these CLUTs be accurate so that the location of events can be accurately identified and so that crystal-specific corrections, such as energy windowing or time alignment, can be applied. While using manual segmentation of the flood image to create the CLUT is a simple and reliable approach, it is both tedious and time consuming for systems with large numbers of crystal elements. In this work we describe the development of an automated algorithm for CLUT generation that uses a Gaussian mixture model paired with thin plate splines (TPS) to iteratively fit a crystal layout template that includes the crystal numbering pattern. Starting from a region of stability, Gaussians are individually fit to data corresponding to crystal locations while simultaneously updating a TPS for predicting future Gaussian locations at the edge of a region of interest that grows as individual Gaussians converge to crystal locations. The algorithm was tested with flood image data collected from 16 detector modules, each consisting of a 409 crystal dual-layer offset LYSO crystal array readout by a 32 pixel SiPM array. For these detector flood images, depending on user defined input parameters, the algorithm runtime ranged between 17.5–82.5 s per detector on a single core of an Intel i7 processor. The method maintained an accuracy above 99.8% across all tests, with the majority of errors being localized to error prone corner regions. This method can be easily extended for use with other detector types through adjustment of the initial template model used.

A Sensitivity Study of Liric Algorithm to User-defined Input Parameters, Using Selected Cases from Thessaloniki’s Measurements

A targeted sensitivity study of the LIRIC algorithm was considered necessary to estimate the uncertainty introduced to the volume concentration profiles, due to the arbitrary selection of user-defined input parameters. For this purpose three different tests were performed using Thessaloniki’s Lidar data. Overall, tests in the selection of the regularization parameters, an upper and a lower limit test were performed. The different sensitivity tests were applied on two cases with different predominant aerosol types, a dust episode and a typical urban case.

Planning of recreational trails in protected areas: Application of regression tree analysis and geographic information systems

One of the most important tasks for managers of many natural protected areas is achieving a balance between conservation of nature and recreational opportunity. This paper presents a framework based on geographic information system (GIS) and regression tree analysis of optimized recreational trail location for flexible, user-defined input parameters. The method utilizes: (1) the GIS to create a database containing field data and existing GIS/cartographic materials; (2) regression tree analysis to establish the relationships between indicators of degradation and environmental and use-related factors for existing trails, as well as to predict trail degradation for potential new trials; (3) least-cost path algorithm within a GIS framework to optimize trail route.The framework was applied to the Gorce National Park in the south of Poland. A large sample (>4500) of the field collected data about degradation of the existing trail network was linked with data about soil, geology, geomorphology and relief, and with information about the type and amount of recreational use. Based on the existing relationship, predicted trail degradation was calculated and routes for two examples of trails (hiking and motorized) were designated. The proposed methodology is objective and quantitative, and can also include knowledge of local stakeholders. The framework has the potential to design new trails (or to re-route old ones), characterized by the best possible solution for recreational and conservation functions to coexist, by routing visitors through trails with the lowest possible impact.