Fitting Process Research Articles

Design of a teleoperated and mixed reality-based electric power live line working robot

This paper presents a novel teleoperation force feedback approach combining graphic and haptic feedback for enhanced telepresence. Visual force feedback is modeled through a fitting process, with the force direction established through coordinate transformations. To construct operational environments, a new scene recognition and reconstruction technique is proposed, based on the principles of binocular stereovision. This method is primarily designed for live-line work scenarios, focusing on power line wires. It involves image preprocessing, target image recognition and extraction, binocular stereo measurements, generating depth point clouds, and ultimately reconstructing three-dimensional models of the target objects. Ultimately, a novel electric power live line working robot system with a strong sense of telepresence is achieved, based on teleoperation and mixed reality.

Stelfi: An R package for fitting Hawkes and log-Gaussian Cox point process models.

Modelling spatial and temporal patterns in ecology is imperative to understand the complex processes inherent in ecological phenomena. Log-Gaussian Cox processes are a popular choice among ecologists to describe the spatiotemporal distribution of point-referenced data. In addition, point pattern models where events instigate others nearby (i.e., self-exciting behaviour) are becoming increasingly popular to infer the contagious nature of events (e.g., animal sightings). While there are existing R packages that facilitate fitting spatiotemporal point processes and, separately, self-exciting models, none incorporate both. We present an R package, stelfi, that fits spatiotemporal self-exciting and log-Gaussian Cox process models using Template Model Builder through a range of custom-written C++ templates. We illustrate the use of stelfi's functions fitting models to Sasquatch (bigfoot) sightings data within the USA. The structure of these data is typical of many seen in ecology studies. We show, from a temporal Hawkes process to a spatiotemporal self-exciting model, how the models offered by the package enable additional insights into the temporal and spatial progression of point pattern data. We present extensions to these well-known models that include spatiotemporal self-excitation and joint likelihood models, which are better suited to capture the complex mechanisms inherent in many ecological data. The package stelfi offers user-friendly functionality, is open source, and is available from CRAN. It offers the implementation of complex spatiotemporal point process models in R for applications even beyond the field of ecology.

Bjet_MCMC: A New Tool to Automatically Fit the Broadband Spectral Energy Distributions of Blazars

Multiwavelength observations are now the norm for studying blazars’ various states of activity, classifying them, and determining the possible underlying physical processes driving their emission. Broadband emission models became unavoidable tools for testing emission scenarios and setting the values of physical quantities such as the magnetic field strength, Doppler factor, or shape of the particle distribution of the emission zone(s). We announce here the first public release of a new tool, Bjet_MCMC, that can automatically fit the broadband spectral energy distributions (SEDs) of blazars. The complete code is available on GitHub and allows for testing leptonic synchrotron self-Compton models with or without external inverse-Compton processes from the thermal environment of supermassive black holes (accretion disk and broad-line region). The code is designed to be user-friendly and computationally efficient. It contains a core written in C++ and a fully parallelized SED fitting method. The original multi-SSC zone model of Bjet is also available on GitHub but is not included in the Markov Chain Monte Carlo fitting process at the moment. We present the features, performance, and results of Bjet_MCMC, as well as user advice.

Comparative study for the elastic scattering of the isobar nuclei 6Li and 6He on 64Zn using different nuclear potentials

The available experimental data for the elastic scattering of the isobar nuclei 6Li and 6He on [Formula: see text]Zn have been reanalyzed in the available energy range using the optical model, double folding (DF) (Sao Paulo), and crystal model (CM). Double-folding (DF) and crystal model (CM) have been used to investigate the experimental data as a real part of the optical potential, while the imaginary part was taken in Woods–Saxon (WS) shape. One value of the real part of the optical potential has been applied to the two systems under consideration. The distance of the closest approach of the 6Li (6He) + [Formula: see text]Zn systems has been extracted from the available experimental data using different methods. The extracted potential from the 6Li and 6He elastically scattered by [Formula: see text]Zn data could be used as the basis for the creation of a simple global optical potential for 6Li and 6He. The extracted normalization factor ([Formula: see text]) was equal to the unity during the fitting process avoiding its unfortunate value (in the range 0.5–0.7) exists in the literature. It was observed that interaction distances vary slowly with the energy of the projectile and strongly depend on the projectile properties. The halo properties of 6He have a significant effect on the calculated reaction cross-sections. Different methods have been applied to 6He and 6Li elastically scattered by [Formula: see text]Zn, where fair agreement between them has been observed.

Process and Outcomes of Fitting Corneoscleral Profilometry-Driven Scleral Lenses for Patients With Ocular Surface Disease.

To assess the feasibility of obtaining cornea scleral profile (CSP) measurements using Scheimpflug imaging and report on the fitting process of free-form custom scleral lenses (SLs) for patients with ocular surface disease (OSD). This prospective study of patients fit with free-form SLs collected data on the following: demographics, indications for wear, corneal and scleral tomography, scan acquisition process, and SL fitting process. Cornea scleral profile scans were acquired on 15 eyes of nine patients. Mean scan time for right eyes was 10.7, and 9.7 min for left eyes. A mean of 2.9 follow-up visits were required to complete SL fitting, with a mean of 2.1 lenses ordered. One eye did not tolerate lens wear, and one eye could not be fit using the CSP scan because of insufficient data. The initial lens ordered was dispensed at the first follow-up visit for seven of the remaining 13 eyes, all of which were ultimately fit successfully in free-form lenses. In this study of profilometry-guided SL fitting for eyes with OSD and low magnitude corneal astigmatism, the number of lenses and follow-up visits required were similar to outcomes of previous studies that described the diagnostic approach to SL fitting. In addition, imaging technology does not negate the need for skilled clinical observation while fitting SLs.

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method.

Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient and reduced scattering coefficient ) and scalp and skull thicknesses. We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance. We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor () estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30mm) on BFi and estimations among DCS-NET, semi-infinite, and three-layer fitting models. DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of and at a source-detector separation of 30mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively. DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

Generalized bivariate mixture model of directional wind speed in mixed wind climates

Probabilistic modeling of the directional wind speed has been an important topic in the fields of wind energy assessment as well as structural wind-resistant design. The mixture model is required for the probabilistic modeling on the directional wind speed as the wind climates often show the mixture nature. For accuracy and robustness concerns in engineering applications, a generalized bivariate mixture (GBM) model of the directional wind speed is developed by using the copula-based bivariate distribution function. In this model, the marginal distributions and connection function for each sub-model can be flexibly defined. In parametrizing the model, the data-driven parametric estimation including a data preprocess and an automatic unsupervised fitting process is presented. The performance of GBM model is verified using two data sets and compared with the offset elliptical normal mixture (OENM) model. The robustness of GBM model is found for its good performance in fitting to the targets with or without smoothing process, and in tackling directional wind speed with the extremely complex probability characteristic.

Permanent magnet synchronous motor inter-turn short circuit diagnosis based on physical-data dual model under oil-drilling environment

Inter-turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motor (PMSM) have gained significant attention due to the growing demand for enhanced reliability and safety in actuation systems. This paper presents a adaptive fault diagnosis approach specifically designed for ITSC faults in PMSM used in oil-drilling applications, where sparse actual data and varying speed conditions pose considerable sparse training data and ITSC feature extraction challenges respectively. To address these challenges, we first construct a physical fault model of PMSM-ITSC and establish a simulation experiment platform to replicate downhole environments with high temperatures and rapidly changing PMSM speeds, ensuring a reliable data source for analysis. Subsequently, we propose a novel adaptive peak-to-peak self-finding method (APPS) that leverages frequency-domain prior knowledge to adaptively extract ITSC fault characteristics, even amidst drastic changes in PMSM speed. Furthermore, we introduce the time-sequence efficient moving window self-attention network (EMWSAN) data model for inferring the stator phase winding state, incorporating Half-sandwich and Cascaded windows group attention operations. This approach significantly reduces computational complexity and network parameters compared to traditional self-attention mechanisms. To expedite the fitting process, we apply transfer learning (TL) theory, transferring knowledge from the physical knowledge to the data model, enabling EMWSAN to be trained more efficiently. As a result, our proposed ITSC fault diagnosis scheme achieves an impressive 96.72% classification accuracy, achieving existing state-of-the-art methods.

-Inflated beta regression model for censored recurrent events.

This research introduces a multivariate -inflated beta regression ( -IBR) modeling approach for the analysis of censored recurrent event data that is particularly useful when there is a mixture of (a) individuals who are generally less susceptible to recurrent events and (b) heterogeneity in duration of event-free periods amongst those who experience events. The modeling approach is applied to a restructured version of the recurrent event data that consists of censored longitudinal times-to-first-event in length follow-up windows that potentially overlap. Multiple imputation (MI) and expectation-solution (ES) approaches appropriate for censored data are developed as part of the model fitting process. A suite of useful analysis outputs are provided from the -IBR model that include parameter estimates to help interpret the (a) and (b) mixture of event times in the data, estimates of mean -restricted event-free duration in a -length follow-up window based on a patient's covariate profile, and heat maps of raw -restricted event-free durations observed in the data with censored observations augmented via averages across MI datasets. Simulations indicate good statistical performance of the proposed -IBR approach to modeling censored recurrent event data. An example is given based on the Azithromycin for Prevention of COPD Exacerbations Trial.

Linear fitting of biconic surfaces for corneal modeling.

This paper presents a method for reconstructing the corneal surface. The proposed method was tested in 56 healthy and 15 post-orthokeratology corneas. The Medmont E300 Corneal Topographer was used to measure the anterior corneal elevation, and custom MATLAB scripts were employed for data analysis, fitting, and other computational processes. The results obtained were compared with the fitting to an ellipsoid and to a biconic, using an alternative method, showing similarities among the different approaches. Additionally, the advantages of this method and the biconic's generality over the ellipsoid were also demonstrated. In conclusion, the method proposed offers an approach with potential applications in the field of visual and ophthalmic optics related with modeling of the cornea and other optical surfaces.

Generic numerical and analytical methods for solving nonlinear oscillators

Nonlinear oscillations are a challenging problem due to the high complexity of the underlying differential equations. This paper focuses on the nonlinear oscillator with cubic and harmonic restoring force as it represents a very general case. In particular, many well-known oscillators (e.g., the Duffing equation, the capillary oscillator, the cubic-quintic Duffing oscillator, the simple pendulum, etc) are subcases of it. Accurate solutions in terms of Fourier series functions for different oscillation amplitudes were derived using an energy-based numerical method and fitting processes. In addition, an analytical approach was developed by finding the derivative of the restoring force function at the point x=c0A, where A is the amplitude of the oscillation, and c0 is a constant. Using this approach, the error was negligible (∼0.02–0.22%) even for A → ∞. The methods and solutions for the nonlinear oscillator with cubic and harmonic restoring force can be equally applied to all its subcases.

A high-energy study of the supernova remnant G296.5+10.0

ABSTRACT We report a detailed multi-wavelength study of the supernova remnant G296.5+10.0 using archival data from XMM–Newton and Fermi-LAT complemented with ATCA observations. In the X-ray band, we performed an adaptive smoothing on the double background subtracted images to construct an X-ray mosaic map with six individual observations. Below 2.0 keV, G296.5+10.0 is asymmetrical, with the south-east side of the radio shell brighter than the south-west one. The spatially resolved X-ray spectral study confirms the thermal origin of the plasma, with enhanced metal abundances, probably arising from ejecta material according to the H i and infrared ($140~{\mu \rm m}$) distributions. In the γ-ray band, we analysed 14 years of accumulated Fermi observations below 500 GeV via different fitting processes. To discuss the origin of the γ-ray emission, we compare the GeV results with H i structures probably associated with the SNR and with the radio spectral indices found at various positions towards the radio shell. Moreover, we identified diverse sources candidates to contribute γ-ray emissions observed. Also, we calculated the lepto-hadronic spectral energy distribution of the remnant for synchrotron, inverse Compton, Bremsstrahlung, and proton–proton processes. The emission at low energies can be explained by electron-synchrotron radiation, with a weak magnetic field of $B=25\, {\rm \mu G}$, while the γ-ray data can be explained by hadronic interactions. Employing the reddening-distance method, we computed a distance of 1.4 kpc for the SNR, implying an age of 14 000 yr.

Cluster Regularization via a Hierarchical Feature Regression

The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparameter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.

Benefit of Hearing-Aid Amplification and Signal Enhancement for Speech Reception in Complex Listening Situations.

A major goal of hearing-device provision is to improve communication in daily life. However, there is still a large gap between the user's daily-life aided listening experience and hearing-aid benefit as assessed with established speech reception measurements in the laboratory and clinical practice. For a more realistic assessment, hearing-aid provision needs to be tested in suitable acoustic environments. In this study, using virtual acoustics, we developed complex acoustic scenarios to measure speech-intelligibility and listening-effort benefit obtained from hearing-aid amplification and signal enhancement strategies. Measurements were conducted using the participants' own devices and a research hearing aid, the Portable Hearing Laboratory (PHL). On the PHL, in addition to amplification, a monaural and a binaural directional filter, as well as a spectral filter were employed. We assessed the benefit from different signal enhancement strategies at the group and the individual level. At the group level, signal enhancement including directional filtering provided a higher hearing-aid benefit in challenging acoustic scenarios in terms of speech intelligibility compared to amplification alone or combined with spectral filtering. However, no difference between monaural and binaural signal enhancement occurred. On an individual level, we found large differences in hearing-aid benefit between participants. While some benefitted from signal-enhancement algorithms, others benefitted from amplification alone, but additional signal enhancement had a detrimental effect. This shows the importance of an individual selection of signal enhancement strategies as a part of the hearing-aid fitting process.

DETECTION AND CHARACTERIZATION OF CUSP SINGULARITIES

Studies on nonlinear analysis of system dynamics have increased in recent years. Since most systems that exist in nature have complex dynamics and therefore exhibit nonlinear behavior; there are various methods and theories developed in this context. Self-similar functions are mathematical functions exhibiting self-similar and scale-invariant behaviors performing fractal structures. Singularities are the basis for producing the self-similar behavior of these functions. Singularity analysis is mainly carried out by using wavelet transform (WT). The cusp singularities are non-oscillating singularities which are characterized by their singularity strength. However, the representation and behavior of this type of singularity differs depending on the sign of the exponent, known as the singularity strength. The cusp singularity functions with negative exponent show irregular behavior progressively different than positively valued functions since the value of the function is undefined at that particular singular point. It is commonly accepted that the singularity strength is studied as Hölder exponent of the cusp function, but by definition, the value of this exponent cannot take negative values. We present a new method to estimate the singularity strength of cusp singularities with negative exponent. The developed method is based on analyzing and redistributing the amplitudes of a cusp function with negative exponent by taking the WT. The redistribution of amplitudes over time is achieved by applying curve fitting process to frequency values of the analyzing function.

Metamodeling for predicting the behavior of airfoils of wind turbine blades: An integration of artificial neural networks

The aim of this paper is to develop a robust metamodel capable of predicting the behavior of wind turbine blade airfoil profiles, despite variations in the input parameters of computational fluid dynamics (CFD) simulations. The metamodel development begins with the specification of essential geometric parameters for the simulations. Subsequently, an empirical analysis of the airfoil profiles is conducted, and the results of the corresponding simulations are presented. These data are used to train and refine the metamodel, which is based on an artificial neural network. The model fitting process is divided into three main stages: training, validation, and testing, during which we strive to minimize the error function using the Levenberg-Marquardt algorithm. In conclusion, we validate our model by a thorough comparison of the results from the metamodel and the CFD simulations, aiming to optimize computation time.

Fractional-Order PID Optimal Tuning Rule based on a FOPDT Process Model with Robustness Constraints

This paper introduces a tuning rule design for fractional order PID controllers. The design is based on a first order plus dead time model for the controlled process. The control problem presented, requires minimizing the integrated absolute error for each control mode, then making a different set of rules for set-point and load-disturbance response. For the proposed optimization, a robustness constraint based on the maximum value of the sensitivity function has been considered so that the tuned controllers have an optimal performance and guarantee the stability of the control system at the time of implementation. By performing a curve fitting process towards the optimal controller parameters, the Model based Robust Tuning rule was designed and its improvement over other tuning methods is validated through examples.

유창성 장애의 언어치료 관련 경시적 계수자료의 유연한 분석

In this study, the goal was to apply a flexible and easy-to-manage analysis method through a model that includes a dispersion parameter in the longitudinal analysis method of count data. In the analysis of count data, it may be inconvenient to change the statistical model depending on whether there is under- or over-dispersion. To compensate for these weaknesses, We have applied a statistical method including a dispersion parameter to the statistical model. The count data covered were included in the diagnosis of fluency disorders and the evaluation of the effectiveness of speech therapy. but research on speech-language therapy is limited by being conducted on small numbers of subjects for several reasons. Therefore, statistical analysis methods that work only on large samples, are complex, or require excessive calculations in the model fitting process were not appropriate for the field. With this background, we applied the Conway-Maxwell Poisson model to longitudinal data analysis of fluency disorders. We evaluated whether the model that reflects the level of data dispersion in the model was consistent with the speech therapy data for fluency disorders by comparing it to the existing Poisson mixed model and linear mixed model. In particular, the Conway-Maxwell Poisson model had the advantage of reflecting well when the degree of dispersion was different for each group or specific subgroup.

Isıl Dayanım Test Raporu İçin Bir Excel Elektronik Tablosunun Kullanımı

Power cables are one of the important parts of electrical power systems. It is important to know the materials that make up the cable or what the life of the cable will be at a given temperature. Therefore, aging tests are performed for the materials used in the cables. One of these tests is the thermal endurance test of the cable. The curve fitting process is performed to the data obtained as a result of this test and the temperature value (the temperature index) corresponding to the 20000-hour life of the cable is found. Automating the process of curve-fitting and finding the temperature index will be useful for companies or research centers that perform this test. Instead of expensive programs such as Matlab or Mathematica, the easy-to-use and accessible Excel program is a very economical and useful solution to evaluate these test results. In this study, the thermal endurance test was briefly described and a spreadsheet was prepared in Excel to evaluate the results of this test. Using the data taken from the measurements made for the EPR material, it has been checked that the spreadsheet works correctly. Such a spreadsheet can be easily used in cable factories to evaluate thermal endurance test results.

​​TECHNICAL VIEW OF EDUCATIONAL PHILOSOPHY IN THE TECHNICAL AGE​

In education philosophy point of view, technology not only refers to the meaning of various education skills, skills, methods and means and all kinds of materialized tools, equipment and information media system, and means the verb in the meaning of technology process, is noun technology of education, bright and structure, is "education technology", for the educators to use knowledge, skills with various tools, media technology system knowledge, cultivating students emotion, hone students will and fitness process of student body, it has the educational, emotional and artistic characteristics. According to different standards, educational technology can be at least divided into original technology and interventional technology, "hard" technology and "soft" technology, general technology and special technology. The practice of educational philosophy and technology should uphold the original intention of education, "improve technical quality" for technology, "soften" hard technology; achieve man-machine collaboration, and make technology serve the overall development of people.