Local Fitting Research Articles

A New Reconstruction Method for Measurement Data With Multiple Outliers

Measurement data sometimes contains a few outliers that have a large deviation from actual value. For such data, the reconstruction results will be greatly distorted around the outliers. To solve this problem, a robust surface approximation method based on moving total least squares (MTLS) is presented in this paper, in which an improved random sample consensus (RANSAC) and classification and regression tree (CART) are introduced into the support domain to detect outliers. Firstly, the residuals of points in the support domain are calculated by the improved RANSAC with an introduced parameter, and then the abnormal residuals are identified by CART algorithm. The points corresponding to abnormal residuals are considered as abnormal points. After the abnormal points are eliminated, the remaining data is processed by weighted total least squares (WTLS) to obtain local fitting parameters. This method can avoid the adverse impact of human intervention and over elimination. The reconstruction results of the numerical simulation and measurement experiment illustrate the effectiveness of our method.

Data Assimilation of High‐Latitude Electric Fields: Extension of a Multi‐Resolution Gaussian Process Model (Lattice Kriging) to Vector Fields

AbstractWe develop a new methodology for the multi‐resolution assimilation of electric fields by extending a Gaussian process model (Lattice Kriging) used for scalar field originally to vector field. This method takes the background empirical model as “a priori” knowledge and fuses real observations under the Gaussian process framework. The comparison of assimilated results under two different background models and three different resolutions suggests that (a) the new method significantly reduces fitting errors compared with the global spherical harmonic fitting (SHF) because it uses range‐limited basis functions ideal for the local fitting and (b) the fitting resolution, determined by the number of basis functions, is adjustable and higher resolution leads to smaller errors, indicating that more structures in the data are captured. We also test the sensitivity of the fitting results to the total amount of input data: (a) as the data amount increases, the fitting results deviate from the background model and become more determined by data and (b) the impacts of data can reach remote regions with no data available. The assimilation also better captures short‐period variations in local PFISR measurements than the SHF and maintains a coherent pattern with the surrounding. The multi‐resolution Lattice Kriging is examined via attributing basis functions into multiple levels with different resolutions (fine level is located in the region with observations). Such multi‐resolution fitting has the smallest error and shortest computation time, making the regional high‐resolution modeling efficient. Our method can be modified to achieve the multi‐resolution assimilation for other vector fields from unevenly distributed observations.

An Intelligent Collaborative System for Robot Dynamics.

In this article, we propose an intelligent collaborative system for robotic navigation and control (CNaC) governed by the Euler-Lagrange equation. First, a state reconstruction based on neural networks navigation (SR-NNN) law is designed to estimate the current position of the robot for intelligent CNaC. The SR-NNN makes full use of partial truth information and the mighty local fitting ability of neural networks. In the absence of landmark, SR-NNN still exhibits navigation performance with high precision. The maximum root-mean-squared error (RMSE) of DR is 0.096 and the maximum RMSE of SR-NNN is 0.053, which has been improved by 55%. In addition, the motion model obtained by SR-NNN online training can avoid the error introduced by the predetermined motion model and overcome the interference of the external environment. The intelligent CNaC still can achieve satisfactory control performance based on the estimated position given by the SR-NNN rather than the ground truth which is formed by postprocessing. The intelligent CNaC has been demonstrated by simulation tracking sample and real experiments, which verifies the effectiveness of the intelligent CNaC.

Effects of Exercise-Diet Therapy on Cognitive Function in Healthy Elderly People Evaluated by Deep Learning Based on Basic Blood Test Data.

Recent studies reported that vascular cognitive impairment in the elderly caused by arteriosclerosis plays an important role in cognitive disorders in both vascular dementia and Alzheimer's disease. In addition, systemic metabolic disorders such as oxygen metabolism dysfunction could be risk of dementia. Based on these findings, we have developed a deep neural network-based screening test (DNN-based test) of cognitive function using basic blood test data, which allowed prediction of cognitive function expressed by Mini Mental State Examination (MMSE) scores. Here, we investigated whether the DNN-based test could be applicable to assessment of the effects of exercise-diet on cognitive function. We studied the following two groups: (1) seven subjects of the local fitness gym (68.6±3.2years old). We evaluated cognitive function by the DNN-based test using blood data before and after the intervention (for 3months). These results were compared with the measured MMSE score. (2) we studied a total of 230 subjects (67.9±7.4years old) who were members of the Tsuminory health class (Apple classroom). We assessed cognitive function by the DNN-based test before and after the intervention (for 2months). We compared the predicted MMSE scores by the DNN-based test before and after the 2-month intervention. In the first group, the MMSE score predicted by the DNN-based test increased from 27.1±0.8 to 27.6±0.7 after the intervention period (p=0.024). The measured MMSE score also increased after exercise, but not significant (P=0.28). In the second group, the exercise-diet therapy increased the predicted MMSE scores in 189 cases (p<0.001). In contrast, the therapy significantly reduced the mean MMSE score (p<0.001). The MMSE score predicted by the DNN-based test were increased by exercise-diet therapy in most subjects. The DNN-based test may be useful to monitor the effect of exercise-diet therapy on cognitive function aged people.

A Systematic Analysis of Community Detection in Complex Networks

Numerous techniques have been proposed by researchers to uncover the hidden patterns of real-world complex networks. Finding a hidden community is one of the crucial tasks for community detection in complex networks. Despite the presence of multiple methods for community detection, identification of the best performing method over different complex networks is still an open research question. In this article, we analyzed eight state-of-the-art community detection algorithms on nine complex networks of varying sizes covering various domains including animal, biomedical, terrorist, social, and human contacts. The objective of this article is to identify the best performing algorithm for community detection in real-world complex networks of various sizes and from different domains. The obtained results over 100 iterations demonstrated that the multi-scale method has outperformed the other techniques in terms of accuracy. Multi-scale method achieved 0.458 average value of modularity metric whereas multiple screening resolution, unfolding fast, greedy, multi-resolution, local fitness optimization, sparse Geosocial community detection algorithm, and spectral clustering, respectively obtained the modularity values 0.455, 0.441, 0.436, 0.421, 0.368, 0.341, and 0.340..

A new look at local ultraluminous infrared galaxies: the atlas and radiative transfer models of their complex physics

ABSTRACT We present the ultraviolet to submillimetre spectral energy distributions (SEDs) of the HERschel Ultra Luminous Infrared Galaxy Survey (HERUS) sample of 42 local ultraluminous infrared galaxies (ULIRGs) and fit them with a Markov chain Monte Carlo code using the CYprus models for Galaxies and their NUclear Spectra (CYGNUS) radiative transfer models for starbursts, active galactic nucleus (AGN) tori, and host galaxy. The Spitzer IRS spectroscopy data are included in the fitting. Our Bayesian SED fitting method takes comparable time to popular energy balance methods but it is more physically motivated and versatile. All HERUS galaxies harbour high rates of star formation but we also find bolometrically significant AGN in all of the galaxies of the sample. We estimate the correction of the luminosities of the AGN in the ULIRGs due to the anisotropic emission of the torus and find that it could be up to a factor of ∼10 for nearly edge-on tori. We present a comparison of our results with the smooth torus model of Fritz et al. and the two-phase models of Siebenmorgen et al. and SKIRTOR. We find that the CYGNUS AGN torus models fit significantly better the SEDs of our sample compared to all other models. We find no evidence that strong AGN appear either at the beginning or end of a starburst episode or that starbursts and AGN affect each other. IRAS 01003−2238 and Mrk 1014 show evidence of dual AGNs in their SED fits, suggesting a minimum dual AGN fraction in the sample of 5 per cent.

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

Abstract This paper introduces a novel reverse engineering (RE) technique for the reconstruction of editable computer-aided design (CAD) models of mechanical parts’ assemblies. The input is a point cloud of a mechanical parts’ assembly that has been acquired as a whole, i.e., without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized CAD geometries (i.e., 2D sketches, 3D parts, or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the adapted geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the geometries’ updates while guaranteeing the possibly imposed constraints and model coherence. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices.

A variational level set model combining with local Gaussian fitting and Markov random field regularization

A variational level set model combining with local Gaussian fitting and Markov random field regularization

Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing.

As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.

Synchronous analysis of cyclo-non-stationary signals: A comprehensive study with aeronautic applications

Synchronous components are common in measured mechanical signals. They usually consist of a periodic waveform generated by a particular rotating organ. The accurate estimation of such components is of high importance in vibration-based health monitoring, offering the possibility to isolate and investigate the corresponding organ contribution. It is also vital for residual analysis in case where the component of interest is random. Traditionally, the synchronous average was a widely used tool for this purpose, providing almost optimal estimation performance and simplicity. However, the applicability of this technique is confined to the case where signals are recorded under (quasi) stationary regimes, which is restrictive for many applications like aeronautics. This paper studies two extensions of this techniques, namely the global and the local synchronous fitting. Both of these techniques aim to estimate, for a given location in the cycle, an estimation of the mean based on a polynomial fitting. Whereas the former performs this estimation through a global polynomial fit, the latter uses a local approach based on the well-known “Savitzky-Golay filter”. Both techniques are presented, and the resulting filter of the local synchronous fitting operation is provided. The performances of these techniques are investigated through simulations, considering the parameters of both approaches. These methods are then successfully tested in three challenging aeronautic applications. The newly proposed local approach has demonstrated superior performances as compared to the global one. The Matlab code of both techniques is also provided.

Shape matters: cost curves and capacity utilization in U.S. manufacturing

This study investigates the shape of cost curves and cost components of twenty industries in the U.S. manufacturing sector in the 1958–2010 period. After aggregating data for the 459 industries of the U.S. Standard Industrial Classification (SIC), I analyzed, at both sectoral and two-digit levels, the behavior of unit variable costs for different levels of output, as represented by a proxy for capacity utilization. In addition, I decomposed the cost structure into payroll, wages, and material costs. Results show that the unit variable costs of the large majority of industries present constancy around the trend of the output series, giving support therefore to the hypothesis of a post-Keynesian inverted L-shaped cost curve that, at the aggregate level, shows a considerable range of constancy in unit direct cost curves around normal capacity utilization. In addition, decomposition in material and labor costs shows that labor hoarding is a generalized phenomenon in the manufacturing sector, shown by the steeper slopes of the local regression fits for payroll and wage costs in relation to material costs.

Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing

The deformation of plastics during production and service means that retired parts often possess different mechanical states, and this can directly affect not only the properties of remanufactured mechanical parts, but also the design of the remanufacturing process itself. In this paper, we describe the stress-strain relationship for remanufacturing, in particular the cyclic deformation of parts, by using the particle swarm optimization (PSO) method to acquire the Yoshida-Uemori (Y-U) hardening model parameters. To achieve this, tension-compression experimental data of AA7075-O, standard PSO, oscillating second-order PSO (OS-PSO) and variable weight PSO (VW-PSO) were acquired separately. The influence of particle numbers on the inverse analysis efficiency was studied based on standard PSO. Comparing the results of PSO variations showed that: (1) standard PSO is able to avoid local solutions and obtain Y-U model parameters to the same degree of precision as the OS-PSO; (2) by adjusting section weight, the VW-PSO could improve local fitting accuracy and adapt to asymmetric deformation; (3) by reducing particle numbers to a certain extent, the efficiency of analysis can be improved while also maintaining accuracy.

Learning the local landscape of protein structures with convolutional neural networks.

One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received much less attention even though it has important applications in both protein engineering and evolutionary biology. Here, we ask whether 3D convolutional neural networks (3D CNNs) can learn the local fitness landscape of protein structure to reliably predict either the wild-type amino acid or the consensus in a multiple sequence alignment from the local structural context surrounding site of interest. We find that the network can predict wild type with good accuracy, and that network confidence is a reliable measure of whether a given prediction is likely going to be correct or not. Predictions of consensus are less accurate and are primarily driven by whether or not the consensus matches the wild type. Our work suggests that high-confidence mis-predictions of the wild type may identify sites that are primed for mutation and likely targets for protein engineering.

DNA sequencing using the RGB image sensor of a consumer digital color camera

A novel method for Sanger DNA sequencing, as part of a compact and inexpensive DNA sequencer, using the RGB image sensor of a digital color camera was developed. Since the RGB image sensor can only detect up to three colors (wavelength bands), while Sanger DNA sequencing is required to detect four or more colors in order to quantify fluorescence of four different fluorophores in a mixed state during capillary electrophoresis, it is difficult to perform Sanger DNA sequencing using an RGB image sensor. Moreover, due to the spectral response of an RGB image sensor, fluorescence of the four fluorophores is detected only on the red and green channels, that is, not on the blue channel. To address the above-described issues, a two-color electropherogram was obtained by capillary electrophoresis of a sequencing sample and reproduced by best local fittings of two-color single peaks of the four fluorophores in chronological order. The fitted single peaks of the four fluorophores respectively give fluorescence intensities of the four fluorophores. That is, the two-color electropherogram is converted to a four-fluorophore electropherogram. The proposed Sanger-DNA-sequencing method was applied to a model sequencing sample, and the DNA sequence of 69–144 bases of the sample was accurately determined. This result is the first successful quantification of four or more fluorophores in a mixed state by using an RGB image sensor.

Theory of preference modelling for communities in scale-free networks

Detecting a community structure on networks is a problem of interest in science and many other domains. Communities are special structures which may consist nodes with some common features. The identification of overlapping communities can clarify not so apparent features about relationships among the nodes of a network. A node in a community can have a membership in a community with a different degree. Here, we introduce a fuzzy based approach for overlapping community detection. A special type of fuzzy operator is used to define the membership strength for the nodes of community. Fuzzy systems and logic is a branch of mathematics which introduces many-valued logic to compute the truth value. The computed truth can have a value between 0 and 1. The preference modelling approach introduces some parameters for designing communities of particular strength. The strength of a community tells us to what degree each member of community is part of a community. As for relevance and applicability of the community detection method on different types of data and in various situations, this approach generates a possibility for the user to be able to control the overlap regions created while detecting the communities. We extend the existing methods which use local function optimization for community detection. The LFM method uses a local fitness function for a community to identify the community structures. We present a community fitness function in pliant logic form and provide mathematical proofs of its properties, then we apply the preference implication of continuous-valued logic. The preference implication is based on two important parameters nu and alpha. The parameter nu of the preference-implication allows us to control the design of the communities according to our requirement of the strength of the community. The parameter alpha defines the sharpness of preference implication. A smaller value of the threshold for community membership creates bigger communities and more overlapping regions. A higher value of community membership threshold creates stronger communities with nodes having more participation in the community. The threshold is controlled by delta which defines the degree of relationship of a node to a community. To balance the creation of overlap regions, stronger communities and reducing outliers we choose a third parameter delta in such a way that it controls the community strength by varying the membership threshold as community evolves over time. We test the theoretical model by conducting experiments on artificial and real scale-free networks. We test the behaviour of all the parameters on different data-sets and report the outliers found. In our experiments, we found a good relationship between nu and overlapping nodes in communities.

Variational integrators for forced Lagrangian systems based on the local path fitting technique

Variational integrators are particularly suitable for simulation of mechanical systems, where features such as symplecticity and momentum preservation are essential. They also exhibit excellent long-time energy behavior even if external forcing is involved. Motivated by this fact, we present a new approach, that is based on the local path fitting technique, to construct variational integrators for forced mechanical systems. The core technology exploited is to fit the local trajectory as the Lagrange interpolation polynomial by requiring that the forced Euler–Lagrange equations hold at the internal interpolation nodes. This operation also yields the essential terms of the discrete forced Euler–Lagrange equations and consequently formulates the final integrator. This new approach not only avoids numerical quadrature involved in the classical construction, but also significantly improves the precision of the resulting integrator, as illustrated by the given examples.

Promoting Inclusion of Adults with Disabilities in Local Fitness Programs: A Needs Assessment

Promoting Inclusion of Adults with Disabilities in Local Fitness Programs: A Needs Assessment

Research on 3D crack segmentation of CT images of oil rock core.

In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background. Secondly the gray level of image background region is replaced by the average gray level of rock core, so that image background does not affect the binary segmentation. Next, median filtering processing is carried out. Finally, an algorithm of local binary fitting (LBF) model is executed to obtain the crack region. The proposed algorithm has been applied to oil rock core CT images with promising results.

Universal Evolutionary Model for Periodical Species

Real-world examples of periodical species range from cicadas, whose life cycles are large prime numbers, like 13 or 17, to bamboos, whose periods are large multiples of small primes, like 40 or even 120. The periodicity is caused by interaction of species, be it a predator-prey relationship, symbiosis, commensalism, or competition exclusion principle. We propose a simple mathematical model, which explains and models all those principles, including listed extremal cases. This rather universal, qualitative model is based on the concept of a local fitness function, where a randomly chosen new period is selected if the value of the global fitness function of the species increases. Arithmetically speaking, the different interactions are related to only four principles: given a couple of integer periods either (1) their greatest common divisor is one, (2) one of the periods is prime, (3) both periods are equal, or (4) one period is an integer multiple of the other.

An active contour model based on local pre-piecewise fitting image

Active contour model (ACM) has been widely used in the field of image segmentation and successfully solved numerous problems. Segmentation of intensity inhomogeneity images is a major focus of current research. To seek a better solution, this paper proposes an active contour model based on local pre-piecewise fitting image. First of all, the piecewise function is pre-fitted by the dilation and erosion operator in gray morphology. Second, a novel energy function is drawn out with a rewritten image fitting function. Third, an optimized gradient descent flow formula is proposed to further improve the robustness of this model. Fourth, an improved regularization term is proposed to optimize the level set function. Experiments have proved that the model can effectively segment images with uneven grayscale. Compared with other models, the proposed model has obvious advantages in robustness to initialization and segmentation efficiency. In addition, the model can also choose various targets by selecting different initial contours.