Robust Modeling Method Research Articles

Auto Takeoff and Precision Terminal-Phase Landing Using an Experimental Optical Flow Model for Global Positioning System/Inertial Navigation System Enhancement

The high estimated position error in current commercial-off-the-shelf (GPS/INS) impedes achieving precise autonomous takeoff and landing (TOL) flight operations. To overcome this problem, in this paper, we propose an integrated global positioning system (GPS)/inertial navigation system (INS)/optical flow (OF) solution in which the OF provides an accurate augmentation to the GPS/INS. To ensure accurate and robust OF augmentation, we have used a robust modeling method to estimate OF based on a set of real-time experiments conducted under various simulated helicopter-landing scenarios. Knowing that the accuracy of the OF measurements is dependent on the accuracy of the height measurements, we have developed a real-time testing environment to model and validate the obtained dynamic OF model at various heights. The performance of the obtained OF model matches the real OF sensor with 87.70% fitting accuracy. An accuracy of 0.006 m/s mean error between the real OF sensor velocity and the velocity of the OF model is also achieved. The velocity measurements of the obtained OF model and the position of the GPS/INS are used in performing a dynamic model-based sensor fusion algorithm. In the proposed solution, the OF sensor is engaged when the vehicle approaches a landing spot that is equipped with a predefined landing pattern. The proposed solution has succeeded in performing a helicopter auto TOL with a maximum position error of 27 cm.

Modelling spatially-resolved diffuse reflectance spectra of a multi-layered skin model by artificial neural networks trained with Monte Carlo simulations.

A robust modelling method was proposed to extract chromophore information in multi-layered skin tissue with spatially-resolved diffuse reflectance spectroscopy. Artificial neural network models trained with a pre-simulated database were first built to map geometric and optical parameters into diffuse reflectance spectra. Nine fitting parameters including chromophore concentrations and oxygen saturation were then determined by solving the inverse problem of fitting spectral measurements from three different parts of the skin. Compared to the Monte Carlo simulation accelerated by a graphics processing unit, the proposed modelling method not only reduced the computation time, but also achieved a better fitting performance.

Integration of weighted LS-SVM and manifold learning for fuzzy modeling

Abstract In this paper, a robust fuzzy modeling method is proposed for strongly nonlinear systems in the presence of noise and/or outliers. The proposed method integrates the advantages of the fuzzy structure, the manifold learning, and the weighted least squares support vector machine (LS-SVM). First, the Gustafson–Kessel clustering algorithm (GKCA) is applied to split the training data set into several subsets to determine the fuzzy rules and premise parameters. Then, a new objective function is constructed based on the fuzzy structure, the weighted LS-SVM, and the manifold regularization, which takes into account robustness and the intrinsic geometry of the data. A solving method is further developed, from which the fuzzy model is achieved and can effectively approximate a nonlinear system with various types of random noise. The proposed method is applied to an artificial case as well as a practical hydraulic actuator, demonstrating its effectiveness in modeling of a nonlinear system even under noise.

Robust Spatiotemporal LS-SVM Modeling for Nonlinear Distributed Parameter System With Disturbance

Most distributed parameter systems (DPS) have a strongly nonlinear spatiotemporal nature and are affected by disturbance. However, most of the existing DPS modeling methods only consider the linear relation between the spatial positions, but neglect the nonlinear one. Additionally, they also do not account for the influence of disturbance. Thus, in this paper, a robust spatiotemporal least squares support vector machine (LS-SVM) modeling method for DPS with disturbance is proposed. First, a spatial kernel function is constructed in order to describe the nonlinear relation between spatial positions. An optimal fusion method is then developed to derive a robust temporal coefficient, from which the influence of disturbance can be rejected. Through the integration of the spatial kernel function and the robust temporal coefficient, a robust spatiotemporal LS-SVM model is constructed. Since this modeling not only considers the nonlinear nature but also takes the influence of disturbance into account, it has the ability to adapt well to the nonlinear spatiotemporal dynamics, even when disturbance is presented. The analysis and proof show that the proposed robust spatiotemporal LS-SVM modeling method has the better robust performance as compared to the existing ones. Case studies not only demonstrate the effectiveness of the proposed method, but also demonstrate its superior robustness than other conventional modeling methods.

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking.

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVR (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. This indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

Spindle thermal error robust modeling using LASSO and LS-SVM

To improve the spindle thermal error prediction accuracy, the least absolute shrinkage and selection operator (LASSO) is used to directly select the temperature-sensitive point subset to guarantee the prediction performance of the thermal error model built by least squares support vector machines (LS-SVM). Taking a horizontal machining center as a test stand, the thermal error experiments with different spindle speed states are carried out. Then the temperature-sensitive points are selected using LASSO. The number of temperature-sensitive points is reduced from 20 to 7. Afterward, the thermal error model is designed by LS-SVM. The prediction performance and generalization performance of the thermal error model are compared with another two thermal error models using gray model (GM) and multiple linear regression (MLR), respectively. The comparison results indicate that the thermal error model derived from LS-SVM shows better prediction performance and generalization performance than those derived from GM and MLR with the highest prediction accuracy increasing about 74.6 and 54.3%, respectively. Thus, the feasibility and effectiveness of the proposed spindle thermal error robust modeling method are validated.

Thermal error robust modeling method for CNC machine tools based on a split unbiased estimation algorithm

In the thermal error compensation technology of CNC machine tools, the core issue is to establish a mathematical model of thermal error with high prediction accuracy and strong robustness. The prerequisite for the mathematical model is choosing optimum temperature sensitive points for modeling. Currently, there are many methods that prioritize reducing the collinearity between temperature sensitive points first before considering the influence weights of temperature sensitive points on thermal error. In this paper, we determined that these methods are unable to ensure that all the temperature sensitive points have high influence weights on thermal error through experimental analysis of the thermal error of Leaderway-V450 CNC machine tools in an idle state. This causes volatility in the temperature sensitive points and decreases the prediction accuracy and robustness of the model. In this regard, we present a new thermal error modeling method called the “Gray relation − Split Unbiased Estimation thermal error robust modeling method”, or the “GR-SUE method”. In the “GR-SUE method”, several temperature sensitive points with the highest influence weights on thermal error are selected directly using the gray relation algorithm. However, the gray relation algorithm causes a significant collinearity problem between temperature sensitive points. Therefore, we improve the multiple linear regression algorithm and propose a split unbiased estimation modeling algorithm to inhibit the influence of collinearity on the prediction accuracy and robustness of the model. Finally, the “GR-SUE method” is verified using numerous thermal error experiments of Leaderway-V450 CNC machine tools at an idle state under different rotation speeds and ambient temperatures. The experimental results show that the “GR-SUE method” can significantly reduce the volatility of temperature sensitive points and improve the prediction accuracy and robustness of the model.

Computational modeling of the bat HKU4 coronavirus 3CLpro inhibitors as a tool for the development of antivirals against the emerging Middle East respiratory syndrome (MERS) coronavirus.

The Middle East respiratory syndrome coronavirus (MERS‐CoV) is an emerging virus that poses a major challenge to clinical management.The 3C‐like protease (3CLpro) is essential for viral replication and thus represents a potential target for antiviral drug development. Presently, very few data are available on MERS‐CoV 3CLpro inhibition by small molecules. We conducted extensive exploration of the pharmacophoric space of a recently identified set of peptidomimetic inhibitors of the bat HKU4‐CoV 3CLpro. HKU4‐CoV 3CLpro shares high sequence identity (81%) with the MERS‐CoV enzyme and thus represents a potential surrogate model for anti‐MERS drug discovery. We used 2 well‐established methods: Quantitative structure‐activity relationship (QSAR)‐guided modeling and docking‐based comparative intermolecular contacts analysis. The established pharmacophore models highlight structural features needed for ligand recognition and revealed important binding‐pocket regions involved in 3CLpro‐ligand interactions. The best models were used as 3D queries to screen the National Cancer Institute database for novel nonpeptidomimetic 3CLpro inhibitors. The identified hits were tested for HKU4‐CoV and MERS‐CoV 3CLpro inhibition. Two hits, which share the phenylsulfonamide fragment, showed moderate inhibitory activity against the MERS‐CoV 3CLpro and represent a potential starting point for the development of novel anti‐MERS agents. To the best of our knowledge, this is the first pharmacophore modeling study supported by in vitro validation on the MERS‐CoV 3CLpro.Highlights MERS‐CoV is an emerging virus that is closely related to the bat HKU4‐CoV.3CLpro is a potential drug target for coronavirus infection.HKU4‐CoV 3CLpro is a useful surrogate model for the identification of MERS‐CoV 3CLpro enzyme inhibitors.dbCICA is a very robust modeling method for hit identification.The phenylsulfonamide scaffold represents a potential starting point for MERS coronavirus 3CLpro inhibitors development.

Cyclic characterization of wave-induced oscillatory and residual response of liquefiable seabed

The paper presents a robust modeling method for a fully coupled effective stress analysis of the wave-induced liquefaction scenarios, based on the Biot consolidation theory. In this context, emphasis is placed on the implementation of a well-calibrated cyclic soil model and an empirical shear-volume coupling equation, which links the increment of volumetric strain per cycle of wave with the shear strain occurring during that particular cycle. Unlike most of the previous investigations using the amplitude of shear stress over the wave period (or the phase-resolved oscillatory shear stresses) as the source term for calculating the residual pore pressure, in this study, the source term is related to the rate of plastic volumetric deformation and implemented into the Biot consolidation equation to consider both generation and partial dissipation of excess pore pressure during wave propagation. Then, the proposed modeling method is incorporated into a dynamic finite difference analysis procedure. Model calibrations are carried out in terms of individual soil element behavior and seabed response under progressive waves, respectively. Overall good agreement demonstrates the reliability of the modeling method for the prediction of wave-induced seabed response. Finally, the paper highlights the potentials of proposed modeling framework to simulate the basic features of wave-induced seabed response (i.e., the coupling of progressive buildup of pore pressure and cyclic softening of soil skeleton), by means of numerical examples. The obtained results show that the cyclic behavior of liquefiable seabed under wave actions can be well captured by the proposed model.

Don't Mention It? Analyzing User-Generated Content Signals for Early Adverse Drug Event Warnings

Post-marketing surveillance entails monitoring, assessing, and detecting adverse events once drugs are approved and released into the market. User-generated content channels such as social media and search query logs are increasingly being leveraged as complementary data sources to traditional databases for post-marketing drug surveillance. However, the existing body of knowledge has leveraged diverse sets of channels, adverse event types, and modeling methods, resulting in varying results and diverging conclusions regarding the viability and efficacy of various online user-generated channels and accompanying modeling methods. The objective of this study is to examine the efficacy and impact of different online user-generated content channels, event characteristics, and event modeling strategies on early detection of adverse drug events. We incorporate a large test bed encompassing millions of tweets, forums postings, and search query logs pertaining to 143 adverse events. We also propose a novel heuristic-based event modeling method capable of improving precision, recall, and timeliness of alerts. Preliminary results shed light on the interplay between user-generated channels and event types, as well as the potential for more robust event modeling methods that go beyond basic mention models. Several current and future research directions are also discussed. The preliminary results reported have important implications for various stakeholder groups, including regulatory agencies, post-marketing monitoring teams, healthcare hedge fund managers, and third-party consumer advocacy groups.

Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm

For thermal error compensation technology of CNC machine tools, the collinearity between temperature sensitive points is the main factor for determining the predicted robustness of thermal error model. The temperature sensitive points are input variables of the thermal error model. This paper studies the thermal error of Leaderway V-450 type CNC machine tools during different seasons. It is found that although the commonly used temperature sensitive point selection methods can significantly reduce the collinearity between temperature sensitive points, the correlation between some of the selected temperature-sensitive points and thermal error is weak. This causes the temperature-sensitive points to be variable and the predicted accuracy and robustness of thermal error to be reduced. Therefore, in this paper, the temperature sensitive points are selected directly by their correlation with thermal error to eliminate variability. However, the experimental results also show that the collinearity between temperature sensitive points is very large. Hence, the ridge regression algorithm is used to establish a thermal error model to inhibit the bad influence of collinearity on the thermal error predicted robustness. Thus, the “robustness ridge regression machine tool thermal error modeling method” is proposed, the “RRR method” for short. In addition, in the “RRR method”, the correlation coefficient is used to measure the correlation between temperature sensitive points and thermal error instead of the commonly used gray correlation; because this paper finds that the gray correlation algorithm is essentially inapplicable for measuring a negative correlation. Based on the thermal error experiment data for the whole year, the “RRR method” is compared with two currently used methods, and the results show that the “RRR method” can significantly enhance the long-term predicted accuracy and robustness of thermal error. Finally, the application effect of practical compensation shows that the “RRR method” is usable and effective.

Disproportionate Collapse Simulations

In recent years the ability to simulate and predict disproportionate collapse has seen growing interest among the scientific community. This is a challenging matter, since many authors have been dealing with the modeling of progressive collapse, and it is now well-established that such problem requires the use of many different non-standard modeling techniques together with extensive calibration. Despite the many papers in the literature, there is still a lack of methodologies tailored for the quantification of the structural robustness and its acceptable level, which clearly depends on the importance and function of the structure. Further, consideration of Performance Criteria as well as Decision Making Strategies, have to be supported by robust (but also efficient) modeling methods that have to include three-dimensional and geometrical non-linear effects. Some of the previously outlined principles about robustness will be discussed following which the authors present a methodology for taking into account that progressive collapse phenomena are governed not only by the dynamic response of the bays adjacent to the triggering event, but also by the residual plastic resources the structure conserves after the initial damage.

Multi‐spatial analysis of forest residue utilization for bioenergy

AbstractThe alternative energy sector is expanding quickly in the USA since passage of the Energy Policy Act of 2005 and the Energy Independence and Security Act of 2007. Increased interest in wood‐based bioenergy has led to the need for robust modeling methods to analyze woody biomass operations at landscape scales. However, analyzing woody biomass operations in regions like the US Inland Northwest is difficult due to highly variable terrain and wood characteristics. We developed the Forest Residue Economic Assessment Model (FREAM) to better integrate with Geographical Information Systems and overcome analytical modeling limitations. FREAM analyzes wood‐based bioenergy logistics systems and provides a modeling platform that can be readily modified to analyze additional study locations. We evaluated three scenarios to test the FREAM’s utility: a local‐scale scenario in which a catalytic pyrolysis process produces gasoline from 181 437 Mg yr−1 of forest residues, a regional‐scale scenario that assumes a biochemical process to create aviation fuel from 725 748 Mg yr−1 of forest residues, and an international scenario that assumes a pellet mill producing pellets for international markets from 272 155 Mg yr−1 of forest residues. The local scenario produced gasoline for a modeled cost of $22.33 GJ−1*, the regional scenario produced aviation fuel for a modeled cost of $35.83 GJ−1 and the international scenario produced pellets for a modeled cost of $10.51 GJ−1. Results show that incorporating input from knowledgeable stakeholders in the designing of a model yields positive results. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd

Moving object detection based on confidence factor and CSLBP features

Moving object detection is a challenging task in many computer vision applications. In this paper, we propose a robust background modelling method for this task. First, the background is updated by an adaptive strategy based on Centre-symmetric Local Binary Patterns. Then, background subtraction is used for detecting moving object. Although the traditional background subtraction technique uses the difference value between the current pixel and its corresponding background pixel for objection detection, our method utilises the confidence factor to determine whether the current pixel is a background or foreground pixel. The confidence factor of the current pixel is calculated in term of the difference values of its neighbourhood pixels. In the experiments, the proposed algorithm is tested on several challenging datasets such as PETS 2009, BMC 2012 and SABS. The experimental results demonstrate that our algorithm can robustly detect moving object under various scenes.

Artificial neural network modeling of diuron and irgarol-based HPLC data and their levels from the seawaters in Izmir, Turkey

ABSTRACTThe LC determination of two well-known antifouling booster biocides, diuron and irgarol, was investigated from the seawaters in İzmir, Turkey. The biocide levels were pre-concentrated through C18 solid-phase extraction cartridges and they were analyzed by the LC-UV method. An artificial neural network (ANN) was used to model the data obtained from LC optimization. Column temperature, percentage of acetonitrile, flow rate, wavelength, pH, and concentration of biocides were used as input parameters. The retention time was selected as output parameter. The best back-propagation algorithm in ANN modeling for diuron and irgarol was found to be the Levenberg–Marquardt algorithm. The limits of detection for diuron and irgarol were calculated as 25.38 and 39.49 ng L−1, respectively. The inter-day and intra-day precisions were obtained less than 13.5% for each biocide. The recovery rate for diuron was 96.9% and for irgarol it was 84.6%. The maximum diuron and irgarol levels were measured as 1779 ng L−1 and 908 ng L−1, respectively. In conclusion, ANN is a robust modeling method to predict the retention time in LC studies. Since diuron and irgarol have been detected in Turkish waters, it is therefore suggested that booster biocides with less impact on the environment should be used in antifouling paint formulas.

Physical growth of the shuar: Height, Weight, and BMI references for an indigenous amazonian population.

Information concerning physical growth among small-scale populations remains limited, yet such data are critical to local health efforts and to foster basic understandings of human life history and variation in childhood development. Using a large dataset and robust modeling methods, this study aims to describe growth from birth to adulthood among the indigenous Shuar of Amazonian Ecuador. Mixed-longitudinal measures of height, weight, and body mass index (BMI) were collected from Shuar participants (n = 2,463; age: 0-29 years). Centile growth curves and tables were created for each anthropometric variable of interest using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Pseudo-velocity and Lambda-Mu-Sigma curves were generated to further investigate Shuar patterns of growth and to facilitate comparison with United States Center for Disease Control and Prevention and multinational World Health Organization growth references. The Shuar are small throughout life and exhibit complex patterns of growth that differ substantially from those of international references. Similar to other Amazonians, Shuar growth in weight compares more favorably to references than growth in height, resulting in BMI curves that approximate international medians. Several additional characteristics of Shuar development are noteworthy, including large observed variation in body size early in life, significant infant growth faltering, extended male growth into adulthood, and a markedly early female pubertal growth spurt in height. Phenotypic plasticity and genetic selection in response to local environmental factors may explain many of these patterns. Providing a detailed reference of growth for the Shuar and other Amazonian populations, this study possesses direct clinical application and affords valuable insight into childhood health and the ecology of human growth.

A Robust Appearance Model and Similarity Measure for Image Matching

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270002/02.jpg"" width=""400"" /> CP3 histogram</div> An ideal similarity measure for matching image should be discriminative, producing a conspicuous correlation peak and suppressing false local maxima. Image matching tasks in practice, however, often involves complex conditions, such as blurring and fluctuating illumination. These may cause the similarity measure to not be discriminative enough. We utilized a robust scene modeling method to model the appearance of an image and propose an associated similarity measure for image matching. The proposed method utilizes a spatio-temporal learning stage to select a group of supporting pixels for each target pixel, then builds a differential statistic model of them to describe the uniqueness of the spatial structure and to provide illumination invariance for robust matching. We utilized this method for image matching in several challenging environments. Experimental results show that the proposed similarity measure produces explicit correlation peaks to achieve robust image matching. </span>

Thermal Error Modeling Method for a CNC Machine Tool Feed Drive System

The disadvantages of the common current thermal error modeling methods for CNC machine tool feed drive systems were analyzed, such as the requirement of many temperature sensors to reach high accuracy and poor applicability of different moving states. A new robust modeling method based on the heat transfer theory is proposed, and the procedure of the thermal tests for a feed drive system is presented. Multiple regression method and robust modeling method based on the heat transfer theory were, respectively, used to establish a thermal error model, and a pointer automatic optimizer was used to optimize the parameters in the robust model. A compensation simulation was conducted under five different moving states using these two modeling methods, and the advantages of the robust modeling method were proved. Finally, the compensation effect of the robust modeling method was verified under a random moving state on a vertical machining center.

ChemInform Abstract: Mathematical Models for Microbial Kinetics in Solid‐State Fermentation: A Review

AbstractReview: 64 refs.

Multimodel Modeling of Doubly Fed Induction Motor

As the electric drives are highly complex and nonlinear systems subject of several disturbances, it is a great problem to represent them via a unique model with sufficient precision and simple structure. Thus, this paper presents a new doubly fed induction motor (DFIM) model based on multimodel approach as a robust modeling method able to replace the complex system by a set of simpler local models. This approach consists of four steps which are clusters estimation, structure identification, parametric identification and local models combination. The collected data on DFIM are, firstly, clustered into several groups through a Chui’s clustering algorithm. Then, the structure identification is performed on each group using the instrumental ratio (RDI) method. Parameters of each sub model are identified using recursive least square (RLS) method. Finally, obtained sub models are combined using the validity concept. A detailed dynamic model of a DFIM with grid-connected stator and PWM inverter connected rotor, side is presented in the dq-synchronous reference frame in order to generate the input/output data. Simulation results are presented and analyzed up under Matlab/Simulink environment