Feasible Parameter Set Research Articles

Goal state driven trajectory optimization

Many applications demand a dynamical system to reach a goal state under kinematic and dynamic (i.e., kinodynamic) constraints. Moreover, industrial robots perform such motions over and over again and therefore demand efficiency, i.e., optimal motion. In many applications, the initial state may not be constrained and can be taken as an additional variable for optimization. The semi-stochastic kinodynamic planning (SKIP) algorithm presented in this paper is a novel method for trajectory optimization of a fully actuated dynamic system to reach a goal state under kinodynamic constraints. The basic principle of the algorithm is the parameterization of the motion trajectory to a vector in a high-dimensional space. The kinematic and dynamic constraints are formulated in terms of time and the trajectory parameters vector. That is, the constraints define a time-varying domain in the high dimensional parameters space. We propose a semi stochastic technique that finds a feasible set of parameters satisfying the constraints within the time interval dedicated to task completion. The algorithm chooses the optimal solution based on a given cost function. Statistical analysis shows the probability to find a solution if one exists. For simulations, we found a time-optimal trajectory for a 6R manipulator to hit a disk in a desired state.

Uncertainty Quantification in Chemical Modeling

A module of PrIMe automated data-centric infrastructure, Bound-to-Bound Data Collaboration (B2BDC), was used for the analysis of systematic uncertainty and data consistency of the H2/CO reaction model (73/17). In order to achieve this purpose, a dataset of 167 experimental targets (ignition delay time and laminar flame speed) and 55 active model parameters (pre-exponent factors in the Arrhenius form of the reaction rate coefficients) was constructed. Consistency analysis of experimental data from the composed dataset revealed disagreement between models and data. Two consistency measures were applied to identify the quality of experimental targets (Quantities of Interest, QoI): scalar consistency measure, which quantifies the tightening index of the constraints while still ensuring the existence of a set of the model parameter values whose associated modeling output predicts the experimental QoIs within the uncertainty bounds; and a newly-developed method of computing the vector consistency measure (VCM), which determines the minimal bound changes for QoIs initially identified as inconsistent, each bound by its own extent, while still ensuring the existence of a set of the model parameter values whose associated modeling output predicts the experimental QoIs within the uncertainty bounds. The consistency analysis suggested that elimination of 45 experimental targets, 8 of which were self- inconsistent, would lead to a consistent dataset. After that the feasible parameter set was constructed through decrease uncertainty parameters for several reaction rate coefficients. This dataset was subjected for the B2BDC framework model optimization and analysis on. Forth methods of parameter optimization were applied, including those unique in the B2BDC framework. The optimized models showed improved agreement with experimental values, as compared to the initially-assembled model. Moreover, predictions for experiments not included in the initial dataset were investigated. The results demonstrate benefits of applying the B2BDC methodology for development of predictive kinetic models.

A Robust Fault Detection Method using a Zonotopic Kaucher Set-membership Approach

This paper presents a robust fault detection method using a zonotopic Kaucher set-membership method. The fault detection approach is based on checking the consistency between the model and the data. Consistency is given if there is an intersection between the feasible parameter set and the nominal parameter set. To allow efficient computation the feasible set is approximated by a zonotope. Due to the usage of Kaucher interval arithmetic the results are mathematically guaranteed. The proposed approach is assessed using an illustrative application based on a well-known four-tank case study. The study shows that it is possible to detect even small errors in a noisy setting.

Set-Membership-Based Fault Detection and Isolation for Robotic Assembly of Electrical Connectors

This paper addresses the fault detection and isolation (FDI) problem for robotic assembly of electrical connectors in the framework of set-membership. Both the fault-free and faulty cases of assembly are modeled by different switched linear models with known switching sequences, bounded parameters, and external disturbances. The locations of switching points of each model are assumed to be inside some areas but the accurate positions are not clear. Given current input/output data, the feasible parameter set of fault-free switched linear model is obtained by sequentially calculating an optimal ellipsoid. If the pair of data is not consistent with any possible submodel, a fault is then detected. The isolation of fault is realized by checking the consistency between the data sequence and each possible fault model one by one. The robustness of the proposed FDI algorithms is proved. The effectiveness of these algorithms is verified by the robotic assembly experiments of mating electrical connectors. Note to Practitioners —In modern robotic assembly tasks, the industrial robots often need to manipulate tiny objects with complex structure. Electrical connectors are a typical kind of these objects and widely used in many industrial fields. To avoid damaging the fragile connectors and accelerate the assembly process, it is required to promptly detect and isolate the certain assembly fault in real time so that the robot can immediately implement an error recovery procedure according to the identified fault. The proposed set-membership-based fault detection and isolation (FDI) methodology satisfies both the timing and fault-isolation requirements for this kind of robotic assembly task. In terms of the set-membership theory, no false alarm will occur if there are sufficient training data for the proposed method. In addition, it turns out that the proposed method can signal an alarm faster than conventional residual-based FDI method from plentiful experiments. Although only the robotic assembly of electrical connectors is investigated, our FDI method can also be applied in the assembly task of other small and complex parts. This is especially useful for increasing the productivity and promoting the automation level of electronic industries.

Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

A recursive technique for tracking the feasible parameter set in bounded error estimation

SummaryIn this paper, a new recursive algorithm is proposed for tracking parameter changes of a time‐varying linear system. Since a bounded error approach is adopted for both modeling the measurement noise and the parameter change process, the problem addressed amounts to the design of a procedure for updating an estimate of the feasible parameter set. The approximating regions considered are in the form of outbounding orthotopes. The novelty of the approach lies in the use of a selection technique which keeps track only of a special subset of the constraints defining the feasible set. These inequalities represent the binding constraints of suitable linear programs of limited size. The devised algorithm is tested on several numerical examples, showing remarkable performance both in computational burden, which is comparable to that of classical recursive estimation algorithms like recursive least squares (RLS) and quality of the set estimate as compared to alternative techniques available in the literature.

Directed strongly regular graphs with rank 6

In this paper, we study 24 possibilities of directed strongly regular graphs with adjacency matrix of rank 6, our proof is based on two effective algorithms. At last, we get 4 families of directed strongly regular graphs with realizable parameter sets, exclude 20 families of directed strongly regular graphs with feasible parameter sets; partially solve the classification problem of directed strongly regular graphs with adjacency matrix of rank 6.

An Agent-Based Approach to Dynamically Represent the Pharmacokinetic Properties of Baicalein.

Baicalein, a typical flavonoid presented in Scutellariae radix, exhibits a unique metabolic profile during first-pass metabolism: parallel glucuronidation and sulfation pathways, with possible substrate inhibition in both pathways. In this project, we aimed to construct an agent-based model to dynamically represent baicalein pharmacokinetics and to verify the substrate inhibition hypothesis. The model consisted of three 3D spaces and two membranes: apical space (S1), intracellular space (S2), basolateral space (S3), apical membrane (M1), and basolateral membrane (M2). In silico enzymes (UDP-glucuronosyltransferases (UGTs) and sulfotransferases (SULTs)) and binder components were placed in S2. The model was then executed to simulate one-pass metabolism experiments of baicalein. With the implementation of a two-site enzyme design, the simulated results captured the preset qualitative and quantitative features of the wet-lab observations. The feasible parameter set showed that substrate inhibition happened in both conjugation pathways of baicalein. The simulation results suggested that the sulfation pathway was dominant at low concentrations and that SULT was more inclined to substrate inhibition than UGT. Cross-model validation was satisfactory. Our findings were consistent with a previously reported catenary model. We conclude that the mechanisms represented by our model are plausible. Our novel modeling approach could dynamically represent the metabolic pathways of baicalein in a Caco-2 system.

A linear programming approach to online set membership parameter estimation for linear regression models

SummaryThis paper presents a new technique for online set membership parameter estimation of linear regression models affected by unknown‐but‐bounded noise. An orthotopic approximation of the set of feasible parameters is updated at each time step. The proposed technique relies on the solution of a suitable linear program, whenever a new measurement leads to a reduction of the approximating orthotope. The key idea for preventing the size of the linear programs from steadily increasing is to propagate only the binding constraints of these optimization problems. Numerical studies show that the new approach outperforms existing recursive set approximation techniques, while keeping the required computational burden within the same order of magnitude. Copyright © 2016 John Wiley & Sons, Ltd.

TuneR: a framework for tuning software engineering tools with hands-on instructions in R

Numerous tools automating various aspects of software engineering have been developed, and many of the tools are highly configurable through parameters. Understanding the parameters of advanced tools often requires deep understanding of complex algorithms. Unfortunately, suboptimal parameter settings limit the performance of tools and hinder industrial adaptation, but still few studies address the challenge of tuning software engineering tools. We present TuneR, an experiment framework that supports finding feasible parameter settings using empirical methods. The framework is accompanied by practical guidelines of how to use R to analyze the experimental outcome. As a proof-of-concept, we apply TuneR to tune ImpRec, a recommendation system for change impact analysis in a software system that has evolved for more than two decades. Compared with the output from the default setting, we report a 20.9% improvement in the response variable reflecting recommendation accuracy. Moreover, TuneR reveals insights into the interaction among parameters, as well as nonlinear effects. TuneR is easy to use, thus the framework has potential to support tuning of software engineering tools in both academia and industry. Copyright © 2016 John Wiley & Sons, Ltd.

A two-phase algorithm for the multiparametric linear complementarity problem

A new two-phase method for solving the multi-parametric linear complementarity problem (mpLCP) with sufficient matrices is presented. In the first phase an initial feasible solution to mpLCP which satisfies certain criteria is determined. In the second phase the set of feasible parameters is partitioned into polyhedral regions such that the solution of the mpLCP, as a function of the parameters, is invariant over each region. The worst-case complexity of the presented algorithms matches that of current methods for nondegenerate problems and is lower than that of current methods for degenerate problems.

The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

Abstract. Heterogeneity of landscape features like terrain, soil, and vegetation properties affects the partitioning of water and energy. However, it remains unclear to what extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from sub-grid topography heterogeneity was incorporated into the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expert-knowledge-based model constraints reduces model uncertainty, and whether (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge-based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as an overall measure of model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 %, respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. In addition, it was shown that suitable semi-quantitative prior constraints in combination with the transfer-function-based regularization approach of mHM can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

Low-Complexity Group Alternate Iterative List Detection for MIMO Systems

In this paper, we propose a low-complexity group alternate iterative list (GAIL) detection algorithm for MIMO systems. By utilizing the recursive interference suppression and successive interference cancellation techniques, the symbol vector can be partitioned into many subgroups. Subsequently, symbols in each subgroup are detected in terms of the K-best detector. The inter-group interference is effectively mitigated in the GAIL algorithm by creating a candidate list and iteratively correcting the unreliable symbols for the detection result. We provide the performance-complexity tradeoff based on different feasible parameter settings. The numerical results demonstrate that the GAIL algorithm can achieve close-to-optimal performance while maintaining low computational complexity. In addition, the running speed of the GAIL algorithm can be dramatically increased using parallel processing in real-time communication systems.

An Inverse Approach for the Determination of\\ Viscous Damping Model of Fibre Reinforced Plastic Beams using Finite Element Model Updating

Investigations have been carried out both numerically and experimentally to settle with a practically feasible set of proportional viscous damping parameters for the accurate prediction of responses of fibre reinforced plastic beams over a chosen frequency range of interest. The methodology needs accurate experimental modal testing, an adequately converged finite element model, a rational basis for correct correlations between these two models, and finally, updating of the finite element model by estimating a pair of global viscous damping coefficients using a gradient-based inverse sensitivity algorithm. The present approach emphasises that the successful estimate of the damping matrix is related to a-priori estimation of material properties, as well. The responses are somewhat accurately predicted using these updated damping parameters over a large frequency range. In the case of plates, determination of in-plane stiffness parameters becomes easier, whereas for beam specimens, transverse material properties play a comparatively greater role and need to be determined. Moreover, for damping matrix parameter estimation, frequency response functions need to be used instead of frequencies and mode shapes. The proposed method of damping matrix identification is able to reproduce frequency response functions accurately even outside the frequency ranges used for identification.

A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator

In this paper the problem of dynamic modeling and parameter estimation of a seven degree of freedom hydraulic manipulator is investigated. The numerical model is developed in Simulink using Sim Mechanic and Simscape toolboxes with unknown/uncertain parameters. The aim of this paper is to develop a mechanism that enables us to find a feasible set of parameters for the robot that is consistent with measurements of the input, output, and states of the system under noisy and unknown operating conditions. As the first step a genetic algorithm is developed to solve an output error system identification problem for a specific joint, i.e. joint 2, such that the parameters of the joint converge to the desired set of parameters within an acceptable accuracy. The results can be straightforwardly extended to all joints of the manipulator.

Physical response of hyperelastic models for composite materials and soft tissues

A hyperelastic model must not only characterize the mechanical response of a composite material such as soft tissue, but also ensure numerical stability by a feasible set of material parameters. Apart from the well-known ill-conditioning problem caused by the incompressibility constraint, the paper indicates another ill-conditioning occurring in any general fibre-reinforced material model for tubular organs when unbalance between the fibre strain energy and the matrix strain energy becomes too large. Specifically, although the Holzapfel model is polyconvex, this problem can be observed as an unphysical behaviour in a physiological deformation range of a tissue such as arterial wall and intestine by thickening in the thickness direction associated with a volume growth of a specimen in a tension test. Particularly, the same problem for a polyconvex modified Fung-type model with the matrix characterized by the neo-Hookean model has been discussed for the first time. By investigating the influence of the shear modulus in these two models, we not only prove the cause of the ill-conditioning but also propose a solution to control the unbalance in the strain energy. The numerical results show significant enhancement of the model stability in overcoming the unphysical deformation.

Set-membership estimation of fiber laser physical parameters from input–output power measurements

The problem of fiber laser parameters estimation from input–output transient power measurements is considered in the paper. The proposed procedure is based on a suitable laser model obtained from the rate and propagation equations. By proper manipulation of the simplified laser equations, the estimation problem is formulated in the bounded error estimation framework, where a priori information on physical parameters can be properly taken into account. First the so-called feasible parameter set is defined. Then, the problem of optimally picking a single model from the set is formulated in terms of central conditional estimation and solved by exploiting recent results by the authors. Properties of the proposed solution are discussed. The proposed algorithm is tested on a set of simulated data obtained from a commercial laser simulator.

A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification

Nonlinear system identification is discussed in a mixed set-membership and statistical setting. A Markov chain Monte Carlo (MCMC) approach is proposed that estimates the feasible parameter set, the minimum volume outer-bounding ellipsoid and the minimum variance estimate. The proposed algorithm is proved to be convergent and enjoys some desirable properties. Further, its computational complexity and numerical accuracy are studied.

A parametric study for the linear fractional programming problem under uncertainty

In this paper, a linear fractional programming problem with interval coefficients in both objective function and constraints is solved. The uncertain objective function is transformed into two deterministic objective functions. The two objective functions are converted into a single objective problem through the linear combination method. Through a modified possibility degree, the uncertain inequality and equality constraints are transformed into deterministic inequality constraints. The existing results are concerning the qualitative and quantitative analysis of basic notions in parametric linear fractional programming problem. These notions are the set of feasible parameters, the solvability set and the stability set of the first kind. A numerical example is investigated to demonstrate the effectiveness of the present approach.

Uncertainty assessment of soil erosion model using particle filtering

Recent advances in computer with geographic information system (GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space. However, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling purposes. Sources of uncertainty involved in modeling include data, model structural, and parameter uncertainty. To deal with the uncertain parameters of a catchment-scale soil erosion model (CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling (PF) is introduced in the CSEM. The proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at outlets. PF provides time-varying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter set. CSEM-PF was applied to a small mountainous catchment of the Yongdam dam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon events. Finally, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.