Efficient Adaptive Method Research Articles

An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review

In the last few years, traffic congestion has become a growing concern due to increasing vehicle ownerships in urban areas. Intersections are one of the major bottlenecks that contribute to urban traffic congestion. Traditional traffic signal control systems cannot adjust the timing pattern depending on road traffic demand. This results in excessive delays for road users. Adaptive traffic signal control in a connected vehicle environment has shown a powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g., delay minimization). Connected vehicle technology, as an emerging technology, is a mobile data platform that enables the real-time data exchange among vehicles and between vehicles and infrastructure. Although several reviews about traffic signal control or connected vehicles have been written, a systemic review of adaptive traffic signal control in a connected vehicle environment has not been made. Twenty-six eligible studies searched from six databases constitute the review. A quality evaluation was established based on previous research instruments and applied to the current review. The purpose of this paper is to critically review the existing methods of adaptive traffic signal control in a connected vehicle environment and to compare the advantages or disadvantages of those methods. Further, a systematic framework on connected vehicle based adaptive traffic signal control is summarized to support the future research. Future research is needed to develop more efficient and generic adaptive traffic signal control methods in a connected vehicle environment.

Adaptive fuzzy tracking control for a class of high-order switched uncertain nonlinear systems

In this paper, adaptive fuzzy tracking control is investigated for a class of high-order switched nonlinear systems. The considered systems possess the characteristic of completely unknown nonlinear functions and arbitrary switchings. An efficient fuzzy adaptive control method is established by combining the common Lyapunov function method with adding a power integrator approach. A common assumption that is widely used in high-order nonlinear systems with or without switching is removed. Because the unknown nonlinearities can be approximated by using fuzzy logic systems, our proposed method is more flexible and applicable. Furthermore, some effective parameters are introduced such that the obtained results can guarantee that the practical output tracking performance can be realized via easily tuning the controller design parameters. On the other hand, to solve the problem of over-parameterization, a result with only one adaptive law is given. Finally, simulation results are addressed to show the effectiveness of the theoretical approaches.

High performance computation of residual stress and distortion in laser welded 301L stainless sheets

Transient thermo-mechanical simulation of stainless plate laser welding process was performed by a highly efficient and accurate approach-hybrid iterative substructure and adaptive mesh method. Especially, residual stress prediction was enhanced by considering various heat effects in the numerical model. The influence of laser welding heat input on residual stress and welding distortion of stainless thin sheets were investigated by experiment and simulation. X-ray diffraction (XRD) and contour method were used to measure the surficial and internal residual stress respectively. Effect of strain hardening, annealing and melting on residual stress prediction was clarified through a parametric study. It was shown that these heat effects must be taken into account for accurate prediction of residual stresses in laser welded stainless sheets. Reasonable agreement among residual stresses by numerical method, XRD and contour method was obtained. Buckling type welding distortion was also well reproduced by the developed thermo-mechanical FEM.

Maneuvering multi-target tracking based on variable structure multiple model GMCPHD filter

The traditional multiple model cardinalized probability hypothesis density (MMCPHD) filter uses a fixed model set for all targets. It is clearly inefficient and may cause the decrease of performance in the situations where the size of the model set is large and the scene is complicated. This paper presents a variable structure multiple model (VSMM) version of the Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter based on the expected mode augmentation (EMA) for the multiple maneuvering target tracking. The GMCPHD filter which is suitable for the VSMM is derived, thus different model sets can be used for different targets. Then EMA algorithm which is an efficient model set adaptation method is introduced and applied to the VSMM version of the GMCPHD (VSMM-GMCPHD) filter. This method costs less computational time and has better estimate precision than that of the traditional multiple model version of GMCPHD (MM-GMCPHD) filter. The gating technique is utilized to further improve the computational efficiency. Simulation results verify the performance of the proposed methods. The estimate precision of VSMM-GMCPHD filters with and without the gate are almost the same, however the former one is more cost-effective. Both of the two VSMM-GMCPHD filters outperform the MM-GMCPHD filter.

Computationally efficient adaptive time step method for the Cahn–Hilliard equation

In this work, we propose a fast and efficient adaptive time step procedure for the Cahn–Hilliard equation. The temporal evolution of the Cahn–Hilliard equation has multiple time scales. For spinodal decomposition simulation, an initial random perturbation evolves on a fast time scale, and later coarsening evolves on a very slow time scale. Therefore, if a small time step is used to capture the fast dynamics, the computation is quite costly. On the other hand, if a large time step is used, fast time evolutions may be missed. Hence, it is essential to use an adaptive time step method to simulate phenomena with multiple time scales. The proposed time adaptivity algorithm is based on the discrete maximum norm of the difference between two consecutive time step numerical solutions. Numerical experiments in one, two, and three dimensions are presented to demonstrate the performance and effectiveness of the adaptive time-stepping algorithm.

Evaluation of the efficiency of an automated system for testing synchronous round rotor machines

On the basis of analysis of normative documents and study of the technological process of tests, an automated system for testing synchronous round rotor electric machines has been developed. This system has a three-level hierarchical structure. Its lower level consists of measurement sensors and actuating units, the middle level is represented by a digital computer system on the basis of the NI PXIe platform by National Instruments, and the highest level is a computerized operator position (COP), which is based on industrial-class PCs. Algorithms and programs for automatic testing of synchronous turbomachines that we developed in the LabVIEW environment form the basis of the virtual instruments of the system. Because of their interconnections, they are combined into a unified software complex (SMTest). The approximation of the synchronous- machine characteristics that were obtained during testing is based on regression equations of different orders and the least-squares method and used in the algorithms to determine the static parameters of a machine. A combined optimizing algorithm, which is based on the least-squares method and the alternating- variable descent method, is used to determine the dynamic parameters of a synchronous machine. To filter possible noise in measuring channels of the voltage and current sensors, a newly developed efficient adaptive method of sliding trigonometric interpolation is used in the algorithms for determining the parameters of sinusoidal signals, which are picked off the voltage and current sensors in steady-state modes, and dots of signal envelopes in transient modes. To reduce the time for obtaining the static characteristics of the machine, the automated system has a designed exciting-current control loop with a PID controller, which provides the desired field forcing. The automated test system’s efficiency was estimated by mathematical simulation of the testing procedure and the experiment. Analysis of the results has shown that the use of this system makes it possible to reduce the complexity of the testing procedure and its duration by a factor of 1.5–1.7, improve the accuracy of the obtained results by a factor of 3–5, and improve testing safety by mitigating risks of injury and emergencies due to incorrect actions of the staff.

Optimal mass transport-based adaptive mesh method for phase-field models of two-phase fluid flows

In this paper, we present an efficient adaptive mesh method for solving phase-field model of a mixture of two-phase incompressible fluid flows. The adaptive mesh is generated by a coordinate transformation that is determined as the solution of the classical problem of the optimal mass transportation, also known as Monge–Kantorovich problem (MKP). The numerical solution of the MKP is computed by taking the gradient of the steady state solution of a parabolic Monge–Ampère equation (PMAE). Several numerical experiments are conducted to demonstrate the performance of the PMAE method for solving phase-field models. The numerical results illustrate that the adaptive mesh computed with PMAE method captures well the moving interface of the phase-field function.

An Incremental Decision Tree for Mining Multilabel Data

Mining with multilabel data is a popular topic in data mining. When performing classification on multilabel data, existing methods using traditional classifiers, such as support vector machines (SVMs), k-nearest neighbor (k-NN), and decision trees, have relatively poor accuracy and efficiency. Motivated by this, we present a new algorithm adaptation method, namely, a decision tree–based method for multilabel classification in domains with large-scale data sets called decision tree for multi-label classification (DTML). We build an incremental decision tree to reduce the learning time and divide the training data and adopt the k-NN classifier at leaves to improve the classification accuracy. Extensive studies show that our algorithm can efficiently learn from multilabel data while maintaining good performance on example-based evaluation metrics compared to nine state-of-the-art multilabel classification methods. Thus, we draw a conclusion that we provide an efficient and effective incremental algorithm adaptation method for multilabel classification especially in domains with large-scale multilabel data.

An efficient space-time adaptive wavelet Galerkin method for time-periodic parabolic partial differential equations

We introduce a multitree-based adaptive wavelet Galerkin algorithm for space-time discretized linear parabolic partial differential equations, focusing on time-periodic problems. It is shown that the method converges with the best possible rate in linear complexity and can be applied for a wide range of wavelet bases. We discuss the implementational challenges arising from the Petrov-Galerkin nature of the variational formulation and present numerical results for the heat and a convection-diffusion-reaction equation.

An efficient adaptive fuzzy learning diagnosis method for e-Learning

Most e-Learning systems are required to establish a flexible content structure and provide suitable learning path, content, or interface by extracting meaningful learning behavior of students for adaptive learning. However, recognizing the students' learning behavior, teaching material, and personal degree are still a challenge that needs to be resolved. In this paper, we propose an efficient fuzzy algorithm using in similarity measurement for selecting suitable content and exams for students, which has been applied to the e-Learning system called Fuzzy Adaptive Learning Diagnosis System (FALDS). The proposed system computes the relationship between exam items and teaching materials depending on the results of practice and then exams to automatically select and marks important paragraphs for the learners. In addition, an efficient system for classifying students into groups so that information for selecting appropriate items for the learners can be provided is proposed. To evaluate the performance, a total of 200 fourth-grade students from six classes participate in the experiment for a school term. The results indicate that by using FALDS to diagnose and assist learning, students in the experimental group outperform those not in the group.

An efficient adaptive method for estimating the distance between mobile sensors

The received signal strength (RSS) is a common source of information used for estimating the distance between two wireless nodes, whether these nodes are stationary or mobile. Minimum mean squared error distance estimation methods that use the RSS require prior knowledge of both the variance of the noise and, in the case of mobile sensors, the dynamics of the nodes' mobility. In mobile applications, where low computational complexity is important, pseudo-optimal estimations are preferred, as they do not require such information. In this case, the maximum likelihood estimator (MLE) is often used. In this paper, we propose an efficient pseudo-optimal log-power based distance estimation method using RSS under lognormal shadowing, that improves the MLE. It does not require a priori knowledge either of the movement dynamics or of the variance of the noise. The method is based on adaptively minimizing the variance of the prediction error, using a random walk model with correlated increments. It is analytically demonstrated that the distance estimation error variance of the proposed method improves the MLE in both the static and mobile cases. We use a simulated velocity model example to compare its performance with other algorithms in this group, such as the linear mean square filter and the Gauss---Newton search.

Short‐range ground moving target indication in forward looking airborne radar based on elevation prefiltering

Clutter suppression for short-range ground moving target indication in forward looking airborne radar is a challenging problem because of the range-dependent clutter. If the pulse repetition frequency is chosen so high that radar clutter is range ambiguous, then additional blind velocities at short-range induced by far-range clutter will occur. To eliminate these additional blind velocities, an efficient beamspace-based adaptive prefiltering method in elevation is proposed in this study to suppress ambiguous far-range clutter. The proposed beamformer can provide nearly optimal adapted elevation pattern because of the improved training data selection strategy. After eliminating the impact of ambiguous far-range clutter, the Doppler-compensation-based space–time adaptive processing method could be carried out to suppress residual range-dependent short-range clutter. In addition, the proposed prefiltering method is computationally efficient, and hence it is suitable for engineering application.

Efficient adaptive response surface method using intelligent space exploration strategy

This article presents a novel intelligent space exploration strategy (ISES), which is then integrated with the adaptive response surface method (ARSM) for higher global optimization efficiency. ISES consists of two novel elements for space reduction and sequential sampling: i) Significant design space (SDS) identification algorithm, which is developed to identify the promising design space and balance local exploitation and global exploration during the search, and ii) An iterative maximin sequential Latin hypercube design (LHD) sampling scheme and tailored termination criteria. Moreover, an adaptive penalty method is developed for handling expensive constraints. The new global optimization strategy, notated as ARSM-ISES, is then tested with numerical benchmark problems on optimization efficiency, global convergence, robustness, and algorithm execution overhead. Comparative results show that ARSM-ISES not only outperforms the original ARSM and IARSM, in general it also converges to better optima with fewer function evaluations and less algorithm execution time as compared to state-of-the-art metamodel-based design optimization algorithms including MPS, EGO, and MSEGO. For high dimensional (HD) problems, ARSM-ISES shows promises as it performs better on chosen test problems than TR-MPS, which is especially designed for solving HD problems. ARSM-ISES is then applied to the optimal design of a lifting surface of hypersonic flight vehicles. Finally, main features and limitations of the proposed algorithm are discussed.

Creeping Ray Tracing Algorithm for Arbitrary NURBS Surfaces Based on Adaptive Variable Step Euler Method

Although the uniform theory of diffraction (UTD) could be theoretically applied to arbitrarilyshaped convex objects modeled by nonuniform rational B-splines (NURBS), one of the great challenges in calculation of the UTD surface diffracted fields is the difficulty in determining the geodesic paths along which the creeping waves propagate on arbitrarilyshaped NURBS surfaces. In differential geometry, geodesic paths satisfy geodesic differential equation (GDE). Hence, in this paper, a general and efficient adaptive variable step Euler method is introduced for solving the GDE on arbitrarilyshaped NURBS surfaces. In contrast with conventional Euler method, the proposed method employs a shape factor (SF)ξto efficiently enhance the accuracy of tracing and extends the application of UTD for practical engineering. The validity and usefulness of the algorithm can be verified by the numerical results.

An efficient adaptive beam-space transformation technique and its application in array processing

Beam-space transformation projects the array data into a lower space, which is not only effective in reducing computation time, improving performance, but also being capable to suppress interference. In contrast to conventional adaptive beam-space transformation method, which often requires adjusting the beam-space matrix and steering vectors online, an efficient adaptive beam-space transformation method is proposed. In the proposed method, the beam-space covariance matrix and the steering vector both have closed-forms, and do not depend on the adaptive beam-space matrix. This eliminates the online adjustment process, and, thus, improves the computational efficiency. Finally, the proposed method can also be applied to the direction of arrival (DOA) estimation. Simulation results demonstrate that it has a better DOA estimation performance than the conventional adaptive method. Furthermore, the proposed method also has another significant advantage, i.e., it is able to suppress moving interference. This can be ascribed to the proposed beam-space matrix which is independent of the historical data, and, thus, effective to avoid the mismatch between the training and application data, since this mismatch often occurs in conventional adaptive methods.

Geometrical method for interpolating S-peaks from cow ECG using a microcontroller

This paper describes a new and computationally efficient adaptive geometrical method for improving the resolution of electrocardiogram (ECG) S-peak detection. The target device is an ECG implant for dairy cows which provides limited calculation power. The method utilises only arithmetic operations and is adaptive to individual ECG curves and electrode installation effects. The method tracks the average heights of the sample points preceding and following the S-wave peak. Comparing these heights to the long-time average values provides an estimate of the offset of the peak sample from the actual peak moment. On average, the prediction of spline interpolation on the original 512 Hz turning point is 0.043 ms more accurate than our geometrical algorithm. Both methods result in less than a 0.7 ms difference from the original data. However, the proposed method is significantly more computationally efficient than cubic spline interpolation.

An adaptive node regeneration technique for the efficient solution of elasticity problems using MDLSM method

An efficient adaptive node regeneration method is proposed in this paper for solving elasticity problems using the mixed discrete least squares meshless (MDLSM) method. The method starts with a point-wise error estimation of the solution produced on an arbitrary initial configuration defined by the user using the MDLSM method. The point-wise error estimate is associated with the support domain of the nodal points and used to calculate the required nodal spacing at each support domain and subsequently generate new nodes at support domain level. A node-removing process is then used to remove some of the nodes created at the overlapping regions of the support domains. To improve the quality of the final configuration, a node-moving procedure based on interpolation of the errors on the original configuration is used to create the final nodal configuration. The proposed method is a single step refinement procedure and is capable of producing nodal configurations of desired accuracy for different problems. The proposed method is used to simulate three benchmark examples from the literature and the results are produced and compared with those of the conventional multi-stage node enrichment method. The results indicate the superior efficiency and effectiveness of the proposed method compared to the available methods.

An adaptive moving grid method for solving convection dominated transport equations in chemical engineering

Convection dominated processes in chemical engineering are frequently accompanied by steep propagating fronts. Numerical simulation of corresponding models with uniform fixed grids requires an excessive amount of grid points along the expected range of the front movement. In this contribution the implementation of an efficient adaptive grid method is presented and applied to two relevant spatially one-dimensional cases, the chlorination stage of the Deacon process and oxygen storage processes in a three-way catalyst. The algorithm exhibits a high accuracy with a much lower number of grid points and a therefore reduced computational effort as opposed to a fixed grid simulation. The present work demonstrates that the algorithm allows for a robust, simple, and fast implementation of the adaptive grid method in common simulation tools and, together with adequate supplementary material, aims to make the method readily accessible to the interested reader.

An efficient adaptive trust-region method for systems of nonlinear equations

We present an adaptive trust-region algorithm to solve systems of nonlinear equations. Using the nonmonotone technique of Grippo, Lampariello and Lucidi, we introduce a new adaptive radius to decrease the total number of iterations and function evaluations. In contrast with the pervious methods, the new adaptive radius ensures that the size of radius is not too large or too small. We show that the sequence generated by the proposed adaptive radius is decreasing, so it prevents the production of too large radius as possible. Furthermore, it is shown that this sequence is reduced slowly, so it prevents the production of the intensely small radius. The global and quadratic convergence of the proposed approach are proved. Preliminary numerical results of our algorithm are also reported which indicate the promising behaviour of the new procedure to solve systems of nonlinear equations.

An efficient adaptive finite element method algorithm with mass conservation for analysis of liquid face seals

To improve lubrication effect and seal performance, complicated geometrical hydrodynamic grooves or patterns are often processed on end faces of liquid lubricated mechanical seals. These structures can lead to difficulties in precisely estimating the seal performance. In this study, an efficient adaptive finite element method (FEM) algorithm with mass conservation was presented, in which a streamline upwind/Petrov-Galerkin (SUPG) weighted residual FEM and a fast iteration algorithm were applied to solve the lubrication equations (Reynolds equation). A mesh adaptation technique was utilized to refine the computation domain based on a residual posterior error estimator. Validation, applicability, and efficiency were verified by comparison among different algorithms and by case studies on seals’ faces with different groove structures. The study investigated the influence of the order of shape function and the mesh number on the leakage balance. Mesh refinement occurred mainly in cavitation zones when cavitation happened, otherwise it occurred in regions with a high pressure gradient. Numerical experiments verified that the proposed algorithm is a fast, effective, and accurate method to simulate lubrication problems in the engineering field apart from end face seals.