Efficient Adaptive Scheme Research Articles

Neuro‐fuzzy Elman wavelet control for nonlinear uncertain systems with fuzzy input and unknown fuzzy disturbances: Application to robotics

SummaryKeeping into view the irregular existence of uncertainties and disturbances in nonlinear systems, a novel adaptive control scheme based on neuro‐fuzzy Elman wavelet neural network (NFEWNN) has been proposed in this work which is capable of accurately modeling system uncertainties. In addition to this, an improvement in an evolutionary algorithm named particle swarm optimization has also been introduced to optimize the initial parameters of the controller. Uncertain non‐linear discrete‐time systems in the non‐strict feedback form with unknown disturbances are taken into consideration. This work aims to design an efficient adaptive control scheme for the proposed systems in the case of fuzzy input and fuzzy external disturbance which makes the control of the system more complex and difficult. The proposed NFEWNN control scheme is capable of tracking the desired trajectory successfully along with accurately modeling system uncertainties with high computational power, low computational burden, and rapid convergence rate. The stability of the controller has been proved using Lyapunov theory and guaranteed convergence of the controller is proved. Finally, real‐world applications of the work have been found in robotics. The controller has been implemented on the robotic manipulator and the dynamic model of aerial robotic vehicles to prove its effectiveness and efficiency.

An efficient adaptive implicit scheme with equivalent continuum approach for two-phase flow in fractured vuggy porous media

This work investigates numerical method and equivalent continuum approach (ECA) of fluid flow in fractured porous media. The commonly used discrete fracture model (DFM) without upscaling needs full discretization of all fractures. It enjoys the merit of capturing each fracture accurately but will get in trouble with mesh partition and low computational efficiency, especially when a complex geometry is involved. In this study, we develop an efficient implicit scheme with adaptive iteration, in which an improved ECA is devised and then integrated in this scheme. Numerical studies show that the proposed numerical scheme improves the convergence condition and computational efficiency. Then, a test is conducted to demonstrate the feasibility of using superposition principle of permeability tensor in upscaling. Based on these, different strategies are applied to simulate fluid flow in fracture networks with a complex geometry. It is demonstrated that the proposed ECA is able to reproduce the results computed by DFM. The accuracy depends on resolution of background grids. The presented method enjoys a low computational cost and desirable convergence performance compared with the standard DFM in which equivalent continuum is not considered.

Joint Iterative Optimization-Based Low-Complexity Adaptive Hybrid Beamforming for Massive MU-MIMO Systems

This paper proposes a joint iterative optimization based hybrid beamforming technique for massive MU-MIMO systems. The proposed technique jointly and iteratively optimizes the transmitter precoders and combiners, aiming to approach the global optimum solution for the system sum-rate maximization problem. The proposed technique develops an adaptive algorithm exploiting the stochastic gradients (SG) of the local beamformers and provides low-complexity closed-form solutions. Furthermore, an efficient adaptive scheme is developed based on the proposed adaptive algorithm and the closed-form solutions. The proposed algorithm requires the signal-to-interference-plus-noise ratio (SINR) feedback from each user and a limited size transition vector to be exchanged between the transmitter and receivers at each step to update beamformers locally. Analytic result shows that the proposed adaptive algorithm achieves low-complexity when the array size is large and is able to converge within a small number of iterations. Simulation result shows that the proposed technique is able to achieve superior performance comparing to the existing state-of-art techniques. In addition, the knowledge of instantaneous channel state information (CSI) is not required as the channels are also adaptively estimated with each coherence time which is a practical assumption since the CSI is usually unavailable or have time-varying nature in real-time applications.

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

PGA: An Efficient Adaptive Traffic Signal Timing Optimization Scheme Using Actor-Critic Reinforcement Learning Algorithm

Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It''s based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.

An Energy Efficient Adaptive Scheduling Scheme (EASS) for Mesh Grid Wireless Sensor Networks

The technology-driven shift in distributed network systems, Internet technology and energy grid digitization have changed the traditional spoke-hub distribution models to the more flexible and giant network-to-networks paradigm known as Energy Cloud. This energy cloud paradigm has the ability to adapt to the large-scale distributed energy resources with dynamic demand–response associated with smart grid and smart homes applications, which allows customers/users to have connectivity of electrical devices with supply grid into one giant energy cloud network. If efficient policies are not adapted in the design and operations of such networks, sensor devices may sense and forward redundant data packets. Hence, valuable energy of the node will be depleted quickly and as a result; the network will be down before accomplishing its intended task. To address the issue of energy-efficiency in energy-cloud at embedded networks, this paper proposes a novel scheme, “Energy Efficient Adaptive Scheduling Scheme (EASS) for Mesh Grid Wireless Sensor Networks”. In EASS, a sensor node configures and schedules its functions/roles according to the contents of sensed data packets and frequency of generated traffic. Like energy cloud, tasks are uniformly distributed on all nodes in mesh-grid in four states – each state of a node is responsible for configuring its components on a specific energy level – which aims to avoid link disconnection and vanish redundant data packets generation. Simulation results show that EASS increases energy-efficiency by 50% due to the four-states model, 62.5% due to alive nodes, 92.60% due to minimized Cluster-Heads overhead and 38.11% due to the reduction in dead nodes ratio.

Dual Mixed Gaussian Quadrature Based Adaptive Scheme for Analytic Functions

An efficient adaptive scheme based on a dual mixed quadrature rule of precision eleven for approximate evaluation of line integral of analytic functions has been constructed. At first, the precision of Gauss-Legendre four point transformed rule is enhanced by using Richardson extrapolation. A suitable convex combination of the resulting rule and the Gauss-Legendre five point rule further enhances the precision producing a new mixed quadrature rule . This mixed rule is termed as dual mixed Gaussian quadrature rule as it acquires a very high precision eleven using Gaussian quadrature rule in two steps. An adaptive quadrature scheme is designed .Some test integrals having analytic function integrands have been evaluated using the dual mixed rule and its constituent rules in non- adaptive mode. The same set of test integrals have been evaluated using those rules as base rules in the adaptive scheme. The dual mixed rule based adaptive scheme is found to be most effective.

Spectrum-Occupancy Aware Cooperative Spectrum Sensing Using Adaptive Detection

In this paper, an efficient adaptive detection scheme is proposed for cognitive radio networks where multiple secondary users (SUs) cooperate to identify idle spectrum bands. Each SU generates a binary local decision, which is then transmitted either directly or via the assistance of intermediate relays to a cognitive radio base station (CRBS), where the global decision is made. The fusion process at the CRBS is performed using the local binary decisions made by the individual SUs. The local binary decisions at the relays and CRBS are regenerated based on dynamic thresholds, which are chosen to minimize the probability of the spectrum sensing error (PSSE) by considering the imbalanced nature of the spectrum occupancy, as well as the reliability of the decisions made by the SUs. The PSSE performance is derived where a closed-form analytical expression is obtained. The analytical results corroborated by Monte Carlo simulation show that using adaptive detection can reduce the PSSE significantly as compared to the conventional detection approach.

Optimal utilization of renewable energy sources in MG connected system with integrated converters: an AGONN Approach

This paper proposes an efficient converter for the usage of hybrid renewable energy sources and reduced switching loss in the Micro Grid associated frameworks. The work resulted in a DC–DC converter for module integration and maximum power point tracking with an efficient adaptive control scheme. The proposed control scheme is a combined execution of both the adaptive grasshopper optimization algorithm (AGOA) and artificial neural network (ANN) named as AGONN strategy. In the proposed strategy, the AGOA plays out the evaluation technique to set up the correct control signals for the system and develops the control signals database for the offline way subject to the power exchange between source side and the load side. Moreover, to train the ANN system for the online way, the achieved dataset is utilized and it drives the control method in less execution time. Also, the objective function is characterized by the system data subject to equality and inequality constraints. The constraints are the accessibility of the renewable energy sources, power demand and the state of charge of storage elements. Batteries are used as an energy source, to balance out and allow the renewable power system units to continue running at a steady and stable output power. By then, the proposed show is executed in MATLAB/Simulink working platform and the execution is surveyed with the current methods.

Correlation-Based Localization Effective in Ensemble-Based History Matching

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191305, “Correlation-Based Adaptive Localization for Ensemble-Based History Matching Applied to the Norne Field Case Study,” by Xiaodong Luo, SPE, Rolf Lorentzen, SPE, Randi Valestrand, SPE, and Geir Evensen, SPE, International Research Institute of Stavanger, prepared for the 2018 SPE Norway One Day Seminar, Bergen, Norway, 18 April. The paper has been peer reviewed and published in the October 2018 SPE Journal. Ensemble-based methods are considered to be state-of-the-art history-matching algorithms. However, in practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history-matching performance. An ensemble history-matching algorithm is equipped customarily with a localization scheme to prevent ensemble collapse. To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations. Introduction In the current work, the authors focus on adopting an efficient adaptive localization scheme, previously established in the literature, for the full Norne Field case study. The adaptive localization scheme exploits the information of sample correlation coefficients between an ensemble of model variables and the corresponding realizations of simulated observations. The adaptive localization scheme uses a data-selection procedure; however, instead of physical distances between the locations of model variables and observations being used for data selection, the magnitudes of the sample correlation coefficients are used for data selection through a hard-thresholding strategy (i.e., keep or kill). To conduct data selection in the adaptive localization scheme, one specifies a positive correlation-threshold value. For a given observation, if the magnitude of the sample correlation coefficient between a model variable and the simulated observation is greater than the threshold value, then the observation will be used to update that model variable. Otherwise, one discards the observation in the update of that model variable. This described data-selection procedure essentially means that a given model variable is updated using only the observations that have significant correlations with the model variable. The rationale behind this hard-thresholding strategy is the interpretation of the magnitude of the correlation coefficient as a measure to detect the causal relation between a model variable and an observation, and the effect is the suppression of spurious correlations caused by a finite sample size. Correlation-based localization can overcome or alleviate the issues arising in distance-based localization, such as the use of nonlocal and time-lapse observations, the need of physical locations for model variables and measurements, and the different degrees of correlations or sensitivities of model variables to observations. Because data selection depends on the magnitudes of sample correlation coefficients between model variables and the corresponding simulated observations, the measurements need not have associated physical locations. Model variables are thus selected by using those observations that exhibit strong-enough correlations, regardless of the physical distances between observations and model variables. As a result, correlation-based localization can be used to localize nonlocal observations. The changes of correlations caused by the effect of time-lapse observations or different types of model variables will be taken into account automatically in correlation-based localization, and this makes the proposed localization scheme more-adaptive and more-flexible in various situations.

A comparative study of Gauss--Laguerre quadrature and an open type mixed quadrature by evaluating some improper integrals

An open type mixed quadrature rule is constructed blending the anti-Gauss 3-point rule with Steffensen's 4-point rule. The analytical convergence of the mixed rule is studied. An adaptive integration scheme is designed based on the mixed quadrature rule. A comparative study of the mixed quadrature rule and the Gauss‒Laguerre quadrature rule is given by evaluating several improper integrals of the form $\int\limits_{0}^{\infty}e^{-x}f(x)dx$. The advantage of implementing mixed quadrature rule in developing an efficient adaptive integration scheme is shown by evaluating some improper integrals.

An efficient adaptive high-order scheme based on the WENO process

In this paper, we propose an efficient adaptive scheme based on the WENO (weighted essentially non-oscillatory) process for the hyperbolic conservation laws. This scheme achieves fifth order accuracy as the fifth order WENO scheme does. Also, due to its adaptive mechanism, the scheme deals with problems which contain both discontinuities and complex smooth regions much better than traditional high order schemes. In addition, the proposed scheme saves computational costs by avoiding solving redundant nonlinear weights in certain places. Systematic analyses and numerical tests show that our adaptive scheme is of a high level of robustness and resolution.

Numerical Blow-Up of Nonlinear Parabolic Integro-Differential Equations on Unbounded Domain

The efficient numerical methods of the nonlinear parabolic integro-differential PDEs on unbounded spatial domains whose solutions blow up in finite time are considered. Based on the unified approach proposed in Zhang et al. (Phys Rev E 78:026709, 2008), Zhang et al. (Phys Rev E 79:046711, 2009), the nonlinear absorbing boundary conditions for one-dimensional and two-dimensional nonlinear parabolic integro-differential PDEs are derived. Thus the original problem on the unbounded spatial domain is reduced to an initial-boundary-value (IBV) problem on a bounded computational domain. Secondly, a simple but efficient adaptive time-stepping scheme for the reduced IBV problem is achieved by using the fixed point method to make the finite difference approximation stable at each time level. At each time level, we also prove that the lower bound and upper bound of the blow-up time can be bounded by the numerical blow-up times of the forward and backward Euler schemes. Finally, the theoretical results are illustrated by a broad range of numerical examples, including a problem with a circle line blow-up.

Bivariate hierarchical Hermite spline quasi-interpolation

Spline quasi-interpolation (QI) is a general and powerful approach for the construction of low cost and accurate approximations of a given function. In order to provide an efficient adaptive approximation scheme in the bivariate setting, we consider quasi-interpolation in hierarchical spline spaces. In particular, we study and experiment the features of the hierarchical extension of the tensor-product formulation of the Hermite BS quasi-interpolation scheme. The convergence properties of this hierarchical operator, suitably defined in terms of truncated hierarchical B-spline bases, are analyzed. A selection of numerical examples is presented to compare the performances of the hierarchical and tensor-product versions of the scheme.

Novel Adaptive Filter (NAF) for Impulse Noise Suppression from Digital Images

In general, it is known that an adaptive filter adjusts its parameters iteratively such as size of the working window, decision threshold values used in two stage detection-estimation based switching filters, number of iterations etc. It is also known that nonlinear filters such as median filters and its several variants are popularly known for their ability in dealing with the unknown circumstances. In this paper an efficient and simple adaptive nonlinear filtering scheme is presented to eliminate the impulse noise from the digital images with an impulsive noise detection and reduction scheme based on adaptive nonlinear filter techniques. The proposed scheme employs image statistics based dynamically varying working window and an adaptive threshold for noise detection with a Noise Exclusive Median (NEM) based restoration. The intensity value of the Noise Exclusive Median (NEM) is derived from the processed pixels in local neighborhood of a dynamically adaptive window. In the proposed scheme use of an adaptive threshold value derived from the noisy image statistics returns more precise results for the noisy pixel detection. The proposed scheme is simple and can be implemented as either a single pass or a multi-pass with a maximum of three iterations with a simple stopping criterion. The goodness of the proposed scheme is evaluated with respect to the qualitative and quantitative measures obtained by MATLAB simulations with standard images added with impulsive noise of varying densities. From the comparative analysis it is evident that the proposed scheme out performs the state-of-art schemes, preferably in cases of high-density impulse noise.

Quantum partition functions of composite particles in a hydrogen-helium plasma via path integral Monte Carlo.

We compute two- and three-body cluster functions that describe contributions of composite entities, like hydrogen atoms, ions H(-), H2(+), and helium atoms, and also charge-charge and atom-charge interactions, to the equation of state of a hydrogen-helium mixture at low density. A cluster function has the structure of a truncated virial coefficient and behaves, at low temperatures, like a usual partition function for the composite entity. Our path integral Monte Carlo calculations use importance sampling to sample efficiently the cluster partition functions even at low temperatures where bound state contributions dominate. We also employ a new and efficient adaptive discretization scheme that allows one not only to eliminate Coulomb divergencies in discretized path integrals, but also to direct the computational effort where particles are close and thus strongly interacting. The numerical results for the two-body function agree with the analytically known quantum second virial coefficient. The three-body cluster functions are compared at low temperatures with familiar partition functions for composite entities.

RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks

WiMAX (Worldwide Interoperability for Microwave Access) constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access (OFDMA) technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplinkwidth ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station (BS). Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods (e.g., learning automata) and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization.

RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks

WiMAX Worldwide Interoperability for Microwave Access constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access OFDMA technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplink width ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station BS. Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods e.g., learning automata and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization.

Sparse-Grid-Based Adaptive Model Predictive Control of HL60 Cellular Differentiation

Quantitative methods such as model-based predictive control are known to facilitate the design of strategies to manipulate biological systems. This study develops a sparse-grid-based adaptive model predictive control (MPC) strategy to direct HL60 cellular differentiation. Sparse-grid sampling and interpolation support a computationally efficient adaptive MPC scheme in which multiple data-consistent regions of the model parameter space are identified and used to calculate a control compromise. The algorithm is evaluated in silico with structural model mismatch. Simulations demonstrate how the multiscenario control strategy more effectively manages the mismatch compared to a single scenario approach. Furthermore, the controller is evaluated in vitro to differentiate HL60 cells in both normal and perturbed environments. The controller-derived input sequence successfully achieves and sustains the specified target level of granulocytes when implemented in the laboratory. The results and analysis given here imply that adoption of this experiment planning technique to direct cell differentiation within more complex tissue engineered constructs will require the use of a reasonably accurate mathematical model and an extension of this algorithm to multiobjective controller design.

An Efficient Adaptive Bitmap-based Selective Tuning Scheme for Spatial Queries in Broadcast Environments

With the advances in wireless communication technology and the advent of smartphones, research on location-based services (LBSs) is being actively carried out. In particular, several spatial index methods have been proposed to provide efficient LBSs. However, finding an optimal indexing method that balances query performance and index size remains a challenge in the case of wireless environments that have limited channel bandwidths and device resources (computational power, memory, and battery power). Thus, mechanisms that make existing spatial indexing techniques more efficient and highly applicable in resource-limited environments should be studied. Bitmap-based Spatial Indexing (BSI) has been designed to support LBSs, especially in wireless broadcast environments. However, the access latency in BSI is extremely large because of the large size of the bitmap, and this may lead to increases in the search time. In this paper, we introduce a Selective Bitmap-based Spatial Indexing (SBSI) technique. Then, we propose an Adaptive Bitmap-based Spatial Indexing (ABSI) to improve the tuning time in the proposed SBSI scheme. The ABSI is applied to the distribution of geographical objects in a grid by using the Hilbert curve (HC). With the information in the ABSI, grid cells that have no objects placed, (i.e., 0-bit information in the spatial bitmap index) are not tuned during a search. This leads to an improvement in the tuning time on the client side. We have carried out a performance evaluation and demonstrated that our SBSI and ABSI techniques outperform the existing bitmap-based DSI (B?DSI) technique.