Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H_?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.     

Improving inter-node communications in multi-core clusters using a contention-free process mapping algorithm

High performance clusters, which are established by connecting many computing nodes together, are known as one of main architectures to obtain extremely high performance. Currently, these systems are moving from multi-core architectures to many-core architectures to enhance their computational capabilities. This trend would eventually cause network interfaces to be a performance bottleneck because these interfaces are few in number and cannot handle multiple network requests at a time. The consequence of such issue would be higher waiting time at the network interface queue and lower performance. In this paper, we tackle this problem by introducing a process mapping algorithm, which attempts to improve inter-node communications in multi-core clusters. Our mapping strategy reduces accesses to the network interface by distributing communication-intensive processes among computing nodes, which leads to lower waiting time at the network interface queue. Performance results for synthetic and real workloads reveal that the proposed strategy improves the performance from 8 % up to 90 % in tested cases compared to other methods.     

An integrated robust probing motion planning and control scheme: A tube-based MPC approach

‘This paper introduces the integration of a probing scheme into a robust MPC-based robot motion planning and control algorithm. The proposed solution tackles the output-feedback tube-based MPC problem using the partially-closed loop strategy to incorporate future measurements in a computationally efficient manner. This combination will provide not only a robust controller but also avoids overly conservative planning which is a drawback of the original implementation of the output-feedback tube-based MPC. The proposed solution is composed of two controllers: (i) a nominal MPC controller with probing feature to plan a globally convergent trajectory in conjunction with active localization, and (ii) an ancillary MPC controller to stabilize the robot motion around the planned trajectory. The performance and real-time implementation of the proposed planning and control algorithms have been verified through both extensive numerical simulations and experiments with a mobile robot.     

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.     

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Multimedia Tools and Applications -     

Buoyancy effects on gaseous slip flow in a vertical rectangular microchannel

Consideration is given to the buoyancy effects on the fully developed gaseous slip flow in a vertical rectangular microduct. Two different cases of the thermal boundary conditions are considered, namely uniform temperature at two facing duct walls with different temperatures and adiabatic other walls (case A) and uniform heat flux at two walls and uniform temperature at other walls (case B). The rarefaction effects are treated using the first-order slip boundary conditions. By means of finite Fourier transform method, analytical solutions are obtained for the velocity and temperature distributions as well as the Poiseuille number. Furthermore, the threshold value of the mixed convection parameter to start the flow reversal is evaluated. The results show that the Poiseuille number of case A is an increasing function of the mixed convection parameter and a decreasing function of the channel aspect ratio, whereas its functionality on the Knudsen number is not monotonic. For case B, the Poiseuille number is decreased by increasing each of the mixed convection parameter, the Knudsen number, and the channel aspect ratio.     

Nonflat surface level pyramid: a high connectivity multidimensional interconnection network

Parallel machines are extensively used to increase computational speed in solving different scientific problems. Various topologies with different properties have been proposed so far and each one is suitable for specific applications. Pyramid interconnection networks have potentially powerful architecture for many applications such as image processing, visualization, and data mining. The major advantage of pyramids which is important for image processing systems is hierarchical abstracting and transferring the data toward the apex node, just like the human being vision system, which reach to an object from an image. There are rapidly growing applications in which the multidimensional datasets should be processed simultaneously. For such a system, we need a symmetric and expandable interconnection network to process data from different directions and forward them toward the apex. In this paper, a new type of pyramid interconnection network called Non-Flat Surface Level (NFSL) pyramid is proposed. NFSL pyramid interconnection networks constructed by L-level A-lateral-base pyramids that are named basic-pyramids. So, the apex node is surrounded by the level-one surfaces of NFSL that are the first nearest level of nodes to apex in the basic pyramids. Two topologies which are called NFSL-T and NFSL-Q originated from Trilateral-base and Quadrilateral-base basic-pyramids are studied to exemplify the proposed structure. To evaluate the proposed architecture, the most important properties of the networks are determined and compared with those of the standard pyramid networks and its variants.     

Analysis,modeling, and design of cascaded defected microstrip structure for planar circuits

In this article, two cascaded defected microstrip structures (CDMSs) are proposed to increase the effective inductance. So the cutoff frequencies of filters made by CDMSs can be controlled. The proposed CDMSs have wider stopband characteristic and sharper transition from passband to stopband than defected microstrip structure (DMS). To show the behavior variations of the effective inductance, the DMS and CDMSs have been analyzed, compared, and their design parameters are formulated. These parameters are necessary for a fast filter design procedure. Also, the models of DMS and CDMSs using the very simple lumped element forms are presented according to the physical parameters. The results of the proposed models have been compared with those obtained by full wave analysis. To verify the proposed DMS model, the comparison in response not only has been considered in amplitude but also in phase of all S‐parameters of proposed model and full wave analysis. It is obvious that the stopband is enhanced by periodically cascading the DMS. Surviving of EMC aspects between DMS, DGS, and CDMS is considered and compared. Finally, the theoretical results are validated by comparison with simulations of the structures and experimental results. The simulations from modeling results are found to be in good agreement with the full wave analysis and measurement results in the range of 0.5–35 GHz. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

Assessing the authenticity of subjective information in the blockchain: a survey and open issues

World Wide Web - Blockchain, with its ever-increasing maturity and popularity, is being used in many different applied computing domains. To document the advancements made, researchers have...