Nonlocal buckling and vibration analysis of thick rectangular nanoplates using finite strip method based on refined plate theory

The buckling and vibration of thick rectangular nanoplates is analyzed in this article. A graphene sheet is theoretically assumed and modeled as a nanoplate in this study. The two-variable refined plate theory (RPT) is applied to obtain the differential equations of the nanoplate. The theory accounts for parabolic variation of transverse shear stress through the thickness of the plate without using a shear correction factor. Besides, the analysis is based on the nonlocal theory of elasticity to take the small-scale effects into account. For the first time, the finite strip method (FSM) based on RPT is employed to study the vibration and buckling behavior of nanoplates and graphene sheets. Hamilton’s principle is employed to obtain the differential equations of the nanoplate. The stiffness, stability and mass matrices of the nanoplate are formed using the FSM. The displacement functions of the strips are evaluated using continuous harmonic function series which satisfy the boundary conditions in one direction and a piecewise interpolation polynomial in the other direction. A matrix eigenvalue problem is solved to find the free vibration frequency and buckling load of the nanoplates subjected to different types of in-plane loadings including the uniform and nonuniform uni-axial and biaxial compression. Comparison studies are presented to verify the validity and accuracy of the proposed nonlocal refined finite strip method. Furthermore, a number of examples are presented to investigate the effects of various parameters (e.g., boundary conditions, nonlocal parameter, aspect ratio, type of loading) on the results.     

Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. An innovative data-driven FDD methodology has been presented in this paper on the basis of a distributed configuration of three adaptive neuro-fuzzy inference system (ANFIS) classifiers for an industrial 440 MW power plant steam turbine with once-through Benson type boiler. Each ANFIS classifier has been developed for a dedicated category of four steam turbine faults. A preliminary set of conceptual and experimental studies has been conducted to realize such fault categorization scheme. A proper selection of four measured variables has been configured to feed each ANFIS classifier with the most influential diagnostic information. This consequently leads to a simple distributed FDD system, facilitating the training and testing phases and yet prevents operational deficiency due to possible cross-correlated measured data effects. A diverse set of test scenarios has been carried out to illustrate the successful diagnostic performances of the proposed FDD system against 12 major faults under challenging noise corrupted measurements and data deformation corresponding to a specific fault time history pattern.     

A co-classification approach to learning from multilingual corpora

We address the problem of learning text categorization from a corpus of multilingual documents. We propose a multiview learning, co-regularization approach, in which we consider each language as a separate source, and minimize a joint loss that combines monolingual classification losses in each language while ensuring consistency of the categorization across languages. We derive training algorithms for logistic regression and boosting, and show that the resulting categorizers outperform models trained independently on each language, and even, most of the times, models trained on the joint bilingual data. Experiments are carried out on a multilingual extension of the RCV2 corpus, which is available for benchmarking.     

Efficient task scheduling for runtime reconfigurable systems

Recent research indicates the promising performance of employing reconfigurable systems to accelerate multimedia and communication applications. Nonetheless, they are yet to be widely adopted. One reason is the lack of efficient operating system support for these platforms. In this paper, we address the problem of runtime task scheduling as a main part of the operating systems. To do so, a new task replacement parameter, called Time-Improvement, is proposed for compiler assisted scheduling algorithms. In contrast with most related approach, we validate our approach using real application workload obtained from an application for multimedia test remotely taken by students. The proposed online task scheduling algorithm outperforms previous algorithms and accelerates task execution from 4% up to 20%.     

Adaptive robust control of uncertain systems with state and input delay

In this paper, adaptive robust control of uncertain systems with multiple time delays in states and input is considered. It is assumed that the parameter uncertainties are time varying norm-bounded whose bounds are unknown but their functional properties are known. To overcome the effect of input delay on the closed loop system stability, new Lyapunov Krasovskii functional will be introduced. It is shown that the proposed adaptive robust controller guarantees globally uniformly exponentially convergence of all system solutions to a ball with any certain convergence rate. Moreover, if there is no disturbance in the system, asymptotic stability of the closed loop system will be established. The proposed design condition is formulated in terms of linear matrix inequality (LMI) which can be easily solved by LMI Toolbox in Matlab. Finally, an illustrative example is included to show the effectiveness of results developed in this paper.     

Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machine, cutting tool and tool access direction (TAD) for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine, cutting tool, and TAD for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. A number of case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.     

Simultaneous state estimation and parameter identification in linear fractional order systems using coloured measurement noise

In the present paper, the identification and estimation problem of a single-input–single-output (SISO) fractional order state-space system will be addressed. A SISO state-space model is considered in which parameters and also state variables should be estimated. The canonical fractional order state-space system will be transformed into a regression equation by using a linear transformation and a shift operator that are appropriate for identification. The identification method provided in this paper is based on a recursive identification algorithm that has the capability of identifying the parameters of fractional order state-space system recursively. Another subject that will be addressed in this paper is a novel fractional order Kalman filter suitable for the systems with coloured measurement noise. The promising performance of the proposed methods is verified using two stable fractional order systems.     

Optimal motion planning for parallel robots via convex optimization and receding horizon

In this paper, the problem of motion planning for parallel robots in the presence of static and dynamic obstacles has been investigated. The proposed algorithm can be regarded as a synergy of convex optimization with discrete optimization and receding horizon. This algorithm has several advantages, including absence of trapping in local optimums and a high computational speed. This problem has been fully analyzed for two three-DOF parallel robots, ie 3s-RPR parallel mechanism and the so-called Tripteron, while the shortest path is selected as the objective function. It should be noted that the first case study is a parallel mechanism with complex singularity loci expression from a convex optimization problem standpoint, while the second case is a parallel manipulator for which each limb has two links, an issue which increases the complexity of the optimization problem. Since some of the constraints are non-convex, two approaches are introduced in order to convexify them: (1) A McCormick-based relaxation merged with a branch-and-prune algorithm to prevent it from becoming too loose and (2) a first-order approximation which linearizes the non-convex quadratic constraints. The computational time for the approaches presented in this paper is considerably low, which will pave the way for online applications.     

A Learning Behavior Based Controller for Maintaining Balance in Robotic Locomotion

The Behavior Based Locomotion Controller (BBLC) extends the applicability of the behavior based control (BBC) architecture to redundant systems with multiple task-space motions. A set of control behaviors are attributed to each task-space motion individually and a reinforcement learning algorithm is used to select the combination of behaviors which can achieve the control objective. The resulting behavior combination is an emergent control behavior robust to unknown environments due to the added learning capability. Hence, the BBLC is applicable to complex redundant systems operating in unknown environments, where the emergent control behaviors can satisfy higher level control objectives such as balance in locomotion. The balance control problem of two robotic systems, a bipedal robot walker and a mobile manipulator, are used to study the performance of this controller. Results show that the BBLC strategy can generate emergent balancing strategies capable of adapting to new unknown disturbances from the environment, using only a small fixed library of balancing behaviors.     

An Efficient Finite Difference Method for The Time‐Delay Optimal Control Problems With Time‐Varying Delay

In this paper, an efficient finite difference method is presented for the solution of time‐delay optimal control problems with time‐varying delay in the state. By using the Pontryagin's maximum principle, the original time‐delay optimal control problem is first transformed into a system of coupled two‐point boundary value problems involving both delay and advance terms. Then the derived system is converted into a system of linear algebraic equations by using a second‐order finite difference formula and a Hermite interpolation polynomial for the first‐order derivatives and delay terms, respectively. The convergence analysis of the proposed approach is provided. The new scheme is also successful for the optimal control of time‐delay systems affected by external persistent disturbances. Numerical examples are included to demonstrate the validity and applicability of the new technique. Some comparative results are included to illustrate the effectiveness of the proposed method.