Deformation behaviour of a cylinder under simultaneous compression and torsion

A number of experiments involving the compression of an Aluminum cylinder with concurrent die rotation were carried out. Two important features were observed: one was that die rotation reduced the degree of bulging and the other was that the compression load decreased. An upper bound analysis with a velocity field consisting of a compound exponential cusp representation was utilized to obtain an approximate analytical solution in a closed form. The theoretical result reproduced the reduction in bulging severity with die rotation as well as the changes in compression pressure.

Multi equal channel angular pressing with rotational dies

The recent successful development of the equal channel angular pressing (ECAP) process in metals provides a feasible solution to produce ultra-fine or nano-grained bulk: materials with tailored material properties. However, ECAP is difficult to scale up commercially due to excessive load requirements. In this paper, a new Multi-ECAP process with die rotation is considered to obtain ultra-fine grain structured materials under a moderate deformation force. It is shown that an addition of torsion results in a reduction in the pressing force and an increase in severity of plastic deformation. An analysis using the upper bound method is found to be useful in predicting the pressing load and flow pattern of ECAP with and without rotational dies. Solutions are obtained for different inclined channel angles under different angular velocities of dies. Relative pressures are presented and some computed solutions are compared with those found by FEM simulation. The theoretical predictions of the pressing load are in good agreement with the simulation results. The amount of plastic deformation is determined by the inclined angle between the two intersecting channels, and the velocity ratio between the angular velocity of dies and the normal component of the punch velocity.

Design of scalar functional observers of order less than (v-1)

This paper presents a new method of designing scalar functional observers of order less than the well-known upper bound (ν - 1). A condition for the existence of observers of order p where 1 ≤ p ≤ (ν - 1) is given. A simple and effective algorithm for solving the constrained generalized Sylvester equation is proposed. Several numerical examples are given to illustrate the attractiveness of the design algorithm.

Aluminium powder forging process using a rotating platen

A torsional upset forging process is analysed on the basis of plasticity theory for powder metal forging. Torsional upset forging is a process to be performed by rotating a lower die with a punch travelling along the longitudinal direction of a work-piece. In this study, an upper bound analysis considering bulging effect, finite element method simulation (DEFORM3D), and experimental research have been performed for the process. A simple kinematically admissible velocity field for a three dimensional deformation is presented for the torsional upset forging of a cylindrical billet. Distributions of stress, strain, and forging load in the process have been obtained, and compared with those in conventional upset forging. In the process, an increase in a friction factor and rotation speed results in a decrease in magnitude of upset force, dead metal zone, and non-homogeneous deformation. This process can reduce forming load, which leads to improvement of die life, and also reduce bulging effect. In addition, the initial sintered-structure and density distribution is improved by the process and surface defect due to high deformation is decreased.

Well-conditioned configurations of fault-tolerant manipulators

Fault-tolerant motion of redundant manipulators can be obtained by joint velocity reconfiguration. For fault-tolerant manipulators, it is beneficial to determine the configurations that can tolerate the locked-joint failures with a minimum relative joint velocity jump, because the manipulator can rapidly reconfigure itself to tolerate the fault. This paper uses the properties of the condition numbers to introduce those optimal configurations for serial manipulators. The relationship between the manipulator's locked-joint failures and the condition number of the Jacobian matrix is indicated by using a matrix perturbation methodology. Then, it is observed that the condition number provides an upper bound of the required relative joint velocity change for recovering the faults which leads to define the optimal fault-tolerant configuration from the minimization of the condition number. The optimization problem to obtain the minimum condition number is converted to three standard Eigen value optimization problems. A solution is for selected optimization problem is presented. Finally, in order to obtain the optimal fault-tolerant configuration, the proposed method is applied to a 4-DoF planar manipulator.

Improved image recovery from compressed data contaminated with impulsive noise

Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in astronomy. However, in the existing CS formulation, the use of the â„“ 2 norm on the residuals is not particularly efficient when the noise is impulsive. This could lead to an increase in the upper bound of the recovery error. To address this problem, we consider a robust formulation for CS to suppress outliers in the residuals. We propose an iterative algorithm for solving the robust CS problem that exploits the power of existing CS solvers. We also show that the upper bound on the recovery error in the case of non-Gaussian noise is reduced and then demonstrate the efficacy of the method through numerical studies.

Exploiting monge structures in optimum subwindow search

Optimum subwindow search for object detection aims to find a subwindow so that the contained subimage is most similar to the query object. This problem can be formulated as a four dimensional (4D) maximum entry search problem wherein each entry corresponds to the quality score of the subimage contained in a subwindow. For n x n images, a naive exhaustive search requires O(n⁴) sequential computations of the quality scores for all subwindows. To reduce the time complexity, we prove that, for some typical similarity functions like Euclidian metric, χ² metric on image histograms, the associated 4D array carries some Monge structures and we utilise these properties to speed up the optimum subwindow search and the time complexity is reduced to O(n³). Furthermore, we propose a locally optimal alternating column and row search method with typical quadratic time complexity O(n²). Experiments on PASCAL VOC 2006 demonstrate that the alternating method is significantly faster than the well known efficient subwindow search (ESS) method whilst the performance loss due to local maxima problem is negligible.

Construction of neural network-based prediction intervals using particle swarm optimization

Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with point forecasts and predictions. This paper adopts and develops the lower upper bound estimation (LUBE) method for construction of PIs using neural network (NN) models. This method is fast and simple and does not require calculation of heavy matrices, as required by traditional methods. Besides, it makes no assumption about the data distribution. A new width-based index is proposed to quantitatively check how much PIs are informative. Using this measure and the coverage probability of PIs, a multi-objective optimization problem is formulated to train NN models in the LUBE method. The optimization problem is then transformed into a training problem through definition of a PI-based cost function. Particle swarm optimization (PSO) with the mutation operator is used to minimize the cost function. Experiments with synthetic and real-world case studies indicate that the proposed PSO-based LUBE method can construct higher quality PIs in a simpler and faster manner.

A case study of ground response due to diaphragm wall installation

The purpose of this research is to investigate the ground response due to diaphragm wall construction using three-dimensional numerical modelling. In this study, the commercial finite difference method software, FLAC3D, and the finite element upper and lower bound limit analysis methods are employed. In addition, a range of factors are investigated. They include the dimensions of the single panel, overconsolidation ration (OCR), soil stiffness (E/su), and the height of the bentonite slurry. The solutions from the numerical upper bound limit analysis method are used for comparison purposes. The results obtained indicate that the above factors do have influence on ground response in terms of its stability and displacements. The discussion in the paper can be utilised as the reference for practical designs.

Combined nonparametric prediction intervals for wind power generation

Prediction intervals (PIs) are a promising tool for quantification of uncertainties associated with point forecasts of wind power. However, construction of PIs using parametric methods is questionable, as forecast errors do not follow a standard distribution. This paper proposes a nonparametric method for construction of reliable PIs for neural network (NN) forecasts. A lower upper bound estimation (LUBE) method is adapted for construction of PIs for wind power generation. A new framework is proposed for synthesizing PIs generated using an ensemble of NN models in the LUBE method. This is done to guard against NN performance instability in generating reliable and informative PIs. A validation set is applied for short listing NNs based on the quality of PIs. Then, PIs constructed using filtered NNs are aggregated to obtain combined PIs. Performance of the proposed method is examined using data sets taken from two wind farms in Australia. Simulation results indicate that the quality of combined PIs is significantly superior to the quality of PIs constructed using NN models ranked and filtered by the validation set.

Short-term load and wind power forecasting using neural network-based prediction intervals

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Construction of neural network-based prediction intervals for short-term electrical load forecasting

Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications.