Sculptured surface machining using triangular mesh slicing

In this paper, an optimized procedure for tool path generation in regional milling is presented. The proposed procedure computes tool paths by slicing a CL-surface (Cutter Location surface), which is a triangular, mesh containing invalid triangles. Tool path generation consists of two steps: firstly, it obtains a set of line segments by slicing the triangular mesh with two-dimensional geometric elements (slicing elements), and, secondly, it extracts a valid tool path from the line segments by removing invalid portions. Two algorithms based on the slicing elements are presented: a ‘line projection’ algorithm based on the plane sweeping paradigm, which works efficiently by using the characteristics of a monotone chain; and a ‘curve projection’ algorithm for the projection of curves, which transforms the curve projection problem into a line projection problem by mapping the XYZ-space of the cylinder surface to the TZ-plane of the unfolded cylinder. The proposed procedure has been implemented and applied to tool path generation in regional milling. Performance tests show the efficiency of the proposed procedure.     

Design of nearly perfect reconstructed non-uniform filter bank by constrained equiripple FIR technique

In this paper, an efficient iterative algorithm is proposed for the design of multi-channel nearly perfect reconstructed non-uniform filter bank. The method employs the constrained equiripple FIR technique to design the prototype filter for filter banks with novelty of exploiting a new perfect reconstruction condition of the non-uniform filter banks instead of using complex objective functions. In the proposed algorithm, passband edge frequency (ω_p) is optimized using linear optimization technique such that the filter coefficients values at quadrature frequency are approximately equal to 0.707. Several design examples are included to illustrate the efficacy of this methodology for designing non-uniform filter bank (NUFB). It was found that the proposed methodology performs better as compared to earlier reported results in terms of reconstruction error (RE), number of iteration (NOI) and computation time (CPU time). The proposed algorithm is very simple, linear in nature, and easy to implement.     

High order stable infinite impulse response filter design using cuckoo search algorithm

In this paper, an efficient technique for optimal design of digital infinite impulse response (IIR) filter with minimum passband error (e _p ), minimum stopband error (e _s ), high stopband attenuation (A _s ), and also free from limit cycle effect is proposed using cuckoo search (CS) algorithm. In the proposed method, error function, which is multi-model and non-differentiable in the heuristic surface, is constructed as the mean squared difference between the designed and desired response in frequency domain, and is optimized using CS algorithm. Computational efficiency of the proposed technique for exploration in search space is examined, and during exploration, stability of filter is maintained by considering lattice representation of the denominator polynomials, which requires less computational complexity as well as it improves the exploration ability in search space for designing higher filter taps. A comparative study of the proposed method with other algorithms is made, and the obtained results show that 90% reduction in errors is achieved using the proposed method. However, computational complexity in term of CPU time is increased as compared to other existing algorithms.     

An adaptive control approach to the attitude control of a flexible rocket

An adaptive notch filter design that considers the body-bending vibration associated with the attitude control of a two-stage sounding rocket is discussed in this paper. The algorithm adapts the parameters while keeping the poles of the notch filter inside the unit circle on the z-plane, and it satisfies the stability conditions of the filter at all times. Only a single parameter of the filter is adapted to simplify the algorithm to speed up the convergence rate. Applying this adaptive notch filter to the two-stage sounding rocket results in stable response characteristics of the attitude control system, which is unstable without adaptation.     

Reliable H ∞ filter design for a class of mixed-delay Markovian jump systems with stochastic nonlinearities and multiplicative noises via delay-partitioning method

This paper is concerned with the problem of reliable H _?? filter design for a class of mixeddelay Markovian jump systems with stochastic nonlinearities and multiplicative noises. The mixed delays comprise both discrete time-varying delay and distributed delay. The stochastic nonlinearities in the form of statistical means cover several well-studied nonlinear functions. And the multiplicative disturbances are in the form of a scalar Gaussian white noise with unit variance. Furthermore, the failures of sensors are quantified by a variable varying in a given interval. A filter is designed to guarantee that the dynamics of the estimation error is asymptotically mean-square stable. Sufficient conditions for the existence of such a filter are obtained by using a new Lyapunov-Krasovskii functional and delaypartitioning method. Then a linear matrix inequality (LMI) approach for designing such a reliable H _?? filter is presented. Finally, the effectiveness of the proposed approach is demonstrated by a numerical example.     

Fast Tensor-Product Solvers: Partially Deformed Three-dimensional Domains

We discuss the numerical solution of partial differential equations in a particular class of three-dimensional geometries; the two-dimensional cross section (in the xy-plane) can have a general shape, but is assumed to be invariant with respect to the third direction. Earlier work has exploited such geometries by approximating the solution as a truncated Fourier series in the z-direction. In this paper we propose a new solution algorithm which also exploits the tensor-product feature between the xy-plane and the z-direction. However, the new algorithm is not limited to periodic boundary conditions, but works for general Dirichlet and Neumann type of boundary conditions. The proposed algorithm also works for problems with variable coefficients as long as these can be expressed as a separable function with respect to the variation in the xy-plane and the variation in the z-direction. For problems where the new method is applicable, the computational cost is very competitive with the best iterative solvers. The new algorithm is easy to implement, and useful, both in a serial and parallel context. Numerical results demonstrating the superiority of the method are presented for three-dimensional Poisson and Helmholtz problems using both low order finite elements and high order spectral element discretizations.     

PID controller frequency-domain tuning for stable, integrating and unstable processes, including dead-time

In the present paper a new tuning procedure is proposed for the ideal PID controller in series with the first-order noise filter. It is based on the recently proposed extension of the Ziegler-Nichols frequency-domain dynamics characterization of a process G_p(s). Measured process characteristics are the ultimate frequency and ultimate gain, the angle of the tangent to the Nyquist curve of the process at the ultimate frequency, and G_p(0). For a large class of processes the same tuning formulae can be effectively applied to obtain closed-loop responses with predictable properties. Load disturbance step responses without the undershoot and reference step responses with negligible overshoot are obtained by analyzing a test batch consisting of stable, integrating and unstable processes, including dead-time and oscillatory dynamics. The proposed tuning makes possible to specify the desired sensitivity to the high frequency measurement noise and the desired maximum sensitivity. Comparison with the optimal ideal PID controller in series with the first-order noise filter is presented and discussed. The extension of the proposed method to the PI controller tuning is direct. Comparison with the optimal PI controller is presented and discussed.     

Perturbation theory-based performance analysis of TLS-Prony method for natural frequency extraction

The total least squares Prony method is used for estimating s-plane natural frequencies from noisy late time response. It consists of three steps: In the first step, the minimum eigenvector of a matrix, whose entries are defined from the noisy late time response, should be computed. In the second step, the roots of a polynomial, whose coefficients are the entries of the minimum eigenvector defined in the first step, are estimated. The roots in the second step are defined as z-plane natural frequencies. In the third step, s-plane natural frequencies are obtained from z-plane natural frequencies. In this paper, a rigorous derivation of the mean square error of the estimator in each step due to an additive noise in the late time response is presented: In the first step, it is shown how the minimum eigenvector of a matrix is perturbed due to an additive noise in the late time response. In the second step, it is derived how the roots of a polynomial are perturbed due to perturbation in the coefficients of the polynomial. In the third step, it is derived how the s-plane natural frequencies are perturbed due to perturbation in z-plane natural frequencies. The mean square error for the first step, the second step and the third step are presented in (26), (36), and (44), respectively, and the validity of these expressions is illustrated in the results using simulated response and experimentally measured data. In conclusion, the mean square errors of the minimum eigenvector, the z plane natural frequencies, and the s plane natural frequencies for the total least squares Prony method can be obtained analytically from (26), (36) and (44) without a computationally intensive Monte-Carlo simulation.     

Networked H∞ filtering for linear discrete-time systems

This paper studies the problem of networked H_∞ filtering for linear discrete-time systems. A new model is proposed as the filtering error system to simultaneously capture the communication constraint, random packet dropout and quantization effects in the networked systems. A sufficient condition is presented for the filtering error system to be mean square exponentially stable with a prescribed H_∞ performance by employing the multiple Lyapunov function method. The obtained condition depends on some parameters of the networked systems, such as the access sequence of nodes, packet dropout rate and quantization density. With these parameters fixed, a design procedure for the desired H_∞ filter is also presented based on the derived condition. Finally, an illustrative example is utilized to show the effectiveness of the proposed method.     

Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification

In this article, an iterative procedure is proposed for the training process of the probabilistic neural network (PNN). In each stage of this procedure, the Q(0)-learning algorithm is utilized for the adaptation of PNN smoothing parameter (σ). Four classes of PNN models are regarded in this study. In the case of the first, simplest model, the smoothing parameter takes the form of a scalar; for the second model, σ is a vector whose elements are computed with respect to the class index; the third considered model has the smoothing parameter vector for which all components are determined depending on each input attribute; finally, the last and the most complex of the analyzed networks, uses the matrix of smoothing parameters where each element is dependent on both class and input feature index. The main idea of the presented approach is based on the appropriate update of the smoothing parameter values according to the Q(0)-learning algorithm. The proposed procedure is verified on six repository data sets. The prediction ability of the algorithm is assessed by computing the test accuracy on 10 %, 20 %, 30 %, and 40 % of examples drawn randomly from each input data set. The results are compared with the test accuracy obtained by PNN trained using the conjugate gradient procedure, support vector machine algorithm, gene expression programming classifier, k–Means method, multilayer perceptron, radial basis function neural network and learning vector quantization neural network. It is shown that the presented procedure can be applied to the automatic adaptation of the smoothing parameter of each of the considered PNN models and that this is an alternative training method. PNN trained by the Q(0)-learning based approach constitutes a classifier which can be treated as one of the top models in data classification problems.     

Noise variance estimation based on dual-channel phase difference for speech enhancement

In this paper, we propose a method for estimating a signal-to-noise ratio (SNR) in order to improve the performance of a dual-microphone speech enhancement algorithm. The proposed method is able to reliably estimate both a priori and a posteriori SNRs by exploring a direction-of-arrival (DOA)-based local SNR that is defined by using spatial cues obtained from dual-microphone signals. The estimated a priori and a posteriori SNRs are then incorporated into a Wiener filter. Consequently, it is shown from an objective perceptual evaluation of speech quality (PESQ) comparison and a subjective listening test that a speech enhancement algorithm employing the proposed SNR estimate outperforms those using conventional single- or dual-microphone speech enhancement algorithms such as the Wiener filter, beamformer, or phase error-based filter under different noise conditions ranging from 0 to 20 dB.     

Direct data-driven filter design for uncertain LTI systems with bounded noise

This paper investigates the filter design problem for linear time-invariant dynamic systems when no mathematical model is available, but a set of initial experiments can be performed where also the variable to be estimated is measured. Instead of using the initial experimental data to identify a model on the basis of which a filter is designed, these data are used to directly design a filter. Assuming norm-bounded disturbances and noises, a Set Membership formulation is followed. For classes of filters with exponentially decaying impulse response, approximating sets are determined that guarantee to contain all the solutions to the optimal filtering problem, where the aim is the minimization of the induced norm from disturbances to the estimation error. A method is proposed for designing almost-optimal linear filters with finite impulse response, whose worst-case filtering error is at most twice the lowest achievable one. In the H_∞ SISO case, an efficient technique is presented, that allows the evaluation of bounds on the guaranteed worst-case filtering error of the designed filter. Numerical examples illustrate the effectiveness of the proposed solution.     

Forwarding-based trajectory tracking control for nonlinear systems with bounded unknown disturbances

There are only a few methods for controlling high-order nonlinear systems, except for standard backstepping technique. Due to the fact that I&I theory proposed by Astolfi and Ortega is able to accomplish the system immersion by selecting a lower-order target system and a vital mapping, a bottom-up recursive procedure for designing tracking control laws for a class of n-dimensional strict-feedback nonlinear systems is focused in our work. The required mappings are transformed into virtual control inputs, and system order reduction is realized as design step is implemented repetitively. A first-order filter is employed at each step to compute analytic derivatives of mappings. The benefit of our proposed algorithm can not only improve the computation efficiency to simplify all controls’ forms, but also suppress the high-frequency noise. While the off-the-manifold coordinate is rendered insensitive to the time-varying and bounded but unknown disturbance, the robust stability can be ultimately guaranteed. A quadrotor helicopter is used to show designing procedures and controller performance via various simulations.     

Design and implementation of look-ahead linear jerk filter for a computerized numerical controlled machine

In this study, an open programmable logic controller (PLC) from Fuji electric prescribed in the Structured Text program was applied to develop a look-ahead linear jerk filter (LALJF) for a computerized numerically controlled (CNC) machine. To ensure the smooth and accurate motion of a tool with a linear change in jerk during real-time machining, the proposed filter was formed by combining a look-ahead algorithm with three modified moving average filters (3MMAF). The look-ahead algorithm performed a single look-ahead step-change in the speed of the speed curve. Based on a step-changing speed profile, given maximal acceleration/deceleration and maximum jerk, the speed curve was modified before it was passed through a linear jerk filter to reduce machining time. The speed commands filtered by the proposed filter stabilize machine table at the beginning and end of its motion, and at any point at which its speed exhibits a step-change. The theoretical and computational aspects of the LALJF are presented together with experimental results of its implementation on an X–Y table. The multiple-step-changing speed curve of a CNC machine and the speed curve of a high-speed measurement system were constructed in order to verify the feasibility and precision of the proposed method.     

A switch-mode information fusion filter based on ISRUKF for autonomous navigation of spacecraft

Aiming at the problem of loss of accuracy using extended Kalman filter (EKF) in case of orbit maneuver, this paper proposes a novel information fusion filtering algorithm-iterated square root unscented Kalman filter (ISRUKF), and then designs a switch-mode information fusion filter based on ISRUKF and extended Kalman filter (EKF). This method combines navigation sensors’ geocentric vector and geocentric distance with starlight angular distance, which efficiently improves the reliability of autonomous navigation. On this basis, the method deduced measurement function of information fusion. With a semi-physical simulation to verify the proposed method, the simulation results for stably running and orbital maneuvering spacecraft show that the switch-mode information fusion filter can reduce the complexity of the algorithm and ensure the accuracy of the estimation. Thus, the proposed switch-mode filter is very suitable for spacecraft autonomous navigation system and other strong nonlinear state estimation fields.     

Fast spatial averaging: an efficient algorithm for 2D mean filtering

We present a new fast spatial averaging technique that efficiently implements operations for spatial averaging or two-dimensional mean filtering. To perform spatial averaging of an M×N image with an averaging filter of size m×n, our proposed method requires approximately 4MN additions and no division. This is very promising, since the major computations required by our algorithm depend only on the size of the original image but not on the size of the averaging filter. To our knowledge, this technique requires the smallest number of additions for mean filtering. Experimental results on various image sizes using different filter sizes confirm that our fast spatial averaging algorithm is significantly faster than other spatial averaging algorithms, especially when the size of the input image is very large.     

LQG/LTR procedure using reduced-order Kalman filters

This paper discusses a linear-quadratic-Gaussian/loop transfer recovery (LQG/LTR) procedure using reduced-order Kalman filters by extending known exact-recovery result. The state-space realisation commonly used for reduced-order observer design is employed. The zero structure intrinsic to the realisation is revealed. Asymptotic recovery is achieved using a non-singular reduced-order Kalman filter with a parameterised set of covariance matrices. The proposed procedure provides a systematic method for directly designing reduced-order LQG controllers without additional coordinate transformations. A numerical design example for a simple multivariable plant is presented to compare the proposed design with the standard LQG/LTR design using a full-order Kalman filter.     

A clustering method combining differential evolution with the K-means algorithm

The present paper considers the problem of partitioning a dataset into a known number of clusters using the sum of squared errors criterion (SSE). A new clustering method, called DE-KM, which combines differential evolution algorithm (DE) with the well known K-means procedure is described. In the method, the K-means algorithm is used to fine-tune each candidate solution obtained by mutation and crossover operators of DE. Additionally, a reordering procedure which allows the evolutionary algorithm to tackle the redundant representation problem is proposed. The performance of the DE-KM clustering method is compared to the performance of differential evolution, global K-means method, genetic K-means algorithm and two variants of the K-means algorithm. The experimental results show that if the number of clusters K is sufficiently large, DE-KM obtains solutions with lower SSE values than the other five algorithms.     

Reduced-order estimation Part 1. Filtering

This paper presents a method for designing an ‘optimum’ unbiased reduced-order filter. For the proposed approach to work, the order of the filter must be greater than a certain minimum determined by the number of independent observations of the system available. The filler is much like a Luenberger observer for the state to be estimated, but with parameters optimized with respect to the noises in the system. A reduced-order innovation process is proposed that has properties similar to those of the full-order innovation process when the reduced filter is optimized. The approach offers the possibility of significant reduction in real-time computational requirements compared with the full-order filter, though at the cost of some loss of performance. The algorithm for the reduced-order filter is simple to implement— quite similar to that of the Kalman filter. An example is presented to compare the performance of the proposed method with the full-order Kalman filter.     

Motor Imagery Electroencephalograph Classification Based on Optimized Support Vector Machine by Magnetic Bacteria Optimization Algorithm

In this paper, an optimized support vector machine (SVM) based on a new bio-inspired method called magnetic bacteria optimization algorithm method is proposed to construct a high performance classifier for motor imagery electroencephalograph based brain–computer interface (BCI). Butterworth band-pass filter and artifact removal technique are combined to extract the feature of frequency band of the ERD/ERS. Common spatial pattern is used to extract the feature vector which are put into the classifier later. The optimization mechanism involves kernel parameters setting in the SVM training procedure, which significantly influences the classification accuracy. Our novel approach aims to optimize the penalty factor parameter C and kernel parameter g of the SVM. The experimental results on the BCI Competition IV dataset II-a clearly present the effectiveness of the proposed method outperforming other competing methods in the literature such as genetic algorithm, particle swarm algorithm, artificial bee colony, biogeography based optimization.