An efficient closed-form design method for nearly perfect reconstruction of non-uniform filter bank

In this paper, an efficient closed form method for the design of multi-channel nearly perfect reconstruction of non-uniform filter bank with the prescribed stopband attenuation and channel overlapping is presented. In this method, the design problem of multi-channel non-uniform filter bank (NUFB) is considered as the design of a prototype filter whose magnitude response at quadrature frequency is 0.707, which is exploited for finding the optimum passband edge frequency through empirical formula instead of using single or multivariable optimization technique. Two main attributes used in assessing the performance of filter bank are peak reconstruction error (PRE) and computational time (CPU time). As compared to existing methods, this method is very simple and easy to implement for NUFBs. To implement this algorithm, a Matlab program has been developed, and several examples are presented to illustrate the performance of proposed method.     

Design of minimum multiplier fractional order differentiator based on lattice wave digital filter

In this paper, a novel design of fractional order differentiator (FOD) based on lattice wave digital filter (LWDF) is proposed which requires minimum number of multiplier for its structural realization. Firstly, the FOD design problem is formulated as an optimization problem using the transfer function of lattice wave digital filter. Then, three optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and cuckoo search algorithm (CSA) are applied to determine the optimal LWDF coefficients. The realization of FOD using LWD structure increases the design accuracy, as only N number of coefficients are to be optimized for Nth order FOD. Finally, two design examples of 3rd and 5th order lattice wave digital fractional order differentiator (LWDFOD) are demonstrated to justify the design accuracy. The performance analysis of the proposed design is carried out based on magnitude response, absolute magnitude error (dB), root mean square (RMS) magnitude error, arithmetic complexity, convergence profile and computation time. Simulation results are attained to show the comparison of the proposed LWDFOD with the published works and it is observed that an improvement of 29% is obtained in the proposed design. The proposed LWDFOD approximates the ideal FOD and surpasses the existing ones reasonably well in mid and high frequency range, thereby making the proposed LWDFOD a promising technique for the design of digital FODs.     

A hybrid cuckoo search and variable neighborhood descent for single and multiobjective scheduling problems

Cuckoo search (CS) is a relatively new meta-heuristic that has proven its strength in solving continuous optimization problems. This papers applies cuckoo search to the class of sequencing problems by hybridizing it with a variable neighborhood descent local search for enhancing the quality of the obtained solutions. The Lévy flight operator proposed in the original CS is modified to address the discrete nature of scheduling problems. Two well-known problems are used to demonstrate the effectiveness of the proposed hybrid CS approach. The first is the NP-hard single objective problem of minimizing the weighted total tardiness time ( \(1|| \sum {T_{w}}\) ) and the second is the multiobjective problem of minimizing the flowtime \(\overline {C}\) and the maximum tardiness T _{m a x} for single machine ( \(1|| (\frac {1}{n}\sum {C}, T_{max})\) ). For the first problem, computational results show that the hybrid CS is able to find the optimal solutions for all benchmark test instances with 40, 50, and 100 jobs and for most instances with 150, 200, 250, and 300 jobs. For the second problem, the hybrid CS generated solutions on and very close to the exact Pareto fronts of test instances with 10, 20, 30, and 40 jobs. In general, the results reveal that the hybrid CS is an adequate and robust method for tackling single and multiobjective scheduling problems.     

Scheduling optimization of flexible manufacturing system using cuckoo search-based approach

In the present work, a cuckoo search (CS)-based approach has been developed for scheduling optimization of a flexible manufacturing system by minimizing the penalty cost due to delay in manufacturing and maximizing the machine utilization time. To demonstrate the application of cuckoo search (CS)-based scheme to find the optimum job, the proposed scheme has been applied with slight modification in its Levy flight operator because of the discrete nature of the solution on a standard FMS scheduling problem containing 43 jobs and 16 machines taken from literature. The CS scheme has been implemented using Matlab, and results have been compared with other soft computing-based optimization approaches like genetic algorithm (GA) and particle swarm optimization found in the literature. The results shown by CS-based approach have been found to outperform the results of existing heuristic algorithms such as GA for the given problem.     

Accurate evaluation of free-form surface profile error based on quasi particle swarm optimization algorithm and surface subdivision

Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline(NURBS) for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system(DCS) and measurement coordinate system(MCS) for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization(QPSO) is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.     

RTDS implementation of an improved sliding mode based inverter controller for PV system

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (R_t), maximum percentage overshoot (MPOS), settling time (T_s), and steady state error (E_ss) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems.     

Numerical investigation on the installation effects of electromagnetic flowmeter downstream of a 90° elbow–laminar flow case

An electromagnetic flowmeter installed downstream of a 90° elbow to measure the flowrate of laminar flow is numerically simulated to investigate installation effects by varying the location of the electromagnetic flowmeter at a distance up to 22D from the elbow, and the angle between the electrodes plane and the symmetry plane of the elbow at ϕ=0, 45 and 90°. Effects of the curvature radius (R_c) and the Reynolds number (Re) based on a diameter D are also scrutinized in the range of 400≤Re≤1500 and R_c=1.5D and 3.0D.For the simulation of an electromagnetic flowmeter, a commercial code FLUENT(ver. 4.4) is applied for flow field analyses and a three-dimensional numerical code is developed for analyzing the magnetic field. The developed code adopts a finite volume method to solve a Poisson-type voltage equation for the magnetic field.It is found that the deviations of the flow signal due to the disturbance from the elbow is strongly dependent on the pattern of axial velocity contours. Cases for ϕ=45° are found to permit significantly better measurement accuracy in comparison with ϕ=0° and ϕ=90°, and the effect of the curvature on the optimum installation distance depends on the Reynolds number. The present numerical simulation method is found to be a useful tool for the performance analysis of the electromagnetic flowmeter.     

改进的布谷鸟算法优化极限学习机的石化轴承故障分类

针对通用的智能故障诊断方法在石化滚动轴承中准确率不理想的问题,提出一种通过改进的布谷鸟算法( CS )优化极限学习机( ELM )使诊断准确率提高的模型。将实测轴承振动信号降噪处理,计算不同嵌入维度下的关联维数作为 ELM 的输入信号;通过改进的布谷鸟算法获取极限学习机最优的隐含层偏置、输入权重,最后输出诊断结果。经过实验证明,该方法可以有效地克服测量信号时的干扰,可以对不同故障下的滚动轴承准确识别,并与多种模型对比,该方法的故障诊断准确率为 97.5% 。     

A simple nonlinear PD controller for integrating processes

Many industrial processes are found to be integrating in nature, for which widely used Ziegler–Nichols tuned PID controllers usually fail to provide satisfactory performance due to excessive overshoot with large settling time. Although, IMC (Internal Model Control) based PID controllers are capable to reduce the overshoot, but little improvement is found in the load disturbance response. Here, we propose an auto-tuning proportional-derivative controller (APD) where a nonlinear gain updating factor α continuously adjusts the proportional and derivative gains to achieve an overall improved performance during set point change as well as load disturbance. The value of α is obtained by a simple relation based on the instantaneous values of normalized error (e_N) and change of error (Δe_N) of the controlled variable. Performance of the proposed nonlinear PD controller (APD) is tested and compared with other PD and PID tuning rules for pure integrating plus delay (IPD) and first-order integrating plus delay (FOIPD) processes. Effectiveness of the proposed scheme is verified on a laboratory scale servo position control system.     

Cuckoo search algorithm for the selection of optimal machining parameters in milling operations

In this research, a new optimization algorithm, called the cuckoo search algorithm (CS) algorithm, is introduced for solving manufacturing optimization problems. This research is the first application of the CS to the optimization of machining parameters in the literature. In order to demonstrate the effectiveness of the CS, a milling optimization problem was solved and the results were compared with those obtained using other well-known optimization techniques like, ant colony algorithm, immune algorithm, hybrid immune algorithm, hybrid particle swarm algorithm, genetic algorithm, feasible direction method, and handbook recommendation. The results demonstrate that the CS is a very effective and robust approach for the optimization of machining optimization problems.     

Knowledge-based substep deterministic optimization of large diameter thin-walled Al-alloy tube bending

Regarding increasing applications with mass quantities, diverse specifications, and close quality tolerance, the precision bending of large diameter thin-walled (LDTW) Al-alloy tube should be efficiently achieved. However, bending of LDTW Al-alloy tube is a highly tri-nonlinear process with possible multi-defect, needing strict coordination of various bending tools and processing parameters. Considering the coupling effects of various forming parameters on multiple defects, this study developed a knowledge-based substep methodology to solve the deterministic optimization of LDTW Al-alloy tube bending with multi-objective and multi-variable under multiple factor constraints. Considering narrow forming window under small bending radii (R _b?<?2D, R _b—bending radius, D—initial tube diameter), a finite element (FE)-based stepwise iterative search method is proposed to optimize key forming parameters of LDTW Al-alloy tube under small R _b, and the search direction is based on bending knowledge. While for large R _b bending with wide optional ranges of forming parameters, a hybrid optimization approach is used by combining virtual design of experiment, FE simulation, approximate response surface model, sequential quadratic programming algorithm, or genetic algorithm. Using orthogonal experimental method, three-dimensional (3D)-FE simulation, experiential data, and analytical formulae, knowledge on key forming parameters, coupling effects on multiple defects, effect significance, and design rules are obtained as well as initial values and design ranges. By several practical bending scenarios with D up to 100 mm, the proposed substep deterministic optimization methodology for LDTW Al-alloy tube bending is evaluated.     

A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence, pattern recognition is very useful in identifying the process problems. In this study, a multiclass SVM (SVM) based classifier is proposed because of the promising generalization capability of support vector machines. In the proposed method type-2 fuzzy c-means (T2FCM) clustering algorithm is used to make a SVM system more effective. The fuzzy support vector machine classifier suggested in this paper is composed of three main sub-networks: fuzzy classifier sub-network, SVM sub-network and optimization sub-network. In SVM training, the hyper-parameters plays a very important role in its recognition accuracy. Therefore, cuckoo optimization algorithm (COA) is proposed for selecting appropriate parameters of the classifier. Simulation results showed that the proposed system has very high recognition accuracy.     

An optimization strategy for die design in the low-density polyethylene annular extrusion process based on FES/BPNN/NSGA-II

An optimization strategy for die design in the polymer extrusion process is proposed in the study based on the finite element simulation, the back-propagation neural network, and the non-dominated sorting genetic algorithm II (NSGA-II). The three-dimensional simulation of polymer melts flow in the extrusion process is conducted using the penalty finite element method. The model for predicting the flow patterns in the extrusion process is established with the artificial neural network based on the simulated results. The non-dominated sorting genetic algorithm II is performed for the search of globally optimal design variables with its objective functions evaluated by the established neural network model. The proposed optimization strategy is successfully applied to the die design in low-density polyethylene (LDPE) annular extrusion process. A constrained multi-objective optimization model is established according to the characteristics of annular extrusion process. The minimum of velocity relative difference, δu, and the minimum of swell ratio, S _w, that, respectively, ensure the extrinsic feature, mechanical property, and dimensional precision of the final products are taken as optimization objectives with a constrained condition on the maximum shear stress. Three important die structure parameters, including the die contraction angle α, the ratio of parallel length to inner radius L/R _i, and the ratio of outer to inner radius R _o /R _i, are taken as design variables. The Phan-Thien–Tanner constitutive model is adopted to describe the viscoelastic rheological characteristics of LDPE whose parameters are fitted by the distributions of material functions detected on the strain-controlled rheometer. The penalty finite element model of polymer melts flowing through out of the extrusion die is derived. A decoupled method is employed to solve the viscoelastic flow problem with the discrete elastic-viscous split-stress algorithm. The simulated results are selected and extracted to constitute the learning samples according to the orthogonal experimental design method. The back propagation algorithm is adopted for the training and the establishment of the predicting model for the optimization objective. A Pareto-optimal set for the constrained multi-objective optimization is obtained using the constrained NSGA-II, and the optimal solution is extracted based on the fuzzy set theory. The optimization for die parameters in the annular extrusion process of low-density polyethylene is performed and the optimization objective is successfully achieved.     

Research on dynamic transmission error of harmonic drive with uncertain parameters by an interval method

Transmission error is one of the most important performance indicators for evaluating harmonic drives, and can have crippling effects on positioning accuracy and stability of industrial robots. However, most of the existing error analysis methods focus on a single factor, and do not consider the uncertainty of dynamic parameters, leading to evident limitations. In the present study, static transmission error (caused by manufacturing and assembly error) and dynamic transmission error (generated by static transmission error and dynamic parameters) of a harmonic drive are modeled. An interval method is developed and used to numerically express uncertain dynamic parameters of the system. Chebyshev polynomials are used to approximate the dynamic differential equations of the harmonic drive, and then the distribution of dynamic transmission error and its relationship with uncertain parameters are discussed in detail. In addition, a global sensitivity analysis is carried out to intuitively demonstrate how much impact each parameter has on dynamic transmission error. Our results suggest that the moment of inertia J_in and the torsional stiffness coefficient k₁ have a large influence on dynamic transmission error. Finally, the proposed method is validated by experiment. The method can be adopted to determine the upper and lower bounds of dynamic transmission error of dynamic systems under the influence of uncertain parameters and provides a theoretical basis for transmission error optimization and compensation.     

Fitting of experimental data using a fractional Kalman-like observer

In this paper, a fractional order Kalman filter (FOKF) is presented, this is based on a system expressed by fractional differential equations according to the Riemann–Liouville definition. In order to get the best fitting of the FOKF, the cuckoo search optimization algorithm (CS) was used. The purpose of using the CS algorithm is to optimize the order of the observer, the fractional Riccati equation and the FOKF tuning parameters. The Grünwald–Letnikov approximation was used to compute the numerical solution of the FOKF. To show the effectiveness of the proposed FOKF, four examples are presented, the brain activity, the cutaneous potential recordings of a pregnant woman, the earthquake acceleration, and the Chua’s circuit response.     

Multi-objective optimization of combustion,performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (P_max)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO _x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, P_max), performance (BTE) and emission (CO, NO _x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, P_max, CO, NO _x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.     

An investigation into the aspheric ultraprecision machining using the response surface methodology

Aspheric ultraprecision machining is increasingly important to the manufacturing industry. The performance of aspheric optical components manufactured by mass-production is largely dependent on the form error of molds and dies. It is believed that productivity of a machining process could be improved if the form error is predictable. In this study, the response surface methodology (RSM) was employed to derive predictive models of rough and compensation cuttings for an aspheric convex mold, with an outer aperture of ϕ12 mm and curve height of 0.6 mm. Two control factors, the depth of cut and spindle speed, were selected for study. The 2K factorial design with four center points was adopted. Two linear models for both rough and compensation cuttings were derived experimentally based on the form errors obtained. The models adequacy was examined through ANOVA (analysis of variance) results for the surface responses. It was found that the linear model of rough cutting is adequate, reflected by the significant regress coefficients and the high R² value. However, the model of compensation cutting was found to be inadequacy.     

A study on accuracy of porous journal air bearing

In order to predict error motion of continuous porous journal air bearing, an accuracy model is established to reveal the relationship among error motion, roundness error and structure parameter under quasi-static conditions. Based on the model, averaging coefficient is defined to quantitatively characterize the error averaging ability. The study finds that whether the bush and shaft roundness errors match is the cause of error motion. The trilobal roundness error of shaft has a major impact on accuracy for a porous journal air bearing with an elliptical bush, while the elliptical roundness error of shaft has a major impact on accuracy for that with a trilobal bush. On the two-dimensional plane of bush wave numbers n₂ = 2~7 and shaft wave numbers n₁ = 2~15, the averaging coefficients are symmetrical along the line n₁ = n₂. The shaft wave numbers which equal integer multiples of prime numbers of bush wave number have no impact on accuracy, while the remaining shaft wave numbers have impact. Among them, those at points n₁ = n₂*i ± 1 are with obvious averaging coefficients and have a major impact on accuracy where i is a positive integer. The main peaks of averaging coefficients appear at the points n₁ = n₂ ± 1, which have the most important impact on accuracy. The theory has many potential applications such as prediction of error motion, structural optimization and selection of parts grinding method, which is of significant importance for design and testing of porous journal air bearings used widely in ultra-precision machine tools.     

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre‐processing step is employed. The method is developed and evaluated on light microscope images of rats’ hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between‐class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi‐threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR.