Search results

1 – 10 of over 13000
Content available
Article
Publication date: 29 July 2020

Abdelhak Boukharouba

Fast iterative algorithms for designing birefringent filters with any specified spectral response are proposed. From the Jones formalism, we derive two polynomials…

Abstract

Fast iterative algorithms for designing birefringent filters with any specified spectral response are proposed. From the Jones formalism, we derive two polynomials representing the transmitted and rejected response of the filter, respectively. Once the coefficients of the filters are obtained, the orientation angle of each birefringent section and the phase shift introduced by each compensator can be determined by an iterative algorithm that gives an efficient solution to the birefringent filter design problem. Afterward, some design examples are presented to demonstrate the effectiveness of the proposed approach. In comparison with results reported in the literature, this approach provides the best performance in terms of accuracy and time complexity.

Details

Applied Computing and Informatics, vol. 17 no. 2
Type: Research Article
ISSN: 2634-1964

Keywords

To view the access options for this content please click here
Article
Publication date: 6 August 2019

Lin Li, Jiadong Xiao, Yanbiao Zou and Tie Zhang

The purpose of this paper is to propose a precise time-optimal path tracking approach for robots under kinematic and dynamic constraints to improve the work efficiency of…

Abstract

Purpose

The purpose of this paper is to propose a precise time-optimal path tracking approach for robots under kinematic and dynamic constraints to improve the work efficiency of robots and guarantee tracking accuracy.

Design/methodology/approach

In the proposed approach, the robot path is expressed by a scalar path coordinate and discretized into N points. The motion between two neighbouring points is assumed to be uniformly accelerated motion, so the time-optimal trajectory that satisfies constraints is obtained by using equations of uniformly accelerated motion instead of numerical integration. To improve dynamic model accuracy, the Coulomb and viscous friction are taken into account (while most publications neglect these effects). Furthermore, an iterative learning algorithm is designed to correct model-plant mismatch by adding an iterative compensation item into the dynamic model at each discrete point before trajectory planning.

Findings

An experiment shows that compared with the sequential convex log barrier method, the proposed numerical integration-like (NI-like) approach has less computation time and a smoother planning trajectory. Compared with the experimental results before iteration, the torque deviation, tracking error and trajectory execution time are reduced after 10 iterations.

Originality/value

As the proposed approach not only yields a time-optimal solution but also improves tracking performance, this approach can be used for any repetitive robot tasks that require more rapidity and less tracking error, such as assembly.

Details

Industrial Robot: the international journal of robotics research and application, vol. 46 no. 6
Type: Research Article
ISSN: 0143-991X

Keywords

To view the access options for this content please click here
Article
Publication date: 6 March 2019

Narinder Kumar and Ashwani Kumar

The purpose of this paper is to analyze annual energy expenditure in the presence of non-linear load and substation voltage harmonics in distribution systems. Economic…

Abstract

Purpose

The purpose of this paper is to analyze annual energy expenditure in the presence of non-linear load and substation voltage harmonics in distribution systems. Economic assessment of non-sinusoidal energy is a challenging task that involves complex computations of harmonic load powers and harmonic line losses.

Design/methodology/approach

The paper evaluates fundamental and non-sinusoidal components of electrical energy by applying backward/forward sweep technique in distorted distribution systems. This work involves harmonic power computations at the substation by including harmonic losses occurring in various lines of the distribution system.

Findings

The paper found that annual energy expenditure significantly depends upon the non-linear load, supply voltage harmonics and type of tariff structure considered in the distribution system. Impact of individual harmonic orders on the energy billing is also assessed.

Originality/value

The paper concludes that considering harmonic distortions in the distribution system analysis would help electricity regulators formulate adequate pricing structures, which would further generate appropriate economic signals for electricity utility and the consumers.

Details

International Journal of Energy Sector Management, vol. 13 no. 1
Type: Research Article
ISSN: 1750-6220

Keywords

To view the access options for this content please click here
Article
Publication date: 20 February 2020

Yassine Selami, Na Lv, Wei Tao, Hongwei Yang and Hui Zhao

The purpose of this paper is to propose cuckoo optimization algorithm (COA)-based back propagation neural network (BPNN) to reduce the effect of the nonlinearities…

Abstract

Purpose

The purpose of this paper is to propose cuckoo optimization algorithm (COA)-based back propagation neural network (BPNN) to reduce the effect of the nonlinearities presented in laser triangulation displacement sensors. The 3D positioning and posture sensor allows access to the third dimension through depth measurement; the performance of the sensor varies according to the level of nonlinearities presented in the system, which leads to inaccuracies in measurement.

Design/methodology/approach

While applying optimization approach, the mathematical model and the relationship between the key parameters in the laser triangulation ranging and the indexes of the measuring system were analyzed.

Findings

Based on the performance of the parametric optimization method, the measurement repeatability reached 0.5 µm with an STD value within 0.17 µm, an expanded uncertainty of measurement was within 5 µm, the angle error variation of the object’s rotational plane was within 0.031 degrees and nonlinearity was recorded within 0.006 per cent in a full scale. The proposed approach reduced the effect of the nonlinearity presented in the sensor. Thus, the accuracy and speed of the sensor were greatly increased. The specifications of the optimized sensor meet the requirements for high-accuracy devices and allow wide range of industrial application.

Originality/value

In this paper, COA-based BPNN is proposed for laser triangulation displacement sensor optimization, on the basis of the mathematical model, clarifying the working space and working angle on the measurement system.

Details

Sensor Review, vol. 40 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

To view the access options for this content please click here
Article
Publication date: 1 June 1991

Meng‐Koon Chua and Douglas C. Montgomery

Three functions are identified and integrated into one unique control scheme for multivariate quality control. The control scheme will identify any out‐of‐control samples…

Abstract

Three functions are identified and integrated into one unique control scheme for multivariate quality control. The control scheme will identify any out‐of‐control samples, select the subset of variables that are out of control, and diagnose the out‐of‐control variables. New control variable selection algorithm and diagnosis methods are proposed and a framework for the control scheme is developed based on simulation results.

Details

International Journal of Quality & Reliability Management, vol. 8 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

To view the access options for this content please click here
Article
Publication date: 7 March 2008

A. Hajnayeb, S.E. Khadem and M.H. Moradi

This paper aims to improve the performance and speed of artificial neural network (ANN)‐ball‐bearing fault detection expert systems by eliminating unimportant inputs and…

Abstract

Purpose

This paper aims to improve the performance and speed of artificial neural network (ANN)‐ball‐bearing fault detection expert systems by eliminating unimportant inputs and changing the ANN structure.

Design/methodology/approach

An algorithm is used to select the best subset of features to boost the success of detecting healthy and faulty ball. Some of the important parameters of the ANN are also optimized to make the classifier achieve the maximum performance.

Findings

It was found that better accuracy can be obtained for ANN with fewer inputs.

Research limitations/implications

The method can be used for other machinery condition‐monitoring systems which are based on ANN.

Practical implications

The results are useful for bearing fault detection systems designers and quality check centers in bearing manufacturing companies.

Originality/value

The algorithm used in this research is faster than in previous studies. Changing ANN parameters improved the results. The system was examined using experimental data of ball‐bearings.

Details

Industrial Lubrication and Tribology, vol. 60 no. 2
Type: Research Article
ISSN: 0036-8792

Keywords

To view the access options for this content please click here
Article
Publication date: 6 September 2018

Ihab Zaqout and Mones Al-Hanjori

The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to…

Abstract

Purpose

The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively.

Design/methodology/approach

Averaged-feature based approach (dimension reduction) and BPNN (generate supervised classifier).

Findings

The recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set.

Originality/value

Averaged-feature based method.

Details

Information and Learning Science, vol. 119 no. 9/10
Type: Research Article
ISSN: 2398-5348

Keywords

To view the access options for this content please click here
Article
Publication date: 1 December 2005

Kai Gao, Yong‐Cheng Wang and Zhi‐Qi Wang

This purpose of this paper is to propose a recommendation approach for information retrieval.

Abstract

Purpose

This purpose of this paper is to propose a recommendation approach for information retrieval.

Design/methodology/approach

Relevant results are presented on the basis of a novel data structure named FPT‐tree, which is used to get common interests. Then, data is trained by using a partial back‐propagation neural network. The learning is guided by users' click behaviors.

Findings

Experimental results have shown the effectiveness of the approach.

Originality/value

The approach attempts to integrate metric of interests (e.g., click behavior, ranking) into the strategy of the recommendation system. Relevant results are first presented on the basis of a novel data structure named FPT‐tree, and then, those results are trained through a partial back‐propagation neural network. The learning is guided by users' click behaviors.

Details

Library Hi Tech, vol. 23 no. 4
Type: Research Article
ISSN: 0737-8831

Keywords

To view the access options for this content please click here
Article
Publication date: 13 March 2007

Şahin Yildirim, İkbal Eski and A. Osman Kurban

To analyse a self‐acting parallel surface thrust bearing using a proposed feedforward neural network.

Abstract

Purpose

To analyse a self‐acting parallel surface thrust bearing using a proposed feedforward neural network.

Design/methodology/approach

Firstly, a one‐piece hydrodynamic thrust bearing with an initially flat surface is analysed, designed and tested. Analysis of the configuration used is particularly simple and gives good agreement with experimental results. Secondly, some artificial neural network types are designed to analyse minimum film thickness for specified load of thrust bearing system.

Findings

A more efficient film shape might result if the length of the cantilever did not increase with radius, since with the configuration used, the deflection of the outer corner was almost three times greater than the deflection of the inner corner, although this effect only becomes acute with regard to film thickness at fairly high loads. The design analysis of an asymmetric cantilever would be more lengthy and less easy to apply. Extrapolation of results for the plain bearing shows that high loads could be carried, but under severe conditions of temperature and clearance.

Research limitations/implications

Owing to finance problems, it was not easy to setup system in real time applications. This approach would be given usefulness elsewhere.

Practical implications

In future, this technique will be implemented for designing experimental neural network predictor on thrust bearing system. Also, this kind of neural predictor will be suitable for complex bearing systems.

Originality/value

A new type of neural network is used to investigate film thickness of thrust bearing system. Quick propagation neural network has given superior performance for designing of model of thrust bearing system. As described and shown in figures and tables, this kind of neural predictor could be employed for analysing such systems in practical analyses.

Details

Industrial Lubrication and Tribology, vol. 59 no. 2
Type: Research Article
ISSN: 0036-8792

Keywords

To view the access options for this content please click here
Article
Publication date: 3 July 2017

Fiaz Ahmad, Kabir Muhammad Abdul Rashid, Akhtar Rasool, Esref Emre Ozsoy, Asif Sabanoviç and Meltem Elitas

To propose an improved algorithm for the state estimation of distribution networks based on the unscented Kalman filter (IUKF). The performance comparison of unscented…

Abstract

Purpose

To propose an improved algorithm for the state estimation of distribution networks based on the unscented Kalman filter (IUKF). The performance comparison of unscented Kalman filter (UKF) and newly developed algorithm, termed Improved unscented Kalman Filter (IUKF) for IEEE-30, 33 and 69-bus radial distribution networks for load variations and bad data for two measurement noise scenarios, i.e. 30 and 50 per cent are shown.

Design/methodology/approach

State estimation (SE) plays an instrumental role in realizing smart grid features like distribution automation (DA), enhanced distribution generation (DG) penetration and demand response (DR). Implementation of DA requires robust, accurate and computationally efficient dynamic SE techniques that can capture the fast changing dynamics of distribution systems more effectively. In this paper, the UKF is improved by changing the way the state covariance matrix is calculated, to enhance its robustness and accuracy under noisy measurement conditions. UKF and proposed IUKF are compared under the cummulative effect of load variations and bad data based on various statistical metrics such as Maximum Absolute Deviation (MAD), Maximum Absolute Per cent Error (MAPE), Root Mean Square Error (RMSE) and Overall Performance Index (J) for three radial distribution networks. All the simulations are performed in MATLAB 2014b environment running on an hp core i5 laptop with 4GB memory and 2.6 GHz processor.

Findings

An Improved Unscented Kalman Filter Algorithm (IUKF) is developed for distribution network state estimation. The developed IUKF is used to predict network states (voltage magnitude and angle at all buses) and measurements (source voltage magnitude, line power flows and bus injections) in the presence of load variations and bad data. The statistical performance of the coventional UKF and the proposed IUKF is carried out for a variety of simulation scenarios for IEEE-30, 33 and 69 bus radial distribution systems. The IUKF demonstrated superiority in terms of: RMSE; MAD; MAPE; and overall performance index J for two measurement noise scenarios (30 and 50 per cent). Moreover, it is shown that for a measurement noise of 50 per cent and above, UKF fails while IUKF performs.

Originality/value

UKF shows degraded performance under high measurement noise and fails in some cases. The proposed IUKF is shown to outperform the UKF in all the simulated scenarios. Moreover, this work is novel and has justified improvement in the robustness of the conventional UKF algorithm.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 36 no. 4
Type: Research Article
ISSN: 0332-1649

Keywords

1 – 10 of over 13000