scholarly journals Efficient transient testing procedure using a novel experience replay particle swarm optimizer for THD-based robust design and optimization of self-X sensory electronics in industry 4.0

2021 ◽  
Vol 10 (2) ◽  
pp. 193-206
Author(s):  
Qummar Zaman ◽  
Senan Alraho ◽  
Andreas König
Keyword(s):  
Industry 4.0 ◽  
Particle Swarm ◽  
Optimization Procedure ◽  
Calibration Method ◽  
Testing Procedure ◽  
Instrumentation Amplifier ◽  
Self Healing ◽  
Particle Swarm Optimizer ◽  
Current Feedback ◽  
Experience Replay

Abstract. This paper aims to improve the traditional calibration method for reconfigurable self-X (self-calibration, self-healing, self-optimize, etc.) sensor interface readout circuit for industry 4.0. A cost-effective test stimulus is applied to the device under test, and the transient response of the system is analyzed to correlate the circuit's characteristics parameters. Due to complexity in the search and objective space of the smart sensory electronics, a novel experience replay particle swarm optimization (ERPSO) algorithm is being proposed and proved a better-searching capability than some currently well-known PSO algorithms. The newly proposed ERPSO expanded the selection producer of the classical PSO by introducing an experience replay buffer (ERB) intending to reduce the probability of trapping into the local minima. The ERB reflects the archive of previously visited global best particles, while its selection is based upon an adaptive epsilon greedy method in the velocity updating model. The performance of the proposed ERPSO algorithm is verified by using eight different popular benchmarking functions. Furthermore, an extrinsic evaluation of the ERPSO algorithm is also examined on a reconfigurable wide swing indirect current-feedback instrumentation amplifier (CFIA). For the later test, we proposed an efficient optimization procedure by using total harmonic distortion analyses of CFIA output to reduce the total number of measurements and save considerable optimization time and cost. The proposed optimization methodology is roughly 3 times faster than the classical optimization process. The circuit is implemented by using Cadence design tools and CMOS 0.35 µm technology from Austria Microsystems (AMS). The efficiency and robustness are the key features of the proposed methodology toward implementing reliable sensory electronic systems for industry 4.0 applications.

Low-cost Indirect Measurement Methods for Self-x Integrated Sensory Electronics for Industry 4.0

2020 ◽  
Vol 87 (s1) ◽  
pp. s79-s84
Author(s):  
Qummar Zaman ◽  
Senan Alraho ◽  
Andreas König
Keyword(s):  
Industry 4.0 ◽  
Degrees Of Freedom ◽  
Performance Metrics ◽  
Low Cost ◽  
Indirect Measurement ◽  
Measurement Methods ◽  
Instrumentation Amplifier ◽  
Performance Parameters ◽  
Current Feedback ◽  
Indirect Measurements

AbstractThe conventional method for testing the performance of reconfigurable sensory electronics of industry 4.0 relies on the direct measurement methods. This approach gives higher accuracy but at the price of extremely high testing cost and does not utilize the new degrees of freedom for measurement methods enabled by industry 4.0. In order to reduce the test cost and use available resources more efficiently, a primary approach, called indirect measurements or alternative testing has been proposed using a non-intrusive sensor. Its basic principle consists in using the indirect measurements, in order to estimate the sensory electronics performance parameters without measuring directly. The non-intrusive property of the proposed method offers better performance of the sensing electronics and virtually applicable to any sensing electronics. Efficiency is evaluated in terms of model accuracy by using six different classical metrics. It uses an indirect current-feedback instrumentation amplifier (InAmp) as a test vehicle to evaluate the performance parameters of the circuit. The device is implemented using CMOS 0.35 μm technology. The achieved maximum value of average expected error metrics is 0.24, and the lowest value of correlation performance metrics is 0.91, which represent an excellent efficiency of InAmp performance predictor.

scholarly journals Gaussian Process Regression Based Robust Optimization with Observer Uncertainty for Reconfigurable Self-x Sensory Electronics for Industry 4.0

2021 ◽  
Vol 88 (s1) ◽  
pp. s83-s88
Author(s):  
Qummar Zaman ◽  
Senan Alraho ◽  
Andreas König
Keyword(s):  
Robust Optimization ◽  
Gaussian Process ◽  
Industry 4.0 ◽  
Performance Metrics ◽  
Optimization Technique ◽  
Gaussian Process Regression ◽  
Search Space ◽  
Instrumentation Amplifier ◽  
Statistical Regression ◽  
Current Feedback

Abstract This paper presents a robust optimization technique for the reconfigurable measurement of sensory electronics for industry 4.0 to obtain a robust solution even in the presence of observer uncertainty using a cost-effective performance measurement method. The extrinsic evaluation of the proposed methodology is performed on an indirect current-feedback instrumentation amplifier (CFIA), which is a fundamental part of sensory systems. To reduce the CFIA device performance evaluation set-up cost, a low-cost test stimulus is applied to the circuit under test, and the output response of the circuit is examined to correlate with the device’s performance parameters. Due to the complexity of the smart sensory electronics search space, the meta-heuristic optimization algorithm is being selected as an optimizer. For objective space or observer uncertainty, the Gaussian process regression from the Bayesian statistical regression process is used to estimate the uncertainty level efficiently. Six different classical metrics have been used to evaluate the regression model accuracy. The highest achieved average expected error metrics value is 0.313, and the minimum value of correlation performance metrics is 0.908. The device is implemented using 0.35 μm austriamicrosystems technology.

scholarly journals A New Simulation-Optimization Approach Using Hybrid Radial Basis Function and Particle Swarm Optimization in Multi-Transmitter Placement Planning

2020 ◽  
Author(s):  
Amir Parnianifard ◽  
Muhammad Saadi ◽  
Manus Pengnoo ◽  
Muhammad Ali Imran ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Simulation Optimization ◽  
Particle Swarm ◽  
Optimization Procedure ◽  
Base Station ◽  
Base Stations ◽  
Optimization Approach ◽  
Particle Swarm Optimizer ◽  
Radial Basis

With the every passing day, the demand for data traffic is increasing and this demand forces the research community not only to look for alternating spectrum for communication but also urges the radio frequency planners to use the existing spectrum smartly. Cell size is shrinking with the every upcoming communication generation which makes the base station placement planning complex and cumbersome. In order to make the next-generation cost-effective, it is important to design the network in such a way which utilizes minimum number of base stations while ensure coverage and quality of service. This paper aims at develop a new approach using hybrid metaheuristic and metamodel applied in multi-transmitter placement planning (MTPP) problem. We apply radial basis function (RBF) metamodel to assist particle swarm optimizer (PSO) in a constrained simulation-optimization (SO) of MTPP to mitigate the associated computational burden of optimization procedure. We evaluate the effectiveness and applicability of proposed algorithm in a case study by simulating MTPP model with two, three, four and five transmitters.

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

2020 ◽  
pp. 147592172097970
Author(s):  
Liangliang Cheng ◽  
Vahid Yaghoubi ◽  
Wim Van Paepegem ◽  
Mathias Kersemans
Keyword(s):  
Feature Selection ◽  
Mahalanobis Distance ◽  
Classification System ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Classification Performance ◽  
Decision Boundary ◽  
Particle Swarm Optimizer ◽  
Selection Phase

The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

scholarly journals Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer

2021 ◽  
Vol 11 (3) ◽  
pp. 1325
Author(s):  
Dalia Yousri ◽  
Magdy B. Eteiba ◽  
Ahmed F. Zobaa ◽  
Dalia Allam
Keyword(s):  
Permanent Magnet ◽  
Linear Optimization ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Synchronous Motor ◽  
Variable Order ◽  
Particle Swarm Optimizer ◽  
Novel Variants ◽  
Fractional Models ◽  
Complex Nonlinear Systems

In this paper, novel variants for the Ensemble Particle Swarm Optimizer (EPSO) are proposed where ten chaos maps are merged to enhance the EPSO’s performance by adaptively tuning its main parameters. The proposed Chaotic Ensemble Particle Swarm Optimizer variants (C.EPSO) are examined with complex nonlinear systems concerning equal order and variable-order fractional models of Permanent Magnet Synchronous Motor (PMSM). The proposed variants’ results are compared to that of its original version to recommend the most suitable variant for this non-linear optimization problem. A comparison between the introduced variants and the previously published algorithms proves the developed technique’s efficiency for further validation. The results emerge that the Chaotic Ensemble Particle Swarm variants with the Gauss/mouse map is the most proper variant for estimating the parameters of equal order and variable-order fractional PMSM models, as it achieves better accuracy, higher consistency, and faster convergence speed, it may lead to controlling the motor’s unwanted chaotic performance and protect it from ravage.

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