Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

This paper deals with the impact of uncertain input parameters on the electrical power generation of galloping-based piezoelectric energy harvester (GPEH). A distributed parameter model for the system is derived and solved by using Newmark beta numerical integration technique. Nonlinear systems tend to behave in a completely different manner in response to a slight change in input parameters. Due to the complex manufacturing process and various technical defects, randomness in system properties is inevitable. Owing to the presence of randomness within the system parameters, the actual power output differs from the expected one. Therefore, stochastic analysis is performed considering uncertainty in aerodynamic, mechanical, and electrical parameters. A polynomial neural network (PNN) based surrogate model is used to analyze the stochastic power output. A sensitivity analysis is conducted and highly influenced parameters to the electric power output are identified. The accuracy and adaptability of the PNN model are established by comparing the results with Monte Carlo simulation (MCS). Further, the stochastic analyses of power output are performed for various degrees of randomness and wind velocities. The obtained results showed that the influence of the electromechanical coefficient on power output is more compared to other parameters.

Polynomial, Neural Network, and Spline Wavelet Models for Continuous Wavelet Transform of Signals

In this paper a modified wavelet synthesis algorithm for continuous wavelet transform is proposed, allowing one to obtain a guaranteed approximation of the maternal wavelet to the sample of the analyzed signal (overlap match) and, at the same time, a formalized representation of the wavelet. What distinguishes this method from similar ones? During the procedure of wavelets’ synthesis for continuous wavelet transform it is proposed to use splines and artificial neural networks. The paper also suggests a comparative analysis of polynomial, neural network, and wavelet spline models. It also deals with feasibility of using these models in the synthesis of wavelets during such studies like fine structure of signals, as well as in analysis of large parts of signals whose shape is variable. A number of studies have shown that during the wavelets’ synthesis, the use of artificial neural networks (based on radial basis functions) and cubic splines enables the possibility of obtaining guaranteed accuracy in approaching the maternal wavelet to the signal’s sample (with no approximation error). It also allows for its formalized representation, which is especially important during software implementation of the algorithm for calculating the continuous conversions at digital signal processors and microcontrollers. This paper demonstrates the possibility of using synthesized wavelet, obtained based on polynomial, neural network, and spline models, during the performance of an inverse continuous wavelet transform.

Performance of Five-Axis Machine Tool and Intelligent Machining Process

In this study, the processing performance of a five-axis machine tool was analyzed to identify processing weaknesses as the basis for subsequent structural improvements. Data were then integrated through the Abductory Induction Mechanism (AIM) polynomial neural network to predict intelligent processing quality, and an in-depth investigation was conducted by importing processing parameters to predict the surface quality of the finished product.The finite element analysis method was used to analyze the static and dynamic characteristics of the whole machine and to test the structural modal frequency and vibration shape. For modal testing, the experiment used various equipment, including impact hammers, accelerometers, and signal extractors. Subsequent planning of modal frequency band processing experiments was conducted to verify the influence of natural frequencies on the processing level. Finally, according to the machine processing characteristics, a processing experiment was planned. The measurement record was used as the training data of the AIM polynomial neural network to establish the processing quality prediction model.After analysis and an actual machine test comparison, the three-axis static rigidity values of the machine were X: 1.63 Kg/µm, Y: 1.93 Kg/µm, and Z: 3.95 Kg/µm. The modal vibration shape maximum error of the machine was within 6.2%. The processing quality prediction model established by the AIM polynomial neural network could input processing parameters to achieve the surface roughness prediction value, and the actual relative error of the Ra value was within 0.1 µm.Based on the results of cutting experiments, the influence of the dynamic characteristics of the machine on the processing quality was obtained, especially in the modal vibration environment, which had an adverse effect on the surface roughness. Hence, the surface roughness of the workpiece processed by the machine could be predicted.

Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. Here providing an appropriate service is considered a major challenge in the heterogeneous environment. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. In order to provide optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. In addition to this, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. In addition to this, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.