Advanced Predictive Control Systems for Elevators Utilizing Intelligent Wavelet Techniques for Fault Signal Analysis

Jun 12, 2025

Journal Journal of Robotics and Control (JRC)

publisher OJS Editorial and Publishing Process

Issue 3

Volume 6

Elevators play a critical role in modern infrastructure, requiring robust predictive control systems to ensure safe and efficient operation. This research work investigates an advanced predictive control system for elevators, integrating intelligent techniques with Wavelet analysis for fault signal analysis. The study aims to enhance the maintenance strategies to minimize downtime and improve reliability. In recent works, the Discrete wavelet transforms with different types like, Daubechies and Symlet, were used for the purpose of decomposition. In this work, the Coiflets Discrete Wavelet Analysis (CDWA), which is classified as one of the well utilized methods for analog signals is applied for the recorded data that obtained from a simulated elevator model for the purpose of enabling the identification of subtle anomalies indicative of potential faults in elevator systems. Concurrently, AI-based intelligent techniques, represented in the use of Backpropagation Multi-Layer Perceptron (BMLP) neural network, are utilized to analyze the decomposed signals, predict impending faults, and recommend proactive maintenance actions. By combining Wavelet analysis with AI-based fault signal analysis, the proposed predictive control system offers a comprehensive approach to elevator maintenance, leading to increased operational efficiency, reduced maintenance costs, and most importantly enhanced safety. The mean square error (MSE) is used to measure the performance of the system, while the convolution matrix is used to assess the accuracy. The findings of this research contribute to the development of smarter and more reliable elevator systems, aligning with the growing demand for intelligent infrastructure in modern urban environments.

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Normalized Clinical Feature Neural Net (NCF-NN) for Cardiovascular Prediction

May 30, 2025

Journal International Research Journal of Innovations in Engineering and Technology (IRJIET)

DOI doi.org/10.47001/IRJIET/2025.905008

Issue 5

Volume 9

Heart disease continues to be one of the leading causes of demise around the world, emphasizing the urgent need for effective early detection mechanisms. Normalized Clinical Feature Neural Net (NCF-NN), a neural network-based technique designed to categorize patients based on the likelihood of cardiovascular issues utilizing 13 clinical characteristics, is proposed in this research. The architecture involves two concealed layers with ReLU activation and L2 regularization, optimized using stochastic gradient descent and binary cross-entropy loss. Leveraging a dataset of 13 standardized clinical attributes extraction, the model attained a prediction accuracy of 98%, an AUC of 0.99, and consistently robust outcomes across other evaluation metrics. These conclusions underscore the model's potential as a practical diagnostic support tool in clinical environments, offering dependable risk prediction and contributing to more informed and proactive cardiovascular care. Separately, some patients exhibited multiple risk factors increasing the complexity of analysis, while others presented with only one or two characteristics highlighting the variance in presentations. The model successfully classified cases along this spectrum demonstrating its ability to evaluate diverse patient profiles.

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Detection of brain stroke in the MRI image using FPGA

Aug 1, 2021

Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)

DOI http://doi.org/10.12928/telkomnika.v19i4.18988

Issue 4

Volume 19

One of the most important difficulties which doctors face in diagnosing is the analysis and diagnosis of brain stroke in magnetic resonance imaging (MRI) images. Brain stroke is the interruption of blood flow to parts of the brain that causes cell death. To make the diagnosis easier for doctors, many researchers have treated MRI images with some filters by using Matlab program to improve the images and make them more obvious to facilitate diagnosis by doctors. This paper introduces a digital system using hardware concepts to clarify the brain stroke in MRI image. Field programmable gate arrays (FPGA) is used to implement the system which is divided into four phases: preprocessing, adjust image, median filter, and morphological filters alternately. The entire system has been implemented based on Zynq FPGA evaluation board. The design has been tested on two MRI images and the results are compared with the Matlab to determine the efficiency of the proposed system. The proposed hardware system has achieved an overall good accuracy compared to Matlab where it ranged between 90.00% and 99.48%

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ASSAS: An automatic smart students attendance system based on normalized cross-correlation

Apr 1, 2021

Journal Bulletin of Electrical Engineering and Informatics

DOI https://doi.org/10.11591/eei.v10i2.2746

Issue 2

Volume 10

A smart student attendance system (SSAS) is presented in this paper. The system is divided into two phases: hardware and software. The Hardware phase is implemented based on Arduino's camera while the software phase is achieved by using image processing with face recognition depended on the cross-correlation technique. In comparison with traditional attendance systems, roll call, and sign-in sheet, the proposed system is faster and more reliable (because there is no action needed by a human being who by its nature makes mistakes). At the same time, it is cheaper when compared with other automatic attendance systems. The proposed system provides a faster, cheaper and reachable system for an automatic smart student attendance that monitors and generates attendance report automatically

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Performance Assessment of a Triangular Integrated Collector Using Neural Networks

Apr 2, 2020

Journal Polytechnic Journal

DOI https://doi.org/10.25156/ptj.v10n1y2020.pp175-181

Issue 1

Volume 10

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Design and Simulate an Attenuator for Multi Types Optical Fiber Using Neural Network

May 9, 2019

Journal International Journal of Enhanced Research in Science,Technology & Engineering

Issue 5

Volume 8

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Comparative Performance of Shot Change Detection Techniques in MPEG Stream

Jan 9, 2016

Journal Journal of Babylon University/Pure and Applied Sciences/ Iraq

Issue 3

Volume 24

Geometric Analysis of Images for 3D Models Reconstruction

May 9, 2009

Journal Al Rafiden Journal of Computer Sciences and Mathematics

Issue 1

Volume 6

Image processing on two images for robot vision

Aug 20, 2008

Thesis of master