Performance Analysis of Transfer Functions in an Artificial Neural Network

Yogitha, R.; Mathivanan, G.

doi:10.1109/iccsp.2018.8524387

Cited by 23 publications

(3 citation statements)

References 6 publications

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“…By adopting a meticulous and well-designed methodology, this study aims to pave the way for further advancements in additive manufacturing technology, benefiting a wide range of industries and applications. Data normalization holds a significant role as a preprocessing step in numerous machine learning algorithms [33], [34]. Raw data and extracted features often manifest distinct scales, which can introduce challenges during the training phase, leading to decreased accuracy and extended training times.…”

Section: Proposed Methodsmentioning

confidence: 99%

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

Dirhamsyah,

Riza,

Rizal

2023

J. meas. eng.

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This study presents a new solution to address challenges encountered in additive manufacturing, specifically in the context of 3D printing, where failures can occur due to complications associated with the nozzle or filament. The proposed solution in this research involves using a time-domain feature extraction method that leverages sound and vibration patterns. By implementing sensors to capture these signals in a controlled and noise-free environment, and then utilizing a Multi-Layer Perceptron (MLP) model trained accurately to predict upcoming signals and vibrations, proactive anticipation of printing outcomes is facilitated, including potential failures. Simulation results obtained using MATLAB for the MLP showcase the effectiveness of this approach, demonstrating remarkably low error rates. Furthermore, through rigorous data validation, the proposed method's ability to accurately identify sound and vibration signals is confirmed. As a result, the likelihood of failures is significantly reduced, thereby preventing defects in the filament. The implications of this solution hold great promise in substantially enhancing the reliability and efficiency of additive manufacturing processes.

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Section: Proposed Methodsmentioning

confidence: 99%

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

Dirhamsyah,

Riza,

Rizal

2023

J. meas. eng.

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“…In this study, the 'rule of thumb' is adapted for the determination of hidden neurons in the hidden layer. Twolayer neural network with tansig and tansig transfer function [38][39] were implemented. The networks are used to evaluate the performance of the neural network.…”

Section: Ann Rice Grain Classificationmentioning

confidence: 99%

Contrastive analysis of rice grain classification techniques: multi-class support vector machine vs artificial neural network

Ibrahim

Kamaruddin

Zabidi

et al. 2020

IJ-AI

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<p>Rice is a staple food for 80% of the population in Southeast Asia. Thus, the quality control and classification of rice grain are crucial for more productive and sustainable production. This paper examines the contrastive analysis of rice grain classification performance between multi-class support vector machine (SVM) and artificial neural network (ANN). The analysis has been tested on three types of rice grain images which are Ponni, Basmati, and Brown rice. A digital image transformation analysis based on shape and color features was developed to classify the three types of rice grain. The performance of the proposed study is evaluated to 90 testing images of each rice variation. The ANN is observed to return higher classification accuracy at 93.34% using Level Sweep image transformation technique. Based on the results, it signifies that the ANN performs better classification than the multiclass SVM.</p>

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“…Yogitha et al introduced the use of CNN and the sparse structure learning algorithm (SSLA) to analyze the images detected by self-driving vehicle sensors. The proposed algorithm could accurately identify fuzzy, dark, and sharp objects, helping self-driving vehicles identify the real environment and drive safely to their destination [20]. Nguyen et al proposed a self-driving vehicle with lane tracking and a GPS navigation system, where the camera can identify the lane and the moving objects.…”

Section: Introductionmentioning

confidence: 99%

Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

Liu,

Lin,

Lai

et al. 2023

Processes

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This study aims to develop an image recognition curve-fitting (IRCF) control strategy integrated with a cloud monitoring technique for application in electric self-driving vehicles (ESDVs) to improve their operation efficiency. The study focuses on an electric vehicle designed to reduce the carbon emissions and promote sustainability. The main camera, combined with the IRCF control strategy, was used to control the ESDV to enhance its operational efficiency. The proposed ESDV employs a pair of cameras to capture images and transmit them to the cloud-based web monitoring platform in real time. This allows the researchers to adjust the control parameters and promptly remove the road obstacles. The ESDV is equipped with a horn, two ultrasonic sensors, and an LED display, which can instantly detect the obstacles ahead of and behind the vehicle. When there are obstacles on the road, the vehicle will automatically stop, and the LED display will provide a visual representation of the obstacles, accompanied by the sounding of the horn as a warning signal. Meanwhile, the secondary camera detects the signal mark and feeds it back to the LED display, thereby informing passengers and other road users about the prevailing driving conditions. The proposed IRCF control strategy was compared with the traditional Hough line detection method on a 110 m ring road. The results revealed that the proposed control strategy outperformed the traditional Hough line detection method in terms of speed, efficiency, and running dexterity. Therefore, integrating the proposed control strategy into the automatic assistance driving system can improve the ESDV’s operation efficiency. Furthermore, the combination of the obstacle detection and signal sign detection functions for the ESDV used in this study can better fulfill the actual ESDV operation requirements on the road.

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Performance Analysis of Transfer Functions in an Artificial Neural Network

Cited by 23 publications

References 6 publications

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

Contrastive analysis of rice grain classification techniques: multi-class support vector machine vs artificial neural network

Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

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