Neural Network based ACC for Optimized safety and comfort

Cherian, Merry; Sathiyan, S. Paul

doi:10.5120/5757-7873

Cited by 6 publications

(3 citation statements)

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“…Moreover, each sample with length "L" is denoted ((L − 2) × 100). 2) Convolutional Neural Network (CNN): A CNN is a deep learning algorithm applied for the prediction of image processing as well as sequential based bioinformatics data [47], [65]- [67]. In this context, a onedimensional (1D) CNN model is very effective for the prediction of the bioinformatics dataset.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

Prediction of Piwi-Interacting RNAs and Their Functions via Convolutional Neural Network

et al. 2021

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In eukaryotic cells, Piwi-interacting RNAs (piRNAs) are the type of short chain noncoding RNA molecules, which interconnect with PIWI proteins. It performs various cellular and genetic functions such as gene-specific protein translation, expression regulation, maintenance, and formulation of germ cells. Seeing the prominent contribution of piRNA in eukaryotic organism cells, many attempts were made to identify it computationally, however, unsatisfactory results were obtained. So, it is requisite to extend the concept of a computational tool in such a way that accurately represents piRNA. In this regard, intelligent and high discriminative deep learning i.e., the convolutional neural network based sequentialcomputational model known as "piRNA-CNN" is carried out for the prediction of piRNA. RNA sequences are mathematically expressed using the natural language processing method namely: word2vec in order to get prominent, relevant, and high variated numerical descriptors. The proposed "piRNA-CNN" model yields an accuracy of 93.83% for the first-layer in which the provided query RNA molecule is predicted as non-piRNA or piRNA. In case of the piRNA, the proposed model identified the query as mRNA deadenylation or without deadenylation in the second layer, and achieved 91.19% of accuracy. The obtained outcomes authenticated that the piRNA-CNN model exposed substantial results matched to the current tools stated in the literature, so far. It is further expected that the suggested predictive tool will assist scientists and researchers to design improved computational tools.

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Section: Deep Learning Approachesmentioning

confidence: 99%

Prediction of Piwi-Interacting RNAs and Their Functions via Convolutional Neural Network

et al. 2021

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“…Adaptive Cruise Control in vehicles has been implemented using three types of neural networks: Backpropogation (BPN), Radial Basis Network (RBN) and Generalized Regression Network (GRN) [14]. The BPN based neural network is a feed forward neural network.…”

Section: B Comparison With Neural Networkmentioning

confidence: 99%

Fault tolerant adaptive neuro-fuzzy based automated cruise controller on FPGA

Jose

Kumar

Hussain

2013

2013 Annual IEEE India Conference (INDICON)

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The objective of the paper is to implement a system which effectively reduces vehicle collisions by using a combination of image processing and neuro-fuzzy based techniques. It uses an image processing module in order to determine the distance of the front vehicle. The distance measurements are given to a sugeno type adaptive neuro-fuzzy system. Since the operation of the system is mission critical in nature, a fault tolerant field programmable gate array (FPGA) implementation of the system is proposed to balance the reliability, availability and performance criteria. Finally, a comparison is made with respect to difference in errors and speed, between neural network and adaptive neuro-fuzzy based methods.

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“…Some other controllers like neural networks [17] or supervised [18] can be mentioned that were used in design of this feedback controller. Neural network designing tries to train the network based on deviation of speeds between real speed of vehicle and reference speed in order to produce desired acceleration and braking corresponding to this deviation speed.…”

Section: Maximum Deceleration Of the Host Vehicle Cannot Bementioning

confidence: 99%

Improvement of Adaptive Cruise Control Performance by Considering Initialization States

Mohtavipour¹,

Gohari²,

Shahhoseini³

2015

ujca

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Design of the controller in an adaptive cruise control (ACC) system is a critical part which prevents the collision between the host vehicle and the front vehicle. In ACC, system starts to work from an initialization state to a stable state after a short time. Passing from the initialization state to the stable state has not been considered in the previous designs. However for a suitable design, the initialization states that can cause a collision and also the unstable states should be considered. Therefore in this paper first by improving the reference signal and then by using a PID controller and optimizing its gains with BFO optimization algorithm, the system will consider the unstable initialization states. Simulation results show that by improving the reference signal and optimizing the gains of PID controller, not only the collision before the stable state is prevented, but also the performance of the controller is improved 74.2% in safety constraint and 15% in comfort constraint in the specific profile of the front vehicle speed that has several driving situations like hard stop and stop & go.

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Neural Network based ACC for Optimized safety and comfort

Cited by 6 publications

References 10 publications

Prediction of Piwi-Interacting RNAs and Their Functions via Convolutional Neural Network

Prediction of Piwi-Interacting RNAs and Their Functions via Convolutional Neural Network

Fault tolerant adaptive neuro-fuzzy based automated cruise controller on FPGA

Improvement of Adaptive Cruise Control Performance by Considering Initialization States

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