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In this work, the potential application of Artificial Neural Network (ANN) was studied to predict the absorption of Carbon Dioxide (CO2) in Ionic Liquid (IL) solutions over wide-ranging operating conditions. A few physical properties had been chosen as input data which were temperature, partial pressure of CO2, molecular weight, acentric value, critical temperature and critical pressure of IL. A sample...
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level...
This paper proposes Neural Network (NN) based data interpolation and design optimization algorithm for an Interior Permanent Magnet Synchronous Machine. Data interpolation using NN is suitable for estimating the performance of an electric machine, because NN is approximate function for representing nonlinear data. To utilize NN as an approximate function, training process is required. After training...
In this paper, we present artificial neural network (ANN) models to predict hard and soft-responses of three configurations of arbiter based physical unclonable functions (PUFs): standard, feed-forward (FF) and modified feed-forward (MFF). The models are trained using data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process. The contributions of this paper are two-fold...
In this paper a Siamese-Twin Random Projection Neural Network (ST-RPNN) is proposed for unsupervised binary hashing of images. ST-RPNN is made of two identical random projection neural networks with hard threshold neurons where the binary code is taken as the neuron outputs. The learning objective is to produce similar binary codes for similar input image pairs and different binary codes otherwise...
Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. It has been postulated that these networks are more efficient for realizing cognitive computing systems compared to second generation networks that are widely used in machine learning algorithms today. In this paper, we review...
The paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine...
Modeling the activity of an ensemble of neurons can provide critical insights into the workings of the brain. In this work we examine if learning based signal modeling can contribute to a high quality modeling of neuronal signal data. To that end, we employ the sparse coding and dictionary learning schemes for capturing the behavior of neuronal responses into a small number of representative prototypical...
In this paper, a Multilayer Perceptron artificial neural network is modeled to estimate complete photonic band-gaps (C-PBGs) of bi-dimensional photonic crystals. Unit cells of square lattice photonic crystals, composed of two silicon round rods and embedded in air, have been designed by an artificial immune network algorithm, and their geometries have been stored in a database along with their C-PGBs...
This paper presents an artificial neural network (ANN) model based design for Hénon chaotic systems, and its equivalent hardware model for hardware co-simulation using Field Programmable Gate Arrays (FPGA). Chaotic generators can be used for the study of chaotic behaviors of brain activities captured by Electroencephalogram (EEG). The ANN model is designed with different fixed-point data format and...
Recently, deep neural networks (DNNs) have demonstrated excellent performance for change detection. The DNN-based background subtraction automatically discovers background features from datasets and outperforms traditional background modeling based on handcraft features and/or subtraction strategies. Most researchers mainly discuss the accuracy of foreground detection and do not analyze how and why...
Artificial neural networks (ANN) have revolutionized the field of machine learning by providing impressive human-like performance in solving real-world tasks in computer vision, speech recognition, or complex strategic games. There is a significant interest in developing non-von Neumann coprocessors for the training of ANNs, where resistive memory devices serve as synaptic elements. However, interdevice...
Temperature resolution is a key factor for the performance of a Distributed Temperature Sensor (DTS). One can define the resolution as the degree of uncertainty in the temperature information. Thus, the temperature measured in a steady-state condition at a given point in the fiber will vary between successive measurements and between adjacent points that are at the same temperature. Temperature resolution...
In this paper, artificial neural networks are modeled to predict complete band-gaps of bi-dimensional photonic crystals. The available data-set has been generated by an integrated artificial immune network and MPB (MIT Photonic Bands) optimization procedure. Two case studies were carried out, considering square lattice photonic crystals composed of two and three silicon round rods embedded in air...
The numerical value discretization is an important task of the data preprocessing phase within the intelligent data analysis. This process allows us to reduce the number of values (among other advantages) with which techniques work, reducing the computational cost when it comes to working with large amounts of data. In this paper a numerical value discretization technique is proposed. Specifically,...
The problems arising in loop electrical network system is a relay setting that follows changes in the system such as power source operation, regular maintenance and damage to powers source. To obtain an adaptive relay which is capable of following the changes in the network system, this paper is proposes the modeling of the coordination of the power system network with the cascade forward neural network...
In order to avoid the serious loss caused by the fire, to achieve the initial fire alarm, multi-sensor system is widely used in fire prediction. In the processing method, it is essentially different from the traditional classical signal. The multi-sensor information fusion system can be merged at different levels. It can be abstracted distributed into three levels: information fusion layer, feature...
Atmospheric aerosol is one of the most important factors that cause the random variation of solar radiation intensity. In view of the problem that the atmospheric aerosol optical depth (AOD) is difficult to obtain real-timely and conveniently with high accuracy, estimation model of AOD using PM concentration is proposed in this paper. Two kinds of modeling methods BP neural network and support vector...
We propose a recursive singular value decomposition (SVD)-based fuzzy extreme learning machine (RSVD-F-ELM) for the online learning in classification or regression analysis. By adopting the same architecture and operation as fuzzy extreme learning machine (F-ELM), which is originally designed for the batch learning, and replacing the Moore-Penrose generalized inverse in F-ELM with a recursive SVD-based...
Emerging nanodevices such as resistive memories are being considered for hardware realizations of a variety of artificial neural networks (ANNs), including highly promising online variants of the learning approaches known as reservoir computing (RC) and the extreme learning machine (ELM). We propose an RC/ELM inspired learning system built with nanosynapses that performs both on-chip projection and...
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