Search results for "Neural Networks"

Prediction of concrete materials compressive strength using surrogate models

2022

Using soft computing methods could be of great interest in predicting the compressive strength of Ultra-High-Performance Fibre Reinforced Concrete (UHPFRC). Therefore, this study developed four soft computing techniques. The models are the Linear- relationship (LR), pure quadratic, M5P-tree (M5P), and artificial neural network (ANN). The models were trained and developed using 306 datasets comprising 11 input parameters, including the curing temperature (T), the water-to-cement ratio (w/c), silica fume (SF), cement content (C), fiber content (Fb), water (W), sand content (S), superplasticizer (SP), fiber aspect ratio (AR) and curing time (t). Experimental results were used and compared to t…

SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

2016

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling tec…

Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

2022

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has …

A Neural Network Model to Forecast Urban Electricity Consumptions from Weather Data

2004

Real-Time Visual Grasp Synthesis Using Genetic Algorithms and Neural Networks

2007

This paper addresses the problem of automatic grasp synthesis of unknown planar objects. In other words, we must compute points on the object’s boundary to be reached by the robotic fingers such that the resulting grasp, among infinite possibilities, optimizes some given criteria. Objects to be grasped are represented as superellipses, a family of deformable 2D parametric functions. They can model a large variety of shapes occurring often in practice by changing a small number of parameters. The space of possible grasp configurations is analyzed using genetic algorithms. Several quality criteria from existing literature together with kinematical and mechanical considerations are considered.…

Efficient FPGA Implementation of a Knowledge-Based Automatic Speech Classifier

2005

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of Automatic Speech Recognition (ASR) systems are comparable to Human Speech Recognition (HSR) only under very strict working conditions, and in general far lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as dete…

Application of EαNets to Feature Recognition of Articulation Manner in Knowledge-Based Automatic Speech Recognition

2006

Speech recognition has become common in many application domains. Incorporating acoustic-phonetic knowledge into Automatic Speech Recognition (ASR) systems design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper, a set of six detectors for the above mentioned attributes is designed based on the E-αNet model of neural networks. This model was chosen for its capability to learn hidden acti…

A Reconfigurable Neural Environment on Active Networks

2000

This paper proposes the deployment of a neural network computing environment on Active Networks. Active Networks are packet-switched computer networks in which packets can contain code fragments that are executed on the intermediate nodes. This feature allows the injection of small pieces of codes to deal with computer network problems directly into the network core, and the adoption of new computing techniques to solve networking problems. The goal of our project is the adoption of a distributed neural network for approaching tasks which are specific of the computer network environment. Dynamically reconfigurable neural networks are spread on an experimental wide area backbone of active no…

EMBER—Embedding Multiple Molecular Fingerprints for Virtual Screening

2022

In recent years, the debate in the field of applications of Deep Learning to Virtual Screening has focused on the use of neural embeddings with respect to classical descriptors in order to encode both structural and physical properties of ligands and/or targets. The attention on embeddings with the increasing use of Graph Neural Networks aimed at overcoming molecular fingerprints that are short range embeddings for atomic neighborhoods. Here, we present EMBER, a novel molecular embedding made by seven molecular fingerprints arranged as different “spectra” to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands&…

Towards a deep-learning-based methodology for supporting satire detection

2021

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers.