Search results for "NEURAL NETWORK"

An Intelligent Tool to Predict Fracture in Sheet Metal Forming Operations

2007

One of the main issues in sheet metal forming operations design is the determination of formability limits in order to prevent necking and fracture. In fact, the ability to predict fracture represents a powerful tool to improve the production quality in mechanical industry. Many researchers investigated the problem here addressed, mainly studying forming limit diagrams (FLD) or developing fracture criteria which are able to foresee fracture defects for different processes. In this paper, the author present some early results of a research project focused on the application of artificial intelligence (AI) for ductile fracture prediction in sheet metal forming operations. The main advantage o…

Realizing Undelayed N-step TD prediction with neural networks

2010

There exist various techniques to extend reinforcement learning algorithms, e.g., eligibility traces and planning. In this paper, an approach is proposed, which combines several extension techniques, such as using eligibility-like traces, using approximators as value functions and exploiting the model of the environment. The obtained method, ‘Undelayed n-step TD prediction’ (TD-P), has produced competitive results when put in conditions of not fully observable environment.

Vector representation of non-standard spellings using dynamic time warping and a denoising autoencoder

2017

The presence of non-standard spellings in Twitter causes challenges for many natural language processing tasks. Traditional approaches mainly regard the problem as a translation, spell checking, or speech recognition problem. This paper proposes a method that represents the stochastic relationship between words and their non-standard versions in real vectors. The method uses dynamic time warping to preprocess the non-standard spellings and autoencoder to derive the vector representation. The derived vectors encode word patterns and the Euclidean distance between the vectors represents a distance in the word space that challenges the prevailing edit distance. After training the autoencoder o…

2021

Dystonia, a debilitating neurological movement disorder, is characterized by involuntary muscle contractions and develops from a complex pathophysiology. Graph theoretical analysis approaches have been employed to investigate functional network changes in patients with different forms of dystonia. In this study, we aimed to characterize the abnormal brain connectivity underlying writer's cramp, a focal hand dystonia. To this end, we examined functional magnetic resonance scans of 20 writer's cramp patients (11 females/nine males) and 26 healthy controls (10 females/16 males) performing a sequential finger tapping task with their non-dominant (and for patients non-dystonic) hand. Functional …

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

2017

[EN] The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In add…

'Niche Selection' and the evolution of a complex behavior in a changing environment--a simulation.

2000

One of the key problems in theoretical biology is the identification of the mechanisms underlying the evolution of complexity. This paper suggests that some difficulties in current models could be avoided by taking account of “niche selection” as proposed by Waddington [21] and subsequent authors [2]. Computer simulations, in which an evolving population of artificial organisms “selects” the niche(s) that maximize their fitness, are compared with a Control Model in which “Niche Selection” is absent. In the simulations the Niche Selection Model consistently produced a greater number of “fit” organisms than the Control Model; although the Niche Selection Model tended, in general, to produce o…

Introduction

1998

Accurate Wound and Lice Detection in Atlantic Salmon Fish Using a Convolutional Neural Network

2022

The population living in the coastal region relies heavily on fish as a food source due to their vast availability and low cost. This need has given rise to fish farming. Fish farmers and the fishing industry face serious challenges such as lice in the aquaculture ecosystem, wounds due to injuries, early fish maturity, etc. causing millions of fish deaths in the fish aquaculture ecosystem. Several measures, such as cleaner fish and anti-parasite drugs, are utilized to reduce sea lice, but getting rid of them entirely is challenging. This study proposed an image-based machine-learning technique to detect wounds and the presence of lice in the live salmon fish farm ecosystem. A new equally di…

Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures

2021

Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using &lsquo

Regularized extreme learning machine for regression problems

2011

Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated. Empirical studies…