Search results for "pattern"
showing 10 items of 4203 documents
Pose classification using support vector machines
2000
In this work a software architecture is presented for the automatic recognition of human arm poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially structured environment can be trained by a human operator to follow particular routes in order to perform its task quickly. The system is able to recognize and classify some different poses of the operator's arms as direction commands like "turn-left", "turn-right", "go-straight", and so on. A binary image of the operator silhouette is obtained from the gray-level input. Next, a slice centered on the silhouette itself is processed in order to compute the eigenvalues …
Identification of the Parameters of Reduced Vector Preisach Model by Neural Networks
2008
This paper presents a methodology for identifying reduced vector Preisach model parameters by using neural networks. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. The network is trained by some hysteresis data, which are generated by using reduced vector Preisach model with preassigned parameters. It is shown how a properly trained network is able to find the parameters needed to best fit a magnetization hysteresis curve.
Hybrid architecture for shape reconstruction and object recognition
1998
The proposed architecture is aimed to recover 3-D- shape information from gray-level images of a scene; to build a geometric representation of the scene in terms of geometric primitives; and to reason about the scene. The novelty of the architecture is in fact the integration of different approaches: symbolic reasoning techniques typical of knowledge representation in artificial intelligence, algorithmic capabilities typical of artificial vision schemes, and analogue techniques typical of artificial neural networks. Experimental results obtained by means of an implemented version of the proposed architecture acting on real scene images are reported to illustrate the system capabilities.
Neural Networks with Multidimensional Cross-Entropy Loss Functions
2019
Deep neural networks have emerged as an effective machine learning tool successfully applied for many tasks, such as misinformation detection, natural language processing, image recognition, machine translation, etc. Neural networks are often applied to binary or multi-class classification problems. In these settings, cross-entropy is used as a loss function for neural network training. In this short note, we propose an extension of the concept of cross-entropy, referred to as multidimensional cross-entropy, and its application as a loss function for classification using neural networks. The presented computational experiments on a benchmark dataset suggest that the proposed approaches may …
Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks
2017
Medical personnel face many difficulties when diagnosing ventricular fibrillation (VF). Its correct diagnosis allows to decide the right medical treatment and, therefore, it is essential to tell it apart adequately from ventricular tachycardia (VT) and other arrhythmias. If the required therapy is not appropriate, the personnel could cause serious injuries or even induce VF. In this work, a diagnosis automatic system for the detection of VF through feature extraction was developed. To verify the validity of this method, an Artificial Neural Network (ANN) classifier was used. The ECG signals used were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database and AHA (2000 series) d…
A new method for optimal synthesis of wavelet-based neural networks suitable for identification purposes
1999
Abstract This paper deals with a new method for optimal synthesis of Wavelet-Based Neural Networks (WBNN) suitable for identification purposes. The method uses a genetic algorithm (GA) combined with a steepest descent technique and least square techniques for both optimal selection of the structure of the WBNN and its training. The method is applied for designing a predictor for a chaotic temporal series
Artificial Neural Networks in Sports: New Concepts and Approaches
2001
Artificial neural networks are tools, which - similar to natural neural networks - can learn to recognize and classify patterns, and so can help to optimise context depending acting. These abilitie...
The Use of Artificial Intelligence in Disaster Management - A Systematic Literature Review
2019
Whenever a disaster occurs, users in social media, sensors, cameras, satellites, and the like generate vast amounts of data. Emergency responders and victims use this data for situational awareness, decision-making, and safe evacuations. However, making sense of the generated information under time-bound situations is a challenging task as the amount of data can be significant, and there is a need for intelligent systems to analyze, process, and visualize it. With recent advancements in Artificial Intelligence (AI), numerous researchers have begun exploring AI, machine learning (ML), and deep learning (DL) techniques for big data analytics in managing disasters efficiently. This paper adopt…
Combining Auto-Encoder with LSTM for WiFi-Based Fingerprint Positioning
2021
Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence o…
Semi-Supervised Support Vector Biophysical Parameter Estimation
2008
Two kernel-based methods for semi-supervised regression are presented. The methods rely on building a graph or hypergraph Laplacian with both the labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). The semi-supervised SVR methods are sucessfully tested in LAI estimation and ocean chlorophyll concentration prediction from remotely sensed images.