Search results for "Neural"

The emergence of a shared action ontology: building blocks for a theory.

2003

To have an ontology is to interpret a world. In this paper we argue that the brain, viewed as a representational system aimed at interpreting our world, possesses an ontology too. It creates primitives and makes existence assumptions. It decomposes target space in a way that exhibits a certain invariance, which in turn is functionally significant. We will investigate which are the functional regularities guiding this decomposition process, by answering to the following questions: What are the explicit and implicit assumptions about the structure of reality, which at the same time shape the causal profile of the brain's motor output and its representational deep structure, in particular of t…

Physical integration: A causal account for consciousness

2014

The issue of integration in neural networks is intimately connected with that of consciousness. In this paper, integration as an effective level of physical organization is contrasted with a methodological integrative approach. Understanding how consciousness arises out of neural processes requires a model of integration in just causal physical terms. Based on a set of feasible criteria (physical grounding, causal efficacy, no circularity and scaling), a causal account of physical integration for consciousness centered on joint causation is outlined.

Energy saving in WWTP: Daily benchmarking under uncertainty and data availability limitations

2016

Efficient management of Waste Water Treatment Plants (WWTPs) can produce significant environmental and economic benefits. Energy benchmarking can be used to compare WWTPs, identify targets and use these to improve their performance. Different authors have performed benchmark analysis on monthly or yearly basis but their approaches suffer from a time lag between an event, its detection, interpretation and potential actions. The availability of on-line measurement data on many WWTPs should theoretically enable the decrease of the management response time by daily benchmarking. Unfortunately this approach is often impossible because of limited data availability. This paper proposes a methodolo…

Domestic demand predictions considering influence of external environmental parameters

2015

A precise prediction of domestic demand is very important for establishing home energy management system and preventing the damage caused by overloading. In this work, active and reactive power consumption prediction model based on historical power usage data and external environment parameter data (temperature and solar radiation) is presented for a typical Southern Norwegian house. In the presented model, a neural network is adopted as a main prediction technique and historical domestic load data of around 2 years are utilized for training and testing purpose. Temperature and global irradiation (which illustrates the solar radiation level quantitatively) are employed as external parameter…

Deep CNN-ELM Hybrid Models for Fire Detection in Images

2018

In this paper, we propose a hybrid model consisting of a Deep Convolutional feature extractor followed by a fast and accurate classifier, the Extreme Learning Machine, for the purpose of fire detection in images. The reason behind using such a model is that Deep CNNs used for image classification take a very long time to train. Even with pre-trained models, the fully connected layers need to be trained with backpropagation, which can be very slow. In contrast, we propose to employ the Extreme Learning Machine (ELM) as the final classifier trained on pre-trained Deep CNN feature extractor. We apply this hybrid model on the problem of fire detection in images. We use state of the art Deep CNN…

Support Vector Machines for Crop Classification Using Hyperspectral Data

2003

In this communication, we propose the use of Support Vector Machines (SVM) for crop classification using hyperspectral images. SVM are benchmarked to well–known neural networks such as multilayer perceptrons (MLP), Radial Basis Functions (RBF) and Co-Active Neural Fuzzy Inference Systems (CANFIS). Models are analyzed in terms of efficiency and robustness, which is tested according to their suitability to real–time working conditions whenever a preprocessing stage is not possible. This can be simulated by considering models with and without a preprocessing stage. Four scenarios (128, 6, 3 and 2 bands) are thus evaluated. Several conclusions are drawn: (1) SVM yield better outcomes than neura…

Real-time image segmentation for anomalies detection using SVM approximation

2003

In this paper, we propose a method of implementation improvement of the decision rule of the support vector machine, applied to real-time image segmentation. We present very high speed decisions (approximately 10 ns per pixel) which can be useful for detection of anomalies on manufactured parts. We propose an original combination of classifiers allowing fast and robust classification applied to image segmentation. The SVM is used during a first step, pre-processing the training set and thus rejecting any ambiguities. The hyperrectangles-based learning algorithm is applied using the SVM classified training set. We show that the hyperrectangle method imitates the SVM method in terms of perfor…

2004

The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to annotate sequences on a large scale. However, these methods can either only be applied for specific subsets, or their results are not formalised, or they do not provide precise confidence estimates for their predictions. We have developed a large-scale annotation system that tackles all of these shortcomings. In our approach, annotation was provided through Gene Ontology terms by applying multiple Support Vector Machines (SVM) for the classification of correct and false predictions. The general performance of the system was bench…

SSPMO: A Scatter Tabu Search Procedure for Non-Linear Multiobjective Optimization

2007

We describe the development and testing of a metaheuristic procedure, based on the scatter-search methodology, for the problem of approximating the efficient frontier of nonlinear multiobjective optimization problems with continuous variables. Recent applications of scatter search have shown its merit as a global optimization technique for single-objective problems. However, the application of scatter search to multiobjective optimization problems has not been fully explored in the literature. We test the proposed procedure on a suite of problems that have been used extensively in multiobjective optimization. Additional tests are performed on instances that are an extension of those consid…

A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme

2014

Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. One can differentiate the SPL from the traditional class of optimization problems by the fact that the former considers the case where the directional information, for example, as inferred from an Oracle (which possibly computes the derivatives), suffices to achieve the optimization-without actually explicitly computing any derivatives. The SPL can be described in terms of a LM (algorithm) attempting to locate a point on a line. The LM interacts with a random environme…