Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Abstract ID: 133 Fast and accurate 3D dose distribution computations using artificial neural networks
2017
In radiation therapy, the trade-off between accuracy and speed is the key of the algorithms used in Treatment Planning Systems (TPS). For photon beams, commercial solutions generally relies on analytic algorithms, biased Monte Carlo, or heavily parallelized Monte Carlo on Graphics Processing Units (GPU). Alternatively, we propose an algorithm using Artificial Neural Network (ANN) to compute the dose distributions resulting from ionizing radiations inside a phantom [1] , [2] . We present an evolution of this platform taking into account modulated field sizes and shapes, and various orientations of the beam to the phantom. Firstly, tomodensitometry-based phantoms are created to validate the d…
Neural Networks as Soft Sensors: a Comparison in a Real World Application.
2006
Physical atmosphere parameters, as temperature or humidity, can be indirectly estimated on the surface of a monument by means of soft sensors based on neural networks, if an ambient air monitoring station works in the neighborhood of the monument itself. Since the soft sensors work as virtual instruments, the accuracy of such measurements has to be analyzed and validated from statistical and metrological points of view. The paper compares different typologies of neural networks, which can be used as soft sensors in a complex real world application: a non invasive monitoring of the conservation state of old monuments. In this context, several designed connessionistic systems, based on radial…
A Study of Perceptron Mapping Capability to Design Speech Event Detectors
2006
Event detection is a fundamental yet critical component in automatic speech recognition (ASR) systems that attempt to extract knowledge-based features at the front-end level. In this context, it is common practice to design the detectors inside well-known frameworks based on artificial neural network (ANN) or support vector machine (SVM). In the case of ANN, speech scientists often design their detector architecture relying on conventional feed-forward multi-layer perceptron (MLP) with sigmoidal activation function. The aim of this paper is to introduce other ANN architectures inside the context of detection-based ASR. In particular, a bank of feed-forward MLPs using sinusoidal activation f…
<title>Real-time face tracking and recognition for video conferencing</title>
2001
This paper describes a system of vision in real time, allowing to detect automatically the faces presence, to localize and to follow them in video sequences. We verify also the faces identities. These processes are based by combining technique of image processing and methods of neural networks. The tracking is realized with a strategy of prediction-verification using the dynamic information of the detection. The system has been evaluated quantitatively on 8 video sequences. The robustness of the method has been tested on various lightings images. We present also the analysis of complexity of this algorithm in order to realize an implementation in real time on a FPGA based architecture.
A neural network-based approach to determine FDTD eigenfunctions in quantum devices
2009
This article combines a Neural Network (NN) algorithm with the Finite Difference Time Domain (FDTD) technique to estimate the eigenfunctions in quantum devices. A NN based on the Least Mean Squares (LMS) algorithm is combined with the FDTD technique to provide a first approach to the confined states in quantum wires. The proposed technique is in good agreement with analytical results and is more efficient than FDTD combined with the Fourier Transform. This technique is used to cal- culate a numerical approximation to the eigenfunctions associated to quan- tum wire potentials. The performance and convergence of the proposed technique are also presented in this article. © 2009 Wiley Periodica…
A New Unsupervised Neural Network for Pattern Recognition with Spiking Neurons
2006
In this paper we propose a three-layered neural network for binary pattern recognition and memorization. Unlike the classic approach to pattern recognition, our net works organizing itself in an unsupervised way, to distinguish beetween different patterns or to recognize similar ones. If we present a binary input to the first layer, after some time steps we could read the output of the net in the third layer, as one and only one neuron activating with high firing rate; the middle layer will act as a generalization layer, i.e. similar pattern will have similar (or the same) representation in the middle layer. We used learning algorithms inspired from other works or from biological data to ac…
Assigning discounts in a marketing campaign by using reinforcement learning and neural networks
2009
In this work, RL is used to find an optimal policy for a marketing campaign. Data show a complex characterization of state and action spaces. Two approaches are proposed to circumvent this problem. The first approach is based on the self-organizing map (SOM), which is used to aggregate states. The second approach uses a multilayer perceptron (MLP) to carry out a regression of the action-value function. The results indicate that both approaches can improve a targeted marketing campaign. Moreover, the SOM approach allows an intuitive interpretation of the results, and the MLP approach yields robust results with generalization capabilities.
A vision system for symbolic interpretation of dynamic scenes using arsom
2001
We describe an artificial high-level vision system for the symbolic interpretation of data coming from a video camera that acquires the image sequences of moving scenes. The system is based on ARSOM neural networks that learn to generate the perception-grounded predicates obtained by image sequences. The ARSOM neural networks also provide a three-dimensional estimation of the movements of the relevant objects in the scene. The vision system has been employed in two scenarios: the monitoring of a robotic arm suitable for space operations, and the surveillance of an electronic data processing (EDP) center.
A Cluster Analysis of Stock Market Data Using Hierarchical SOMs
2016
The analysis of stock markets has become relevant mainly because of its financial implications. In this paper, we propose a novel methodology for performing a structured cluster analysis of stock market data. Our proposed method uses a tree-based neural network called the TTOSOM. The TTOSOM performs self-organization to construct tree-based clusters of vector data in the multi-dimensional space. The resultant tree possesses interesting mathematical properties such as a succinct representation of the original data distribution, and a preservation of the underlying topology. In order to demonstrate the capabilities of our method, we analyze 206 assets of the Italian stock market. We were able…
Safer Reinforcement Learning for Agents in Industrial Grid-Warehousing
2020
In mission-critical, real-world environments, there is typically a low threshold for failure, which makes interaction with learning algorithms particularly challenging. Here, current state-of-the-art reinforcement learning algorithms struggle to learn optimal control policies safely. Loss of control follows, which could result in equipment breakages and even personal injuries.