Search results for "machine"

Matlab/Simulink-Based Modeling for Industrial Electric Vehicle

2023

The land transport sector has passed through multiple phases of evolution in design, development, and manufacturing of vehicles. In particular, the construction site continues to progress towards the autonomous vehicles (also called self-driving), which were one of its big trends and have become a hot topic in the industrial and academic world. By now, with this new technology of autonomous driving, we can ensure safety by reducing the number of road accidents, also the environmental impact and energy consumption is lessened. The modeling and simulation phases had become a mandatory step to design, characterize and simulate vehicle dynamics while reducing the cost of development. As they pr…

Machine learning methods to forecast temperature in buildings

2013

Efficient management of energy in buildings saves a very important amount of resources (both economic and technological). As a consequence, there is a very active research in this field. One of the keys of energy management is the prediction of the variables that directly affect building energy consumption and personal comfort. Among these variables, one can highlight the temperature in each room of a building. In this work we apply different machine learning techniques along with other classical ones for predicting the temperatures in different rooms. The obtained results demonstrate the validity of these techniques for predicting temperatures and, therefore, for the establishment of optim…

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…

Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

2013

We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user’s trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the…

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…

Multi-Temporal Image Classification with Kernels

2009

An improved MSD-based method for PD defects classification

2006

The new proposed method of pattern recognition is based on the application of Multi-resolution Signal Decomposition (MSD) technique of wavelet transform. This technique has showed off interesting properties in capturing the embedded horizontal, vertical and diagonal variations within an image obtained from the PD pattern in a separable form. This feature was exploited to identify in the PD pattern's MSD, relative at various family of partial discharge sources, some detail images typical of a single discharge phenomenon. The classification of a generic PD phenomenon is feasible through a comparison between its detail images and the detail images typical of a single discharge phenomenon. Test…

A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images

2007

This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. T…

Graph matching for efficient classifiers adaptation

2011

In this work we present an adaptation algorithm focused on the description of the measurement changes under different acquisition conditions. The adaptation is carried out by transforming the manifold in the first observation conditions into the corresponding manifold in the second. The eventually non-linear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the labeled samples in the first are projected into the second domain, thus allowing the application of any classifier in the transformed domain. Experiments on VHR series of images show the validity of the proposed method …