Search results for "vector [form factor]"
showing 10 items of 770 documents
Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia
2020
Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are a…
An affordable contactless security system access for restricted area
2016
International audience; We present in this paper a security system based on identity verification process and a low-cost smart camera , intended to avoid unauthorized access to restricted area. The Le2i laboratory has a longstanding experience in smart cameras implementation and design [1], for example in the case of real-time classical face detection [2] or human fall detection [3]. The principle of the system, fully thought and designed in our laboratory, is as follows: the allowed user presents a RFID card to the reader based on Odalid system [4]. The card ID, time and date of authorized access are checked using connection to an online server. In the same time, multi-modality identity ve…
Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors
2022
This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor t…
Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.
2016
This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.
Prediction of banana quality indices from color features using support vector regression
2015
Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acid…
Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity
2020
Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…
Machine Learning Methods for One-Session Ahead Prediction of Accesses to Page Categories
2004
This paper presents a comparison among several well-known machine learning techniques when they are used to carry out a one-session ahead prediction of page categories. We use records belonging to 18 different categories accessed by users on the citizen web portal Infoville XXI. Our first approach is focused on predicting the frequency of accesses (normalized to the unity) corresponding to the user’s next session. We have utilized Associative Memories (AMs), Classification and Regression Trees (CARTs), Multilayer Perceptrons (MLPs), and Support Vector Machines (SVMs). The Success Ratio (SR) averaged over all services is higher than 80% using any of these techniques. Nevertheless, given the …
Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework
2019
This article presents a framework for accurate fault diagnostics in inverter-fed induction machinery operating under variable speed and load conditions within very short time windows. Condition indicators based on fault characteristic frequencies observed over the extended Park's vector modulus are fused with deep features extracted using stacked autoencoders to generate a multidimensional feature space for fault classification using support vector machine. The proposed approach is demonstrated in a laboratory setup to detect the most commonly occurring faults, namely, the stator turns fault, broken rotor bars fault and bearing fault with an accuracy > 98% within a short time window of 2–3 …
Spectrum Hole Detection for Cognitive Radio through Energy Detection using Random Forest
2020
The growth of wireless data is the major driving force for an exponential increase in wireless communication. Cognitive Radio is one of the emerging wireless technologies that can be used for smart utility networks. Optimum utilization of the wireless spectrum is the objective of Cognitive Radio. Finding a spectrum hole through intelligent means is essential for the success of Cognitive Radio. Dynamic spectrum allocation is also an efficient technique for spectrum allocation. It will lead to a better spectrum utilization. In this paper, some of the machine learning techniques are used to find a frequency range for dynamic spectrum allocation. Different machine learning techniques such as Lo…