Search results for "pattern recognition"
showing 10 items of 2301 documents
Restricted compositions and permutations: from old to new Gray codes
2011
Any Gray code for a set of combinatorial objects defines a total order relation on this set: x is less than y if and only if y occurs after x in the Gray code list. Let @? denote the order relation induced by the classical Gray code for the product set (the natural extension of the Binary Reflected Gray Code to k-ary tuples). The restriction of @? to the set of compositions and bounded compositions gives known Gray codes for those sets. Here we show that @? restricted to the set of bounded compositions of an interval yields still a Gray code. An n-composition of an interval is an n-tuple of integers whose sum lies between two integers; and the set of bounded n-compositions of an interval si…
Promotion et Développement d'un Master Erasmus Mundus - L'Exemple du VIBOT
2011
Cet article decrit l’offre de formation a l’internationale proposee au Centre Universitaire Condorcet du Creusot (Universite de Bourgogne) dans le domaine de la vision par ordinateur et de la robotique. Il presente l’organisation particuliere de ces formations et les actions de support mises en place pour en assurer la perennite.
A New Approach to Partial Discharge Detection Under DC Voltage: Application to Different Materials
2021
Usability of a new Direct Current Periodic waveform for partial discharge qualification in HVDC systems is exemplified by tests performed on different materials.
Early detection and classification of bearing faults using support vector machine algorithm
2017
Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine oper…
Mass calibration of the energy axis in ToF- E elastic recoil detection analysis
2016
We report on procedures that we have developed to mass-calibrate the energy axis of ToF-E histograms in elastic recoil detection analysis. The obtained calibration parameters allow one to transform the ToF-E histogram into a calibrated ToF-M histogram.
Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network
2019
Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density es…
Towards Open Domain Chatbots—A GRU Architecture for Data Driven Conversations
2018
Understanding of textual content, such as topic and intent recognition, is a critical part of chatbots, allowing the chatbot to provide relevant responses. Although successful in several narrow domains, the potential diversity of content in broader and more open domains renders traditional pattern recognition techniques inaccurate. In this paper, we propose a novel deep learning architecture for content recognition that consists of multiple levels of gated recurrent units (GRUs). The architecture is designed to capture complex sentence structure at multiple levels of abstraction, seeking content recognition for very wide domains, through a distributed scalable representation of content. To …
Optimized Class-Separability in Hyperspectral Images
2016
International audience; Image visualization techniques are mostly based on three bands as RGB color composite channels for human eye to characterize the scene. This, however, is not effective in case of hyper-spectral images (HSI) because they contain dozens of informative spectral bands. To eliminate redundancy of spectral information among these bands, dimensionality reduction (DR) is applied while at the same trying to retain maximum information. In this paper, we propose a new method of information-preserved hyper-spectral satellite image visualization that is based on fusion of unsupervised band selection techniques and color matching function (CMF) stretching. The results show consist…
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
2019
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
2016
Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squar…