The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Z. Styczynski - One of the best experts on this subject based on the ideXlab platform.
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Statistical Matrix Representation of Time-Varying Electrical Signals. Application to Wind Generator Currents
International Journal of Distributed Energy Resources, 2007Co-Authors: Vanya Ignatova, Pierre Granjon, Seddik Bacha, Z. StyczynskiAbstract:The development of the technology over the years and the liberalization of the energy market have brought many technical and economical profits, but they have also modified power system operation. In order to accurately analyze the new operational conditions and characteristics, a voluminous measurement data are required. It is, therefore, very important to store this data in efficient way without loosing any important information. This paper deals with the statistical description of measurement data. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix, and transitions number matrix. In order to evaluate the degree of preservation of the time information, two applications of the statistical matrices are investigated: reconstruction and prediction. Indeed, the availability of the information about the time evolution of the Recorded data can be applied to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behaviour in the future. The methods are illustrated on real measurement data and applied in the case of wind generator measured currents.
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Statistical Matrix Representation of Time-Varying Harmonics. Reconstruction and Prediction Applications
2006Co-Authors: Vanya Ignatova, Z. Styczynski, Pierre Granjon, Seddik BachaAbstract:Power system currents and voltages magnitudes are time variant due to continual changes in system configuration and load conditions. This paper deals with the statistical description of measured electrical Signals. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix and transitions number matrix. Their performance is further analyzed in the paper by investigating two of their applications – reconstruction and prediction. In deed, the availability of the information about the time evolution of the Recorded data can be used to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behavior in the future. Both applications are illustrated on measurement data acquired from a real power network.
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Transition matrices for statistical representation of time-varying electrical Signals. Application to wind generator currents.
2006Co-Authors: Vanya Ignatova, Pierre Granjon, Seddik Bacha, Z. StyczynskiAbstract:The development of the technology over the years and the liberalization of the en-ergy market have brought many technical and economical profits, but they have also modified power system operation. In order to accurately analyze the new op-erational conditions and characteristics, a voluminous measurement data are re-quired. It is, therefore, very important to store this data in efficient way without loosing any important information. This paper deals with the statistical description of measurement data. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix and transitions number matrix. In order to evaluate the degree of preservation of the time information, two applications of the statisti-cal matrices are investigated: reconstruction and prediction. In deed, the availabil-ity of the information about the time evolution of the Recorded data can be applied to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behaviour in the future. The methods are illustrated on real measurement data and applied in the case of wind generator measured currents.
Vanya Ignatova - One of the best experts on this subject based on the ideXlab platform.
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Statistical Matrix Representation of Time-Varying Electrical Signals. Application to Wind Generator Currents
International Journal of Distributed Energy Resources, 2007Co-Authors: Vanya Ignatova, Pierre Granjon, Seddik Bacha, Z. StyczynskiAbstract:The development of the technology over the years and the liberalization of the energy market have brought many technical and economical profits, but they have also modified power system operation. In order to accurately analyze the new operational conditions and characteristics, a voluminous measurement data are required. It is, therefore, very important to store this data in efficient way without loosing any important information. This paper deals with the statistical description of measurement data. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix, and transitions number matrix. In order to evaluate the degree of preservation of the time information, two applications of the statistical matrices are investigated: reconstruction and prediction. Indeed, the availability of the information about the time evolution of the Recorded data can be applied to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behaviour in the future. The methods are illustrated on real measurement data and applied in the case of wind generator measured currents.
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Statistical Matrix Representation of Time-Varying Harmonics. Reconstruction and Prediction Applications
2006Co-Authors: Vanya Ignatova, Z. Styczynski, Pierre Granjon, Seddik BachaAbstract:Power system currents and voltages magnitudes are time variant due to continual changes in system configuration and load conditions. This paper deals with the statistical description of measured electrical Signals. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix and transitions number matrix. Their performance is further analyzed in the paper by investigating two of their applications – reconstruction and prediction. In deed, the availability of the information about the time evolution of the Recorded data can be used to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behavior in the future. Both applications are illustrated on measurement data acquired from a real power network.
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Transition matrices for statistical representation of time-varying electrical Signals. Application to wind generator currents.
2006Co-Authors: Vanya Ignatova, Pierre Granjon, Seddik Bacha, Z. StyczynskiAbstract:The development of the technology over the years and the liberalization of the en-ergy market have brought many technical and economical profits, but they have also modified power system operation. In order to accurately analyze the new op-erational conditions and characteristics, a voluminous measurement data are re-quired. It is, therefore, very important to store this data in efficient way without loosing any important information. This paper deals with the statistical description of measurement data. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix and transitions number matrix. In order to evaluate the degree of preservation of the time information, two applications of the statisti-cal matrices are investigated: reconstruction and prediction. In deed, the availabil-ity of the information about the time evolution of the Recorded data can be applied to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behaviour in the future. The methods are illustrated on real measurement data and applied in the case of wind generator measured currents.
Seddik Bacha - One of the best experts on this subject based on the ideXlab platform.
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Statistical Matrix Representation of Time-Varying Electrical Signals. Application to Wind Generator Currents
International Journal of Distributed Energy Resources, 2007Co-Authors: Vanya Ignatova, Pierre Granjon, Seddik Bacha, Z. StyczynskiAbstract:The development of the technology over the years and the liberalization of the energy market have brought many technical and economical profits, but they have also modified power system operation. In order to accurately analyze the new operational conditions and characteristics, a voluminous measurement data are required. It is, therefore, very important to store this data in efficient way without loosing any important information. This paper deals with the statistical description of measurement data. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix, and transitions number matrix. In order to evaluate the degree of preservation of the time information, two applications of the statistical matrices are investigated: reconstruction and prediction. Indeed, the availability of the information about the time evolution of the Recorded data can be applied to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behaviour in the future. The methods are illustrated on real measurement data and applied in the case of wind generator measured currents.
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Statistical Matrix Representation of Time-Varying Harmonics. Reconstruction and Prediction Applications
2006Co-Authors: Vanya Ignatova, Z. Styczynski, Pierre Granjon, Seddik BachaAbstract:Power system currents and voltages magnitudes are time variant due to continual changes in system configuration and load conditions. This paper deals with the statistical description of measured electrical Signals. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix and transitions number matrix. Their performance is further analyzed in the paper by investigating two of their applications – reconstruction and prediction. In deed, the availability of the information about the time evolution of the Recorded data can be used to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behavior in the future. Both applications are illustrated on measurement data acquired from a real power network.
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Transition matrices for statistical representation of time-varying electrical Signals. Application to wind generator currents.
2006Co-Authors: Vanya Ignatova, Pierre Granjon, Seddik Bacha, Z. StyczynskiAbstract:The development of the technology over the years and the liberalization of the en-ergy market have brought many technical and economical profits, but they have also modified power system operation. In order to accurately analyze the new op-erational conditions and characteristics, a voluminous measurement data are re-quired. It is, therefore, very important to store this data in efficient way without loosing any important information. This paper deals with the statistical description of measurement data. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix and transitions number matrix. In order to evaluate the degree of preservation of the time information, two applications of the statisti-cal matrices are investigated: reconstruction and prediction. In deed, the availabil-ity of the information about the time evolution of the Recorded data can be applied to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behaviour in the future. The methods are illustrated on real measurement data and applied in the case of wind generator measured currents.
Pierre Granjon - One of the best experts on this subject based on the ideXlab platform.
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Statistical Matrix Representation of Time-Varying Electrical Signals. Application to Wind Generator Currents
International Journal of Distributed Energy Resources, 2007Co-Authors: Vanya Ignatova, Pierre Granjon, Seddik Bacha, Z. StyczynskiAbstract:The development of the technology over the years and the liberalization of the energy market have brought many technical and economical profits, but they have also modified power system operation. In order to accurately analyze the new operational conditions and characteristics, a voluminous measurement data are required. It is, therefore, very important to store this data in efficient way without loosing any important information. This paper deals with the statistical description of measurement data. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix, and transitions number matrix. In order to evaluate the degree of preservation of the time information, two applications of the statistical matrices are investigated: reconstruction and prediction. Indeed, the availability of the information about the time evolution of the Recorded data can be applied to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behaviour in the future. The methods are illustrated on real measurement data and applied in the case of wind generator measured currents.
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Statistical Matrix Representation of Time-Varying Harmonics. Reconstruction and Prediction Applications
2006Co-Authors: Vanya Ignatova, Z. Styczynski, Pierre Granjon, Seddik BachaAbstract:Power system currents and voltages magnitudes are time variant due to continual changes in system configuration and load conditions. This paper deals with the statistical description of measured electrical Signals. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix and transitions number matrix. Their performance is further analyzed in the paper by investigating two of their applications – reconstruction and prediction. In deed, the availability of the information about the time evolution of the Recorded data can be used to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behavior in the future. Both applications are illustrated on measurement data acquired from a real power network.
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Transition matrices for statistical representation of time-varying electrical Signals. Application to wind generator currents.
2006Co-Authors: Vanya Ignatova, Pierre Granjon, Seddik Bacha, Z. StyczynskiAbstract:The development of the technology over the years and the liberalization of the en-ergy market have brought many technical and economical profits, but they have also modified power system operation. In order to accurately analyze the new op-erational conditions and characteristics, a voluminous measurement data are re-quired. It is, therefore, very important to store this data in efficient way without loosing any important information. This paper deals with the statistical description of measurement data. A matrix representation is chosen in order to preserve the information about the temporal evolution of the Recorded Signal. Two matrix forms are investigated: transitions probabilities (Markov) matrix and transitions number matrix. In order to evaluate the degree of preservation of the time information, two applications of the statisti-cal matrices are investigated: reconstruction and prediction. In deed, the availabil-ity of the information about the time evolution of the Recorded data can be applied to restore the original Signal from its corresponding matrix form. Another possible application is the forecasting of the electrical Signals behaviour in the future. The methods are illustrated on real measurement data and applied in the case of wind generator measured currents.
Sebastian Hitziger - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Waveform Learning: A Framework for Modeling Variability in Neurophysiological Signals
IEEE Transactions on Signal Processing, 2017Co-Authors: Sebastian Hitziger, Maureen Clerc, Sandrine Saillet, Christian Bénar, Théodore PapadopouloAbstract:When analyzing brain activity such as local field potentials (LFP), it is often desired to represent neural events by stereotypic waveforms. Due to the non-deterministic nature of the neural responses, an adequate waveform estimate typically requires to record multiple repetitions of the neural events. It is common practice to segment the Recorded Signal into event-related epochs and calculate their average. This approach suffers from two major drawbacks: (i) epoching can be problematic, especially in the case of overlapping neural events and (ii) variability of the neural events across epochs (such as varying onset latencies) is not accounted for, which may lead to a distorted average. In this paper, we propose a novel method called adaptive waveform learning (AWL). It is designed to learn multi-component representations of neural events while explicitly capturing and compensating for waveform variability, such as changing latencies or more general shape variations. Thanks to its generality, it can be applied to both epoched (i.e., segmented) and continuous (i.e., non-epoched) Signals by making the corresponding specializations to the algorithm. We evaluate AWL's performance and robustness to noise on simulated data and demonstrate its empirical utility on an electrophysiological recording containing intracranial epileptiform discharges (epileptic spikes).
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Modeling the variability of electrical activity in the brain
2015Co-Authors: Sebastian HitzigerAbstract:This thesis investigates the analysis of brain electrical activity. An important challenge is the presence of large variability in neuroelectrical recordings, both across different subjects and within a single subject, for example, across experimental trials. We propose a new method called adaptive waveform learning (AWL). It is general enough to include all types of relevant variability empirically found in neuroelectric recordings, but can be specialized for different concrete settings to prevent from overfitting irrelevant structures in the data. The first part of this work gives an introduction into the electrophysiology of the brain, presents frequently used recording modalities, and describes state-of-the-art methods for neuroelectrical Signal processing. The main contribution of this thesis consists in three chapters introducing and evaluating the AWL method. We first provide a general Signal decomposition model that explicitly includes different forms of variability across Signal components. This model is then specialized for two concrete applications: processing a set of segmented experimental trials and learning repeating structures across a single Recorded Signal. Two algorithms are developed to solve these models. Their efficient implementation based on alternate minimization and sparse coding techniques allows the processing of large datasets. The proposed algorithms are evaluated on both synthetic data and real data containing epileptiform spikes. Their performances are compared to those of PCA, ICA, and template matching for spike detection.