The Experts below are selected from a list of 6219 Experts worldwide ranked by ideXlab platform

Thirimachos Bourlai - One of the best experts on this subject based on the ideXlab platform.

  • on the effectiveness of Statistical Hypothesis Testing in infrared based face recognition in heterogeneous environments
    Advances in Social Networks Analysis and Mining, 2016
    Co-Authors: Neeru Narang, Thirimachos Bourlai
    Abstract:

    In this work, our objective is to study the impact of Statistical Hypothesis tests for the purpose of improving heterogeneous face recognition (FR). A series of tests are conducted to find the most suitable type of Statistical analysis test (parametric vs. non-parametric). To conduct the experiments, we used a multi-spectral face database (visible and Near-IR) collected under challenging conditions, i.e. at night time and at four different standoff distances, namely 30, 60, 90 and 120 meters. Next, the selected Statistical analysis test is used to find the Statistical significance of; (i) image restoration, (ii) fusion of scores. First, Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Then the Statistical analysis reveals which descriptors result in higher recognition performance. Finally, Statistical Hypothesis tests are performed to explore the impact of data stratification (grouping of gallery and probe sets) in terms of ethnicity, gender. A set of face identification studies are performed. Experimental results suggest that our proposed image restoration approach, fusion schemes and the usage of stratification result in a significantly better performance results than the baseline, e.g. the rank-one score is improved from 50% to 71% when using image restoration, to 73% when using fusion of scores and to 75% (i.e. in the case of Testing FR accuracy only on the female Asian class) when employing database stratification.

  • ASONAM - On the effectiveness of Statistical Hypothesis Testing in infrared-based face recognition in heterogeneous environments
    2016 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016
    Co-Authors: Neeru Narang, Thirimachos Bourlai
    Abstract:

    In this work, our objective is to study the impact of Statistical Hypothesis tests for the purpose of improving heterogeneous face recognition (FR). A series of tests are conducted to find the most suitable type of Statistical analysis test (parametric vs. non-parametric). To conduct the experiments, we used a multi-spectral face database (visible and Near-IR) collected under challenging conditions, i.e. at night time and at four different standoff distances, namely 30, 60, 90 and 120 meters. Next, the selected Statistical analysis test is used to find the Statistical significance of; (i) image restoration, (ii) fusion of scores. First, Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Then the Statistical analysis reveals which descriptors result in higher recognition performance. Finally, Statistical Hypothesis tests are performed to explore the impact of data stratification (grouping of gallery and probe sets) in terms of ethnicity, gender. A set of face identification studies are performed. Experimental results suggest that our proposed image restoration approach, fusion schemes and the usage of stratification result in a significantly better performance results than the baseline, e.g. the rank-one score is improved from 50% to 71% when using image restoration, to 73% when using fusion of scores and to 75% (i.e. in the case of Testing FR accuracy only on the female Asian class) when employing database stratification.

F Kanaya - One of the best experts on this subject based on the ideXlab platform.

  • on the converse theorem in Statistical Hypothesis Testing
    IEEE Transactions on Information Theory, 1993
    Co-Authors: Kenji Nakagawa, F Kanaya
    Abstract:

    Simple Statistical Hypothesis Testing is investigated by making use of the divergence geometric method. The asymptotic behavior of the minimum value of the error probability of the second kind under the constraint that the error probability of the first kind is bounded above by exp(-rn) is looked for, where r is a given positive number. If r is greater than the divergence of the two probability measures, the so-called converse theorem holds. It is shown that the condition under which the converse theorem holds can be divided into two separate cases by analyzing the geodesic connecting the two probability measures, and, as a result, an explanation is given for the Han-Kobayashi linear function f/sub T/(X). >

  • on the converse theorem in Statistical Hypothesis Testing for markov chains
    IEEE Transactions on Information Theory, 1993
    Co-Authors: Kenji Nakagawa, F Kanaya
    Abstract:

    Hypothesis Testing for two Markov chains is considered. Under the constraint that the error probability of the first kind is less than or equal to exp(-rn), the error probability of the second kind is minimized. The geodesic that connects the two Markov chains is defined. By analyzing the geodesic, the power exponents are calculated and then represented in terms of Kullback-Leibler divergence. >

Pierre Borgnat - One of the best experts on this subject based on the ideXlab platform.

  • Statistical Hypothesis Testing with time frequency surrogates to check signal stationarity
    International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Cedric Richard, Andre Ferrari, Hassan Amoud, Paul Honeine, Patrick Flandrin, Pierre Borgnat
    Abstract:

    An operational framework is developed for Testing stationarity relatively to an observation scale. The proposed method makes use of a family of stationary surrogates for defining the null Hypothesis of stationarity. As a further contribution to the field, we demonstrate the strict-sense stationarity of surrogate signals and we exploit this property to derive the asymptotic distributions of their spectrogram and power spectral density. A Statistical Hypothesis Testing framework is then proposed to check signal stationarity. Finally, some results are shown on a typical model of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.

  • ICASSP - Statistical Hypothesis Testing with time-frequency surrogates to check signal stationarity
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Cedric Richard, Andre Ferrari, Hassan Amoud, Paul Honeine, Patrick Flandrin, Pierre Borgnat
    Abstract:

    An operational framework is developed for Testing stationarity relatively to an observation scale. The proposed method makes use of a family of stationary surrogates for defining the null Hypothesis of stationarity. As a further contribution to the field, we demonstrate the strict-sense stationarity of surrogate signals and we exploit this property to derive the asymptotic distributions of their spectrogram and power spectral density. A Statistical Hypothesis Testing framework is then proposed to check signal stationarity. Finally, some results are shown on a typical model of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.

Francesc Pozo - One of the best experts on this subject based on the ideXlab platform.

  • wind turbine fault detection through principal component analysis and Statistical Hypothesis Testing
    Advances in Science and Technology, 2016
    Co-Authors: Francesc Pozo, Yolanda Vidal
    Abstract:

    This work addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data are compared, a Statistical Hypothesis Testing is used to make a decision on whether or not the wind turbine presents some fault. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large wind turbine in the presence of wind turbulence and realistic fault scenarios.

  • wind turbine fault detection through principal component analysis and Statistical Hypothesis Testing
    Energies, 2015
    Co-Authors: Francesc Pozo, Yolanda Vidal
    Abstract:

    This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of data—the baseline and the data from the current wind turbine—are compared, a Statistical Hypothesis Testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.

  • a structural damage detection indicator based on principal component analysis and Statistical Hypothesis Testing
    Smart Materials and Structures, 2014
    Co-Authors: Luis Eduardo Mujica, Francesc Pozo, Magda Ruiz, Jose Rodellar, Alfredo Guemes
    Abstract:

    A comprehensive Statistical analysis is performed for structural health monitoring (SHM). The analysis starts by obtaining the baseline principal component analysis (PCA) model and projections using measurements from the healthy or undamaged structure. PCA is used in this framework as a way to compress and extract information from the sensor-data stored for the structure which summarizes most of the variance in a few (new) variables into the baseline model space. When the structure needs to be inspected, new experiments are performed and they are projected into the baseline PCA model. Each experiment is considered as a random process and, consequently, each projection into the PCA model is treated as a random variable. Then, using a random sample of a limited number of experiments on the healthy structure, it can be inferred using the ?2 test that the population or baseline projection is normally distributed with mean ?h and standard deviation ?h. The objective is then to analyse whether the distribution of samples that come from the current structure (healthy or not) is related to the healthy one. More precisely, a test for the equality of population means is performed with a random sample, that is, the equality of the sample mean ?s and the population mean ?h is tested. The results of the test can determine that the Hypothesis is rejected (?h????c and the structure is damaged) or that there is no evidence to suggest that the two means are different, so the structure can be considered as healthy. The results indicate that the test is able to accurately classify random samples as healthy or not.

Yolanda Vidal - One of the best experts on this subject based on the ideXlab platform.

  • wind turbine fault detection through principal component analysis and Statistical Hypothesis Testing
    Advances in Science and Technology, 2016
    Co-Authors: Francesc Pozo, Yolanda Vidal
    Abstract:

    This work addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data are compared, a Statistical Hypothesis Testing is used to make a decision on whether or not the wind turbine presents some fault. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large wind turbine in the presence of wind turbulence and realistic fault scenarios.

  • wind turbine fault detection through principal component analysis and Statistical Hypothesis Testing
    Energies, 2015
    Co-Authors: Francesc Pozo, Yolanda Vidal
    Abstract:

    This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of data—the baseline and the data from the current wind turbine—are compared, a Statistical Hypothesis Testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.