The Experts below are selected from a list of 130671 Experts worldwide ranked by ideXlab platform
Xinyang Yue - One of the best experts on this subject based on the ideXlab platform.
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Technical note: Common characteristics of directional spreading-steepness Joint Distribution in freak wave events
Ocean Science, 2016Co-Authors: Shouhua Liu, Xinyang YueAbstract:Abstract. Seven freak wave incidents previously documented in the real ocean in combination with model hindcast simulations are used to study the variations associated with freak-wave-related parameters, such as wave steepness, directional spreading, and frequency bandwidth. Unlike the strong correlations between the freak wave parameters and freak waves' occurrence which were obtained in experimental and physical research, the correlations are not clear in the freak waves occurring in the real ocean. Wave directional spreading–steepness Joint Distribution is introduced and common visual features were found in the Joint Distribution when freak waves occur among seven “freakish” sea states. The visual features show that freak wave incidents occur when the steepness is large and directional spreading is small. Besides large steepness and small directional spreading, a long-duration, relatively rough sea state is also necessary for the freak wave generation. The Joint Distribution is more informative than any single statistical wave parameter. The continuous sea states of local large steepness and small directional spreading are supposed to generate freak waves, and two-dimensional Distribution visualization is found to be a useful tool for freak waves' forecast. The common visual features of Joint Distributions supply an important cue for the theoretical and experimental research.
H. Rue - One of the best experts on this subject based on the ideXlab platform.
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On a Joint Distribution of two successive surf parameters
Coastal Processes, 2009Co-Authors: Dag Myrhaug, H. RueAbstract:A Joint Distribution of two successive surf parameters is provided, and it is represented by a bivariate lognormal Distribution. Consequently the Joint Distribution of two successive breaker indices is represented by a bivariate lognormal Distribution. The application of the surf parameter Distribution is exemplified by estimating the probability of two successive breakers on slopes by using wave parameters corresponding to typical field conditions.
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Joint Distribution of successive wave periods revisited
Journal of Ship Research, 1998Co-Authors: Dag Myrhaug, H. RueAbstract:A Joint Distribution of successive wave periods is presented. First, the analysis includes comparisons between observed wave data and an empirical as well as theoretical Distributions of wave period. Secondly, a Joint Distribution of successive wave periods based on a best fit of a Weibull Distribution to the wave period Distribution is developed. Comparisons are made with observed wave data as well as with the previous approach of Myrhaug & Rue (1993). It appears that the revised approach is in better agreement with the data.
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Note on a Joint Distribution of Successive Wave Periods
Journal of Ship Research, 1993Co-Authors: Dag Myrhaug, H. RueAbstract:A Joint Distribution of successive wave periods is presented. The analysis includes comparisons with observed wave data obtained from measurements in deep water at sea on the Norwegian continental shelf. It appears that the Joint Distribution of successive wave periods describes the measured data reasonably well in an intermediate wave period range.
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Joint Distribution of Successive Wave Steepness Parameters
Journal of Offshore Mechanics and Arctic Engineering, 1993Co-Authors: Dag Myrhaug, H. RueAbstract:In this paper, a Joint Distribution of wave steepness parameters for two successive waves is presented. The wave steepness parameters considered herein are the crest front steepness and the total wave steepness. The Joint Distribution of wave steepness parameters for two successive waves is represented by a two-dimensional Weibull Distribution with the parameters [alpha]=0.84 and [beta]=1.40. The application of the results is illustrated by an example. Overall these results seem to be physically sound, although they are valid for the particular sea state chosen. The present approach has some basis in measured wave data, but comparison with data on the Joint Distribution of steepness parameters for two successive waves are needed before any conclusion can be drawn on the ability of this approach to describe measured wave data. Such a data base should be established from carefully designed field measurements in order to have the possibility to measure nonlinear properties of the waves. However, at present this Joint Distribution of steepness parameters for two successive waves should represent a useful tool for engineering applications.
Shouhua Liu - One of the best experts on this subject based on the ideXlab platform.
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Technical note: Common characteristics of directional spreading-steepness Joint Distribution in freak wave events
Ocean Science, 2016Co-Authors: Shouhua Liu, Xinyang YueAbstract:Abstract. Seven freak wave incidents previously documented in the real ocean in combination with model hindcast simulations are used to study the variations associated with freak-wave-related parameters, such as wave steepness, directional spreading, and frequency bandwidth. Unlike the strong correlations between the freak wave parameters and freak waves' occurrence which were obtained in experimental and physical research, the correlations are not clear in the freak waves occurring in the real ocean. Wave directional spreading–steepness Joint Distribution is introduced and common visual features were found in the Joint Distribution when freak waves occur among seven “freakish” sea states. The visual features show that freak wave incidents occur when the steepness is large and directional spreading is small. Besides large steepness and small directional spreading, a long-duration, relatively rough sea state is also necessary for the freak wave generation. The Joint Distribution is more informative than any single statistical wave parameter. The continuous sea states of local large steepness and small directional spreading are supposed to generate freak waves, and two-dimensional Distribution visualization is found to be a useful tool for freak waves' forecast. The common visual features of Joint Distributions supply an important cue for the theoretical and experimental research.
Dag Myrhaug - One of the best experts on this subject based on the ideXlab platform.
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On a Joint Distribution of two successive surf parameters
Coastal Processes, 2009Co-Authors: Dag Myrhaug, H. RueAbstract:A Joint Distribution of two successive surf parameters is provided, and it is represented by a bivariate lognormal Distribution. Consequently the Joint Distribution of two successive breaker indices is represented by a bivariate lognormal Distribution. The application of the surf parameter Distribution is exemplified by estimating the probability of two successive breakers on slopes by using wave parameters corresponding to typical field conditions.
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Joint Distribution of successive wave periods revisited
Journal of Ship Research, 1998Co-Authors: Dag Myrhaug, H. RueAbstract:A Joint Distribution of successive wave periods is presented. First, the analysis includes comparisons between observed wave data and an empirical as well as theoretical Distributions of wave period. Secondly, a Joint Distribution of successive wave periods based on a best fit of a Weibull Distribution to the wave period Distribution is developed. Comparisons are made with observed wave data as well as with the previous approach of Myrhaug & Rue (1993). It appears that the revised approach is in better agreement with the data.
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Note on a Joint Distribution of Successive Wave Periods
Journal of Ship Research, 1993Co-Authors: Dag Myrhaug, H. RueAbstract:A Joint Distribution of successive wave periods is presented. The analysis includes comparisons with observed wave data obtained from measurements in deep water at sea on the Norwegian continental shelf. It appears that the Joint Distribution of successive wave periods describes the measured data reasonably well in an intermediate wave period range.
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Joint Distribution of Successive Wave Steepness Parameters
Journal of Offshore Mechanics and Arctic Engineering, 1993Co-Authors: Dag Myrhaug, H. RueAbstract:In this paper, a Joint Distribution of wave steepness parameters for two successive waves is presented. The wave steepness parameters considered herein are the crest front steepness and the total wave steepness. The Joint Distribution of wave steepness parameters for two successive waves is represented by a two-dimensional Weibull Distribution with the parameters [alpha]=0.84 and [beta]=1.40. The application of the results is illustrated by an example. Overall these results seem to be physically sound, although they are valid for the particular sea state chosen. The present approach has some basis in measured wave data, but comparison with data on the Joint Distribution of steepness parameters for two successive waves are needed before any conclusion can be drawn on the ability of this approach to describe measured wave data. Such a data base should be established from carefully designed field measurements in order to have the possibility to measure nonlinear properties of the waves. However, at present this Joint Distribution of steepness parameters for two successive waves should represent a useful tool for engineering applications.
Lawrence Carin - One of the best experts on this subject based on the ideXlab platform.
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Jointgan multi domain Joint Distribution learning with generative adversarial nets
arXiv: Learning, 2018Co-Authors: Yunchen Pu, Weiyao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence CarinAbstract:A new generative adversarial network is developed for Joint Distribution matching. Distinct from most existing approaches, that only learn conditional Distributions, the proposed model aims to learn a Joint Distribution of multiple random variables (domains). This is achieved by learning to sample from conditional Distributions between the domains, while simultaneously learning to sample from the marginals of each individual domain. The proposed framework consists of multiple generators and a single softmax-based critic, all Jointly trained via adversarial learning. From a simple noise source, the proposed framework allows synthesis of draws from the marginals, conditional draws given observations from a subset of random variables, or complete draws from the full Joint Distribution. Most examples considered are for Joint analysis of two domains, with examples for three domains also presented.
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ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
arXiv: Machine Learning, 2017Co-Authors: Hao Liu, Ricardo Henao, Changyou Chen, Liqun Chen, Lawrence CarinAbstract:We investigate the non-identifiability issues associated with bidirectional adversarial training for Joint Distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched Joint Distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as Joint Distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.
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NIPS - ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
2017Co-Authors: Hao Liu, Ricardo Henao, Changyou Chen, Liqun Chen, Lawrence CarinAbstract:We investigate the non-identifiability issues associated with bidirectional adversarial training for Joint Distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched Joint Distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as Joint Distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.
