The Experts below are selected from a list of 88977 Experts worldwide ranked by ideXlab platform
Henrik Sandberg - One of the best experts on this subject based on the ideXlab platform.
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SiMpLIfy: A toolbox for Structured Model reduction
2015 European Control Conference (ECC), 2015Co-Authors: Martin Biel, Farhad Farokhi, Henrik SandbergAbstract:In this paper, we present a toolbox for Structured Model reduction developed for MATLAB. In addition to Structured Model reduction methods using balanced realizations of the subsystems, we introduce a numerical algorithm for Structured Model reduction using a subgradient optimization algorithm. We briefly present the syntax for the toolbox and its features. Finally, we demonstrate the applicability of various Model reduction methods in the toolbox on a Structured mass-spring mechanical system.
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ECC - SiMpLIfy: A toolbox for Structured Model reduction
2015 European Control Conference (ECC), 2015Co-Authors: Martin Biel, Farhad Farokhi, Henrik SandbergAbstract:In this paper, we present a toolbox for Structured Model reduction developed for MATLAB. In addition to Structured Model reduction methods using balanced realizations of the subsystems, we introduce a numerical algorithm for Structured Model reduction using a subgradient optimization algorithm. We briefly present the syntax for the toolbox and its features. Finally, we demonstrate the applicability of various Model reduction methods in the toolbox on a Structured mass-spring mechanical system.
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Dissipativity-Preserving Model Reduction for Large-Scale Distributed Control Systems
IEEE Transactions on Automatic Control, 2015Co-Authors: Takako Ishizaki, Koji Kashima, Henrik Sandberg, Takayuki Ishizaki, Kenji Kashima, Jun-ichi Imura, Kazuyuki AiharaAbstract:© 1963-2012 IEEE.We propose a dissipativity-preserving Structured Model reduction method for distributed control systems. As a fundamental tool to develop Structured Model reduction, we first establish dissipativity-preserving Model reduction for general linear systems on the basis of a singular perturbation approximation. To this end, by deriving a tractable expression of singular perturbation Models, we characterize dissipativity preservation in terms of a projection-like transformation of storage functions, and we show that the resultant approximation error is relevant to the sum of neglected eigenvalues of an index matrix. Next, utilizing this dissipativity-preserving Model reduction, we develop a Structured controller reduction method for distributed control systems. The major significance of this method is to preserve the spatial distribution of dissipative controllers and to provide an a priori bound for the performance degradation of closed-loop systems in terms of the H2-norm. The efficiency of the proposed method is verified through a numerical example of vibration suppression control for interconnected second-order systems.
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Coherency-independent Structured Model reduction of power systems
2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2015Co-Authors: Christopher Sturk, Luigi Vanfretti, Yuwa Chompoobutrgool, Henrik SandbergAbstract:This paper proposes a new Model reduction algorithm for power systems based on an extension of balanced truncation. The algorithm is applicable to power systems which are divided into a study area which requires a high-fidelity Model and an external area, making up most of the power system, which is to be reduced. The division of the power system can be made arbitrarily and does not rely on the identification of coherent generators. The proposed algorithm yields a reduced order system with a full nonlinear description of the study area and a reduced linear Model of the external area.
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Structured Model reduction of interconnected linear systems based on singular perturbation
2013 American Control Conference, 2013Co-Authors: Takayuki Ishizaki, Henrik Sandberg, Kenji Kashima, Jun-ichi Imura, Karl Henrik Johansson, Kazuyuki AiharaAbstract:This paper proposes a singular perturbation approximation that preserves system passivity and an interconnection topology among subsystems. In the first half of this paper, we develop a singular perturbation approximation valid for stable linear systems. Using the relation between the singular perturbation and the reciprocal transformation, we derive a tractable expression of the error system in the Laplace domain, which provides a novel insight to regulate the approximating quality of reduced Models. Then in the second half, we develop a Structured singular perturbation approximation that focuses on a class of interconnected systems. This Structured approximation provides a reduced Model that not only possesses fine approximating quality, but also preserves the original interconnection topology and system passivity.
Thomas S Huang - One of the best experts on this subject based on the ideXlab platform.
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LEGO-MM: LEarning Structured Model by Probabilistic loGic Ontology Tree for MultiMedia
IEEE Transactions on Image Processing, 2017Co-Authors: Jinhui Tang, Guo-jun Qi, Shiyu Chang, Qi Tian, Thomas S HuangAbstract:Recent advances in multimedia ontology have resulted in a number of concept Models, e.g., large-scale concept for multimedia and Mediamill 101, which are accessible and public to other researchers. However, most current research effort still focuses on building new concepts from scratch, very few work explores the appropriate method to construct new concepts upon the existing Models already in the warehouse. To address this issue, we propose a new framework in this paper, termed LEarning Structured Model by Probabilistic loGic Ontology Tree for MultiM edia (LEGO 1 -MM), which can seamlessly integrate both the new target training examples and the existing primitive concept Models to infer the more complex concept Models. LEGO-MM treats the primitive concept Models as the lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, we first formulate the logic operations to be the lego connectors to combine the existing concept Models hierarchically in probabilistic logic ontology trees. Then, we incorporate new target training information simultaneously to efficiently disambiguate the underlying logic tree and correct the error propagation. Extensive experiments are conducted on a large vehicle domain data set from ImageNet. The results demonstrate that LEGO-MM has significantly superior performance over the existing state-of-the-art methods, which build new concept Models from scratch.
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Multimedia LEGO: Learning Structured Model by Probabilistic Logic Ontology Tree
2013 IEEE 13th International Conference on Data Mining, 2013Co-Authors: Shiyu Chang, Guo-jun Qi, Yong Rui, Jinhui Tang, Qi Tian, Thomas S HuangAbstract:Recent advances in Multimedia research have generated a large collection of concept Models, e.g., LSCOM and Media mill 101, which become accessible to other researchers. While most current research effort still focuses on building new concepts from scratch, little effort has been made on constructing new concepts upon the existing Models already in the warehouse. To address this issue, we develop a new framework in this paper, termed LEGO, to seamlessly integrate both the new target training examples and the existing primitive concept Models. LEGO treats the primitive concept Models as a lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, LEGO first formulates the logic operations to be the lego connectors to combine existing concept Models hierarchically in probabilistic logic ontology trees. LEGO then simultaneously incorporates new target training information to efficiently disambiguate the underlying logic tree and correct the error propagation. We present extensive experimental results on a large vehicle domain data set from Image Net, and demonstrate significantly superior performance over existing state-of-the-art approaches which build new concept Models from scratch.
Vataya Boonpiam - One of the best experts on this subject based on the ideXlab platform.
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Tree-Structured Model selection and simulated-data adaptation for environmental and speaker robust speech recognition
2007 International Symposium on Communications and Information Technologies, 2007Co-Authors: Nattanun Thatphithakkul, Boontee Kruatrachue, Chai Wutiwiwatchai, Sanparith Marukatat, Vataya BoonpiamAbstract:This paper proposes the use of tree-Structured Model selection and simulated-data in maximum likelihood linear regression (MLLR) adaptation for environment and speaker robust speech recognition. The objective of this work is to solve major problems in robust speech recognition system, namely unknown speaker and unknown environmental noise. The proposed solution is composed of two components. The first one is based on a tree-Structured Model for selecting a speaker-dependent Model that best matches to the input speech. The second component uses simulated-data to adapt the selected acoustic Model to fit with the unknown noise. The proposed technique can thus alleviate both problems simultaneously. Experimental results show that the proposed system achieves a higher recognition rate than the system using only the input speech in adaptation and the system using a multi-conditioned acoustic Model.
Jinhui Tang - One of the best experts on this subject based on the ideXlab platform.
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LEGO-MM: LEarning Structured Model by Probabilistic loGic Ontology Tree for MultiMedia
IEEE Transactions on Image Processing, 2017Co-Authors: Jinhui Tang, Guo-jun Qi, Shiyu Chang, Qi Tian, Thomas S HuangAbstract:Recent advances in multimedia ontology have resulted in a number of concept Models, e.g., large-scale concept for multimedia and Mediamill 101, which are accessible and public to other researchers. However, most current research effort still focuses on building new concepts from scratch, very few work explores the appropriate method to construct new concepts upon the existing Models already in the warehouse. To address this issue, we propose a new framework in this paper, termed LEarning Structured Model by Probabilistic loGic Ontology Tree for MultiM edia (LEGO 1 -MM), which can seamlessly integrate both the new target training examples and the existing primitive concept Models to infer the more complex concept Models. LEGO-MM treats the primitive concept Models as the lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, we first formulate the logic operations to be the lego connectors to combine the existing concept Models hierarchically in probabilistic logic ontology trees. Then, we incorporate new target training information simultaneously to efficiently disambiguate the underlying logic tree and correct the error propagation. Extensive experiments are conducted on a large vehicle domain data set from ImageNet. The results demonstrate that LEGO-MM has significantly superior performance over the existing state-of-the-art methods, which build new concept Models from scratch.
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Multimedia LEGO: Learning Structured Model by Probabilistic Logic Ontology Tree
2013 IEEE 13th International Conference on Data Mining, 2013Co-Authors: Shiyu Chang, Guo-jun Qi, Yong Rui, Jinhui Tang, Qi Tian, Thomas S HuangAbstract:Recent advances in Multimedia research have generated a large collection of concept Models, e.g., LSCOM and Media mill 101, which become accessible to other researchers. While most current research effort still focuses on building new concepts from scratch, little effort has been made on constructing new concepts upon the existing Models already in the warehouse. To address this issue, we develop a new framework in this paper, termed LEGO, to seamlessly integrate both the new target training examples and the existing primitive concept Models. LEGO treats the primitive concept Models as a lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, LEGO first formulates the logic operations to be the lego connectors to combine existing concept Models hierarchically in probabilistic logic ontology trees. LEGO then simultaneously incorporates new target training information to efficiently disambiguate the underlying logic tree and correct the error propagation. We present extensive experimental results on a large vehicle domain data set from Image Net, and demonstrate significantly superior performance over existing state-of-the-art approaches which build new concept Models from scratch.
Simon Jennings - One of the best experts on this subject based on the ideXlab platform.
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evaluation and management implications of uncertainty in a multispecies size Structured Model of population and community responses to fishing
Methods in Ecology and Evolution, 2015Co-Authors: Robert Thorpe, Will Le J F Quesne, Fay Luxford, Jeremy S Collie, Simon JenningsAbstract:1. Implementation of an ecosystem approach to fisheries requires advice on trade-offs among fished species and between fisheries yields and biodiversity or food web properties. However, the lack of explicit representation, analysis and consideration of uncertainty in most multispecies Models has limited their application in analyses that could support management advice. 2. We assessed the consequences of parameter uncertainty by developing 78 125 multispecies size-Structured fish community Models, with all combinations of parameters drawn from ranges that spanned parameter values estimated from data and literature. This unfiltered ensemble was reduced to 188 plausible Models, the filtered ensemble (FE), by screening outputs against fish abundance data and ecological principles such as requiring species' persistence. 3. Effects of parameter uncertainty on estimates of single-species management reference points for fishing mortality (FMSY, fishing mortality rate providing MSY, the maximum sustainable yield) and biomass (BMSY, biomass at MSY) were evaluated by calculating probability distributions of estimated reference points with the FE. There was a 50% probability that multispecies FMSY could be estimated to within ±25% of its actual value, and a 50% probability that BMSY could be estimated to within ±40% of its actual value. 4. Signal-to-noise ratio was assessed for four community indicators when mortality rates were reduced from current rates to FMSY. The slope of the community size spectrum showed the greatest signal-to-noise ratio, indicating that it would be the most responsive indicator to the change in fishing mortality F. Further, the power of an ongoing international monitoring survey to detect predicted responses of size spectrum slope was higher than for other size-based metrics. 5. Synthesis and applications: Application of the ensemble Model approach allows explicit representation of parameter uncertainty and supports advice and management by (i) providing uncertainty intervals for management reference points, (ii) estimating working values of reference points that achieve a defined reduction in risk of not breaching the true reference point, (iii) estimating the responsiveness of population, community, food web and biodiversity indicators to changes in F, (iv) assessing the performance of indicators and monitoring programmes and (v) identifying priorities for data collection and changes to Model structure to reduce uncertainty.
