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@@ Nonprofit organizations, with their service goals and humanistic approach, have the potential of serving as a vital pillar of a harmonious society in China. However, nonprofit management – as a matter of public regulation and ...
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@@ Nonprofit organizations, with their service goals and humanistic approach, have the potential of serving as a vital pillar of a harmonious society in China. However, nonprofit management – as a matter of public regulation and organizational administration – appears to be quite neglected in public management research and education in China.
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摘要 :
@@ Nonprofit organizations, with their service goals and humanistic approach, have the potential of serving as a vital pillar of a harmonious society in China. However, nonprofit management – as a matter of public regulation and ...
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@@ Nonprofit organizations, with their service goals and humanistic approach, have the potential of serving as a vital pillar of a harmonious society in China. However, nonprofit management – as a matter of public regulation and organizational administration – appears to be quite neglected in public management research and education in China.
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The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are no...
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The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are nonlinear mappings of the on-line observed signals of dynamical systems, where the main features are the uncertainties in both the system's structure and external disturbances, and the non-stationarity and dependency of the system signals. Thus, a key difficulty in establishing a mathematical theory of adaptive systems lies in how to deal with complicated nonlinear stochastic dynamical systems which describe the adaptation processes. In this paper, we will illustrate some of the basic concepts, methods and results through some simple examples. The following fundamental questions will be discussed: How much information is needed for estimation? How to deal with uncertainty by adaptation? How to analyze an adaptive system? What are the convergence or tracking performances of adaptation? How to find the proper rate of adaptation in some sense? We will also explore the following more fundamental questions: How much uncertainty can be dealt with by adaptation ? What are the limitations of adaptation ? How does the performance of adaptation depend on the prior information ? We will partially answer these questions by finding some "critical values" and establishing some "Impossibility Theorems" for the capability of adaptation, for several basic classes of nonlinear dynamical control systems with either parametric or nonparametric uncertainties.
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摘要 :
The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are no...
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The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are nonlinear mappings of the on-line observed signals of dynamical systems, where the main features are the uncertainties in both the system's structure and external disturbances, and the non-stationarity and dependency of the system signals. Thus, a key difficulty in establishing a mathematical theory of adaptive systems lies in how to deal with complicated nonlinear stochastic dynamical systems which describe the adaptation processes. In this paper, we will illustrate some of the basic concepts, methods and results through some simple examples. The following fundamental questions will be discussed: How much information is needed for estimation? How to deal with uncertainty by adaptation? How to analyze an adaptive system? What are the convergence or tracking performances of adaptation? How to find the proper rate of adaptation in some sense? We will also explore the following more fundamental questions: How much uncertainty can be dealt with by adaptation ? What are the limitations of adaptation ? How does the performance of adaptation depend on the prior information ? We will partially answer these questions by finding some "critical values" and establishing some "Impossibility Theorems" for the capability of adaptation, for several basic classes of nonlinear dynamical control systems with either parametric or nonparametric uncertainties.
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In the framework of the littlest Higgs model with T parity, we study the W_H /Z_h +q- and W_H-pair productions at the CERN Large Hadron Collider up to the QCD next- to-leading order (NLO). The kinematic distributions of final deca...
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In the framework of the littlest Higgs model with T parity, we study the W_H /Z_h +q- and W_H-pair productions at the CERN Large Hadron Collider up to the QCD next- to-leading order (NLO). The kinematic distributions of final decay products and the theoretical dependence of the cross section on the factorization/renormalization scale are analyzed. We adopt the PROSPINO scheme in the QCD NLO calculations to avoid double counting and keep the convergence of the perturbative QCD description. By using the subtraction scheme, the QCD NLO corrections enhance the leading order cross section with a K-factor in the range of 1.00 ~1.43 for W_h(Z_h)q- production process, and in the range of 1.09 ~1.22 for the W_h pair production process.
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The architecture of distributed satellite cluster network (DSCN) is presented and the characteristics of DSCN topology change are illustrated. On the basis of analyzing the acquisition method of network status and route calculatio...
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The architecture of distributed satellite cluster network (DSCN) is presented and the characteristics of DSCN topology change are illustrated. On the basis of analyzing the acquisition method of network status and route calculation, we proposed a heuristic algorithm Ant Colony Optimization (ACO) based traffic classified routing (ATCR) algorithm for DSCN. Simulation results shows that, ATCR algorithm can balance network traffic effectively, and the end-to-end delay of every traffic class is less than TCD algorithm. The end-to-end delay of traffic class A and class B is less than ACO algorithm which does not use traffic classification. ATCR has a better performance on packet delivery ratio than ACO and TCD because ATCR reduces the number of heavy load link as well as packet loss caused by congestion.
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The architecture of distributed satellite cluster network (DSCN) is presented and the characteristics of DSCN topology change are illustrated. On the basis of analyzing the acquisition method of network status and route calculatio...
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The architecture of distributed satellite cluster network (DSCN) is presented and the characteristics of DSCN topology change are illustrated. On the basis of analyzing the acquisition method of network status and route calculation, we proposed a heuristic algorithm Ant Colony Optimization (ACO) based traffic classified routing (ATCR) algorithm for DSCN. Simulation results shows that, ATCR algorithm can balance network traffic effectively, and the end-to-end delay of every traffic class is less than TCD algorithm. The end-to-end delay of traffic class A and class B is less than ACO algorithm which does not use traffic classification. ATCR has a better performance on packet delivery ratio than ACO and TCD because ATCR reduces the number of heavy load link as well as packet loss caused by congestion.
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The multimedia content analysis community has made significant effort to bridge the gap between low-level features and high-level semantics perceived by human cognitive systems such as real-world objects and concepts. In the two f...
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The multimedia content analysis community has made significant effort to bridge the gap between low-level features and high-level semantics perceived by human cognitive systems such as real-world objects and concepts. In the two fields of multimedia analysis and brain imaging, both topics of low-level features and high level semantics are extensively studied. For instance, in the multimedia analysis field, many algorithms are available for multimedia feature extraction, and benchmark datasets are available such as the TRECVID. In the brain imaging field, brain regions that are responsible for vision, auditory perception, language, and working memory are well studied via functional magnetic resonance imaging (fMRI). This paper presents our initial effort in marrying these two fields in order to bridge the gaps between low-level features and high-level semantics via fMRI brain imaging. Our experimental paradigm is that we performed fMRI brain imaging when university student subjects watched the video clips selected from the TRECVID datasets. At current stage, we focus on the three concepts of sports, weather, and commercial-/advertisement specified in the TRECVID 2005. Meanwhile, the brain regions in vision, auditory, language, and working memory networks are quantitatively localized and mapped via task-based paradigm fMRI, and the fMRI responses in these regions are used to extract features as the representation of the brain's comprehension of semantics. Our computational framework aims to learn the most relevant low-level feature sets that best correlate the fMRI-derived semantics based on the training videos with fMRI scans, and then the learned models are applied to larger scale test datasets without fMRI scans for category classifications. Our result shows that: 1) there are meaningful couplings between brain's fMRI responses and video stimuli, suggesting the validity of linking semantics and low-level features via fMRI; 2) The computationally learned low-level feature sets from fMRI-derived semantic features can significantly improve the classification of video categories in comparison with that based on original low-level features.
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摘要 :
The multimedia content analysis community has made significant effort to bridge the gap between low-level features and high-level semantics perceived by human cognitive systems such as real-world objects and concepts. In the two f...
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The multimedia content analysis community has made significant effort to bridge the gap between low-level features and high-level semantics perceived by human cognitive systems such as real-world objects and concepts. In the two fields of multimedia analysis and brain imaging, both topics of low-level features and high level semantics are extensively studied. For instance, in the multimedia analysis field, many algorithms are available for multimedia feature extraction, and benchmark datasets are available such as the TRECVID. In the brain imaging field, brain regions that are responsible for vision, auditory perception, language, and working memory are well studied via functional magnetic resonance imaging (fMRI). This paper presents our initial effort in marrying these two fields in order to bridge the gaps between low-level features and high-level semantics via fMRI brain imaging. Our experimental paradigm is that we performed fMRI brain imaging when university student subjects watched the video clips selected from the TRECVID datasets. At current stage, we focus on the three concepts of sports, weather, and commercial-/advertisement specified in the TRECVID 2005. Meanwhile, the brain regions in vision, auditory, language, and working memory networks are quantitatively localized and mapped via task-based paradigm fMRI, and the fMRI responses in these regions are used to extract features as the representation of the brain's comprehension of semantics. Our computational framework aims to learn the most relevant low-level feature sets that best correlate the fMRI-derived semantics based on the training videos with fMRI scans, and then the learned models are applied to larger scale test datasets without fMRI scans for category classifications. Our result shows that: 1) there are meaningful couplings between brain's fMRI responses and video stimuli, suggesting the validity of linking semantics and low-level features via fMRI; 2) The computationally learned low-level feature sets from fMRI-derived semantic features can significantly improve the classification of video categories in comparison with that based on original low-level features.
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Strapdown stellar-inertial integrated attitude determination based on low-cost micro-electromechanical system (MEMS) gyroscopes and a complementary metal-oxide-semiconductor transistor active pixel star sensor is one of the most e...
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Strapdown stellar-inertial integrated attitude determination based on low-cost micro-electromechanical system (MEMS) gyroscopes and a complementary metal-oxide-semiconductor transistor active pixel star sensor is one of the most effective methods for nano-spacecrafts attitude determination. However, the accuracy of attitude determination is low because of non-linearity of the system. Thus, an adaptive segmented information fusion method based on the UKF is presented by taking UKF+QUEST and UKF+optimal REQUEST as two modes of information fusion that can be adaptively switched between. Initially, the gyro drift estimation error is inaccurate, and the UKF+QUEST mode is adopted to quickly estimate the gyro drifts. When the mean-square error matrix of the UKF tends to stabilization, the information fusion mode is adaptively switched to the UKF+optimal REQUEST dual-filter model. The hybrid simulation results show this method not only has higher accuracy in attitude determination but also can quickly estimate gyro drifts.
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