2,953 research outputs found
Efficient duration modelling in the hierarchical hidden semi-Markov models and their applications
Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, 1989]. Despite its widespread success in many cases, the standard HMM often fails to model more complex data whose elements are correlated hierarchically or over a long period. Such problems are, however, frequently encountered in practice. Existing efforts to overcome this weakness often address either one of these two aspects separately, mainly due to computational intractability. Motivated by this modeling challenge in many real world problems, in particular, for video surveillance and segmentation, this thesis aims to develop tractable probabilistic models that can jointly model duration and hierarchical information in a unified framework. We believe that jointly exploiting statistical strength from both properties will lead to more accurate and robust models for the needed task. To tackle the modeling aspect, we base our work on an intersection between dynamic graphical models and statistics of lifetime modeling. Realizing that the key bottleneck found in the existing works lies in the choice of the distribution for a state, we have successfully integrated the discrete Coxian distribution [Cox, 1955], a special class of phase-type distributions, into the HMM to form a novel and powerful stochastic model termed as the Coxian Hidden Semi-Markov Model (CxHSMM). We show that this model can still be expressed as a dynamic Bayesian network, and inference and learning can be derived analytically.Most importantly, it has four superior features over existing semi-Markov modelling: the parameter space is compact, computation is fast (almost the same as the HMM), close-formed estimation can be derived, and the Coxian is flexible enough to approximate a large class of distributions. Next, we exploit hierarchical decomposition in the data by borrowing analogy from the hierarchical hidden Markov model in [Fine et al., 1998, Bui et al., 2004] and introduce a new type of shallow structured graphical model that combines both duration and hierarchical modelling into a unified framework, termed the Coxian Switching Hidden Semi-Markov Models (CxSHSMM). The top layer is a Markov sequence of switching variables, while the bottom layer is a sequence of concatenated CxHSMMs whose parameters are determined by the switching variable at the top. Again, we provide a thorough analysis along with inference and learning machinery. We also show that semi-Markov models with arbitrary depth structure can easily be developed. In all cases we further address two practical issues: missing observations to unstable tracking and the use of partially labelled data to improve training accuracy. Motivated by real-world problems, our application contribution is a framework to recognize complex activities of daily livings (ADLs) and detect anomalies to provide better intelligent caring services for the elderly.Coarser activities with self duration distributions are represented using the CxHSMM. Complex activities are made of a sequence of coarser activities and represented at the top level in the CxSHSMM. Intensive experiments are conducted to evaluate our solutions against existing methods. In many cases, the superiority of the joint modeling and the Coxian parameterization over traditional methods is confirmed. The robustness of our proposed models is further demonstrated in a series of more challenging experiments, in which the tracking is often lost and activities considerably overlap. Our final contribution is an application of the switching Coxian model to segment education-oriented videos into coherent topical units. Our results again demonstrate such segmentation processes can benefit greatly from the joint modeling of duration and hierarchy
Canceling Quadratic Divergences in a Class of Two-Higgs-Doublet Models
The Newton-Wu conditions for the cancellation of quadratic divergences in a
class of two-Higgs-doublet models are analyzed as to how they may be satisfied
with a typical extension of the Standard Model of particle interactions.Comment: 5 pages, no figur
Scalar Mass Bounds in Two Supersymmetric Extended Electroweak Gauge Models
In two recently proposed supersymmetric extended electroweak gauge models,
the reduced Higgs sector at the 100-GeV energy scale consists of only two
doublets, but they have quartic scalar couplings different from those of the
minimal supersymmetric standard model. In the SU(2) X SU(2) X U(1) model, there
is an absolute upper bound of about 145 GeV on the mass of the lightest neutral
scalar boson. In the SU(3) X U(1) model, there is only a parameter-dependent
upper bound which formally goes to infinity in a particular limitComment: 9 pages (6 figures not included), UCRHEP-T128 (July 1994
Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations
Next-generation power grids will likely enable concurrent service for
residences and plug-in electric vehicles (PEVs). While the residence power
demand profile is known and thus can be considered inelastic, the PEVs' power
demand is only known after random PEVs' arrivals. PEV charging scheduling aims
at minimizing the potential impact of the massive integration of PEVs into
power grids to save service costs to customers while power control aims at
minimizing the cost of power generation subject to operating constraints and
meeting demand. The present paper develops a model predictive control (MPC)-
based approach to address the joint PEV charging scheduling and power control
to minimize both PEV charging cost and energy generation cost in meeting both
residence and PEV power demands. Unlike in related works, no assumptions are
made about the probability distribution of PEVs' arrivals, the known PEVs'
future demand, or the unlimited charging capacity of PEVs. The proposed
approach is shown to achieve a globally optimal solution. Numerical results for
IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this
approach
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