Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas...
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Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.
Intensive task-oriented repetitive physical therapies provided by individualized interaction between the patient and a rehabilitation specialist can improve hand motor performance in patients survived from stroke and ...
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Intensive task-oriented repetitive physical therapies provided by individualized interaction between the patient and a rehabilitation specialist can improve hand motor performance in patients survived from stroke and traumatic brain injury. However, the therapy process is long and expensive and difficult to evaluate quantitatively and objectively. The goal of this research is to develop a novel wearable device for robotic assisted hand repetitive therapy. We designed a pneumatic muscle (PM) driven therapeutic device that is wearable and provides assistive forces required for grasping and release movements. The robot has two distinct degrees of freedom at the thumb and the fingers. The embedded sensors feedback position and force information for robot control and quantitative evaluation of task performance. It has the potential of providing supplemental at-home therapy in addition to in the clinic treatment.
Since the introduction of biomechanical models into cardiac image analysis, there have been a number of efforts to estimate patient-specific cardiac kinematic functions and material properties, assuming the unobservab...
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ISBN:
(纸本)9781424406715;1424406714
Since the introduction of biomechanical models into cardiac image analysis, there have been a number of efforts to estimate patient-specific cardiac kinematic functions and material properties, assuming the unobservable driving forces (a.k.a. the input forces) as known or constructible from boundary conditions. In this paper, we present a multiframe estimation framework which simultaneously recovers the cardiac motion parameters and the input forces from periodic medicalimage sequences. It is realized through a Kalman filter which generates the residual innovation sequences, followed by an on-line recursive least-squares filter that uses the residual innovation sequences to compute the values of input forces and to correct the estimations of cardiac kinematic functions. We demonstrate the ability of the framework on synthetic data and magnetic resonance image sequences
Meshfree particle method (MPM) exhibits improved flexibility and accuracy in dealing with problems with large deformation, complex geometry and material discontinuities. In this paper, we present a MPM based framework...
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ISBN:
(纸本)9781424406715;1424406714
Meshfree particle method (MPM) exhibits improved flexibility and accuracy in dealing with problems with large deformation, complex geometry and material discontinuities. In this paper, we present a MPM based framework for the simultaneous shape recovery and motion tracking of the left ventricle. The myocardium is modeled as an anisotropic elastic body accounting for the fiber directions, represented by sampling nodes bounded by endocardial and epicardial boundaries. Cardiac dynamics is then driven by external force constructed individually for each node through integration of measures of image edgeness, image derived salient feature coherence, prior tissue distribution model, and temporal motion model. The displacement field throughout the cardiac cycle is obtained when the total energy of the elastic body is minimized to reach equilibrium state. Experiments with 3D canine MR images illustrate the benefits and potentials of such effort.
Noninvasive imaging of cardiac electrophysiology has been an active area with increasing clinical significance. Instead of equivalent-source-based approaches, however, noninvasive 3D cardiac transmembrane potentials (...
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ISBN:
(纸本)1424406714
Noninvasive imaging of cardiac electrophysiology has been an active area with increasing clinical significance. Instead of equivalent-source-based approaches, however, noninvasive 3D cardiac transmembrane potentials (TMP) mapping remains a formidable goal due to the remote and integrative nature of body surface potential recordings. We recognized the incorporation of a priori physiological knowledge as one desired paradigm and presented a model-based general Bayesian framework for 3D TMP mapping from body surface potential maps (BSPMs), based on optimal integration of a priori physiological models and patient-specific observations within their respective uncertainties. An alternative being the use of additional observations for volumetric cardiac electrophysiological information, we also worked on recovering cardiac electrical excitation pattern from tomographic medicalimage sequences. Furthermore, aiming for a model-based data fusion framework wherein information from BSPMs and medicalimage sequences are integrated, we presented the dynamic image guided mapping of 3D cardiac electrophysiology.
Proper spatial and temporal constraints are essential for image-based motion recovery of deforming objects. Since biological organs, such as the heart, are typically composed of fibrous tissues of anisotropic nature, ...
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Proper spatial and temporal constraints are essential for image-based motion recovery of deforming objects. Since biological organs, such as the heart, are typically composed of fibrous tissues of anisotropic nature, one must adopt realistic spatial models, in addition to those important considerations for temporal modeling, in order to properly regularize the object behavior for kinematics recovery. We present a biomechanically constrained state space analysis framework for the multiframe estimation of the heart motion and deformation. While the anisotropic physical constraints enforce spatial regulations on the myocardial behavior and spatial filtering of the image data measurements, statistical filtering techniques impose temporal constraints to incorporate multiframe information. Implemented within a mesh-free particle representation and computation framework, excellent experimental results are achieved for both synthetic data with known ground truth and canine magnetic resonance image sequences with known clinical gold standard.
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