The sampling storage method which used in the current data stream could not respond data tendency effectively. For the problem, this paper presents a new processing method based on curve fitting. A weighted least-squa...
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Temporal lobe epilepsy (TLE) is a neurological disease that affects millions of individuals in the world. Majority of TLE patients suffer from refractory seizures. Determining abnormal/damaged regions of the brain tha...
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The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquir...
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ISBN:
(纸本)9781424466238
The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.
Inattentive and impaired drivers are a major cause of road accidents. Especially when impaired by drugs, fatigue or physical handicaps, the skill levels, driving habits, capabilities and decisions of a human driver ar...
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ISBN:
(纸本)9781424465880
Inattentive and impaired drivers are a major cause of road accidents. Especially when impaired by drugs, fatigue or physical handicaps, the skill levels, driving habits, capabilities and decisions of a human driver are adversely affected. In such cases, the ability of a driver to safely operate a vehicle may be augmented considerably by well tuned and driver adaptive warning and assistance systems. Data from 105 drivers were collected in the Drive Safe project on an approximately 30 km route containing both city and highway traffic. The data was used to develop methods for determining inattentive/impaired drivers. This paper is on a lane keeping driver assistant system that is activated once such an inattentive/impaired driver who cannot perform a good task of lane keeping by himself is determined. Robust, parameter space based and velocity scheduled control design techniques carried out in the COMES toolbox are used for designing the lane keeping controller. A camera based image processing algorithm for lane detection and tracking is used. The image processing algorithm and the lane keeping assistant controlsystem are evaluated first using offline simulations and then using more realistic, real time hardware-in-the-loop simulations. While a relatively simple linear model is used for lane keeping controller design, evaluation of the designed controller also uses the high fidelity, high order, realistic and nonlinear vehicle model in Carmaker HiL. A PC is used for processing video frames coming from an in-vehicle camera pointed towards the road ahead. Lane detection and tracking computations including fitting of composite Bezier curves to curved lanes are carried out in this PC. A dSpace microautobox is available for obtaining the lane data from the PC and the Carmaker vehicle data from the dSpace compact simulator used and calculating the required steering actions and sending them to the Carmaker vehicle model. Offline and real time simulation results demonstrate t
This paper aims to model and solve the Sum and Product Riddle in public announcement logic. A dynamic epistemic model is proposed, that is the linear temporal combination of the epistemic model of environment and the ...
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This paper aims to model and solve the Sum and Product Riddle in public announcement logic. A dynamic epistemic model is proposed, that is the linear temporal combination of the epistemic model of environment and the epistemic models after each announcement, such that the authors' model checking technique for temporal epistemic logic can be extended to support the modeling and verification of public announcement logic. This model checking method not only can help to find all solutions, but also verify related temporal epistemic properties. Such characteristic is not fully supported by the current version of MCK, MCMAS and DEMO. The authors implemented the proposed method in the symbolic model checker MCTK via OBDD and verified the sum and product riddle. The experimental results show that the proposed method is correct and efficient.
This paper considers analysis and synthesis of discrete-time networked controlsystems (NCSs), where the plant has additive uncertainty and the controller is updated with the sensor information at stochastic time inte...
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This paper considers analysis and synthesis of discrete-time networked controlsystems (NCSs), where the plant has additive uncertainty and the controller is updated with the sensor information at stochastic time intervals. It is shown that the problem is linked to robust control of linear discrete-time stochastic systems and a new small gain theorem is established. Based on this result, sufficient conditions are given for ensuring mean square stability of the NCS, and the genetic algorithm is utilised to design the controller of the NCS based on a linear matrix inequality technique.
To answer the problem of ambiguous design levels for large-scale distributed simulation systems, this paper proposes a hierarchical system model based on the quotient space theory. This model consists of a system glob...
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The sampling storage method which used in the current data stream could not respond data tendency effectively. For the problem, this paper presents a new processing method based on curve fitting. A weighted least...
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The sampling storage method which used in the current data stream could not respond data tendency effectively. For the problem, this paper presents a new processing method based on curve fitting. A weighted least square principle is used to fit the cached stream data and better model description is obtained. Then the fitting results are analyzed by clustering algorithm, which serves as a classifier for polynomial fitting parameters According to the clustering result, the appropriate window size will be given to fit the periodic stream data. Comparing the function solutions with the actual data, the different methods are adopted to store data according to the comparison result. The experimental results indicate that the proposed method has better fitting accuracy and compression ratio, could meet the requirement of data stream processing. And the data tendency could be responded effectively by the fitting results.
Aim at the hysteretic nonlinearity characteristic of the giant magnetostrictive actuator in control, an internal model control method based on support vector regression is presented in this paper. Its models are built...
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Aim at the hysteretic nonlinearity characteristic of the giant magnetostrictive actuator in control, an internal model control method based on support vector regression is presented in this paper. Its models are built by support vector regression using the input and output data of the actuator and the internal model control is achieved. The simulation results show the method in this paper has better control precision.
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