The generating rule method is presented for incompatible and incomplete information of test data based on Bayesian theory. Firstly, the rule's conditional probability is calculated when the certainty (reliability)...
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The generating rule method is presented for incompatible and incomplete information of test data based on Bayesian theory. Firstly, the rule's conditional probability is calculated when the certainty (reliability) of the test data is the prior probability and the samples (supportability) is posterior probability. Then, Those rules whose conditional probability is bigger than a given threshold value should be preserved. Lastly, the rule is generated by logic conjunction and disjunction of all the preserved rules. The example and application analysis indicate that the algorithm is clear, the calculating process is simple and it can be easily applied to computer programs, moreover, this method can avoid the knowledge distortion and the rule losing to the maximum for generating rule.
This paper presents a new interface framework which has been added to Easy Java Simualtions' environment in order to improve its graphical features for 3D modeling. These new 3D capabilities provide users a set of...
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This paper presents a new interface framework which has been added to Easy Java Simualtions' environment in order to improve its graphical features for 3D modeling. These new 3D capabilities provide users a set of new view elements which can be used to develop models with a high degree of reality. In this way, Easy Java Simulations becomes a powerful tool to easily and quickly create 3D realistic simulations.
We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a mult...
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ISBN:
(纸本)9781424441259
We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of the energy function, a two-step optimization strategy is used. In the first step, shape parameters are optimized based on the analytic derivatives of the energy function. In the second step, shapes of the structures are fine-tuned using a level set method. The proposed method is shown to be superior to some popular methods in the literature using a dataset of 64 patients with mesial temporal lobe epilepsy. In addition, the method can be used for lateralization with accuracy close to that of manual segmentation.
Because of strong coupling, nonlinear, and time-varying characteristics, it is difficult to control complex spacecraft. By means of combining with controlled object dynamics and performance requirements, characteristi...
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Because of strong coupling, nonlinear, and time-varying characteristics, it is difficult to control complex spacecraft. By means of combining with controlled object dynamics and performance requirements, characteristic modeling and control approach is an effective way to solve this problem. Aimed at the flexible satellites described by using characteristic modeling approach, with the help of fuzzy rules, fuzzy dynamic characteristic modeling method and intelligent adaptive controller are designed to control this complex spacecraft. Meanwhile, based on the satellite system simulation platform, we validate the correctness and efficiency of the proposed modeling and control method by comparing with the simulation results of the other similar methods.
In recent years, dynamics model and control of space robot system are the hot topics in the research field. In this paper, a new dynamics model and control strategy of space robotic system with a flexible manipulator ...
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In recent years, dynamics model and control of space robot system are the hot topics in the research field. In this paper, a new dynamics model and control strategy of space robotic system with a flexible manipulator and a liquid fuel tank are investigated. Based on Lagrange equation method, the dynamics model of the space robotic system coupling with liquid sloshing, flexibility vibration and base movement is derived. The elastic deflection of the flexible manipulator is described by the assumed mode method and equivalent mechanical model is adopted instead of liquid sloshing under the environment of low-gravity. The inverse dynamics control algorithm combined with PD control method is performed to solve the trajectory tracking problem. Some simulation results are given to verify the effectiveness of the proposed method.
The support vector domain description (SVDD) is a popular kernel method for outlier detection, which tries to fit a class of data with a sphere and uses a few target objects to support its decision boundary. The probl...
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Dynamic programming provides a method to solve hybrid optimal control problems. This contribution extends existing numerical methods originally developed for purely continuous systems, to a class of hybrid systems wit...
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ISBN:
(纸本)9783902661593
Dynamic programming provides a method to solve hybrid optimal control problems. This contribution extends existing numerical methods originally developed for purely continuous systems, to a class of hybrid systems with autonomous as well as controlled switching behavior. The hybrid dynamics is approximated by a locally consistent discrete Markov decision process. The original optimal control problem is then reformulated for the Markov decision process and solved by standard dynamic programming methods. The convergence of the discrete approximation to the original problem is ensured. The viability of the numerical scheme is illustrated by a two gear transmission system used previously in literature.
A communication-based distributed model predictive control scheme for a set of dynamically decoupled autonomous systems (agents) is proposed. The individual dynamics are described by discrete-time linear systems. Loca...
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In current manufacturing systems, short times for product development and ramp-up as well as high production rates and customized products are of increasing importance. To push the efficiency (and thus the economic ga...
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A temporal point process is a stochastic time series of binary events that occurs in continuous time. In computational neuroscience, the point process is used to model neuronal spiking activity; however, estimating th...
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A temporal point process is a stochastic time series of binary events that occurs in continuous time. In computational neuroscience, the point process is used to model neuronal spiking activity; however, estimating the model parameters from spike train is a challenging problem. The state space point process filtering theory is a new technique for the estimation of the states and parameters. In order to use the stochastic filtering theory for the states of neuronal system with the Gaussian assumption, we apply the extended Kalman filter. In this regard, the extended Kalman filtering equations are derived for the point process observation. We illustrate the new filtering algorithm by estimating the effect of visual stimulus on the spiking activity of object selective neurons from the inferior temporal cortex of macaque monkey. Based on the goodness-offit assessment, the extended Kalman filter provides more accurate state estimate than the conventional methods.
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