Accurate registration of 3-D point clouds is a common problem in computer vision. This paper presents a new two-stage algorithm for point clouds registration. A novel local invariant feature which is a k-dimensional v...
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ISBN:
(纸本)9781612847719
Accurate registration of 3-D point clouds is a common problem in computer vision. This paper presents a new two-stage algorithm for point clouds registration. A novel local invariant feature which is a k-dimensional vector is proposed and used in our coarse registration stage. Two new structural constraints combined with the Iterative Closest Point (ICP) algorithm are adopted in our fine registration stage. The accuracy and effectiveness of our algorithm is visually and quantitatively demonstrated by the comparative experiments on synthetic and real 3-D data.
Nowadays, decision tree is widely used as one of the most powerful tools in data mining. However, to construct an optimization decision tree is a complete NP problem. So a new method about how to construct decision tr...
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This paper investigates an social learning model with time-varying weights, in which the individual updates her belief through observing private signal caused by social event and communicating with those regarded as n...
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Abstract In this paper, the model predictive control strategy based on input and output data sets for partial differential equation (PDE) unknown spatially-distributed system (SDS) is proposed. The control aim is that...
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Abstract In this paper, the model predictive control strategy based on input and output data sets for partial differential equation (PDE) unknown spatially-distributed system (SDS) is proposed. The control aim is that the outputs of low-dimensional temporal model reach the set points. Thus, it makes the control design easily and reduces the computational burden. The low-dimensional model is obtained by principal component analysis (PCA) method, and the state of the low-dimensional model is estimated based on spatially-distributed output. The terminal constraints are used to transform the cost function along an infinite prediction horizon into finite prediction horizon. The simulations demonstrated show the accuracy and efficiency of the proposed method.
Abstract This paper considers the stability and stabilization of a class of switched polynomial nonlinear systems. Firstly, based on time-dependent switching control and a set of auxiliary matrices, new explicit suffi...
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Abstract This paper considers the stability and stabilization of a class of switched polynomial nonlinear systems. Firstly, based on time-dependent switching control and a set of auxiliary matrices, new explicit sufficient conditions are developed to ensure asymptotical stability of the studied class of systems. Moreover, switching rule design is considered for this class of systems with state-dependent switching control. Sufficient conditions for stability and stabilization are given in polynomial matrix inequalities (PMIs), and these conditions can be verified by sum of squares technique which solves linear matrix inequality feasibility problem in essence. A numerical example is provided to illustrate the effectiveness of the proposed methods.
Abstract The predicted mean vote (PMV) index is widely used to evaluate the indoor thermal comfort with indoor environmental and human factors considered. However, PMV is difficult to control as its mathematical model...
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Abstract The predicted mean vote (PMV) index is widely used to evaluate the indoor thermal comfort with indoor environmental and human factors considered. However, PMV is difficult to control as its mathematical model is complicated and uncertain. Moreover, spatial distributions of environmental factors are neglected by using one PMV index in a room. In this paper, Computational Fluid Dynamics (CFD) technology is applied for simulation of the environmental factors in order to accurately describe PMV index. To deal with measurement noises or other system uncertainties, an Interval type-2 fuzzy model of PMV is developed and a new GK-GA-based modeling method is proposed. The essential issue of type-2 fuzzy modeling lies in the appropriate choice of secondary membership functions. In this study, the primary membership function is gained through G-K algorithm, and the secondary membership function is determined through Genetic Algorithm (GA). The consequent of the fuzzy rules is identified by least squared algorithm. Simulation results show that the type-2 fuzzy model is favorable to minimize the influence of uncertainties and the proposed method is effective and with good accuracy.
An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertaint...
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For a class of nonlinear networked controlsystem (NCS) with network-induced delay and packed dropout, which is influenced by external disturbance with limited energy in transmission, a T-S fuzzy model is employed to ...
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ISBN:
(纸本)9781424490103
For a class of nonlinear networked controlsystem (NCS) with network-induced delay and packed dropout, which is influenced by external disturbance with limited energy in transmission, a T-S fuzzy model is employed to represent the nonlinear controlled plant. By using appreciate Lyapunov-Krasovskii function, a H ∞ integrality sufficient condition against actuator failures is derived based on delay-dependent approach, and the H ∞ fault-tolerant controller gain can be obtained via solving several linear matrix inequalities(LMIs). Because of the lower bound of the time-delay and free weighting matrixes are introduced, and the model transformation has not been used. Then a less conservative result is obtained. Finally, an example is used to illustrate the effectiveness and feasibility of proposed approach.
A novel control algorithm is applied to control superheated steam temperature in power plants. Since the disturbances existed in practical processes are probably non-Gaussian, the performance index is constructed by m...
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Vector quantization is one of high performance and popular methods for data compression. But it is extremely time consuming during the encoding process. In this paper, a fast encoding algorithm for vector quantization...
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ISBN:
(纸本)9781612848396
Vector quantization is one of high performance and popular methods for data compression. But it is extremely time consuming during the encoding process. In this paper, a fast encoding algorithm for vector quantization is proposed to save the computation time. This algorithm uses two characteristics of a vector, Hadamard transform (HT) and variance. The methods using one of these features was already proposed, they handles these features separately. Here, the proposed algorithm put forward a new inequality which utilizes these features simultaneously to rejects more codewords which are impossible to be the nearest codeword in the distortion computations stage. This method produces the same output as conventional full search algorithm. The simulation results show that the effectiveness of the proposed algorithm is outstanding.
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