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Currently environmental distribution maps, such as for soil fertility, rainfall and foliage, are widely used in the natural resource management and policy making. One typical example is to predict the grazing capacity in particular geographical regions. This paper uses a discovering approach to choose a prediction model for real-world environmental data. The approach consists of two steps: (1) model selection which determines the type of prediction model, such as linear or non-linear; (2) model optimisation which aims at using less environmental data for prediction but without any loss on accuracy. The latter step is achieved by automatically selecting non-redundant features without using specific models. Various experimental results on real-world data illustrate that using specific linear model can work pretty well and fewer environment distribution maps can quickly make better/comparable prediction with the benefit of lower cost of data collection and computation. [ABSTRACT FROM AUTHOR]

This paper describes a control strategy for mobile robot Auto-seeking the Welded joint. Through this control strategy the mobile robot can find position of the weld joint automatically and adjust welding torch to parallel to it. Many simulation experiments results demonstrate the effectiveness and correctness of the control algorithm. [ABSTRACT FROM AUTHOR]

Hybrid actuators, which are a combination of two types of motor and mechanism, have good flexibility. In this paper, the kinematics analysis for a hybrid five-bar actuator is introduced. An optimization design of hybrid actuator is performed with reference to kinematics objective function. By the use of the properties of ergodicity, stochastic property, and "regularity" of chaos, a hybrid optimization algorithm is proposed based on chaos optimization algorithm (COA) and a gradient-based search. The efficiency of COA is much higher than some stochastic algorithms such as simulated anneal algorithm (SAA) and genetic algorithm (GA) when COA is used to a kind of continuous problems. The chaos optimization algorithm can improve the efficiency of searching in the whole field by gradually shrinking the area of optimization variable. The precision of optimum dimensions obtained by using the hybrid optimization method can be improved evidently. Finally, a numerical example is carried out, and the simulation results show that the optimization method is feasible and satisfactory in the design of hybrid actuator. [ABSTRACT FROM AUTHOR]

Based on the analysis of existent evaluation methods for coaxiality errors, an intelligent evaluation method is provided in this paper. The evolutional optimum model and the calculation process are introduced in detail. According to characteristics of coaxiality error evaluation, ant colony optimization (ACO) algorithm is proposed to evaluate the minimum zone error. Compared with conventional optimum evaluation methods such as simplex search and Powell method, it can find the global optimal solution, and the precision of calculating result is very good. Then, the objective function calculation approaches for using the ACO algorithm to evaluate minimum zone error are formulated. Finally, the control experiment results evaluated by different method such as the least square, simplex search, Powell optimum methods and GA, indicate that the proposed method does provide better accuracy on coaxiality error evaluation, and it has fast convergent speed as well as using computer expediently and popularizing application easily. [ABSTRACT FROM AUTHOR]

In this paper, we present an optimised learning algorithm for learning the parametric prior models for high-order Markov random fields (MRF) of colour images. Compared to the priors used by conventional low-order MRFs, the learned priors have richer expressive power and can capture the statistics of natural scenes. Our proposed optimal learning algorithm is achieved by simplifying the estimation of partition function without compromising the accuracy of the learned model. The parameters in MRF colour image priors are learned alternatively and iteratively in an EM-like fashion by maximising their likelihood. We demonstrate the capability of the proposed learning algorithm of high-order MRF colour image priors with the application of colour image denoising. Experimental results show the superior performance of our algorithm compared to the state-of-the-art of colour image priors in [1], although we use a much smaller training image set. [ABSTRACT FROM AUTHOR]

Any quantum algorithm has to be implemented by quantum circuit. However, due to the existence of decoherence time and the difficulty of importing ancillae qubits, we introduce a new method which can greatly reduce the operating time steps by making good use of relaxing qubits without new ancillae qubits.This concept presented in this paper can be generalized to the other quantum networks. [ABSTRACT FROM AUTHOR]

Network random delays directly cause the degradations of networked control systems. A methodology based on model predictive control (MPC) is proposed to overcome the non-deterministic delays in data communication of the network. The general algorithm of dynamic matrix control (DMC) has been improved to make it suitable for network condition. When data packets can not arrive at target nodes in sequence, the predictive value of the system output can be used to take the place of the actual measure value by the controller, and the predictive value of control input will be acted as the required control value which coordinate the whole control system. The experiment results based on Motor Ethernet Control Open Platform (MECOP) show the effectiveness of the real-time networked control strategy. [ABSTRACT FROM AUTHOR]

Vibration control is a pivotal study subject for vehicle suspension system. In this paper, the physical model of semi-active quarter-coach suspension was established by the base of the theories of Buckingham's Pi theorem. According to the characteristics of the semi-active suspension of diesel-truck, a hybrid intelligent control algorithm-Fuzzy cerebellar model articulation control combined the Fuzzy logic control with cerebellar model articulation control techniques was presented and used to perform online control of semi-active suspension for the first time, novel weight-update laws were derived that guarantee the stability of closed-loop system, both information retrieval and learning rules were described by algebraic equations in matrix form. The results of experiment of closed -loop excited by three typical vibration signals showed that the control strategy proposed here can obviously reduce the value of mean square root of vertical acceleration for semi-active suspension system, compared with the traditional control strategy. [ABSTRACT FROM AUTHOR]