Novel mesh technique and its application in the wind field simulation for flexible spatial structure

In this paper, novel mesh techniques are proposed for wind field simulation of flexible spatial structure. For mesh generation, an interpolation strategy is presented to obtain a mesh system with variable density. Two spatial structure examples are used to examine the efficiency and applicability of this technique. Then based on the structured mesh system generated by the technique, the mesh nodal coordinates are updated to adapt the moving boundary conditions by means of the mapping interpolation functions and some examples are given to verify the effectiveness. Furthermore, the constrained counterforce distribution technique and projection interpolation strategy are developed to implement the data exchange on the interaction surface of wind and structure. Finally, the computational accuracy is numerically validated.     

Grafting watermelon seedling production management system based on process control strategy

Greenhouse environmental management, seedling bed scheduling, and fertilizer and water management are the core technologies of seedling nursery. Precision management and control of the whole production process guarantees high grated seedling quality. In order to improve management efficiency of grafted watermelon seedling production and seedling quality, this paper designs and develops a grafted watermelon seedling production management system based on process control methods by process control strategy, seedling bed planning design, and handheld device technology.     

Image fusion scheme based on nonsubsampled contourlet and block-based cosine transform

A kind of nonsubsampled contourlet and block-based cosine transform (NSCBCT) is developed, and its application in image fusion is studied in this paper. The construction of filtering banks is based on the nonsubsampled contourlet transform (NSCT) and block-based discrete cosine transform (B-DCT). We combine NSCT and B-DCT to design filters that lead to NSCBCT with better singularity representation than either of them in isolation. A design framework based on the hybrid approach is proposed, which allows for the fast implementation based on NSCT and B-DCT respectively. In addition, a new image fusion scheme based on NSCBCT for multispectral and panchromatic satellite images is proposed. Firstly, because it adopts NSCT, the fused satellite images have higher spatial resolution than those based on wavelets. Secondly, based on the localized high frequency information provided by B-DCT, the proposed fusion scheme can reduce the spectral distortion of the fused image further. Experimental results show that the proposed fusion method is able to increase the spatial resolution and reduce the spectral distortion of the fused image at the same time.     

Nonlinear analyses of exchange rates of six emerging markets

This paper presents some empirical evidences on the presence of nonlinearity of exchange rates of six emerging markets by using Brock-Dechert-Scheinkman (BDS) test and Volterra-Wiener-Korenberg (VWK) model, respectively. The nonlinear dependences are found in the exchange rates of six emerging markets. Furthermore, this paper applies the VWK model with surrogate data method to detect if their nonlinear dependences are deterministic or not. The results show that the above exchange rates are deterministic and nonlinear time series. These imply that the exchange rate markets do not conform to the requirements of the random walk hypothesis. Therefore, the nonlinear dynamic model should be used to analyze the exchange rates.     

TDOA Passive Location Based on Cuckoo Search Algorithm

This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival(TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization(PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram′er-Rao lower bound(CRLB) and quickly achieve the global optimal solutions.     

Adaptive Sliding-Mode Tracking Control for an Uncertain Nonlinear SISO Servo System with a Disturbance Observer

An adaptive sliding mode controller with a disturbance observer (ASMC-DO) is proposed for the control of a single-input and single-output (SISO) servo system which has uncertain parameters, nonlinear friction, disturbance and input saturation. It is difficult to choose the suitable value of the parameters. The newly designed adaptive method is used to reduce the effects of system time-varying parameters, such as the moment of inertia and the damp coefficient. The robustness of object is improved. A DO is selected to approximate the compound disturbance and to render the estimate error convergent in finite time. The stability and the convergence of the closed-loop system are proved by using the Lyapunov theory. Experimental results show that the proposed ASMC-DO can better satisfy the influence of variable parameters and external disturbance to the control precision of the SISO servo system than other two controllers. The effectiveness of the proposed controller is showed. The control input stability and robust performances of the input saturation system are enhanced and the chattering is reduced.     

Adaptive Multi-Objective Optimization of Bionic Shoulder Joint Based on Particle Swarm Optimization

To get the movement mode and driving mechanism similar to human shoulder joint, a six degrees of freedom (DOF) serial-parallel bionic shoulder joint mechanism driven by pneumatic muscle actuators (PMAs) was designed. However, the structural parameters of the shoulder joint will affect various performances of the mechanism. To obtain the optimal structure parameters, the particle swarm optimization (PSO) was used. Besides, the mathematical expressions of indexes of rotation ranges, maximum bearing torque, discrete dexterity and muscle shrinkage of the bionic shoulder joint were established respectively to represent its many-sided characteristics. And the multi-objective optimization problem was transformed into a single-objective optimization problem by using the weighted-sum method. The normalization method and adaptive-weight method were used to determine each optimization index’s weight coefficient; then the particle swarm optimization was used to optimize the integrated objective function of the bionic shoulder joint and the optimal solution was obtained. Compared with the average optimization generations and the optimal target values of many experiments, using adaptive-weight method to adjust weights of integrated objective function is better than using normalization method, which validates superiority of the adaptive-weight method.     

Structural Optimization of Hatch Cover Based on Bi-directional Evolutionary Structure Optimization and Surrogate Model Method

Weight reduction has attracted much attention among ship designers and ship owners. In the present work, based on an improved bi-directional evolutionary structural optimization (BESO) method and surrogate model method, we propose a hybrid optimization method for the structural design optimization of beam-plate structures, which covers three optimization levels: dimension optimization, topology optimization and section optimization. The objective of the proposed optimization method is to minimize the weight of design object under a group of constraints. The kernel optimization procedure (KOP) uses BESO to obtain the optimal topology from a ground structure. To deal with beam-plate structures, the traditional BESO method is improved by using cubic box as the unit cell instead of solid unit to construct periodic lattice structure. In the first optimization level, a series of ground structures are generated based on different dimensional parameter combinations, the KOP is performed to all the ground structures, the response surface model of optimal objective values and dimension parameters is created, and then the optimal dimension parameters can be obtained. In the second optimization level, the optimal topology is obtained by using the KOP according to the optimal dimension parameters. In the third optimization level, response surface method (RSM) is used to determine the section parameters. The proposed method is applied to a hatch cover structure design. The locations and shapes of all the structural members are determined from an oversized ground structure. The results show that the proposed method leads to a greater weight saving, compared with the original design and genetic algorithm (GA) based optimization results.     

GNN-CRC: Discriminative Collaborative Representation-Based Classification via Gabor Wavelet Transformation and Nearest Neighbor

Collaborative representation-based classification (CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor (NN) features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.