Applied Mechanics and Materials Vols. 511-512

Abstract: Cooperative control of multiple autonomous underwater vehicles (AUVs) plays an important role on marine scientific investigation and marine development. The formation of multi-AUV can significantly enhance applications on the marine sampling, imaging, surveillance and communications. Compared to the formation control of multi-robot, the formation control of multi-AUV is particularly difficult, especially on controlling attitude and direction of AUV; what is more, the communication method among AUVs is acoustic. When communication distance increases, the communication qualities deteriorate quickly; this mainly makes time-delay, signal attenuation and distortion. Although formation control of multiple AUVs obtains a wide range of attention in recent years, the fruits on formation control problem are less than ones on land multi-robot problems. For example, Fiorelli conducted a collaborative and adaptive sampling research of multi-AUV at the Monterey Bay ^[; Yu and Ura carried out the cable-based modular fast-moving and obstacle-avoidance experiments, and presented an interconnected multi-AUV system with three-dimension sensors. On the aspect of formation control framework ^[2-, [ proposed a four-layer cooperative control strategy based on hierarchical structure; [ proposed a hierarchical control framework based on hybrid model. In addition, Yang converted a nonholonomic system to a chain one and designed a controller to implement a leader-follower formation for multiple AUVs in [. The formation control for multiple autonomous underwater vehicles is rather different than the control methods for other vehicles, because the formation control for AUVs is of its characteristics, such as the large-scale distribution in space. The finite-time consensus controller designing based on finite-time control and consensus problem has important theoretical and practical significance. The decentralized controller methods for the autonomous underwater vehicle are applied more and more, but they ignore the coupling relationship between them. Another method is that an AUV is modeling as an agent, but this method ignores attitude characteristics of AUVs (pitch, roll and yaw). In this paper, we consider the cooperative control problem in three dimensional spaces. Finite-time formation for Autonomous Underwater Vehicles (AUVs) with constraints on communication range is investigated. We proposed a two-layer finite-time consensus control law, to avoid leading to collapse on formation because of failure leader, all AUVs are arrayed in the same level and each AUV can obtain global formation information. Finally, the simulation results show the effectiveness of the control strategy.

Abstract: The paper proposed a novel neural network ensemble algrithm (NNNEA) whose individual was generated by clonal selection algorithm. NNNEA can produced individuals of ensemble with better difference than other algrithm. NNNEA was used for predicting ciruit functions and finding sneak circuit. The inputs of NNNEA are states of switches, and the outputs are states of functional components. NNNEA predicted all possible functions of circuit. The sneak circuits can be discovered by comparing the predicted with designed functions. Although there are several limitations of NNNNEA, the results revealed that NNNNEA can exactly discover sneak circuits.

Abstract: To improve the data transmission reliability of wireless meter reading system, a bidirectional routing algorithm is proposed. The routing algorithm is developed to make all nodes in the network establish optimal routing and register a complete neighbor table for routing maintenance. The neighbor exchange mechanism based on Hello Packet can timely detect changes in network topology and solve the problem of nodes flexibly coming in or out of the network. Simulation and system testing have all verified the reliability and usefulness of the algorithm which can satisfy the need of wireless meter reading application.

Abstract: High-pressure circuit breakers are very important and it undertakes the disconnection and connection control of high voltage transmission lines. It is one of the equipment of substation daily inspection. State of breakers are judged by open and close characters label, so a shape-prior active contour model to realize state automatic recognition of breaker images collected by inspection robot is presented in this paper. Shape-prior active contour model combines the shape information with CV model to build energy functional model, then set up initial position curve by a priori knowledge and drives the curve evolution in minimize energy functional process, the curve position is the character label contour when energy functional shows minimum. We do experiment for the algorithm on different images, demonstrate that the algorithm based on known character contour, have good segmentation results of circuit breaker in the image character recognition accuracy and applicability when the circuit breaker character is actually partial occlusion, local deformation, scale changes.

Abstract: In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

Abstract: According to the characteristics of complex mechanical product, we put forward case matching algorithm in case-based reasoning(CBR) ,which uses grey correlation theory,combines with the variation coefficient method and Euclidean distance.And case retrieval and matching model was established.An example of CNC machine tool design was provided to validate and prove its efficiency and feasibility

Abstract: Aim to the traditional acceleration gesture recognition system on PC platform had the problem of high power consumption, hard to carry and low recognition rate, the paper proposes a novel gesture recognition algorithm. The algorithm first sampled the gestures signal acceleration by acceleration sensor, and then segmented and smoothing filtered the collected original signal. After preprocessing, extracted the feature value and segmented the feature value according to segments signal energy. Finally for all the segments used the improved DTW(Dynamic Time Warping) algorithm[1] to match the extracted signal features with the predefined template feature respectively and integrated the matching results of them, then concluded the final recognition results. We apply the proposed algorithm to the smartphone and test the system. Testing result shows that: The novel algorithm can improve the recognition rate and enable the system to real-time and accuracy recognizes gestures.

Abstract: Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

Abstract: When we manipulate high dimensional data with Elman neural network, many characteristic variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the accuracy of recognition finally. Factor Analysis (FA) concentrates the information that is carried by numerous original indexes which form the index system, and then stores it to the factor, and can according to the precision that the actual problem needs, through controlling the number of the factors, to adjust the amount of the information. In this paper we make full use of the advantages of FA and the properties of Elman neural network structures to establish FA-Elman algorithm. The new algorithm reduces dimensions by FA, and carry on network training and simulation with low dimensional data that we get, which obviously simplifies the network structure, and in the process, improves the training speed and generalization capacity of the Elman neural network.

Abstract: The convergence rate is very important in the distributed consensus problems, especially, for the distributed consensus algorithms based on large-scale complex networks. In order to accelerate the convergence rate of the distributed consensus algorithms, in the paper, we propose an optimized topology model by adding randomly a few shortcuts to the nearest neighbor coupling networks, and the shortcuts follow a normal distribution. By analyses and simulations, the results show that the algebraic connectivity of the new model is bigger than that of the NMW model, and the convergence rate of the distributed consensus based on the new model is higher than that based on the NMW model