Applied Mechanics and Materials Vols. 373-375

Abstract: The extraction methods of both the shape feature based on Fourier descriptors and the motion feature in time domain were introduced. These features were fused to get a hybrid feature which had higher distinguish ability. This combined representation was used for human action recognition. The experimental results show the proposed hybrid feature has efficient recognition performance in the Weizmann action database .

Abstract: Berth scheduling operation is an important problem in container terminal. The mathematic model of this problem is described in this paper and an improved particle swarm optimization algorithm is introduced to obtain the optimal scheduling solution. A floating-point allocation rule is used to encode the particles in the discrete space. A local search method is combined with PSO to avoid precocity. Finally the experiments are done to prove the improved PSO in this paper can resolve the berth scheduling problem and get better solution and convergence speed than the basic PSO.

Abstract: To improve the ability of aircraft sortie generation, we propose a genetic method to model the aircraft spotting allocation optimal problem, and transform it to a constrained optimal problem. Sortie path and time from parking spot to catapult are calculated by pseudospectral method, and then the genetic algorithm is given in detail. Finally, an aircraft sortie scenario is given to prove our method. Simulation results testify our method satisfies the requirement of scheduling optimal and can help commanders on flight deck to control aircraft spot allocation.

Abstract: In this paper, we propose a context-awareness based personalized recommender system in the pervasive application space. The recommender system comprises the personalized recommender engine and the distributed context management framework. With a hybrid approach, the personalized recommender engine combines those contexts into the decisions on recommendations to get more comprehensive recommendation effectiveness. In contrast with existing middleware of context-awareness, the recommender system has an ability of user-centric recommendation. At the end of this paper, an emphasis is put on the metrics of the effectiveness of the recommender system.

Abstract: DEM matching algorithm without control points is the base for detecting deformation method, the deformation existing in DEMs is treated as gross error. The detectability and locatability of multiple gross errors is very critical according to the surveying error and reliability theory. The interactive observations number (ION) derived from gross error judgement matrix is adapted in this paper. The relationship between zero-column vectors in judge matrix and the rank of the coefficient matrix of DEM matching is discussed in theory, and prove none zero-column vectors exist with real date sets. ION Equal to the number of redundant observations. Experimental results show that DEM matching algorithm has the detectability and locatability of multiple gross errors, and the deformation can be correctly detected by robust estimators in prearranged confidence level.

Abstract: A good model is necessary in order to design a controller of a system off-line. It is especially beneficial in the implementation of new advanced control schemes in Unmanned Aerial Vehicle (UAV). Considering the safety and benefit of an off-line tuning of the UAV controllers, this paper identifies a dynamic MIMO UAV nonlinear system which is derived based on the collection of input-output data taken from a test flights (36250 samples data). These input-output sample flight data are grouped into two flight data sets. The first flight data set, a chirp signal, is used for training the neural network in order to determine parameters (weights) for the network. Validation of the network is performed using the second data set, which is not used for training, and is a representation of UAV circular flight movement. An artificial neural network is trained using the training data set and thereafter the network is excited by the second set input data set. The predicted outputs based on our proposed Neural Network model is similar to the desired outputs (roll, pitch and yaw) which has been produced by real UAV system.

Abstract: In recent years, people have been paying increasingly attention on monitoring the quality of drinking water, which becomes rather necessary after natural disasters such as the Beijing 7.21 rainstorm, considering that the drinking water is one of the main medium for epidemic spreading. Most of the existing evaluation methods have their bases on concise mathematical models, which often fail to describe the complex essential nonlinear relations between the water quality and the chemical material in it. In this paper, we propose the evaluation method by using the SOM neural network, a unsupervised method that is able to classify, and therefore evaluate, given water samples. In order to promote the convergence rate and the precision of SOM neural network when dealing with high dimensional and highly correlated samples, we add a PCA preprocessing procedure. Experiment results demonstrate that the improved SOM neural network could evaluate the water quality with high precision.

Abstract: Volleyball is a popular exercise. There are not only lots of sports enthusiasts but also many professional athletes. In a variety of tactics and globalization of volleyball sport, the match situation becomes complex and intense a lot in the relative. However, for the professional athletes, the focus of training is still just the specific skills and fitness training, so only doing the traditional training courses will make the athletes more difficult to get the winning. Therefore, in this paper, a new training conception is proposed to enhance the volleyball players successful blocking rate by neural network.

Abstract: Command ships driving simulation system based on existing virtual reality technology teaching conditions, mentioned about "teachers, students, simulation system" for the characteristics of ship driving command interactive teaching mode, built a simulation system, Simulation, computer network and multimedia technology etc as the foundation, connected to the innovative teaching mode of teaching and learning.

Abstract: Target tracking is one of the interesting tasks of mobile robots navigation. This paper presents a fuzzy logic controller (FLC) for a mobile robot chasing a moving target. The proposed control strategy computes both the linear and angular velocities of the robot in order to regulate its perceptual state according to the targets motion. Then, a particle swarm optimization (PSO) algorithm is applied to tune the scaling factors of the FLC for a better performance in term of target tracking. Simulation results demonstrate the effectiveness of the proposed control approach.