Applied Mechanics and Materials Vols. 48-49

Abstract: A visual modelling approach and its computational technique were proposed to represent and simulate a kind of immune system, which is comprised of immune cells and immune molecules etc. To study natural immune system and artificial immune system according to information theories and computational methodologies, the hierarchical model of the immune system was proposed, more faithful and suitable for visual simulation than traditional models. The hierarchical immune system basically consisted of innate immune tier, adaptive immune tier and immune cell tier. Thus, the tri-tier model of the immune system was seamless and coherent with the architecture of the artificial immune system, so that the research on the natural immune system and the research on the artificial one could improve and synchronize each other. Though the structure and features of the natural immune system were difficult to measure and test, the tri-tier architecture and qualitative features of the artificial immune system were built, changed and verified. To validate the new approach to visualize and explore the natural immune system, many experiments were tested on the tri-tier artificial immune system. At last, the visual results of the simulations show that the visual modelling approach can provide an effective and better way of understanding the natural immune system.

Abstract: Miniature unmanned helicopter (MUH) is a controlled member which is very complicated, due to their some characteristics such as highly nonlinear, close coupled, time-variation, open-loop unstable etc. The traditional method of identification is a whole model method. Although those can solve some hard problem, the time-variation is not treated well. The paper introduces a method of model building for miniature unmanned helicopter (MUH), based on local least square support vector machine. Namely the nearest samples to the predicted sample are selected online, and model building is finished by those samples with prediction. The feature of this method is that using the idea of local model building updates the model online, and the global model building brings the low ability of model generalization. In the last, compared with the traditional method of least square support vector machine in the experiment, the results show the algorithm is more effective.

Abstract: Rate-dependent hysteresis is a strongly nonlinear phenomenon which exists in the giant magnetostrictive actuator (GMA); it has influence in the precision and stability of active vibration control. It is highly important in the control theory and control engineering that the influence of hysteresis is eliminated by the modeling of rate-dependent hysteresis for GMA. So an online intelligent modeling method, which is based on an improved online least squares support vector machines (IOLS-SVM), is presented for identifying rate-dependent hysteresis nonlinearity for GMA, and is used to online real-time training. The data measured in the experiment are used for modeling. The numerical simulation shows the effectiveness of the method.

Abstract: Many mobility models have been proposed for the simulation of vehicular sensor network, however, the existing models seldom consider the sociological Aspects of vehicle movement. The paper firstly analyzed the social characteristic of mobility of the real vehicular traces and gained the conclusion that is short average distance, limited node degree and high cluster coefficient. Based on it, a mobility model based social network is proposed and turned into a simulation, which consists of growing and evolvement of social networks, geographical location mapping of vehicles and node dynamics. The simulation results proved that the model’s characteristic is similar to real trace.

Abstract: On remote sensing imaging platform, quality of image is normally degraded by aliasing. The low-pass filter is commonly used to dealiasing. However low-pass filter introduces error for frequencies above its cutoff frequency. And the removed aliasing is also valuable information. To retain the valuable information, we propose a restoration based on band-pass filter. Firstly, the image is transformed into frequency domain. A restoration of reciprocal cell is adopted. It is based on geometrical characteristics of sensors. As a result, the superposition parts are separated from spectrum inside the bandwidth. Then aliasing spectrum is put into right position. Inverse filter is used to deblurring and remove the color noise. Finally, the shift invariance wavelet is combined to reduce the white noise. The test results indicate that the proposed restoration is better than conventional restorations. Valuable information of the restored spectrum is more than degraded spectrum. So this proposed method will be beneficial in the field of practical projects.

Abstract: This paper is devoted to the study of consensus problem of multi-agent systems with a time-varying reference state in directed networks with both switching topology and time-delay. Stability analysis is performed based on a proposed Lyapunov–Krasovskii function. Sufficient conditions based on linear matrix inequalities (LMIs) are given to guarantee that multi-agent consensus on a time-varying reference state can be achieved under arbitrary switching of the network topology even if the network communication is affected by time-delay. These consensus algorithms are also extended to consensus formation among the agents. Finally, simulation example is given to validate our theoretical results.

Abstract: With the development of computer network and automation technology, network production of NC machine tool has become trend in enterprise. Aim at communicate interface of NC machine tool, a NC machine tool control system design based on CAN bus was presented. Asynchronism serial communication software between DNC host computer with NC system and hardware realization of CAN bus communication and the key technologies for realizing system were discussed in detail.

Abstract: This paper is concerned with delay-dependent stability for systems with interval time varying delay. By defining a new Lyapunov functional which contains a triple-integral term with the idea of decomposing the delay interval of time-varying delay, an improved criterion of asymptotic stability is derived in term of linear matrix inequalities. The criterion proves to be less conservative with fewer matrix variables than some previous ones. Finally, a numerical example is given to show the effectiveness of the proposed method.

Abstract: This work puts forward a parameter-less and practical immune optimization mechanism in noisy environments to deal with single-objective chance-constrained programming problems without prior noisy information. In this practical mechanism, an adaptive sampling scheme and a new concept of reliability-dominance are established to evaluate individuals, while three immune operators borrowed from several simplified immune metaphors in the immune system and the idea of fitness inheritance are utilized to evolve the current population, in order to weaken noisy influence to the optimized quality. Under the mechanism, three kinds of algorithms are obtained through changing its mutation rule. Experimental results show that the mechanism can achieve satisfactory performances including the quality of optimization, noise compensation and performance efficiency.

Abstract: A new type of high precision back propagation (BP) neural network model was proposed and applied to nonlinear time series for improving its prediction accuracy. In order to optimize the neural network structure, it uses the correlation analysis to select the number of input node for BP neural network at first. Second, it uses grey clustering method to select the initial number of hidden node for BP neural network, then using the grey correlation analysis method to analyze the correlation degree between hidden node output and network output and according to the size of correlation degree to delete the redundant hidden nodes. Meanwhile, in order to improve model prediction accuracy, it increases the direct connection between the input layer and output layer. Finally, prediction results show that the proposed model has good prediction capability.