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To achieve online testing of high-frequency electric resistance welding (HF-ERW) tube quality, forecasting models were established for welding defect conditions with collected high-speed images of the joint melting phenomenon, based on a radial basis function neural network (RBFNN). Firstly, the dimensions of the collected image samples were deduced by principal component analysis (PCA). Then, the reduced-dimension image samples were set as inputs of both BPNN (back-propagation neural network) and, for comparison, RBFNN, which were trained so that the model parameters were optimized. Finally, the testing sample set was identified by trained networks. The experimental results show that RBFNN had better generalization ability for HF-ERW images than BPNN, which meant that the recognition rate of low-heat input status reached 100%, while the recognition rate of overheating input status reached 97.67%. They also show that the welding quality detection system based on a neural network is very effective and has a strong guiding significance for welding quality control.

For the purpose of solving the high quality required in some image based measurement systems, a new calibration algorithm for a non-uniform illumination field image is proposed on the basis of some existing algorithm. In this algorithm, by analysing the characteristics of the potential function for target detection, a new potential function for target detection was designed. A measurement function for standardisation of variety was deduced thereafter. After that, the mapping function from the original image to the background calibration image was given. Numerical simulations showed that the proposed algorithm can restrain the non-uniform background illumination field and calibrate the strong fluctuant background of the illumination field image and thus improve the quality of the image. Meanwhile, the algorithm is simple to carry out and very suitable in practice.

The complexity of taxi operation system grows out of the inherent dynamics and randomness of taxi services. A simulation model of cruising taxi operation system is presented, through which, the dynamic features of the taxis 24-hour available service can be recurred. A new method of determining the system optimal taxi fleet size is developed. These are helpful to taxi supervisor for providing better service.

The deficiency of predistortion performance exists in indirect NN (Neural Network)-predistorter learning methods for nonlinear high power amplifiers (HPAs), and direct NN-predistorter learning methods possess great computational complexity. To circumvent these problems, in this paper we propose a novel NN-predistorter learning method with its structure developed by using some properties of nonlinear operators and its corresponding algorithm derived by using an approximation formula. The proposed method is based on the identification of NN post-distorter of the HPA, and then directly implements the efficient Levenberg-Marquardt back propagation algorithm. Thus, compared with the direct NN-predistorter learning method, our proposed method reduces the computational complexity and still keeps slightly better predistortion performance. Theoretical analysis and simulation results also show our proposed method outperforms the indirect NN-predistorter learning method in the term of about 5 dB adjacent channel power ratio improvement.

TinyOS is a key element of the software infrastructure for the research and development involved in realizing wireless sensor networks (WSNs). In this paper, a priority-based scheduling strategy is proposed and implemented. Furthermore, the proposed approach and the original mechanism are tested and compared in the laboratory environment. Test result indicates that the modified strategy greatly improved the system performance for real-time tasks.

Our goal is to establish a simple technique for dynamic human body tracking based on image sequence. In this paper, we present a novel method of template matching and predictive tracking for moving body in clutter background. The method exploits the fact that moving human body changed in approximate rigid parts. First, we use the moving difference to find the region of interest, then we make a template of a target through the morphology algorithm using the skeleton of the object, and by revising the mask combined Kalman predictive tracking method to archive real-time tracking. It has been proved that in clutter background and noisy situation this algorithm is still efficient.

A HPN(Hybrid Petri Nets) traffic model, composed of discrete Petri Nets and TdPN(Timed Petri Nets), is proposed for modeling dynamic traffic flows at non-signalized T-intersections in urban traffic networks. This model is used to solve two primary problems. One is how to formulate dynamic traffic flows at intersections. Another is how to simulate their behavior accurately and continuously. The discrete Petri Nets represent the Levels of traffic priority, and the TdPN computes the corresponding time-varying quantities. The simulation results indicate that the model can represent the characteristics of dynamic traffic flows at non-signalized T-intersections and meet the requirements for modeling the urban transportation networks.

Aiming at the complex dynamic feature of large ship, an intelligent control structure based on Library-similar Knowledge-increasable Neural Network Group is presented. This compounded control structure using the dynamic knowledge-increasable learning capability of the neural network groups, solve the problems of online identification and online design of the controller, so that the high precise output tracking control of uncertain nonlinear large ship can be realized. Simulating results show that it is feasible and effective.

Aiming at the complex dynamic feature of large ship, an intelligent control structure based on Library-similar Knowledge-increasable Neural Network Group is presented. This compounded control structure using the dynamic knowledge-increasable learning capability of the neural network groups, solve the problems of online identification and online design of the controller, so that the high precise output tracking control of uncertain nonlinear large ship can be realized. Simulating results show that it is feasible and effective.

Aiming at the complex dynamic feature of large ship, an intelligent control structure based on Library-similar Knowledge-increasable Neural Network Group is presented. This compounded control structure using the dynamic knowledge-increasable learning capability of the neural network groups, solve the problems of online identification and online design of the controller, so that the high precise output tracking control of uncertain nonlinear large ship can be realized. Simulating results show that it is feasible and effective.

Flash memory is a kind of common storage device. Its characteristics of flexibility, low power, and so on offer excellent qualifications for embedded system and mobile system. But flash memory must be written after erasure operation, and the most important thing is that the erasure operation times are very limitable. For assurance of long time availability, data must be distributed over all memory space reasonably and politic, which brings forward challenge for storage system designer. This paper analyses the data structure and physical characteristics of typical flash memory. And a static trigger wear-leveling strategy based on classifying data with trigger condition is brought forward, called STWL. STWL forces these static data to move over all memory space according to the trigger condition so as to avoid some certain data blocks being damaged in advance. An experiment is carried out to simulate this strategy using VHDL. We construct a 4M bytes RAM as flash memory simulation model, a static wear-leveling unit to implement STWL and an excitation generation unit to yield memory store/load operations, As a result, the wear-leveling rate improves. 33% of space recycle times can be reduced and the biggest gap of number of erasing times of data block decreases from 883% to 38%.