Measuring & controlling system based on industrial Ethernet (called IMMCS: IP Mode Measurement andControl System)has been a research focus at present. According to uncertainty of decision elements of multi-sensor cooperative measurement in IMMCS, a new decision method for IP mode multi-sensor cooperative measurement based on synergetics is brought forward. The cooperative measurement mechanism in IMMCS is described based on self-organization of synergetics, a decision model with the dynamic cooperation-competitionharmony course is constructed to form a three-layer feedback decision method. The method first selects the clustering function of sum of error squares. By means of transforming rule of sample set based on proportional clustering, the region vectors are acquired. Then the transformation is accomplished from region vectors to adjoint vectors through M-P generalized inverse calculation. Finally, ensuring several parameters of selforganization decision process, the universal formula on multidimensional uncertainty is derived in several different conditions. The application in IP mode on-line measurement and control of alcohol concentration is also presented in this paper. Through running the measuring & controlling strategies, the operation results are displayed. Thereinto, the iterative steps was 3 and the iterative error of order parameters was 2.4726e-016. The experimental results show the decision method is reliable, reduces greatly computing quantity and calculating time, decreases effectively the uncertainty of cooperative measurement decision in IMMCS. It can be generalized to decision research of complicated system.

No abstract

The resolution problem of welding seam ultrasonic imaging system is studied in this paper, a triple-stage stepwise solutions is proposed, the measured ultrasonic signal is processed by super-resolution algorithm based on wavelet transform, signal processing result shows: the method is feasible to improve image quality of ultrasound imaging equipment and enhances its ability to identify subtle defects in welding seam. Moreover, the algorithms is under further improving and enhancing in ultrasonic image..

According to the nonlinear dynamic characteristics and uncertainty of Multi-sensor Integrated System(MSIS), the Multisensor Cooperative Measurement Mechanism(MSCMM) is studied based on Synergetics. By dint of analogy method, the MSCMM is described deeply and its model with the dynamic cooperation-competition-harmony course is proposed. Then dynamics equation of MSCMM model is established. With the Slaving Principle(SP), eliminating stable modes, the potential function of order parameters equation is acquired. Through deducing instability limit of potential function, the cooperative degree ξ is obtained. After the MSIS becomes stable, the uniform expression of accuracy η of multidimension uncertainty is achieved. Sequentially, the assessment index system is confirmed, including cooperative degree ξ , accuracy η , iterative error ψ and stability steps L . Finally, the application in multi-parameter safety monitoring for electric transmission line is introduced. The experiment results show that the MSCMM possesses self-association and self-recover, enlarges greatly time-space range of multi-sensor system and decreases effectively uncertainties.

This paper presents work on a natural crack identification problem from eddy current testing (ECT) signals. ECT is a widely used in-service Nondestructive testing (NDT) technique. A crucial problem in ECT is to inverse flaw profile from testing signals. Iterative inversion algorithms are commonly used to solve this problem. Typical iterative inversion approaches use a numerical forward model to predict the measurement signal from a given defect profile. But the use of numerical models is computationally intensive. In this study, the reconstruction of natural crack shapes from the ECT signals is realized by utilizing artificial neural networks as the forward solver and applying a metaheuristics-based optimization method. The crack is successfully reconstructed that verified both the efficiency of the artificial neural network forward scheme and the feasibility of the metaheuristics-based inversion method.