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In modern petroleum extraction industry, Internet of Things (IoT) technology is widely used, and various of sensors constitute a typical cyber-physical systems (CPS). There are more requirements of interoperability and interaction between well site device systems. In such industrial critical domain, high-confidence is also important. This paper presents the requirements and running environment of developing high-confidence CPS. To satisfy the requirements of interoperability and data exchange among heterogeneous well site systems, a foundation framework with well defined interfaces and quality of service constraints was established. Based on this framework, designing high-confidence petroleum extraction CPS using Model-Driven Architecture approach is proposed, the long-lived models produced in the process that can be applied to any implementation technologies and platforms through model transformation which make the systems more portable. A case study shows the detail of the development process and using communication bridges to build relationships between models. At last some important problems which need to be paid more attention in model transformation process are discussed.

An enhancement algorithm for weather fax map with zero-mean Gaussian noise is proposed based on BEMD and self features of meteorological facsimile maps. First decompose the noised meteorological facsimile map to IMF 1 and a residue, then estimate the variance of the noise via BEMD and IMF 1 through the intrinsic relation between the variances of IMF 1 and the noise. According to the current time and location of the ship and the frequency loaded in the meteorologic electrograph, the type and the geographic information of the meteorological facsimile map is obtained. Then the noised map subtracts the geographic information after map matching. Last perform non-local mean filtering and binary value optimization on key words and marks to obtain the refined map. Experiments prove that the proposed algorithm has better performance for the enhancement of meteorological facsimile maps.

This paper presents an alterative bi-dimensional empirical mode decomposition based on the assisted images constructed in advance according to needed bandwidth. Firstly, the given assisted image and its negative counterpart are added to the original image respectively to construct the two images to be decomposed. Secondly, the decomposed IMFs from the two images are added together to get the IMFs, in which the added images are canceled out with less mode-mixing and boundary effects. The contribution of the method lies in the low time-cost and controllable bandwidth, which exist in ASBEMD. Lastly, the efficiency and performance of the method are given through theoretical analysis and its application in remote sensing image fusion, which outperforms some previous approaches.