Internal valve leakage in a natural gas pipeline seriously impairs the safe operation on pipelines, and the recognition of leakages has therefore been a major concern of the industry. In this study, a novel leakage detection scheme based on kernel principal component analysis (kernel PCA) and the support vector machine (SVM) classifier for the recognition of the leakage level is constructed. Using this approach, the acoustic signal of the leakage is obtained as the feature source using an acoustic emission (AE) sensor. The kernel PCA is used to reduce the dimensionality of the features and extract the optimal features for the classification process, and the SVM is applied to perform the recognition of the leakage levels. The performance of the classification process based on kernel PCA and the classifier are evaluated in terms of the accuracy, Cohen's kappa number and training time. The experimental results demonstrate that the intelligent recognition model based on kernel PCA and SVM classifier is very effective for recognizing the leakage level of a valve in a natural gas pipeline.The inevitable events of gas pipeline valve leakage during the gas transportation process pose serious problems to the availability, reliability, and economy of the pipeline. Thus, the possibly used safety measures focused on early detection of both small and large leakages is not only a guarantee for safer operation but also a help for reducing the costs of the industry. To monitor the valve yield condition for substantial cost savings and safer working conditions, a great number of methods have been developed. However, the currently available leakage detection methods provide little capability for the quantitative recognition of leakage levels.

No abstract

No abstract

Owing to the increased demand of long endurance flight, lightweight, and high strength envelope becoming the main point of the stratospheric airship design, especially the research on the envelope mechanical properties a novel stratospheric airship envelope material was developed in this paper. According to the structure of the woven fabric composite, nonlinearity and orthotropy are two main characteristics of the envelope. Uniaxial tensile test was performed to study the stress-strain curves of the envelope material. It is seen that the force-displacement curve can be divided into two significant nonlinear regions and three quasi-linear regions by the analysis of the test results. To analyze the forcedisplacement curve with different regions by the statistical method, Monte Carlo simulation based on the Ising model was developed. It is found that the simulation curves coincide well with the experiment curves both in the warp and weft directions. And the results show that parameters of the envelope material including fabric strength, functional layer strength, interfacial bonding strength, and so on, are the important factors affecting the mechanical properties and they decide the force-displacement curve shape.

Image enhancement has been an important technique for image analysis. The purpose of enhancement is suppressing noises and enhancing image details. However, most of algorithms only focus on the noise suppressing or detail enhancing. In this paper, an algorithm which could both suppressing noises and enhancing details through combining the adaptive median filter and Wallis filter is proposed. The adaptive median filter and Wallis filter are combined through the alternative strategy. Also, the strategy similar to the multi-scale enhancement is also performed to further enhancing the images. Experiments on various images verified that, the proposed algorithm performs effectively for both the noise suppressing and detail enhancing.