An ECT System Based on Improved RBF Network and Adaptive Wavelet Image Enhancement for Solid/Gas Two-phase Flow

Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance.To solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image enhancement.Firstly,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured.Then,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image feature-enhanced.Through multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be constructed.Finally,the image is reconstructed with adjusting wavelet coefficients.In this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm.Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].

Building Warships and Nurturing Technical Talent at the Foochow Navy Yard during the Self-Strengthening Movement

In 1866, after the Opium Wars, the Chinese official Zuo Zongtang established the Foochow Navy Yard, which aimed to facilitate the independent construction of modern warships. French advisers, engineers, teachers, and craftsmen were hired, and a series of French schools for naval construction, drawing, and apprenticeship were set up. Previous studies have nearly exhausted the historical material on the Foochow Navy Yard, but few of them give an exact evaluation on its shipbuilding and educational levels. This paper traces the French sources on the shipbuilding technology and educational system at the Foochow Navy Yard and conducts a comparative study. With the guidance and assistance of foreigners, the Foochow Navy Yard gained the ability to assemble ships and imitate engines, while it remained necessary to import design drawings and structural components. The most outstanding students that the navy yard nurtured may have reached the level of école Polytechnique graduates, but the quality of students was hard to maintain. The backwardness of its conceptualization and the lack of financial and political support also contributed to its decline.

Combining supervised classifiers with unlabeled data

Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.

“融”出新模式 “合”出大格局

随着5G、大数据、区块链等技术的迅猛发展,传播格局和舆论生态发生了深刻变化。传统媒体承受了前所未有的压力,也面临着重大的时代机遇。济南日报报业集团坚持"融合赋能,伴生城市成长"的"大融合"理念,全方位推动媒体融合向纵深发展。