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A novel methodology based deep belief networks and multiple models (DBNs-MMs) is presented to accomplish fault detection for complex systems. And firstly, historical datasets are collected and processed to train the DBNs, so that DBNs can be constructed to learn the nonlinear dynamic characteristics of complex system, and so a model with a specific architecture and some initial intelligence will be built up. Secondly, the operation condition and real-time measurement data are employed in DBNs-MMs to get a series of network outputs. Finally, the residuals can be obtained by comparing the measurement output and each DBN output. Then, the fault detection can be achieved by employing a properly adaptive threshold for each residual. Some faulty cases of the complex cryogenic propellant loading system have been used to demonstrate the effectiveness of the proposed fault detection methodology, and the result has shown its excellent performance.

In this paper, a novel multi-focus image fusion approach is presented. Firstly, a joint dictionary is constructed by combining several sub-dictionaries which are adaptively learned from source images using K-singular value decomposition (K-SVD) algorithm. The proposed dictionary constructing method does not need any prior knowledge, and no external pre-collected training image data is required either. Secondly, sparse coefficients are estimated by the batch orthogonal matching pursuit (batch-OMP) algorithm. It can effectively accelerate the sparse coding process. Finally, a maximum weighted multi-norm fusion rule is adopted to accurately reconstruct fused image from sparse coefficients and the joint dictionary. It can enable the fused image to contain most important information of the source images. To comprehensively evaluate the performance of the proposed method, comparison experiments are conducted on several multi-focus images and manually blurred images. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques, in terms of visual and quantitative evaluations.

Multi-modality medical image fusion technology can integrate the complementary information of different modality medical images, obtain more precise, reliable and better description of lesions. Dictionary learning based image fusion draws a great attention in researchers and scientists, for its high performance. The standard learning scheme uses entire image for dictionary learning. However, in medical images, the informative region takes a small proportion of the whole image. Most of the image patches have limited and redundant information. Taking all the image patches for dictionary learning brings lots of unvalued and redundant information, which can influence the medical image fusion quality. In this paper, a novel dictionary learning approach is proposed for image fusion. The proposed approach consists of three steps. Firstly, a novel image patches sampling scheme is proposed to obtain the informative patches. Secondly, a local density peaks based clustering algorithm is conducted to classify the image patches with similar image structure information into several patch groups. Each patch group is trained to a compact sub-dictionary by K-SVD. Finally the sub-dictionaries are combined to a complete, informative and compact dictionary. In this dictionary,only important and useful information which can effectively describe the medical image are selected. To show the efficiency of the proposed dictionary learning approach, the sparse coefficient vectors are estimated by a simultaneous orthogonal matching pursuit (SOMP) algorithm with the trained dictionary, and fused by max-L1 rules. The comparative experimental results and analyses reveal that the proposed method achieves better image fusion quality than existing state-of-the-art methods.