Abstract-THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD. The nonparametric decentralized detection problem is investigated, in which the joint distribution of the environmental event and the sensors' observations are not known and only a set of training samples are available. The system features rate constraints, i.e., integer bit constraints on sensors' transmissions, different qualities of observations, additional observations to the fusion center, and multi-level tree-structured network. Our study adopts the kernel-based nonparametric approach proposed by Nguyen, Wainwright, and Jordan with the following generalization. A weighted count kernel is introduced so that the corresponding reproducing kernel Hilbert space (RKHS) (over which the fusion center's decision rule is optimized) allows the fusion center's decision rule to count information from sensors and its own observations differently. In order to find the optimal decision rules, our optimization is solved by alternatively and recursively conducting three optimization steps: finding the optimal weight parameters in the weighted count kernel for selecting the best associated RKHS, finding the best optimal decision rule for the fusion center over the identified RKHS, and finding the local decision rules for sensors. Generalization to multilevel tree-structured network is also discussed. Finally numerical results are provided to demonstrate the performance based on the proposed weighted count kernel.
Drilling pump is the “heart” of drilling construction. The key to accurate fault diagnosis is to extract useful fault features from noisy raw signals. In order to improve the accuracy of fault diagnosis of drilling pump fluid end, this paper proposes a fault diagnosis method based on multi-scale convolutional neural network (MSCNN) combined with the snake optimization optimized maximum correlation kurtosis deconvolution (SO-IMCKD). First, the SO algorithm is employed to optimize the filter length and the shift number of IMCKD to process the raw signal, enhancing the fault features from the raw signal. Second, the continuous wavelet transform (CWT) is used to convert the enhanced signals into time-frequency images which are input into an established MSCNN to extract the fault feature more effectively. Finally, by changing the training batchsize of the MSCNN model, the identification effect of the model to the normal state, minor damage, and serious damage of the fluid end is analyzed. The identification of nine states of the fluid end is successfully carried out, with an average diagnostic accuracy of 99.93%. Moreover, the adaptability of the proposed method is verified with the Mechanical Failure Prevention Technology Association (MFPT) dataset. The method has high accuracy and good adaptability, which has desired prospect for drilling pump fault diagnosis and bearing fault diagnosis.
In this paper, we propose a simple interactive way for a novel type of image synthesis called image rearrangement whose goal is to construct a new image based on some objects cropped from source images. The synthesis results are obtained by copying patches from the source images in a globally consistent way. The patch copying problem is formulated with the Markov random field model, and belief propagation is used as the optimization tool. To speed up our algorithm, a two-step belief propagation and a multiscale patch copying scheme are taken. Experimental results indicate that our algorithm obtains satisfactory results in both performance and efficiency.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.