The Method of Communication System Fault Diagnosis Based on Deep Belief Net

To meet the need of fault diagnosis for military communication system, an effective method based on deep belief (DBN) net is proposed. During the fault diagnosis, the bottom layer of DBN model is used to receive the input fault signals to extract the fault features and the fault classification results will be outputted after softmax classified. Accordingly, algorithms for DBN model and training and RBM parameter learning have been designed. To reduce the running time, parallel solutions based on MapReduce framework have been provided. In order to test and verify the effect of DBN fault diagnosis, the communication experiment system is built in the laboratory which the output signals of the transmitter and the receiver are measured and collected as the original data for further learning and training. Compared with the traditional fault diagnosis methods, it can be found that DBN method has high accuracy in fault diagnosis and the process is simple and friendly. It is impossible to realize real-time diagnosis and online diagnosis for the communication system. The research can be applicated to the health management of communication equipment, and it will provide advanced technical support and software program for the health of communication equipment

Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network

The prediction of underwater acoustic transmission loss in the sea plays a key role in generating situational awareness in complex naval battles and assisting underwater operations. However, the traditional classical underwater acoustic transmission loss models do not consider the regional hydrological elements, and the performance of underwater acoustic transmission loss prediction under complex environmental conditions in a wide range of sea areas is limited. In order to solve this problem, we propose a deep learning-based underwater acoustic transmission loss prediction method. First, we studied the application domains of typical underwater acoustic transmission loss models (ray model, normal model, fast field program model, parabolic equation model), analyzed the constraint rules of its characteristic parameters, and constructed a dataset according to the rules. Then, according to the characteristics of the dataset, we built a DBN (deep belief net) neural network model and used DBN to train and learn the dataset. Through the DBN method, the adaptation and calculation of the underwater acoustic transmission loss model under different regional hydrological elements were carried out in a simulation environment. Finally, the new method was verified with the measured transmission loss data of acoustic sea trials in a certain sea area. The results show that the RMSE error between the underwater acoustic transmission loss calculated by the new method and the measured data was less than 6.5 dB, the accuracy was higher than that of the traditional method, and the prediction speed was faster, the result was more accurate, and had a wide range of adaptability in complex seas.

Enhancing the prediction of disease–gene associations with multimodal deep learning

Motivation Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease–gene associations. Results In this study, we propose a method to predict disease–gene associations by multimodal DBN (dgMDL). Specifically, latent representations of protein-protein interaction networks and gene ontology terms are first learned by two DBNs independently. Then, a joint DBN is used to learn cross-modality representations from the two sub-models by taking the concatenation of their obtained latent representations as the multimodal input. Finally, disease–gene associations are predicted with the learned cross-modality representations. The proposed method is compared with two state-of-the-art algorithms in terms of 5-fold cross-validation on a set of curated disease–gene associations. dgMDL achieves an AUC of 0.969 which is superior to the competing algorithms. Further analysis of the top-10 unknown disease–gene pairs also demonstrates the ability of dgMDL in predicting new disease–gene associations. Availability and implementation Prediction results and a reference implementation of dgMDL in Python is available on https://github.com/luoping1004/dgMDL. Supplementary information Supplementary data are available at Bioinformatics online.