Energy efficiency (EE) and spectrum efficiency (SE) have received significant attentions on optimizing the network performance in cognitive radio networks. In this paper, an EE+SE tradeoff based target is considered for the primary users (PUs) and the secondary users (SUs). First of all, considering the orthogonal frequency division multiple access-based resource allocation (RA) for the underlying SUs, we formulate an objective function through minimizing a weighted sum of the secondary interference power, where the network performance of both PUs and SUs are guaranteed by the constraints on quality of service, power consumption and data rate. However, it is a NP-hard problem. In order to solve it, we propose a damped three dimensional (D3D) message-passing algorithm (MPA) based on deep learning. Specifically, a feed-forward neural network is devised and an analogous back propagation algorithm is developed to learn the optimal parameters of the D3D-MPA. To improve the computational efficiency of the allocation and the learning, a suboptimal RA scheme is deduced based on a damped two dimensional MPA. Finally, simulation results are provided to confirm the effectiveness of our proposed scheme.

Deep Learning-Inspired Message Passing Algorithm for Efficient Resource Allocation in Cognitive Radio Networks

Condition monitoring and fault diagnosis are of significance to improve the safety and reliability of motors, given their widespread applications in virtually every branch of the industry. Sequential data modeling based on recurrent neural network and its variants have drawn increasing attention because the temporal nature of motor signals can be well leveraged for motor analysis. One common drawback of prior research is that signals measured on motors are typically analyzed with a fixed time window, making it difficult to tradeoff between global state estimation and local feature extraction. This paper presents a deep-learning-based model termed multiresolution & multisensor fusion network for motor fault diagnosis, through multiscale analysis of motor vibration and stator current signals. Specifically, vibration and current signals are first segmented by analysis windows of varying lengths to create a new data stream for the joint representation and temporal encoding of the original sensor signals, based on two network structures: convolutional neural network and long short-term memory. The advantage of the developed method is that it automatically learns the discriminative features through the network training process, without requiring manual feature selection as is typically the case in prior methods. By considering the temporal dependence of the signals being analyzed, the developed multiresolution fusion technique not only improves the effectiveness of feature extraction but is also adaptive to varying motor speed. Two case studies demonstrate the advantages of the developed method.

Multilevel Information Fusion for Induction Motor Fault Diagnosis

/pdf/an-introduction-to-key-technology-in-artificial-intelligence-4lvpkvt4f6.pdf

An Introduction to Key Technology in Artificial Intelligence and big Data Driven e-Learning and e-Education

Evidence theory is widely used in real applications such as target recognition because of its efficiency in evidential sensor data fusing. However, counter-intuitive results may be obtained in the ...

https://journals.sagepub.com/doi/pdf/10.1177/1550147719841295

A new method to measure the divergence in evidential sensor data fusion

This study aims to develop an integrated approach for solving probabilistic linguistic multi-criteria decision-making (MCDM) problems. This study first reveals the limitations in the existing metho...

Probabilistic linguistic multi-criteria decision-making based on evidential reasoning and combined ranking methods considering decision-makers’ psychological preferences

Fault diagnosis is a typical multisensor information fusion problem. The information obtained from different sensors, such as sound, pressure, vibration, and temperature, can be considered as a piece of evidence. From the viewpoint of the evidence theory, the problem of multisensor fault diagnosis can be viewed as the problem of evidence fusion and decision. However, the information obtained from different sensors may be inaccurate, uncertain, fuzzy, or even conflict, so how to set up the fault diagnosis architecture of a distributed multisensor system and combine the conflict evidence should be taken into consideration. In this paper, the classical Dempster–Shafer evidence theory is described and the disadvantage of a classical Dempster's combination rule is discussed. In order to solve the counter-intuitive result when using the classical Dempster's combination rule, the Euclidean distance is proposed to characterize the differences between different pieces of evidence, and then the support degree of each evidence is generated and the weighted pieces of evidence can be combined directly using the classical Dempster's combination rule. Numerical simulation examples indicate that the proposed method has a better performance of analyzing the conflict between different pieces of evidence, especially for high conflict evidence. Therefore, compared with the existing methods, it has better applicability. According to the requirement of the Dempster–Shafer evidence theory, the fault diagnosis architecture of a distributed multisensor system is analyzed in detail, and a fault case of a rotating machine is used to illustrate that the proposed model is effective and superior, which can be used in practice.

Multisensor Fault Diagnosis Modeling Based on the Evidence Theory

In this paper, we base the grey relational analysis and interval number as a method for recognizing the digital signal modulation. Firstly, the wavelet energy spectrum entropy, power spectrum entropy and bispectral entropy are selected as the characteristic of the signal modulation type. Then, considering the situation of the received signal is not stable, this paper combined the idea of interval number with the sequences of the grey relational analysis. Finally, this paper analyzed the recognition results of the classical grey relational analysis and grey relational analysis based on the interval number theory. The simulation experiment indicates that the grey relational analysis can be applied to the communication signal modulation recognition.

The method research of signal recognition based on information entropy and grey relational analysis

Tick-borne infectious diseases pose a serious health threat in certain regions of the world. Emerging infectious diseases caused by novel tick-borne pathogens have been reported that are causing particular concern. Several tick-borne diseases often coexist in the same foci, and a single vector tick can transmit two or more pathogens at the same time, which greatly increases the probability of co-infection in host animals and humans and can lead to an epidemic of tick-borne disease. The lack of epidemiological data and information on the specific clinical symptoms related to co-infection with tick-borne pathogens means that it is not currently possible to accurately and rapidly distinguish between a single pathogen infection and co-infection with multiple pathogens, which can have serious consequences. Inner Mongolia in the north of China is endemic for tick-borne infectious diseases, especially in the eastern forest region. Previous studies have found that more than 10% of co-infections were in host-seeking ticks. However, the lack of data on the specific types of co-infection with pathogens makes clinical treatment difficult. In our study, we present data on the co-infection types and the differences in co-infection among different ecological regions through genetic analysis of tick samples collected throughout Inner Mongolia. Our findings may aid clinicians in the diagnosis of concomitant tick-borne infectious diseases.

/pdf/co-infection-of-tick-borne-bacterial-pathogens-in-ticks-in-f4yb5ls5.pdf

Xuhong Yin

Papers

Multisensor Fault Diagnosis Modeling Based on the Evidence Theory

The method research of signal recognition based on information entropy and grey relational analysis

Co-infection of tick-borne bacterial pathogens in ticks in Inner Mongolia, China