In rotating machinery fault diagnosis, domain adaptation (DA) transfer learning-based framework has been attracting great attentions to tackle the problem of inconsistent feature distribution and insufficient labeled fault feature data. However, most of the existing approaches mainly focus on either the cross-domain distribution alignment or manifold subspace learning, which faces two critical limitations: 1) it is hard to overcome the feature distortions when aligning the distribution in the original feature space and 2) subspace learning is insufficient to decrease the distribution divergence. To address the above limitations, this work proposes an intelligent fault diagnosis scheme based on supervised DA with manifold embedding and key features selection. It first applies maximal overlap discrete wavelet packet transform (MODWPT) to process the vibration signals and performs the statistical feature extraction. In order to ensure that the selected key features are conductive to domain adaption, the fault discriminative ability and domain invariance of the features are investigated based on the domain differences and Laplacian score. Then, it presents a new supervised domain adaption with manifold embedding for the distribution alignment in manifold subspace by taking the class information and neighboring relationships into account. Finally, an intra classifier is learned to predict the unlabeled target domain. The proposed fault diagnosis scheme is evaluated using a set of practical data sets of motor and bearing. The extensive experimental results demonstrate that it significantly outperforms the comparative models and achieves much more effective fault diagnosis under different working conditions.

Being simple and portable, the three-dimensional (3D) convolution network has achieved great success in action recognition. However, its applicability in spatiotemporal feature learning is not evident. This study aims to improve the 3D convolution model and propose a flexible and significant attention module for the extraction of spatiotemporal information. Our first contribution is a self-additive attention module and a feature-based attention module, which is a simple yet effective method for measuring the spatiotemporal importance of a video. In self-additive attention, the spatiotemporal fusion between the frames is defined intuitively, where we set equivalent weights between the video frames manually. Further, the feature-based attention that is trained adaptively by the 3D convolution process combines the spatiotemporal information from the feature map. This study also focuses on attention fusion in learning the spatiotemporal characteristics for 3D convolution. The proposed attention fusion method exhibits outstanding performance in comparison to the recently developed attention modules and the latest 3D networks when applied to the data from the UCF101 and HMDB51 datasets. The experiments show consistent improvements, affirming the robustness of the method in extracting spatiotemporal attention.

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted [Formula: see text]-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85[Formula: see text]m in the exhibition hall and 1.28[Formula: see text]m in the laboratory, outperforming the other systems.

The Internet of Things (IoT) has been recognized as the next technological revolution. It faces two challenges: 1) how to achieve energy efficient communication for the battery constrained devices and 2) how to connect a very large number of devices to the Internet with low latency, high efficiency, and reliability. To address these problems, this paper proposes two methods based on Kalman filter (KF), termed as extensions of predicable KF (EPKF). They locally reduce the unnecessary transmission (access) of end devices to the network (Internet) utilizing the spatial and temporal correlations with low algorithmic overhead. Each transmitting device (TD) independently controls its transmission using the temporal correlation; and the receiving device (RD) exploits the spatial correlation among the TDs to further improve the reconstruction quality. The reconstruction problem in the RD is nonlinear. To reduce the computation complexity, an in-depth analysis of the local estimate error is conducted and the approximated linear solutions are thereupon obtained. They are fundamental methods applicable to any IoT monitored/controlled physical system that can be modeled as a linear state space representation. The pedestrian-position application is used as a case study to demonstrate the efficiency in the simulation. Remarkably, the EPKF methods using the linear combinations of the local estimates from multiple TDs reduce the transmission rate to 10%, while achieving the same reconstruction quality as using KF in the traditional manner.

In this paper, a multi-user cooperative game is proposed to stimulate selfish user nodes to participate in cooperative relaying in wireless ad hoc networks. Without using the traditional reputation-mechanisms or pricing-mechanisms, we resort to the resource-exchange mechanism by assuming a source node could reward the relaying nodes by, in return, forwarding data that are originated from these relaying nodes. Then the cooperation stimulating problem can be formulated as a multi-player cooperative bargaining game. We prove that there exists a unique Nash bargaining solution (NBS) of the game and propose a fast Particle Swarm Optimizer (PSO) algorithm to solve the NBS. Simulation results show that the NBS-based incentive strategy achieves social optimality, i.e., all cooperative nodes could achieve significant rate-gains in comparison with direct transmission. Moreover, the relaying nodes could also get fairness rewards by the source node according to the level of contribution that they have made to improve the performance of the source node.

In this paper, a cooperative game is proposed to perform a fair and efficient resource allocation for the time division multiple access (TDMA) based cooperative communication networks. In the considered system, two selfish user nodes can act as a source as well as a potential relay for each other. A transmission node with energy limitation is willing to seek cooperative relaying only if the data-rate achieved through cooperation is not lower than that achieved without cooperation by consuming the same amount of energy. The cooperative strategy of a node can be defined as the number of data-symbols and power that it is willing to contribute for relaying purpose. We formulate this two-node fair and efficient resource sharing problem as a bargaining game. Since the Nash bargaining solution (NBS) to the game is computationally complex to obtain, a low-complexity algorithm to search the suboptimal NBS is proposed. Simulation results show that the NBS results are fair in that both nodes could experience better performance than if they work independently. And the NBS results are efficient in that the performance loss of the game to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair.

For the problem of rock burst, which seriously influences the security of mines at present, the text concludes the predicting researches and summarizes the representative characteristic parameters of acoustic emission and the characteristic parameters of attribute rock. On such a base these parameters are adopted to establish the predicted model for rock burst which is based on the fuzzy logic ore. With the gathered data from Sanhejian coal mine and the information about rock burst which is applied to the model, the result has proved to be satisfied. Thus, it helps to build the foundation that the fuzzy logic is practically applied to forecasting rock burst.