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Summary In the past several years, a series of breakthrough research advancements have been achieved by leveraging wireless signals such as Wi‐Fi in various emerging applications, including healthcare, behavior recognition, positioning, and target detection. Compared to traditional human behavior sensing methods, Wi‐Fi signals human behavior sensing technology has many advantages, including non‐line‐of‐sight, sensor device‐free sensing, passive sensing, ease of deployment, and no need for lights. Data mining undoubtedly plays a critical role in making Wi‐Fi‐based human behavior detection intelligent enough to facilitate convenient services and environments. We study Wi‐Fi signals mining using the data mining process and review the developmental process of Wi‐Fi data mining. This covers the methods of Wi‐Fi data mining, including signal acquisition, preprocessing, feature extraction to training, and classification. We then propose WHSecurity, a whole home intrusion detection and tracking system that is based on all of the methods covered above. Finally, WHSecurity includes a deep learning‐based data mining process called multiview learning for the decision‐making on intrusion detection and tracking. Experimental outcomes show that the WHSecurity approach performs superior in terms of intrusion detection and tracking performance. [ABSTRACT FROM AUTHOR]

In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for backpropagation is derived by constraining the addition of bias voltage in spiking neurons. The proposed differential encoding method can effectively exploit the correlation between the data and improve the performance of the proposed model, and the new learning rule can take complete advantage of the modulation properties of bias on the spike firing threshold. We experiment with the proposed model on the environmental sound dataset RWCP and the image dataset MNIST and Fashion-MNIST, respectively, and assign various conditions to test the learning ability and robustness of the proposed model. The experimental results demonstrate that the proposed model achieves near-optimal results with a smaller time step by maintaining the highest accuracy and robustness with less training data. Among them, in MNIST dataset, compared with the original spiking neural network with the same network structure, we achieved a 0.39% accuracy improvement. [ABSTRACT FROM AUTHOR]

Abstract: To better equip with a non-expert to carry out the diagnosis operations, a new method for intelligent fault identification of rotating machinery based on the empirical mode decomposition (EMD), dimensionless parameters, fault decision table (FDT), MLEM2 rule induction algorithm and improved rule matching strategy (IRMS) is proposed in this paper. EMD is used to preprocess the vibration signals for mining the fault characteristic information more accurately. Then, dimensionless parameters are extracted from both the decomposed signals in time domain and envelop spectrum in frequency domain respectively to form the conditional attributes of a FDT. Moreover, MLEM2 algorithm is run directly on the FDT to generate decision rules imbedded in the data. To make the following classification process more robust, the IRMS is adopted to resolve the conflicting and non-matching problems. Finally, data of rolling element bearings with four typical working conditions is used to evaluate the performance of the proposed method. The testing result demonstrates that the method has high accuracy and systematically good performance. It is proved to be a convenient, concise, interpretable and reliable way to diagnose bearings’ faults. The advantages are also confirmed by the comparisons with the other two approaches, i.e. the principal component analysis (PCA) and probabilistic neural network (PNN) based method as well as the wavelet transform (WT) and genetic algorithm (GA) based one. Furthermore, thank to the FDT working as a data interface, the method is more transplantable, therefore it may be applied to diagnose other types of rotating machines effectively. [Copyright &y& Elsevier]