Sampling Adaptive Learning Algorithm for Mobile Blind Source Separation

Huang, Jingwen; Sun, Jianshan

doi:10.1155/2018/5048419

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…So far, there have been many effective and distinctive blind source separation algorithms. Typical algorithms include fast fixed-point algorithm [16], natural gradient algorithm [17], EASI Algorithm [18], and JADE algorithm [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Miao

Zhao

2020

Mathematical Problems in Engineering

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When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It is difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. In these environments, the vibration signals are first denoised with the synchronous cumulative average noise reduction (SCA) method, and the denoised signals were separated with the improved fast independent component analysis (FastICA) algorithm. The results of simulation test and rotor fault experiments demonstrate that the novel method can effectively extract fault features, certifying its superiority in comparison with previous methods. Therefore, it is likely to be useful and practical in the fault detection area, especially under the condition of strong noise and vibration interferences.

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Section: Introductionmentioning

confidence: 99%

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Miao

Zhao

2020

Mathematical Problems in Engineering

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“…There have been many effective blind source separation algorithms with different characteristics, including the fast fixed-point algorithm [3], natural gradient algorithm [29], Equivariant Adaptive Separation via Independence (EASI) algorithm [30], and Joint Approximation Diagonalization of Eigen-matrices (JADE) algorithm [31]. These have been widely used for fault diagnosis [32,33], image processing [34], speech recognition [35], earthquake prediction [36], and other fields. Lu et al [37] proposed a source contribution quantitative estimation method based on underdetermined blind source separation to identify the major vibration and radiation noise.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)

et al. 2020

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Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.

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Multimodal interaction aware embedding for location-based social networks

Yang

Wang

et al. 2023

AIC

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Location-based social networks (LBSNs) have greatly promoted the development of the field of human mobility mining. However, the sparsity, multimodality and heterogeneity nature of the user check-in data remains a great concern for learning high-quality user or other entities representations, especially in the downstream application tasks, such as point-of-interest (POI) recommendation. Most existing methods focus on user preference modeling based on sequential POI tags without exploring the interaction between different modalities (e.g., user-user interactions, user-timestamp interactions, user-POI interactions, etc.). To this end, we introduce a multimodal interaction aware embedding framework to generate reliable entity embeddings on the heterogeneous socio-spatial network. At its core, first, multi-modal interaction sub-graph sampling techniques are designed to capture the heterogeneous contexts; then, a self-supervised contrastive learning technique is leveraged to extract intra-modality and inter-modality interactions in a light way. We conduct experiments on the next-POI recommendation tasks based on three real-world datasets. Experimental results demonstrate the superiority of our model over the state-of-the-art embedding learning algorithms.

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Sampling Adaptive Learning Algorithm for Mobile Blind Source Separation

Cited by 4 publications

References 16 publications

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)

Multimodal interaction aware embedding for location-based social networks

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