Search

The remaining useful life (RUL) prediction plays a pivotal role in the predictive maintenance of industrial manufacturing systems. However, one major problem with the existing RUL estimation algorithms is the assumption of a single health degradation trend for different machine health stages. To improve the RUL prediction accuracy with various degradation trends, this article proposes an algorithm dubbed degradation-aware long short-term memory (LSTM) autoencoder (AE) (DELTA). First, the Hilbert transform is adopted to evaluate the degradation stage and factor with the real-time sensory signal. Second, we adopt LSTM AE to predict RUL based on multisensor time-series data and the degradation factor. Distinct from the existing studies, the proposed framework is able to dynamically model the degradation factor and explore latent variables to improve RUL prediction accuracy. The performance of DELTA is evaluated with the open-source FEMTO bearing data set. Compared with the existing algorithms, DELTA achieves appreciable improvements in the RUL prediction accuracy. [ABSTRACT FROM AUTHOR]

Data in the form of graphs are prevalent, ranging from biological and social networks to citation graphs and the Web. In particular, most real-world graphs are heterogeneous, containing objects of multiple types, which present new opportunities for many problems on graphs. Consider a typical proximity search problem on graphs, which boils down to measuring the proximity between two given nodes. Most earlier studies on homogeneous or bipartite graphs only measure a generic form of proximity, without accounting for different “semantic classes”—for instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct semantic classes. Learning these semantic classes are made possible on heterogeneous graphs through the concept of metagraphs. In this study, we identify metagraphs as a novel and effective means to characterize the common structures for a desired class of proximity. Subsequently, we propose a family of metagraph-based proximity, and employ a learning-to-rank technique that automatically learns the right parameters to suit the desired semantic class. In terms of efficiency, we develop a symmetry-based matching algorithm to speed up the computation of metagraph instances. Empirically, extensive experiments reveal that our metagraph-based proximity substantially outperforms the best competitor by more than 10 percent, and our matching algorithm can reduce matching time by more than half. As a further generalization, we aim to derive a general node and edge representation for heterogeneous graphs, in order to support arbitrary machine learning tasks beyond proximity search. In particular, we propose the finer-grained anchored metagraph, which is capable of discriminating the roles of nodes within the same metagraph. Finally, further experiments on the general representation show that we can outperform the state of the art significantly and consistently across various machine learning tasks. [ABSTRACT FROM AUTHOR]

In this paper, a novel bacterial foraging algorithm (BFA)-Gaussian support vector classifier machine (GSVCM) model was proposed to improve the fatigue classification accuracy of electromyography (EMG) signals. This optimization mechanism involves the kernel parameter setting in the GSVCM training procedure, which significantly influences the classification accuracy. Experiments were conducted based on the EMG signal to differentiate the normal and fatigue status. In the proposed method, the EMG signals were decomposed into intrinsic mode functions by ensemble empirical mode decomposition (EEMD) before the mean instantaneous frequency could be obtained by Hilbert transform (HT). Finally, the fatigue statistical features can be extracted from fast Fourier transform and EEMD—HT. The application of this model to the fatigue status recognition of EMG signal indicated that further significant enhancement of the classification accuracy can be achieved by the proposed BFA-GSVCM classification system. The diagnostic method is effective and feasible. [ABSTRACT FROM AUTHOR]

The identification of network motifs has important applications in numerous domains, such as pattern detection in biological networks and graph analysis in digital circuits. However, mining network motifs is computationally challenging, as it requires enumerating subgraphs from a real-life graph, and computing the frequency of each subgraph in a large number of random graphs. In particular, existing solutions often require days to derive network motifs from biological networks with only a few thousand vertices. To address this problem, this paper presents a novel study on network motif discovery using Graphical Processing Units (GPUs). The basic idea is to employ GPUs to parallelize a large number of subgraph matching tasks in computing subgraph frequencies from random graphs, so as to reduce the overall computation time of network motif discovery. We explore the design space of GPU-based subgraph matching algorithms, with careful analysis of several crucial factors (such as branch divergences and memory coalescing) that affect the performance of GPU programs. Based on our analysis, we develop a GPU-based solution that (i) considerably differs from existing CPU-based methods in how it enumerates subgraphs, and (ii) exploits the strengths of GPUs in terms of parallelism while mitigating their limitations in terms of the computation power per GPU core. With extensive experiments on a variety of biological networks, we show that our solution is up to two orders of magnitude faster than the best CPU-based approach, and is around $20$ times more cost-effective than the latter, when taking into account the monetary costs of the CPU and GPUs used. [ABSTRACT FROM PUBLISHER]

The Fe/X (X = C, Si, Ge) films were deposited on glass substrates by magnetron sputtering, and then they were annealed in vacuum. Their magnetic and transport properties are compared. For Fe/C films, the annealing process gives rise to the enhancement of saturation magnetization, the increase of the Fe particle size, and the reduction of the magnetoresistance (MR). For Fe/Si films, the annealing process causes the appearance of the Fe particles and the enhancement of MR. For Fe/Ge films, no Fe particles can be formed in the films. The unsaturated MR was observed in the as-deposited Fe/Ge film. Thus, the microstructures, magnetism, and transport properties can be tuned by the group-IV elements and the postannealing process. This paper may be beneficial to the group-IV-based spintronics. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Integrated Oversampling (INOS) method that can handle highly imbalanced time series classification. We introduce an enhanced structure preserving oversampling (ESPO) technique and synergistically combine it with interpolation-based oversampling. ESPO is used to generate a large percentage of the synthetic minority samples based on multivariate Gaussian distribution, by estimating the covariance structure of the minority-class samples and by regularizing the unreliable eigen spectrum. To protect the key original minority samples, we use an interpolation-based technique to oversample a small percentage of synthetic population. By preserving the main covariance structure and intelligently creating protective variances in the trivial eigen dimensions, ESPO effectively expands the synthetic samples into the void area in the data space without being too closely tied with existing minority-class samples. This also addresses a key challenge for applying oversampling for imbalanced time series classification, i.e., maintaining the correlation between consecutive values through preserving the main covariance structure. Extensive experiments based on seven public time series data sets demonstrate that our INOS approach, used with support vector machines (SVM), achieved better performance over existing oversampling methods as well as state-of-the-art methods in time series classification. [ABSTRACT FROM PUBLISHER]

The deployment of a sensor node to manage a group of sensors and collate their readings for system health monitoring is gaining popularity within the manufacturing industry. Such a sensor node is able to perform real-time configurations of the individual sensors that are attached to it. Sensors are capable of acquiring data at different sampling frequencies based on the sensing requirements. The different sampling rates affect power consumption, sensor lifespan, and the resultant network bandwidth usage due to the data transfer incurred. These settings also have an immediate impact on the accuracy of the diagnostics and prognostics models that are employed for system health monitoring. In this paper, we propose a novel adaptive classification system architecture for system health monitoring that is well suited to accommodate and take advantage of the variable sampling rate of sensors. As such, our proposed system is able to yield a more effective health monitoring system by reducing the power consumption of the sensors, extending the sensors' lifespan, as well as reducing the resultant network traffic and data logging requirements. We also propose an ensemble based learning method to integrate multiple existing classifiers with different feature representations, which can achieve significantly better, stable results compared with the individual state-of-the-art techniques, especially in the scenario when we have very limited training data. This result is extremely important in many real-world applications because it is often impractical, if not impossible, to hand-label large amounts of training data. [ABSTRACT FROM AUTHOR]

[Co(O.6 nm)/ZnO(x nm)][sub60] = 0.3 - 3) films were deposited on glass substrates by magnetron sputtering, and then annealed in vacuum. The large negative magnetoresistance of - 10.4% is observed in the as-deposited [Co(O.6 nm)IZnO(O.4 nm)] [sub60] film. When ZnO layer thickness is increased from 0.3 to 3 nm, MR ratios of the as-deposited Co/ZnO films are gradually decreased and MR ratios of the annealed films are increased, which might be attributed to the transformation of the structures from the multilayers to granular films during annealing. And the postannealing process makes the magnetism of the films transform from superparamagnetism to ferromagnetism due to Co precipitation in granular films after annealing. [ABSTRACT FROM AUTHOR]