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This paper proposes a feasible anti-disturbance constrained control algorithm for a class of typical permanent magnet synchronous motor (PMSM) driven-based single-joint flexible manipulator systems with input saturation and angular velocity constraint. Firstly, by actuating with a surface-mounted PMSM, and setting the d-axis current as zero, the single-joint flexible manipulator is converted into a normal state space model. Secondly, compared with classical harmonic or linear disturbance, the exogenous disturbance model with configurable parameters and the associated disturbance observer (DO) are continuously introduced to dynamically estimate those unknown irregular disturbance. Moreover, by combining the convex hull representation of the saturating input with the suggested adaptive law, an efficient adaptive active anti-disturbance controller is designed to ensure the stability of closed-loop manipulator systems. By using convex optimization technique, not only the dynamic tracking for the rotation position but also the angular velocity constraint can be guaranteed simultaneously. Finally, simulation results for three different kinds of disturbances are showed to demonstrate the superiority of the proposed method.

This paper studies the problem of resolving data inconsistency from multiple sources in managing data related to power equipment for China’s state grid corporation. This paper proposes HENGE, a HEtetrogeneous Network GEneration model, to automatically align inconsistent devices from multiple sources, i.e., the same devices with multiple entries with different values in each source. HENGE builds multiple data sources into a heterogeneous graph, and captures complex physical and semantic relationships among devices. HENGE combines feature and relational information and improves alignment accuracy by feature-enhanced residual graph encoder and disentangled representation learning. HENGE can learn from a small amount of labeled data through a uniformity autoencoder trained on an unsupervised generation task. Experiments on two real-world datasets demonstrate the capability of HENGE in resolving inconsistent device entries in multiple sources.

As important power cables for the mining mobile equipment, such as mining shearers, ethylene propylene rubber (EPR) cables often suffer from the combining effect of various stresses on site. In order to evaluate the aging state of EPR insulation, the paper carried out the multi-stress accelerated aging test on the insulation of coal mining flexible EPR cables. The infrared spectrum, elongation at break and polarization and depolarization current of cable insulation samples were measured. The insulation state of EPR cable was evaluated by using the aging factor obtained from isothermal relaxation current. Based on the failure standard of 50% elongation at break, the life formula of EPR insulation was deduced. The experimental and analytical results show that the tiny air gap was torn to the larger air gap under the mechanical stress, which leads to the increase of oxygen infiltration and promotes the thermo-oxygen reaction. The fracture energy of EPR decreases, and mechanical aging is accelerated. Aging factor and elongation at break can indicates the deterioration degree of EPR insulation. Based on the least square method and Arrhenius equation, the service life of the insulation of coal mining flexible EPR cables is deduced.

Empirical mode decomposition (EMD) is an extensively utilized tool in a time-frequency analysis. However, disturbances, such as impulse noise, can result in a mode-splitting effect, in which one physically meaningful component is split into two or more intrinsic mode functions (IMFs). In this paper, we propose a novel method, minimum arclength EMD (MA-EMD), to robustly decompose time series data with impulse-like noises. The idea is to apply a minimum arclength criterion to adjust the knot positions of impulses during the sifting process in EMD. In this way, the impulse-like artifact is extracted with the first IMF, and the mode splitting effect of the latter decomposition is alleviated. Furthermore, when the first IMF contains the desired information, we separate the spikes and the first IMF by adding a pair of masking signals. For using this masking-aided MA-EMD (MAMA-EMD) method, we also mathematically derived the appropriate ranges of the frequency and the amplitude of the masking signal. The MAMA-EMD is utilized to deal with the simulated Duffing wave and four real-world data, including electrical current, vibration signals, the cyclic alternating pattern in sleep EEG (electroencephalography), and circadian of core body temperature. The results show that the MA-EMD and MAMA-EMD have a sound improvement when encountering impulse noises.

Social recommender is an active research area. Most previous social recommenders adopt existing social networks to augment recommendations which are based on user preferences. In this contribution, we propose to simultaneously infer the social influence network and the user preferences in a matrix factorization framework. Furthermore, we assume that the influence strength is dependent on the social roles of users. We present an incremental clustering algorithm to detect dynamic social roles. Comprehensive experiments on real data sets demonstrate the efficiency and effectiveness of our model to generate precise recommendations.

Traditional methods of analyzing genes mainly focus on a single gene. However, they are time consuming, costly, and neglect the interaction between genes. To overcome the shortcomings of traditional methods, many computational techniques have been proposed for gene analysis. Computational methods posit that the interaction between genes forms a network. By analyzing the topological characteristics of the network, a type of node, “hubs,”is identified. Only a small fraction of nodes is called “hubs,”but they interact with many partners in the network. Several methods are used to find “hubs”in a network. However, they are not comprehensive. Nevertheless, searching for more information about genes in a network remains challenging. In this paper, we integrate HumanNet and a framework to find a structural hole (SH) spanner. We classify genes into three classes (SH, normal, and non-SH). We also classify genes into three classes according to degree and betweenness. Enrichment analysis reveals that the SH genes are enriched for an essential one. Furthermore, enrichment analyses of disease mutations, viruses, and drugs consistently show that the SH genes are preferred targets and are important in cellular state transition. Functional enrichment shows that the biological function that the SH genes over represent is related to cell function. We demonstrate that by finding the structural hole spanner in HumanNet, we can find the SH genes that are essential and indispensable in cellular life. In addition, we identify the SH genes that are key players in cellular state transition.