Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Fabrication of reduced Graphene Oxide supported Gd3+ doped V2O5 nanorod arrays for superior photocatalytic and antibacterial activities

In the current report, pure V₂O₅, a series of Gd doped V₂O₅ (1 wt%, 3 wt%, 5 wt% and 10 wt%) and graphene integrated Gd–V₂O₅ photocatalysts have been prepared using a facile wet chemical approach. The effect of Gd⁺³ ions substitution and RGO support on V₂O₅ was studied by the different analytical techniques. X-ray diffraction (XRD) results showed the orthorhombic crystal structure of synthesized samples with crystallize size in range of 22–35 nm. Morphological analysis showed nanorods and nanorod arrays like appearance of V₂O₅, Gd–V₂O₅ and GdV-₂O₅/RGO, respectively. Gd–V₂O₅ and Gd–V₂O₅/RGO exhibited enhanced optical response in the visible region along with decrease in the band gap values for Gd doped V₂O₅ samples. BET surface area of Gd–V₂O₅ and Gd- V₂O₅/RGO was calculated as 12.39 g/m² and 15.35 g/m² that was found to be higher than pristine V₂O₅. To study the photocatalytic activity of synthesized photocatalysts, methylene blue (MB) was chosen as model pollutant. Among the Gd doped V₂O₅ samples, highest photocatalytic activity (45.62%) was achieved by optimal concentration of 5 wt% Gd–V₂O₅ that is accredited to effective separation of electron-hole pairs. While Gd–V₂O₅/RGO showed 2.1 times higher dye removal (97.12%) than unsupported Gd–V₂O₅, under the visible light irradiation. The significantly high photocatalytic activity of Gd–V₂O₅/RGO is due to the synergistic effect aroused by combined action of Gd⁺³ ions doping and advantageous properties of highly conductive and large surfaced graphene. Recycling experiments for V₂O₅ derivatives showed good stability and recyclability of photocatalysts. Additionally, Gd–V₂O₅/RGO was found to be more potential anti-bacterial agent than V₂O₅ and Gd–V₂O₅.     

On the design of secure primitives for real world applications

This editorial paper provides an overview of new advances on hardware security challenges and promising solutions for real world applications. It first presents the increasing hardware vulnerabilities with the emergence of diverse computing platforms. Then, it details the technical challenges, research opportunities, and potential solutions to cyber-attacks on -physical- devices.     

Approximation Optimal Vibration Control with Applications to Jacket Offshore Platforms with Nonlinear Dynamic Characteristics

International Journal of Control, Automation and Systems - The vibration control problem of offshore jacket platforms is studied. The model of offshore platforms with nonlinear interactions of the...     

Multiple clusterings of heterogeneous information networks

Machine Learning - Traditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world data. To deal with this...     

Effects of the film thickness and poling electric field on photovoltaic performances of (Pb,La)(Zr,Ti)O3 ferroelectric thin film-based devices

The ferroelectric photovoltaic (FPV) effect obtained in inorganic perovskite ferroelectric materials has received much attention because of its large potential in preparing FPV devices with superior stability, high open-circuit voltage (V_oc) and large short-circuit current density (J_sc). In order to obtain suitable thickness for the ferroelectric thin film as light absorption layer, in which, the sunlight can be fully absorbed and the photo-generated electrons and holes are recombined as few as possible, we prepare Pb_0.93La_0.07(Zr_0.6Ti_0.4)_0.9825O₃ (PLZT) ferroelectric thin films with different layer numbers by the sol-gel method and based on these thin films, obtain FPV devices with FTO/PLZT/Au structure. By measuring photovoltaic properties, it is found that the device with 4 layer-PLZT thin film (~300 nm thickness) exhibits the largest V_oc and J_sc and the photovoltaic effect obviously depends on the value and direction of the poling electric field. When the device is applied a negative poling electric field, both the V_oc and J_sc are significantly higher than those of the device applied the positive poling electric field, due to the depolarization field resulting from the remnant polarization in the same direction with the built-in electric field induced by the Schottky barrier, and the higher the negative poling electric field, the larger the V_oc and J_sc. At a -333 kV/cm poling electric field, the FPV device exhibits the most superior photovoltaic properties with a V_oc of as high as 0.73 V and J_sc of as large as 2.11 μA/cm². This work opens a new way for developing ferroelectric photovoltaic devices with good properties.     

Integral barrier Lyapunov function-based adaptive fuzzy output feedback control for nonlinear delayed systems with time-varying full-state constraints

In this article, an adaptive fuzzy output feedback control method is presented for nonlinear time-delay systems with time-varying full state constraints and input saturation. To overcome the problem of time-varying constraints, the integral barrier Lyapunov functions (IBLFs) integrating with dynamic surface control (DSC) are applied for the first time to keep the state from violating constraints. The effects of unknown time delays can be removed by using designed Lyapunov-Krasovskii functions (LKFs). An auxiliary design system is introduced to solve the problem of input saturation. The unknown nonlinear functions are approximated by the fuzzy logic systems (FLS), and the unmeasured states are estimated by a designed fuzzy observer. The novel controller can guarantee that all signals remain semiglobally uniformly ultimately bounded and satisfactory tracking performance is achieved. Finally, two simulation examples illustrate the effectiveness of the presented control methods.     

Looking ahead: Joint small group detection and tracking in crowd scenes

Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.