Uneven illumination surface defects inspection based on convolutional neural network

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination

Collective flow in 2.76 A TeV and 5.02 A TeV Pb+Pb collisions

In this paper, we study and predict flow observables in 2.76 A TeV and 5.02 A TeV Pb +Pb collisions, using the iEBE-VISHNU hybrid model with TRENto and AMPT initial conditions and with different forms of the QGP transport coefficients. With properly chosen and tuned parameter sets, our model calculations can nicely describe various flow observables in 2.76 A TeV Pb +Pb collisions, as well as the measured flow harmonics of all charged hadrons in 5.02 A TeV Pb +Pb collisions. We also predict other flow observables, including $v_n(p_T)$ of identified particles, event-by-event $v_n$ distributions, event-plane correlations, (Normalized) Symmetric Cumulants, non-linear response coefficients and $p_T$-dependent factorization ratios, in 5.02 A TeV Pb+Pb collisions. We find many of these observables remain approximately the same values as the ones in 2.76 A TeV Pb+Pb collisions. Our theoretical studies and predictions could shed light to the experimental investigations in the near future.Comment: 17 pages, 11 figure

Tidal disruption rate suppression by the event horizon of spinning black holes

The rate of observable tidal disruption events (TDEs) by the most massive black holes (BHs) is suppressed due to direct capture of stars by the event horizon. This suppression effect depends on the shape of the horizon and holds the promise of probing the spin distribution of dormant BHs at the centers of galaxies. By extending the frozen-in approximation commonly used in the Newtonian limit, we propose a general relativistic criterion for the tidal disruption of a star of given interior structure. The rate suppression factor is then calculated for different BH masses, spins, and realistic stellar populations. We find that either a high BH spin (> 0.5) or a young stellar population (< 1 Gyr) allows TDEs to be observed from BHs significantly more massive than 10^8 solar masses. We call this spin-age degeneracy (SAD). This limits our utility of the TDE rate to constrain the BH spin distribution, unless additional constraints on the age of the stellar population or the mass of the disrupted star can be obtained by modeling the TDE radiation or the stellar spectral energy distribution near the galactic nuclei.Comment: 19 pages, 14 figures, 3 tables; submitted to MNRA

Spectral flow, twisted modules and MLDE of quasi-lisse vertex algebras

We calculate the fusion rules among $\mathbb{Z}_2$-twisted modules $L_{\mathfrak{sl}_2}(\ell,0)$ at admissible levels. We derive a series MLDEs for normalized characters of ordinary twisted modules of quasi-lisse vertex algebras. Examples include affine VOAs of type $A_1^{(1)}$ at boundary admissible level, admissible level $k=-1/2$, $A^{(1)}_{2}$ at boundary admissible level $k=-3/2$, and $\mathrm{BP}^{k}$-algebra with special value $k=-9/4$. We also derive characters of some non-vacuum modules for affine VOA of type $D_4$ at non-admissible level $-2$ from spectral flow automorphism

Design of Magnesium Phosphate Cement Based Composite for High Performance Bipolar Plate of Fuel Cells

In this work, we report a comprehensive study on a magnesium phosphate cement (MPC) based composite as the construction material for high performance bipolar plates of fuel cells. MPC with partial replacement of fly ash was employed as the binding matrix. Some carbon-based materials, such as graphite, carbon black, carbon fiber, and multi-walled carbon nanotubes were used to construct the conductive phase. A simple hot-press process was applied to produce the composite. The formula and the structure of the composite was modified and adjusted to optimize the properties of the composite to meet the US DOE 2015 technical targets, including the introducing of a reinforcement support. Finally, all the technical targets such as electrical conductivity (\u3e100 S cm-1), the flexural strength (\u3e25 MPa), the corrosion resistance ( \u3c 1 μA cm-2), and gas permeability ( \u3c 10-5 cm3 (s cm2)-1) were achieved as well as low cost ( \u3c 5 $ per kW). The optimized formula and the detailed procedures to fabricate the MPC based composite were concluded

Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network

Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model\u27s predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system

Hydrodynamic Collectivity in Proton--Proton Collisions at 13 TeV

In this paper, we investigate the hydrodynamic collectivity in proton--proton (pp) collisions at 13 TeV, using iEBE-VISHNU hybrid model with HIJING initial conditions. With properly tuned parameters, our model simulations can remarkably describe all the measured 2-particle correlations, including integrated and differential elliptic flow coefficients for all charged and identified hadrons ($K_S^0$, $\Lambda$). However, our model calculations show positive 4-particle cumulant $c_{2}\{4\}$ in high multiplicity pp collisions, and can not reproduce the negative $c_{2}\{4\}$ measured in experiment. Further investigations on the HIJING initial conditions show that the fluctuations of the second order anisotropy coefficient $\varepsilon_{2}$ increases with the increase of its mean value, which leads to a similar trend of the flow fluctuations. For a simultaneous description of the 2- and 4- particle cumulants within the hydrodynamic framework, it is required to have significant improvements on initial condition for pp collisions, which is still lacking of knowledge at the moment.Comment: 7 pages, 6 figures, published versio

Cyclic performance evaluation of a polyethylenimine/silica adsorbent with steam regeneration using simulated NGCC flue gas and actual flue gas of a gas-fired boiler in a bubbling fluidized bed reactor

To accelerate the deployment of Carbon Capture and Storage (CCS) based on the solid amine adsorbents towards a practical scale application relevant to Natural Gas Combined Cycle (NGCC) power plants, this study has evaluated the cyclic performance of a polyethylenimine/silica adsorbent of kg scale in a laboratory scale bubbling fluidized bed reactor. A high volumetric concentration 80?90 vol% of steam mixed with N2 and CO2 has been used as the stripping gas during a typical temperature swing adsorption (TSA) cycle. Both the simulated NGCC flue gas and the actual flue gas from a domestic gas boiler have been used as the feed gas of the CO2 capture tests with the solid adsorbent. Various characterization has been carried out to elucidate the possible reasons for the initial capacity decline under the steam regeneration conditions. The effect of presence of CO2 in the stripping gas has also been studied by comparing the working capacities using different regeneration strategies. It has been demonstrated that the breakthrough and equilibrium CO2 adsorption capacities can be stabilized at approximately 5.9 wt% and 8.6 wt%, respectively, using steam regeneration for both the simulated and actual natural gas boiler flue gases. However, using a concentration of 15 vol% CO2 in the stripping gas has resulted in a significantly low working capacity at a level of 1.5 wt%, most likely due to the incomplete C