Hydrogen is a clean-burning fuel that can be converted to other forms. of energy without generating any greenhouse gases. Currently, hydrogen is stored either by compression to high pressure (>700 bar) or cryogenic cooling to liquid form (<23 K). Therefore, it is essential to develop safe, reliable, and energy-efficient storage technology that can store hydrogen at lower pressures and temperatures. In this work, we systematically designed 2902 Mg-alkoxide-functionalized covalent-organic frameworks (COFs) and performed high-throughput (HT) computational screening for hydrogen storage applications at 111, 231, and 296 K. To accurately model the interaction between Mg-alkoxide sites and molecular hydrogen, we performed MP2 calculations to compute the hydrogen binding energy for different types of functionalized models, and the data were subsequently used to fit modified-Morse force field (FF) parameters. Using the developed FF models, we conducted HT grand canonical Monte Carlo (GCMC) simulations to compute hydrogen uptakes for both original and functionalized COFs. The generated data were subsequently used to evaluate the materials’ gravimetric and volumetric storage performance at various temperatures (111, 231, and 296 K). Finally, we developed machine learning (ML) models to predict the hydrogen storage performance of functionalized structures based on the features of the original structures. The developed model showed excellent performance with a mean absolute error (MAE) of 0.061 wt % and 0.456 g/L for predicting the gravimetric and volumetric deliverable capacities, enabling a quick evaluation of structures in a hypothetical COF database. The screening results demonstrated that the Mg-alkoxide functionalization yields greater improvements in volumetric H2 storage capacities for COFs with smaller pores compared to those with larger (mesoporous) pores.@en

This paper proposes a multioral image change detection algorithm based on adaptive parameter estimation, which is used to solve the problems of severe interference of coherent speckle noise and the retention of detailed information about changing regions in synthetic aperture radar remote sensing images. The change area in the initial differential image has local consistency and global prominence. By detecting the significant area to locate similar change areas, the coherent speckle noise outside the area can be eliminated. The use of hierarchical FCM clustering to automatically generate training samples can improve the reliability of training samples. In addition, in order to increase the distinction between the changed area and the non-changed area, a sparse automatic encoder is used to extract the changed features and generate a change detection map. Experiments using 4 sets of SAR images show that the algorithm can effectively reduce the effect of speckle noise on detection accuracy, the extraction of changing areas is more complete and meticulous, and the false detection rate is greatly reduced. Since the images in different time phases will be disturbed by weather, clouds, sea water, etc., the target segmentation algorithm can be used to extract the target of interest and highlight the changing area. Principal component analysis and k-means clustering method are used to reduce the influence of isolated pixels, and change information is extracted to obtain different images. The experiment uses four sets of image data of islands and reefs. The experiment proves that the algorithm can well eliminate external interference, improve the accuracy of change detection, and have a good detection effect on the area of islands and reefs. The adaptive parameter estimation plays a good role in the detection of changing areas, and the visual effect is better, which can improve the accuracy of the detection results.

@en

In this paper, a coupled-inductor-based LCC resonant converter with the primary-parallel-secondary-series (PPSS) configuration is proposed to achieve output-voltage sharing ability for HV generator applications. The PPSS configuration of the LCC resonant converter with the voltage multipliers is introduced to achieve high output-voltage and increase the output-power level. However, the variations of the magnetizing inductance, leakage inductance, and winding capacitance of the HV transformer and voltage multiplier impact on the output-voltage sharing performance. Subsequently, the resonant inductors in the primary side of the conventional LCC resonant converters with the PPSS configuration are coupled to achieve the output-voltage sharing without any additional circuits and control efforts. Furthermore, an analytical equivalent circuit model considering the magnetizing inductor of the HV transformer is derived to analyze the output-voltage sharing ability. Moreover, the design method for the coupled inductors considering the output-voltage sharing performance affected by the leakage inductance of the coupled inductors is presented. Finally, the output-voltage sharing performance of the proposed coupled-inductor-based LCC resonant converter with the PPSS configuration is validated by the experimental results of a 50-V input, 5-kV output 100-W prototype. The prototype experimental results show that the unbalance voltage degree decreases from 67.7% to 8.5% with the utilization of the coupled inductor.

@en

Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.

@en