互联网时代下的学校图书管理信息化建设

如何在互联网时代打造一个信息化的管理模式,已经成为广大学校图书管理发展过程中亟待处理的问题。文章就互联网时代下的高职图书管理信息化建设进行了详细探讨,以期能够给广大同仁提供一些借鉴参考,共同为图书管理工作的现代化改革和发展贡献力量。     

智能化电子信息技术发展及应用分析

文章首先对智能化电子信息技术进行了深入的研究,而后分析了该技术在应用过程中出现的问题,最后结合该技术的相关特点给出了相应的问题解决措施,希望能够对智能化电子信息技术的发展提供帮助。     

Theoretical prediction of structure,electronic and optical properties of VH2 hydrogen storage material

The vanadium hydrides have better hydrogen storage capacity in comparison to the other metal hydrides. Although the structure of VH₂ hydride has been reported, the structural stability, electronic and optical properties of VH₂ hydride are unclear. To solve these problems, we apply the first-principles method to study the structural stability, electronic and optical properties of VH₂ hydrides. Similar to the metal dihydrides, four possible VH₂ hydrides such as the cubic (Fm-3m), tetragonal (I4/mmm), tetragonal (P4₂/mnm) and orthorhombic (Pnma) are designed. The result shows that the cubic VH₂ hydride is a thermodynamic and dynamical stability. In particular, the tetragonal (I4/mmm) and the orthorhombic (Pnma) VH₂ hydrides are firstly predicted. It is found that these VH₂ hydrides show metallic behavior. The electronic interaction of V (d-state)-H (s-state) is beneficial to improve the hydrogen storage in VH₂ hydride. In addition, the formation of V–H bond can improve the structural stability of VH₂ hydride. Based on the analysis of optical properties, it is found that all VH₂ hydrides show the ultraviolet response. Compared to the tetragonal and orthorhombic VH₂ hydrides, the cubic VH₂ hydride has better storage optical properties. Therefore, we believe that the VH₂ hydride is a promising hydrogen storage material.     

Polymer electrets and their applications

Polymer electrets have revealed great potential application in electromechanical devices because of the low weight, large quasi-piezoelectric sensitivity, and excellent flexibility. For an electret, a permanent and macroscopic electric field exists on the surface, principally led by a macroscopic electrostatic charge on the surface or a net orientation of polar groups inside the object. Here, progress in the development of polymer electrets is reviewed. After a brief retrospect of the research courses and those typical polymer electrets that are classified into fluorine polymer and nonfluorine polymer, we present a survey on the charging methods, including corona, soft X-ray, contact, thermal and monoenergetic particle beams. The latest representative applications (i.e., power harvesting, sensors, field effect transistors, and biomedicine) based on polymer electrets are also summarized. Finally, we complete this review with a discussion on perspectives and challenges in this field.     

IRS辅助的SWIPT系统中基于能效优先的波束成形设计与优化

以智能反射面(intelligent reflecting surface,IRS)辅助的无线携能通信(simultaneous wireless information and power transfer,SWIPT)系统为背景,研究了该系统中基于能效优先的多天线发送端有源波束成形与IRS无源波束成形联合设计与优化方法。以最大化接收端的最小能效为优化目标,构造在发送端功率、接收端能量阈值、IRS相移等多约束下的非线性优化问题,用交替方向乘子法(alternating direction method of multipliers,ADMM)求解。采用Dinkelbach算法转化目标函数,通过奇异值分解(singular value decomposition,SVD)和半定松弛(semi-definite relaxation,SDR)得到发送端有源波束成形向量。采用SDR得到IRS相移矩阵与反射波束成形向量。结果表明,该系统显著降低了系统能量收集(energy harvesting,EH)接收端的能量阈值。当系统总电路功耗为?15 dBm时,所提方案的用户能效为300 KB/J。当IRS反射阵源数与发送天线数均为最大值时,系统可达最大能效。     

Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Fabrication of barium titanate ceramics via digital light processing 3D printing by using high refractive index monomer

A digital light processing (DLP) technology has been developed for 3D printing lead-free barium titanate (BTO) piezoelectric ceramics. By comparing the curing and rheological properties of slurries with different photosensitive monomer, a high refractive index monomer acryloyl morpholine (ACMO) was chosen, and a design and preparation method of BTO slurry with high solid content, low viscosity and high curing ability was proposed. By further selecting the printing parameters, the single-layer exposure time was reduced and the forming efficiency has been greatly improved. Sintered specimens were obtained after a nitrogen-air double-step debinding and furnace sintering process, and the BTO ceramics fabricated with 80 wt% slurry shows the highest relative density (95.32 %) and piezoelectric constant (168.1 pC/N). Furthermore, complex-structured BTO ceramics were prepared, impregnated by epoxy resin and finally assembly made into hydrophones, which has significance for the future design and manufacture of piezoelectric ceramic-based composites that used in functional devices.