Feasibility study of large-scale mass customization 3D printing framework system with a case study on Nanjing Happy Valley East Gate

At present, the development and implementation of digital transformation are the keys to promoting high-quality industry development. The new digital fabrication method of robotic 3D printing is a research area being studied by many to tackle the issue of the declining productivity of traditional construction methods. Although many studies have been done, most of the current 3D printing projects are facing limitations in terms of scale. In order to bridge the gap, this article proposed a mass customization 3D printing framework system for large-scale projects. This article discusses how mass customization is made possible through the joint operation of the FUROBOT software and 3D printing hardware. By taking the east gate of Nanjing Happy Valley Plaza as a case study, the article demonstrates and studies the feasibility of the large-scale mass customization 3D printing framework system.     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.     

变电设备智能检修系统设计与实现

针对目前变电站内设备检修工作存在的人员技能水平参差不齐、检修计划和方案管理低效、检修作业指导书缺乏可视化指导和现场安全监管难等问题,基于增强现实技术及激光雷达定位技术提出并研发了变电设备智能检修系统.该系统综合应用生产管理系统等多系统的变电设备大数据,可实现检修计划智能管理、检修方案智能编制功能,为变电设备检修策划提供了智能技术支持.该系统实现了检修预演功能、检修过程智能管控功能,有效管控检修作业过程质量和安全.该系统在500kV文山变电站的成功应用,验证了系统的实用性及有效性.     

CYCLIC DEFORMATION BEHAVIOUR OF Cu SINGLE CRYSTALS ORIENTED FORDOUBLE SLIP Ⅱ. Persistent Slip Bands and Deformation Bands

用光学显微镜和扫描电镜研究了双滑移取向（［０３４］，［１１７］）Ｃｕ单晶循环饱和后的表面形貌，塑性分切应变幅（γｐｌ）低于１０~（－３）时，［０３４］晶体表面上要为主滑移系的驻留滑移带（ＰＳＢｓ）占据，次滑移只在边缘区域启动，其ＰＳＢｓ细窄（＜１μｍ），体积百分数在１％以下．γｐｌ＞１０~（－３）时，次滑移开始在试样的中部启动，同时，表面出现二种贯穿晶体的宏观形变带（ＤＢＩ，ＤＢＩＩ），滑移带在形变带内集中．［１１７］晶体在γｐｌ＝４．４×１０~（－４）时，双滑移现象已十分明显．γｐｌ＞１０~（－３）时，表面也形成与前者相似的形变带．ＤＢＩ的惯习面与主滑移面平行（［０３４］晶体）或接近（［１１７］晶体），ＤＢＩＩ的惯习面则与前者垂直，文章讨论了形变带形成的可能原因．     

基于碳化硅器件的无线充电系统电源设计

碳化硅(SiC)器件耐高频高温、散热性能好、导通电阻小,应用于无线充电系统可有效提高无线充电系统的效率。文中首先对比了SiC材料与Si材料的特性,在此基础上研制了一套基于SiC器件的无线充电系统电源装置。该装置由整流模块和逆变模块构成,输入端接市电,且装置的整流模块具有功率因数校正功能。文中详细给出了整流模块的整流桥选型策略、滤波电路参数设计方法、Boost电路功率器件和无源元件设计原则及开关管控制电路设计方法,还给出了逆变模块的开关管选型策略、开关管驱动电路和开关管保护电路的设计方法。最后,实验结果验证了方案的有效性和可行性,输入侧实现了高功率因数,逆变电路开关管电压振荡得到抑制,实验样机的效率峰值可达98.2%。     

双滑移取向Cu单晶的循环形变行为──Ⅱ．滑移带和形变带SCIEI

用光学显微镜和扫描电镜研究了双滑移取向（［０３４］，［１１７］）Ｃｕ单晶循环饱和后的表面形貌，塑性分切应变幅（γｐｌ）低于１０^（－３）时，［０３４］晶体表面上要为主滑移系的驻留滑移带（ＰＳＢｓ）占据，次滑移只在边缘区域启动，其ＰＳＢｓ细窄（＜１μｍ），体积百分数在１％以下．γｐｌ＞１０^（－３）时，次滑移开始在试样的中部启动，同时，表面出现二种贯穿晶体的宏观形变带（ＤＢＩ，ＤＢＩＩ），滑移带在形变带内集中．［１１７］晶体在γｐｌ＝４．４×１０^（－４）时，双滑移现象已十分明显．γｐｌ＞１０^（－３）时，表面也形成与前者相似的形变带．ＤＢＩ的惯习面与主滑移面平行（［０３４］晶体）或接近（［１１７］晶体），ＤＢＩＩ的惯习面则与前者垂直，文章讨论了形变带形成的可能原因．     

DQ型高效催化剂在环管PP装置上的应用

介绍了使用DQ型球形高效催化剂在上海石油化工股份有限公司塑料事业部70kt/a环管连续法工业聚丙烯生产装置上应用研究的结果，并对结果进行了讨论。     

Multimodal Pre-training Method for Vision-language Understanding and Generation

Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like loss functions (masked language modeling and image-text matching) during pre-training. Despite their good performance in the understanding of downstream tasks, such as visual question answering, image-text retrieval, and visual entailment, these methods cannot generate information. To tackle this problem, this study proposes Unified multimodal pre-training for Vision-Language understanding and generation (UniVL). The proposed UniVL is capable of handling both understanding tasks and generation tasks. It expands existing pre-training paradigms and uses random masks and causal masks simultaneously, where causal masks are triangular masks that mask future tokens, and such pre-trained models can have autoregressive generation abilities. Moreover, several vision-language understanding tasks are turned into text generation tasks according to specifications, and the prompt-based method is employed for fine-tuning of different downstream tasks. The experiments show that there is a trade-off between understanding tasks and generation tasks when the same model is used, and a feasible way to improve both tasks is to use more data. The proposed UniVL framework attains comparable performance to recent vision-language pre-training methods in both understanding tasks and generation tasks. Moreover, the prompt-based generation method is more effective and even outperforms discriminative methods in few-shot scenarios.     

滴状冷凝强化含不凝气的蒸气冷凝传热机制

为了深入考察滴状冷凝强化混合蒸气冷凝传热传质过程的作用机制，在竖直表面上设计了完全滴状（DWC）、没有液滴向下脱落运动的条形分割滴膜共存（DFC）和膜状（FWC）3种冷凝形态的实验表面。对纯蒸气及含不凝气蒸气的冷凝传热过程进行了分析和实验研究，结果表明纯蒸气滴状冷凝与滴膜共存冷凝传热特性相近；而对含不凝气的冷凝，滴膜共存表面与膜状冷凝表面的传热特性相近；不凝气摩尔分数分别为0.9%、4.8%时，滴状冷凝较其他两种形态下的冷凝传热系数提高了30%～80%。其主要原因是由于混合蒸气冷凝传热阻力主要由气相边界层控制，滴膜共存冷凝并没有使气相的扩散传质过程得到强化，而完全滴状冷凝与设计的滴膜共存冷凝的区别在于后者仅存在小液滴的合并运动而没有大液滴向下脱落和对表面冲刷过程。根据二者实验结果的分析，认为滴状冷凝的大液滴脱落运动是影响气相传质的主要因素，大液滴脱落过程对气相边界层的扰动和剪切作用强化了气液界面传热传质特性。     

一种洛伦兹力微机械谐振磁传感器建模及数值仿真

针对微机械洛伦兹力谐振磁传感器灵敏度受驱动控制电路和谐振频率漂移影响问题,利用平均周期法分析了鉴相器为异或门的锁相环频率跟踪电路的动力学模型,并求解出频率跟踪的稳定条件和稳态振幅平衡点.设计的磁传感器数值建模及仿真实验表明:当锁相环积分器系数小于临界值时,环路频率跟踪稳定;当锁相环积分器系数大于临界值时,频率跟踪振荡;稳态振幅与理论分析一致,灵敏度与接口电路无关;频率跟踪动态过程与压控振荡器初始频率ω0、积分系数kI、滤波器时间常数ξ有关.