红外吸收法测硅铁中的超低硫

应用高频红外碳硫仪,建立了硅铁合金中超低硫测定方法,对选择适当的测定条件如助熔剂、分析时间等进行了探讨,最佳测定条件:最大工作电流为400～480 mA;载气纯度为99.99%;陶瓷坩埚需经1 100℃灼烧2 h后,可使空白降至最小;硫元素最短分析时间为40 s,比较水平为5.00.     

Calculation of single adsorption isotherms from gravimetrically measured binary gas mixture adsorption isotherms on activated carbon at high pressures

A method is developed for the calculation of single-component adsorption isotherms from gravimetrically measured binary gas mixture adsorption isotherms at high pressures, at two temperatures and for different mole fractions of the gas phase. The adsorption of nitrogen/methane on active carbon Norit R1 is taken as an experimental example.     

快速高吸油性丙烯酸酯树脂的合成研究

以丙烯酸丁酯与甲基丙烯酸甲酯为共聚单体，在聚合体系中引入致孔剂和大分子增容剂，采用悬浮聚合法合成了快速高吸油性树脂，考察了致孔剂用量、增容剂用量、乙酸乙酯／异戊醇混合致孔剂质量比等对高吸油性树脂吸油性能的影响。结果表明：通过引入致孔剂乙酸乙酯，不仅使树脂对甲苯和汽油的吸油率分别达到了3300％和1000％，而且吸油速度加快，2h内可达到饱和吸油；在致孔的基础上引入增容剂，可使树脂对汽油的吸收率显著提高，达到了1500％。     

聚丙烯工程化研究

采用纳米填充、交联和转晶的复合技术对聚丙烯进行了改性，优选了纳米碳酸钙为填料。当β成核剂用量为0．5％、助交联剂为3％时，复合材料的综合性能达到最好，接近普通工程塑料的各项力学性能。红外光谱分析和熔体质量流动速率的测定表明，复合材料体系产生了微交联；DSC分析发现，PP材料的聚集态结构中含有β球晶。     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives

The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data, various privacy-preserving schemes have been introduced in the recent past. However, technological advancement in CPSSs requires the modifications of previous techniques to suit its dynamics. Meanwhile, differential privacy has emerged as an effective method to safeguard CPSSs data privacy. To completely comprehend the state-of-the-art developments and learn the field’s research directions, this article provides a comprehensive review of differentially private data fusion and deep learning in CPSSs. Additionally, we present a novel differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems , and various future research directions for CPSSs.     

基于ZYNQ飞艇囊体应变在线监测系统设计

针对当前高空飞艇囊体的应变实时测量装置无法同时具备体积小和实时测量的工作属性,创新性地用光学器件和感光元件配合,对工作中飞艇囊体两个固定点的相对位移量进行测量,提出了一种能够在线监测飞艇囊体应变的测量装置,能够实现应变在线测量且测量精度高;采用TCD1500C电荷耦合器件作为数据来源;主芯片采用ZYNQ处理器,ZYNQ能够具备FPGA的并行处理能力和Cortex的数据调度能力,能够处理CCD高速数据;通过自定义IP核,使用PS端与PL端互联AXI协议,实现了高速、大量数据传输;为了得到应变信号,采用了将应变信号转化为位移信号进行采集的方法,之后将位移信号采集存储至SD卡中并周期性显示到上位机上,实现地面对飞艇囊体应变的实时监控;实现了飞艇囊体应变的实时测量并把测量精度提高至0.1％.     

Experimental investigations of machining characteristics and removal mechanisms of advanced ceramics in high speed deep grinding

Machining characteristics and surface integrity of advanced ceramics, including alumina, alumina–titania, and yttria partially stabilized tetragonal zirconia, were studied under high speed deep grinding conditions. Material removal mechanisms involved in the grinding processes were explored. The material removal in the grinding of alumina and alumina–titania was dominated by grain dislodgement or lateral cracking along grain boundaries. The removal for zirconia was via both local micro fracture and ductile cutting. It was found that under a feed rate of 500 mm/min and for all the wheel speeds used, an increase in the wheel depth of cut (DOC) from 0.1–2 mm slightly improved the ground surface finish, but greatly prolonged the wheel life. This increase did not deepen the subsurface damage layer for the alumina and alumina–titania, but resulted in a slightly deeper damage layer for the zirconia.     

高精度冷轧不锈钢管生产

介绍了用ＬＤ型冷轧管机生产高精度冷轧不锈钢管的经验和质量保证措施。     

硬质合金磨削温度的实验研究

采用半人工和人工热电偶法，对硬质合金YG6和YT30平面磨削的磨削温度进行了实验研究。并对磨削参数与磨削温度的关系，进行了理论分析及实验验证。结果表明：磨削深度和工作台速度增加，磨削温度升高，但工作台速度的改变，对磨削温度的影响较小．