智能建造技术协同创新主体互动关系研究

文章分析智能建造技术创新主体的特点，界定并划分智能建造技术协同创新主体互动关系的范畴和类别，建立智能建造协同创新的多方演化博弈模型，揭示智能建造技术协同创新主体互动关系。主要结论为：①智能建造技术协同创新呈现级联性、函数性、动态平衡性、不确定性和指数性特征；②智能建造技术协同创新主体互动关系分为同质性的内部主体互动和异质性的内部主体与外部主体互动，表现出利益共生、成长进化和共享协同的特征；③创新主体之间的互动行为有利于对创新资源进行有效配置，推动智能建造技术的应用。     

盾构隧道施工物料无人运输发展现状及关键技术

盾构隧道施工物料无人运输有助于提升运输效率、降低运输成本、减少运输安全事故率。与公路汽车无人驾驶、港口车辆无人驾驶、轨道交通无人驾驶等逐步成熟的无人驾驶技术不同，在隧道场景下实施施工物料无人运输存在运输物料种类繁多、运输调度困难、地下定位信号拒止、狭窄车道频繁会车、行车路面工况复杂、地上地下联动响应慢等诸多挑战。从实现盾构隧道施工物料无人运输的关键问题分析入手，综述当前盾构隧道施工物料运输方式、运输需求、无人运输发展现状及存在的挑战，提出盾构隧道施工物料无人运输的五大关键技术：多种类物料智能货控能效管理技术，无人化垂直装卸门机控制技术，多传感器融合同步定位与地图构建技术，隧道复杂环境路径规划与自主避障技术，隧-地一体化联动响应的高可靠性无线通信技术。     

Gelling behavior of MgAl2O4 slurries with a common dispersant

MgAl₂O₄ transparent ceramics were shaped by a commonly used polyacrylic acid (PAA), which acted as both dispersant and gelling agent. The spinel slurries were prepared by ball-milling MgAl₂O₄ powder, PAA, and water in an attrition mill. The gelling of slurries happened at room temperature in air atmosphere without any other organic additive. The gelling mechanism was the formation of chelates between Mg²⁺ and carboxyl groups (-COO⁻) of PAA. The frequency-based testing method was applied to investigate the gelling process of the as-prepared slurry. In addition, a novel in situ characterization method based on a modified indentation testing was invented to better understand the strengthening of the wet green body with time and to guide when demolding could be carried out. After sintering, transparent MgAl₂O₄ ceramics with high in-line transmittance were resulted.     

毫米波三维异质异构集成技术的进展与应用

三维异质异构集成技术是实现电子信息系统向着微型化、高效能、高整合、低功耗及低成本方向发展的最重要方法,也是决定信息化平台中微电子和微纳系统领域未来发展的一项核心高技术。文章详细介绍了毫米波频段三维异质异构集成技术的优势、近年来的发展趋势以及面临的挑战。利用硅基MEMS 光敏复合薄膜多层布线工艺可实现异质芯片的低损耗互连,同时三维集成高性能封装滤波器、高辐射效率封装天线等无源元件,还能很好地处理布线间的电磁兼容和芯片间的屏蔽问题。最后介绍了一款新型毫米波三维异质异构集成雷达及其在远距离生命体征探测方面的应用。     

Deep learning-based symbol detection algorithm in IMDD-OOFDM system

In the current research on intensity-modulation and direct-detection optical orthogonal frequency division multiplexing ( IMDD-OOFDM ) system, effective channel compensation is a key factor to improve system performance. In order to improve the efficiency of channel compensation, a deep learning-based symbol detection algorithm is proposed in this paper for IMDD-OOFDM system. Firstly, a high-speed data streams symbol synchronization algorithm based on a training sequence is used to ensure accurate symbol synchronization. Then the traditional channel estimation and channel compensation are replaced by an echo state network (ESN) to restore the transmitted signal. Finally, we collect the data from the system experiment and calculate the signal-to-noise ratio (SNR). The analysis of the SNR optimized by the ESN proves that the ESN-based symbol detection algorithm is effective in compensating nonlinear distortion.     

Semi-supervised Learning with Graph Convolutional Networks Based on Hypergraph

Neural Processing Letters - Graph convolutional networks (GCNs), which rely on graph structures to aggregate information of neighbors to output robust node embeddings, have been becoming a popular...     

直通链路技术的发展与展望

直通链路技术己广泛应用于车联网场景。对于直通链路技术的潜在技术方向给出可行的建议，包括传统直通链路技术的增强方向，如载波聚合、使用非授权频谱等；侧行链路对于中继场景的应用扩展，包括终端到终端之间的中继，以及中继的多链接场景；在高精度定位场景使用直通链路技术。并且，给出直通链路技术与各种新技术的融合应用，如智能反射面与区块链技术，从而解决直通链路技术自身的缺陷。     

Engineering A-site cation deficiency into LaCoO3 thin sheets for improved microwave absorption performance

Journal of Materials Science - Rationally designing microwave absorption materials with highly efficient and tunable bandwidth is in great demand but remains a huge challenge. In this study,...