六维加速度感知在机器人、航空航天、超精密加工等领域均具有迫切的应用需求，已成为高端装备向“超精尖”发展的一项核心技术。并联式六维加速度传感器具有结构紧凑、动力学解耦精度高等突出优势，其测量性能与感知机构的操作性能有关。从工作机理的角度，阐述了并联式六维加速度感知机构与并联机器人、并联式六维力感知机构之间的差异。从数学和力学的角度，系统阐释了并联式六维加速度感知机构的静刚度、奇异性、基频共振和故障修复四项操作性能的表征和优化方法，并剖析了现有方法尚不具备自适应性的原因。接着，阐明六维加速度标定平台的必要性和基本要求，并分析了四种原理方案的优缺点。最后，指出挖掘感知机构“性能-结构-激励”三者之间的内在联系，并突破几何/拓扑结构的重构、标定平台的设计和优化这些关键问题，有助于充分发挥并联式六维加速度感知机构的性能优势。

中子照相系统因其特定的成像结构，使得成像后的中子照相图像不可避免地存在几何不锐度。为提升中子照相图像的质量，本文提出一种基于改进Richardson–Lucy算法的中子照相图像几何不锐度修正方法，该方法首先根据几何不锐度的形成原理构建点扩散函数，然后通过拉普拉斯算子和中值滤波方法去除图像中的γ白斑噪声，最后采用Richardson–Lucy算法对图像进行复原。采用线对模板样品进行测试，并与4种现有的中子照相图像几何不锐度修正算法进行对比，验证了本文算法可将衡量中子照相图像几何不锐度修正效果的平均梯度、空间频率指标分别提升60.23%和29.90%，能够实现几何不锐度的修正，同时解决图像复原过程中γ白斑噪声的放大问题，为实现高分辨率的中子照相提供重要技术支撑。

Holographic imaging poses significant challenges when facing real-time disturbances introduced by dynamic environments. The existing deep-learning methods for holographic imaging often depend solely on the specific condition based on the given data distributions, thus hindering their generalization across multiple scenes. One critical problem is how to guarantee the alignment between any given downstream tasks and pretrained models. We analyze the physical mechanism of image degradation caused by turbulence and innovatively propose a swin transformer-based method, termed train-with-coherence-swin (TWC-Swin) transformer, which uses spatial coherence (SC) as an adaptable physical prior information to precisely align image restoration tasks in the arbitrary turbulent scene. The light-processing system (LPR) we designed enables manipulation of SC and simulation of any turbulence. Qualitative and quantitative evaluations demonstrate that the TWC-Swin method presents superiority over traditional convolution frameworks and realizes image restoration under various turbulences, which suggests its robustness, powerful generalization capabilities, and adaptability to unknown environments. Our research reveals the significance of physical prior information in the optical intersection and provides an effective solution for model-to-tasks alignment schemes, which will help to unlock the full potential of deep learning for all-weather optical imaging across terrestrial, marine, and aerial domains.