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In view of the high-sensitivity vibration effect of precision instrument laboratory buildings under the influence of surrounding traffic loads, field micro-vibration tests under various working conditions were carried out based on actual projects. Combined with numerical simulation, measured data served as input loads to simulate the vibration effect of various traffic loads on the floor of a building structure, and the structural vibration laws under the comprehensive action of various loads were analyzed. The vibration isolation effect of the isolation ditch on the oblique orthogonal load was investigated according to the corresponding safety index. The results show that the main frequency components of the site vibration frequency caused by various traffic loads are approximately 25 Hz and that the root-mean-square speed value is stable below VC-E, which meets the design requirements. Under the comprehensive action of multiple loads, the site structure will produce a vibration amplification effect, which is obvious when all types of loads are distributed symmetrically and the curve distribution is controlled by load factors with large amplitudes. The isolation effect of a small isolation ditch is best when it is located close to the source and the building. The depth of the isolation ditch must be greater than the maximum depth of the source to achieve better results, and the width has little influence. The effect of a small isolation trench on vertical vibration is poor, and the oblique orthogonal triaxial load has a counteracting effect on the vertical component. Thus, additional structural vibration isolation measures are needed. The research results provide a reference for engineering vibration isolation, damping measures, and structural design. [ABSTRACT FROM AUTHOR]

Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so consequently the performance of the method still needs to be improved. Therefore, we combine causal inference with reinforcement learning and propose a new framework for knowledge graph reasoning. By combining the counterfactual method in causal inference, our method can obtain more information as prior knowledge and integrate it into the control strategy in the reinforcement model. The proposed method mainly includes the steps of relationship importance identification, reinforcement learning framework design, policy network design, and the training and testing of the causal reinforcement learning model. Specifically, a prior knowledge table is first constructed to indicate which relationship is more important for the problem to be queried; secondly, designing state space, optimization, action space, state transition and reward, respectively, is undertaken; then, the standard value is set and compared with the weight value of each candidate edge, and an action strategy is selected according to the comparison result through prior knowledge or neural network; finally, the parameters of the reinforcement learning model are determined through training and testing. We used four datasets to compare our method to the baseline method and conducted ablation experiments. On dataset NELL-995 and FB15k-237, the experimental results show that the MAP scores of our method are 87.8 and 45.2, and the optimal performance is achieved. [ABSTRACT FROM AUTHOR]

In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway's length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The accuracy of the shear-phase retrieval method is critical for accurate wavefront aberration measurements in double-grating Ronchi lateral shearing interferometry. Currently, the suppression of the interferences of unwanted diffractions generated by the Ronchi grating at the image plane is eliminated by adding more phase shifts, which increases the measurement time. Here, a stepped phase-shifting algorithm is proposed to suppress the unwanted diffractions and retrieve the shear phase between ±1st orders accurately with fewer phase shifts, and the measurement efficiency can be increased by 25% at least. The minimum number of phase shifts, which depends on the high diffraction orders existing in the interferograms, is analyzed. The proposed method was verified via numerical simulations and experiments. The wavefront measurement was validated via a comparison with the results of point-diffracted interferometry, and the root-mean-square difference was within 2.0 nm. [ABSTRACT FROM AUTHOR]

• A new shear-phase retrieval algorithm for double-grating Ronchi lateral shearing interferometry is proposed. • The algorithm allows for adjustable shear ratios without any hardware change. • The algorithm can reject unwanted diffraction orders, verified by the spectral analysis using its frequency transfer function. • Significant improvement on the accuracy of wavefront measurement is demonstrated in both experiment and numerical simulation. Accurate wavefront measurement using double-grating Ronchi lateral shearing interferometry significantly relies on the accuracy of shear-phase retrieval. Traditional methods cannot effectively suppress the interferences of unwanted diffractions generated by the Ronchi grating at the image plane. In this paper, we propose a method that uses 3 N +1 shear interferograms to retrieve the shear phase, where N is a positive integer. The selection of N depends on the number of the diffraction orders existing in the shearogram. According to the numerical aperture of the optical system under test and the period of image grating, the interferograms corresponding to the specific 3 N +1 phase shifts are obtained, from which the shear phase is calculated. For validation, the frequency response of the proposed algorithm is characterized, and the improved accuracy of wavefront measurement is demonstrated by comparisons with the existing 8-frame, 10-frame and 13-frame methods. [ABSTRACT FROM AUTHOR]

Magnification and distortion are two important parameters for high-precision imaging systems. Point diffraction interferometers (PDIs) can measure the magnification, distortion, and wavefront aberration of imaging systems with high precision. However, determining the precise pinhole alignment of the classical PDI is difficult. A new method for measurement of the magnification and distortion based on a dual-fiber point diffraction interferometer (DFPDI) is proposed. The end faces of two fibers are placed on the object plane of the optics under test and imaged to the image plane. The distance between the image points in the x and y directions are proportional to the Z2 and Z3 Zernike coefficients of the wavefront measurement result, respectively. The measurements of the image placement shift and precise alignment of the point diffraction pinhole are realized rapidly with high accuracy. The feasibility of the method is verified experimentally. The wavefront aberration, magnification, and distortion of a 5 × reduction lens with numerical aperture (NA) of 0.3 is measured jointly. The measurement uncertainties (3σ) of the magnification in the x and y directions and distortion are 756 ppm, 793 ppm, and 0.233 μm, respectively. Error analysis shows that the position error of the object- and image-plane stages is the main error source. An improved measurement scheme with a pinhole–pinhole pairs array in the object plane and a pinhole–window pairs array in the image plane is proposed. The influence of the position errors of the stages is eliminated with optimized measurement procedure. The DFPDI's measurement repeatability (3σ) of the Z2 and Z3 coefficients is 0.65 and 0.33 nm, respectively, corresponding measurement uncertainties (3σ) of the magnification (in the x and y directions) and distortion can reach 1.88 ppm, 1.69 ppm, and 0.812 nm, respectively. [ABSTRACT FROM AUTHOR]