GedankenNet: Self-supervised learning of hologram reconstruction using physics consistency

The past decade has witnessed transformative applications of deep learning in various computational imaging, sensing and microscopy tasks. Due to the supervised learning schemes employed, most of these methods depend on large-scale, diverse, and labeled training data. The acquisition and preparation of such training image datasets are often laborious and costly, also leading to biased estimation and limited generalization to new types of samples. Here, we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labeled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks. Without prior knowledge about the sample types to be imaged, the self-supervised learning model was trained using a physics-consistency loss and artificial random images that are synthetically generated without any experiments or resemblance to real-world samples. After its self-supervised training, GedankenNet successfully generalized to experimental holograms of various unseen biological samples, reconstructing the phase and amplitude images of different types of objects using experimentally acquired test holograms. Without access to experimental data or the knowledge of real samples of interest or their spatial features, GedankenNet's self-supervised learning achieved complex-valued image reconstructions that are consistent with the Maxwell's equations, meaning that its output inference and object solutions accurately represent the wave propagation in free-space. This self-supervised learning of image reconstruction tasks opens up new opportunities for various inverse problems in holography, microscopy and computational imaging fields.Comment: 30 pages, 6 Figure

Safety Index Synthesis via Sum-of-Squares Programming

Control systems often need to satisfy strict safety requirements. Safety index provides a handy way to evaluate the safety level of the system and derive the resulting safe control policies. However, designing safety index functions under control limits is difficult and requires a great amount of expert knowledge. This paper proposes a framework for synthesizing the safety index for general control systems using sum-of-squares programming. Our approach is to show that ensuring the non-emptiness of safe control on the safe set boundary is equivalent to a local manifold positiveness problem. We then prove that this problem is equivalent to sum-of-squares programming via the Positivstellensatz of algebraic geometry. We validate the proposed method on robot arms with different degrees of freedom and ground vehicles. The results show that the synthesized safety index guarantees safety and our method is effective even in high-dimensional robot systems

Incorporate Modeling Uncertainty into the Decision Making of Passive System Reliability Assessment

International audiencePassive safety features will play crucial roles in the development of the future generation nuclear power plant technologies. However because of the insufficient experiences, researches and validations are still necessary with the aim to prove the actual performance and reliability of the passive systems. Uncertain resources, which will influence the reliability and performance of such systems, can be divided into two groups: modeling uncertainties and parametric uncertainties. Up to now, the researchers have as good as established ways to quantify the effects caused by the parameter uncertainties, e.g. the variation of physical parameters (environment temperature, fabrication error, etc.) and have already got a number of achievements. In addition to the parameter uncertainty, the modeling uncertainty, e.g. uncertain physical phenomenon, uncertainties by different modeling techniques, etc. shall also be an important contributor to the passive system performance. How to take into account the effect caused by this kind of factors, there hasn't any mature approaches. In this paper, a survey of researches about the modeling uncertainty from the open literature is presented. A framework to incorporate the modeling uncertainty into the decision making of passive system reliability assessment will be proposed based on the survey and the discussion