Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight

Scene transfer for vision-based mobile robotics applications is a highly relevant and challenging problem. The utility of a robot greatly depends on its ability to perform a task in the real world, outside of a well-controlled lab environment. Existing scene transfer end-to-end policy learning approaches often suffer from poor sample efficiency or limited generalization capabilities, making them unsuitable for mobile robotics applications. This work proposes an adaptive multi-pair contrastive learning strategy for visual representation learning that enables zero-shot scene transfer and real-world deployment. Control policies relying on the embedding are able to operate in unseen environments without the need for finetuning in the deployment environment. We demonstrate the performance of our approach on the task of agile, vision-based quadrotor flight. Extensive simulation and real-world experiments demonstrate that our approach successfully generalizes beyond the training domain and outperforms all baselines

A new approach to stability analysis of discrete-time recurrent neural networks with time-varying delay

In this paper, the problem of stability analysis of discrete-time recurrent neural networks with time-varying delay is studied. Based on the general assumption of time delay (that is 0 < dm ≤ d(k) ≤ dM), we represent d(k) as dm+h(k) with 0 ≤ h(k) ≤ dM-dm, and introduce a new Lyapunov functional with the idea of delay partitioning. A new stability criterion is then obtained by utilizing the most updated techniques for achieving delay dependence, which is characterized in terms of linear matrix inequalities (LMIs) and can be easily checked by utilizing the efficient LMI toolbox. The merit of the proposed stability lies in its less conservatism than most of the existing results, which is well illustrated via an example

A study of asymptotic stability for delayed recurrent neural networks

This paper addresses the problem of asymptotic stability for discrete-time recurrent neural networks with time-varying delay. The analysis starts with a general assumption that the time-varying delay may be expressed as the lower bound plus the length of an interval over which the delay varies. Then the delay partitioning technique is used to establish a new delay-dependent sufficient condition under which the asymptotic stability of recurrent neural networks with time-varying delay can be guaranteed. The new stability criterion takes the form of linear matrix inequalities, thus lending itself to being readily checkable by the available software package. The obtained theoretical result is further illustrated by numerical results, including their superiority over the existing results on asymptotic stability of delayed recurrent neural networks

Automatic Construction of REDIM Reduced Chemistry with a Detailed Transport and Its Application to CH4 Counterflow Flames

Extinction limits are important quantities of counterflow diffusion flames. An accurate prediction of extinction limits is necessary for the design of engineering combustion devices involving flame quenching. In this work, the reaction-diffusion manifold (REDIM) reduced chemistry with a detailed transport model is applied for the numerical investigation of extinction limits of counterflow diffusion flames. Unlike other tabulated flamelet models where very detailed information about a particular combustion system is required, the REDIM reduced chemistry can be generated based on the detailed reaction mechanisms, requiring only a minimal additional knowledge of the considered combustion system. Recently, an automatic generation of the REDIM has been introduced and tested for premixed flames. This newly developed algorithm starts with a 1D reduced model, and any higher dimension of the REDIM reduced model can be constructed automatically without any additional information. Such an algorithm largely simplifies the generation of the REDIM reduced chemistry. The focus of this work is to apply this newly developed algorithm for the construction of two-dimensional (2D) and three-dimensional (3D) REDIMs for counterflow diffusion flames. It is shown how 2D and 3D REDIM reduced chemistry can be generated automatically in a generic way according to a hierarchical concept. An oxygen-enriched MILD combustion system CH4/CO2 versus the O-2/CO2 counterflow diffusion flame, whose extinction strain rates had been measured experimentally, is selected as an illustrative example for discussion and validation. The relative errors of predicted extinction strain rates using a 3D REDIM are much less than the experimental uncertainty and the differences using different detailed chemical mechanisms

Interlaminar Mechanical Properties and Toughening Mechanism of Highly Thermally Stable Composite Modified by Polyacrylonitrile Nanofiber Films

This work concentrated on the interlaminar mechanical properties and toughening mechanism of carbon fiber-reinforced bismaleimide resin (CF/BMI) composites modified by polyacrylonitrile (PAN) nanofiber films. The PAN nanofiber films were prepared by electrospinning. End-notched flexure (ENF) and short-beam strength tests were conducted to assess the mode II fracture toughness (GIIc) and interlaminar shear strength (ILSS). The results showed that the GIIc and ILSS of PAN-modified specimens are 1900.4 J/m2 and 93.1 MPa, which was 21.4% and 5.4% higher than that of the virgin specimens (1565.5 J/m2 and 88.3 MPa), respectively. The scanning electron microscopy (SEM) images of the fracture surface revealed that the PAN nanofiber films toughen the composite on two scales. On the mesoscopic scale, the composite laminates modified by PAN formed a resin-rich layer with high strength and toughness, which made the crack propagate across the layers. At the microscopic scale, the crack propagation between two-dimensional nanofiber films led to constant pull-out and breakage of the nanofibers. As a result, the interlaminar fracture toughness of the composite laminates improved

Improving Electromagnetic Interference Shielding While Retaining Mechanical Properties of Carbon Fiber-Based Composites by Introducing Carbon Nanofiber Sheet into Laminate Structure

The demands for carbon fiber reinforced composites (CFRCs) are growing in the aviation industry for fuel consumption savings, despite the increasing risk of electromagnetic interference (EMI). In this work, polyacrylonitrile (PAN) sheets were prepared by electrospinning. Carbon nanofiber (CNF) sheets were obtained by the carbonization of PAN sheets. The laminate structures of the CF reinforced bismaleimide (BMI)-based composites were specially designed by introducing two thin CNF sheets in the upper and bottom plies, according to EMI shielding theory. The results showed that the introduction of CNF sheets led to a substantial increase in the EMI shielding effectiveness (SE) by 35.0% compared with CFRCs free of CNF sheets. The dominant EMI shielding mechanism was reflection. Noticeably, the introduction of CNF sheets did not impact the interlaminar shear strength (ILSS) of CFRCs, indicating that the strategy provided in this work was feasible for fabricating CFRCs with a high EMI shielding performance without sacrificing their mechanical properties. Therefore, the satisfactory EMI shielding and ILSS properties, coupled with a high service temperature, made BMI-based composites a promising candidate in some specific fields, such as high-speed aircrafts and missiles

DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues

Abstract Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3–9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68)