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Automated human emotion recognition from facial expressions is a well-studied problem and still remains a very challenging task. Some efficient or accurate deep learning models have been presented in the literature. However, it is quite difficult to design a model that is both efficient and accurate at the same time. Moreover, identifying the minute feature variations in facial regions for both macro and micro-expressions requires expertise in network design. In this paper, we proposed to search for a highly efficient and robust neural architecture for both macro and micro-level facial expression recognition. To the best of our knowledge, this is the first attempt to design a NAS-based solution for both macro and micro-expression recognition. We produce lightweight models with a gradient-based architecture search algorithm. To maintain consistency between macro and micro-expressions, we utilize dynamic imaging and convert microexpression sequences into a single frame, preserving the spatiotemporal features in the facial regions. The EmoNAS has evaluated over 13 datasets (7 macro expression datasets: CK+, DISFA, MUG, ISED, OULU-VIS CASIA, FER2013, RAF-DB, and 6 micro-expression datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC, MEGC2019 challenge). The proposed models outperform the existing state-of-the-art methods and perform very well in terms of speed and space complexity.

Airdrop testing of parachutes is a complicated endeavor that requires the custom design and certification of many critical components. The most direct path to certifying a component is to perform full scale testing with margin over the maximum loads expected to be seen in operation. However, other constraints often preclude the opportunity to perform full scale testing. In this paper, we present a case study where a problem arises in a joint that had been certified with a full scale test. There was no time or budget available to repeat the full scale testing after a redesign of the joint. Instead, we present a method of testing each failure mode at the component level to support a certification by analysis approach. The analysis itself was not complicated, but tradeoffs had to be made between different failure modes to arrive at the optimal design. The same approach was also applied back to the original joint to confirm that the failure mode that was not seen in full scale testing would have been caught by the proposed analysis. In the end, the new design was certified by analysis and worked without issue for the final six airdrop tests that used this joint.

NASA's Orion Capsule Parachute Assembly System (CPAS) Project is now in the qualification phase of testing, and the Adams simulation has continued to evolve to model the complex dynamics experienced during the test article extraction and separation phases of flight. The ability to initiate tests near the upper altitude limit of the Orion parachute deployment envelope requires extractions from the aircraft at 35,000 ft-MSL. Engineering development phase testing of the Parachute Test Vehicle (PTV) carried by the Carriage Platform Separation System (CPSS) at altitude resulted in test support equipment hardware failures due to increased energy caused by higher true airspeeds. As a result, hardware modifications became a necessity requiring ground static testing of the textile components to be conducted and a new ground dynamic test of the extraction system to be devised. Force-displacement curves from static tests were incorporated into the Adams simulations, allowing prediction of loads, velocities and margins encountered during both flight and ground dynamic tests. The Adams simulation was then further refined by fine tuning the damping terms to match the peak loads recorded in the ground dynamic tests. The failure observed in flight testing was successfully replicated in ground testing and true safety margins of the textile components were revealed. A multi-loop energy modulator was then incorporated into the system level Adams simulation model and the effect on improving test margins be properly evaluated leading to high confidence ground verification testing of the final design solution.

Fluid flows that are smooth at low speeds become unstable and then turbulent at higher speeds. This phenomenon has traditionally been investigated by linearizing the equations of flow and looking for unstable eigenvalues of the linearized problem, but the results agree poorly in many cases with experiments. Nevertheless, it has become clear in recent years that linear effects play a central role in hydrodynamic instability. A reconciliation of these findings with the traditional analysis can be obtained by considering the 'pseudospectra' of the linearized problem, which reveals that small perturbations to the smooth flow in the form of streamwise vortices may be amplified by factors on the order of 10(exp 5) by a linear mechanism, even though all the eigenmodes are stable. The same principles apply also to other problems in the mathematical sciences that involve non-orthogonal eigenfunctions.