Abstract: Disclosed is a servo matching control mid-infrared differential dual-wavelength laser based on Nd:MgO:PPLN, The 813 nm semiconductor pumping source, the energy transmitting optical fiber, the first focusing lens, the second focusing lens, the first 45-degree beam splitter, the mid-infrared idle frequency light output mirror, the polarized crystal, the servo motor, the mid-infrared parametric light total reflection mirror, the microprogrammed control unit, the second 45-degree beam splitter, the electro-optical crystal and the 1093 nm fundamental frequency light total reflection mirror are sequentially placed from right to left in a straight cavity of the laser; and the 1084 nm fundamental frequency light total reflection mirror is placed in a bent-shape cavity of the laser, corresponding to a position of the second 45-degree beam splitter, such the second 45-degree beam splitter can reflect incident light to the 1084 nm fundamental frequency light total reflection mirror.

Abstract: The systems and methods described herein are directed to a Co-Augmentation framework that may learn new rules and labels simultaneously from unlabeled data with a small set of seed rules and a few manually labeled training data. The augmented rules and labels are further used to train supervised neural network models. Specifically, the systems and methods described herein include two major components: a rule augmenter, and a label augmenter. The rule augmenter is directed to learning new rules, which can be used to obtain weak labels from unlabeled data. The label augmenter is directed to learning new labels from unlabeled data. The Co-Augmentation framework is an iterative learning process which generates and refines a high precision set. At each iteration, both the rule augmenter and label augmenter will contribute new and more accurate labels to the high precision set, which is in turn used to train both the rule augmenter and label augmenter.

Abstract: A DFOS system and machine learning method that automatically localizes manholes, which forms a key step in a fiber optic cable mapping process. Our system and method utilize weakly supervised learning techniques to predict manhole locations based on ambient data captured along the fiber optic cable route. To improve any non-informative ambient data, we employ data selection and label assignment strategies and verify their effectiveness extensively in a variety of settings, including data efficiency and generalizability to different fiber optic cable routes. We describe post-processing steps that bridge the gap between classification and localization and combining results from multiple predictions.

Abstract: A multi-stage tracking control system includes at least a main actuator and a microactuator. The tracking control system is switched to a single-stage mode so that the main actuator alone provides tracking. A disturbance signal is applied to the microactuator while in the single-stage mode, and a failure condition of the microactuator is determined based on a position error generated in response to the disturbance signal.

Abstract: A multi-stage tracking control system includes at least a main actuator and a microactuator. The tracking control system is switched to a single-stage mode so that the main actuator alone provides tracking. A disturbance signal is applied to the microactuator while in the single-stage mode, and a failure condition of the microactuator is determined based on a position error generated in response to the disturbance signal.

Abstract: A method of extracting taxane products from biomass, which involves feeding the biomass into an pressurized liquid extraction unit and contacting the biomass with a halogenated C1 to C2 alkane solvent at a temperature of 100° C. or less and at sufficient pressure to keep the solvent in liquid form, to extract a stream of taxanes and solvent. The stream of taxanes and solvent are then cooled arid the solvent is stripped from the taxanes. The taxanes are finally passed through either a normal phase liquid chromatograph or a reverse phase liquid chromatograph.