Detecting the Unexpected via Image Resynthesis

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes can appear at test time. The main trends in this area either leverage the notion of prediction uncertainty to flag the regions with low confidence as unknown, or rely on autoencoders and highlight poorly-decoded regions. Having observed that, in both cases, the detected regions typically do not correspond to unexpected objects, in this paper, we introduce a drastically different strategy: It relies on the intuition that the network will produce spurious labels in regions depicting unexpected objects. Therefore, resynthesizing the image from the resulting semantic map will yield significant appearance differences with respect to the input image. In other words, we translate the problem of detecting unknown classes to one of identifying poorly-resynthesized image regions. We show that this outperforms both uncertainty- and autoencoder-based methods

Charge collection mechanisms in a sub-micron grated MSM photodector: field analysis

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (July 14, 2006)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2005.Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.Enhanced charge collection mechanisms due to sub-micron wall-like silicon structures on the active surface of a metal-semiconductor-metal (MSM) photodetector were studied through the analysis of transmitted electromagnetic energy with and without the structural changes. Analysis shows that rearranging the walls to a square lattice (cubic or rectangular shaped structures) could improve the charge collection efficiency further. Further studies were done to show that there exists a certain critical percentage of the area covered by the gratings over the detector after which the charge collection efficiency starts to decrease

Power Angle Control Scheme for Integration of UPQC in Grid Connected PV System

The quality of electric power is greatly affected by the proliferation of non-linear loads in electrical energy processing applications like switched mode power supplies, electric motor drives, battery chargers, etc., The custom power devices like UPQC has gained more importance in power quality arena as it gives the best solution for all power quality issues. UPQC is the combination of both shunt and series active power filters connected through a common DC link capacitor. The shunt active power filter is the most corrective measure to remove the current related problems, power factor improvement by supplying reactive power and regulates DC link voltage. The series APF acts as controlled voltage source and corrects voltage related problems, like sag or swell, flickering, harmonics, etc.,. As a combination of both of these, UPQC improves service reliability. In the present work, shunt inverter control is based on modified active- reactive (p-q) power theory, uses High selectivity filter (HSF) for reference current generation. The series APF uses Power Angle Control (PAC) scheme for compensating sag/swell, interruption and voltage related problems along with sharing a part of load reactive power demand with shunt APF and thus ease its loading and makes the utilization of UPQC to be optimal. The topology uses three phase three leg inverters for both shunt APF and series APF. The gating signals were generated using Hysteresis controller. The output of High step-Up DC-DC Converter is used to work as DC voltage source for both APFs. The input voltage for the converter is provided by Photo Voltaic array incorporated with P&O MPPT technique. The use of high step-up DC-DC converter is for high voltage gain with better efficiency. The present topology avoids the PLL in shunt active power filter. The simulation results are presented to show the effectiveness of the three phase, three-wire PV-UPQC and here obtained an acceptable THD for source current and kept load voltag

Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is available. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained stochastic optimal control with dynamics learning and feedback control. We derive an iterative convex optimization algorithm that solves an \underline{Info}rmation-cost \underline{S}tochastic \underline{N}onlinear \underline{O}ptimal \underline{C}ontrol problem (Info-SNOC). The optimization objective encodes both optimal performance and exploration for learning, and the safety is incorporated as distributionally robust chance constraints. The dynamics are predicted from a robust regression model that is learned from data. The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints. A stable feedback controller is used to execute the motion plan and collect data for model learning. We prove the safety of rollout from our exploration method and reduction in uncertainty over epochs, thereby guaranteeing the consistency of our learning method. We validate the effectiveness of Info-SNOC by designing and implementing a pool of safe trajectories for a planar robot. We demonstrate that our approach has higher success rate in ensuring safety when compared to a deterministic trajectory optimization approach.Comment: Submitted to RA-L 2020, review-

Trajectory Optimization for Chance-Constrained Nonlinear Stochastic Systems

This paper presents a new method of computing a sub-optimal solution of a continuous-time continuous-space chance-constrained stochastic nonlinear optimal control problem (SNOC) problem. The proposed method involves two steps. The first step is to derive a deterministic nonlinear optimal control problem (DNOC) with convex constraints that are surrogate to the SNOC by using generalized polynomial chaos (gPC) expansion and tools taken from chance-constrained programming. The second step is to solve the DNOC problem using sequential convex programming (SCP) for trajectory generation. We prove that in the unconstrained case, the optimal value of the DNOC converges to that of SNOC asymptotically and that any feasible solution of the constrained DNOC is a feasible solution of the chance-constrained SNOC because the gPC approximation of the random variables converges to the true distribution. The effectiveness of the gPC-SCP method is demonstrated by computing safe trajectories for a second-order planar robot model with multiplicative stochastic uncertainty entering at the input while avoiding collisions with a specified probability

Physiological and Molecular Characterization of Hydroxyphenylpyruvate Dioxygenase (HPPD)-inhibitor Resistance in Palmer Amaranth (Amaranthus palmeri S. Wats.)

Citation: Nakka, S., Godar, A. S., Wani, P. S., Thompson, C. R., Peterson, D. E., Roelofs, J., & Jugulam, M. (2017). Physiological and Molecular Characterization of Hydroxyphenylpyruvate Dioxygenase (HPPD)-inhibitor Resistance in Palmer Amaranth (Amaranthus palmeri S. Wats.). Frontiers in Plant Science, 8, 12. doi:10.3389/fpls.2017.00555Herbicides that inhibit hydroxyphenylpyruvate dioxygenase (HPPD) such as mesotrione are widely used to control a broad spectrum of weeds in agriculture. Amaranthus palmeri is an economically troublesome weed throughout the United States. The first case of evolution of resistance to HPPD-inhibiting herbicides in A. palmeri was documented in Kansas (KS) and later in Nebraska (NE). The objective of this study was to investigate the mechansim of HPPD-inhibitor (mesotrione) resistance in A. palmeri. Dose response analysis revealed that this population (KSR) was 10-18 times more resistant than their sensitive counterparts (MSS or KSS). Absorbtion and translocation analysis of [C-14] mesotrione suggested that these mechanisms were not involved in the resistance in A. palmeri. Importantly, mesotrione (>90%) was detoxified markedly faster in the resistant populations (KSR and NER), within 24 hours after treatment (HAT) compared to sensitive plants (MSS, KSS, or NER). However, at 48 HAT all populations metabolized the mesotrione, suggesting additional factors may contribute to this resistance. Further evaluation of mesotrione-resistant A. palmeri did not reveal any specific resistance-conferring mutations nor amplification of HPPD gene, the molecular target of mesotrione. However, the resistant populations showed 4- to 12-fold increase in HPPD gene expression. This increase in HPPD transcript levels was accompanied by increased HPPD protein expression. The significant aspects of this research include: the mesotrione resistance in A. palmeri is conferred primarily by rapid detoxification (non-target-site based) of mesotrione; additionally, increased HPPD gene expression (target-site based) also contributes to the resistance mechanism in the evolution of herbicide resistance in this naturally occurring weed species

Automated Rendezvous and Docking Using Tethered Formation Flight

This paper analyzes capture strategies for tether-based autonomous rendezvous and docking. Once both spacecrafts are connected by tethers, docking is achieved through the use of reaction wheels and tether motors without the use of propellant. Autonomous rendezvous and docking is crucial for many upcoming missions including on-orbit servicing and potential Mars missions. The tether-based capture strategies investigated are a spin-up tether deployment and a free-flying child spacecraft attaching the tether. These strategies are compared to a traditional two-agent propulsive docking strategy. The capture strategies are simulated from initial orbit through to completed dock, with the total fuel consumption and dock time compared, along with initial pointing/location requirements. In addition to having lower fuel cost, the tether-based strategies are also more reliable due to redundancy, since tethers can be reeled back in and multiple tethers can be stored for use in case of primary tether failure