A framework for modeling and simulation of control, navigation, and surveillance for unmanned aircraft separation assurance

The integration of Unmanned Aircraft Systems in the National Airspace System (UASNAS) problem has received much attention because of the growing number of variety of mission types and the rapid growth of UAS market. Among the many challenging UASNAS problems, separation assurance is considered to be particularly complex, having many interactions among the elements in different levels of abstraction and coupling effects between the different disciplinary domains. In order to explore the separation assurance problem, an analytic model should capture diverse operational scenarios, vehicle dynamics, and subsystem functions such as sensor/surveillance, control, navigation and communications. This has major implications on the analytic model requirements, especially in regard to modeling scope, resolution (or fidelity), and computational expense. The objective of this thesis is to formulate and demonstrate improvements in modeling and simulation of fully integrated UAS to enable systems analysis across the levels of abstraction and multiple disciplines. This work also quantitatively characterizes collision avoidance as a critical element of separation assurance in terms of system behaviors across the levels of abstraction and multiple disciplines. To address these objectives, this thesis contributes to four areas: (1) a statistical gain-scheduling method to improve computational efficiency without a loss of accuracy or fidelity, (2) a hybrid collision avoidance algorithm using a machine learning technique that improves computational runtime as well as optimal trajectory cost, (3) a two-layer obstacle avoidance algorithm for a multi-obstacle environment, (4) a rapid, data-driven and grid-based urban modeling methodology using airborne LiDAR sources. The proposed modeling and simulation capability provides insights into the interaction between system of systems, systems, and subsystems that cannot be characterized by a conventional modeling and simulation environment. To illustrate the collision avoidance problem, this thesis examines the navigation of a fixed wing UAV in a dense urban environment.Ph.D

The survival of holes as thru-holes in thru-hole epitaxy: How can holes in 2D layers not sealed off during multiple stacking of those layers?

Thru-hole epitaxy was recently reported to be able to grow readily detachable domains crystallographically aligned with the underlying substrate over 2D mask material transferred onto a substrate. While the experimental demonstration of thru-hole epitaxy of GaN over multiple stacks of $h$-BN was evident, the detailed mechanism of how small holes in each stack of $h$-BN survived as thru-holes during multiple stacking of $h$-BN was not intuitively clear. Here, we show through Monte Carlo simulations that in what conditions holes in each stack of 2D mask layers can survive as thru-holes during multiple stacking. If more holes are laterally connected, the survival rate of thru-holes increases resulting in the epitaxial connectedness. This computational work verifies and supports that thru-hole epitaxy is attributed to the epitaxial connectedness established by thru-holes survived even through multiple stacks.Comment: 14 pages, 6 figure

Rational Perturbation of the Fluorescence Quantum Yield in Emission‐Tunable and Predictable Fluorophores (Seoul‐Fluors) by a Facile Synthetic Method Involving C—H Activation†

Abstract Fluorescence imaging enables the uniquely sensitive observation of functional‐ and molecular‐recognition events in living cells. However, only a limited range of biological processes have been subjected to imaging because of the lack of a design strategy and difficulties in the synthesis of biosensors. Herein, we report a facile synthesis of emission‐tunable and predictable Seoul‐Fluors, 9‐aryl‐1,2‐dihydrolopyrrolo[3,4‐b]indolizin‐3‐ones, with various R1 and R2 substituents by coinage‐metal‐catalyzed intramolecular 1,3‐dipolar cycloaddition and subsequent palladium‐mediated C—H activation. We also showed that the quantum yields of Seoul‐Fluors are controlled by the electronic nature of the substituents, which influences the extent of photoinduced electron transfer. On the basis of this understanding, we demonstrated our design strategy by the development of a Seoul‐Fluor‐based chemosensor 20 for reactive oxygen species that was not accessible by a previous synthetic route

Coverage path planning for a UAS imagery mission using column generation with a turn penalty

© Copyright 2019 IEEEThis paper introduces a novel Coverage Path Planning (CCP) algorithm for a Unmanned Aerial Systems (UAS) imagery mission. The proposed CPP algorithm is a vehicle-routing-based approach using a column generation method. In general, one of the main issues of the traditional arc-based vehicle routing approaches is imposing a turn penalty in a cost function because a turning motion of vehicle requires the more amount of energy than a cruise motion. However, the conventional vehicle-routing-based approaches for the CPP cannot capture a turning motion of the vehicle. This limitation of the arc-based mathematical model comes from the property of turning motions, which should be evaluated from two arcs because a turn motion occurs at a junction of the arcs. In this paper, to mitigate the limitation, a route-based model using column generation approach with a turn penalty is proposed. To demonstrate the proposed CPP approach, numerical simulations are conducted with a conventional CPP algorithm

Arabidopsis BAG1 Functions as a Cofactor in Hsc70-Mediated Proteasomal Degradation of Unimported Plastid Proteins

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Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the full-precision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bit-width reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy

Effects of dietary supplementation with fermented and non-fermented brown algae by-products on laying performance, egg quality, and blood profile in laying hens

Objective This study was conducted to investigate the effects of dietary supplementation with fermented and non-fermented brown algae by-products on the laying performance, egg quality, relative organ weight, and blood profile of laying hens. Methods Hy-Line Brown chickens (n = 180; 70-week-old) were randomly divided into 5 groups with 4 replicates per group (3 hens per cage, 4 cages per replicate), and fed with 5 experimental diets, namely the basal control diet (CON) or the control diet supplemented with 0.5% brown seaweed (BS), 0.5% seaweed fusiforme (SF), 0.5% fermented brown seaweed (FBS), or 0.5% fermented seaweed fusiforme (FSF), for 4 weeks. Results Egg production rate and egg mass were greater in the BS group than in the other groups (p<0.05), and the SF and FSF groups had greater egg production than the control group (p<0.05). Egg weight was higher in the BS group than in the other groups (p<0.05). There were no differences in eggshell color, egg yolk color, eggshell strength, or eggshell thickness among the groups. There was no difference in Haugh units among the treatment groups, except for the FSF group, which had a significantly lower value (p<0.05). The non-fermented groups had greater relative organ weights, particularly the liver and cecum, than the other groups (p<0.05). Regarding blood profile, the supplemented-diet groups had higher albumin levels than the control group (p<0.05). The FBS group had higher total cholesterol and triglyceride levels than the other groups (p<0.05). The BS and FBS groups had higher glutamic pyruvic transaminase levels than the other groups (p<0.05). Conclusion This study demonstrated that dietary brown algae supplementation can improve egg-laying performance; however, supplementation with fermented seaweeds had no positive effect on the egg-laying performance of hens

Successful Management of a Rare Case of Stent Fracture and Subsequent Migration of the Fractured Stent Segment Into the Ascending Aorta in In-Stent Restenotic Lesions of a Saphenous Vein Graft

Stent fracture is a complication following implantation of drug eluting stents and is recognized as one of the risk factors for in-stent restenosis. We present the first case of successfully managing a stent fracture and subsequent migration of the fractured stent into the ascending aorta that occurred during repeat revascularization for in-stent restenosis of an ostium of saphenous vein graft after implantation of a zotarolimus-eluting stent. Although the fractured stent segment had migrated into the ascending aorta with a pulled balloon catheter, it was successfully repositioned in the saphenous vein graft using an inflated balloon catheter. Then, the fractured stent segment was successfully connected to the residual segment of the zotarolimus-eluting stent by covering it with an additional sirolimuseluting stent