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Urban air mobility (UAM), a transformative concept for the transport of passengers and cargo, faces several integration challenges in complex urban environments. Community acceptance of aircraft noise is among the most noticeable of these challenges when launching or scaling up a UAM system. Properly managing community noise is fundamental to establishing a UAM system that is environmentally and socially sustainable. In this work, we develop a holistic and equitable approach to manage UAM air traffic and its community noise impact in urban environments. The proposed approach is a hybrid approach that considers a mix of different noise mitigation strategies, including limiting the number of operations, cruising at higher altitudes, and ambient noise masking. We tackle the problem through the lens of network system control and formulate a multi-objective optimization model for managing traffic flow in a multi-layer UAM network while concurrently pursuing demand fulfillment, noise control, and energy saving. Further, we use a social welfare function in the optimization model as the basis for the efficiency-fairness trade-off in both demand fulfillment and noise control. We apply the proposed approach to a comprehensive case study in the city of Austin and perform design trade-offs through both visual and quantitative analyses., Comment: 30 pages, 15 figures

Unmanned Aerial Systems (UAS), an integral part of the Advanced Air Mobility (AAM) vision, are capable of performing a wide spectrum of tasks in urban environments. The societal integration of UAS is a pivotal challenge, as these systems must operate harmoniously within the constraints imposed by regulations and societal concerns. In complex urban environments, UAS safety has been a perennial obstacle to their large-scale deployment. To mitigate UAS safety risk and facilitate risk-aware UAS operations planning, we propose a novel concept called \textit{3D virtual risk terrain}. This concept converts public risk constraints in an urban environment into 3D exclusion zones that UAS operations should avoid to adequately reduce risk to Entities of Value (EoV). To implement the 3D virtual risk terrain, we develop a conditional probability framework that comprehensively integrates most existing basic models for UAS ground risk. To demonstrate the concept, we build risk terrains on a Chicago downtown model and observe their characteristics under different conditions. We believe that the 3D virtual risk terrain has the potential to become a new routine tool for risk-aware UAS operations planning, urban airspace management, and policy development. The same idea can also be extended to other forms of societal impacts, such as noise, privacy, and perceived risk., Comment: 33 pages, 19 figures

In the vision of urban air mobility, air transport systems serve the demands of urban communities by routing flight traffic in networks formed by vertiports and flight corridors. We develop a routing algorithm to ensure that the air traffic flow fairly serves the demand of multiple communities subject to stochastic network capacity constraints. This algorithm guarantees that the flight traffic volume allocated to different communities satisfies the \emph{alpha-fairness conditions}, a commonly used family of fairness conditions in resource allocation. It further ensures robust satisfaction of stochastic network capacity constraints by bounding the coherent risk measures of capacity violation. We prove that implementing the proposed algorithm is equivalent to solving a convex optimization problem. We demonstrate the proposed algorithm using a case study based on the city of Austin. Compared with one that maximizes the total served demands, the proposed algorithm promotes even distributions of served demands for different communities.

Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass classification can be categorized as (i) decomposition into binary (ii) extension from binary and (iii) hierarchical classification. Decomposing multiclass classification into a set of binary classifications that can be efficiently solved by using binary classifiers, called class binarization, which is a popular technique for multiclass classification. Neuroevolution, a general and powerful technique for evolving the structure and weights of neural networks, has been successfully applied to binary classification. In this paper, we apply class binarization techniques to a neuroevolution algorithm, NeuroEvolution of Augmenting Topologies (NEAT), that is used to generate neural networks for multiclass classification. We propose a new method that applies Error-Correcting Output Codes (ECOC) to design the class binarization strategies on the neuroevolution for multiclass classification. The ECOC strategies are compared with the class binarization strategies of One-vs-One and One-vs-All on three well-known datasets Digit, Satellite, and Ecoli. We analyse their performance from four aspects of multiclass classification degradation, accuracy, evolutionary efficiency, and robustness. The results show that the NEAT with ECOC performs high accuracy with low variance. Specifically, it shows significant benefits in a flexible number of binary classifiers and strong robustness., Comment: 14 pages, 17 figures

Accurate modeling of aircraft environmental impact is pivotal to the design of operational procedures and policies to mitigate negative aviation environmental impact. Aircraft environmental impact segmentation is a process which clusters aircraft types that have similar environmental impact characteristics based on a set of aircraft features. This practice helps model a large population of aircraft types with insufficient aircraft noise and performance models and contributes to better understanding of aviation environmental impact. Through measuring the similarity between aircraft types, distance metric is the kernel of aircraft segmentation. Traditional ways of aircraft segmentation use plain distance metrics and assign equal weight to all features in an unsupervised clustering process. In this work, we utilize weakly-supervised metric learning and partial information on aircraft fuel burn, emissions, and noise to learn weighted distance metrics for aircraft environmental impact segmentation. We show in a comprehensive case study that the tailored distance metrics can indeed make aircraft segmentation better reflect the actual environmental impact of aircraft. The metric learning approach can help refine a number of similar data-driven analytical studies in aviation., Comment: 32 pages, 11 figures

Urban air mobility (UAM), as envisioned by aviation professionals, will transport passengers and cargo at low altitudes within urban and suburban areas. To operate in urban environments, precise air traffic management, in particular the management of traffic overflows due to physical and operational disruptions will be critical to ensuring system safety and efficiency. To this end, we propose UAM network design with reserve capacity, i.e., a design where alternative landing options and flight corridors are explicitly considered as a means of improving contingency management. Similar redundancy considerations are incorporated in the design of many critical infrastructures, yet remain unexploited in the air transportation literature. In our methodology, we first model how disruptions to a given UAM network might impact on the nominal traffic flow and how this flow might be re-accommodated on an extended network with reserve capacity. Then, through an optimization problem, we select the locations and capacities for the backup vertiports with the maximal expected throughput of the extended network over all possible disruption scenarios, while the throughput is the maximal amount of flights that the network can accommodate per unit of time. We show that we can obtain the solution for the corresponding bi-level and bi-linear optimization problem by solving a mixed-integer linear program. We demonstrate our methodology in the case study using networks from Milwaukee, Atlanta, and Dallas--Fort Worth metropolitan areas and show how the throughput and flexibility of the UAM networks with reserve capacity can outcompete those without., Comment: 44 pages, 10 figures

Neuromorphic hardware as a non-Von Neumann architecture has better energy efficiency and parallelism than the conventional computer. Here, with numerical modeling spin-orbit torque (SOT) device using current-induced SOT and Joule heating effects, we acquire its magnetization switching probability as a function of the input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working. We further demonstrate that the artificial spiking neural network (SNN) built by this SOT device can perform unsupervised handwritten digit recognition with the accuracy of 80% and logic operation learning. Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevices, Comment: 8 pages, 5 figures

We experimentally study the dynamical modes excited by current-induced spin-orbit torque and its electrostatic gating effect in a 3-terminal planar nano-gap spin Hall nano-oscillator (SHNO) with a moderate interfacial perpendicular magnetic anisotropy (IPMA). Both quasilinear propagating spin-wave and localized "bullet" modes are achieved and controlled by varying the applied in-plane magnetic field and driving current. The minimum linewidth shows a linear dependence on the actual temperature of the active area, confirming single-mode dynamics based on the nonlinear theory of spin-torque nano-oscillation with a single mode. The observed electrostatic gating tuning oscillation frequency arises from voltage-controlled magnetic anisotropy and threshold current of SHNO via modification of the nonlinear damping and/or the interfacial spin-orbit coupling of the magnetic multilayer. In contrast to previously observed two-mode coexistence degrading the spectral purity in Py/Pt-based SHNOs with a negligible IPMA, a single coherent spin-wave mode with a low driven current can be achieved by selecting the ferromagnet layer with a suitable IPMA because the nonlinear mode coupling can be diminished by bringing in the PMA field to compensate the easy-plane shape anisotropy. Moreover, the simulations demonstrate that the experimentally observed current and gate-voltage modulation of auto-oscillation modes are also closely associated with the nonlinear damping and mode coupling, which are determined by the ellipticity of magnetization precession. The demonstrated nonlinear mode coupling mechanism and electrical control approach of spin-wave modes could provide the clue to facilitate the implementation of the mutual synchronization map for neuromorphic computing applications in SHNO array networks., Comment: 11 pages, 10 figures

We study spin-transfer-torque driven magnetization dynamics of a perpendicular magnetic tunnel junction (MTJ) nanopillar. Based on the combination of spin-torque ferromagnetic resonance and microwave spectroscopy techniques, we demonstrate that the free layer (FL) and the weak pinned reference layer (RL) exhibit distinct dynamic behaviors with opposite frequency vs. field dispersion relations. The FL can support a single coherent spin-wave (SW) mode for both parallel and antiparallel configurations, while the RL exhibits spin-wave excitation only for the antiparallel state. These two SW modes corresponding to the FL and RL coexist at an antiparallel state and exhibit a crossover phenomenon of oscillation frequency with increasing the external magnetic field, which could be helpful in the mutual synchronization of auto-oscillations for SW-based neuromorphic computing., Comment: 13 pages, 5 figures

In this paper, we propose an evolutionary predatorprey robot system which can be generally implemented from simulation to the real world. We design the closed-loop robot system with camera and infrared sensors as inputs of controller. Both the predators and prey are co-evolved by NeuroEvolution of Augmenting Topologies (NEAT) to learn the expected behaviours. We design a framework that integrate Gym of OpenAI, Robot Operating System (ROS), Gazebo. In such a framework, users only need to focus on algorithms without being worried about the detail of manipulating robots in both simulation and the real world. Combining simulations, real-world evolution, and robustness analysis, it can be applied to develop the solutions for the predator-prey tasks. For the convenience of users, the source code and videos of the simulated and real world are published on Github.