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1. Toward a Bias-Aware Future for Mixed-Initiative Visual Analytics

   Coscia, A; Chau, D H: (January 2020, Workshop on Trust and Expertise in Visual Analytics (TREX))

   null (Ed.)

   Mixed-initiative visual analytics systems incorporate well-established design principles that improve users' abilities to solve problems. As these systems consider whether to take initiative towards achieving user goals, many current systems address the potential for cognitive bias in human initiatives statically, relying on fixed initiatives they can take instead of identifying, communicating and addressing the bias as it occurs. We argue that mixed-initiative design principles can and should incorporate cognitive bias mitigation strategies directly through development of mitigation techniques embedded in the system to address cognitive biases in situ. We identify domain experts in machine learning adopting visual analytics techniques and systems that incorporate existing mixed-initiative principles and examine their potential to support bias mitigation strategies. This examination considers the unique perspective these experts bring to visual analytics and is situated in existing user-centered systems that make exemplary use of design principles informed by cognitive theory. We then suggest informed opportunities for domain experts to take initiative toward addressing cognitive biases in light of their existing contributions to the field. Finally, we contribute open questions and research directions for designers seeking to adopt visual analytics techniques that incorporate bias-aware initiatives in future systems. 

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2. Investigating Errors Observed during UAV-Based Vertical Measurements Using Computational Fluid Dynamics

   https://doi.org/10.3390/drones6090253  

   Hedworth, Hayden; Page, Jeffrey; Sohl, John; Saad, Tony (September 2022, Drones)

   Unmanned Aerial Vehicles (UAVs) are a popular platform for air quality measurements. For vertical measurements, rotary-wing UAVs are particularly well-suited. However, an important concern with rotary-wing UAVs is how the rotor-downwash affects measurement accuracy. Measurements from a recent field campaign showed notable discrepancies between data from ascent and descent, which suggested the UAV downwash may be the cause. To investigate and explain these observed discrepancies, we use high-fidelity computational fluid dynamics (CFD) simulations to simulate a UAV during vertical flight. We use a tracer to model a gaseous pollutant and evaluate the impact of the rotor-downwash on the concentration around the UAV. Our results indicate that, when measuring in a gradient, UAV-based measurements were ∼50% greater than the expected concentration during descent, but they were accurate during ascent, regardless of the location of the sensor. These results provide an explanation for errors encountered during vertical measurements and provide insight for accurate data collection methods in future studies. 

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3. UAV Assisted Cellular Networks With Renewable Energy Charging Infrastructure: A Reinforcement Learning Approach

   https://doi.org/10.1109/MILCOM52596.2021.9653034  

   Sherman, Michelle; Shao, Sihua; Sun, Xiang; Zheng, Jun (November 2021, MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM))

   Deploying unmanned aerial vehicle (UAV) mounted base stations with a renewable energy charging infrastructure in a temporary event (e.g., sporadic hotspots for light reconnaissance mission or disaster-struck areas where regular power-grid is unavailable) provides a responsive and cost-effective solution for cellular networks. Nevertheless, the energy constraint incurred by renewable energy (e.g., solar panel) imposes new challenges on the recharging coordination. The amount of available energy at a charging station (CS) at any given time is variable depending on: the time of day, the location, sunlight availability, size and quality factor of the solar panels used, etc. Uncoordinated UAVs make redundant recharging attempts and result in severe quality of service (QoS) degradation. The system stability and lifetime depend on the coordination between the UAVs and available CSs. In this paper, we develop a reinforcement learning time-step based algorithm for the UAV recharging scheduling and coordination using a Q-Learning approach. The agent is considered a central controller of the UAVs in the system, which uses the ϵ -greedy based action selection. The goal of the algorithm is to maximize the average achieved throughput, reduce the number of recharging occurrences, and increase the life-span of the network. Extensive simulations based on experimentally validated UAV and charging energy models reveal that our approach exceeds the benchmark strategies by 381% in system duration, 47% reduction in the number of recharging occurrences, and achieved 66% of the performance in average throughput compared to a power-grid based infrastructure where there are no energy limitations on the CSs. 

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4. Testing and Evaluation of Radio Frequency Immunity of Unmanned Aerial Vehicles For Bridge Inspection

   https://doi.org/10.1109/AERO50100.2021.9438457  

   Mahama, Edward; Walpita, Thisara; Karimoddini, Ali; Eroglu, Abdullah; Goudarzi, Navid; Cavalline, Tara; Khan, Mubbashar (June 2021, 2021 IEEE Aerospace Conference)

   Recent technological advances have led to an increase in the adoption of Unmanned Aerial Vehicles (UAVs) in a variety of use-case scenarios. In particular, Departments of Transportation in several states in the United States have been exploring the use of UAVs for bridge and infrastructure inspections to improve safety and reduce the costs of the inspection process. UAVs are remotely piloted from a cockpit or a ground station via radio channels. The UAV's state information and payload information are also transmitted to the cockpit/ground station via radio frequency (RF) signals. The RF channels that are commonly used by most UAVs are 72-73, 902-928 and 2400-2483.5 MHz bands, which is also shared by several other communication protocols such as, WiFi and ZigBee networks, and therefore, the interference effects with the other services on the UAV's operation performance cannot be overlooked, particularly to maintain the minimum distance from the close by surfaces while flying alongside and underneath the bridges to achieve the best results. The loss of signal or even signal strength during such close flights can cause damage to the UAV. Especially while inspecting the bridges located in the urban areas that involve a lot of RF communication around due to presence of sever RC devices providing different services. Conventional Electromagnetic Compatibility (EMC) adherence requirements imposed on electronic systems are not adequate for UAVs due to their airborne nature and the presence of the other RF sources in the environment. Thus, in this work, we investigate the compliance of EMC requirements by designing and conducting field experiments to expose the UAVs to electromagnetic interference and distortions that are likely to be encountered during the UAV operation. The results of this work will enable us to assess the level of RF immunity of the general-purpose UAVs to aid in the selection of a suitable UAV platform for bridge inspection and develop safety procedures for minimizing the impact of RF interference. 

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5. Design and Characteristics of a New Transformable UAV with both Coplanar and Omnidirectional Features

   https://doi.org/10.1109/ICUAS54217.2022.9836144  

   Lu, Shi; Rodriguez, Armando; Tsakalis, Konstantinos; Chen, Yan (June 2022, 2022 International Conference on Unmanned Aircraft Systems (ICUAS))

   To broaden and promote the applications of unmanned aerial vehicles (UAVs), UAVs with agile and omnidirectional mobility enabled by full or over actuation are a growing field of research. However, the balance of motion agility and force (energy) efficiency is challenging for a fixed UAV structure. This paper presents the new design of a transformable UAV, which can operate as a coplanar hexacopter or as an omnidirectional multirotor based on different operation modes. The UAV has 100% force efficiency for launching or landing tasks in the coplanar mode. In the omnidirectional mode, the UAV is fully actuated in the air for agile mobility in six degrees of freedom (DOFs). Models and control design are developed to characterize the motion of the transformable UAV. Simulation results are presented to validate the transformable UAV design and the enhanced UAV performance, compared with a fixed structure. 

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