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1. UNMANNED AERIAL VEHICLES AS EDUCATIONAL TECHNOLOGY SYSTEMS IN CONSTRUCTION ENGINEERING EDUCATION

   Ivan Mutis Pablo Antonenko (April 2022, Journal of information technology in construction)

   Robert Amor (Ed.)

   Integrating complex spatio-temporal cognitive tasks such as in-situ planning and trade coordination of job site activities is a continuous challenge to learners in Construction Engineering (CE) courses. Spatial information in this context addresses how physical resources are related to one another at a job site, whereas temporal information defines work sequences and hierarchies that transform physical resources. This paper discusses the impacts of using an innovative learning environment for supporting spatio-temporal cognition in CE education using aerial visualizations from Unmanned Aerial Vehicles (UAVs). Learners experience a unique, ‘birds-eye view’ of the spatio-temporal dynamics of a job site. The effects were on improved abilities to apply, analyze, and synthesize any form of design representation to situations and physical contexts. Our findings demonstrate that participants in the intervention group outperformed the control group on measures of learning and motivation, which underscores the potential of UAVs as an educational technology system in CE education. 

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2. Object Detectors for Construction Resources Using Unmanned Aerial Vehicles

   https://doi.org/10.1061/(ASCE)SC.1943-5576.0000598  

   Mutis, Ivan; Joshi, Virat Arun; Singh, Abhishek (November 2021, Practice Periodical on Structural Design and Construction)

   Project control operations in construction are mostly executed via direct observations and the manual monitoring of progress and performance of construction tasks on the job site. Project engineers move physically within job-site areas to ensure activities are executed as planned. Such physical displacements are error-prone and ineffective in cost and time, particularly in larger construction zones. It is critical to explore new methods and technologies to effectively assist performance control operations by rapidly capturing data from materials and equipment on the job site. Motivated by the ubiquitous use of unmanned aerial vehicles (UAVs) in construction projects and the maturity of computer-vision-based machine-learning (ML) techniques, this research investigates the challenges of object detection—the process of predicting classes of objects (specified construction materials and equipment)—in real time. The study addresses the challenges of data collection and predictions for remote monitoring in project control activities. It uses these two proven and robust technologies by exploring factors that impact the use of UAV aerial images to design and implement object detectors through an analytical conceptualization and a showcase demonstration. The approach sheds light on the applications of deep-learning techniques to access and rapidly identify and classify resources in real-time. It paves the way to shift from costly and time-consuming job-site walkthroughs that are coupled with manual data processing and input to more automated, streamlined operations. The research found that the critical factor to develop object detectors with acceptable levels of accuracy is collecting aerial images with for adequate scales with high frequencies from different positions of the same construction areas. 

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3. Contract-Theoretic Resource Control in Wireless Powered Communication Public Safety Systems

   https://doi.org/10.1109/GLOBECOM42002.2020.9348200  

   Patrizi, Nathan; Fragkos, Georgios; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon (December 2020, 2020 IEEE Global Communications Conference)

   null (Ed.)

   Recent technological advances in the use of Unmanned Aerial Vehicles (UAVs) and Wireless Powered Communications (WPC) have enabled the energy efficient operation of the Public Safety Networks (PSN) during disaster scenarios. In this paper, an energy efficient information flow and energy harvesting framework capturing users' risk-aware characteristics is introduced based on the principles of Contract Theory. To better support the operational effectiveness of the proposed framework, users are clustered in rescue groups following a socio-physical-aware group formation mechanism, while rescue leaders for each group are selected. A reinforcement learning approach is applied to enable the optimal matching between the UAVs and the rescue leaders in a distributed and efficient manner. The proposed contract-theoretic framework models the UAVs-victims relation based on a labor market setting via offering rewards to the users (incentives) in order to compensate them for their invested labor (reporting information). Detailed numerical results demonstrate the benefits and superiority of the proposed framework under different settings. 

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4. Mining Interpretable Spatio-Temporal Logic Properties for Spatially Distributed Systems

   https://doi.org/10.1007/978-3-030-88885-5_7  

   Mohammadinejad, Sara; Deshmukh, Jyotirmoy V; Nenzi, Laura (October 2021, International Symposium on Automated Technology for Verification and Analysis)

   null (Ed.)

   The Internet-of-Things, complex sensor networks, multi-agent cyber-physical systems are all examples of spatially distributed systems that continuously evolve in time. Such systems generate huge amounts of spatio-temporal data, and system designers are often interested in analyzing and discovering structure within the data. There has been considerable interest in learning causal and logical properties of temporal data using logics such as Signal Temporal Logic (STL); however, there is limited work on discovering such relations on spatio-temporal data. We propose the first set of algorithms for unsupervised learning for spatio-temporal data. Our method does automatic feature extraction from the spatio-temporal data by projecting it onto the parameter space of a parametric spatio-temporal reach and escape logic (PSTREL). We propose an agglomerative hierarchical clustering technique that guarantees that each cluster satisfies a distinct STREL formula. We show that our method generates STREL formulas of bounded description complexity using a novel decision-tree approach which generalizes previous unsupervised learning techniques for Signal Temporal Logic. We demonstrate the effectiveness of our approach on case studies from diverse domains such as urban transportation, epidemiology, green infrastructure, and air quality monitoring. 

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5. Distributed Constraint Optimization Problem for Coordinated Response of Unmanned Aerial Vehicles and Ground Vehicles

   https://doi.org/10.1109/CISS50987.2021.9400249  

   Darko, Justice; Folsom, Larkin; Deshpande, Niharika; Park, Hyoshin (March 2021, The 55th Annual Conference on Information Sciences and Systems)

   While there has been significant progress on statistical theories in the information community, there is a lack of studies in information-theoretic distributed resource allocation to maximize information gain. With advanced technologies of unmanned aerial vehicles (UAVs) in response to corresponding revised FAA regulations, this study focuses on developing a new framework for utilizing UAVs in incident management. As a result of new computing technologies, predictive decision-making studies have recently improved ERV allocations for a sequence of incidents; however, these ground-based operations do not simultaneously capture network-wide information. This study incorporates a real-time aerial view using UAVs with three key improvements. First, aerial observations update the status of the freeway shoulder, allowing an ERV to safely travel at full speed. Second, observing parameters of the congestion shockwave provides accurate measurements of the true impact of an incident. Third, real-time information can be gathered on the clearance progress of an incident scene. We automate UAV and ERV allocation while satisfying constraints between these vehicles using a distributed constraint optimization problem (DCOP) framework. To find the optimal assignment of vehicles, the proposed model is formulated and solved using the Max-Sum approach. The system utility convergence is presented for different scenarios of grid size, number of incidents, and number of vehicles. We also present the solution of our model using the Distributed Stochastic Algorithm (DSA). DSA with exploration heuristics outperformed the Max-Sum algorithm when probability threshold p=0.5 but degrades for higher values of p. 

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