Architectural Model to Enable Power System Tradeoff Studies

We continue the development of an overall architectural model for an all-electric ship using a physics-based simulation environment to perform fully-integrated simulation of electrical, hydrodynamic, thermal, and structural components of the ship operating in a seaway. The goal of this architectural model is to develop an early-stage design tool capable of performing tradeoff studies on concepts such as AC vs. DC distribution, frequency and voltage level, inclusion of reduction gears, energy and power management options, and effect of arrangements and topology. The results of the studies will be presented in standard metrics including cost, weight, volume, efficiency/fuel consumption, reliability and survivability. We will specifically look at the hull, mechanical and electrical (HM&E) systems that support the ship and its missions; specifically, the electrical generation and distribution system, propulsion equipment, fresh- and saltwater pumping and distribution, control systems, and structural components

Investigating Hastily-Formed Collaborative Networks

This research explores both the human and technical aspects of the network centric environment in the context of a major disaster or incident of national significance. The National Incident Management System (NIMS) is viewed by the authors as a social network, and an organizational topology is developed to improve its effectiveness. A rapid Network Deployment Kit (RNDK) using commercial off the shelf (COTS) wireless networking technology is also proposed that facilitates immediate NIMS implementation. The integration of logical and technical analyses forms a comprehensive systems engineering proposal to facilitate collaboration in a net-centric environment. It is envisioned that the methodology used herein to derive and evaluate comprehensive networks proves extendable to other contexts thereby contributing to the netcentric body of knowledge

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

Quantify resilience enhancement of UTS through exploiting connect community and internet of everything emerging technologies

This work aims at investigating and quantifying the Urban Transport System (UTS) resilience enhancement enabled by the adoption of emerging technology such as Internet of Everything (IoE) and the new trend of the Connected Community (CC). A conceptual extension of Functional Resonance Analysis Method (FRAM) and its formalization have been proposed and used to model UTS complexity. The scope is to identify the system functions and their interdependencies with a particular focus on those that have a relation and impact on people and communities. Network analysis techniques have been applied to the FRAM model to identify and estimate the most critical community-related functions. The notion of Variability Rate (VR) has been defined as the amount of output variability generated by an upstream function that can be tolerated/absorbed by a downstream function, without significantly increasing of its subsequent output variability. A fuzzy based quantification of the VR on expert judgment has been developed when quantitative data are not available. Our approach has been applied to a critical scenario (water bomb/flash flooding) considering two cases: when UTS has CC and IoE implemented or not. The results show a remarkable VR enhancement if CC and IoE are deploye

Automating 3D Wireless Measurements with Drones

Wireless signals and networks are ubiquitous in today’s world. Though more reliable than ever, wireless networks still struggle with weak coverage, blind spots, and interference. Having a strong understanding of wireless signal propagation is essential for increasing coverage, optimizing performance, and minimizing interference for wireless networks. Extensive studies have been done on the propagation of wireless signals, and many theoretical models have been made to simulate wireless signal propagation. Unfortunately, models of signal propagation are often not accurate in reality, and real- world signal measurements are required for validation. Existing methods for collecting wireless measurements involve human researchers walking to each location of interest and manually collecting measurements, which requires large amounts of time and effort, or placing sensors at each location of interest, which is costly. We propose Drone- Sense: a system for measuring wireless signals using autonomous drones. DroneSense reduces the time and effort required for measurement collection, and is affordable and accessible to all users. This is significant in the field of wireless networking as it provides researchers with an efficient method to quickly analyze wireless coverage and test their wireless propagation models

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin