Technical Workshop: Advanced Helicopter Cockpit Design

Information processing demands on both civilian and military aircrews have increased enormously as rotorcraft have come to be used for adverse weather, day/night, and remote area missions. Applied psychology, engineering, or operational research for future helicopter cockpit design criteria were identified. Three areas were addressed: (1) operational requirements, (2) advanced avionics, and (3) man-system integration

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

Cognitively Sensitive User Interface for Command and Control Applications

While there are broad guidelines for display or user interface design, creating effective human-computer interfaces for complex, dynamic systems control is challenging. Ad hoc approaches which consider the human as an afterthought are limiting. This research proposed a systematic approach to human / computer interface design that focuses on both the semantic and syntactic aspects of display design in the context of human-in-the-loop supervisory control of intelligent, autonomous multi-agent simulated unmanned aerial vehicles (UAVs). A systematic way to understand what needs to be displayed, how it should be displayed, and how the integrated system needs to be assessed is outlined through a combination of concepts from naturalistic decision making, semiotic analysis, and situational awareness literature. A new sprocket-based design was designed and evaluated in this research. For the practical designer, this research developed a systematic, iterative design process: design using cognitive sensitive principles, test the new interface in a laboratory situation; bring in subject matter experts to examine the interface in isolation; and finally, incorporate the resulting feedback into a full-size simulation. At each one of these steps, the operator, the engineer and the designer reexamined the results

Multi-authored monograph

Unmanned aerial vehicles. Perspectives. Management. Power supply : Multi-authored monograph / V. V. Holovenskiy, T. F. Shmelova,Y. M. Shmelev and oth.; Science Editor DSc. (Engineering), T. F. Shmelova. – Warsaw, 2019. – 100 p. - ISBN 978-83-66216-10-5.У монографії аналізуються можливі варіанти енергопостачання та управління безпілотними літальними апаратами. Також розглядається питання прийняття рішення оператором безпілотного літального апарату при управлінні у надзвичайних ситуаціях. Рекомендується для фахівців, аспірантів і студентів за спеціальностями 141 - «Електроенергетика, електротехніка та електромеханіка», 173 - «Авіоніка» та інших суміжних спеціальностей.The monograph analyzes the possible options for energy supply and control of unmanned aerial vehicles. Also, the issue of decision-making by the operator of an unmanned aerial vehicle in the management of emergencies is considered.

Mitigating Complexity in Air Traffic Control: The Role of Structure-Based Abstractions

Cognitive complexity is a limiting factor on the capacity and efficiency of the Air Traffic Control (ATC) system. A multi-faceted cognitive ethnography approach shows that structure, defined as the physical and informational elements that organize and arrange the ATC environment, plays an important role in helping controllers mitigate cognitive complexity. Key influences of structure in the operational environment and on controller cognitive processes are incorporated into a cognitive process model. Controllers are hypothesized to internalize the structural influences in the form of abstractions simplifying their working mental model of the situation. By simplifying their working mental model, these structure-based abstractions reduce cognitive complexity.FAA grants 96-C-001 and # 06-G-006

AN INTRODUCTION TO FRAMEWORK ADAPTATIONS FOR ADDITIONAL ASSURANCE OF A DEEP NEURAL NETWORK WITHIN NAVAL TEST AND EVALUATION

The complexity of modern warfare has rapidly outmatched the capacity of a human brain to accomplish the required tasks of a defined mission set. Task-shedding mundane tasks would prove immensely beneficial, freeing the warfighter to solve more complex issues; however, most tasks that a human might find menial, and shed-worthy, prove vastly abstract for a computer to solve. Advances in Deep Neural Network technology have demonstrated extensive applications as of late. As DNNs become more capable of accomplishing increasingly complex tasks, and the processors to run those neural nets continue to decrease in size, incorporation of DNN technology into legacy and next-generation aerial Department of Defense platforms has become eminently useful and advantageous. The assimilation of DNN-based systems using traditional testing methods and frameworks to produce artifacts in support of platform certification within Naval Airworthiness, however, proves prohibitive from a cost and time perspective, is not factored for agile development, and would provide an incomplete understanding of the capabilities and limitations of a neural network. The framework presented in this paper provides updated methodologies and considerations for the testing and evaluation and assurance of neural networks in support of the Naval Test and Evaluation process.Commander, United States NavyApproved for public release; distribution is unlimited

Selecting Metrics to Evaluate Human Supervisory Control Applications

The goal of this research is to develop a methodology to select supervisory control metrics. This methodology is based on cost-benefit analyses and generic metric classes. In the context of this research, a metric class is defined as the set of metrics that quantify a certain aspect or component of a system. Generic metric classes are developed because metrics are mission-specific, but metric classes are generalizable across different missions. Cost-benefit analyses are utilized because each metric set has advantages, limitations, and costs, thus the added value of different sets for a given context can be calculated to select the set that maximizes value and minimizes costs. This report summarizes the findings of the first part of this research effort that has focused on developing a supervisory control metric taxonomy that defines generic metric classes and categorizes existing metrics. Future research will focus on applying cost benefit analysis methodologies to metric selection. Five main metric classes have been identified that apply to supervisory control teams composed of humans and autonomous platforms: mission effectiveness, autonomous platform behavior efficiency, human behavior efficiency, human behavior precursors, and collaborative metrics. Mission effectiveness measures how well the mission goals are achieved. Autonomous platform and human behavior efficiency measure the actions and decisions made by the humans and the automation that compose the team. Human behavior precursors measure human initial state, including certain attitudes and cognitive constructs that can be the cause of and drive a given behavior. Collaborative metrics address three different aspects of collaboration: collaboration between the human and the autonomous platform he is controlling, collaboration among humans that compose the team, and autonomous collaboration among platforms. These five metric classes have been populated with metrics and measuring techniques from the existing literature. Which specific metrics should be used to evaluate a system will depend on many factors, but as a rule-of-thumb, we propose that at a minimum, one metric from each class should be used to provide a multi-dimensional assessment of the human-automation team. To determine what the impact on our research has been by not following such a principled approach, we evaluated recent large-scale supervisory control experiments conducted in the MIT Humans and Automation Laboratory. The results show that prior to adapting this metric classification approach, we were fairly consistent in measuring mission effectiveness and human behavior through such metrics as reaction times and decision accuracies. However, despite our supervisory control focus, we were remiss in gathering attention allocation metrics and collaboration metrics, and we often gathered too many correlated metrics that were redundant and wasteful. This meta-analysis of our experimental shortcomings reflect those in the general research population in that we tended to gravitate to popular metrics that are relatively easy to gather, without a clear understanding of exactly what aspect of the systems we were measuring and how the various metrics informed an overall research question