Relational oriented systems engineering framework for flight training

The integration of systems of systems (SoS) associated with a flight training mission directly reflects the problem of developing a system engineering process for the design of live, virtual and constructive (LVC) experiments. Due to the complexity and disparity of the technology in a flight training SoS (FTSoS), modeling and analysis of architecture is becoming increasingly important. Relational Oriented Systems Engineering (ROSE) methodology is used to develop a framework for simulation and analysis of a navigational SoS for a typical aircraft. The framework can be used for both the prescription of navigation systems entering and exiting the SoS and for the analysis of pilot behavior as navigation quality of service (QoS) changes. ROSE offers a novel approach to developing a model-based systems engineering (MBSE) process for simulation and analysis of a complex SoS problem

Upset Simulation and Training Initiatives for U.S. Navy Commercial Derived Aircraft

Militarized versions of commercial platforms are growing in popularity due to many logistical benefits in the form of commercial off-the-shelf (COTS) parts, established production methods, and commonality for different certifications. Commercial data and best practices are often leveraged to reduce procurement and engineering development costs. While the developmental and cost reduction benefits are clear, these militarized aircraft are routinely operated in flight at significantly different conditions and in significantly different manners than for routine commercial flight. Therefore they are at a higher risk of flight envelope exceedance. This risk may lead to departure from controlled flight and/or aircraft loss1. Historically, the risk of departure from controlled flight for military aircraft has been mitigated by piloted simulation training and engineering analysis of typical aircraft response. High-agility military aircraft simulation databases are typically developed to include high angles of attack (AoA) and sideslip due to the dynamic nature of their missions and have been developed for many tactical configurations over the previous decades. These aircraft simulations allow for a more thorough understanding of the vehicle flight dynamics characteristics at high AoA and sideslip. In recent years, government sponsored research on transport airplane aerodynamic characteristics at high angles of attack has produced a growing understanding of stall/post-stall behavior. This research along with recent commercial airline training initiatives has resulted in improved understanding of simulator-based training requirements and simulator model fidelity.2-5 In addition, inflight training research over the past decade has produced a database of pilot performance and recurrency metrics6. Innovative solutions to aerodynamically model large commercial aircraft for upset conditions such as high AoA, high sideslip, and ballistic damage, as well as capability to accurately account for scaling factors, is necessary to develop realistic engineering and training simulations. Such simulations should significantly reduce the risk of departure from controlled flight, loss of aircraft, and ease the airworthiness certification process. The characteristics of commercial derivative aircraft are exemplified by the P-8A Multi-mission Maritime Aircraft (MMA) aircraft, and the largest benefits of initial investigation are likely to be yielded from this platform. The database produced would also be utilized by flight dynamics engineers as a means to further develop and investigate vehicle flight characteristics as mission tactics evolve through the years ahead. This paper will describe ongoing efforts by the U.S. Navy to develop a methodology for simulation and training for large commercial-derived transport aircraft at unusual attitudes, typically experienced during an aircraft upset. This methodology will be applied to a representative Navy aircraft (P-8A) and utilized to develop a robust simulation that should accurately represent aircraft response in these extremes. Simulation capabilities would then extend to flight dynamics analysis and simulation, as well as potential training applications. Recent evaluations of integrated academic, ground-based simulation, and in-flight upset training will be described along with important lessons learned, specific to military requirements

Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Potential applications of simulation modelling techniques in healthcare: lessons learned from aerospace and military

The Aerospace and Military areas are to do with complex missions and situations. Modelling and Simulation (M&S) has been applied in many areas of defence ranging from space sciences, satellite engineering to multi-warfare (air warfare, undersea warfare), air & missile defence, acquisition, tactical military trainings & exercises, national security analysis and strategic decision making & planning, etc. The application of simulation modelling techniques in healthcare would improve the provision of healthcare services; however, their application has been much relatively feeble in the healthcare sector as compared to the defence sector. This paper presents results from a systematic literature survey on applications of modelling simulation techniques in the Aerospace & Military. The knowledge gained or lessons learned from the survey were finally used to analyze the potential applications of the simulation modelling techniques to the healthcare sector. Results show that in the defence sector, Distributed Simulation has now become a widely adopted technique. However, System Dynamics (SD) and Discrete Event Simulation (DSE) have also gained relative attention. From this survey it becomes clear that various simulation modelling techniques are useful for specific purposes and have potential applications in the healthcare sector

CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using side-channel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.Comment: 15 pages, 16 figure