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Cyberspace is "the environment formed by physical and non-physical components to store, modify, and exchange data using computer networks" (NATO CCDCOE). Beyond that, it is an environment where people interact. IT attacks are hostile, non-cooperative interactions that can be described with conflict theory. Applying conflict theory to IT security leads to different objectives for end-user education, requiring different formats like agency-based competence developing games.
Design and Development of a Hot S-Parameter Measurement System for Plasma and Magnetron Applications
(2020)
Game-based learning is a promising approach to anti-phishing education, as it fosters motivation and can help reduce the perceived difficulty of the educational material. Over the years, several prototypes for game-based applications have been proposed, that follow different approaches in content selection, presentation, and game mechanics. In this paper, a literature and product review of existing learning games is presented. Based on research papers and accessible applications, an in-depth analysis was conducted, encompassing target groups, educational contexts, learning goals based on Bloom’s Revised Taxonomy, and learning content. As a result of this review, we created the publications on games (POG) data set for the domain of anti-phishing education. While there are games that can convey factual and conceptual knowledge, we find that most games are either unavailable, fail to convey procedural knowledge or lack technical depth. Thus, we identify potential areas of improvement for games suitable for end-users in informal learning contexts.
One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.
This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.
Component failures within water supply systems can lead to significant performance losses. One way to address these losses is the explicit anticipation of failures within the design process. We consider a water supply system for high-rise buildings, where pump failures are the most likely failure scenarios. We explicitly consider these failures within an early design stage which leads to a more resilient system, i.e., a system which is able to operate under a predefined number of arbitrary pump failures. We use a mathematical optimization approach to compute such a resilient design. This is based on a multi-stage model for topology optimization, which can be described by a system of nonlinear inequalities and integrality constraints. Such a model has to be both computationally tractable and to represent the real-world system accurately. We therefore validate the algorithmic solutions using experiments on a scaled test rig for high-rise buildings. The test rig allows for an arbitrary connection of pumps to reproduce scaled versions of booster station designs for high-rise buildings. We experimentally verify the applicability of the presented optimization model and that the proposed resilience properties are also fulfilled in real systems.