TY - JOUR A1 - Kreyer, Jörg A1 - Müller, Marvin A1 - Esch, Thomas T1 - A Calculation Methodology for Predicting Exhaust Mass Flows and Exhaust Temperature Profiles for Heavy-Duty Vehicles JF - SAE International Journal of Commercial Vehicles N2 - The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition. In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile. In addition to the use of road gradient profile data, an evaluation of the continuously recorded distance signal, which represents the distance between the test vehicle and the road user ahead, is included in the prediction model. Using a Fourier analysis, the trajectory of the vehicle speed is determined for a defined prediction horizon. To verify the method, a holistic simulation model consisting of several hierarchically structured submodels has been developed. A map-based submodel of a combustion engine is used to determine the EGR and EAT exhaust gas mass flows and exhaust gas temperature profiles. All simulation results are validated on the basis of the recorded vehicle and environmental data. Deviations from the predicted values are analyzed and discussed. Y1 - 2020 U6 - http://dx.doi.org/10.4271/02-13-02-0009 SN - 1946-3928 VL - 13 IS - 2 SP - 129 EP - 143 PB - SAE International CY - Warrendale, Pa. ER - TY - JOUR A1 - Köhler, Klemens T1 - A conflict theory perspective of IT attacks – consequences for IT security education N2 - 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. Y1 - 2020 IS - Preprint ER - TY - JOUR A1 - Gossmann, Matthias A1 - Thomas, Ulrich A1 - Horváth, András A1 - Dragicevic, Elena A1 - Stoelzle-Feix, Sonja A1 - Jung, Alexander A1 - Raman, Aravind Hariharan A1 - Staat, Manfred A1 - Linder, Peter T1 - A higher-throughput approach to investigate cardiac contractility in vitro under physiological mechanical conditions JF - Journal of Pharmacological and Toxicological Methods Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.vascn.2020.106843 VL - 105 IS - Article 106843 PB - Elsevier CY - New York, NY ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalili, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 U6 - http://dx.doi.org/10.1109/ACCESS.2020.2999898 SN - 2169-3536 VL - 8 IS - Art. 9108222 SP - 111381 EP - 111393 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalil, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 SN - 2169-3536 U6 - http://dx.doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Roepke, Rene A1 - Köhler, Klemens A1 - Drury, Vincent A1 - Schroeder, Ulrik A1 - Wolf, Martin A1 - Meyer, Ulrike T1 - A pond full of phishing games - analysis of learning games for anti-phishing education JF - Model-driven Simulation and Training Environments for Cybersecurity. MSTEC 2020 N2 - 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. Y1 - 2020 SN - 978-3-030-62433-0 U6 - http://dx.doi.org/10.1007/978-3-030-62433-0_32020 N1 - Lecture Notes in Computer Science, vol 12512 SP - 41 EP - 60 PB - Springer CY - Cham ER - TY - JOUR A1 - Ketelhut, Maike A1 - Brügge, G. M. A1 - Göll, Fabian A1 - Braunstein, Bjoern A1 - Albracht, Kirsten A1 - Abel, Dirk T1 - Adaptive iterative learning control of an industrial robot during neuromuscular training JF - IFAC PapersOnLine N2 - To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur. KW - Iterative learning control KW - Robotic rehabilitation KW - Adaptive control Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.ifacol.2020.12.741 SN - 2405-8963 VL - 53 IS - 2 SP - 16468 EP - 16475 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bung, Daniel Bernhard A1 - Erpicum, Sébastien A1 - Tullis, Blanke P. T1 - Advances in hydraulic structures engineering JF - Journal of Hydraulic Engineering Y1 - 2020 U6 - http://dx.doi.org/10.1061/(ASCE)HY.1943-7900.0001851 SN - 0733-9429 (Druckausgabe) SN - 1943-7900 (Online-Ausgabe) VL - 147 IS - 1 PB - ASCE CY - Reston, Va. ER - TY - JOUR A1 - Morat, Mareike A1 - Faude, Oliver A1 - Hanssen, Henner A1 - Ludyga, Sebastian A1 - Zacher, Jonas A1 - Eibl, Angi A1 - Albracht, Kirsten A1 - Donath, Lars T1 - Agility Training to Integratively Promote Neuromuscular, Cognitive, Cardiovascular and Psychosocial Function in Healthy Older Adults: A Study Protocol of a One-Year Randomized-Controlled Trial JF - International Journal of Environmental Research and Public Health N2 - Exercise training effectively mitigates aging-induced health and fitness impairments. Traditional training recommendations for the elderly focus separately on relevant physiological fitness domains, such as balance, flexibility, strength and endurance. Thus, a more holistic and functional training framework is needed. The proposed agility training concept integratively tackles spatial orientation, stop and go, balance and strength. The presented protocol aims at introducing a two-armed, one-year randomized controlled trial, evaluating the effects of this concept on neuromuscular, cardiovascular, cognitive and psychosocial health outcomes in healthy older adults. Eighty-five participants were enrolled in this ongoing trial. Seventy-nine participants completed baseline testing and were block-randomized to the agility training group or the inactive control group. All participants undergo pre- and post-testing with interim assessment after six months. The intervention group currently receives supervised, group-based agility training twice a week over one year, with progressively demanding perceptual, cognitive and physical exercises. Knee extension strength, reactive balance, dual task gait speed and the Agility Challenge for the Elderly (ACE) serve as primary endpoints and neuromuscular, cognitive, cardiovascular, and psychosocial meassures serve as surrogate secondary outcomes. Our protocol promotes a comprehensive exercise training concept for older adults, that might facilitate stakeholders in health and exercise to stimulate relevant health outcomes without relying on excessively time-consuming physical activity recommendations. KW - agility KW - prevention KW - healthy aging KW - community dwelling KW - psychosocial Y1 - 2020 U6 - http://dx.doi.org/10.3390/ijerph17061853 SN - 1660-4601 VL - 17 IS - 6 SP - 1 EP - 14 PB - MDPI CY - Basel ER - TY - JOUR A1 - Götten, Falk A1 - Havermann, Marc A1 - Braun, Carsten A1 - Marino, Matthew A1 - Bil, Cees T1 - Airfoil drag at low-to-medium reynolds numbers: A novel estimation method JF - AIAA Journal N2 - This paper presents a novel method for airfoil drag estimation at Reynolds numbers between 4×10⁵ and 4×10⁶. The novel method is based on a systematic study of 40 airfoils applying over 600 numerical simulations and considering natural transition. The influence of the airfoil thickness-to-chord ratio, camber, and freestream Reynolds number on both friction and pressure drag is analyzed in detail. Natural transition significantly affects drag characteristics and leads to distinct drag minima for different Reynolds numbers and thickness-to-chord ratios. The results of the systematic study are used to develop empirical correlations that can accurately predict an airfoil drag at low-lift conditions. The new approach estimates a transition location based on airfoil thickness-to-chord ratio, camber, and Reynolds number. It uses the transition location in a mixed laminar–turbulent skin-friction calculation, and corrects the skin-friction coefficient for separation effects. Pressure drag is estimated separately based on correlations of thickness-to-chord ratio, camber, and Reynolds number. The novel method shows excellent accuracy when compared with wind-tunnel measurements of multiple airfoils. It is easily integrable into existing aircraft design environments and is highly beneficial in the conceptual design stage. Y1 - 2020 U6 - http://dx.doi.org/10.2514/1.J058983 SN - 1533-385X VL - 58 IS - 7 SP - 2791 EP - 2805 PB - AIAA CY - Reston, Va. ER -