TY - CHAP A1 - Steger, Fabian A1 - Nitsche, Alexander A1 - Miley, Cayler A1 - Schweiger, Hans-Georg A1 - Belski, Iouri ED - Huda, Nazmul ED - Inglis, David ED - Tse, Nicholas ED - Town, Graham T1 - Laboratory Learning: Hands-on versus Simulated Experiments T2 - Proceedings of the 28th Annual Conference of the Australasian Association for Engineering Education (AAEE 2017) N2 - CONTEXT Many universities and vocational training institutions conduct laboratories as simulated experiments. This is due to the costs and supervision needs to conduct hands-on labs safely. Numerous studies have presented mixed opinions on whether hands-on laboratory work is more conducive to learning than a simulated laboratory. Most of the studies put students from experimental and control groups in significantly different conditions. Therefore, it is hard to reach any definite conclusion regarding the influence of the learning mode onto the learning achievements. PURPOSE This study compares learning outcomes of student laboratory work in an energy storages course conducted in two different modes: first as a practical hands-on exercise and second using computer-based simulations. APPROACH In order to provide reliable insights, this study implements optimized research methodology to avoid any other effect (e.g. learning synchronicity/distance learning/instructions) on the learning outcome rather than the effect of the learning mode itself. The student laboratory experiments were created in a manner that they could be conducted in both modes in the same way and using a single set of instructions. To ensure a comparable group environment for the individual student, the students were arranged into two similar groups based on the student's practical experience. In this crossover study, the groups were taught the same topics by means of interchanging learning modes. RESULTS To evaluate the influence of each mode on student learning, short written tests regarding the previous experiment were conducted at the beginning of the subsequent laboratory session. 102 students have taken part in the study in two years. Overall learning results of hands-on experiments were slightly better than those of simulated laboratories (Cohen's d=0.25), the difference in performance was statistically significant (p<0.02). Through solicited feedback on each laboratory session, in hands-on mode more students expressed they have acquired new insights/comprehensions (76% vs. 66%, Cohen's d=0.23, small effect, p<0.07). CONCLUSIONS Following the strategy not to optimize the lessons individually to the learning mode, other influences on the learning outcome, which were usually mixed, were excluded. The students' subjective opinions show advantages of the hands-on mode. Based on the objective data, a weak, but significant outcome to better knowledge acquisition with hands-on laboratory experiments was achieved. This observation is against the trend of the literature in the last years towards better or equal learning with nontraditional labs. Some of the excluded factors might have a stronger influence on student learning than estimated previously. To get a clear view, the authors recommend isolated research. KW - Hands-on vs. simulated experiment KW - battery experiment KW - learning-mode comparison Y1 - 2017 UR - https://researchers.mq.edu.au/en/publications/proceedings-of-the-28th-annual-conference-of-the-australasian-ass UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-15581 SN - 978-0-646-98026-3 SP - 940 EP - 947 PB - Macquarie University CY - Sydney ER - TY - CHAP A1 - Kolb, Jan Christopher A1 - Wech, Lothar A1 - Schwabe, Martin A1 - Ruzok, Christopher A1 - Trost, Christoph ED - Riener, Andreas ED - Appel, Alexandra ED - Dorner, Wolfgang ED - Huber, Thomas ED - Kolb, Jan Christopher ED - Wagner, Harry T1 - Technische Aspekte des automatisierten Fahrens am Projekt des autonomen Shuttlebusses in Bad Birnbach T2 - Autonome Shuttlebusse im ÖPNV N2 - Die Entwickler von selbstfahrenden Fahrzeugen sind sich einig, dass es nach heutigem Stand der Technik über eine Milliarde Testkilometer bedarf, um ein autonom fahrendes Fahrzeug zu entwickeln. Daher überrascht es nicht, dass der „autonome“ Shuttlebus in Bad Birnbach lediglich als teilautomatisiert einzustufen ist. Dennoch ist das Projekt wertvoll, um Erkenntnisse für die Entwicklung von autonomen Fahrzeugen zu erlangen und deren Einsatz im öffentlichen Personennahverkehr (ÖPNV) beurteilen zu können. Im vorliegenden Teilprojekt wurden innerhalb von vier Monaten über dreihundert Situationen dokumentiert, in denen die Fahrt unplanmäßig unterbrochen wurde. Diese wurden anschließend ausgewertet und teilweise in Versuchen nachgestellt. Es hat sich gezeigt, dass der Shuttlebus im aktuellen Betriebsmodell unfallfrei fahren kann. Allerdings ist für das unfall- und unterbrechungsfreie Fahren der durch die Zulassungsauflagen vorgeschriebene Operator an Bord zwingend erforderlich. Dieser muss an verschiedenen Stellen der Strecke – inklusive der Haltestellen – die Weiterfahrt bestätigen, den Bus um Hindernisse lenken und bei einem drohenden Unfall eingreifen. Ohne Operator käme es zur Kollision, da die Technik des Busses nicht alle kritischen Situationen erkennt. Der Bus kann im aktuellen Entwicklungsstand weder fahrerlos betrieben noch als zuverlässige Ergänzung im ÖPNV eingesetzt werden. Der Bus stellt dennoch einen wichtigen Schritt in Richtung autonomen Fahrens dar, welches weiterhin im Rahmen zukünftiger Forschungs- und Entwicklungsprojekte vorangetrieben werden muss. UR - https://doi.org/10.1007/978-3-662-59406-3_5 Y1 - 2020 UR - https://doi.org/10.1007/978-3-662-59406-3_5 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-17520 SN - 978-3-662-59405-6 SN - 978-3-662-59406-3 SP - 57 EP - 91 PB - Springer Vieweg CY - Berlin, Heidelberg ER - TY - JOUR A1 - Müller, Marcus A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang T1 - Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies JF - Journal of Advances in Information Technology N2 - In road traffic, critical situations pass by as quickly as they appear. Within the blink of an eye, one has to come to a decision, which can make the difference between a low severity, high severity or fatal crash. Because time is important, a machine learning driven Crash Severity Predictor (CSP) is presented which provides the estimated crash severity distribution of an imminent crash in less than 0.2ms. This is 63⋅ 103 times faster compared to predicting the same distribution through computationally expensive numerical simulations. With the proposed method, even very complex crash data, like the results of Finite Element Method (FEM) simulations, can be made available ahead of a collision. Knowledge, which can be used to prepare occupants and vehicle to an imminent crash, activate and adjust safety measures like airbags or belt tensioners before of a collision or let self-driving vehicles go for the maneuver with the lowest crash severity. Using a real-world crash test it is shown that significant safety potential is left unused if instead of the CSP-proposed driving maneuver, no or the wrong actions are taken. UR - https://doi.org/10.12720/jait.9.1.15-24 KW - crash severity KW - vehicle safety KW - reliable prediction KW - machine learning Y1 - 2018 UR - https://doi.org/10.12720/jait.9.1.15-24 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24644 SN - 1798-2340 VL - 9 (2018) IS - 1 SP - 15 EP - 24 PB - Engineering and Technology Publishing CY - Rowland Heights (CA) ER - TY - JOUR A1 - Nebl, Christoph A1 - Steger, Fabian A1 - Schweiger, Hans-Georg T1 - Discharge Capacity of Energy Storages as a Function of the Discharge Current–Expanding Peukert’s equation JF - International Journal of Electrochemical Science N2 - In 1897 Wilhelm Peukert tested lead-acid batteries with constant current and observed that a single equation can describe the relationship between the discharge capacity of the battery and a constant discharge current. In this article the dependence of the discharge capacity of lithium-ion battery cells, electrochemical double-layer capacitors and lithium capacitors are investigated from low to very high discharge rates. From low to intermediate discharge rates, these energy storage devices show ideal Peukert behavior, but a deviation was observed at high discharge rates. The cells provide less charge than predicted by Peukert’s Law. To describe this deviation, a new equation has been derived by expanding Peukert’s law to very discharge rates. It is capable to describe the discharge behavior of lithium-ion battery cells, electrochemical double-layer capacitors and lithium capacitors from low to high discharge rates in an unequivocal way. UR - https://doi.org/10.20964/2017.06.51 KW - discharge capacity KW - lithium-ion cell KW - electrochemical double-layer capacitors KW - lithium capacitors KW - capacity KW - Peukert equation KW - discharge current Y1 - 2017 UR - https://doi.org/10.20964/2017.06.51 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-17129 SN - 1452-3981 VL - 12 IS - 6 SP - 4940 EP - 4957 PB - ESG CY - Belgrad ER - TY - CHAP A1 - Reway, Fabio A1 - Funk Drechsler, Maikol A1 - Wachtel Granado, Diogo A1 - Huber, Werner T1 - Validity Analysis of Simulation-based Testing concerning Free-space Detection in Autonomous Driving T2 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems UR - https://doi.org/10.5220/0009573705520558 KW - automated driving KW - testing KW - validation KW - simulation Y1 - 2020 UR - https://doi.org/10.5220/0009573705520558 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18804 SN - 978-989-758-419-0 SN - 2184-495X SP - 552 EP - 558 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Nebl, Christoph A1 - Kotzur, Frank-Oliver A1 - Koch, Daniel A1 - Schweiger, Hans-Georg T1 - Prediction of constant power delivery of lithium-ion cells at high loads JF - Journal of Energy Storage N2 - Available energy and available capacity are key factors for dimensioning batteries. Discharge duration of a battery and its dependency on discharge current amplitude are well described by Peukert’s law from low to medium current ranges. Other equations describe this dependency up to very high current rates, including the currents occurring during a short circuit. It is not current and capacity, but energy and power which are the key parameters for dimensioning battery systems. Thus, the available power of battery cells, vs. the discharge duration was investigated from low to high constant power discharge loads. Based on the results of these experiments, a correlation of the maximum discharge duration for low to medium discharge power pulses, similar to the Peukert’s equation was found. Additionally, a new equation is proposed, describing the power range from low to very high discharge power rates. The result of this work simplifies the design of battery systems, its electromechanical components, as well as improves the prediction of available boost power cost-effective way e.g. for hybrid electric vehicles. UR - https://doi.org/10.1016/j.est.2020.101552 KW - Constant power discharge KW - Lithium-ion batteries KW - Peukert equation KW - High load amplitudes KW - Constant power prediction KW - Discharge power Y1 - 2020 UR - https://doi.org/10.1016/j.est.2020.101552 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9239 SN - 2352-1538 VL - 2020 IS - 30 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Nadarajan, Parthasarathy A1 - Botsch, Michael A1 - Sardina, Sebastian T1 - Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic JF - Journal of Advances in Information Technology N2 - This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed representation of the future traffic scenario is of significant importance for autonomous driving and for all active safety systems. In order to predict the future spacetime representation of the traffic scenario, first the type of traffic scenario is identified and then the machine learning algorithm maps the current state of the scenario to possible future states. The input to the machine learning algorithms is the current state representation of a traffic scenario, termed as the Augmented Occupancy Grid (AOG). The output is the probabilistic space-time representation which includes uncertainties regarding the behaviour of the traffic participants and is termed as the Predicted Occupancy Grid (POG). The novel architecture consists of two Stacked Denoising Autoencoders (SDAs) and a set of Random Forests. It is then compared with the other two existing architectures that comprise of SDAs and DeconvNet. The architectures are validated with the help of simulations and the comparisons are made both in terms of accuracy and computational time. Also, a brief overview on the applications of POGs in the field of active safety is presented. UR - https://doi.org/10.12720/jait.9.1.1-9 KW - Predicted Occupancy Grid KW - Active vehicle safety KW - Autoencoders KW - Random Forest KW - DeconvNet Y1 - 2018 UR - https://doi.org/10.12720/jait.9.1.1-9 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24653 SN - 1798-2340 VL - 9 IS - 1 SP - 1 EP - 9 PB - Engineering and Technology Publishing CY - Rowland Heights ER -