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<title>Ponencias / Comunicaciones de congresos IOI</title>
<link>https://hdl.handle.net/10259/5504</link>
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<dc:date>2026-05-07T11:22:05Z</dc:date>
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<title>New ways to evaluate learning. Assessing teamwork using TPM and a Poka-Yoke design</title>
<link>https://hdl.handle.net/10259/5294</link>
<description>New ways to evaluate learning. Assessing teamwork using TPM and a Poka-Yoke design
López Perea, Eva María; Mariscal Saldaña, Miguel Ángel; García Herrero, Susana
In this paper we present a hands-on experiment for measuring learning&#13;
through teamwork applied to solving a real problem.&#13;
The experiment is part of the Production Systems course and involves&#13;
designing a Poka-Yoke, but not theoretically, as is usually the case, rather an&#13;
actual working mechanism. To this end, a practical problem is proposed for&#13;
which a physical machine has to be designed to solve the problem. As part of&#13;
the same exercise, a TPM is developed, also applied to a real case, such as&#13;
assembling a bicycle.&#13;
In the case of the Poka-Yoke, two simultaneous objectives are pursued: to&#13;
avoid a defective product, and to maximize the production per unit time. The&#13;
final score is assigned based on a measurement of these two parameters.&#13;
Once the exercise is assigned, the teamwork is verified to be measured&#13;
efficiently, even when the number of students is high. The physical design of&#13;
the elements, as well as the simultaneous engagement by all the students in the exercise, served to considerably raise the motivation of the students.
Trabajo presentado en: 2nd International Conference on Higher Education Advances, HEAd’16. 2016. Valencia
</description>
<dc:date>2016-01-01T00:00:00Z</dc:date>
</item>
<item rdf:about="https://hdl.handle.net/10259/5293">
<title>Risks management and cobots. Identifying critical variables</title>
<link>https://hdl.handle.net/10259/5293</link>
<description>Risks management and cobots. Identifying critical variables
Mariscal Saldaña, Miguel Ángel; González Pérez, J.; Khalid, Azfar; Gutiérrez Llorente, José Manuel; García Herrero, Susana
A collaborative robot or a "Cobot" is the name of a robot that can share a workspace with operators&#13;
in the absence of a protective fence or with only partial protection. They represent a new and expanding sector of&#13;
industrial robotics. This investigation draws from the latest international rules and safety parameters related to&#13;
work with collaborative robots. Its detailed research is motivated by the design of a collaborative industrial robot&#13;
system, hazard elimination, risk reduction, and different collaborative operations, such as power and force&#13;
limiting, collaborative operation design, and end-effector safety requirements, among others. The purpose of our&#13;
study is to analyze the most important variables that must be controlled in accordance with the desired use of the&#13;
Cobot, according to ISO / TS 15066, ISO / TR 20218-1and some other generic safety regulations on machines and&#13;
industrial robots. A series of observations and appreciations on the use of the Cobot will also be presented.
Trabajo presentado en: 29th European Safety and Reliability Conference (ESREL), 22–26 September 2019, Hannover
</description>
<dc:date>2019-01-01T00:00:00Z</dc:date>
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<item rdf:about="https://hdl.handle.net/10259/5245">
<title>Vulnerability of cyclists on the road. A probabilistic analysis of the database of traffic injuries in Spain focusing on type of involved vehicle and driver culpability</title>
<link>https://hdl.handle.net/10259/5245</link>
<description>Vulnerability of cyclists on the road. A probabilistic analysis of the database of traffic injuries in Spain focusing on type of involved vehicle and driver culpability
García Herrero, Susana; Aldred, Rachel; Anaya Boig, Esther; Mariscal Saldaña, Miguel Ángel
The goal of this research is to explore the role of the collision partner – vehicle type and driver culpability – in incidents involving injuries to people cycling. Previous research has explored a range of factors affecting cyclist injury severity, but were more frequently focused on cyclist behaviour and/or road conditions.&#13;
The database for our study includes a total of 12,318 drivers or riders of any vehicles involved in traffic injuries with victims in Spain in 2016, of which 7,488 are injured bicycle riders. The database used in our research was provided by Spain's National Traffic Department (Dirección General de Tráfico - DGT).&#13;
This research uses Bayesian machine learning techniques. These have been recently used to study the severity of traffic injuries, since they provide a sound methodology for analyzing their causes and risks and predicting the probability of traffic injuries with serious injuries or fatalities.&#13;
We have found proof that involvement of heavy vehicles substantially increases the likelihood of cyclists being killed or seriously injured, and that drivers are more likely than cyclists to be held responsible for the injury.
Trabajo presentado en: 29th European Safety and Reliability Conference (ESREL), 22–26 September 2019, Hannover.
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<dc:date>2019-01-01T00:00:00Z</dc:date>
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