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| 2. |
- Edelbro, Catrin
(författare)
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Numerical modelling of observed fallouts in hard rock masses using an instantaneous cohesion-softening friction-hardening model
- 2009
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Ingår i: Tunnelling and Underground Space Technology. - : Elsevier BV. - 0886-7798 .- 1878-4364. ; 24:4, s. 398-409
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Tidskriftsartikel (refereegranskat)abstract
- The work presented in this paper focuses on compressive stress-induced brittle fallouts in hard rock masses, which are massive or sparsely fractured and subjected to intermediate to high in situ stresses. The results of numerical modelling, using a linear-elastic, brittle plastic material model with cohesion-softening friction-hardening (CSFH) behaviour, were compared with observed fallouts for six cases. The objective was to study how well the results of a CSFH model agrees with observed fallouts with respect to location, depth, and shape. All six cases were well documented with respect to virgin stresses, fallout characteristics, rock mass properties, and rock behaviour. The modelling results showed that shear strain localization (shear bands) developed for all cases. The depth of the intersected shear bands were used as a fallout indicator. Furthermore, the location and shape of the observed fallouts could be predicted fairly accurately. The predicted fallout depth was in good agreement with observed fallouts for three of the cases. Using both yielded elements and intersecting shear bands as fallout indicators results in a better prediction of fallout than using just one indicator.
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| 3. |
- Gao, Yuan
(författare)
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Machine Behavior Development and Analysis using Reinforcement Learning
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- We are approaching a future where robots and humans will co-exist and co-adapt. To understand how can a robot co-adapt with humans, we need to understand and develop efficient algorithms suitable for our interactive purposes. Not only it can help us to advance the field of robotics but also it can help us to understand ourselves. A subject Machine Behavior, proposed by Iyad Rahwan in a recent Science article, studies algorithms and the social environments in which algorithms operate. What this paper's view tells us is that, when we would like to study any artificial robot we create, like natural science, a two-step method based on logical positivism should be applied. That is, we need to, on one hand, provide a complicated theory based on logical deduction, and on another hand, empirically setup experiments to conduct.Reinforcement learning (RL) is a computational model that helps us to build a theory to explain the interactive process. Integrated with neural networks and statistics, the current RL is able to obtain a reliable learning representation and adapt over interactive processes. It might be one of the first times that we are able to use a theoretical framework to capture uncertainty and adapt automatically during interactions between humans and robots. Though some limitations are observed in different studies, many positive aspects have also been revealed. Additionally, considering the potentials of these methods people observed from related fields e.g. image recognition, physical human-robot interaction and manipulation, we hope this framework will bring more insights to the field of robotics. The main challenge in applying Deep RL to the field of social robotics is the volume of data. In traditional robotics problems such as body control, simultaneous localization and mapping and grasping, deep reinforcement learning often takes place only in a non-human environment. In such an environment, the robot can learn infinitely in the environment to optimize its strategies. However, applications in social robotics tend to be in a complex environment of human-robot interaction. Social robots require human involvement every time they learn in such an environment, which leads to very expensive data collection. In this thesis, we will discuss several ways to deal with this challenge, mainly in terms of two aspects, namely, evaluation of learning algorithms and the development of learning methods for human-robot co-adaptation.
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| 5. |
- Beohar, Harsh, 1984-, et al.
(författare)
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Spinal test suites for software product lines
- 2014
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Ingår i: Proceedings. - Sydney : Open Publishing Association. ; , s. 44-55
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Konferensbidrag (refereegranskat)abstract
- A major challenge in testing software product lines is efficiency. In particular, testing a product line should take less effort than testing each and every product individually. We address this issue in the context of input-output conformance testing, which is a formal theory of model-based testing. We extend the notion of conformance testing on input-output featured transition systems with the novel concept of spinal test suites. We show how this concept dispenses with retesting the common behavior among different, but similar, products of a software product line. © H. Beohar & M.R. Mousavi.
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| 6. |
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| 7. |
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Pengar till kulturminnesvården
- 1985
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Ingår i: Kulturminnesvård. - Stockholm : Riksantikvarieämbetet. - 0346-9077. ; :1, s. 19-20
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- Kulturminnesvård, lämnar som vanligt en översikt över kulturpengarna i årets budgetproposition
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| 8. |
- Crema, Andrea, et al.
(författare)
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Helping Hand grasp rehabilitation : Preliminary assessment on chronic stroke patients
- 2017
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Ingår i: 8th International IEEE EMBS Conference on Neural Engineering, NER 2017. - 9781538619162 ; , s. 146-149
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Konferensbidrag (refereegranskat)abstract
- The Helping Hand (HH) system is a novel grasp rehabilitation platform aimed at simplifying the clinical usage of wearable electrode arrays for neuromuscular electrical stimulation (NMES). In a randomized dose-matched, clinical study we evaluate usability and effectiveness of the HH treatment, and of other enriched upper limb rehabilitation treatments, and compare the outcomes. This paper shows the preliminary clinical results of the trial on 5 chronic stroke patients throughout a 9 weeks, 3 hours per week, hand preshaping training.
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