1,062 research outputs found
Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge
No full textAutonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system dynamics. An example of a typical ADS problem that is used in this thesis is that of a wheeled robot driving through puddles of mud that, when entered, alters the velocity of the robot. When the robot is in such a puddle, it is in an ADS 'mud', and when it is not, it is in an ADS 'free'. ADS can be indirectly inferred through the analysis of lower-level data such as the velocity of the robot. The goal of this thesis is to design a general abstract discrete state estimator (ADSE) operating with limited prior knowledge. An ADSE is a hierarchical system for detecting changes in ADS. The ADSE should be general; applicable to multiple ADSE problems. The ADSE should further operate under limited prior knowledge: only assuming that the amount of ADS and the ADS that describes the regular operation are known. The basis for the ADSE designed in this thesis is a Gaussian hidden Markov model (GHMM), a hidden Markov model enhanced with Gaussian emissions. Randomly generated experiments are done on a simple but general ADSE problem. Two unsupervised learning methods derived from Expectation Maximization are evaluated, namely Baum-Welch (BW) and forward extraction (FWE). FWE is introduced in this thesis and is a simpler implementation of Viterbi extraction, leveraging assumptions of ADSE to in theory gain computational efficiency. We found that both BW and FWE exhibit superior performance compared to a likelihood-based baseline estimator when the maximum score of the learning curve is considered. When the final score is considered, in some cases, FWE displays a deteriorating learning curve, resulting in worse final scores compared to the baseline. Furthermore, it was found that the lower the overlap coefficient (therefore the less similar the ADS), the higher the maximum reached score. It was further shown that BW exhibits better convergence than FWE to the true model parameters. Besides this, FWE obtained comparable or in some cases even superior scores compared to BW. In general, from the results, the diversity of the experiments conducted, and the assumptions made we can conclude that the GHMM can be a general method for an ADSE with limited prior knowledge. To quantify the suitability of the GHMM for ADSE, further research should include the evaluation of different ADSE methods on the same problem. There exists a tradeoff between the lower computational cost FWE and the more stable but more computationally intensive BW learning. Therefore, future research can include a combination of these methods. Other extensions include extending the GHMM to a Gaussian mixture hidden Markov model to allow for the modeling of more complex distributions, or the application to multiple states or a changing environment.https://github.com/Wouter-deBoer/adseMechanical Engineering | Vehicle Engineering | Cognitive Robotic
embalming and reperfusion of porcine kidneys
No full text<p>These are the data of the following article:</p>
<p>Understanding Thiel embalming in pig kidneys to develop a new circulation model</p>
<p>First author: Wouter Willaert</p
Nederland op een kantelpunt: Interview met Wouter Veldhuis over het Stedelijk Netwerk Nederland en het sociaal netwerk van woonwijken
Full text linkDe stedenbouwkundige en architect Wouter Veldhuis en landschapsarchitect Jannemarie de Jonge zijn per 1 december 2020 Rijksadviseur voor de fysieke leefomgeving. Later in september 2021 komt daar de architect Francesco Veenstra bij als Rijksbouwmeester en dan is het nieuwe trio College van Rijksadviseurs weer compleet. De uitdagingen voor het college zijn groot. De ruimteclaims die er liggen in stad en land, de hooggestemde ambities om klimaatneutraal en circulair te zijn in 2050, de roep om een minister voor de fysieke leefomgeving en of wonen en weer een echt ministerie met budget. Het enorme probleem op de woningmarkt en de druk om één miljoen woningen ergens bij te bouwen. Op 24 april sprak het team van 1M Homes initiative van de TU Delft met de nieuw benoemde rijksadviseur voor de fysieke leefomgeving Wouter Veldhuis over de aanstaande veranderingen
Does Indonesia have a"low-pay"civil service?
Full text linkGovernment officials and polcy analysts maintain that Indonesia's civil servants are poorly paid and have been for decades. This conclusion is supported by anecdotal evidence and casual empiricism. The authors systematically analyze the realtionship between government and private compensation levels using data from two large household surveys carried out by Indonesia's Central Bureau of Statistics: the 1998 Sakernas and 1999 Susenas. The results suggest that government workers with a high school education or less, representing three-quarters of the civil service, earn a pay premium over their private sector counterparts. Civil servants with more than a high school education earn less than they would in the private sector but, on average, the premium is far smaller than commonly is alleged and is in keeping with public/private differentials in other countries. These results prove robust to varying econometric specifications and cast doubt on low pay as an explanation for government corruption.Decentralization,Public Health Promotion,Health Monitoring&Evaluation,National Governance,Knowledge Economy,Health Monitoring&Evaluation,NationalGovernance,Knowledge Economy,Education for the Knowledge Economy,Parliamentary Government
Iterative learning control for LTV systems with applications to an industrial robot
Full text linkIndustrial robots are widely used in industry because of their dexterity, the high manipulation speed and the relatively low price. However, the applicability of these robots is limited by the mediocre accuracy resulting from the low bandwidth of standard industrial controllers. Fortunately, the repeatability of industrial robots is often much better than their tracking accuracy, which can be exploited to improve the accuracy by the application of Iterative Learning Control (ILC). ILC is a control technique that reduces the tracking error along a trajectory that is traced repeatedly by the iterative refinement of a feedforward signal. The tracking accuracy of industrial robots can be improved substantially with ILC by reducing the frequency components of the tracking error beyond the low bandwidth of the standard industrial controller. Below this bandwidth the non-linear dynamics of the robot mechanism are linearised by the controller, but at higher frequencies the closed-loop dynamics depend on the configuration of the robot mechanism. These configuration dependent dynamics can be approximated as linear time-varying (LTV) for small deviations from the repetitive large-scale motion. Therefore, two ILC algorithms for systems with LTV dynamics are developed in this thesis. The norm-optimal ILC algorithm iteratively computes the feedforward that minimises a weighted sum of the norm of the error and the growth of the feedforward. The error is predicted from an LTV dynamic model. The computation of the optimal feedforward is formulated as a finite-time optimal control problem and it is shown that this optimisation problem can be solved using an existing, computationally efficient algorithm. The robust ILC algorithm iteratively computes the feedforward that optimises the reduction of the error for an LTV dynamic model with a given uncertainty. A sufficient condition is derived under which the feedforward reduces the error with a specified fraction for the worst case effect of the uncertainty. This condition takes the finite length of the iteration and the LTV dynamics into account. The computation of the optimal feedforward is formulated as a finitetime dynamic game and the check of the convergence condition is formulated as an anti-causal optimal control problem. It is shown that the dynamic game and the optimal control problem can be solved using existing, computationally efficient algorithms. Convergence analysis shows that the proposed ILC algorithms make the error converge to zero with an adjustable convergence rate if the dynamic model is sufficiently accurate. Increasing the convergence rate reduces the allowable model error. A model error that is too large results in divergence of the tracking error. The allowable model error can be increased by using a robustness filter that removes the components of the feedforward to which the dynamic response is not modelled sufficiently accurate. However, the removed components of the feedforward cannot be used to compensate for the error, which typically results in a non-zero error after convergence. The proposed algorithms are suited for systems with LTV dynamics, they are computationally efficient and they are able to reduce the error monotonically with an adjustable convergence rate. This unique combination of properties makes the algorithms suited for improving the tracking accuracy of industrial robots and other systems with LTV dynamics in practice. The performance of the ILC algorithms is tested experimentally by the application to the industrial St¨aubli RX90 robot. The setpoints for the position of the robot are adjusted with ILC to reduce the tracking error at its end-effector, which is measured with an optical sensor. The experimental results show that the proposed ILC algorithms are able to reduce the measured tracking error substantially, especially if an LTV model of the configuration dependent highfrequency dynamics of the robot is used. The reduction of the tracking error is sufficient for the application of the robot to laser welding of complex trajectories at high speed
Optimization of the capacity of a rose sorting system using discrete event simulation
No full textIn Rose cultivation companies in the Netherlands, there is a demand for a higher sorting capacity on the existing sorting systems. The objective for this research is to advice which part of the sorting system needs to be adjusted to gain a higher sorting capacity. For the current sorting systems, five bottlenecks are defined, the bottlenecks limit the sorting capacity. To be able to forecast the effect of machine adjustments, a discrete event simulation model has been constructed, using Simulink and Matlab. This simulation model is verified, matched and validated using data of on an existing rose sorting system. Results of a single day validation showed that the time to process the roses can be simulated with a 97% accuracy. Subsequently, five different simulations are executed. In each simulation, one of the five bottlenecks is removed or reduced. With the results of these simulations the capacity limitation due to each bottleneck is quantified. However entirely removing a bottleneck is not feasible in reality for all bottlenecks. A last situation is simulated where all feasible bottleneck reductions are combined. This showed that the time to sort all roses is reduced by 35%.Marine Technology | Transport Engineering and Logistic
Single-component organic solar cells-Perspective on the importance of chemical precision in conjugated block copolymers
Full text linkThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors acknowledge financial support from Hasselt University, the Research Foundation-Flanders (FWO Vlaanderen; projects W000620N, I006320N, and 1S99620N), and the European Research Council (ERC; grant agreement 864625)
From exemplar to copy: the scribal appropriation of a Hadewijch manuscript computationally explored
Full text linkThis study is devoted to two of the oldest known manuscripts in which the
oeuvre of the medieval mystical author Hadewijch has been preserved: Brussels,
KBR, 2879-2880 (ms. A) and Brussels, KBR, 2877-2878 (ms. B). On the basis of
codicological and contextual arguments, it is assumed that the scribe who
produced B used A as an exemplar. While the similarities in both layout and
content between the two manuscripts are striking, the present article seeks to
identify the differences. After all, regardless of the intention to produce a
copy that closely follows the exemplar, subtle linguistic variation is
apparent. Divergences relate to spelling conventions, but also to the way in
which words are abbreviated (and the extent to which abbreviations occur). The
present study investigates the spelling profiles of the scribes who produced
mss. A and B in a computational way. In the first part of this study, we will
present both manuscripts in more detail, after which we will consider prior
research carried out on scribal profiling. The current study both builds and
expands on Kestemont (2015). Next, we outline the methodology used to analyse
and measure the degree of scribal appropriation that took place when ms. B was
copied off the exemplar ms. A. After this, we will discuss the results
obtained, focusing on the scribal variation that can be found both at the level
of individual words and n-grams. To this end, we use machine learning to
identify the most distinctive features that separate manuscript A from B.
Finally, we look at possible diachronic trends in the appropriation by B's
scribe of his exemplar. We argue that scribal takeovers in the exemplar impacts
the practice of the copying scribe, while transitions to a different content
matter cause little to no effect
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