188,990 research outputs found
Ant Colony Optimization for Control
No full textThe very basis of this thesis is the collective behavior of ants in colonies. Ants are an excellent example of how rather simple behavior on a local level can lead to complex behavior on a global level that is beneficial for the individuals. The key in the self-organization of ants is communication through pheromones. When an ant forages for food, it is biased to search along trails of stronger pheromone concentrations. The moment it finds food, it will walk back to the nest while depositing pheromones and thereby contributing to the reinforcement of a successful trail. Inspired by this mechanism, research within an engineering context has led to the development of the field of Ant Colony Optimization (ACO). Specifically developed for efficiently solving combinatorial optimization problems, ACO has been successfully applied to routing in road traffic and Internet networks. In this thesis, we take the principles behind ACO to the domain of control policy learning. A control policy is a mapping from states to actions and our objective is to develop methods to learn the optimal control policy for a given dynamic system by interacting with it. We call our methods Ant Colony Learning (ACL) and their power lies in the fact that there is a set of ants, from which each ant interacts with the system and influences the other ants through updating pheromone levels associated with the visited state-action pairs. In experiments involving control problems that have a discrete state space and deterministic state transitions, it turns out that ACL converges quickly to the optimal solution. We also observe that increasing the number of ants in the algorithm results in a decrease of the number of trials required for convergence to the optimal policy. An analytical study of the convergence behavior of ACL reveals that for systems with discrete and noiseless state transitions, the expected policy converges to the optimal policy in the case of using only one ant. Another major part of this thesis deals with the application of ACL to control problems with continuous state spaces. In order to capture a continuous space with a finite number of elements, we study two ways of partitioning the state space and their incorporation in the ACL framework. In crisp ACL, the state space is partitioned using bins. Each state measurement is assigned to exactly one bin, which leads to the introduction of discretization noise, rendering an originally deterministic system non-deterministic and restricting the performance of the algorithm. We find that a better way of partitioning the state space is by using fuzzy triangular membership functions. The continuous state measurement then belongs to multiple membership functions to a certain degree. With fuzzy partitioning, the continuity of the state variables is preserved and no non-determinism is introduced. We call this method fuzzy ACL. The developed generalized ACL algorithm unifies both crisp and fuzzy ACL. The behavior and performance of crisp and fuzzy ACL are further studied using simulation experiments. We study the influence of the local and global pheromone decay rates, the number of ants, and the density of the state space partitioning grid on the learning performance. Especially, the performance of crisp ACL improves for a small local pheromone decay rate, while fuzzy ACL outperforms crisp ACL over the whole line. In general, crisp ACL is much more sensitive to the choice of the pheromone decay parameters than fuzzy ACL. We find that using more ants leads to faster convergence, but that the number of ants does not need to be extremely large to obtain a satisfactory performance. With regard to the scaling of ACL, crisp ACL reveals a slow, but gradual improvement of the learning for an increasing state space partitioning density. Fuzzy ACL, on the other hand, improves more rapidly and requires fewer ants to learn a better control policy. Finally, we present a general modeling framework for swarms of moving agents. It turns out that ACL fits within this framework and as such can be unified with other swarm intelligence techniques. In the future, this could result in beneficial integration of elements from other swarm intelligence techniques into ACL, or the other way around.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin
The canonical tensor fields of type on
No full textsummary:We prove that every natural affinor on is proportional to the identity affinor if dim
A Roman Veteran and his Skilful Administrator: Gemellus and Epagathus in Light of Unpublished Papyri
No full textInnovative biogas multi-stage R&D plant – preliminary project experiences
No full textThis paper summarizes the challenges and problems faced in designing, building and authorizing for operation of a novel cascade anaerobic digestion plant introducing a novel analytical method. For the R&D plant itself problems are mostly associated with preliminary unknown substrate characteristics and sizing whereas the challenges are mostly of administrative nature, i.e. authorizations and location. For the analytical method the problems and challenges are the application of an analytical method to a new field of application
Resolving RD and R D ∗ anomalies in adjoint SU(5)
No full textAbstract We investigate the R D and R D ∗ anomalies in the context of non-minimal SU(5), where Higgs sector is extended by adjoint 45-dimensional multiplet. One of the light spectrum of this model could be the scalar triplet leptoquark that is contained in this multiplet. We demonstrate that this particular scalar leptogquark mediation of the transition b → cτν is capable of simultaneously accounting for both R D and R D ∗ anomalies. We further emphasize that another Yukawa coupling controls its contribution to b → sℓ + ℓ − , ensuring that R K and R K ∗ remain consistent with the standard model predictions
D * polarization vs. R D ∗ anomalies in the leptoquark models
No full textAbstract Polarization measurements in B ¯ → D ∗ τ ν ¯ are useful to check consistency in new physics explanations for the R D and R D * anomalies. In this paper, we investigate the D * and τ polarizations and focus on the new physics contributions to the fraction of a longitudinal D * polarization (F L D * ), which is recently measured by the Belle collaboration F L D * = 0.60 ± 0.09, in model-independent manner and in each single leptoquark model (R2, S1 and U1) that can naturally explain the R D ∗ anomalies. It is found that ℬ(B c + → τ + ν) severely restricts deviation from the Standard Model (SM) prediction of F L,SM D * = 0.46±0.04 in the leptoquark models: [0.43, 0.44], [0.42, 0.48], and [0.43, 0.47] are predicted as a range of F L D * for the R2, S1, and U1 leptoquark models, respectively, where the current data of R D ∗ is satisfied at 1σ level. It is also shown that the τ polarization observables can much deviate from the SM predictions. The Belle II experiment, therefore, can check such correlations between R D ∗ and the polarization observables, and discriminate among the leptoquark models
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The AST view of ES knowledge management: Insights from world’s fastest SAP R/3 implementation
No full textOrganizations invest substantial resources in Enterprise Systems (ES) expecting positive outcomes for the organization. Implementing an ES is a lengthy-costly undertaking, with general upheaval for many of the organizations. Many organizations therefore are seriously considering rapid implementations of ES to reduce related resources. A rapid ES implementation requires effective management of knowledge (KM) as the extent of the engagement of external and internal parties (consultants and vendors with the client) is limited. This research paper introduces a theoretical model to assess the impact of KM in a rapid implementation of SAP R/3 that had completed in a record time of three weeks. Using the Adaptive Structuration Theory (AST) this paper proposes a theoretical model 1) to identify the KM enablers and KM strategies of an rapid ES implementation that facilitate knowledge creation, retention and transfer and 2) to determine the importance of knowledge transfer modes in a rapid ES implementation
A tree -base for without cofinal branches
No full textsummary:We prove an analogue to Dordal's result in P.L. Dordal, {\it A model in which the base-matrix tree cannot have cofinal branches\/}, J. Symbolic Logic {\bf 52} (1980), 651--664. He obtained a model of ZFC in which there is a tree -base for with no branches yet of height . We establish that this is also possible for using a natural modification of Mathias forcing
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