Search results for "Bust"
showing 10 items of 1000 documents
ESP for small scale wood combustion
2009
In this paper new ESP technology developed for fine particle processes are presented. The ESP is based on diffusion charging of particles using sonic jet charger. The ESP concept has been tested using a commercial 20kW wood pellet burner. The removal efficiency was measured to be 80% for submicron particles.
An experimental and kinetic modeling study on the oxidation of 1,3-dioxolane
2021
International audience; The modern catalytic or enzymatic advances allow the production of novel biofuel. Among them, 1,3dioxolane can be produced from formaldehyde and ethylene glycol, both can be obtained from biomass. In this study, the oxidation of 1,3-dioxolane is studied at stoichiometric conditions. The ignition delay times of 1,3-dioxolane/O 2 /inert mixtures were measured in a shock tube and in a rapid compression machine at pressures of 20 to 40 bar and temperatures ranging from 630 to 1300 K. The pressure profiles recorded in the rapid compression machine show a first stage of ignition enlightening the influence of the low temperature chemistry of combustion. Furthermore, mole fr…
In-plane conductive heat tansfer in solid and porous planar structures
2011
Methods for determining the in-plane thermal diffusivity in a planar sample geometry were developed. These methods were tested and verified by measuring planar metal samples with known thermal properties. The techniques used were based on heating the sample at one edge and recording the evolution of the temperature field in the sample by a thermographic camera. The temperature fields at different times were processed and then fitted by a solution to a heat equation describing the experimental setup, thermal diffusivity as one of the fitting parameters. In the first experimental setup the sample was placed in a weak constant flow of air, and the situation was improved in the second setup by …
An Efficient, Robust, and Scalable Trust Management Scheme for Unattended Wireless Sensor Networks
2012
Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between visits by the sink. The absence of an online trusted third party, i.e., an on-site sink, makes existing trust management schemes used in legacy wireless sensor networks not applicable to UWSNs directly. In this paper, we propose a trust management scheme for UWSNs to provide efficient, robust and scalable trust data storage. For trust data storage, we employ geographic hash table to efficiently identify data storage nodes and to significantly reduce storage cost. We demonstrate, through detailed analyses and extensive simulations, that the proposed…
Extending the sGLOH descriptor
2015
This paper proposes an extension of the sGLOH keypoint descriptor [3] which improves its robustness and discriminability. The sGLOH descriptor can handle discrete rotations by a cyclic shift of its elements thanks to its circular structure, but its performance can decrease when the keypoint relative rotation is in between two sGLOH discrete rotations. The proposed extension couples together two sGLOH descriptors for the same patch with different rotations in order to cope with this issue and it can be also applied straightly to the sCOr and sGOr matching strategies of sGLOH. Experimental results show a consistent improvement of the descriptor discriminability, while different setups can be …
Feature Ranking of Large, Robust, and Weighted Clustering Result
2017
A clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the evaluation techniques need to take this into account. The purpose of this article is to advance the automatic knowledge discovery from a robust clustering result on the population level. For this purpose, we derive a novel ranking method by generalizing the computation of the Kruskal-Wallis H test statistic from sample to population level with two different approaches. Application of these enlargements to both the input variables used in clustering and to metadata provides a…
Robust control in uncertain multi-inventory systems and consensus problems
2008
Abstract We consider a continuous time linear multi–inventory system with unknown demands bounded within ellipsoids and controls bounded within polytopes. We address the problem of ∈-stabilizing the inventory since this implies some reduction of the inventory costs. The main results are certain conditions under which ∈-stabilizability is possible through a saturated linear state feedback control. The idea of this approach is similar to the consensus problem solution for a network of continuous time dynamic agents, where each agent evolves according to a first order dynamics has bounded control and it is subject to unknown but bounded disturbances. In this context, we derive conditions under…
Transcriptomic and Bioinformatic Analyses Identifying a Central Mif-Cop9-Nf-kB Signaling Network in Innate Immunity Response of Ciona robusta
2023
The Ascidian C. robusta is a powerful model for studying innate immunity. LPS induction activates inflammatory-like reactions in the pharynx and the expression of several innate immune genes in granulocyte hemocytes such as cytokines, for instance, macrophage migration inhibitory factors (CrMifs). This leads to intracellular signaling involving the Nf-kB signaling cascade that triggers downstream pro-inflammatory gene expression. In mammals, the COP9 (Constitutive photomorphogenesis 9) signalosome (CSN) complex also results in the activation of the NF-kB pathway. It is a highly conserved complex in vertebrates, mainly engaged in proteasome degradation which is essential for maintaining proc…
Explainable Reinforcement Learning with the Tsetlin Machine
2021
The Tsetlin Machine is a recent supervised machine learning algorithm that has obtained competitive results in several benchmarks, both in terms of accuracy and resource usage. It has been used for convolution, classification, and regression, producing interpretable rules. In this paper, we introduce the first framework for reinforcement learning based on the Tsetlin Machine. We combined the value iteration algorithm with the regression Tsetlin Machine, as the value function approximator, to investigate the feasibility of training the Tsetlin Machine through bootstrapping. Moreover, we document robustness and accuracy of learning on several instances of the grid-world problem.
Semi-Supervised Classification Method for Hyperspectral Remote Sensing Images
2004
A new approach to the classification of hyperspectral images is proposed. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning met…