536 research outputs found
Extended fast search clustering algorithm: widely density clusters, no density peaks
CFSFDP (clustering by fast search and find of density peaks) is recently
developed density-based clustering algorithm. Compared to DBSCAN, it needs less
parameters and is computationally cheap for its non-iteration. Alex. at al have
demonstrated its power by many applications. However, CFSFDP performs not well
when there are more than one density peak for one cluster, what we name as "no
density peaks". In this paper, inspired by the idea of a hierarchical
clustering algorithm CHAMELEON, we propose an extension of CFSFDP,E_CFSFDP, to
adapt more applications. In particular, we take use of original CFSFDP to
generating initial clusters first, then merge the sub clusters in the second
phase. We have conducted the algorithm to several data sets, of which, there
are "no density peaks". Experiment results show that our approach outperforms
the original one due to it breaks through the strict claim of data sets.Comment: 18 pages, 10 figures, DBDM 201
Measuring optical vortices by means of dual shearing-type Sagnac interferometers
Measuring the positions of optical vortices is an essential part in the
researches of speckles and adaptive optics. The measurement accuracy is
restricted by the performance of optical devices and the properties of optical
vortices, such as density and size. In order to achieve high accuracy and wide
range of application, the dual shearing-type Sagnac interferometers is proposed
using two shearing plates to adjust the precision of optical vortices
measurement. The shearing displacements are able to balance the measuring
precision and the value of the intensity ratio point to provide optimum
measurement performance. This method is useful for the observation of optical
vortices with different sizes and densities, especially for the high density
condition
Mechanical Parameter Inversion in Sandstone Diversion Tunnel and Stability Analysis during Operation Period
A large number of experimental studies show that the mechanical parameters of deep buried surrounding rock show significant attenuation characteristics with the increase of strain from the rheological acceleration stage to the attenuation stage. However, the existing numerical models all take mechanical parameters as constants when describing the rheological behavior of surrounding rocks, which can only be applied to the stability analysis of the shallowly buried tunnel. Therefore, this work proceeding from the actual project, improved the sandstone rheological constitutive model and optimized the algorithm of parameter inversion, and put forward a long-term stability analysis model that can accurately reflect the rheological characteristics of surrounding rocks under the complex geological condition including high stress induced by great depth and high seepage pressure. In the process, a three-dimensional nonlinear rheological damage model was established based on Burgers rheological model by introducing damage factors into the derivation of the sandstone rheological constitutive model to accurately describe the rheological behaviors of the deep buried tunnel. And BP (Back Propagation) neural network optimized by the multi-descendant genetic algorithm is used to invert the mechanical parameters in the model, which improves the efficiency and precision of parameter inversion. Finally, the rheological equation was written by using parametric programming language and incorporated into the general finite element software ANSYS to simulate the rheological behavior of the tunnel rock mass at runtime. The results of the model analysis are in good agreement with the monitoring data in the later stage. The research results can provide a reference for the stability analysis of similar projects
Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Review
In the realm of data protection, a striking disconnect prevails between
traditional domains of doctrinal, legal, theoretical, and policy-based
inquiries and a burgeoning body of empirical evidence. Much of the scholarly
and regulatory discourse remains entrenched in abstract legal principles or
normative frameworks, leaving the empirical landscape uncharted or minimally
engaged. Since the birth of EU data protection law, a modest body of empirical
evidence has been generated but remains widely scattered and unexamined. Such
evidence offers vital insights into the perception, impact, clarity, and
effects of data protection measures but languishes on the periphery,
inadequately integrated into the broader conversation. To make a meaningful
connection, we conduct a comprehensive review and synthesis of empirical
research spanning nearly three decades (1995- March 2022), advocating for a
more robust integration of empirical evidence into the evaluation and review of
the GDPR, while laying a methodological foundation for future empirical
research
TransSC: Transformer-based Shape Completion for Grasp Evaluation
Currently, robotic grasping methods based on sparse partial point clouds have
attained a great grasping performance on various objects while they often
generate wrong grasping candidates due to the lack of geometric information on
the object. In this work, we propose a novel and robust shape completion model
(TransSC). This model has a transformer-based encoder to explore more
point-wise features and a manifold-based decoder to exploit more object details
using a partial point cloud as input.
Quantitative experiments verify the effectiveness of the proposed shape
completion network and demonstrate it outperforms existing methods. Besides,
TransSC is integrated into a grasp evaluation network to generate a set of
grasp candidates. The simulation experiment shows that TransSC improves the
grasping generation result compared to the existing shape completion baselines.
Furthermore, our robotic experiment shows that with TransSC the robot is more
successful in grasping objects that are randomly placed on a support surface
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