142 research outputs found
Structure controllability of complex network based on preferential matching
Minimum driver node sets (MDSs) play an important role in studying the
structural controllability of complex networks. Recent research has shown that
MDSs tend to avoid high-degree nodes. However, this observation is based on the
analysis of a small number of MDSs, because enumerating all of the MDSs of a
network is a #P problem. Therefore, past research has not been sufficient to
arrive at a convincing conclusion. In this paper, first, we propose a
preferential matching algorithm to find MDSs that have a specific degree
property. Then, we show that the MDSs obtained by preferential matching can be
composed of high- and medium-degree nodes. Moreover, the experimental results
also show that the average degree of the MDSs of some networks tends to be
greater than that of the overall network, even when the MDSs are obtained using
previous research method. Further analysis shows that whether the driver nodes
tend to be high-degree nodes or not is closely related to the edge direction of
the network
An efficient algorithm for finding all possible input nodes for controlling complex networks
Understanding structural controllability of a complex network requires to
identify a Minimum Input nodes Set (MIS) of the network. It has been suggested
that finding an MIS is equivalent to computing a maximum matching of the
network, where the unmatched nodes constitute an MIS. However, maximum matching
of a network is often not unique, and finding all MISs may provide deep
insights to the controllability of the network. Finding all possible input
nodes, which form the union of all MISs, is computationally challenging for
large networks. Here we present an efficient enumerative algorithm for the
problem. The main idea is to modify a maximum matching algorithm to make it
efficient for finding all possible input nodes by computing only one MIS. We
rigorously proved the correctness of the new algorithm and evaluated its
performance on synthetic and large real networks. The experimental results
showed that the new algorithm ran several orders of magnitude faster than the
existing method on large real networks
Altering nodes types in controlling complex networks
Controlling a complex network towards a desired state is of great importance
in many applications. A network can be controlled by inputting suitable
external signals into some selected nodes, which are called driver nodes.
Previous works found there exist two control modes in dense networks:
distributed and centralized modes. For networks with the distributed mode, most
of the nodes can be act as driver nodes; and those with the centralized mode,
most of the nodes never be the driver nodes. Here we present an efficient
algorithm to change the control type of nodes, from input nodes to redundant
nodes, which is done by reversing edges of the network. We conclude four
possible cases when reversing an edge and show the control mode can be changed
by reversing very few in-edges of driver nodes. We evaluate the performance of
our algorithm on both synthetic and real networks. The experimental results
show that the control mode of a network can be easily changed by reversing a
few elaborately selected edges, and the number of possible driver nodes is
dramatically decreased. Our methods provide the ability to design the desired
control modes of the network for different control scenarios, which may be used
in many application regions
Efficient target control of complex networks based on preferential matching
Controlling a complex network towards a desire state is of great importance
in many applications. Existing works present an approximate algorithm to find
the driver nodes used to control partial nodes of the network. However, the
driver nodes obtained by this algorithm depend on the matching order of nodes
and cannot get the optimum results. Here we present a novel algorithm to find
the driver nodes for target control based on preferential matching. The
algorithm elaborately arrange the matching order of nodes in order to minimize
the size of the driver nodes set. The results on both synthetic and real
networks indicate that the performance of proposed algorithm are better than
the previous one. The algorithm may have various application in controlling
complex networks
Scenario description language for automated driving systems : a two level abstraction approach
The complexities associated with Automated Driving Systems (ADSs) and their interaction with the environment pose a challenge for their safety evaluation. Number of miles driven has been suggested as one of the metrics to demonstrate technological maturity. However, the experiences or the scenarios encountered by the ADSs is a more meaningful metric, and has led to a shift to scenario-based testing approach in the automotive industry and research community. Variety of scenario generation techniques have been advocated, including real-world data analysis, accident data analysis and via systems hazard analysis. While scenario generation can be done via these methods, there is a need for a scenario description language format which enables the exchange of scenarios between diverse stakeholders (as part of the systems engineering lifecycle) with varied usage requirements. In this paper, we propose a two-level abstraction approach to scenario description language (SDL) - SDL level 1 and SDL level 2. SDL level 1 is a textual description of the scenario at a higher abstraction level to be used by regulators or system engineers. SDL level 2 is a formal machine-readable language which is ingested by testing platform e.g. simulation or test track. One can transform a scenario in SDL level 1 into SDL level 2 by adding more details or from SDL level 2 to SDL level 1 by abstracting
Facile construction of nanofibrous ZnO photoelectrode for dye-sensitized solar cell applications
A facile method to prepare nanofibrous ZnO photoelectrodes with tunable thicknesses by
electrospinning is reported. A “self-relaxation layer” is formed spontaneously between ZnO
nanofibers and fluorine-doped SnO2 FTO substrate, which facilitates the release of interfacial tensile stress during calcination, resulting in good adhesion of ZnO film to FTO substrate. Dye-sensitized solar cells DSSCs based on the nanofibrous ZnO photoelectrodes are fabricated and an energy conversion efficiency of 3.02% is achieved under irradiation of AM 1.5 simulated sunlight with a power density of 100 mW cm−2, which shows good promise of electrospun nanofibrous ZnO as the photoelectrode in DSSCs
Deep learning for remote sensing image classification:A survey
Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel?wise and scene?wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL?based RS methods is also provided. Finally, the challenges and potential directions for further research are discussedpublishersversionPeer reviewe
Interaction of multiple vortices over a double delta wing
Interaction of strake and wing vortices over a 70◦/50◦double delta wing were studied experimentally in a wind tunnel using particle image velocimetry (PIV) measurements. The upstream effect of the wing vortex on the formation of the strake vortex was identified. A dual-vortex structure of the strake vortices was observed before the wing vortex developed. Further downstream, wing and strake vortices rotated around each other slowly initially, and then faster with downstream distance, at an increasing rate with increasing incidence. Prior to vortex breakdown, both wing and strake vortices were found meandering in relatively small regions. The correlation between the instantaneous locations of the vortices increases if the vortices become sufficiently close to each other. The proper orthogonal decomposition (POD) analysis of the instantaneous velocity fields suggested that, for both wing and strake vortices, the most energetic mode was displacement in the first helical mode. The most energetic mode reveals out-of-phase displacements when the vortices are close to each other
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