217 research outputs found
Synthesis of mesoporous composite materials of nitrogen-doped carbon and silica using a reactive surfactant approach
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Mesoporous composite materials of nitrogen-doped carbon and silica were synthesised in a one-step-process applying a soft templating procedure. The template used in the sol–gel synthesis of the silica is a cationic surfactant with distinct reactivity to form nitrogen-doped graphitic carbon upon heating. This reactivity is derived from the combination of the dicyanamide anion with a nitrogen-containing pyridinium cation, as it is known from ionic liquids used as nitrogen-doped carbon precursors. Thus applying this surfactant in a conventional sol–gel synthesis yields a silica gel doped with a precursor for N-doped carbon. By subsequent annealing mesoporous composite materials of silica and nitrogen-doped carbon are obtained
Nitrogen- and phosphorus-co-doped carbons with tunable enhanced surface areas promoted by the doping additives
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.1-Butyl-3-methyl-pyridinium-dicyanamide (BMP-dca) is carbonised with tetra-alkyl-phosphonium-bromide additives yielding nitrogen- and phosphorus-co-doped carbons with enhanced BET surface areas promoted by the additives.DFG, EXC 314, Unifying Concepts in Catalysi
Data-free Black-box Attack based on Diffusion Model
Since the training data for the target model in a data-free black-box attack
is not available, most recent schemes utilize GANs to generate data for
training substitute model. However, these GANs-based schemes suffer from low
training efficiency as the generator needs to be retrained for each target
model during the substitute training process, as well as low generation
quality. To overcome these limitations, we consider utilizing the diffusion
model to generate data, and propose a data-free black-box attack scheme based
on diffusion model to improve the efficiency and accuracy of substitute
training. Despite the data generated by the diffusion model exhibits high
quality, it presents diverse domain distributions and contains many samples
that do not meet the discriminative criteria of the target model. To further
facilitate the diffusion model to generate data suitable for the target model,
we propose a Latent Code Augmentation (LCA) method to guide the diffusion model
in generating data. With the guidance of LCA, the data generated by the
diffusion model not only meets the discriminative criteria of the target model
but also exhibits high diversity. By utilizing this data, it is possible to
train substitute model that closely resemble the target model more efficiently.
Extensive experiments demonstrate that our LCA achieves higher attack success
rates and requires fewer query budgets compared to GANs-based schemes for
different target models
A Transferable Intersection Reconstruction Network for Traffic Speed Prediction
Traffic speed prediction is the key to many valuable applications, and it is
also a challenging task because of its various influencing factors. Recent work
attempts to obtain more information through various hybrid models, thereby
improving the prediction accuracy. However, the spatial information acquisition
schemes of these methods have two-level differentiation problems. Either the
modeling is simple but contains little spatial information, or the modeling is
complete but lacks flexibility. In order to introduce more spatial information
on the basis of ensuring flexibility, this paper proposes IRNet (Transferable
Intersection Reconstruction Network). First, this paper reconstructs the
intersection into a virtual intersection with the same structure, which
simplifies the topology of the road network. Then, the spatial information is
subdivided into intersection information and sequence information of traffic
flow direction, and spatiotemporal features are obtained through various
models. Third, a self-attention mechanism is used to fuse spatiotemporal
features for prediction. In the comparison experiment with the baseline, not
only the prediction effect, but also the transfer performance has obvious
advantages.Comment: 14 pages, 12 figure
Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction
Traffic prediction plays an important role in the realization of traffic
control and scheduling tasks in intelligent transportation systems. With the
diversification of data sources, reasonably using rich traffic data to model
the complex spatial-temporal dependence and nonlinear characteristics in
traffic flow are the key challenge for intelligent transportation system. In
addition, clearly evaluating the importance of spatial-temporal features
extracted from different data becomes a challenge. A Double Layer - Spatial
Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The
lower layer of DL-STFEE is spatial-temporal feature extraction layer. The
spatial and temporal features in traffic data are extracted by multi-graph
graph convolution and attention mechanism, and different combinations of
spatial and temporal features are generated. The upper layer of DL-STFEE is the
spatial-temporal feature evaluation layer. Through the attention score matrix
generated by the high-dimensional self-attention mechanism, the
spatial-temporal features combinations are fused and evaluated, so as to get
the impact of different combinations on prediction effect. Three sets of
experiments are performed on actual traffic datasets to show that DL-STFEE can
effectively capture the spatial-temporal features and evaluate the importance
of different spatial-temporal feature combinations.Comment: 39 pages, 14 figures, 5 table
Progress and summary of reinforcement learning on energy management of MPS-EV
The high emission and low energy efficiency caused by internal combustion
engines (ICE) have become unacceptable under environmental regulations and the
energy crisis. As a promising alternative solution, multi-power source electric
vehicles (MPS-EVs) introduce different clean energy systems to improve
powertrain efficiency. The energy management strategy (EMS) is a critical
technology for MPS-EVs to maximize efficiency, fuel economy, and range.
Reinforcement learning (RL) has become an effective methodology for the
development of EMS. RL has received continuous attention and research, but
there is still a lack of systematic analysis of the design elements of RL-based
EMS. To this end, this paper presents an in-depth analysis of the current
research on RL-based EMS (RL-EMS) and summarizes the design elements of
RL-based EMS. This paper first summarizes the previous applications of RL in
EMS from five aspects: algorithm, perception scheme, decision scheme, reward
function, and innovative training method. The contribution of advanced
algorithms to the training effect is shown, the perception and control schemes
in the literature are analyzed in detail, different reward function settings
are classified, and innovative training methods with their roles are
elaborated. Finally, by comparing the development routes of RL and RL-EMS, this
paper identifies the gap between advanced RL solutions and existing RL-EMS.
Finally, this paper suggests potential development directions for implementing
advanced artificial intelligence (AI) solutions in EMS
The exceptional sediment load of fine-grained dispersal systems: Example of the Yellow River, China
Sedimentary dispersal systems with fine-grained beds are common, yet the physics of sediment movement within them remains poorly constrained. We analyze sediment transport data for the best-documented, fine-grained river worldwide, the Huanghe (Yellow River) of China, where sediment flux is underpredicted by an order of magnitude according to well-accepted sediment transport relations. Our theoretical framework, bolstered by field observations, demonstrates that the Huanghe tends toward upper-stage plane bed, yielding minimal form drag, thus markedly enhancing sediment transport efficiency. We present a sediment transport formulation applicable to all river systems with silt to coarse-sand beds. This formulation demonstrates a remarkably sensitive dependence on grain size within a certain narrow range and therefore has special relevance to silt-sand fluvial systems, particularly those affected by dams
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