1,087 research outputs found
Mezoporozni katalizator PtSnO2/C s poveÄanom katalitiÄkom aktivnoÅ”Äu za elektrooksidaciju etanola
In this paper, we report the synthesis, characterization, and electrochemical evaluation of a mesoporous PtSnO2/C catalyst, called PtSnO2(M)/C, with a nominal Pt : Sn ratio of 3 : 1. BrunauerāEmmettāTeller and transmission electron microscopy characterizations showed the obvious mesoporous structure of SnO2 in PtSnO2(M)/C catalyst. X-ray photoelectron spectroscopy analysis exhibited the interaction between Pt and mesoporous SnO2. Compared with Pt/C and commercial PtSnO2/C catalysts, PtSnO2(M)/C catalyst has a lower active site, but higher catalytic activity for ethanol electro-oxidation reaction (EOR). The enhanced activity could be attributed to Pt nanoparticles deposited on mesoporous SnO2 that could decrease the amount of poisonous intermediates produced during EOR by the interaction between Pt and mesoporous SnO2.
This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom radu prikazana je sinteza, karakterizacija i elektrokemijska ocjena mezoporoznog katalizatora PtSnO2/C (PtSnO2(M)/C), s nominalnim omjerom Pt : Sn od 3 : 1. Karakterizacije Brunauer-Emmett-Tellerovom metodom i transmisijskom elektronskom mikroskopijom pokazale su oÄitu mezoporoznu strukturu SnO2 u katalizatoru PtSnO2(M)/C. Analiza rendgenskom fotoelektronskom spektroskopijom pokazala je interakciju izmeÄu Pt i mezoporoznog SnO2. U usporedbi s Pt/C i komercijalnim katalizatorima PtSnO2/C, katalizator PtSnO2(M)/C ima slabije aktivno mjesto, ali veÄu katalitiÄku aktivnost za reakciju elektrooksidacije etanola (EOR). PoboljÅ”ana aktivnost mogla bi se pripisati nanoÄesticama Pt pohranjenim na mezoporoznom SnO2, Å”to bi moglo smanjiti koliÄinu otrovnih meÄuprodukata proizvedenih tijekom elektrooksidacije etanola interakcijom izmeÄu Pt i mezoporoznog SnO2.
Ovo djelo je dano na koriÅ”tenje pod licencom Creative Commons Imenovanje 4.0 meÄunarodna
A Computationally Efficient Neural Video Compression Accelerator Based on a Sparse CNN-Transformer Hybrid Network
Video compression is widely used in digital television, surveillance systems,
and virtual reality. Real-time video decoding is crucial in practical
scenarios. Recently, neural video compression (NVC) combines traditional coding
with deep learning, achieving impressive compression efficiency. Nevertheless,
the NVC models involve high computational costs and complex memory access
patterns, challenging real-time hardware implementations. To relieve this
burden, we propose an algorithm and hardware co-design framework named NVCA for
video decoding on resource-limited devices. Firstly, a CNN-Transformer hybrid
network is developed to improve compression performance by capturing
multi-scale non-local features. In addition, we propose a fast algorithm-based
sparse strategy that leverages the dual advantages of pruning and fast
algorithms, sufficiently reducing computational complexity while maintaining
video compression efficiency. Secondly, a reconfigurable sparse computing core
is designed to flexibly support sparse convolutions and deconvolutions based on
the fast algorithm-based sparse strategy. Furthermore, a novel heterogeneous
layer chaining dataflow is incorporated to reduce off-chip memory traffic
stemming from extensive inter-frame motion and residual information. Thirdly,
the overall architecture of NVCA is designed and synthesized in TSMC 28nm CMOS
technology. Extensive experiments demonstrate that our design provides superior
coding quality and up to 22.7x decoding speed improvements over other video
compression designs. Meanwhile, our design achieves up to 2.2x improvements in
energy efficiency compared to prior accelerators.Comment: Accepted by DATE 202
TOAST: Transfer Learning via Attention Steering
Transfer learning involves adapting a pre-trained model to novel downstream
tasks. However, we observe that current transfer learning methods often fail to
focus on task-relevant features. In this work, we explore refocusing model
attention for transfer learning. We introduce Top-Down Attention Steering
(TOAST), a novel transfer learning algorithm that keeps the pre-trained
backbone frozen, selects task-relevant features in the output, and feeds those
features back to the model to steer the attention to the task-specific
features. By refocusing the attention only, TOAST achieves state-of-the-art
results on a number of transfer learning benchmarks, while having a small
number of tunable parameters. Compared to fully fine-tuning, LoRA, and prompt
tuning, TOAST substantially improves performance across a range of fine-grained
visual classification datasets (e.g., 81.1% -> 86.2% on FGVC). TOAST also
outperforms the fully fine-tuned Alpaca and Vicuna models on
instruction-following language generation. Code is available at
https://github.com/bfshi/TOAST.Comment: Code is available at https://github.com/bfshi/TOAS
Draining the Water Hole: Mitigating Social Engineering Attacks with CyberTWEAK
Cyber adversaries have increasingly leveraged social engineering attacks to
breach large organizations and threaten the well-being of today's online users.
One clever technique, the "watering hole" attack, compromises a legitimate
website to execute drive-by download attacks by redirecting users to another
malicious domain. We introduce a game-theoretic model that captures the salient
aspects for an organization protecting itself from a watering hole attack by
altering the environment information in web traffic so as to deceive the
attackers. Our main contributions are (1) a novel Social Engineering Deception
(SED) game model that features a continuous action set for the attacker, (2) an
in-depth analysis of the SED model to identify computationally feasible
real-world cases, and (3) the CyberTWEAK algorithm which solves for the optimal
protection policy. To illustrate the potential use of our framework, we built a
browser extension based on our algorithms which is now publicly available
online. The CyberTWEAK extension will be vital to the continued development and
deployment of countermeasures for social engineering.Comment: IAAI-20, AICS-2020 Worksho
Point Normal Orientation and Surface Reconstruction by Incorporating Isovalue Constraints to Poisson Equation
Oriented normals are common pre-requisites for many geometric algorithms
based on point clouds, such as Poisson surface reconstruction. However, it is
not trivial to obtain a consistent orientation. In this work, we bridge
orientation and reconstruction in implicit space and propose a novel approach
to orient point clouds by incorporating isovalue constraints to the Poisson
equation. Feeding a well-oriented point cloud into a reconstruction approach,
the indicator function values of the sample points should be close to the
isovalue. Based on this observation and the Poisson equation, we propose an
optimization formulation that combines isovalue constraints with local
consistency requirements for normals. We optimize normals and implicit
functions simultaneously and solve for a globally consistent orientation. Owing
to the sparsity of the linear system, an average laptop can be used to run our
method within reasonable time. Experiments show that our method can achieve
high performance in non-uniform and noisy data and manage varying sampling
densities, artifacts, multiple connected components, and nested surfaces
A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.
As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network ā extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries
Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving
A semantic map of the road scene, covering fundamental road elements, is an
essential ingredient in autonomous driving systems. It provides important
perception foundations for positioning and planning when rendered in the
Bird's-Eye-View (BEV). Currently, the prior knowledge of hypothetical depth can
guide the learning of translating front perspective views into BEV directly
with the help of calibration parameters. However, it suffers from geometric
distortions in the representation of distant objects. In addition, another
stream of methods without prior knowledge can learn the transformation between
front perspective views and BEV implicitly with a global view. Considering that
the fusion of different learning methods may bring surprising beneficial
effects, we propose a Bi-Mapper framework for top-down road-scene semantic
understanding, which incorporates a global view and local prior knowledge. To
enhance reliable interaction between them, an asynchronous mutual learning
strategy is proposed. At the same time, an Across-Space Loss (ASL) is designed
to mitigate the negative impact of geometric distortions. Extensive results on
nuScenes and Cam2BEV datasets verify the consistent effectiveness of each
module in the proposed Bi-Mapper framework. Compared with exiting road mapping
networks, the proposed Bi-Mapper achieves 2.1% higher IoU on the nuScenes
dataset. Moreover, we verify the generalization performance of Bi-Mapper in a
real-world driving scenario. The source code is publicly available at
https://github.com/lynn-yu/Bi-Mapper.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L). The source
code is publicly available at https://github.com/lynn-yu/Bi-Mappe
- ā¦