640 research outputs found
What Can Help Pedestrian Detection?
Aggregating extra features has been considered as an effective approach to
boost traditional pedestrian detection methods. However, there is still a lack
of studies on whether and how CNN-based pedestrian detectors can benefit from
these extra features. The first contribution of this paper is exploring this
issue by aggregating extra features into CNN-based pedestrian detection
framework. Through extensive experiments, we evaluate the effects of different
kinds of extra features quantitatively. Moreover, we propose a novel network
architecture, namely HyperLearner, to jointly learn pedestrian detection as
well as the given extra feature. By multi-task training, HyperLearner is able
to utilize the information of given features and improve detection performance
without extra inputs in inference. The experimental results on multiple
pedestrian benchmarks validate the effectiveness of the proposed HyperLearner.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
Building cluster control to enable grid reliability and efficiency support
Power system operators are actively seeking solutions to increase electric grid power flexibility and inertia, to accommodate deeper renewable integration. Buildings account for 75% of the total electricity use in the US and have great potential for grid reliability support at various time and spatial scales. Due to the limited bidding power of individual buildings, grid services are often provided by a fleet of small buildings managed by tailored coordination strategies. This dissertation presents two families of control methods for building cluster energy management based on the control time frequency and inter-building coordination mode: (1) dictatorial load modulating control strategies formulated under a specific context of distribution voltage regulation, and (2) market-based load shifting control achieved through a game-theoretic control framework. Load modulating represents the ability to balance power supply and demand within seconds in response to the grid signal. Therefore, the load modulating can enable distribution voltage support by controlling flexible loads in the building clusters to let their power use follow volatile solar photovoltaic output, as a means to mitigate fluctuations in the net demand and maintain a stable voltage. Load shifting represents the ability to change the timing of electricity use. In load shifting the typical time duration is 1 to 4 hours, and response time is less than 1 hour. The game-theoretic control strategies allow coordinative load shifting in which individual entities determine their control actions in their own interests while coordination is achieved indirectly through a market mechanism, with the goal of flattening the total load curve of the building cluster
Fast time-stepping discontinuous Galerkin method for the subdiffusion equation
The nonlocality of the fractional operator causes numerical difficulties for
long time computation of the time-fractional evolution equations. This paper
develops a high-order fast time-stepping discontinuous Galerkin finite element
method for the time-fractional diffusion equations, which saves storage and
computational time. The optimal error estimate of the current time-stepping discontinuous Galerkin
method is rigorous proved, where denotes the number of time intervals,
is the degree of polynomial approximation on each time subinterval, is the
maximum space step, , is the order of finite element space, and
can be arbitrarily small. Numerical simulations verify the
theoretical analysis.Comment: 21 pages, 1 figure,4 table
Hierarchical porous carbons derived from leftover rice for high performance supercapacitors
Abstract(#br)Biomass-derived porous carbons have been extensively investigated as potential electrode materials of electrochemical energy storage devices. Herein, hierarchical porous carbons with high specific surface area and large mesoporosity are successfully prepared from leftover rice, a common meal surplus, benefiting from its unique swelled structure and the activation effect of potassium hydroxide. The hierarchical porous carbons exhibit outstanding electrochemical energy storage performances in 1 M TEABF 4 /PC (propylene carbonate) electrolyte, including a high specific capacitance of 153.2 F g −1 at 0.2 A g −1 based on the active material, a high specific energy density of 22.6 Wh kg −1 at a power density of 21,503 W kg −1 based on the cells and over 87% capacitance retentions after 10,000 cycles at 1 A g −1 . Such excellent electrochemical performances demonstrate that leftover rice can be potentially applied as bioresource for high property porous carbon electrode materials of supercapacitors
Integration of Phase Change Material-Based Storage in Air Distribution Systems to Increase Building Power Flexibility
This paper presents a novel energy storage solution by incorporating phase change material (PCM) in the building supply-air duct to increase a building’s thermal storage capacity. This solution has various advantages compared to PCM-integrated walls including more effective heat transfer (forced convection and greater temperature differentials). During off-peak hours, the system runs at a supply-air temperature below the material’s solidification point to charge the PCM with cooling energy. During on-peak hours, a higher supply-air temperature is utilized so that the stored energy can be discharged into the supply-air. This shifts a portion of the building’s cooling load from the on-peak hours to the off-peak hours. A numerical model for the melting and solidification of PCM in the duct was developed and modified using experimental data. Whole building energy simulations were conducted by coupling the PCM model with EnergyPlus DOE prototypical building model in a Simulink co-simulation platform. Simulations were performed for three cities in different climate zones over a three-month cooling season (June to August), and the PCM storage reduced the on-peak energy consumption by 20-25%. The electricity cost and payback period were determined using current time-of-use electricity rates
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Considering the balance of performance and efficiency, sampled point and
voxel methods are usually employed to down-sample dense events into sparse
ones. After that, one popular way is to leverage a graph model which treats the
sparse points/voxels as nodes and adopts graph neural networks (GNNs) to learn
the representation for event data. Although good performance can be obtained,
however, their results are still limited mainly due to two issues. (1) Existing
event GNNs generally adopt the additional max (or mean) pooling layer to
summarize all node embeddings into a single graph-level representation for the
whole event data representation. However, this approach fails to capture the
importance of graph nodes and also fails to be fully aware of the node
representations. (2) Existing methods generally employ either a sparse point or
voxel graph representation model which thus lacks consideration of the
complementary between these two types of representation models. To address
these issues, in this paper, we propose a novel dual point-voxel absorbing
graph representation learning for event stream data representation. To be
specific, given the input event stream, we first transform it into the sparse
event cloud and voxel grids and build dual absorbing graph models for them
respectively. Then, we design a novel absorbing graph convolutional network
(AGCN) for our dual absorbing graph representation and learning. The key aspect
of the proposed AGCN is its ability to effectively capture the importance of
nodes and thus be fully aware of node representations in summarizing all node
representations through the introduced absorbing nodes. Finally, the event
representations of dual learning branches are concatenated together to extract
the complementary information of two cues. The output is then fed into a linear
layer for event data classification
Tencent AVS: A Holistic Ads Video Dataset for Multi-modal Scene Segmentation
Temporal video segmentation and classification have been advanced greatly by
public benchmarks in recent years. However, such research still mainly focuses
on human actions, failing to describe videos in a holistic view. In addition,
previous research tends to pay much attention to visual information yet ignores
the multi-modal nature of videos. To fill this gap, we construct the Tencent
`Ads Video Segmentation'~(TAVS) dataset in the ads domain to escalate
multi-modal video analysis to a new level. TAVS describes videos from three
independent perspectives as `presentation form', `place', and `style', and
contains rich multi-modal information such as video, audio, and text. TAVS is
organized hierarchically in semantic aspects for comprehensive temporal video
segmentation with three levels of categories for multi-label classification,
e.g., `place' - `working place' - `office'. Therefore, TAVS is distinguished
from previous temporal segmentation datasets due to its multi-modal
information, holistic view of categories, and hierarchical granularities. It
includes 12,000 videos, 82 classes, 33,900 segments, 121,100 shots, and 168,500
labels. Accompanied with TAVS, we also present a strong multi-modal video
segmentation baseline coupled with multi-label class prediction. Extensive
experiments are conducted to evaluate our proposed method as well as existing
representative methods to reveal key challenges of our dataset TAVS
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