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 $O(N^{-p-1} + h^{m+1} + \varepsilon N^{r\alpha})$ of the current time-stepping discontinuous Galerkin method is rigorous proved, where $N$ denotes the number of time intervals, $p$ is the degree of polynomial approximation on each time subinterval, $h$ is the maximum space step, $r\ge1$, $m$ is the order of finite element space, and $\varepsilon>0$ 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