Learn More
With the increasing availability of wearable cameras, research on first-person view videos (egocentric videos) has received much attention recently. While some effort has been devoted to collecting various egocentric video datasets, there has not been a focused effort in assembling one that could capture the diversity and complexity of activities related to(More)
In this paper, we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem, using both the video and sensor data. First, we experiment and extend a multi-stream Convolutional Neural Network to learn the spatial and temporal features from egocentric videos. Second, we propose a multistream Long(More)
Image blur and image noise are common distortions during image acquisition. In this paper, we systematically study the effect of image distortions on the deep neural network (DNN) image classifiers. First, we examine the DNN classifier performance under four types of distortions. Second, we propose two approaches to alleviate the effect of image distortion:(More)
With the increasing availability of wearable devices, research on egocentric activity recognition has received much attention recently. In this paper, we build a Multimodal Egocentric Activity dataset which includes egocentric videos and sensor data of 20 fine-grained and diverse activity categories. We present a novel strategy to extract temporal(More)
The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by including text information in the classification, making this a(More)
With the large-scale development of microgrids-(MGs), adjacent area could formulate multi-microgrids (MMGs) due to the mutual connection and supply, stimulating the problem about efficient and economic operation in multi-microgrids and becoming one of the pivotal technologies. This paper presents distributed predict optimal operation algorithm and control(More)
Wind/PV/Battery micro-grid hybrid simulation research on operational control is an important link in the development of renewable energy power generation technology. This paper establishes a model of micro power unit control system based on RTDS and a digital physical hybrid simulation model of micro power unit control system based on RTDS and controller,(More)
The incremental conductance (INC) algorithm is one of the most extensive maximum power point tracking (MPPT) techniques for photovoltaic (PV) system to make full utilization of solar energy. However, the conventional INC algorithm can't track the MPP for PV systems effectively in rapidly changing atmospheric conditions. A novel region partition MPPT(More)
  • 1