For many social events such as public performances, multiple hand-held cameras may capture the same event. This footage is often collected by amateur cinematographers who typically have little control over the scene and may not pay close attention to the camera. For these reasons, each individually captured video may fail to cover the whole time of the event, or may lose track of interesting foreground content such as a performer. We introduce a new algorithm that can synthesize a single smooth video sequence of moving foreground objects captured by multiple hand-held cameras. This allows later viewers to gain a cohesive narrative experience that can transition between different cameras, even though the input footage may be less than ideal. We first introduce a graph-based method for selecting a good transition route. This allows us to automatically select good cut points for the hand-held videos, so that smooth transitions can be created between the resulting video shots. We also propose a method to synthesize a smooth photorealistic transition video between each pair of hand-held cameras, which preserves dynamic foreground content during this transition. Our experiments demonstrate that our method outperforms previous state-of-the-art methods, which struggle to preserve dynamic foreground content.

Current image-editing tools do not match up to the demands of personalized image manipulation, one application of which is changing clothes in usercaptured images. Previous work can change single color clothes using parametric human warping methods. In this paper, we propose an image-based clothes changing system, exploiting body factor extraction and content-aware image warping. Image segmentation and mask generation are first applied to the user input. Afterwards, we determine joint positions via a neural network. Then, body shape matching is performed and the shape of the model is warped to the user’s shape. Finally, head swapping is performed to produce realistic virtual results. We also provide a supervision and labeling tool for refinement and further assistance when creating a dataset.

Extracting background features for estimating the camera path is a key step in many video editing and enhancement applications. Existing approaches often fail on highly dynamic videos that are shot by moving cameras and contain severe foreground occlusion. Based on existing theories, we present a new, practical method that can reliably identify background features in complex video, leading to accurate camera path estimation and background layering. Our approach contains a local motion analysis step and a global optimization step. We first divide the input video into overlapping temporal windows, and extract local motion clusters in each window. We form a directed graph from these local clusters, and identify background ones by finding a minimal path through the graph using optimization. We show that our method significantly outperforms other alternatives, and can be directly used to improve common video editing applications such as stabilization, compositing and background reconstruction.

Low carbon emission is one of the main targets for smart grid planning. To achieve this goal, intermittent energies such as wind and solar are integrated to the power systems increasingly. However, this may create huge challenges to the power system operators for balancing the generation and demand at all times and guaranteeing the system reliability at the same time. With high penetration of renewable energies, power system operators are compelled to curtail the loads when the power system cannot rely on power from renewable energies continuously due to strong dependence on the environment. As an important defense to protect the power network from collapsing and to keep the system integrating, load shedding has been designed and proposed for decades. However, most of the shedding schemes consider the load increasing instead of lack of generation. This paper applies a load shedding scheme with considering both voltage and frequency changes when the generation is inadequate since the power system cannot obtain the expected renewable generation and renewable energies are highly penetrated into the grid.

With more uncertainty and variability existing in smart grid, deterministic load flow, which is used to analyze the operation conditions on a daily routine and planning the power systems for future investment, could not solve the problems with consideration of renewable generation intermittence and load variation. Nowadays, with more attentions are paid to the environment, carbon emission problem is one of the main concerns in smart grid strategies and planning. It is a great opportunity and challenge for planners to take good care of all stakeholders' interests. Multi-objectives need to be considered when making a critical decision. This paper presents result comparisons between probabilistic optimal load flow and probabilistic load flow by considering both carbon emission and minimization of power loss of the entire grid. The framework is applied to the modified IEEE 14-bus system, which is modeled in Power Factory Dig SILENT, with intermittent wind energy source and load variation consideration.

With the deployments of smart grid technologies and communication networks in modern power systems, frequency relays, which are used for the purpose of power system protection, are to achieve more advanced and reliable performance. Load shedding strategies, which prevent power systems from suffering frequency instability based on the real time data frequency relays, are facing a challenge to adequately and accurately take shedding actions, as power system loads are varying all the time. On the generation side, increasingly non-dispatch able and inflexible renewable power generations being integrated to the system complicates generation predictions and results in frequent power imbalance. This paper proposes a load shedding scheme with different magnitudes and load shedding orders for distribution networks, with its effect on power system frequency stability verified in a benchmark IEEE 33-bus distribution system, which is simulated in Dig SILENT Power Factory package.

In this paper, Decision Support System (DSS) from 30 years ago to the near future will be studied to investigate the development trend of DSS. A typical DSS architecture has been described and the requirement of the DSS functionality implementation is illustrated in this article. Because all of the DSSs are projected-oriented, researchers are focused on developing general architecture to adapt as many cases as possible. Researchers begin to investigate the dynamic interaction and multi-criteria for the DSS application. In the near future, with the development of cloud computing, some cloud-based and agent-based DSSs will be adopted to various applications.