With the increasing integration of renewable energy, microgrids face challenges in maintaining grid stability due to variability and uncertainty in energy supply. Enhancing microgrid flexibility through demand respons...
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ISBN:
(数字)9798331523527
ISBN:
(纸本)9798331523534
With the increasing integration of renewable energy, microgrids face challenges in maintaining grid stability due to variability and uncertainty in energy supply. Enhancing microgrid flexibility through demand response is essential, but accurately assessing demand response potential is complex due to high-dimensional and noisy data. This paper proposes a framework combining Random Matrix Theory (RMT) for high-dimensional data analysis and a Deep Auto-Encoder Network (DAE) for feature extraction and visualization. RMT identifies hidden correlations between microgrid users and environmental factors, optimizing demand response strategies. The DAE reduces data dimensionality, providing clear visual insights into user behavior and demand response potential. A case study based on data from a southern city in China demonstrates the effectiveness of the proposed method. Results show seasonal variations in demand response potential, highlighting the need for tailored strategies. This approach enhances situational awareness and supports renewable energy integration in microgrids.
Visualizing a scholar’s scientific impact is important for many challenging tasks in academia such as tenure evaluation and award selection. Existing visualization and profiling approaches do not focus on the analysi...
Visualizing a scholar’s scientific impact is important for many challenging tasks in academia such as tenure evaluation and award selection. Existing visualization and profiling approaches do not focus on the analysis of individual scholar’s impact, or they are too abstract to provide detailed interpretation of high-impact scholars. This work builds over a new scholar-centric impact-oriented profiling method called GeneticFlow. We propose a visualization design of scholar’s self-citation graphs using a time-dependent, hierarchical representation method. The graph visualization is augmented with color-coded topic information trained with cutting-edge deep learning techniques, and also temporal trend chart to illustrate the dynamics of topic/impact evolution. The visualization method is validated on a benchmark dataset established for the visualization field. visualization results reveal key patterns of high-impact scholars and also demonstrate its capability to serve ordinary researchers for their impact visualization task.
This study is conducted to develop a collaborative visualization framework in the cross-field working group. The ultimate goal of this project is to provide a proper framework that can be used to develop a platform to...
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Ultra-high voltage transmission technology plays an important role in optimal energy allocation and large-scale clean energy consumption because of its advantages of large transmission capacity, long distance and high...
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This study explores the design of an electric vehicle modeling and visualization system based on industrial CT and mixed reality technology. Firstly, the principles of CT technology and the composition and data proces...
This study explores the design of an electric vehicle modeling and visualization system based on industrial CT and mixed reality technology. Firstly, the principles of CT technology and the composition and data processing methods of industrial CT systems are introduced. Then, the imaging principles and calculation methods of CT are discussed in detail, including the Beer-Lambert law and data reconstruction algorithms. Subsequently, a method utilizing OpenGL for modeling and visualization is proposed, encompassing the overall design of the visualization system and its operational workflow. Through this research, a novel approach is established, combining CT and mixed reality technology, to provide fresh insights and methods for the related field.
Media integration is the future development trend. New media play a role in promoting the development of informationvisualization. informationvisualization in the new media environment is undergoing media changes, a...
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Methods for digital processing of brain MRI data have been developed, including methods for isolating clusters of transplanted mesenchymal stem cells in T2 and SWI modes. Methods of cognitive 2D and 3D visualization o...
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An introductory course on distributed systems typically exposes students to some basic synchronization distributed algorithms. This is often the first exposure for these students to the topic of distributed algorithms...
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ISBN:
(纸本)9781728184784
An introductory course on distributed systems typically exposes students to some basic synchronization distributed algorithms. This is often the first exposure for these students to the topic of distributed algorithms. In addition, in a systems course, these algorithms are typically covered in an informal way, avoiding proofs of correctness or complexity analysis. Hence, this first exposure can be challenging to students. visualization of these algorithms can help alleviate some of these challenges. We present a suite to visualize six basic algorithms on total ordering, critical sections, and leader election.
The introduction of cloud computing technologies as well as the growth of geospatial big data have both helped to make smart city initiatives more realistically achievable. Using geospatial Big data, cities have the p...
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Ultra High Definition Television (UHDTV) offers a better immersive audiovisual experience than HDTV by improving the aesthetic sense of the content [1]. How-ever, it may lead to an increase of both encoding time compl...
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ISBN:
(纸本)9781665478939
Ultra High Definition Television (UHDTV) offers a better immersive audiovisual experience than HDTV by improving the aesthetic sense of the content [1]. How-ever, it may lead to an increase of both encoding time complexity and compression artifacts at lower bitrates. To address this challenge, a low-latency pre-processing algorithm named COntent-aware frame Dropping Algorithm (CODA) is proposed to predict the optimized framerate per video segment in streaming scenarios. The optimized framerate $(\\hat{f})$ for every video segment at each target bitrate is modelled as an exponential decay (increasing) function whose decay rate is directly proportional to the temporal characteristics $(h)$ [2] [3] of the video and the target bitrate $(b)$, and inversely proportional to the spatial characteristics $(E)$ of the video. The encoding is carried out with the predicted framerate, saving encoding time and improving visual quality at lower bitrates. At the decoder side, the video is upscaled in the temporal domain to the original framerate $(f_{max})$ for display.
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