摘要 :
Virtual Reality (VR) improves the user's experience when interacting with the virtual world, and could revolutionarily transform the designs of many interactive systems. However, providing VR from untethered mobile devices is diff...
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Virtual Reality (VR) improves the user's experience when interacting with the virtual world, and could revolutionarily transform the designs of many interactive systems. However, providing VR from untethered mobile devices is difficult due to their limited local capabilities. Existing VR solutions address this difficulty by rendering VR frames at remote computing facilities, but are limited to rendering every VR frame separately. A tremendous amount of VR frame data, hence, needs to be transmitted to mobile devices over low-bandwidth wireless links and seriously impairs VR performance. In this paper, we aim to remove this performance constraint on highly dynamic VR applications with complicated scenes and intensive user movement, by adaptively reusing the redundant VR pixels across multiple VR frames. We leverage the unique characteristics of image warping used in current VR applications, and fundamentally expand the scope of image warping to the entire VR lifespan to precisely capture the fluctuations of VR scene due to VR dynamics. We implemented our design over Android OS and Unity VR application engine, and demonstrated that our design can maximize the mobile VR performance over highly dynamic VR scenarios with 95% less amount of VR frame data being transmitted, by completely removing the pixel redundancy across VR frames.
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摘要 :
Virtual Reality (VR) improves the user's experience when interacting with the virtual world, and could revolutionarily transform the designs of many interactive systems. However, providing VR from untethered mobile devices is diff...
展开
Virtual Reality (VR) improves the user's experience when interacting with the virtual world, and could revolutionarily transform the designs of many interactive systems. However, providing VR from untethered mobile devices is difficult due to their limited local capabilities. Existing VR solutions address this difficulty by rendering VR frames at remote computing facilities, but are limited to rendering every VR frame separately. A tremendous amount of VR frame data, hence, needs to be transmitted to mobile devices over low-bandwidth wireless links and seriously impairs VR performance. In this paper, we aim to remove this performance constraint on highly dynamic VR applications with complicated scenes and intensive user movement, by adaptively reusing the redundant VR pixels across multiple VR frames. We leverage the unique characteristics of image warping used in current VR applications, and fundamentally expand the scope of image warping to the entire VR lifespan to precisely capture the fluctuations of VR scene due to VR dynamics. We implemented our design over Android OS and Unity VR application engine, and demonstrated that our design can maximize the mobile VR performance over highly dynamic VR scenarios with 95% less amount of VR frame data being transmitted, by completely removing the pixel redundancy across VR frames.
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摘要 :
Improving the precision of wind power forecasting can be helpful to dispatch efficiency. In this paper, to examine the time varying characteristics in the high order moments of wind power time series, an improved auto-regressive c...
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Improving the precision of wind power forecasting can be helpful to dispatch efficiency. In this paper, to examine the time varying characteristics in the high order moments of wind power time series, an improved auto-regressive conditional density (ARCD) model for wind power forecasting is proposed. First, a generalized form of ARCD model is presented. Furthermore, from three different aspects: skewed conditional distribution, selected mathematical restriction and proper time-varying structure, the improved ARCD model is deduced. The proposed model can depict the information implicated in moments higher than the second order (variance) in wind power time series. Based on historical coastal wind power data of China, the time-varying high order moments of the wind power time series are highlighted based on the proposed model, and parameters of the ARCD model are estimated by the conditional maximum likelihood estimation (CMLE) method. Then, the mathematical relationship between time-varying parameter and conditional high order moments is analyzed considering the practical range of the time-varying parameter. The feasibility and effectiveness of the proposed wind power forecasting model are validated by model comparison. Case study clearly indicates that the ARCD model can provide effective forecasting results and outperform the alternative wind power forecasting models.
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