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Wind power plants (WPPs) have been rapidly installed worldwide as an alternative source to thermal power plants. Nevertheless, since the outputs of WPPs constantly fluctuates due to variations in wind speed, WPPs expose power systems to power quality degradation, such as frequency fluctuation. This paper develops an optimal control method of energy storage systems (ESSs) that utilizes WPP output prediction to mitigate WPP output fluctuation. In the proposed method, an output reference of ESS can be obtained as the solution of an optimization problem. Specifically, the proposed method regulates the state of charge of ESS within its appropriate range by minimizing a cost function. At the same time, the minimization of ESS output and multiple grid codes related to the mitigation of WPP output fluctuation are considered as constraints. As a result, the proposed method enables us to mitigate the output fluctuation of WPP sufficiently by an ESS with small rated power. The effectiveness of the proposed method is demonstrated through comparative analysis with conventional methods via scenario simulations.

Active studies have been conducted on video saliency prediction, which predicts human visual attention toward videos. Most deep learning-based video saliency prediction models implicitly learn features that contribute to video saliency prediction, greatly improving accuracy. This study proposes a model called optical flow-based feature warping ViNet (OFF-ViNet). This model explicitly adds a Warping module, which is a mechanism that considers future predictions based on object motion in addition to implicitly learned features. The Warping module spatially warps the hierarchical features extracted by the 3D convolutional backbone based on the optical flow to obtain a feature representation that predicts the future. Compared with existing models, OFF-ViNet achieves better and competitive accuracy with state-of-the-art models on video saliency prediction datasets, particularly on UCF-Sports, which contains several videos with moving objects.

It has been suggested that luminance edges in retinal images are potential cues for glossiness perception, particularly when the perception relies on low-luminance specular regions. However, a previous study has shown only statistical correlations between luminance edges and perceived glossiness, not their causal relations. Additionally, although specular components should be embedded at various spatial frequencies depending on the micro-roughness on the object surface, it is not well understood what spatial frequencies are essential for glossiness perception on objects with different micro-roughness. To address these issues, we examined the impact of a sub-band contrast enhancement on the perceived glossiness in the two conditions of stimuli: the Full condition where the stimulus had natural specular components and the Dark condition where it had specular components only in dark regions. Object images with various degrees of surface roughness were generated as stimuli, and their contrast was increased in various spatial-frequency sub-bands. The results indicate that the enhancement of the sub-band contrast can significantly increase perceived glossiness as expected. Furthermore, the effectiveness of each spatial frequency band depends on the surface roughness in the Full condition. However, effective spatial frequencies are constant at a middle spatial frequency regardless of the stimulus surface roughness in the Dark condition. These results suggest that, for glossiness perception, our visual system depends on specular-related information embedded in high spatial frequency components but may change the dependency on spatial frequency based on the surface luminance to be judged.