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For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan. [ABSTRACT FROM AUTHOR]

This paper presents a system applied to unmanned aerial vehicles based on Robot Operating Systems (ROSs). The study addresses the challenges of efficient object detection and real-time target tracking for unmanned aerial vehicles. The system utilizes a pruned YOLOv4 architecture for fast object detection and the SiamMask model for continuous target tracking. A Proportional Integral Derivative (PID) module adjusts the flight attitude, enabling stable target tracking automatically in indoor and outdoor environments. The contributions of this work include exploring the feasibility of pruning existing models systematically to construct a real-time detection and tracking system for drone control with very limited computational resources. Experiments validate the system's feasibility, demonstrating efficient object detection, accurate target tracking, and effective attitude control. This ROS-based system contributes to advancing UAV technology in real-world environments. [ABSTRACT FROM AUTHOR]

This paper presents a framework that can automatically analyze the images and comments in user-uploaded location databases. The proposed framework integrates image processing and natural language processing techniques to perform scene classification, data cleaning, and comment summarization so that the cluttered information in user-uploaded databases can be presented in an organized way to users. For scene classification, RGB image features, segmentation features, and the features of discriminative objects are fused with an attention module to improve classification accuracy. For data cleaning, incorrect images are detected using a multilevel feature extractor and a multiresolution distance calculation scheme. Finally, a comment summarization scheme is proposed to overcome the problems of unstructured sentences and the improper usage of punctuation marks, which are commonly found in customer reviews. To validate the proposed framework, a system that can classify and organize scenes and comments for hotels is implemented and evaluated. Comparisons with existing related studies are also performed. The experimental results validate the effectiveness and superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

In this article, we propose a pose transfer framework that can deal with large body motion to generate dance videos. To solve the problem of body shape deformation from large movements, a multiple scale region extractor (MSRE) is proposed. The features of each body region can be extracted from multiple layers of the encoder according to the body key points and passed through shortcuts to the decoder to reduce the spatial information loss. We add a region style loss calculated by the style representations of the body regions to the loss function to improve the quality of the generated images. In addition, the concept of learnable region normalization is integrated into the proposed framework to prevent introducing undesired mean and variance shifts by the corrupted regions during normalization. The experiments have shown that the proposed system can significantly improve the pose generation results compared with existing methods, especially when there are large body movements in the dancing poses. [ABSTRACT FROM AUTHOR]

In this article, we propose a system for automatically classifying and cleaning location images in large-scale image databases uploaded by arbitrary users. Detecting incorrect scenes uploaded by users and maintaining the correctness of the database through automatic data cleaning are essential because human inspection is not feasible for verifying massive amounts of data. In this study, we compared different feature extractors using deep convolutional neural networks trained using big data. We designed a multilevel extractor to improve feature extraction. Moreover, a detector based on multiresolution dissimilarity calculation was designed to overcome the issue of large intraclass distances and successfully identify incorrect scenes. The proposed system was validated using a highly challenging dataset with 138,000 images collected from Google Places. The experiments show that the multilevel extractor and the detector based on multiresolution dissimilarity calculation can improve the accuracy in identifying incorrect scenes and achieve satisfying data cleaning results. [ABSTRACT FROM AUTHOR]