Accurate translation of aerial imagery to maps is a direction of great value and challenge in mapping, a method of generating maps that does not require using vector data as traditional mapping methods do. The tremendous progress made in recent years in image translation based on generative adversarial networks has led to rapid progress in aerial image-to-map translation. Still, the generated results could be better regarding quality, accuracy, and visual impact. This paper proposes a supervised model (SAM-GAN) based on generative adversarial networks (GAN) to improve the performance of aerial image-to-map translation. In the model, we introduce a new generator and multi-scale discriminator. The generator is a conditional GAN model that extracts the content and style space from aerial images and maps and learns to generalize the patterns of aerial image-to-map style transformation. We introduce image style loss and topological consistency loss to improve the model’s pixel-level accuracy and topological performance. Furthermore, using the Maps dataset, a comprehensive qualitative and quantitative comparison is made between the SAM-GAN model and previous methods used for aerial image-to-map translation in combination with excellent evaluation metrics. Experiments showed that SAM-GAN outperformed existing methods in both quantitative and qualitative results.
With the power grid load increasing, the problem of grid voltage stability is increasingly prominent, and the possibility of voltage instability is also growing. In order to improve the voltage stability, this paper analyzed how the voltage stability was influenced by different reactive power injection based on the simplified L-indicator of on-line voltage stability monitoring. According to the basic differential property of the simplified L-indicator, a general and normative analytical algorithm about reactive power optimization was deduced. The analytical algorithm can calculate the load node injected reactive power, and then the network can run in the optimal steady state on the basis of the calculation results. According to the simulation results of IEEE-14, IEEE-30, IEEE-57 and IEEE-118, the feasibility and effectiveness of the proposed algorithm to improve voltage stability and reduce the risk of grid collapse were verified.
Association rule (AR) mining represents a challenge in the field of data mining. Mining ARs using traditional algorithms generates a large number of candidate rules, and even if we use binding measures such as support, reliability, and lift, there are still several rules to keep, and domain experts are needed to extract the rules of interest from the remaining rules. The focus of this paper is on whether we can directly provide rule rankings and calculate the proportional relationship between the items in the rules. To address these two questions, this paper proposes a modified FP-Growth algorithm called FP-GCID (novel FP-Growth algorithm based on Cluster IDs) to generate ARs; in addition, a new method called Mean-Product of Probabilities (MPP) is proposed to rank rules and compute the proportion of items for one rule. The experiment is divided into three phases: the DBSCAN (Density-Based Scanning Algorithm with Noise) algorithm is used to cluster the geographic interest points and map the obtained clusters into corresponding transaction data; FP-GCID is used to generate ARs, which contain cluster information; and MPP is used to choose the best rule based on the rankings. Finally, a visualization of the rules is used to validate whether the two previously stated requirements were fulfilled.
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