GIPC-GAN: an end-to-end gradient and intensity joint proportional constraint generative adversarial network for multi-focus image fusion

Abstract As for the problems of boundary blurring and information loss in the multi-focus image fusion method based on the generative decision maps, this paper proposes a new gradient-intensity joint proportional constraint generative adversarial network for multi-focus image fusion, with the name of GIPC-GAN. First, a set of labeled multi-focus image datasets using the deep region competition algorithm on a public dataset is constructed. It can train the network and generate fused images in an end-to-end manner, while avoiding boundary errors caused by artificially constructed decision maps. Second, the most meaningful information in the multi-focus image fusion task is defined as the target intensity and detail gradient, and a jointly constrained loss function based on intensity and gradient proportional maintenance is proposed. Constrained by a specific loss function to force the generated image to retain the information of target intensity, global texture and local texture of the source image as much as possible and maintain the structural consistency between the fused image and the source image. Third, we introduce GAN into the network, and establish an adversarial game between the generator and the discriminator, so that the intensity structure and texture gradient retained by the fused image are kept in a balance, and the detailed information of the fused image is further enhanced. Last but not least, experiments are conducted on two multi-focus public datasets and a multi-source multi-focus image sequence dataset and compared with other 7 state-of-the-art algorithms. The experimental results show that the images fused by the GIPC-GAN model are superior to other comparison algorithms in both subjective performance and objective measurement, and basically meet the requirements of real-time image fusion in terms of running efficiency and mode parameters quantity

Model Predictive PI Circulating Current Control for Modular Multilevel Converter

Significant circulating currents in the modular multilevel converter (MMC) increase system losses and complicate heat-sink design. Conventional PI and PR controllers can achieve steady-state error adjustment, but are sensitive to parameter changes and model uncertainty, heavily relying on coordinate transformations and careful design of model parameters. Model predictive control (MPC) has the characteristics of simple design, good robustness, and excellent dynamic response; however, it encountered the complexity of adjusting weighting factors. This paper proposed circulating the current model predictive proportional integral control (MPPIC) method in abc reference frame. This hybrid control solution utilized the predictive model and traditional PI algorithm to combine the advantages of nonlinear and linear control. Compared with existing suppression methods, this method avoided complex mathematical operations and a selection of weight coefficients, was easy to implement, and can effectively suppress circulating currents under different modulation ratios. Simulations were conducted on MATLAB/Simulink to verify the effectiveness of the proposed control strategy. MPPIC can not only distinctly suppress the circulating currents, but also reduce the overall voltage fluctuation of sub-modules capacitors under different modulation ratios, and had almost no any adverse effect on the performance of MMC

Insights into Alpine-Karst-Type Tufa Deposits in Geological Environmental Records: A Case Study of the Calcareous Tufa Profile of the Jiuzhaigou Natural Reserve on the Eastern Margin of the Tibetan Plateau

To study the geological environmental records of alpine-karst-type tufa deposits in the eastern margin of the Tibetan Plateau, the calcareous tufa profile exposed by the “8.8” Jiuzhaigou earthquake was taken as the research object and combined with a field geological investigation. Further, the petrography, sedimentology, chronology, and elemental geochemistry of the calcareous tufa were studied and analyzed. The results show the following. (1) The Sparkling Lake calcareous tufa profile was deposited under the background of a warm and humid climate during the Holocene. The growth pattern follows a bottom-to-top deposition. (2) At 750 ± 30–300 ± 30 aB.P., the calcareous tufa layers were gray-black as a whole, and the changes in mineral composition and elemental geochemistry indicate a fluctuating upward trend for temperature and precipitation during this period. (3) The formation of two sets of black peat layers in the upper part of the tufa calcareous profile is due to the synergistic action of multiple factors caused by strong tectonic activity. In conclusion, the deposition mechanism of the calcareous tufa in Jiuzhaigou was controlled by paleoclimate hydrology and glaciation for a long time, while strong tectonic activity over a short period of time considerably changed the color, structure, element content, and mineral composition of the calcareous tufa