Potential evapotranspiration (PET) is crucial for water resources assessment. In this regard, the FAO (Food and Agriculture Organization)-Penman-Monteith method (PM) is commonly recognized as a standard method for PET estimation. However, due to requirement of detailed meteorological data, the application of PM is often constrained in many regions. Under such circumstances, an alternative method with similar efficiency to that of PM needs to be identified. In this study, three radiation-based methods, Makkink (Mak), Abtew (Abt), and Priestley-Taylor (PT), and five temperature-based methods, Hargreaves-Samani (HS), Thornthwaite (Tho), Hamon (Ham), Linacre (Lin), and Blaney-Criddle (BC), were compared with PM at yearly and seasonal scale, using long-term (50 years) data from 90 meteorology stations in southwest China. Indicators, viz. (videlicet) Nash-Sutcliffe efficiency (NSE), relative error (Re), normalized root mean squared error (NRMSE), and coefficient of determination (R 2 ) were used to evaluate the performance of PET estimations by the above-mentioned eight methods. The results showed that the performance of the methods in PET estimation varied among regions; HS, PT, and Abt overestimated PET, while others underestimated. In Sichuan basin, Mak, Abt and HS yielded similar estimations to that of PM, while, in Yun-Gui plateau, Abt, Mak, HS, and PT showed better performances. Mak performed the best in the east Tibetan Plateau at yearly and seasonal scale, while HS showed a good performance in summer and autumn. In the arid river valley, HS, Mak, and Abt performed better than the others. On the other hand, Tho, Ham, Lin, and BC could not be used to estimate PET in some regions. In general, radiation-based methods for PET estimation performed better than temperature-based methods among the selected methods in the study area. Among the radiation-based methods, Mak performed the best, while HS showed the best performance among the temperature-based methods.
To alleviate environmental pollution and improve the efficient use of energy, energy systems integration (ESI)—covering electric power systems, heat systems and natural gas systems—has become an important trend in energy utilization. The traditional power flow calculation method, with the object as the power system, will prove difficult in meeting the requirements of the coupled energy flow analysis. This paper proposes a generalized energy flow (GEF) analysis method which is suitable for an ESI containing electricity, heat and gas subsystems. First, the models of electricity, heat, and natural gas networks in the ESI are established. In view of the complexity of the conventional method to solve the gas network including the compressor, an improved practical equivalent method was adopted based on different control modes. On this basis, a hybrid method combining homotopy and the Newton-Raphson algorithm was executed to compute the nonlinear equations of GEF, and the Jacobi matrix reflecting the coupling relationship of multi-energy was derived considering the grid connected mode and island modes of the power system in the ESI. Finally, the validity of the proposed method in multi-energy flow calculation and the analysis of interacting characteristics was verified using practical cases.
Emotion recognition has drawn consistent attention from researchers recently. Although gesture modality plays an important role in expressing emotion, it is seldom considered in the field of emotion recognition. A key reason is the scarcity of labeled data containing 3D skeleton data. Some studies in action recognition have applied graph-based neural networks to explicitly model the spatial connection between joints. However, this method has not been considered in the field of gesture-based emotion recognition, so far. In this work, we applied a pose estimation based method to extract 3D skeleton coordinates for IEMOCAP database. We propose a self-attention enhanced spatial temporal graph convolutional network for skeleton-based emotion recognition, in which the spatial convolutional part models the skeletal structure of the body as a static graph, and the self-attention part dynamically constructs more connections between the joints and provides supplementary information. Our experiment demonstrates that the proposed model significantly outperforms other models and that the features of the extracted skeleton data improve the performance of multimodal emotion recognition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.