For some applications such as decision making, it would % help to have predicted y(t+1) once y(t) is available, but before the % actual y(t+1) occurs. The network can be made to return its output a % timestep early by removing one delay so that its minimal tap delay is now ...
Modeling Asphaltene Precipitation-Part II: Comparative Study for Asphaltene Precipitation Curve Prediction Methodsdoi:10.31026/j.eng.2025.01.03EQUATIONS of stateCUBIC equationsPETROLEUMASPHALTENEOIL fieldsLOW temperaturesAsphaltenes' solubility in crude oils is frequently affected by temperature...
In the current study, we set out to examine the viability of a novel approach to modeling human personality. Research in psychology suggests that people’s personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM potentially leaves room...
Modeling or predicting electron flow in reactions26 can also be considered as a variant of graph-based methods. Besides, some semi-template-based methods also improve performance by identifying reaction sites followed by recovering graphs or sequences5,27,28,29. Translation-based approaches formalize...
Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China. Geomatics, Natural Hazards and Risk, 2024, 15(1): 2344804. DOI:10.1080/19475705.2024.2344804 56. Gao, B., He, Y., Chen, X. et al. A Deep Neural ...
Common social interaction modeling methods can be generally classified into three types [16]. Show abstract Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning 2023, Transportation Research Part C: Emerging Technologies Citation Excerpt : Both methods showed ...
Statistical modeling and decision science. Amsterdam: Elsevier Science. MATH Google Scholar Wright, M. N., & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software, 77(1), 1–17. Article Google Scholar ...
this framework needs to be trained by specimens from not the same markets. But in addition to using market history modeling and various other variables (futures, options) as input data, it uses the ten pieces of data that are close to the data variables of the day. In this algorithm, all...
5.2.1. Modeling of imperfection Using the proposed hybrid method and an appropriate imperfection, we are now able to accurately simulate the wrinkling behavior under complicated boundary conditions in a predictive manner. However, the modeling of the imperfection is difficult. In general, it is commo...
(1) As a basic deep-learning model only considering the temporal modeling in the FTP task, A1 suffers from the largest prediction errors, i.e., 0.9472 km in the MDE metric, which makes it challenging to support delicate trajectory operation management. To improve the prediction accuracy,...