Visual SLAM (abbreviates ‘simultaneous localization and mapping’) is a promising solution for environment mapping. This study is devoted to a description of a semantically ensembled SLAM framework. For structural indoor scenes, the structured lines and planes can serve as the newly added constraints to improve the positioning accuracy. In this paper, we propose to comprehensively incorporate point-line-plane primitives and construct a tightly coupled camera pose estimator without any environment assumptions. In particular, the maximum number of extracted lines features is numerically determined. We further integrate a lightweight object mapping pipeline with the designed pose estimator. In this pipeline, the leveraging of fitted plane and cuboid landmarks enables an online, CPU-based dense mapping. The tests on ICL-NUIM and TUM benchmark datasets illustrate that, in comparison to ORB-SLAM2, PL-SLAM (Point and Line based SLAM), SP-SLAM (Supposed Plane SLAM) and PLP-SLAM (Point, Line and Plane fused SLAM), our design leads to superior performances in global consistency and system drift elimination. The feature detection and multi-level navigation map reconstruction results are simultaneously provided.
Shale gas plays an important role in supplementing energy demand and reducing carbon footprint. A precise and effective prediction of shale gas production is important for optimizing completion parameters. This paper established a gated recurrent unit and multilayer perceptron combined neural network (GRU-MLP model) to forecast multistage fractured horizontal shale gas well production. A nondominated sorting genetic algorithm II (NSGA II) was introduced into the model to enable its automatic architectural optimization. In addition, embedded discrete fracture models (EDFM) and a reservoir simulator were used to generate training datasets. Meanwhile, a sensitivity analysis was carried out to find the variable’s importance and support the history matching. The results illustrated that the GRU-MLP model can precisely and efficiently predict the productivity of multistage fractured horizontal shale gas in a rapid and effective manner. Additionally, the model fits better at peak values of shale gas production. The GRU-MLP hybrid model has a higher accuracy within an acceptable computational time range compared to recurrent neural networks (RNN), long short-term memory (LSTM), and GRU models. The mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) for shale gas production generated by GRU-MLP model were 3.90% and 3.93%, respectively, values 84.87% and 84.88% smaller than those of the GRU model. Consequently, compared with a purely data-driven method, the physics-constrained data-driven method behaved better. The main results of the study will hopefully contribute to the intelligent development of shale gas production prediction.
In order to simulate the interruption process of mechanical DC circuit breaker in actual DC power grid and obtain various stress characteristics of DC circuit breaker in actual interruption process, it is necessary to establish a stable and effective flexible DC system model with good dynamic performance. In this paper, the controller models of system level, converter station level and converter valve level are established for the 10kV flexible DC system, and then the steady-state characteristics and dynamic characteristics of power step of the system are simulated. After analyzing the variation of voltage, current and power and dynamic response speed of converter station, the simulation results show that DC voltage and active power of the system operate stably according to the reference value and the fluctuation error of DC voltage is less than 1%. The internal and external loop controllers all track the dynamic changes of their respective command values rapidly within 0.5s. The control performance of the flexible DC system model can meet the requirement of the interruption simulation of DC circuit breakers.
Due to the uncertainty and randomness, the chaotic behavior of nonlinear system has become an important factor affecting the stability of the system. Therefore, it is very necessary to ensure that the nonlinear system has strong robustness against the external stochastic interference. This paper presents a new dynamic surface controller for nonlinear systems with time delays. In order to be closer to the actual engineering complex operation environment, the fractional calculus operator is added in the system. The dynamic surface control and time-delay effect is first applied to the second-order nonlinear fractional-order system. To deal with the problem of number explosion and time-delay in traditional inversion methods, a fractional-order filter and an alpha-order Pade approximation method are designed. After fully considering the tracking error, virtual control error and filtering error, the dynamic surface controller is established and its stability is verified. It can be seen from the simulation results that the system tracks the set function trajectory in finite time, and the controller has a good effect.
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