An essential function for automated vehicle technologies is accurate localization. It is difficult, however, to achieve lane-level accuracy with low-cost Global Navigation Satellite System (GNSS) receivers due to the biased noisy pseudo-range measurements. Approaches such as Differential GNSS can improve the accuracy, but usually require an enormous amount of investment in base stations. The emerging connected vehicle technologies provide an alternative approach to improving the localization accuracy. It has been shown in this paper that localization accuracy can be enhanced by fusing GNSS information within a group of connected vehicles and matching the configuration of the group to a digital map to eliminate the common bias in localization. A Rao-Blackwellized particle filter (RBPF) was used to jointly estimate the common biases of the pseudo-ranges and the vehicles positions. Multipath biases, which are non-common to vehicles, were mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation was exploited through the prediction-update process. The proposed approach was compared to the existing static and smoothed static methods in the intersection scenario. Simulation results show that the proposed algorithm reduced the estimation error by fifty percent and reduced the estimation variance by two orders of magnitude.
A new method to search for high-rate convolutional codes is achieved by means of a pruned trellis. This makes possible a reduced search procedure that can not be accomplished by standard methods. This new search technique makes use of the concept of the expanded column distance function of a convolutional code. By use of this search procedure, codes are found with an optimum distance profile followed by a maximisation of dfree. A number of systematic convolution al codes of high rates 3/4, 4/5, 5/6, 6/7, and 3/5 are found and listed in this paper.
In the existing enrollment teaching training, the low ratio of teachers / students causes small training scale and low training efficiency. In order to solve this problem, an interactive enrollment system based on AI is designed. The system mainly includes four modules: speech recognition module, evaluation and analysis module, comprehensive data module and scenario interface module. It realizes the functions of human-computer interaction, scenario design/import, realtime automatic scoring, teaching data analysis and so on. The introduction of enrollment system will effectively improve the training efficiency and expand the training scale.
Downward continuation is a critical task in potential field processing, including gravity and magnetic fields, which aims to transfer data from one observation surface to another that is closer to the source of the field. Its effectiveness directly impacts the success of detecting and highlighting subsurface anomalous sources. We treat downward continuation as an inverse problem that relies on solving a forward problem defined by the formula for upward continuation, and we propose a new physics-trained deep neural network (DNN)-based solution for this task. We hard-code the upward continuation process into the DNN's learning framework, where the DNN itself learns to act as the inverse problem solver and can perform downward continuation without ever being shown any ground truth data. We test the proposed method on both synthetic magnetic data and real-world magnetic data from West Antarctica. The preliminary results demonstrate its effectiveness through comparison with selected benchmarks, opening future avenues for the combined use of DNNs and established geophysical theories to address broader potential field inverse problems, such as density and geometry modelling.
Mode transition is an important control challenge for dual fuel engines, particularly for marine applications where fuel quality and composition may vary over a wide range. Feedback control is critical for dealing with fuel uncertainties and assuring robust performance. In this paper, a model-based approach is pursued for dual fuel engine mode switch control. A mean-value control-oriented model for a marine dual fuel engine is constructed in MATLAB™/ Simulink™environment. This model is used to emulate the mode transition process for shipboard generator set applications. Three different control architectures are examined for feedback control based on engine speed regulation during mode transitions. Based on the metric that reflects engine speed tracking error, it is found that a Multiple Input Single Output (MISO) architecture with feedback corrections applied to both gas fuel and diesel is advantageous versus architectures that apply corrections to only one (either diesel or gas) fuel command.
Model order reduction for nonlinear systems has been a challenging problem with limited methods and tools available. One approach is balanced truncation of empirically obtained Gramians (Lall et al., 1999) or controllability/ observability covariance matrices (Hahn et al., 2002). Empirical Gramians provide information about the controllability/ observability which is then used to transform a system so that only the subspaces that are most important to the input-to-output behavior are retained. While this provides an open-loop reduced order model, it may not be the best choice for robust controller and estimator (compensator) design. In this paper, an approach for obtaining empirical control and filter Riccati covariance matrices is proposed, and the empirical Riccati covariance matrices are used to obtain a reduced order model for systematic closed-loop compensator design. The proposed reduction method is demonstrated on a spatially discretized catalytic rod model, for which a constrained nonlinear model predictive controller and an extended Kalman filter are designed with the reduced order model. The efficacy of the proposed approach is established by comparing input and output responses of the resulting closed-loop system to those of the full order compensator and reduced compensators designed using other common balanced truncations methods.
Internal model control (IMC), which explicitly incorporates a plant model and a plant inverse as its components, has an intuitive control structure and simple tuning philosophy, making it appealing to industrial applications. Combining the IMC structure with adaptation through the certainty equivalence principle leads to adaptive IMC (AIMC), where the plant model is identified and the plant inverse is derived by inverting the estimated model. In [1], [2], we proposed the composite adaptive IMC (CAIMC) for a first-order plant and successfully applied it to the boost-pressure control problem of a turbocharged gasoline engine system. Within the IMC control structure, the plant model and the plant inverse are simultaneously identified to minimize modeling errors and further reduce the tracking error. Through theoretical analysis, simulations, and experimental validation, CAIMC was shown to demonstrate better performance compared to AIMC. In this paper the design procedure of CAIMC is generalized to a n-th order plant, and stability and asymptotic performance are established and analyzed under proper conditions.
The fundamentals and design principles of model reference adaptive control (MRAC) are described. The controller structure and adaptive algorithms are delineated. Stability and convergence properties are summarized.
<p>Supplementary Fig. S1. CA9 fails to enhance the cytolytic activity of NK-92 cells in vitro. Supplementary Fig. S2. NHE1 enhances in vitro cytotoxicity of NK-92 cells without affecting proliferation or survival. Supplementary Fig. S3. Representative three-dimensional (3D) reconstruction of confocal microscopic images of antibody-activated NK-92. Supplementary Fig. S4. NHE1 increases the protein expression of c-Myc in NK-92 cells. Supplementary Fig. S5. NHE1 enhances in vitro cytotoxicity of NK-92MI. Supplementary Table S1. Nucleotide sequence of codon-optimized, constitutively active human NHE1 cDNA. Supplementary Table S2. Differentially expressed genes between Na+/H+-exchanger 1 (NHE1)-expressing and empty vector NK-92 cells, ranked by log2(fold change).</p>
Cabin heating demand and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs), especially in congested traffic conditions. This paper presents an integrated power and thermal management (i-PTM) scheme for the optimization of power split, engine thermal management, and cabin heating of HEVs. A control-oriented model of a power split HEV, including power and thermal loops, is developed and experimentally validated against data collected from a 2017 Toyota Prius HEV. Based on this model, the dynamic programming (DP) technique is adopted to derive a bench-mark for minimal fuel consumption, using 2-dimensional (power split and engine thermal management) and 3-dimensional (power split, engine thermal management, and cabin heating) formulations. Simulation results for a real-world congested driving cycle show that the engine thermal effect and the cabin heating requirement can significantly influence the optimal behavior for the power management, and substantial potential on fuel saving can be achieved by the i-PTM optimization as compared to conventional power and thermal management strategies.
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