A Model of Generating Visual Place Cells Based on Environment Perception and Similar Measure

It is an important content to generate visual place cells (VPCs) in the field of bioinspired navigation. By analyzing the firing characteristic of biological place cells and the existing methods for generating VPCs, a model of generating visual place cells based on environment perception and similar measure is abstracted in this paper. VPCs’ generation process is divided into three phases, including environment perception, similar measure, and recruiting of a new place cell. According to this process, a specific method for generating VPCs is presented. External reference landmarks are obtained based on local invariant characteristics of image and a similar measure function is designed based on Euclidean distance and Gaussian function. Simulation validates the proposed method is available. The firing characteristic of the generated VPCs is similar to that of biological place cells, and VPCs’ firing fields can be adjusted flexibly by changing the adjustment factor of firing field (AFFF) and firing rate’s threshold (FRT)

Jet mixing optimization using machine learning control

We experimentally optimize mixing of a turbulent round jet using machine learning control (MLC) following Li et al (2017). The jet is manipulated with one unsteady minijet blowing in wall-normal direction close to the nozzle exit. The flow is monitored with two hotwire sensors. The first sensor is positioned on the centerline 5 jet diameters downstream of the nozzle exit, i.e. the end of the potential core, while the second is located 3 jet diameters downstream and displaced towards the shear-layer. The mixing performance is monitored with mean velocity at the first sensor. A reduction of this velocity correlates with increased entrainment near the potential core. Machine Learning Control (MLC) is employed to optimize sensor feedback, a general open-loop broadband frequency actuation and combinations of both. MLC has identified the optimal periodic forcing with a small duty cycle as the best control policy employing only 400 actuation measurements, each lasting for 5 seconds. This learning rate is comparable if not faster than typical optimization of periodic forcing with two free parameters (frequency and duty cycle). In addition, MLC results indicate that neither new frequencies nor sensor feedback improves mixing further-contrary to many of other turbulence control experiments. The optimality of pure periodic actuation may be attributed to the simple jet flapping mechanism in the minijet plane. The performance of sensor feedback is shown to face a challenge for small duty cycles. The jet mixing results demonstrate the untapped potential of MLC in quickly learning optimal general control policies, even deciding between open- and closed-loop control.Comment: 15 pages, 14 figure

Tuning the electronic, ion transport, and stability properties of Li-rich Manganese-based oxide materials with oxide perovskite coatings: a first-principles computational study

Lithium-rich manganese-based oxides (LRMO) are regarded as promising cathode materials for powering electric applications due to their high capacity (250 mAh g–1) and energy density (~900 Wh kg–1). However, poor cycle stability and capacity fading have impeded the commercialization of this family of materials as battery components. Surface modification based on coating has proven successful in mitigating some of these problems, but a microscopic understanding of how such improvements are attained is still lacking, thus impeding systematic and rational design of LRMO-based cathodes. In this work, first-principles density functional theory (DFT) calculations are carried out to fill out such a knowledge gap and to propose a promising LRMO-coating material. It is found that SrTiO3 (STO), an archetypal and highly stable oxide perovskite, represents an excellent coating material for Li1.2Ni0.2Mn0.6O2 (LNMO), a prototypical member of the LRMO family. An accomplished atomistic model is constructed to theoretically estimate the structural, electronic, oxygen vacancy formation energy, and lithium-transport properties of the LNMO/STO interface system, thus providing insightful comparisons with the two integrating bulk materials. It is found that (i) electronic transport in the LNMO cathode is enhanced due to partial closure of the LNMO band gap (~0.4 eV) and (ii) the lithium ions can easily diffuse near the LNMO/STO interface and within STO due to the small size of the involved ion-hopping energy barriers. Furthermore, the formation energy of oxygen vacancies notably increases close to the LNMO/STO interface, thus indicating a reduction in oxygen loss at the cathode surface and a potential inhibition of undesirable structural phase transitions. This theoretical work therefore opens up new routes for the practical improvement of cost-affordable lithium-rich cathode materials based on highly stable oxide perovskite coatings.Peer ReviewedPostprint (published version

Hybrid Random Regret Minimization and Random Utility Maximization in the Context of Schedule-Based Urban Rail Transit Assignment

Route choice is one of the most critical passenger behaviors in public transit research. The utility maximization theory is generally used to model passengers’ route choice behavior in a public transit network in previous research. However, researchers have found that passenger behavior is far more complicated than a single utility maximization assumption. Some passengers tend to maximize their utility while others would minimize their regrets. In this paper, a schedule-based transit assignment model based on the hybrid of utility maximization and regret minimization is proposed to study the passenger route choice behavior in an urban rail transit network. Firstly, based on the smart card data, the space-time expanded network in an urban rail transit was constructed. Then, it adapts the utility maximization (RUM) and the regret minimization theory (RRM) to analyze and model the passenger route choice behavior independently. The utility values and the regret values are calculated with the utility and the regret functions. A transit assignment model is established based on a hybrid of the random utility maximization and the random regret minimization (RURM) with two kinds of hybrid rules, namely, attribute level hybrid and decision level hybrid. The models are solved by the method of successive algorithm. Finally, the hybrid assignment models are applied to Beijing urban rail transit network for validation. The result shows that RRM and RUM make no significant difference for OD pairs with only two alternative routes. For those with more than two alternative routes, the performance of RRM and RUM is different. RRM is slightly better than RUM in some of the OD pairs, while for the other OD pairs, the results are opposite. Moreover, it shows that the crowd would only influence the regret value of OD pair with more commuters. We conclude that compared with RUM and RRM, the hybrid model RURM is more general. Document type: Articl