A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle

This paper addresses the problem of optimizing charging/discharging schedules of electric vehicles (EVs) when participate in demand response (DR). As there exist uncertainties in EVs' remaining energy, arrival and departure time, and future electricity prices, it is quite difficult to make charging decisions to minimize charging cost while guarantee that the EV's battery state-of-the-charge (SOC) is within certain range. To handle with this dilemma, this paper formulates the EV charging scheduling problem as a constrained Markov decision process (CMDP). By synergistically combining the augmented Lagrangian method and soft actor critic algorithm, a novel safe off-policy reinforcement learning (RL) approach is proposed in this paper to solve the CMDP. The actor network is updated in a policy gradient manner with the Lagrangian value function. A double-critics network is adopted to synchronously estimate the action-value function to avoid overestimation bias. The proposed algorithm does not require strong convexity guarantee of examined problems and is sample efficient. Comprehensive numerical experiments with real-world electricity price demonstrate that our proposed algorithm can achieve high solution optimality and constraints compliance

Standardized Flow-control Signals to Support Chaining of Identity Providers

Many online services, such as websites or apps, facilitate user sign-up or sign-in via different identity providers (IDPs). When a service permits the use of multiple IDPs, displaying the UIs of all of these at the same time is not suitable from a user experience point of view. In such cases, each of the available IDP options needs to be shown one by one. However, owing to various issues regarding how libraries and/or Application Programming Interfaces (APIs) of different IDPs handle the authentication action flow, it is difficult for online services to integrate with multiple IDPs in such a chained manner. To tackle this problem, the techniques described in this disclosure provide a chainable API to facilitate standardized communication between an online service that requests user authentication and an IDP that holds the user’s credentials

Blue Phosphorene Oxide: Strain-tunable Quantum Phase Transitions and Novel 2D Emergent Fermions

Tunable quantum phase transitions and novel emergent fermions in solid state materials are fascinating subjects of research. Here, we propose a new stable two-dimensional (2D) material, the blue phosphorene oxide (BPO), which exhibits both. Based on first-principles calculations, we show that its equilibrium state is a narrow-bandgap semiconductor with three bands at low energy. Remarkably, a moderate strain can drive a semiconductor-to-semimetal quantum phase transition in BPO. At the critical transition point, the three bands cross at a single point at Fermi level, around which the quasiparticles are a novel type of 2D pseudospin-1 fermions. Going beyond the transition, the system becomes a symmetry-protected semimetal, for which the conduction and valence bands touch quadratically at a single Fermi point that is protected by symmetry, and the low-energy quasiparticles become another novel type of 2D double Weyl fermions. We construct effective models characterizing the phase transition and these novel emergent fermions, and we point out several exotic effects, including super Klein tunneling, supercollimation, and universal optical absorbance. Our result reveals BPO as an intriguing platform for the exploration of fundamental properties of quantum phase transitions and novel emergent fermions, and also suggests its great potential in nanoscale device applications.Comment: 23 pages, 5 figure

Multi-AUV Cooperative Target Hunting based on Improved Potential Field in a Surface-Water Environment

In this paper, target hunting aims to detect target and surround the detected target in a surface-water using Multiple Autonomous Underwater Vehicles (multi-AUV) in a given area. The main challenge in multi-AUV target hunting is the design of AUV\u27s motion path and coordination mechanism. To conduct the cooperative target hunting by multi-AUV in a surface-water environment, an integrated algorithm based on improved potential field (IPF) is proposed. First, a potential field function is established according to the information of the surface-water environment. Then, the dispersion degree, the homodromous degree, and district-difference degree are introduced to increase the cooperation of the multi-AUV system. Finally, the target hunting is solved by embedding the three kinds of degree into the potential field function. The simulation results show that the proposed approach is applicable and feasible for multi-AUV cooperative target hunting

4-[(7-Fluoro­quinazolin-4-yl)­oxy]aniline

In the mol­ecule of the title compound, C14H10FN3O, the bicyclic quinazoline system is effectively planar, with a mean deviation from planarity of 0.0140 (3) Å. The quinazoline heterocyclic system and the adjacent benzene ring make a dihedral angle of 85.73 (9)°. Two inter­molecular N—H⋯N hydrogen bonds contribute to the stability of the crystal structure. In addition, a weak π–π stacking inter­action [centroid–centroid distance = 3.902 (2) Å] is observed