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This paper elaborates the design and analysis of cross-aperture-coupled twin port ceramic radiator. Stimulation of alumina ceramic using a cross slot helps to produce circular waves within 7.35–7.8 GHz. The polarization diversity concept helps to improve the separation level by above 25 dB. Loading of double negative unit cell made metasurface (MS) improves the antenna gain over 11.5 dBi within the working spectrum. Machine learning (ML) techniques, i.e. Decision Tree and Random Forest are utilized to predict the |S11|/Axial ratio parameters. Experimental verification/ML prediction and optimized simulated consequences confirm that the structured radiator works efficiently between 7.21 and 8.2 GHz with over 25 dB isolation between the ports. Directive pattern and decent values of (MIMO) parameters make the radiator applicable for the 6G communication system.
Task and motion planning (TAMP) problems combine abstract causal relations from preconditions to effects with computational geometry, kinematics, and dynamics. This chapter is about the integration of planning for motion/manipulation with planning for abstract actions. It introduces the main sampling-based algorithms for motion planning. Manipulation planning is subsequently introduced. A few approaches specific to TAMP are then presented.
This chapter is about planning approaches with explicit time in the descriptive and operational models of actions, as well as in the models of the expected evolution of the world not caused by the actor. It describes a planning algorithm that handles durative and concurrent activities with respect to a predicted dynamics. Section 17.1 presents a knowledge representation for modeling actions and tasks with temporal variables using temporal refinement methods. Temporal plans and planning problems are defined as chronicles, i.e., collections of assertions and tasks with explicit temporal constraints. A planning algorithm with temporal refinement methods is developed in Section 17.2. The basic techniques for managing temporal and domain constraints are then presented in Section 17.3.
Local eddy viscosity and diffusivity models are widely used to understand and predict turbulent flows. However, the local approximations in space and time are not always valid for actual turbulent flows. Recently, a non-local eddy diffusivity model for turbulent scalar flux was proposed to improve the local model and was validated using direct numerical simulation (DNS) of homogeneous isotropic turbulence with an inhomogeneous mean scalar (Hamba 2022 J. Fluid Mech.950, A38). The model was modified using the scale-space energy density in preparation for application to inhomogeneous turbulence (Hamba 2023 J. Fluid Mech.977, A11). In this paper, the model is further improved by incorporating the effects of turbulence anisotropy, inhomogeneity and wall boundaries. The needed inputs from the flow to evaluate the model are the Reynolds stress and the energy dissipation rate. With the improved model, one- and two-dimensional profiles ofthe non-local eddy diffusivity in turbulent channel flow are evaluated and compared with the exact DNS values. The DNS results reveal a contribution to the scalar flux from the mean scalar gradient in a wide upstream region. Additionally, the temporal profile of the non-local eddy diffusivity moves downstream, diffuses anisotropically and is tilted towards the bottom wall. The model reproduces this behaviour of mean flow convection and anisotropic turbulent diffusion well. These results indicate that the non-local eddy diffusivity model is useful for gaining insights into scalar transport in inhomogeneous turbulence.
This chapter is about two key aspects of learning with deterministic models: learning heuristics to speed up the search for a solution plan and the automated synthesis of the model itself. We discuss how to learn heuristics for exploring parts of the search space that are more likely to lead to solutions. We then address the problem of how to learn a deterministic model, with a focus on learning action schemas.
A ground vortex engendered by the interaction of uniform flow over a plane surface with suction into a cylindrical conduit whose axis is normal to the cross-flow and parallel to the ground plane is investigated in wind tunnel experiments. The formation and evolution of the columnar vortex and its ingestion into the conduit’s inlet are explored using planar/stereo particle image velocimetry over a broad range of formation parameters that include the speeds of the inlet and cross-flows and the cylinder’s elevation above the ground plane with specific emphasis on the role of the surface vorticity layer in the vortex initiation and sustainment. The present investigations show that the appearance of a ground vortex within the inlet face occurs above a threshold boundary of two dimensionless formation parameters, namely the inlet’s momentum flux coefficient and its normalised elevation above the ground surface. Transitory initiations of wall-normal columnar vortices are spawned within a countercurrent shear layer that forms over the ground plane within a streamwise domain on the inlet’s leeward side by the suction flow into the duct. At low suction speeds, these wall-normal vortices are advected downstream with the cross-flow but when their celerity is reversed with increased suction, they are advected towards the cylinder’s inlet, gain circulation and stretch along their centrelines and become ingested into the inlet at a threshold defined by the formation parameters. Finally, the present investigations demonstrated that reduction of the countercurrent shear within the wall vorticity layer by deliberate, partial bypass of the inlet face flow through the periphery of the cylindrical duct can significantly delay the ingestion of the ground vortex to higher level thresholds of the formation parameters.
The motivations for acting and planning with probabilistic models are about handling uncertainty in a quantitative way, with optimal or near-optimal decisions. The future is never entirely and precisely predictable. Uncertainty can be due to exogenous events in the environment, from nature and other actors, to noisy sensing and information gathering actions, to possible failures and outcomes of imprecise or intrinsically nondeterministic actions. Models are necessarily incomplete. Knowledge about open environments is partial. Part of what may happen can be only be modeled with uncertainty. Even in closed predictable environments, complete deterministic models may be too complex to develop. The three chapters in Part III tackle acting, planning, and learning in a probabilistic setting.
This chapter is about planning techniques for solving MDP problems. It presents algorithms that seeks optimal or near-optimal solution policies for a domain. Most of the chapter is focused on indefinite-horizon goal reachability domains that have positive costs and a safe solution; they may have dead ends, but those are avoidable. The chapter presents dynamic programming algorithms, heuristics search methods and their heuristics, linear programming methods, and online and Monte Carlo tree search techniques.