Steady-State Planning in Expected Reward Multichain MDPs

The planning domain has experienced increased interest in the formal synthesis of decision-making policies. This formal synthesis typically entails finding a policy which satisfies formal specifications in the form of some well-defined logic. While many such logics have been proposed with varying degrees of expressiveness and complexity in their capacity to capture desirable agent behavior, their value is limited when deriving decision-making policies which satisfy certain types of asymptotic behavior in general system models. In particular, we are interested in specifying constraints on the steady-state behavior of an agent, which captures the proportion of time an agent spends in each state as it interacts for an indefinite period of time with its environment. This is sometimes called the average or expected behavior of the agent and the associated planning problem is faced with significant challenges unless strong restrictions are imposed on the underlying model in terms of the connectivity of its graph structure. In this paper, we explore this steady-state planning problem that consists of deriving a decision-making policy for an agent such that constraints on its steady-state behavior are satisfied. A linear programming solution for the general case of multichain Markov Decision Processes (MDPs) is proposed and we prove that optimal solutions to the proposed programs yield stationary policies with rigorous guarantees of behavior.

Steady State Behavior of a Single-Switch Non-isolated DC-DC SEPIC Converter Topology with Improved Static Voltage Gain

This paper presents the analysis of steady state behavior of a single switch non-isolated Single Ended Primary Inductance Converter (SEPIC) topology for achieving high DC voltage gain using diode-capacitor voltage multiplier. A voltage boosting module consisting of inductor and capacitor in addition with two diodes is introduced in the conventional SEPIC configuration in order to derive the DC-DC conversion technology proposed in this work. The voltage gain of the converter is extended using a diode-capacitor voltage multiplier cell. The converter suggested in this work has a single controlled switch. Hence, the conduction losses and the control complexity of the switch are very much reduced. The open loop configuration of the proposed non-isolated converter is described under continuous inductor current mode. The voltage boosting capability of the presented converter is compared with that of the existing modified SEPIC structure. The presented positive output converter topology has low switch voltage-current stress compared to the existing modified SEPIC topology given in the literature. The inductor and capacitor components of the suggested converter are so chosen that the DC output voltage and current waveforms show very low percentage of ripples. A DC voltage level of 24 V is given as input to the proposed converter. The DC voltage obtained across the load terminals is around 370 V which is achievable with low duty ratio (= 0.7) of the active switch. The voltage conversion ratio is very much influenced by the variation of the duty cycle of the power switch. In this work, the converter topology is presented and its various modes of operation are explained with equivalent circuits. The PSIM software platform is effectively and efficiently utilized to validate the performance of the converter. The obtained results convey that the proposed DC-DC conversion technology with extended voltage gain has the capability to maintain the steady-state output voltage and current profiles with almost negligible amount of ripples owing to the use of suitably designed non-dissipative elements in LC filter.

An Efficient Fast and Convergence-Controlled Algorithm for Sidelobes Simultaneous Reduction (SSR) and Spatial Filtering

In this paper, an efficient sidelobe levels (SLL) reduction and spatial filtering algorithm is proposed for linear one-dimensional arrays. In this algorithm, the sidelobes are beamspace processed simultaneously based on its orientation symmetry to achieve very deep SLL at much lower processing time compared with recent techniques and is denoted by the sidelobes simultaneous reduction (SSR) algorithm. The beamwidth increase due to SLL reduction is found to be the same as that resulting from the Dolph-Chebyshev window but at considerably lower average SLL at the same interelement spacing distance. The convergence of the proposed SSR algorithm can be controlled to guarantee the achievement of the required SLL with almost steady state behavior. On the other hand, the proposed SSR algorithm has been examined for spatial selective sidelobe filtering and has shown the capability to effectively reduce any angular range of the radiation pattern effectively. In addition, the controlled convergence capability of the proposed SSR algorithm allows it to work at any interelement spacing distance, which ranges from tenths to a few wavelength distances, and still provide very low SLL.

Steady-state multiphysics analysis of stator bar using finite element method

The electrical insulation is one of the main sources of failures in hydro-generators, therefore it is important to research the insulation system of stator bars. In this paper, it is developed a steady-state multiphysics analysis of a stator bar using the Finite Element Method to assess its steady-state behavior in the electrical, magnetic and thermal domains. Different aspects are analyzed in simulations, such as capacitance, mechanical stress and thermal effects. Numerical results are compared with experimental measurements for validation.