Characterisation of Passive Intermodulation in Passive RF Devices with X-parameter

A novel approach to the modelling of passive intermodulation (PIM) generation in passive components with distributed weak nonlinearities is outlined. Based upon the formalism of X-parameters, it provides a unified framework for co-design of antenna beamforming networks, filters, combiners, phase shifters and other passive and active devices containing nonlinearities at RF front-end. The effects of discontinuities and complex circuit layouts can be efficiently evaluated with the aid of the equivalent networks of the canonical nonlinear elements. The main concepts are illustrated by examples of numerical simulations of PIM generation in the transmission lines and comparison with the measurement results.

Cavity-aided quantum parameter estimation in a bosonic double-well Josephson junction

We describe an apparatus designed to make non-demolition measurements on a Bose-Einstein condensate (BEC) trapped in a double-well optical cavity. This apparatus contains, as well as the bosonic gas and the trap, an optical cavity. We show how the interaction between the light and the atoms, under appropriate conditions, can allow for a weakly disturbing yet highly precise measurement of the population imbalance between the two wells and its variance. We show that the setting is well suited for the implementation of quantum-limited estimation strategies for the inference of the key parameters defining the evolution of the atomic system and based on measurements performed on the cavity field. This would enable {\it de facto} Hamiltonian diagnosis via a highly controllable quantum probe.

An Architecture of a Multi-Agent System for SCADA - Dealing With Uncertainty, Plans and Actions

This paper presents a multi-agent system approach to address the difficulties encountered in traditional SCADA systems deployed in critical environments such as electrical power generation, transmission and distribution. The approach models uncertainty and combines multiple sources of uncertain information to deliver robust plan selection. We examine the approach in the context of a simplified power supply/demand scenario using a residential grid connected solar system and consider the challenges of modelling and reasoning with
uncertain sensor information in this environment. We discuss examples of plans and actions required for sensing, establish and discuss the effect of uncertainty on such systems and investigate different uncertainty theories and how they can fuse uncertain information from multiple sources for effective decision making in
such a complex system.

Absolute Magnitudes of Pan-STARRS 1 Asteroids

It is well known that the absolute magnitudes (H) in the MPCORB and ASTORB orbital element catalogs suffer from a systematic offset. Juric at al. (2002) found 0.4 mag offset in the SDSS data and detailed light curve studies of WISE asteroids by Pravec et al. (2012) revealed size-dependent offsets of up to 0.5 mag. The offsets are thought to be caused by systematic errors introduced by earlier surveys using different photometric catalogs and filters. The next generation asteroid surveys provide an order of magnitude more asteroids and well-defined and calibrated magnitudes. The Pan-STARRS 1 telescope (PS1) has observed hundreds of thousands asteroids, submitted more than 2 million detections to the Minor Planet Center (MPC) and discovered almost 300 NEOs since the beginning of operations in late 2010. We transformed the observed apparent magnitudes of PS1-detected asteroids from the gP1,rP1,iP1,yP1,zP1 and wP1-bands into Johnson photometric system by assuming the mean S and C-type asteroid color (Fitzsimmons 2011 - personal communication, Schlafly et al. 2012, Magnier et al. 2012 - in preparation) and calculated the absolute magnitude (H) in the V-band and its uncertainty (Bowell et al., 1989) for more than 200,000 known asteroids having on average 6.7 detections per object. The H error with respect to the MPCORB catalog revealed a mean offset of -0.49+0.30 mag in good agreement with published values. We will also discuss the statistical and systematical errors in H and slope parameter G.

Analysing Web-Orchestrations Under Stress Using Uncertainty Profiles

An orchestration is a multi-threaded computation that invokes a number of remote services. In practice, the responsiveness of a web-service fluctuates with demand; during surges in activity service responsiveness may be degraded, perhaps even to the point of failure. An uncertainty profile formalizes a user's perception of the effects of stress on an orchestration of web-services; it describes a strategic situation, modelled by a zero-sum angel–daemon game. Stressed web-service scenarios are analysed, using game theory, in a realistic way, lying between over-optimism (services are entirely reliable) and over-pessimism (all services are broken). The ‘resilience’ of an uncertainty profile can be assessed using the valuation of its associated zero-sum game. In order to demonstrate the validity of the approach, we consider two measures of resilience and a number of different stress models. It is shown how (i) uncertainty profiles can be ordered by risk (as measured by game valuations) and (ii) the structural properties of risk partial orders can be analysed.

Uncertainty in the Cloud: An Angel-Daemon Approach to Modelling Performance

Uncertainty profiles are used to study the effects of contention within cloud and service-based environments. An uncertainty profile provides a qualitative description of an environment whose quality of service (QoS) may fluctuate unpredictably. Uncertain environments are modelled by strategic games with two agents; a daemon is used to represent overload and high resource contention; an angel is used to represent an idealised resource allocation situation with no underlying contention. Assessments of uncertainty profiles are useful in two ways: firstly, they provide a broad understanding of how environmental stress can effect an application’s performance (and reliability); secondly, they allow the effects of introducing redundancy into a computation to be assessed

Energy Saving - Another Perspective for Parameter Optimization of P and PI Controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

Mathematical models are useful tools for simulation, evaluation, optimal operation and control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the model parameters of these two type of cells efficiently, a biogeography-based optimization algorithm with mutation strategies (BBO-M) is proposed. The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Numerical experiments have been conducted on ten benchmark functions with 50 dimensions, and the results show that BBO-M can produce solutions of high quality and has fast convergence rate. Then, the proposed BBO-M is applied to the model parameter estimation of the two type of cells. The experimental results clearly demonstrate the power of the proposed BBO-M in estimating model parameters of both solar and fuel cells.