Load monitoring is the practice of measuring electrical signals in a domestic environment in order to identify which electrical appliances are consuming power. One reason for developing a load monitoring system is to reduce power consumption by increasing consumers’ awareness of which appliances consume the most energy. Another example of an application of load monitoring is activity sensing in the home for the provision of healthcare services. This paper outlines the development of a load disaggregation method that measures the aggregate electrical signals of a domestic environment and extracts features to identify each power consuming appliance. A single sensor is deployed at the main incoming power point, to sample the aggregate current signal. The method senses when an appliance switches ON or OFF and uses a two-step classification algorithm to identify which appliance has caused the event. Parameters from the current in the temporal and frequency domains are used as features to define each appliance. These parameters are the steady-state current harmonics and the rate of change of the transient signal. Each appliance’s electrical characteristics are distinguishable using these parameters. There are three Types of loads that an appliance can fall into, linear nonreactive, linear reactive or nonlinear reactive. It has been found that by identifying the load type first and then using a second classifier to identify individual appliances within these Types, the overall accuracy of the identification algorithm is improved.

This paper presents a detailed examination of a novel data center network (DCN) that can satisfy the high capacity and low latency requirements of modern cloud computing applications. This reconfigurable architecture called AgileDCN uses fast-switching optical components with a centralized control function and workload scheduler. By providing a highly flexible optical network fabric between server racks, very high network efficiencies can be achieved even under imbalanced loading patterns. Our simulation results show that, at high (70%) loads, TCP flow completion times in the AgileDCN are significantly lower than in an equivalent electronic leaf-spine network.

In this paper we propose an analytic framework for estimating blocking probability in an asynchronous optical burst switch with wavelength conversion and a multi-channel feed-back fibre delay line. Burst arrivals are of general renewal type and burst lengths exponentially distributed. Example solutions for gamma-distributed arrivals are compared with discrete-event simulations. To the authors' knowledge, this is the first analytic model of such a switch for general renewal arrival statistics.

A new data dimension-reduction method, called Internal Information Redundancy Reduction (IIRR), is proposed for application to Optical Emission Spectroscopy (OES) datasets obtained from industrial plasma processes. For example in a semiconductor manufacturing environment, real-time spectral emission data is potentially very useful for inferring information about critical process parameters such as wafer etch rates, however, the relationship between the spectral sensor data gathered over the duration of an etching process step and the target process output parameters is complex. OES sensor data has high dimensionality (fine wavelength resolution is required in spectral emission measurements in order to capture data on all chemical species involved in plasma reactions) and full spectrum samples are taken at frequent time points, so that dynamic process changes can be captured. To maximise the utility of the gathered dataset, it is essential that information redundancy is minimised, but with the important requirement that the resulting reduced dataset remains in a form that is amenable to direct interpretation of the physical process. To meet this requirement and to achieve a high reduction in dimension with little information loss, the IIRR method proposed in this paper operates directly in the original variable space, identifying peak wavelength emissions and the correlative relationships between them. A new statistic, Mean Determination Ratio (MDR), is proposed to quantify the information loss after dimension reduction and the effectiveness of IIRR is demonstrated using an actual semiconductor manufacturing dataset. As an example of the application of IIRR in process monitoring/control, we also show how etch rates can be accurately predicted from IIRR dimension-reduced spectral data.