State Characteristic Clustering for Nonintrusive Load Monitoring with Stochastic Behaviours in Smart Grids

Integrating the nonintrusive load monitoring (NILM) technology into smart meters poses challenges in demand-side management (DSM) of the smart grid when capturing detailed power information and stochastic consumption behaviours, due to the difficulties in accurately detecting load operation states in real household environments with the limited information available. In this paper, a state characteristic clustering (SCC) approach is presented for promoting the performance of event detection in NILM, which makes full use of multidimensional characteristic information. After identifying different types of state domains in an established multidimensional characteristic space, we design a sliding window difference search method (SWDS) to extract their initial clustering centre. Meanwhile, the mean-shift updating and iterating procedures are conducted to find the potential terminal stable state according to the probability density function. The above control strategy considers the transient events and stable states in a time-series dataset simultaneously, which thus allows the exact state of complex events to be obtained in a fluctuating environment. Moreover, a multisegment computing scheme is applied for fast computing in the state characteristic clustering process. Experiments of three different cases on both our real household dataset and REDD public dataset are provided to reveal the higher performance of the proposed SCC approach over the existing related methods.

Improved Influenza Diagnostics through Thermal Contrast Amplification

Influenza poses a serious health threat and creates an economic burden for people around the world. The accurate diagnosis of influenza is critical to the timely clinical treatment of patients and the control of outbreaks to protect public health. Commercially available rapid influenza diagnostic tests (RIDTs) that are operated by visual readout are widely used in clinics to screen influenza infections, but RIDTs suffer from imperfect analytical sensitivity, especially when the virus concentration in the sample is low. Fortunately, the sensitivity can be simply improved through an add-on signal amplification step, i.e., thermal contrast amplification (TCA). To demonstrate the advantage of TCA for influenza diagnosis, we conducted a prospective cohort study on 345 clinical specimens collected for influenza A and B testing during the 2017–2018 influenza season. All samples were tested using the Quidel QuickVue Influenza A + B test, followed by a TCA readout, and then confirmatory polymerase chain reaction testing. Through the TCA detecting sub-visual weak positives, TCA reading improved the overall influenza sensitivity by 53% for influenza A and 33% for influenza B over the visual RIDTs readings. Even though the specificity was compromised slightly by the TCA protocol (relative decrease of 0.09% for influenza A and 0.01% for influenza B), the overall performance was still better than that achieved by visual readout based on comparison of their plots in receiver operating characteristic space and F1 scores (relative increase of 14.5% for influenza A and 12.5% for influenza B). Performing a TCA readout on wet RIDTs also improved the overall TCA performance (relative increase in F1 score of 48%). Overall, the TCA method is a simple and promising way to improve the diagnostic performance of commercial RIDTs for infectious diseases, especially in the case of specimens with low target analytes.

Selection of Significant Features of Objects in the Classification Data Processing

The formation of a characteristic space in classification problems can be divided into two stages: the choice of the initial description of objects and the formation of an informative description of objects on the basis of a reduction in the dimension of the space of the original description

Dynamic path dependent landslide susceptibility modelling

This contribution tests the added value of including landslide path dependency in statistically-based landslide susceptibility modelling. A conventional pixel-based landslide susceptibility model was compared with a model that includes landslide path dependency, and with a purely path dependent landslide susceptibility model. To quantify path dependency among landslides, we used a Space-Time Clustering (STC) measure derived from Ripley's space-time K function implemented on a point-based multi-temporal landslide inventory from the Collazzone study area in central Italy. We found that the values of STC obey an exponential decay curve with characteristic time scale of 17 years, and characteristic space scale of 60 meters. This exponential space-time decay of the effect of a previous landslide on landslide susceptibility was used as the landslide path dependency component of susceptibility models. We found that the performance of the conventional landslide susceptibility model improved considerably when adding the effect of landslide path dependency. In fact, even the purely path dependent landslide susceptibility model turned out to perform better than the conventional landslide susceptibility model. The conventional plus path dependent and path dependent landslide susceptibility model and their resulted maps are dynamic and change over time unlike conventional landslide susceptibility maps.