Multi-modal video event recognition based on association rules and decision fusion

In this paper, we propose a multi-modal event recognition framework based on the integration of feature fusion, deep learning, scene classification and decision fusion. Frames, shots, and scenes are identified through the video decomposition process. Events are modeled utilizing features of and relations between the physical video parts. Event modeling is achieved through visual concept learning, scene segmentation and association rule mining. Visual concept learning is employed to reveal the semantic gap between the visual content and the textual descriptors of the events. Association rules are discovered by a specialized association rule mining algorithm where the proposed strategy integrates temporality into the rule discovery process. In addition to frames, shots and scenes, the concept of scene segment is proposed to define and extract elements of association rules. Various feature sources such as audio, motion, keypoint descriptors, temporal occurrence characteristics and fully connected layer outputs of CNN model are combined into the feature fusion. The proposed decision fusion approach employs logistic regression to formulate the relation between dependent variable (event type) and independent variables (classifiers' outputs) in terms of decision weights. Multi-modal fusion-based scene classifiers are employed in the event recognition. Rule-based event modeling and multi-modal fusion capability are shown to be promising approaches for event recognition. The decision fusion results are promising and the proposed algorithm is open to the fusion of new sources for further improvements. The proposal is also open to new event type integrations. The accuracy of the proposed methodology is evaluated on the CCV and Hollywood2 dataset for event recognition and results are compared with the benchmark implementations in the literature

INTEGRATED INSTANCE-BASED AND KERNEL METHODS FOR POWER QUALITY KNOWLEDGE MODELING

In this paper, an integrated knowledge discovery strategy for high dimensional spatial power quality event data is proposed. Real time, distributed measuring of the electricity transmission system parameters provides huge number of time series power quality events. The proposed method aims to construct characteristic event distribution and interaction models for individual power quality sensors and the whole electricity transmission system by considering feasibility, time and accuracy concerns. In order to construct the knowledge and prediction model for the power quality domain; feature construction, feature selection, event clustering, and multi-class support vector machine supervised learning algorithms are employed

Data Mining Framework for Power Quality Event Characterization of Iron and Steel Plants

In this paper, a power quality (PQ) knowledge discovery and modeling framework has been developed for both temporal and spatial PQ event data collected from transformer substations supplying iron and steel (I&S) plants. PQ event characteristics of various I&S plants have been obtained based on clustering and rule discovery techniques. The data are collected by the PQ analyzers, which detect the voltage sags, swells, and interruptions according to the IEC Standard 61000-4-30. The constructed clustering strategy ensures feasible system monitoring by reducing unmanageable number of PQ events collected by the distributed PQ measurement systems into event clusters count. An abstraction for event representation has been developed, through which representative feature bags are constructed for each event to be used in the similarity decisions. The developed model has been applied satisfactorily to PQ event data obtained from 15 major transformer substations supplying heavy industry zones of the transmission system up to a five-year time period and from two additional transformer substations supplying some other industrial zones, for comparison purposes. The developed PQ data mining framework, which is used to identify PQ event distributions based on the event descriptions given in the IEEE Std. 1159, provides a useful analysis and evaluation infrastructure for taking countermeasures against the most probable event occurrences, specific to those feeders of I&S plant transformer substations

Choice-based Lettings, Social Landlords and Equitable Housing Outcomes: Structure, Agency and Reflexivity

Developed from the Delft model, Choice Based Lettings systems (CBL), have been extensively adopted by social landlords for allocating housing in the UK. Despite the purported neutrality and customer orientation of the service, its implementation appears to perpetuate existing ethnic disparities by increasing the concentration of minoritised ethnic (ME) communities in areas of deprivation. Notably, little attention has been paid to the systems and processes of social landlords from the perspectives of individuals from ME communities who have engaged with these systems. To help fill this gap, this UKRI-funded study uses a critical realist multi-levelled governance framework to unravel the complex interplay between the structural constraints inherent in social housing and digital platforms, the racialised social processes of engaging with these systems and the scope for reflexivity and agency among ME individuals. By analysing verbal and visual data from in-depth interviews with 37 ME tenants of social housing and follow up interviews with 15 individuals as well as data from an online survey of 55 applicants from ME communities in England and Scotland, we reveal the ways in which the systems and processes of social landlords hinder engagement with CBL systems. We argue that there is considerable scope for social landlords to work in collaboration with ME communities, web developers, data scientists, service providers, and policymakers, to allow more equitable housing outcomes

Assessment of extensive countrywide electrical power quality measurements through a database architecture

This paper describes countrywide electrical power quality (PQ) assessments of the Turkish Electricity Transmission System through a genuine PQ database. The database stores the output of mobile PQ measurement systems which are established at 172 transformer substations of the transmission system. At 601 measurement points in these substations, which are potentially critical in terms of PQ, measurements are carried out by the mobile systems for a period of 1 week. PQ parameters defined in the IEC-61000-4-30 standard in addition to power values are calculated from acquired raw data by the mobile-monitoring system and the resulting data are transferred to the PQ database. The database, based on a novel PQ data model, enables its users to take PQ snapshots of the transmission system countrywide and can be accessed through several interfaces including a visual query interface, a natural language interface, and a map interface. The overall PQ status of the measured points, representing the characteristics of the transmission system, is assessed through these interfaces and problematic points are determined while deriving important conclusions about the transmission system's PQ behavior. Moreover, several novel PQ assessment methods are proposed and their applications on the PQ data are demonstrated especially through the map interface