Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Fuzzy logic to evaluate driving maneuvers: An integrated approach to improve training

Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us to build better training programs. Currently, there is a need to develop and evaluate Advanced Driving Assistance Systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel Intelligent Driver Training System (IDTS) that analyses crash risks for a given driving situation, providing avenues for improvement and personalisation of driver training programs. The analysis takes into account numerous variables acquired synchronously from the Driver, the Vehicle and the Environment (DVE). The system then segments out the manoeuvres within a drive. This paper further presents the usage of fuzzy set theory to develop the safety inference rules for each manoeuvre executed during the drive. This paper presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving manoeuvres and then provide a comprehensive analysis of the drive used to give feedback to novice drivers.

Common values in assessing health outcomes from disease and injury: Disability weights measurement study for the Global Burden of Disease Study 2010

BACKGROUND Measurement of the global burden of disease with disability-adjusted life-years (DALYs) requires disability weights that quantify health losses for all non-fatal consequences of disease and injury. There has been extensive debate about a range of conceptual and methodological issues concerning the definition and measurement of these weights. Our primary objective was a comprehensive re-estimation of disability weights for the Global Burden of Disease Study 2010 through a large-scale empirical investigation in which judgments about health losses associated with many causes of disease and injury were elicited from the general public in diverse communities through a new, standardised approach. METHODS We surveyed respondents in two ways: household surveys of adults aged 18 years or older (face-to-face interviews in Bangladesh, Indonesia, Peru, and Tanzania; telephone interviews in the USA) between Oct 28, 2009, and June 23, 2010; and an open-access web-based survey between July 26, 2010, and May 16, 2011. The surveys used paired comparison questions, in which respondents considered two hypothetical individuals with different, randomly selected health states and indicated which person they regarded as healthier. The web survey added questions about population health equivalence, which compared the overall health benefits of different life-saving or disease-prevention programmes. We analysed paired comparison responses with probit regression analysis on all 220 unique states in the study. We used results from the population health equivalence responses to anchor the results from the paired comparisons on the disability weight scale from 0 (implying no loss of health) to 1 (implying a health loss equivalent to death). Additionally, we compared new disability weights with those used in WHO's most recent update of the Global Burden of Disease Study for 2004. FINDINGS 13,902 individuals participated in household surveys and 16,328 in the web survey. Analysis of paired comparison responses indicated a high degree of consistency across surveys: correlations between individual survey results and results from analysis of the pooled dataset were 0·9 or higher in all surveys except in Bangladesh (r=0·75). Most of the 220 disability weights were located on the mild end of the severity scale, with 58 (26%) having weights below 0·05. Five (11%) states had weights below 0·01, such as mild anaemia, mild hearing or vision loss, and secondary infertility. The health states with the highest disability weights were acute schizophrenia (0·76) and severe multiple sclerosis (0·71). We identified a broad pattern of agreement between the old and new weights (r=0·70), particularly in the moderate-to-severe range. However, in the mild range below 0·2, many states had significantly lower weights in our study than previously. INTERPRETATION This study represents the most extensive empirical effort as yet to measure disability weights. By contrast with the popular hypothesis that disability assessments vary widely across samples with different cultural environments, we have reported strong evidence of highly consistent results.

Application of TS-Fuzzy Controller for Active Power and DC Capacitor Voltage Control in DFIG-Based Wind Energy Conversion Systems

This chapter focuses on the implementation of the TS (Tagaki-Sugino) fuzzy controller for the Doubly Fed Induction Generator (DFIG) based wind generator. The conventional PI control loops for mantaining desired active power and DC capacitor voltage is compared with the TS fuzzy controllers. DFIG system is represented by a third-order model where electromagnetic transients of the stator are neglected. The effectiveness of the TS-fuzzy controller on the rotor speed oscillations and the DC capacitor voltage variations of the DFIG damping controller on converter ratings is also investigated. The results from the time domain simulations are presented to elucidate the effectiveness of the TS-fuzzy controller over the conventional PI controller in the DFIG system. The proposed TS-fuzzy con-troller can improve the fault ride through capability of DFIG compared to the conventional PI controller.

Rough Fuzzy Control of SVC for Power System Stability Enhancement

Modern power systems have become more complex due to the growth in load demand, the installation of Flexible AC Transmission Systems (FACTS) devices and the integration of new HVDC links into existing AC grids. On the other hand, the introduction of the deregulated and unbundled power market operational mechanism, together with present changes in generation sources including connections of large renewable energy generation with intermittent feature in nature, have further increased the complexity and uncertainty for power system operation and control. System operators and engineers have to confront a series of technical challenges from the operation of currently interconnected power systems. Among the many challenges, how to evaluate the steady state and dynamic behaviors of existing interconnected power systems effectively and accurately using more powerful computational analysis models and approaches becomes one of the key issues in power engineering. The traditional computing techniques have been widely used in various fields for power system analysis with varying degrees of success. The rapid development of computational intelligence, such as neural networks, fuzzy systems and evolutionary computation, provides tools and opportunities to solve the complex technical problems in power system planning, operation and control.

Fuzzy-DL perception for multi-robot systems

For future planetary robot missions, multi-robot-systems can be considered as a suitable platform to perform space mission faster and more reliable. In heterogeneous robot teams, each robot can have different abilities and sensor equipment. In this paper we describe a lunar demonstration scenario where a team of mobile robots explores an unknown area and identifies a set of objects belonging to a lunar infrastructure. Our robot team consists of two exploring scout robots and a mobile manipulator. The mission goal is to locate the objects within a certain area, to identify the objects, and to transport the objects to a base station. The robots have a different sensor setup and different capabilities. In order to classify parts of the lunar infrastructure, the robots have to share the knowledge about the objects. Based on the different sensing capabilities, several information modalities have to be shared and combined by the robots. In this work we propose an approach using spatial features and a fuzzy logic based reasoning for distributed object classification.

An application of fuzzy DL-Based semantic perception to soil container classification

Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.

Fuzzy-logic based frequency controller for wind farms augmented with energy storage systems

Displacement of conventional synchronous generators by non-inertial units such as wind or solar generators will result in reduced-system inertia affecting under-frequency response. Frequency control is important to avoid equipment damage, load shedding, and possible blackouts. Wind generators along with energy storage systems can be used to improve the frequency response of low-inertia power system. This paper proposes a fuzzy-logic based frequency controller (FFC) for wind farms augmented with energy storage systems (wind-storage system) to improve the primary frequency response in future low-inertia hybrid power system. The proposed controller provides bidirectional real power injection using system frequency deviations and rate of change of frequency (RoCoF). Moreover, FFC ensures optimal use of energy from wind farms and storage units by eliminating the inflexible de-loading of wind energy and minimizing the required storage capacity. The efficacy of the proposed FFC is verified on the low-inertia hybrid power system.

Generalizing the use of geographical weights in biodiversity modelling

Aim Determining how ecological processes vary across space is a major focus in ecology. Current methods that investigate such effects remain constrained by important limiting assumptions. Here we provide an extension to geographically weighted regression in which local regression and spatial weighting are used in combination. This method can be used to investigate non-stationarity and spatial-scale effects using any regression technique that can accommodate uneven weighting of observations, including machine learning. Innovation We extend the use of spatial weights to generalized linear models and boosted regression trees by using simulated data for which the results are known, and compare these local approaches with existing alternatives such as geographically weighted regression (GWR). The spatial weighting procedure (1) explained up to 80% deviance in simulated species richness, (2) optimized the normal distribution of model residuals when applied to generalized linear models versus GWR, and (3) detected nonlinear relationships and interactions between response variables and their predictors when applied to boosted regression trees. Predictor ranking changed with spatial scale, highlighting the scales at which different species–environment relationships need to be considered. Main conclusions GWR is useful for investigating spatially varying species–environment relationships. However, the use of local weights implemented in alternative modelling techniques can help detect nonlinear relationships and high-order interactions that were previously unassessed. Therefore, this method not only informs us how location and scale influence our perception of patterns and processes, it also offers a way to deal with different ecological interpretations that can emerge as different areas of spatial influence are considered during model fitting.

“I understand. I am a participant”: Navigating the ‘fuzzy’ boundaries of visual methods in qualitative longitudinal research

In this chapter we discuss how utilising the participatory visual methodology, photovoice, in an aged care context with its unique communal setting raised several ‘fuzzy boundary’ ethical dilemmas. To illustrate these challenges, we draw on immersive field notes from an ongoing qualitative longitudinal research (QLR) exploring the lived experience of aged care from the perspective of older residents, and focus on interactions with one participant, 81 year old Cassie. We explore how the camera, which is integral to the photovoice method, altered the researcher/participant ethical dynamics by becoming a continual ‘connector’ to the researcher. The camera took on a distinct agency, acting as a non-threatening ‘portal’ that lengthened contact, provided informal opportunities to alter the relationship dynamics and enabled unplanned participant revelation.