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The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

An aircraft engine control system consists of a large scale of control parameters and variables because of the complex structure of aero-engine. Monitoring and adjusting control variables and parameters such as detecting, isolating and reconfiguring the system faults/failures depend on the controller design. Developing a robust controller is based on an accurate mathematical model.

In this study, a small-scale turboprop engine is modeled. Simulation is carried out on MATLAB/Simulink for design and off-design operating conditions. Both steady-state and transient conditions (from idle to maximum thrust levels) are tested. The performance parameters of compressor and turbine components are predicted via trained Neuro-Fuzzy model (ANFIS) based on component maps. Temperature, rotational speed, mass flow, pressure and other parameters are generated by using thermodynamic formulas and conservation laws. Considering these calculated values, error calculations are made and compared with the cycle data of the engine at the related simulation conditions.

Simulation results show that the designed engine model’s simulation values have acceptable accuracy for both design and off-design conditions from idle to maximum power operating envelope considering cycle data. The designed engine model can be adapted to other types of gas turbine engines.

Different from other literature studies, in this work, a small-scale turboprop engine is modeled. Furthermore, for performance prediction of compressor and turbine components, ANFIS structure is applied.

Mortgage defaults within a neighborhood may tip the scales whereby a vicious cycle of disinvestment and deterioration in the surrounding neighborhoods begins. This paper aims to examine the impact that mortgage default has on properties in the same ZIP code and neighboring ZIP codes.

Hypothesizing that neighborhoods' susceptibility to cascade failure can be measured by the rate of acceleration of mortgage failures within the neighborhood, the paper introduces a model to investigate whether or not this vicious cycle is such that mortgage failures multiply, and there is a tipping point at which the downward cycle accelerate.

The paper applies the model to data for the Los Angeles metropolitan area for the period 2006-2007 and finds evidence of a tipping point.

The paper is limited by the availability of data with respect to both time and space.

A failure tipping point will provide a signal that mortgage crisis is pending. Reacting to this signal could allow financial markets to avert such crises in the future.

Some neighborhoods may resist being labelled as one with significant mortgage failure activity. This resistance may cause a negative reaction to these results and implementation for the findings.

To-date, no evidence of a mortgage failure tipping point has been discovered in the literature.

Recently, there has been increased attention to community- and neighborhood-level influences on rates and experiences of intimate partner violence (IPV). The purpose of this paper is to describe the use of geographic information systems (GIS) to geographically analyze these influences in order to enhance community-level understanding of and responses to IPV.

The authors review existing literature supporting the needs for this level of analysis, and then they present eight steps for researchers and practitioners to use when applying GIS to analyze IPV.

This is a conceptual paper.

This paper offers researchers and practitioners suggested strategies for using GIS analyses to examine community-level influences on IPV in future research.

The practical implications of using GIS analyses are discussed, including ways that the findings of these analyses can be used to enhance community-level resources to prevent and respond to IPV.

This innovative, interdisciplinary approach offers new insights into understanding and addressing IPV at a community level.

To date, there has been minimal research used to apply GIS analyses to the problem of IPV in communities. This paper presents a framework for future researchers and practitioners to apply this methodology to expand on community-level understanding of IPV.

The purpose of this research is to examine why communities facing the same disaster recover differentially and determine pathways to successful disaster recovery in the research setting of New Orleans neighborhoods affected by Hurricane Katrina. While previous studies suggest that there are a variety of pathways to recovery, a broader cross-case comparison is necessary to generalize these pathways into a recovery framework. Specifically, this study seeks to determine what pre-disaster and post-disaster causal factors, alone or in combination, were important to recovery following Hurricane Katrina.

This paper presents a cross-case comparative study of neighborhood-level recovery. Based on prior work, which used the Delphi method to determine hypothesized causal factors and indicators of recovery, data was collected through publically available sources, including the US Census, the Greater New Orleans Community Data Center and previously completed studies for 18 damaged neighborhoods. Fuzzy-set qualitative comparative analysis was used due to its ability to analyze both quantitative and qualitative data for smaller case studies.

The results show that there are multiple pathways combining pre-disaster community factors and post-disaster actions that led to recovery, as measured by population return. For example, economic capacity is nearly sufficient for recovery, but a combination of low social vulnerability, post-disaster community participation, a high proportion of pre-World War II housing stock and high amounts of post-disaster funds also led to recovery.

This research uses a novel method to link pre-disaster measures of resilience and vulnerability to recovery outcomes and, through cross-case comparison, generates results that will enable researchers to develop a theory of sustainable community recovery.

This research aims to address the issue of safe navigation for autonomous vehicles in highly challenging outdoor environments. Indeed, robust navigation of autonomous mobile robots over long distances requires advanced perception means for terrain traversability assessment.

The use of visual systems may represent an efficient solution. This paper discusses recent findings in terrain traversability analysis from RGB-D images. In this context, the concept of point as described only by its Cartesian coordinates is reinterpreted in terms of local description. As a result, a novel descriptor for inferring the traversability of a terrain through its 3D representation, referred to as the unevenness point descriptor (UPD), is conceived. This descriptor features robustness and simplicity.

The UPD-based algorithm shows robust terrain perception capabilities in both indoor and outdoor environment. The algorithm is able to detect obstacles and terrain irregularities. The system performance is validated in field experiments in both indoor and outdoor environments.

The UPD enhances the interpretation of 3D scene to improve the ambient awareness of unmanned vehicles. The larger implications of this method reside in its applicability for path planning purposes.

This paper describes a visual algorithm for traversability assessment based on normal vectors analysis. The algorithm is simple and efficient providing fast real-time implementation, since the UPD does not require any data processing or previously generated digital elevation map to classify the scene. Moreover, it defines a local descriptor, which can be of general value for segmentation purposes of 3D point clouds and allows the underlining geometric pattern associated with each single 3D point to be fully captured and difficult scenarios to be correctly handled.

Achieving the elimination of racial differences in test performance, as set forth in the No Child Left Behind Act of 2001 (NCLB), requires education policies that engage the reality that African American test performances are not only about race but also about gender and residential status. In an effort to inform education policymaking with research that explores race–gender and residential inequality, I assess the growth of reading gaps in school and non-school contexts using a national and city sample of children from the Early Childhood Longitudinal, Kindergarten Cohort 1998–1999. I found that inequality in test performances was greater in the city than elsewhere, and African American boys shoulder a disproportionate educational burden related to city residency and enrollment in city schools. Additionally, children in city neighborhoods – where drugs and burglary are big problems – experience large shortfalls in reading in school and non-school contexts. I conclude with a discussion of the study’s implications for future educational policy, practice, and research, especially NCLB, which mandates that public schools achieve parity among racial groups by the end of the 2013–2014 academic year.

This study aims to examine the main factors and the related impacts that have caused a negative shift in the social perception of tourism among residents of Barcelona. Namely, it contextualises the recent evolution of the impacts and the social perception of tourism among the city’s residents; analyses the relationship between the social perception of tourism and different tourist, real estate, demographic and economic factors; and lastly, it identifies the social impacts that majorly influence the negative perception among residents in every neighbourhood.

This study applies quantitative and qualitative techniques to a selection of five neighbourhoods of Barcelona. First, the character of the neighbourhoods was analysed, and external statistical information was later provided to understand the state and evolution of the factors that shape perceptions of tourism. Secondly, representatives of the community movements were interviewed in-depth. This consecutive qualitative approach enabled the comprehension of how these factors shape the residents’ perception.

The results showed that residents generally shared similar perceptions despite variations among neighbourhoods. Perceived negative effects included not only the most direct consequences of tourism such as anti-social behaviour and congestion of public spaces but also indirect ones such as population displacement and the weakening of social structures.

This study’s innovation lies in linking objective statistical data that describe the reality of a tourist neighbourhood (housing prices, number of available beds, family income, etc.), to the subjective perceptions of its residents. Thus, it is possible to identify the perceived impacts of tourism (which have an impact on the local population’s satisfaction), and relate these to the true evolution of tourism variables in the neighbourhood. This contrasted reading between perception and reality is important for future initiatives for the regulation of tourism in the city.

The purpose of this paper is to investigate the link between childhood neighbourhood ethnic composition and short- and long-run economic outcomes of second-generation immigrants and natives in Sweden.

The author uses Swedish longitudinal register data and apply regression analysis methods to investigate the correlation between three ethnic neighbourhood variables(share of immigrants, share of immigrants with the same ethnic background and share of immigrants with other descent) in childhood with short- and long-run economic outcomes (earnings, unemployment, reliance on social assistance and educational attainment).

The results show that second-generation immigrants raised in immigrant-dense neighbourhoods have a lower probability to continue to higher education, whereas, their earnings, unemployment and social assistance tendencies are unaffected. On the contrary, natives’ earnings and educational attainment are negatively correlated with, and the probability of social assistance and unemployment are positively associated with a high immigrant concentration. Moreover, the social assistance and unemployment of non-Nordic second-generation immigrants appears to be negatively correlated with the neighbourhood share of co-ethnics and positively correlated with the neighbourhood proportion of other ethnic groups. Overall, the author finds that the results are very similar in the short and long run.

This paper expands the literature on children and ethnic segregation and in contrast to earlier research in this context, it focuses on second-generation immigrants and their performance in comparison to natives. This study contributes to this research area by investigating a large variety of outcomes, looking at both immigrant, own ethnic group and other ethnic group concentration and including both short- and long-run correlations.