Generalised damage identification scheme via sparse representation

Structures inevitably deteriorate during their service lives. Therefore, the methods capable of identifying and assessing various damages in a structure timely and accurately have drawn increasing attention. From a broader perspective, structural damage identification problem can be regarded as a pattern recognition problem by using sparse representation techniques. The unknown signal/feature from a damaged structure can be associated to a known type of signal/feature in a “dictionary”, leading to damage identification. From this new angle, an innovative damage identification scheme has been proposed by the authors. In this paper, two important techniques of this scheme are further discussed, namely the construction of dictionary and the choice of parameters. The numerical simulated soil-pipe system is used for verifying the performance of the proposed method. The results demonstrate that this damage identification scheme will be a promising tool for structural health monitoring.

K-Center: an approach on the multi-source identification of information diffusion

The global diffusion of epidemics, computer viruses, and rumors causes great damage to our society. It is critical to identify the diffusion sources and timely quarantine them. However, most methods proposed so far are unsuitable for diffusion with multiple sources because of the high computational cost and the complex spatiotemporal diffusion processes. In this paper, based on the knowledge of infected nodes and their connections, we propose a novel method to identify multiple diffusion sources, which can address three main issues in this area: 1) how many sources are there? 2) where did the diffusion emerge? and 3) when did the diffusion break out? We first derive an optimization formulation for multi-source identification problem. This is based on altering the original network into a new network concerning two key elements: 1) propagation probability and 2) the number of hops between nodes. Experiments demonstrate that the altered network can accurately reflect the complex diffusion processes with multiple sources. Second, we derive a fast method to optimize the formulation. It has been proved that the proposed method is convergent and the computational complexity is O(mn log α) , where α = α (m,n) is the slowly growing inverse-Ackermann function, n is the number of infected nodes, and m is the number of edges connecting them. Finally, we introduce an efficient algorithm to estimate the spreading time and the number of diffusion sources. To evaluate the proposed method, we compare the proposed method with many competing methods in various real-world network topologies. Our method shows significant advantages in the estimation of multiple sources and the prediction of spreading time.

Negative perceptions about self-control and identification with gender-role stereotypes related to binge eating, problem drinking, and to co-morbidity among adolescents

Purpose
To examine the role of both positive and negative styles of self-control, and gender-role stereotypes in binge eating and problem drinking.

Method
Participants were 428 adolescent boys and 555 girls from predominantly Anglo-Australian backgrounds who attended regional state schools in New South Wales, Australia. Students completed standardized questionnaires that assessed problem drinking, binge eating, self-control styles, and identification with gender-role stereotypes. ANOVA and post hoc Tukey tests were conducted to examine differences among adolescents who reported problems in binge eating, drinking, and both domains.

Results
Adolescents who reported eating and drinking problems also reported a high negative and a low positive sense of self-control coupled with self-identification with the traits that typically describe negative dimensions of gender-role stereotypes. Regardless of gender, problem drinking was mainly related to traits of negative masculinity (bossy, noisy aggressive, etc.) whereas binge eating was mainly related to negative femininity (shy, needs approval from others, etc.). Participants who reported eating and drinking symptoms recorded low scores on positive control, high scores on negative control, and also high scores on the negative dimensions of masculinity and femininity.

Summary
A negative and passive style of self-control coupled with an identification with negative dimensions of gender summarizes the type of self-regulation that is implicated in both binge eating and problem drinking, and co-morbid symptoms. There is a need for interventions working toward a more balanced gender self-concept and a positive sense of self-control.

Identification of general and double aggregation operators using monotone smoothing

Aggregation operators model various operations on fuzzy sets, such as conjunction, disjunction and averaging. Recently double aggregation operators have been introduced; they model multistep aggregation process. The choice of aggregation operators depends on the particular problem, and can be done by fitting the operator to empirical data. We examine fitting general aggregation operators by using a new method of monotone Lipschitz smoothing. We study various boundary conditions and constraints which determine specific types of aggregation.

Identification of general aggregation operators by Lipschitz approximation

Aggregation operators model various operations on fuzzy sets, such as conjunction, disjunction and aver aging. The choice of aggregation operators suitable for a particular problem is frequently done by fitting the parameters of the operator to the observed data. This paper examines fitting general aggregation operators by using a new method of Lipschitz approximation.

Blind identification of FIR MIMO channels by decorrelating subchannels

We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel to be identifiable up to a scaling and permutation using the second-order statistics of the channel output. This condition is that the channel matrix is irreducible (but not necessarily column-reduced), and the input signals are mutually uncorrelated and of distinct power spectra. We also show that this condition is necessary in the sense that no single part of the condition can be further weakened without another part being strengthened. While the above condition is a strong result that sets a fundamental limit of blind identification, there does not yet exist a working algorithm under that condition. In the second part of this paper, we show that a method called blind identification via decorrelating subchannels (BIDS) can uniquely identify an FIR MIMO channel if a) the channel matrix is nonsingular (almost everywhere) and column-wise coprime and b) the input signals are mutually uncorrelated and of sufficiently diverse power spectra. The BIDS method requires a weaker condition on the channel matrix than that required by most existing methods for the same problem.

Students perceptions of learning outcomes from group-based, problem-based teaching and learning activities

Objective: A study aimed at exploring the variation in perceptions of learning outcomes reported by undergraduate nursing students enrolled in a problem-based learning subject in a pre-registration Bachelor of Nursing course (BN).
Method: Students were asked to respond to four open-ended questions which focussed on their learning outcomes in the different teaching/learning modalities of the subject. Data were analysed in two phases using a modified phenomenographic analysis. In the first phase a set of categories of description were developed from the student responses to questions related to the learning modalities. In the second phase the individual responses were classified in terms of the categories. Finally, correlations between the learning modalities were identified. In this paper the approach to analysis, the process of category identification and the correlations between the learning modalities will be described and the implications for further research and teaching will be discussed.
Results: The findings indicated that there were two distinct groups of student responses. Inward focussed students who described outcomes in terms of their own learning and students whose focus was outward i.e. describing learning in terms of patient care and how learning relates to that care. Another important result shows the relationship between the learning modalities and outcomes. From the students' perspective, the most sophisticated outcomes of the lectures and laboratories were ideas and skills to be used and applied in clinical settings. Whereas, the group-based activities in which clinical problems were presented to the students in the form of Situation Improvement Packages (SIPS) focussed their attention on the clinical setting which constituted a preparation for the realities of clinical practice.
Conclusion: The findings from this study indicate that students perceive their learning in the group based teaching/learning modality (SIPS) as effective in focussing them on the reality of their role in the clinical practice environment while lectures and laboratories provided the skills and knowledge required for this setting.

Sex-role traits and the comorbidity of symptoms of disordered eating and problem drinking

The symptoms of problem drinking and disordered eating were studied independently in relation to sex-role traits and also for evidence of comorbidity in a student sample of 217 women. The participants completed surveys that assessed positive and negative sex-role traits, reported drinking levels, alcohol dependence, problem drinking, bulimic symptoms, dietary restraint, and drive for thinness. Eating symptoms were related to both the negative and positive traits of Femininity, but self-descriptions involving negative traits (passivity, dependence, unassertiveness, etc.) showed the strongest relationship. High scores on identification with the traits typically labelled as Masculinity were related to drinking but there was an important difference between drinking per se (which was related to Positive Masculinity) and drinking found to be associated with drinking problems, which was related to Negative Masculinity (aggression, showing-off, rudeness, etc.). Feminine traits were also related to drinking. Low identification with the traits of Negative Femininity was associated with non-problem drinking, whereas low identification with the traits of Positive Femininity were associated with problem-related drinking. Young women who displayed comorbid symptoms described themselves by a high identification with the traits of both Negative Masculinity and Negative Femininity. It was argued that comorbidity reveals a more extreme form of the sex-role conflict previously described in relation to disordered control over both eating and drinking when considered independently.

Problem and solution trees : a practical approach for identifying potential interventions to improve population nutrition

Population nutrition problems have a diversity of contributory factors and, ideally, multi-sectoral solutions should be developed by the relevant stakeholders, based on a common understanding of these factors. The problem and solution tree approach is a participatory process of working through the layers of determinants and then developing potential interventions for a specific issue, using the available data and expertise. We tailored this approach for non-communicable disease-related nutrition problems in Pacific Islands and applied it in several countries. The process led to the identification of a considerable range of determinants of unhealthy diets and potential interventions to improve the situation. This practical approach also offered the additional benefit of developing stakeholder awareness in the issues. Problem trees are a relatively simple tool to implement, easy to adapt to differing needs, can generate a wealth of information and can be more widely used.

A brain inspired approach for multi-view patterns identification

Biologically human brain processes information in both uniimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is
demonstrated and discussed with some experimental results.

Hierarchical rule generalisation for speaker identification in fiction books

This paper presents a hierarchical pattern matching and generalisation technique which is applied to the problem of locating the correct speaker of quoted speech found in fiction books. Patterns from a training set are generalised to create a small number of rules, which can be used to identify items of interest within the text. The pattern matching technique is applied to finding the Speech-Verb, Actor and Speaker of quotes found in ction books. The technique performs well over the training data, resulting in rule-sets many times smaller than the training set, but providing very high accuracy. While the rule-set generalised from one book is less effective when applied to different books than an approach based on hand coded heuristics, performance is comparable when testing on data closely related to the training set.

Spectral element model updating for damage identification using clonal selection algorithm

A spectral element model updating procedure is presented to identify damage in a structure using Guided wave propagation results. Two damage spectral elements (DSE1 and DSE2) are developed to model the local (cracks in reinforcement bar) and global (debonding between reinforcement bar and concrete) damage in one-dimensional homogeneous and composite waveguide, respectively. Transfer matrix method is adopted to assemble the stiffness matrix of multiple spectral elements. In order to solve the inverse problem, clonal selection algorithm is used for the optimization calculations. Two displacement-based functions and two frequency-based functions are used as objective functions in this study. Numerical simulations of wave propagation in a bare steel bar and in a reinforcement bar without and with various assumed damage scenarios are carried out. Numerically simulated data are then used to identify local and global damage of the steel rebar and the concrete-steel interface using the proposed method. Results show that local damage is easy to be identified by using any considered objective function with the proposed method while only using the wavelet energy-based objective function gives reliable identification of global damage. The method is then extended to identify multiple damages in a structure. To further verify the proposed method, experiments of wave propagation in a rectangular steel bar before and after damage are conducted. The proposed method is used to update the structural model for damage identification. The results demonstrate the capability of the proposed method in identifying cracks in steel bars based on measured wave propagation data.

Multiple identification and risks : examination of peer factors across multiracial and single-race youth

Multiracial youth are thought to be more vulnerable to peer-related risk factors than are single-race youth. However, there have been surprisingly few well-designed studies on this topic. This study empirically investigated the extent to which multiracial youth are at higher risk for peer influenced problem behavior. Data are from a representative and longitudinal sample of youth from Washington State (N = 1,760, mean age = 14.13, 50.9% girls). Of those in the sample, 225 youth self-identified as multiracial (12.8%), 1,259 as White (71.5%), 152 as Latino (8.6%), and 124 as Asian American (7.1%). Results show that multiracial youth have higher rates of violence and alcohol use than Whites and more marijuana use than Asian Americans. Higher levels of socioeconomic disadvantage and single-parent family status partly explained the higher rates of problem behaviors among multiracial youth. Peer risk factors of substance-using or antisocial friends were higher for multiracial youth than Whites, even after socioeconomic variables were accounted for, demonstrating a higher rate of peer risks among multiracial youth. The number of substance-using friends was the most consistently significant correlate and predictor of problems and was highest among multiracial youth. However, interaction tests did not provide consistent evidence of a stronger influence of peer risks among multiracial youth. Findings underscore the importance of a differentiated understanding of vulnerability in order to better target prevention and intervention efforts as well as the need for further research that can help identify and explain the unique experiences and vulnerabilities of multiracial youth.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

A monotonicity index for the monotone fuzzy modeling problem

In this paper, the problem of maintaining the (global) monotonicity and local monotonicity properties between the input(s) and the output of an FIS model is addressed. This is known as the monotone fuzzy modeling problem. In our previous work, this problem has been tackled by developing some mathematical conditions for an FIS model to observe the monotonicity property. These mathematical conditions are used as a set of governing equations for undertaking FIS modeling problems, and have been extended to some advanced FIS modeling techniques. Here, we examine an alternative to the monotone fuzzy modeling problem by introducing a monotonicity index. The monotonicity index is employed as an approximate indicator to measure the fulfillment of an FIS model to the monotonicity property. It allows the FIS model to be constructed using an optimization method, or be tuned to achieve a better performance, without knowing the exact mathematical conditions of the FIS model to satisfy the monotonicity property. Besides, the monotonicity index can be extended to FIS modeling that involves the local monotonicity problem. We also analyze the relationship between the FIS model and its monotonicity property fulfillment, as well as derived mathematical conditions, using the Monte Carlo method.