Remaining useful life prediction using elliptical basis function network and Markov Chain

This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.

A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Evaluation of public hospital performance in Khanh Hoa Province : Vietnam in connection with patient and staff satisfaction surveys

This research aimed to develop a framework for performance evaluation of public hospitals in Vietnam that is culturally, socially, and politically appropriate. The research included both qualitative and quantitative methods and identified and validated novel instruments to measure patient satisfaction and job satisfaction of hospital staff and to determine a set of hospital indicators that reflect the quality of hospital performance. New models for understanding the determinants of patient and staff satisfaction were developed along with a new performance indicator framework for hospital performance. These instruments will now be applied to the evaluation of hospital services in Khanh Hoa Province, permitting longer term evaluation of their effectiveness in changing system wide performance and satisfaction.

Searching for a feminist voice : film festivals and negotiating the tension between expectation and intent

International Film Festivals play a vital role in shaping filmmakers’ careers. This paper presents some initial findings from a current major research project, highlighting the significance of particular festival programming of emerging female directors from developing nations. Some filmmakers showcased at festivals actively privilege the voices of women in their films as a means of commenting on pressing cultural and political issues. Ironically, other filmmakers do not subscribe to the label of “feminist” or “woman filmmaker”, even if their respective films represent a strongly coded woman’s point of view. Tensions also arise inevitably when scrutinising women filmmakers from developing nations within a first world film festival context. The expectations of the researcher, the festival, film critics and audiences inevitably must negotiate with the original intentions of the filmmaker. This paper explores the significance of women filmmakers in attendance at the Brisbane International Film Festival (2009) and the International Film Festival Rotterdam (2010).

On the optimality of sample-based estimates of the expectation of the empirical minimizer

We study sample-based estimates of the expectation of the function produced by the empirical minimization algorithm. We investigate the extent to which one can estimate the rate of convergence of the empirical minimizer in a data dependent manner. We establish three main results. First, we provide an algorithm that upper bounds the expectation of the empirical minimizer in a completely data-dependent manner. This bound is based on a structural result due to Bartlett and Mendelson, which relates expectations to sample averages. Second, we show that these structural upper bounds can be loose, compared to previous bounds. In particular, we demonstrate a class for which the expectation of the empirical minimizer decreases as O(1/n) for sample size n, although the upper bound based on structural properties is Ω(1). Third, we show that this looseness of the bound is inevitable: we present an example that shows that a sharp bound cannot be universally recovered from empirical data.

Aerial SLAM with a single camera using visual expectation

Micro aerial vehicles (MAVs) are a rapidly growing area of research and development in robotics. For autonomous robot operations, localization has typically been calculated using GPS, external camera arrays, or onboard range or vision sensing. In cluttered indoor or outdoor environments, onboard sensing is the only viable option. In this paper we present an appearance-based approach to visual SLAM on a flying MAV using only low quality vision. Our approach consists of a visual place recognition algorithm that operates on 1000 pixel images, a lightweight visual odometry algorithm, and a visual expectation algorithm that improves the recall of place sequences and the precision with which they are recalled as the robot flies along a similar path. Using data gathered from outdoor datasets, we show that the system is able to perform visual recognition with low quality, intermittent visual sensory data. By combining the visual algorithms with the RatSLAM system, we also demonstrate how the algorithms enable successful SLAM.

Improving recall in appearance-based visual SLAM using visual expectation

In this paper, we present a new algorithm for boosting visual template recall performance through a process of visual expectation. Visual expectation dynamically modifies the recognition thresholds of learnt visual templates based on recently matched templates, improving the recall of sequences of familiar places while keeping precision high, without any feedback from a mapping backend. We demonstrate the performance benefits of visual expectation using two 17 kilometer datasets gathered in an outdoor environment at two times separated by three weeks. The visual expectation algorithm provides up to a 100% improvement in recall. We also combine the visual expectation algorithm with the RatSLAM SLAM system and show how the algorithm enables successful mapping

Supply chain management systems benefits : the expectation-confirmation theory perspective

Organizations adopt a Supply Chain Management System (SCMS) expecting benefits to the organization and its functions. However, organizations are facing mounting challenges to realizing benefits through SCMS. Studies suggest a growing dissatisfaction among client organizations due to an increasing gap between expectations and realization of SCMS benefits. Further, reflecting the Enterprise System studies such as Seddon et al. (2010), SCMS benefits are also expected to flow to the organization throughout its lifecycle rather than being realized all at once. This research therefore proposes to derive a lifecycle-wide understanding of SCMS benefits and realization to derive a benefit expectation management framework to attain the full potential of an SCMS. The primary research question of this study is: How can client organizations better manage their benefit expectations of SCM systems? The specific research goals of the current study include: (1) to better understand the misalignment of received and expected benefits of SCM systems; (2) to identify the key factors influencing SCM system expectations and to develop a framework to manage SCMS benefits; (3) to explore how organizational satisfaction is influenced by the lack of SCMS benefit confirmation; and (4) to explore how to improve the realization of SCM system benefits. Expectation-Confirmation Theory (ECT) provides the theoretical underpinning for this study. ECT has been widely used in the consumer behavior literature to study customer satisfaction, post-purchase behavior and service marketing in general. Recently, ECT has been extended into Information Systems (IS) research focusing on individual user satisfaction and IS continuance. However, only a handful of studies have employed ECT to study organizational satisfaction on large-scale IS. The current study will enrich the research stream by extending ECT into organizational-level analysis and verifying the preliminary findings of relevant works by Staples et al. (2002), Nevo and Chan (2007) and Nevo and Wade (2007). Moreover, this study will go further trying to operationalize the constructs of ECT into the context of SCMS. The empirical findings of the study commence with a content analysis, through which 41 vendor reports and academic reports are analyzed yielding sixty expected benefits of SCMS. Then, the expected benefits are compared with the benefits realized at a case organization in the Fast Moving Consumer Goods industry sector that had implemented a SAP Supply Chain Management System seven years earlier. The study develops an SCMS Benefit Expectation Management (SCMS-BEM) Framework. The comparison of benefit expectations and confirmations highlights that, while certain benefits are realized earlier in the lifecycle, other benefits could take almost a decade to realize. Further analysis and discussion on how the developed SCMS-BEM Framework influences ECT when applied in SCMS was also conducted. It is recommended that when establishing their expectations of the SCMS, clients should remember that confirmation of these expectations will have a long lifecycle, as shown in the different time periods in the SCMS-BEM Framework. Moreover, the SCMS-BEM Framework will allow organizations to maintain high levels of satisfaction through careful mitigation and confirming expectations based on the lifecycle phase. In addition, the study reveals that different stakeholder groups have different expectations of the same SCMS. The perspective of multiple stakeholders has significant implications for the application of ECT in the SCMS context. When forming expectations of the SCMS, the collection of organizational benefits of SCMS should represent the perceptions of all stakeholder groups. The same mechanism should be employed in the measurements of received SCMS benefits. Moreover, for SCMS, there exists interdependence of the satisfaction among the various stakeholders. The satisfaction of decision-makers or the authorized staff is not only driven by their own expectation confirmation level, it is also influenced by the confirmation level of other stakeholders‘ expectations in the organization. Satisfaction from any one particular stakeholder group can not reflect the true satisfaction of the client organization. Furthermore, it is inferred from the SCMS-BEM Framework that organizations should place emphasis on the viewpoints of the operational and management staff when evaluating the benefits of SCMS in the short and middle term. At the same time, organizations should be placing more attention on the perspectives of strategic staff when evaluating the performance of the SCMS in the long term.

Supply chain benefits expectation management framework

Organizations invest heavily in Supply Chain Management Systems expecting the benefits promised by the software vendors and the implementation partners. However, both academic and industry reports suggest that there is growing dissatisfaction among client organizations due to an increasing gap in benefits purported by the software vendors and benefits realised by the client. In order to better manage expectations of the client organization, this study proposes a Benefit Expectation Management Framework for Supply Chain Management Systems, based on Expectation-Confirmation Theory. This study derives 60 expected benefits of Supply Chain Management Systems through 41 vendor-reported customer stories and academic papers. Through comparing those benefits with the received benefits by a case organization that has implemented SAP Supply Chain Management Systems for seven years, two salient factors – long timetable and multiple stakeholders – have been identified as the controlling factors affecting the confirmation level of Supply Chain Management System expectations and further impacting the satisfaction of a client organization. The case study also highlights the likely causes for realized benefits and enduring issues in relation to the Supply Chain Management Systems.

Relationship between expectation management and client retention in online Cognitive Behavioural Therapy

Background Engaging clients from the outset of psychotherapy is important for therapeutic success. However, there is little research evaluating therapists’ initial attempts to engage clients. This article reports retrospective analysis of data from a trial of online Cognitive Behavioural Therapy (CBT) for depression. Qualitative and quantitative methods were used to evaluate how therapists manage clients’ expectations at the outset of therapy and its relationship with client retention in the therapeutic intervention. Aims To develop a system to codify expectation management in initial sessions of online CBT and evaluate its relationship with retention. Method Initial qualitative research using conversation analysis identified three different communication practices used by therapists at the start of first sessions: no expectation management, some expectation management, and comprehensive expectation management. These findings were developed into a coding scheme that enabled substantial inter-rater agreement (weighted Kappa = 0.78; 95% CI: 0.52 to 0.94) and was applied to all trial data. Results Adjusting for a range of client variables, primary analysis of data from 147 clients found comprehensive expectation management was associated with clients remaining in therapy for 1.4 sessions longer than those who received no expectation management (95% CI: -0.2 to 3.0). This finding was supported by a sensitivity analysis including an additional 21 clients (1.6 sessions, 95% CI: 0.2 to 3.1). Conclusions Using a combination of qualitative and quantitative methods, this study suggests a relationship between expectation management and client retention in online CBT for depression, which has implications for professional practice. A larger prospective study would enable a more precise estimate of retention.

Electrophysiological responses to violations of expectation from eye gaze and arrow cues

Isolating processes within the brain that are specific to human behavior is a key goal for social neuroscience. The current research was an attempt to test whether recent findings of enhanced negative ERPs in response to unexpected human gaze are unique to eye gaze stimuli by comparing the effects of gaze cues with the effects of an arrow cue. ERPs were recorded while participants (N¼30) observed a virtual actor or an arrow that gazed (or pointed) either toward (object congruent) or away from (object incongruent) a flashing checkerboard. An enhanced negative ERP (N300) in response to object incongruent compared to object congruent trials was recorded for both eye gaze and arrow stimuli. The findings are interpreted as reflecting a domain general mechanism for detecting unexpected events.

The influence of injury cause, contact-sport participation, and personal knowledge on expectation of outcome from mild traumatic brain injury

Objective: This study investigated the influence of injury cause, contact-sport participation, and prior knowledge of mild traumatic brain injury (mTBI) on injury beliefs and chronic symptom expectations of mTBI. Method: A total of 185 non-contact-sport players (non-CSPs) and 59 contact-sport players (CSPs) with no history of mTBI were randomly allocated to one of two conditions in which they read either a vignette depicting a sport-related mTBI (mTBIsport) or a motor-vehicle-accident-related mTBI (mTBIMVA). The vignettes were otherwise standardized to convey the same injury parameters (e.g., duration of loss of consciousness). After reading a vignette, participants reported their injury beliefs (i.e., perceptions of injury undesirability, chronicity, and consequences) and their expectations of chronic postconcussion syndrome (PCS) and posttraumatic stress disorder (PTSD) symptoms. Results: Non-CSPs held significantly more negative beliefs and expected greater PTSD symptomatology and greater PCS affective symptomatology from an mTBIMVA vignette thann mTBIsport vignette, but this difference was not found for CSPs. Unlike CSPs, non-CSPs who personally knew someone who had sustained an mTBI expected significantly less PCS symptomatology than those who did not. Despite these different results for non-CSPs and CSPs, overall, contact-sport participation did not significantly affect injury beliefs and symptom expectations from an mTBIsport. Conclusions: Expectations of persistent problems after an mTBI are influenced by factors such as injury cause even when injury parameters are held constant. Personal knowledge of mTBI, but not contact sport participation, may account for some variability in mTBI beliefs and expectations. These factors require consideration when assessing mTBI outcome.