313 research outputs found

    Deep Learning-Based Machinery Fault Diagnostics

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    This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

    Applications of Belief Functions in Business Decisions: A Review

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    This is the author's final draft. The publisher's official version is available from: .In this paper, we review recent applications of Dempster-Shafer theory (DST) of belief functions to auditing and business decision-making. We show how DST can better map uncertainties in the application domains than Bayesian theory of probabilities. We review the applications in auditing around three practical problems that challenge the effective application of DST, namely, hierarchical evidence, versatile evidence, and statistical evidence. We review the applications in other business decisions in two loose categories: judgment under ambiguity and business model combination. Finally, we show how the theory of linear belief functions, a new extension of DST, can provide an alternative solution to a wide range of business problems

    A New Residual Life Prediction Method for Complex Systems Based on Wiener Process and Evidential Reasoning

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    A new residual life prediction method for complex systems based on Wiener process and evidential reasoning is proposed to predict the residual life of complex systems effectively. Moreover, the better maintenance strategies and decision supports are provided. For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. The process of parameters estimation and the probability density function (PDF) of the residual life are deduced. To improve the accuracy of the residual life prediction results, the evidential reasoning (ER) is used to integrate the prediction results of Wiener process. Finally, a case study of gyroscope is examined to illustrate the feasibility and effectiveness of the proposed method, compared with fuzzy theory, which provides an important reference for the optimization of the reliability of complex systems and improvement

    Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

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    Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

    BIOMEDICAL ONTOLOGIES: EXAMINING ASPECTS OF INTEGRATION ACROSS BREAST CANCER KNOWLEDGE DOMAINS

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    The key ideas developed in this thesis lie at the intersection of epistemology, philosophy of molecular biology, medicine, and computer science. I examine how the epistemic and pragmatic needs of agents distributed across particular scientific disciplines influence the domain-specific reasoning, classification, and representation of breast cancer. The motivation to undertake an interdisciplinary approach, while addressing the problems of knowledge integration, originates in the peculiarity of the integrative endeavour of sciences that is fostered by information technologies and ontology engineering methods. I analyse what knowledge integration in this new field means and how it is possible to integrate diverse knowledge domains, such as clinical and molecular. I examine the extent and character of the integration achieved through the application of biomedical ontologies. While particular disciplines target certain aspects of breast cancer-related phenomena, biomedical ontologies target biomedical knowledge about phenomena that is often captured within diverse classificatory systems and domain-specific representations. In order to integrate dispersed pieces of knowledge, which is distributed across assorted research domains and knowledgebases, ontology engineers need to deal with the heterogeneity of terminological, conceptual, and practical aims that are not always shared among the domains. Accordingly, I analyse the specificities, similarities, and diversities across the clinical and biomedical domain conceptualisations and classifications of breast cancer. Instead of favouring a unifying approach to knowledge integration, my analysis shows that heterogeneous classifications and representations originate from different epistemic and pragmatic needs, each of which brings a fruitful insight into the problem. Thus, while embracing a pluralistic view on the ontologies that are capturing various aspects of knowledge, I argue that the resulting integration should be understood in terms of a coordinated social effort to bring knowledge together as needed and when needed, rather than in terms of a unity that represents domain-specific knowledge in a uniform manner. Furthermore, I characterise biomedical ontologies and knowledgebases as a novel socio-technological medium that allows representational interoperability across the domains. As an example, which also marks my own contribution to the collaborative efforts, I present an ontology for HER2+ breast cancer phenotypes that integrates clinical and molecular knowledge in an explicit way. Through this and a number of other examples, I specify how biomedical ontologies support a mutual enrichment of knowledge across the domains, thereby enabling the application of molecular knowledge into the clinics

    Supporting Large Scale Collaboration and Crowd-Based Investigation in Economics: A Computational Representation for Description and Simulation of Financial Models

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    Finance should be studied as a hard science, where scientific methods apply. When a trading strategy is proposed, the underlying model should be transparent and defined robustly to allow other researchers to understand and examine it thoroughly. Any reports on experimental results must allow other researchers to trace back to the original data and models that produced them. Like any hard sciences, results must be repeatable to allow researchers to collaborate and build upon each other’s results. Large-scale collaboration, when applying the steps of scientific investigation, is an efficient way to leverage crowd science to accelerate research in finance. Unfortunately, the current reality is far from that. Evidence shows that current methods of investigation in finance in most cases do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of financial decisions at all levels, from personal investment choices to overreaching global economic policies, rely on some variation of try-and-error and are mostly non-scientific by definition. We lack transparency for procedures and evidence, proper explanation of market events, predictability on effects, or identification of causes. There is no clear demarcation of what is inherently scientific, and as a consequence, the line between fake and true is blurred. In this research, we advocate the use of a next-generation investigative approach leveraging forces of human diversity, micro-specialized crowds, and proper computer-assisted control methods associated with accessibility, reproducibility, communication, and collaboration. This thesis is structured in three distinctive parts. The first part defines a set of very specific cognitive and non-cognitive enablers for crowd-based scientific investigation: methods of proof, large-scale collaboration, and a domain-specific computational representation. These enablers allow the application of procedures of structured scientific investigation powered by crowds, a “collective brain in which neurons are human collaborators”. The second part defines a specialized computational representation to allow proper controls and collaboration in large-scale in the field of economics. A computational representation is a role-based representation system based on facets, contributions, and constraints of data, and used to define concepts related to a specific domain of knowledge for crowd-based investigation. The third and last part performs an end-to-end investigation of a non-trivial problem in finance by measuring the actual performance of a momentum strategy in technical analysis, applying formal methods of investigation developed over the first and second part of this research

    Maintenance optimisation for systems with multi-dimensional degradation and imperfect inspections

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    In this paper, we develop a maintenance model for systems subjected to multiple correlated degradation processes, where a multivariate stochastic process is used to model the degradation processes, and the covariance matrix is employed to describe the interactions among the processes. The system is considered failed when any of its degradation features hits the pre-specified threshold. Due to the dormancy of degradation-based failures, inspection is implemented to detect the hidden failures. The failed systems are replaced upon inspection. We assume an imperfect inspection, in such a way that a failure can only be detected with a specific probability. Based on the degradation processes, system reliability is evaluated to serve as the foundation, followed by a maintenance model to reduce the economic losses. We provide theoretical boundaries of the cost-optimal inspection intervals, which are then integrated into the optimisation algorithm to relieve the computational burden. Finally, a fatigue crack propagation process is employed as an example to illustrate the effectiveness and robustness of the developed maintenance policy. Numerical results imply that the inspection inaccuracy contributes significantly to the operating cost and it is suggested that more effort should be paid to improve the inspection accuracy
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