A knowledge base system approach to inspection scheduling for fixed offshore platforms

In the offshore oil and gas industry in the UK, one of the most common forms of structure is the fixed steel jacket type of offshore platform. These are highly redundant structures subject to many random or uncertain factors. In particular, they are subject to uncertainties in the load distribution through the components, and to time-varying and cyclic loads leading to deterioration through fatigue. Operators are required to ensure the integrity of these structures by carrying out periodic inspections and repairing when necessary. Decisions on inspection, repair and maintenance (IRM) actions on structures involves making use of various tools and can be a complex problem. Traditionally, engineering judgement is employed to schedule inspections and deterministic analyses are used to confirm decisions. The use of structural reliability methods may lead to more rational scheduling of IRM actions. Applying structural reliability analysis to the production of rational inspection strategies, however, requires understanding the inspection procedure and making use of the appropriate information on inspection techniques. There are difficulties in collecting input data and the interpreted results need to be combined to form a rational global solution for the structure which takes into account practical constraints. The development of a knowledge base system (KBS) for reliability based inspection scheduling (RISC) provides a way of making use of complex quantitative objective analyses for scheduling. This thesis describes the development of a demonstrator RISC KBS. The general problems of knowledge representation and scheduling are discussed and schemes from Artificial Intelligence are proposed. Additionally, a system for automated inspection is described and its role in IRM of platforms is considered. A RISC System integrating suitable databases with fatigue fracture mechanics based reliability analysis within a KBS framework will enable operators to develop rational IRM scheduling strategies

Improving the Resilience of Wireless Sensor Networks Against Security Threats: A Survey and Open Research Issues

Wireless Sensor Network (WSN) technology has gained importance in recent years due to its various benefits, practicability and extensive utilization in diverse applications. The innovation helps to make real-time automation, monitoring, detecting and tracking much easier and more effective than previous technologies. However, as well as their benefits and enormous potential, WSNs are vulnerable to cyber-attacks. This paper is a systematic literature review of the security-related threats and vulnerabilities in WSNs. We review the safety of and threats to each WSN communication layer and then highlight the importance of trust and reputation, and the features related to these, to address the safety vulnerabilities. Finally, we highlight the open research areas which need to be addressed in WSNs to increase their flexibility against security threats

A Comparative Study Of The Effects Of Learning Style Prescriptions And/Or Modality-Based Instruction On The Spelling Achievement Of Fifth-Grade Students

Problem Learning style has been studied extensively across the United States since the early 1970s. Much research has involved Rita and Kenneth Dunns\u27 model and associated Learning Style Inventory. In 1991, Robert Zenhausern developed the Homework Disc software program which correlates with the Dunns\u27 work and yields learning style prescriptions for students. The use of these study strategies at the elementary level has not been broadly examined. Therefore, this study investigated the effects of prescriptions on spelling achievement of fifth-grade students. Method A total of 65 students (33 males, 32 females) in three intact groups participated in this study. The Control Group received instruction and studied in a traditional manner, an Instructional Group received modality-based instruction determined by preferences on the Learning Stvle Inventory. and an Individualized Group received similar instruction and independently applied prescription information. Four null hypotheses were formulated. The first three dealt with differences between pre- and posttests of the three groups and were analyzed using repeated measures analysis of variance and a priori tests. The final hypothesis concerned differences among adjusted posttest means of the three groups and was tested by both 3-way and 1-way analysis of covariance. Newman-Keuls tests were additionally done to identify the location of identified differences. Results 1. Modality-based instruction alone did not significantly increase spelling achievement. 2. Spelling achievement was significantly increased (p \u3c .05) when students independently applied learning style prescription information to completion of homework in addition to receiving modality-based instruction in the classroom. Conclusions Learning style, experience, and personality are intricately connected, making complete individualization in the classroom nearly impossible. Therefore, students should be taught how to capitalize on their own preferences in order to increase learning

A Survey on Energy Efficient Routing Protocols in Wireless Sensor Networks

Energy efficiency is one of the critical issues in the Wireless Sensor Networks (WSNs), since sensor devices are tiny and integrated with a limited capacity battery. In most of the advanced applications, WSNs operate in very harsh areas and not under supervision of human controls. Routing protocols play a significant role in energy balancing by incorporating the techniques that can reduce control overhead, proper data aggregation method and feasible path selection. It demands a unique requirement due to its frequent topology changes and distributive nature. One of the major concerns in the design of routing protocol in WSNs is efficient energy usage and prolonging Network lifetime. This paper mainly discusses different issues related to energy efficiency in routing protocols of all categories. It incorporates most recent routing protocols which improves the energy efficiency in various application environments. This paper also provides comprehensive details of each protocol which emphasize their principles and explore their advantages and limitations. These protocols belong to different classifications based on Network Structures, communication model, topology and QoS parameters. It also includes more relevant and prominent comparisons with all recent State-of-Art works

Funding of Higher Education in Tennessee: A Qualitative Study of the Perceptions of State Legislators and Higher Education Leaders

The purpose of this study was to identify issues that are considered important to the legislators and higher education leaders of Tennessee in making decisions that affect the funding of higher education. A further purpose was to identify actions that such individuals believe should be taken by higher education leaders to ensure that higher education is accountable and worthy of continued or increased financial support. Using a qualitative research design, interviews were held with 10 legislators and 6 higher education leaders selected in accordance with the concept of purposeful sampling. Legislative participants included five members from the Senate and five members from the House of Representatives. All participants served on either the Education Committee or Finance Ways and Means Committee within their chamber. Higher education leaders consisted of a university president, the President of the University of Tennessee System, Chancellor of the Tennessee Board of Regents, Executive Director of the Tennessee Higher Education Commission, Comptroller of the Treasury, and a member of the University of Tennessee Board of Trustees. Issues identified from the interviews were reduced to eight categories: (a) issues affecting higher education and (b) findings regarding the accountability of higher education. The issues category was divided into eight categories: (a) financial issues that was further subdivided into funding issues, accountability issues, capital expenditures, taxes, fees, and other general financial issues; (b) administrative structure and costs; (c) quality outcomes; (d) faculty issues; (e) technology; (f) program duplication; (g) relationship to K-12 education; and (h) other general issues. Issues that emerged related to accountability included the measurement of educational outcomes and the communication of those results to legislators and the public. Based on the findings of this study, three recommendations are offered: (1) a committee consisting of appropriate representatives should be established to study the issue of accountability and determine appropriate measurements that will provide relevant information; (2) leaders in higher education should make a concerted effort to improve communication with legislators and their staffs; and (3) those in higher education must improve their communication with the public

A Train Protection Logic Based on Topological Manifolds for Virtual Coupling

Virtual coupling is a promising innovation aimed at increasing railway capacity. Compared to current railway signaling systems, it allows two or more trains to run with reduced headway between them. However, such reduced headways are a challenge to safety. In this work we consider this challenge by formally describing and verifying an approach to virtual coupling. We propose a general modeling method based on topological manifolds to describe the protection logic for virtual coupling train control systems. We also describe the basic train control elements in topological terms and analyze the line condition of our virtual coupling logic. We establish that the line condition safety requirements and its representation as a manifold are equivalent and further provide a formal definition of the concept of a movement authority with manifold notations. This allows us to consider the dynamic behavior of trains and a series of theorems that establish the correctness of our protection logic for virtual coupling. Finally, we apply the presented methods to a case study. The results show that the proposed method provides a suitable way to realize a virtual coupling logic safely

The Impact of an Integrated Study Skills Program on University of La Verne Adult Undergraduates at Vandenberg Air Force Base and Naval Air Station North Island

The Problem: There have been a number of recent attempts to provide unique educational opportunities in learning strategies and study skills for adult students. A difficulty in removing the student from the classroom to provide this specialized training is that many students are unable to transfer the acquired skills back into a variety of different contexts. A second problem is removing faculty from the ongoing class and educating them in workshops that address learning strategies and study skills. Many faculty find it difficult to incorporate these skills while meeting the demands of fulfilling course content objectives. The Research: The purpose of this study was to assess the impact of an integrated study skills program on adult undergraduates attending courses through the University of La Verne at Vandenberg Air Force Base (VAFB) and Naval Air Station North Island (NASNI). The study attempted to discover whether three methodologies of an integrated study skills program differed on affective behavioral outcomes of intact groups. Data from a preliminary survey, Learning and Study Strategies Inventory (LASSI) and exit survey were analyzed suing a variety of statistical measures: frequency counts, multivariate analysis and qualitative interpretation. The Results: The academic self-concepts, or how one views oneself as a student, of business students were significantly higher than non-business students in Time Management, Information Processing, use of Support Aids, Self Testing, Motivation and Concentration in treatment groups A and B. Non-business students improved over business students in the control group. Students liked having the opportunity to sample learning strategies and study skills, but were divided on whether to have a film presentation or an instructor-led group. Faculty expressed an interest in students being exposed to learning strategies and study skills, but some faculty were concerned about integrating the study skills program into course curriculum

Incorporating sensor measurements using data assimilation and machine learning to improve the accuracy of thermal finite element models

Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2022.Complex systems are commonly encountered in engineering. Such systems have a degree of unpredictability, which can lead to undesirable results. The digital twin concept has been proposed as a method to obtain more information about the system so that its behaviour can be better predicted. To construct a digital twin of a system, a high-fidelity model that predicts the evolution of the system over time is required. Classically, such high fidelity models would be either physics-based or data-driven, though both of these approaches have disadvantages that may make them unsuitable for application in a digital twin. A hybrid model is a combination of physics-based and data-driven models that seeks to exploit the advantages of both approaches, and is a promising candidate for producing a model that is suitable for application in a digital twin. This work investigates the training of hybrid models of real engineering systems. It considers systems that are dynamic and that are partially observed. Physics-based models of these systems are available in the form of partial differential equations that are solved numerically using the finite element method. To reduce the computational cost associated with such models, surrogate models are constructed. The construction of surrogate models involves a dimensionality reduction step in the form of proper orthogonal decomposition (POD), as well as a prediction step that involves the training of a data-driven model to predict the evolution of the POD coefficients over time. Hybrid models are then trained using the surrogate models as their physics-based component. The training of hybrid models takes place using a combination of data assimilation and machine learning. The machine learning-data assimilation (ML-DA) algorithm that is documented in the literature is used, together with two algorithms proposed in this work, called the data assimilation-observation (DA-O) and per-step DA-O algorithms. It is the nature of the application of this methodology of training hybrid models that allows this work to make a contribution relative to the published literature. Previous uses of data assimilation in the training of hybrid models perform investigations using simplified problems that allow direct use of the physics-based model in the hybrid model. Meanwhile, the increased computational demands of the real engineering problem considered in this work necessitates the use of a surrogate model of the physics-based component of the hybrid model. Surrogate models have been previously applied in hybrid models constructed to solve engineering problems. However, these approaches do not apply naturally to applications such as digital twins where observations are continuously available. The use of data assimilation in this work allows it to address this shortcoming. The proposed methodology of training hybrid models is evaluated using two case studies. The first case study involves a thermal simulation model of a small section of the freeboard of a process converter. Surrogate models of the simulation model are constructed using different data-driven function approximation techniques, such as Gaussian process regression, Support Vector Machines (SVMs) and neural networks. These surrogate models are then used to train hybrid models using simulated observations and the DA-O, per-step DA-O and ML-DA algorithms. When 30 of the 29077 nodes of the simulation model are observed, the per-step DA-O algorithm produces the best hybrid model in terms of a root mean square error (RMSE) metric calculated using the analysis states estimated during data assimilation. The ML-DA algorithm which performed next best is, however, easier to apply to different numbers of observed nodes and is potentially less sensitive to observation noise. The ML-DA algorithm is subsequently used to investigate the effect of using different numbers of observed nodes. While there is a benefit to using a greater number of observed nodes, the trained hybrid models still outperform the physics-based model when as few as two of the 29077 nodes of the simulation model are observed. These results indicate that the training of hybrid models for sparsely observed systems is feasible. The second case study considered in this work involves a thermal half model of the process converter freeboard for which actual sensor observations are available. Surrogate models are again constructed using different data-driven function approximation techniques, and these are used in the training of hybrid models. Only the ML-DA algorithm is now used for the training of hybrid models. Simulated sensor observations are used at first to understand whether improvements in predictive performance that hybrid models make relative to physics-based models on the observed nodes extend to larger subsets of the nodes of the system. It is found that when the performance of the hybrid models is evaluated in terms of the RMSE calculated using analysis states estimated during data assimilation, it is possible that improvements in performance are made on the observed nodes but not on larger subsets of the nodes. When the RMSE is instead calculated using predictions of the evolution of the system over 30 time steps, the performance of the hybrid models on the observed nodes correlates with their performance on larger subsets of the nodes. When real observations are used to train hybrid models, the trained hybrid models improve on the performance of physics-based models on the observed nodes in terms of the RMSE calculated using analysis states and in terms of the RMSE calculated using 30 time step predictions. The improvement in performance on the latter could indicate that this improvement on the observed nodes extends to larger subsets of nodes of the system. There are, however, other possible explanations for this improvement.Centre for Asset Integrity ManagementUniversity of Pretoria Masters Research BursaryMechanical and Aeronautical EngineeringMEngUnrestricted (Mechanical Engineering

Oceanus.

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