Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Treatment of Choice or A Last Resort? A Review of Residential Mental Health Placements For Children and Adolescents

Residential treatment is often regarded as a treatment of ‘last resort’ and, increasingly, residential treatment programs are being asked to address the needs of very troubled children and adolescents. This paper is an effort to summarize what is currently known about the effects of residential treatment for children and adolescents. The review is organized into two sections: studies of the effectiveness of group home residential treatment and studies of the effectiveness of residential treatment delivered in residential treatment centres. In both areas, we attempt to identify trends within treatment, as well as patterns found in the literature that characterize post residential treatment adaptation. We also discuss several additional factors that appear to share a relationship with residential treatment outcomes crossing both short-term and long-term trends. We conclude our review with suggestions for future directions in residential treatment for children and adolescents

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

Validation of an Instrument for Assessing Conceptual Change with Respect to The Theory of Evolution By Secondary Biology Students

This pilot study evaluated the validity of a new quantitative, closed-response instrument for assessing student conceptual change regarding the theory of evolution. The instrument has two distinguishing design features. First, it is designed not only to gauge student mastery of the scientific model of evolution, but also to elicit a trio of deeply intuitive tendencies that are known to compromise many students’ understanding: the projection of intentional agency, teleological directionality, and immutable essences onto biological phenomena. Second, in addition to a section of conventional multiple choice questions, the instrument contains a series of items where students may simultaneously endorse both scientifically normative propositions and intuitively appealing yet unscientific propositions, without having to choose between them. These features allow for the hypothesized possibility that the three intuitions are partly innate, themselves products of cognitive evolution in our hominin ancestors, and thus may continue to inform students’ thinking even after instruction and conceptual change. The test was piloted with 340 high school students from diverse schools and communities. Confirmatory factor analysis and other statistical methods provided evidence that the instrument already has strong potential for validly distinguishing students who hold a correct scientific understanding from those who do not, but that revision and retesting are needed to render it valid for gauging students’ adherence to intuitive misconceptions. Ultimately the instrument holds promise as a tool for classroom intervention studies by conceptual change researchers, for diagnostic testing and data gathering by instructional leaders, and for provoking classroom dialogue and debate by science teachers

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies