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

Aerospace bibliography, fifth edition

Bibliography of references, periodicals, and educational materials related to space fligh

Encourage. Empowering People. Annual Report 2012

Peter Löscher, President of the Siemens Stiftung Board of Trustees, on behalf of the Board: Siemens Stiftung aims to contribute to positive changes in society with technical solutions, concrete concepts, and platforms for knowledge transfer. Cooperating with various stakeholders is a fundamental requirement for increasing the impact of its projects and anchoring them for the long term. For that reason, Siemens Stiftung seeks to cooperate with other foundations and non-governmental organizations as well as with government institutions, businesses, and the scientific community. Partnerships allow complementary approaches, skills, and resources to be bundled and sustainable programs to be developed. The previous fiscal year, in particular, delivers impressive examples of how such partnership models can increase the effectiveness of project work

SciTech News Volume 71, No. 2 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14 Reviews Sci-Tech Book News Reviews 16 Advertisements IEEE

SciTech News Volume 71, No. 3 (2017)

Columns and Reports From the Editor.........................3 Division News Science-Technology Division....5 Chemistry Division....................8 Conference Report, Marion E, Sparks Professional Development Award Recipient..9 Engineering Division................10 Engineering Division Award, Winners Reflect on their Conference Experience..15 Aerospace Section of the Engineering Division .....18 Architecture, Building Engineering, Construction, and Design Section of the Engineering Division................20 Reviews Sci-Tech Book News Reviews...22 Advertisements IEEE..........................................

Англійська мова для студентів електромеханічних спеціальностей

Навчальний посібник розрахований на студентів напряму підготовки 6.050702 Електромеханіка. Містить уроки, що структуровані за тематичними розділами, граматичний коментар, короткі англо-український і українсько- англійський словники та додатки, які спрямовані на закріплення загальних навичок володіння англійською мовою. Акцентований на ɨсобливості термінології, що застосовується у науково-технічній галузі, зокрема, в електромеханіці та виконання запропонованих завдань, що буде сприяти формуванню навичок перекладу з англійської та української мов, сприйняттю письмової та усної англійської мови, вмінню письмового викладення англійською мовою науково-технічних та інших текстів під час професійної діяльності, спілкуванню з професійних та загальних питань тощо

An analysis of the application of AI to the development of intelligent aids for flight crew tasks

This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research