Tekoälyn hyödyntäminen ydinvoimalaitosvaatimusten analysoinnissa

Nuclear power plant projects are often characterized by two factors: they are time-consuming and capital-intensive. These current challenges include descriptive and non-harmonized requirements demanded in the nuclear power industry resulting in the adaptation to a new licensing domain being very data-intensive, laborious, and tardy. Furthermore, the sheer volume of these requirements also poses a challenge. Nevertheless, by utilizing artificial intelligence in the analysis of nuclear power plant requirements, licensing and engineering could be facilitated and errors reduced in the allocation of requirements. This Master’s thesis develops an algorithm capable of recognizing natural language to classify nuclear power plant requirements into predefined categories by utilizing supervised machine learning. The study was performed in close cooperation with an AI company, Selko Technologies Oy, being responsible for the development of the algorithm based on the classified set of requirements and the needs of Fortum. The algorithm consists of a nuclear power industry-specific language model involving a long short-term memory network, and a classifier based on a feedforward neural network. The language model and classifier were trained by using the YVL Guides issued by the Finnish Radiation and Nuclear Safety Authority (STUK). For training the classifier, a small selection of the requirements were classified according to the two-level predefined hierarchy. The algorithm was tested on the selected YVL Guides and a set of requirements issued by the Office for Nuclear Regulation in United Kingdom. The results include a predetermined requirements hierarchy, the content of the categories, natural language processing algorithm, requirements classified by both the experts and algorithm, and model accuracies in each test case. The accuracies of the classification tasks are promising indicating that the current methods are suitable for categorizing natural language as long as there is a qualified and sufficient amount of training data in place. The conclusions also suggest proceeding to research the capability of the models in other requirements analysis related tasks, such as atomizing long requirements and combining similar requirements into one.Ydinvoimalaitosprojektit ovat usein pitkäkestoisia ja pääomaintensiivisiä. Yhtenä projektien ominaisena haasteena voidaan pitää suurta määrää kuvailevia ja epäyhtenäisiä vaatimuksia. Lisäksi ydinvoimalaitosdesignin vieminen ja suunnittelun sopeuttaminen uuteen lisensiointiympäristöön vaatii paljon tiedonhallintaa. Lisäksi se on työlästä ja hidasta. Tekoälyn hyödyntäminen ydinvoimalaitosvaatimusten analysoimisessa voisi nopeuttaa lisensiointi- ja suunnitteluprosesseja, sekä vähentää virheitä vaatimusten allokoinnissa. Tässä diplomityössä on kehitetty luonnollisen kielen prosessointiin kykenevä algoritmi ydinvoimalaitosvaatimusten luokitteluun. Työssä vaatimukset on luokiteltu ennalta määrättyihin kategorioihin ohjattua koneoppimista hyödyntämällä. Tutkimus on tehty yhteistyössä tekoäly-yrityksen Selko Technologies Oy:n kanssa, joka on vastannut algoritmin kehittämisestä Fortumin toimittaman luokitellun vaatimusjoukon ja tarpeiden perusteella. Algoritmi koostuu ydinvoima-alan kielimallista ja luokittelijasta. Kielimalli pohjautuu pitkään lyhytaikaisen muistin verkkoon ja luokittelija myötäkytkettyyn neuroverkkoon. Kielimallin ja luokittelijan kouluttamiseen on käytetty Suomen säteily- ja ydinturvallisuusviranomaisen Säteilyturvakeskuksen (STUK) Ydinturvallisuusohjeita. Luokittelijan kouluttamista varten tietty osa vaatimuksista on kategorisoitu kaksitasoisen ennalta määritellyn hierarkian mukaisesti. Algoritmin testaukseen on käytetty sekä valittua Ydinturvallisuusohjeiden vaatimusjoukkoa että Yhdistyneiden kuningaskuntien ydinturvallisuusviranomaisen (ONR) yhtä vaatimusjoukkoa. Työn tuloksena syntyi ennalta määritetty vaatimushierarkia sekä luonnollista kieltä prosessoiva algoritmi. Lisäksi työssä määriteltiin, mitä asioita kuuluu eri vaatimusluokkiin. Määrittelyn jälkeen sekä asiantuntijat että algoritmi luokittelivat työssä käytetyn datan. Mallin tarkkuus ja käytettävyys pystyttiin testaamaan lopuksi testidatalla. Saadut tarkkuudet vaatimusten luokittelussa ovat lupaavia ja osoittavat, että nykyiset menetelmät soveltuvat hyvin luonnollisen kielen luokitteluun, mikäli vain koulutusdata on laadukasta ja sitä on riittävästi. Tutkimusta voitaisiin jatkaa kokeilemalla mallien soveltumista myös muissa vaatimusten analysointiin liittyvissä tehtävissä. Näitä ovat esimerkiksi pitkien vaatimusten pilkkominen lyhempiin ja selkeämmin määriteltyihin lauseisiin sekä samanlaisten vaatimusten yhdistäminen yhdeksi vaatimukseksi

2005 AAPP Monograph Series

The African American Professors Program (AAPP) at the University of South Carolina is proud to publish the fifth edition of its annual monograph series. The program recognizes the significance of offering its scholars avenue to engage actively in research and publish papers related thereto. Parallel with the publication of their refereed manuscripts is the opportunity to gain visibility among scholars throughout institutions worldwide. Scholars who have contributed manuscripts for this monograph are to be commended for adding this additional responsibility to their academic workload. Writing across disciplines adds to the intellectual diversity of these papers. From neophytes, relatively speaking, to an array of very experienced individuals, the chapters have been researched and comprehensively written. Founded in 1997 through the Department of Educational Leadership and Policies in the College of Education, AAPP was designed to address the underrepresentation of African American professors on college and university campuses. Its mission is to expand the pool of these professors in critical academic and research areas. Sponsored by the University of South Carolina, the W. K. Kellogg Foundation, and the South Carolina General Assembly, the program recruits doctoral students for disciplines in which African Americans currently are underrepresented among faculty in higher education. The continuation of this monograph series is seen as responding to a window of opportunity to be sensitive to an academic expectation of graduates as they pursue career placement and, at the same time, one that allows for the dissemination of AAPP products to a broader community. The importance of this monograph series has been voiced by one of our 2002 AAPP graduates, Dr. Shundele LaTjuan Dogan, a recent Administrative Fellow at Harvard University and now a Program Officer for the Southern Education Foundation, Atlanta, Georgia. Dr. Dogan wrote: One thing in particular that I want to thank you for is having the African American Professors Program scholars publish articles for the monograph. I have to admit that writing the articles seemed like extra work at the time. However, in my recent interview process, organizations have asked me for samples of my writing. Including an article from a published monograph helped to make my portfolio much more impressive. You were \u27right on target\u27 in having us do the monograph series. (MPP 2003 Monograph, p. xi) The African American Professors Program offers this 2005 publication as a contribution to its readership and hopes that you will be inspired by this select group of manuscripts. John McFadden, Ph.D. The Benjamin Elijah Mays Professor Director, African American Professors Program University of South Carolinahttps://scholarcommons.sc.edu/mcfadden_monographs/1007/thumbnail.jp

Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning

Transportation and power generation have historically relied upon Internal Combustion Engines (ICEs). However, because of environmental impact and inefficiency, considerable research has been devoted to improving their performance. Alternative fuels are necessary because of environmental concerns and the depletion of non-renewable fuel stocks. Biodiesel has the potential to reduce emissions and improve sustainability when compared to diesel fuel. Several researchers have examined using nanofluids to increase biodiesel performance in internal combustion engines. Due to their thermal and physical properties, nanoparticles in a host fluid improve engine combustion and efficiency. This comprehensive review examines three key areas for improving ICE efficiency: biodiesel as an alternative fuel, application of nanofluids, and artificial intelligence (AI)/machine learning (ML) integration. The integration of AI/ML in nanoparticle-infused biodiesel offers exciting possibilities for optimizing production processes, enhancing fuel properties, and improving engine performance. This article first discusses, the benefits of biodiesel concerning the environment and various difficulties associated with its usage. The review then explores the effects and characteristics of nanofluids in IC engines, aiming to know their impact on engine emissions and performance. After that, this review discusses the utilization of AI/ML techniques in enhancing the biodiesel-nanofluid combustion process. This article sheds light on the ongoing efforts to make ICE technology more environmentally friendly and energy-efficient by examining current research and emerging patterns in these fields. Finally, the review presents the challenges and future perspectives of the field, paving the way for future research and improvement

Question-driven text summarization with extractive-abstractive frameworks

Automatic Text Summarisation (ATS) is becoming increasingly important due to the exponential growth of textual content on the Internet. The primary goal of an ATS system is to generate a condensed version of the key aspects in the input document while minimizing redundancy. ATS approaches are extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) and then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate form and then constructs the summary using different sentences than the originals. The hybrid approach combines both the extractive and abstractive approaches. The query-based ATS selects the information that is most relevant to the initial search query. Question-driven ATS is a technique to produce concise and informative answers to specific questions using a document collection. In this thesis, a novel hybrid framework is proposed for question-driven ATS taking advantage of extractive and abstractive summarisation mechanisms. The framework consists of complementary modules that work together to generate an effective summary: (1) discovering appropriate non-redundant sentences as plausible answers using a multi-hop question answering system based on a Convolutional Neural Network (CNN), multi-head attention mechanism and reasoning process; and (2) a novel paraphrasing Generative Adversarial Network (GAN) model based on transformers rewrites the extracted sentences in an abstractive setup. In addition, a fusing mechanism is proposed for compressing the sentence pairs selected by a next sentence prediction model in the paraphrased summary. Extensive experiments on various datasets are performed, and the results show the model can outperform many question-driven and query-based baseline methods. The proposed model is adaptable to generate summaries for the questions in the closed domain and open domain. An online summariser demo is designed based on the proposed model for the industry use to process the technical text