An ebd-enabled design knowledge acquisition framework

Having enough knowledge and keeping it up to date enables designers to execute the design assignment effectively and gives them a competitive advantage in the design profession. Knowledge elicitation or acquisition is a crucial component of system design, particularly for tasks requiring transdisciplinary or multidisciplinary cooperation. In system design, extracting domain-specific information is exceedingly tricky for designers. This thesis presents three works that attempt to bridge the gap between designers and domain expertise. First, a systematic literature review on data-driven demand elicitation is given using the Environment-based Design (EBD) approach. This review address two research objectives: (i) to investigate the present state of computer-aided requirement knowledge elicitation in the domains of engineering; (ii) to integrate EBD methodology into the conventional literature review framework by providing a well-structured research question generation methodology. The second study describes a data-driven interview transcript analysis strategy that employs EBD environment analysis, unsupervised machine learning, and a range of natural language processing (NLP) approaches to assist designers and qualitative researchers in extracting needs when domain expertise is lacking. The second research proposes a transfer-learning method-based qualitative text analysis framework that aids researchers in extracting valuable knowledge from interview data for healthcare promotion decision-making. The third work is an EBD-enabled design lexical knowledge acquisition framework that automatically constructs a semantic network -- RomNet from an extensive collection of abstracts from engineering publications. Applying RomNet can improve the design information retrieval quality and communication between each party involved in a design project. To conclude, this thesis integrates artificial intelligence techniques, such as Natural Language Processing (NLP) methods, Machine Learning techniques, and rule-based systems to build a knowledge acquisition framework that supports manual, semi-automatic, and automatic extraction of design knowledge from different types of the textual data source

Koneoppimiskehys petrokemianteollisuuden sovelluksille

Machine learning has many potentially useful applications in process industry, for example in process monitoring and control. Continuously accumulating process data and the recent development in software and hardware that enable more advanced machine learning, are fulfilling the prerequisites of developing and deploying process automation integrated machine learning applications which improve existing functionalities or even implement artificial intelligence. In this master's thesis, a framework is designed and implemented on a proof-of-concept level, to enable easy acquisition of process data to be used with modern machine learning libraries, and to also enable scalable online deployment of the trained models. The literature part of the thesis concentrates on studying the current state and approaches for digital advisory systems for process operators, as a potential application to be developed on the machine learning framework. The literature study shows that the approaches for process operators' decision support tools have shifted from rule-based and knowledge-based methods to machine learning. However, no standard methods can be concluded, and most of the use cases are quite application-specific. In the developed machine learning framework, both commercial software and open source components with permissive licenses are used. Data is acquired over OPC UA and then processed in Python, which is currently almost the de facto standard language in data analytics. Microservice architecture with containerization is used in the online deployment, and in a qualitative evaluation, it proved to be a versatile and functional solution.Koneoppimisella voidaan osoittaa olevan useita hyödyllisiä käyttökohteita prosessiteollisuudessa, esimerkiksi prosessinohjaukseen liittyvissä sovelluksissa. Jatkuvasti kerääntyvä prosessidata ja toisaalta koneoppimiseen soveltuvien ohjelmistojen sekä myös laitteistojen viimeaikainen kehitys johtavat tilanteeseen, jossa prosessiautomaatioon liitettyjen koneoppimissovellusten avulla on mahdollista parantaa nykyisiä toiminnallisuuksia tai jopa toteuttaa tekoälysovelluksia. Tässä diplomityössä suunniteltiin ja toteutettiin prototyypin tasolla koneoppimiskehys, jonka avulla on helppo käyttää prosessidataa yhdessä nykyaikaisten koneoppimiskirjastojen kanssa. Kehys mahdollistaa myös koneopittujen mallien skaalautuvan käyttöönoton. Diplomityön kirjallisuusosa keskittyy prosessioperaattoreille tarkoitettujen digitaalisten avustajajärjestelmien nykytilaan ja toteutustapoihin, avustajajärjestelmän tai sen päätöstukijärjestelmän ollessa yksi mahdollinen koneoppimiskehyksen päälle rakennettava ohjelma. Kirjallisuustutkimuksen mukaan prosessioperaattorin päätöstukijärjestelmien taustalla olevat menetelmät ovat yhä useammin koneoppimiseen perustuvia, aiempien sääntö- ja tietämyskantoihin perustuvien menetelmien sijasta. Selkeitä yhdenmukaisia lähestymistapoja ei kuitenkaan ole helposti pääteltävissä kirjallisuuden perusteella. Lisäksi useimmat tapausesimerkit ovat sovellettavissa vain kyseisissä erikoistapauksissa. Kehitetyssä koneoppimiskehyksessä on käytetty sekä kaupallisia että avoimen lähdekoodin komponentteja. Prosessidata haetaan OPC UA -protokollan avulla, ja sitä on mahdollista käsitellä Python-kielellä, josta on muodostunut lähes de facto -standardi data-analytiikassa. Kehyksen käyttöönottokomponentit perustuvat mikropalveluarkkitehtuuriin ja konttiteknologiaan, jotka osoittautuivat laadullisessa testauksessa monipuoliseksi ja toimivaksi toteutustavaksi

A method and application of machine learning in design

This thesis addresses the issue of developing machine learning techniques for the acquisition and organization of design knowledge to be used in knowledge-based design systems. It presents a general method of developing machine learning tools in the design domain. An identification tree is introduced to distinguish different approaches and strategies of machine learning in design. Three existing approaches are identified: the knowledge-oriented, the learner-oriented, and the design-oriented approach. The learner-oriented approach is critical, which focuses on the development of new machine learning tools for design knowledge acquisition. Four strategies that are suitable for this approach are: specialization, generalization, integration and exploration. A general method, called MLDS (Machine Learning in Design with 5 steps), of developing machine learning techniques in the design domain is presented. It consists of the following steps: 1) identify source data and target knowledge; 2) determine source representation and target representation; 3) identify the background knowledge available; 4) identify the features of data, knowledge and domain; and 5) develop (specialize, generalize, integrate or explore) a machine learning tool. The method is elaborated step by step and the dependencies between the components are illustrated with a corresponding framework. To assist in characterising the data, knowledge and domain, a set of formal measures are introduced. They include density of dataset, size of description space, homogeneity of dataset, complexity of domain, difficulty of domain, stability of domain, and usage of knowledge. Design knowledge is partitioned into two main types: empirical and causal. Empirical knowledge is modelled as empirical associations in categories of design attributes or empirical mappings between these meaningful categories. Eight types of empirical mappings are distinguished. Among them the mappings from one multiple dimensional space to another are recognized as the most important for both knowledge-based design systems and machine learning in design. The MLDS method is applied to the preliminary design of a learning model for the integration of design cases and design prototypes. Both source and target representations use the framework of design prototypes. The function-behaviour-structure categorization of design prototypes is used as background knowledge to improve both supervised and unsupervised learning in this task. Many-to-many mappings and time- or order-dependent data are discovered as the most important characteristics of the design domain for machine learning. Multiple attribute prediction and the capture of design concept ‘drift’ are identified as challenging tasks for machine learning in design. After the possibilities and limitations of solving the problem by modifying existing learning methods (both supervised and unsupervised) are considered, a learning model is created by integrating several learning techniques. The basic scheme of this model is that of goal-driven concept formation, which consists of flexible categorization, extensive generalization, temporary suspension, and cognitively-based sequence prediction in design. The learning process is described as follows: each time one category of attributes is treated as the predictive feature set and the remaining as the predicted feature set; a conceptual hierarchy or decision tree is constructed incrementally according the predictive features of design cases (but statistical information is generalized with both feature sets); whenever the predictive or the predicted feature set of a node becomes homogeneous, the construction process at that branch will temporarily suspend until a new case arrives and breaks this homogeneity; frequency—based prediction at indeterminate nodes is replaced with a cognitively-based sequence prediction, which allows the more recent cases to have stronger influence on the determination of the default or predicted values. An advantage of this scheme is that with the single learning algorithm, all the types of empirical mappings between function, behaviour and structure or between design problem specification and design solution description can be generalized from design cases. To enrich the indexing facilities in a conceptual hierarchy and improve its case retrieval ability, extensive generalization based memory organizations are investigated as alternatives for concept formation. An integration of the above learning techniques reduces the memory requirement of some existing extensive generalization models to a level applicable to practical problems in the design domain. The MLD5 method is particularly useful in the preliminary design of a learning system for the identification of a learning problem and of suitable strategies for solving the problem in the domain. Although the MLDS method is developed and demonstrated in the context of design, it is independent of any particular design problems and is applicable to some other domains as well. The cognitive model of sequence-based prediction developed with this method can be integrated with general concept formation methods to improve their performance in those domains where concepts drift or knowledge changes quickly, and where the degree of indeterminacy is high

An Ontology Approach for Knowledge Acquisition and Development of Health Information System (HIS)

This paper emphasizes various knowledge acquisition approaches in terms of tacit and explicit knowledge management that can be helpful to capture, codify and communicate within medical unit. The semantic-based knowledge management system (SKMS) supports knowledge acquisition and incorporates various approaches to provide systematic practical platform to knowledge practitioners and to identify various roles of healthcare professionals, tasks that can be performed according to personnel’s competencies, and activities that are carried out as a part of tasks to achieve defined goals of clinical process. This research outcome gives new vision to IT practitioners to manage the tacit and implicit knowledge in XML format which can be taken as foundation for the development of information systems (IS) so that domain end-users can receive timely healthcare related services according to their demands and needs

Learning and discovery in incremental knowledge acquisition

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required