Supporting engineering design using knowledge based systems technology with a case study in electricity distribution network design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis explores the architectural requirements of engineering design support systems based on knowledge based systems technology. The exploration is based on an understanding of the nature of designing as a professional activity and on the extent to which designers' competence can be modelled. Attention is focused on certain salient aspects of designers' competent behaviour. The theoretical study leads to the specification of requirements to be satisfied by a knowledge based system which will support designers in their professional setting and to the proposal of some knowledge based system components which will meet the requirements identified. The theoretical aspect of the thesis is complemented by a case study based on a designer of high voltage electricity distribution networks. The case study illustrates the theoretical component of the thesis and the methodological basis for the work. The practical realizability of the components of the knowledge based systems architecture proposed are demonstrated using the results of the analysis of the knowledge elicited in the case study without prejudicing the general applicability of the ideas. An object-oriented knowledge engineering software development environment is used to demonstrate how some components of the design situation represented can be implemented.Financial support provided by Brunel University

An MDE Approach for Domain based Architectural Components Modelling.

International audienceComponent Based Software Engineering (CBSE) is a popular and widely adopted software engineering paradigm that has proven his usefulness and success to increase reusability and efficiency in various application domains. In this paper, we propose a common metamodel of a component to support all the requirements of CBSE taking into account the specificities of each domain. The resulting modeling framework serves primarily to capture the basic concepts of concerns related to component systems development based on the clear separation between the development process, interactions and the domain knowledge. As a proof of concept, we are evaluating the feasibility of our approach through the CCM component model applied to an use case for building systems having real-time requirements

Specifying An Expanded Framework for Classifying and DescribingDecision Support Systems

This article defines an expanded conceptual framework for classifying and describing Decision Support Systems (DSS) that consists of one primary dimension and three secondary dimensions. The primary dimension is the dominant technology component or driver of decision support. The three secondary dimensions are the targeted users, the specific purpose of the system and the primary deployment or enabling technology. Five generic DSS types are identified and defined based upon the dominant technology component, including Communications-driven, Data-driven, Document-driven, Knowledge-driven, and Model-driven Decision Support Systems. Specific targeted users like individuals, groups, or customers can use any of the five generic types of DSS. Also, a DSS can be created for a decision- specific or a more general purpose. Finally, in the framework, the DSS deployment and enabling technology may be a mainframe computer, a client/server LAN, a spreadsheet or a web-based technology architecture. The goal in defining an expanded DSS framework is to help researchers better identify meaningful, homogeneous categories for research and to help Information Systems professionals describe and explain the various types of decision support systems

Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature

Design knowledge for deep-learning-enabled image-based decision support systems — evidence from power line maintenance decision-making [in press]

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. This paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements from literature and the application field are derived. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact\u27s capability to capture selected faults (regarding insulators and safety pins) on unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. This paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Approximate reasoning using terminological models

Term Subsumption Systems (TSS) form a knowledge-representation scheme in AI that can express the defining characteristics of concepts through a formal language that has a well-defined semantics and incorporates a reasoning mechanism that can deduce whether one concept subsumes another. However, TSS's have very limited ability to deal with the issue of uncertainty in knowledge bases. The objective of this research is to address issues in combining approximate reasoning with term subsumption systems. To do this, we have extended an existing AI architecture (CLASP) that is built on the top of a term subsumption system (LOOM). First, the assertional component of LOOM has been extended for asserting and representing uncertain propositions. Second, we have extended the pattern matcher of CLASP for plausible rule-based inferences. Third, an approximate reasoning model has been added to facilitate various kinds of approximate reasoning. And finally, the issue of inconsistency in truth values due to inheritance is addressed using justification of those values. This architecture enhances the reasoning capabilities of expert systems by providing support for reasoning under uncertainty using knowledge captured in TSS. Also, as definitional knowledge is explicit and separate from heuristic knowledge for plausible inferences, the maintainability of expert systems could be improved

INDIVIDUAL NEGOTIATION SUPPORT IN GROUP DSS

Negotiation support is an important aspect of multiperson decision support systems. Besides mechanisms for representing and evolving group joint problem representations, such DSS should also provide an environment in which decision makers are supported in developing, analyzing and reinforcing their individual negotiation position. Recognizing the diversity of research approaches to negotiation modeling in the literature, this paper synthesizes an integrated model from which a knowledge-based individual negotiation support environment using tools from different areas can be designed. Role and architecture of such a component are described in the context of MEDIATOR, a database-centered negotiation support system under development at NYU.Information Systems Working Papers Serie