250,880 research outputs found

    Aided diagnosis of structural pathologies with an expert system

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    Sustainability and safety are social demands for long-life buildings. Suitable inspection and maintenance tasks on structural elements are needed for keeping buildings safely in service. Any malfunction that causes structural damage could be called pathology by analogy between structural engineering and medicine. Even the easiest evaluation tasks require expensive training periods that may be shortened with a suitable tool. This work presents an expert system (called Doctor House or DH) for diagnosing pathologies of structural elements in buildings. DH differs from other expert systems when it deals with uncertainty in a far easier but still useful way and it is capable of aiding during the initial survey 'in situ', when damage should be detected at a glance. DH is a powerful tool that represents complex knowledge gathered from bibliography and experts. Knowledge codification and uncertainty treatment are the main achievements presented. Finally, DH was tested and validated during real surveys.Peer ReviewedPostprint (author's final draft

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

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    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

    Future scenarios to inspire innovation

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    In recent years and accelerated by the economic and financial crisis, complex global issues have moved to the forefront of policy making. These grand challenges require policy makers to address a variety of interrelated issues, which are built upon yet uncoordinated and dispersed bodies of knowledge. Due to the social dynamics of innovation, new socio-technical subsystems are emerging, however there is lack of exploitation of innovative solutions. In this paper we argue that issues of how knowledge is represented can have a part in this lack of exploitation. For example, when drivers of change are not only multiple but also mutable, it is not sensible to extrapolate the future from data and relationships of the past. This paper investigates ways in which futures thinking can be used as a tool for inspiring actions and structures that address the grand challenges. By analysing several scenario cases, elements of good practice and principles on how to strengthen innovation systems through future scenarios are identified. This is needed because innovation itself needs to be oriented along more sustainable pathways enabling transformations of socio-technical systems

    The nature and evaluation of commercial expert system building tools, revision 1

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    This memorandum reviews the factors that constitute an Expert System Building Tool (ESBT) and evaluates current tools in terms of these factors. Evaluation of these tools is based on their structure and their alternative forms of knowledge representation, inference mechanisms and developer end-user interfaces. Next, functional capabilities, such as diagnosis and design, are related to alternative forms of mechanization. The characteristics and capabilities of existing commercial tools are then reviewed in terms of these criteria

    An Approach to the Development of Hybrid Architecture of Expert Systems

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    The knowledge acquisition process is a crucial stage in the technology of expert systems. However, this process is not well defined. One of the promising structured source of learning can be found in the recent work on neural network technology. Neural network can serve as a knowledge base of expert system that does classification tasks. The combination of these two technologies emerges new systems called neural expert systems. Neural expert systems allow us to generate a knowledge base automatically from training examples. Also, they have an ability to handle partial and noisy data. Despite the advances of these systems, debugging their knowledge bases is still a big problem. Neural networks still have some problems such as providing explanation facilities, managing the architecture of network and accelerating the training time. The concept of a rough set bas been proposed as a new mathematical tool to deal with uncertain and imprecise data. Using this tool to approach the problem of data reduction and data dependency has emerged as a powerful technique in applications of expert systems, decision support systems, machine learning, and pattern recognition. Two methods based on rough set analysis were developed and merged with the development of neural expert systems forming a new hybrid architecture of expert systems called a rough neural expert system. The first method works as a preprocessor for neural network. within the architecture, and it is called a pre-processing rough engine, while the second one was added to the architecture for building a new structure of inference engine called a rough neural inference engine. Consequently, a new architecture of knowledge base was designed. This new architecture was based on the connectionist of neural network and the reduction of rough set analysis. The proposed design was implemented using an environment of object-oriented programming. Four objects and three modules were developed using C++ programming language. The performance of the proposed system was evaluated by an application to the field of medical diagnosis using a real example of hepatitis diseases. Data for this application was obtained from researchers working on a related study. Also, the proposed work. was compared with some related works. The comparing results indicate that the new methods have improved the inference procedures of the expert systems. The findings from this study have showed that this new architecture has some properties over the conventional architectures of expert systems

    A knowledge based system for linking information to support decision making in construction

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    This work describes the development of a project model centred on the information and knowledge generated and used by managers. It describes a knowledge-based system designed for this purpose. A knowledge acquisition exercise was undertaken to determine the tasks of project managers and the information necessary for and used by these tasks. This information was organised into a knowledge base for use by an expert system. The form of the knowledge lent itself to organisation into a link network. The structure of the knowledge-based system, which was developed, is outlined and its use described. Conclusions are drawn as to the applicability of the model and the final system. The work undertaken shows that it is feasible to benefit from the field of artificial intelligence to develop a project manager assistant computer program that utilises the benefit of information and its link
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