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Intelligent Systems Engineering

Issue 1 • Date Spring 1994

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Displaying Results 1 - 5 of 5
  • Neural network based classification system for texture images with its applications

    Page(s): 27 - 36
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1384 KB)  

    A new approach to interconnecting multilayer feedforward neural networks for tackling the problems of texture classification is proposed. The resulting classification system classifies textures via two stages; one to compress original co-occurrence feature patterns of high dimensionality to lower dimensional principal feature patterns, and the other to perform actual classification of textures using the principal features. Each stage is efficiently implemented by a trained multilayer feedforward neural network. Such a cascaded use of neural networks significantly reduces the computational complexity that is otherwise encountered in classifying large-scale texture images. Two practical applications of the system are provided, showing the direct applicability of the approach for real problem-solving View full abstract»

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  • Responsibility modelling as a technique for organisational requirements definition

    Page(s): 20 - 26
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (692 KB)  

    The paper proposes that the concept of responsibility provides a means of identifying and specifying, in a way that is meaningful both to users and systems designers, the organisational requirements that arise when a technical system is placed in a social context. The paper shows how organisational structure may be represented as a network of responsibility relationships, how requirements arise from the discharge of obligations associated with responsibilities, and how these concepts have been applied to the particular example of specifying user requirements for clinical workstations in acute hospitals View full abstract»

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  • On-line process fault diagnosis using fuzzy neural networks

    Page(s): 37 - 47
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1080 KB)  

    The paper describes a new technique for online process fault diagnosis using fuzzy neural networks. The fuzzy neural network considered in this paper is obtained by adding a fuzzification layer to a conventional feed-forward neural network. The fuzzification layer converts the increment in each online measurement and controller output into three fuzzy sets; `increase', `steady' and `decrease', with corresponding membership functions. The feed-forward neural network then classifies abnormalities, represented by fuzzy increments in online measurements and controller outputs, into various categories. The fuzzification layer can compress training data, and thereby ease training effort. Robustness of the diagnosis system is enhanced by adopting a fuzzy approach in representing abnormalities in the process. Applications of the proposed technique to the fault diagnosis of a continuous stirred tank reactor system demonstrate that the technique is robust to measurement noise, capable of diagnosing incipient faults, and requires fewer training data examples than a conventional network approach View full abstract»

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  • Scheduling theory and practice: Minsk group results

    Page(s): 1 - 8
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (680 KB)  

    The paper surveys scheduling theory results obtained mainly at the Institute of Engineering Cybernetics of the Academy of Sciences of Belarus, and published on the whole in Russian. The survey deals with the job-shop, flow-shop and open-shop scheduling problems, and with sequencing jobs on one machine and on a set of parallel machines. The more complicated problems and applied aspects of scheduling theory are also observed. Some trends of further research in scheduling and sequencing are outlined View full abstract»

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  • Knowledge based simulation for process monitoring and regulatory control

    Page(s): 9 - 19
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (924 KB)  

    A knowledge-based simulation is described for the monitoring and regulatory control of a juice purification process in cane sugar production. In the paper, the problem description is analysed and a set of requirements are identified. To meet these requirements, a scheme is presented that organises the domain knowledge into suitable structured modules, such as object classes, libraries, rule sets, rules, instance objects etc. A number of user interface facilities are developed to monitor and exercise the application, such as tuning the PID regulators, fault diagnosis, restoring the normal status, etc. Two scenarios are described to explain the remedial operations carried out when a regulator is out of tune or faulty, or when a valve is stuck. The system can be used with the actual process in a sugar factory or with other similar process control applications View full abstract»

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Aims & Scope

Intelligent Systems Engineering was published by the IET between 1992 and 1994.

Full Aims & Scope