Realising intelligent virtual design

This paper presents a vision and focus for the CAD Centre research: the Intelligent Design Assistant (IDA). The vision is based upon the assumption that the human and computer can operate symbiotically, with the computer providing support for the human within the design process. Recently however the focus has been towards the development of integrated design platforms that provide general support irrespective of the domain, to a number of distributed collaborative designers. This is illustrated within the successfully completed Virtual Reality Ship (VRS) virtual platform, and the challenges are discussed further within the NECTISE, SAFEDOR and VIRTUE projects

Realising intelligent virtual design

This paper presents a vision and focus for the CAD Centre research: the Intelligent Design Assistant (IDA). The vision is based upon the assumption that the human and computer can operate symbiotically, with the computer providing support for the human within the design process. Recently however the focus has been towards the development of integrated design platforms that provide general support irrespective of the domain, to a number of distributed collaborative designers. This is illustrated within the successfully completed Virtual Reality Ship (VRS) virtual platform, and the challenges are discussed further within the NECTISE, SAFEDOR and VIRTUE projects

Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods

The job of software effort estimation is a critical one in the early stages of the software development life cycle when the details of requirements are usually not clearly identified. Various optimization techniques help in improving the accuracy of effort estimation. The Support Vector Regression (SVR) is one of several different soft-computing techniques that help in getting optimal estimated values. The idea of SVR is based upon the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. Further, the SVR kernel methods can be applied in transforming the input data and then based on these transformations, an optimal boundary between the possible outputs can be obtained. The main objective of the research work carried out in this paper is to estimate the software effort using use case point approach. The use case point approach relies on the use case diagram to estimate the size and effort of software projects. Then, an attempt has been made to optimize the results obtained from use case point analysis using various SVR kernel methods to achieve better prediction accuracy.Comment: 13 pages, 6 figures, 11 Tables, International Journal of Information Processing (IJIP

Expert System-Based Exploratory Approach to Cost Modeling of Reinforced Concrete Office Building

Expert system is a conventional method that is in use in cost modeling, considering its advantage over traditional regression method. It is based on this fact, that this study aimed at deploying neural network in cost modeling of reinforced concrete office building. One hundred (100) samples were selected at random and divided into two; one part was used to develop network algorithm while the second part was used for model validation. Neural network was used to generate the model algorithm; the model is divided into 3 modules: the data optimization module, criteria selection with initializing and terminating modules. Regression analysis was carried out and model validated with Jackknife re-sampling technique. The colinearity analysis indicates high level of tolerance and -0.07403 lowest variation prediction quotients to 0.66639 highest variation quotients. Also the Regression coefficient (R-square) value for determining the model fitness is 0.034 with standard error of 0.048 this attest to the fitness of the model generated. The model is flexible in accommodating new data and variables, thus, it allows for regular updating

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security