Decentralized neural network variable structure controller design for wood drying process
This paper investigates the development and evaluation of a robust control system for a wood drying kiln process incorporating decentralized variable structure control (DVSC) such that the moisture content of lumber will reach and be stabilized at the desired set point. A description of the dynamics of the wood drying process by means of the time-delay neural network is also presented, in which the back-propagation algorithm was implemented for testing, training and validation. Then this identified model is used for simulation purpose and controller design. For comparison purpose, a conventional proportional-integral-derivative (PID) controller is also employed and system performance is evaluated through simulations. The results are evaluated to tune the controller parameters to achieve good performance in the wood-drying kiln and the DVSC strategy promises improved performance. The control system developed in this study may be applied in industrial wood-drying kilns, with a clear potential for improved quality and increased speed of drying.
Modeling of temperature-humidity for wood drying based on time-delay neural network
The temperature-humidity models of wood drying were developed based on Time-delay neural network and the identification structures of Time-delay neural network were given. The controlling model and the schedule model, which revealed the relation between controlling signal and temperature-humidity and the relation between wood moisture content and temperature-humidity of wood drying, were separately presented. The models were simulated by using the measured data of the experimental drying kiln. The numerical simulation results showed that the modeling method was feasible, and the models were effective.
Toward Building a Linked Open Data Cloud to Predict and Regulate Social Relations in the Saudi Society
Background: The trend in producing linked open data to publish high-quality interlinked data has gained widespread traction in recent years. Various sectors are producing linked open data to increase public access and ensure transparency, in addition to a better utilization of government data, namely linked open government data. Problem Definition: As compared to the developed countries, Saudi Arabia lags behind in benefiting from this new era of ubiquitous web of data, despite its publication of government related data in non-linked format. In the context of Saudi open government data, the full potential of multi-category data published by various government agencies at different portals is not being realized as the data are not published in open data format and remain unlinked to other existing datasets. Methodology: To bridge this gap, this study presents a framework to extract and generate semantically enriched data from various data sources under different domains. The framework was used to produce the Saudi linked open government data cloud by interlinking data entities with each other and with external existing open datasets. Results: The effectiveness of our approach is validated by applying it to a socially significant issue, i.e., divorce rate, in Saudi Arabia. By posing smart queries to semantically enriched data, we were able to perform an in-depth analysis of different factors related to increasing divorce rates in Saudi Arabia. Arguably, without using linked open data and related technologies such analysis would not have been possible. Finally, we also present a simulated visual environment for better understanding and communication of such analysis for decision and policy makers.