Integrating VR and knowledge-based technologies to facilitate the development of operator training systems and scenarios to improve process safety

Process safety can be regarded of paramount importance since any malfunction or mal-operation of a hazardous processing plant may lead to accidents that will cause damage to properties, injury to people and may even result in fatalities. This project investigates how Virtual Reality (VR) and knowledge-based (in particular rule-based) technologies may be combined to provide an effective tool for implementing operator training systems to deal with different scenarios for any given plant. VR is one of the fastest developing visualisation technologies. Through VR, a trainee can be immersed in the realistic simulated environment, which is helpful in providing operating experience without having to worry about causing any accidents or operational difficulties of the real plant. However, it is necessary to provide flexible ways of capturing and specifying the expertise for evaluating the action of the trainee without hard coding everything into the simulation system. The proposed solution is to couple the VR simulation tool with a knowledge-based tool, or more specifically a rule-based tool. The VR tool is responsible purely for the user interaction and updating the state of the simulated plant. On the other hand, a set of expert rules is specified in the rule-base in a high level declarative format. Every time the plant changes state, the rule-based tool will check the new state of the plant against its set of rules. If the plant is in an undesirable or unsafe state then an appropriate warning will be issued or an appropriate message will be passed to the VR tool. Different training scenarios can be easily developed by changing the plant description and/or the rule set. This paper describes the overall system architecture and provides some details about the separate tools. An example is used to illustrate the working of the system. On-going research issues will also be highlighted and discussed. © 2006 Taylor & Francis Grou

AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS

In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel’s lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure

VR, HF and rule-based technologies applied and combined for improving industrial safety

Industrial safety can be regarded as a major issue of industrial environments nowadays. This is why industries are currently spending huge amounts of resources to improve safety in all levels by reducing risks of causing damages to equipment, human injuries or even fatalities. This paper describes how Virtual Reality, Human Factors and Rule-based technologies are used in the framework of the VIRTHUALIS Integrated Project towards industrial training, safety management and accident investigation. The paper focuses mainly on the VR system specification and basic modules, while at the same time it presents the main system modules that synthesize the tool as a whole

Redox, iron, and nutritional status of children during swimming training

Effects of exercise training oil important determinants of children's long-term health, Such as redox and iron Status, have not been adequately investigated. The aim of the present study was to examine changes in markers of the redox, iron and nutritional status of boy and girl swimmers during a prolonged period of training. 11 boys and 13 girls, aged 10-11 years, were members of a swimming club. They were assessed at the beginning of the training season, at 13 weeks and at 23 weeks through blood sampling and recording of the diet. Reduced glutathione increased at 13 and 23 weeks, whereas oxidised glutathione decreased at 13 weeks, resulting in an increase of the reduced/oxidised glutathione ratio at 13 and 23 weeks. Total antioxidant capacity, catalase, thiobarbituric acid-reactive Substances, hemoglobin, transferrin saturation and ferritin did not change significantly. Carbohydrate intake was below 50% of energy and fat intake was above 40% of energy. Intakes of saturated fatty acids and cholesterol were excessive. Iron intake was adequate but intakes of folate, vitamin E, calcium and magnesium did not meet the recommended daily allowances. No significant differences were found between sexes in any of the parameters measured. In Conclusion, child swimmers improved the redox status Of glutathione during training, although the intake of antioxidant nutrients did not change. The iron status was not impaired by training. Suboptimal intake of several nutrients suggests the need for nutritional monitoring and education of children athletes. (C) 2008 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved