On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis

In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies

Control Room Requirements for Voltage Control in Future Power Systems

In future power grids, a large integration of renewable energy sources is foreseen, which will impose serious technical challenges to system operators. To mitigate some of the problems that renewable energy sources may bring, new voltage and frequency control strategies must be developed. Given the expected evolution of technologies and information systems, these new strategies will benefit from increasing system observability and resources controllability, enabling a more efficient grid operation. The ELECTRA IRP project addressed the new challenges that future power systems will face and developed new grid management and control functionalities to overcome the identified problems. This work, implemented in the framework of ELECTRA, presents an innovative functionality for the control room of the cell operator and its application in assistance with the voltage control designed for the Web-of-Cells. The voltage control method developed uses a proactive mode to calculate the set-points to be sent to the flexible resources, each minute, for a following 15-min period. This way, the voltage control method developed is able to mitigate voltage problems that may occur, while, at the same time, contributes to reduce the energy losses. To enable a straightforward utilization of this functionality, a user interface was created for system operators so they can observe the network state and control resources in a forthright manner accordingly.The work presented in this paper was funded by the ELECTRA IRP project (Seventh Framework Programme FP7 2007/2013) under grant agreement No. 609687

Space Station - Key to the Future

Possible future benefits of space station

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed

The development of a Human Factors Readiness Level tool for implementing industrial human-robot collaboration

The concept of industrial human-robot collaboration (HRC) is becoming increasingly attractive as a means for enhancing manufacturing productivity and product. However, due to traditional preventive health and safety standards, there have been few operational examples of true HRC, so it has not been possible to explore the organisational human factors that need to be considered by manufacturing organisations to realise the benefits of industrial HRC until recently. Charalambous, Fletcher and Webb (2015) made the first attempt to identify the key organisational human factors for the successful implementation of industrial HRC through an industrial exploratory case study. This work enabled (i) development of a theoretical framework of key organisational human factors relevant to industrial HRC and (ii) identification of these factors as enablers or barriers. Although identifying the key organisational human factors (HF) was an important step, it presented a crucial question: when should practitioners involved in HRC design and implementation consider these factors? New industrial processes are typically designed and implemented using a maturity or readiness evaluation system, but these do not incorporate of or link to any formal considerations of HF. The aim of this paper is to expand on the previous findings and link the key human factors in the theoretical framework directly to a recognised industrial maturity readiness level system to develop a new Human Factors Readiness Level (HFRL) tool for system design practitioners to optimise successful implementation of industrial HRC

Task analysis of discrete and continuous skills: a dual methodology approach to human skills capture for automation

There is a growing requirement within the field of intelligent automation for a formal methodology to capture and classify explicit and tacit skills deployed by operators during complex task performance. This paper describes the development of a dual methodology approach which recognises the inherent differences between continuous tasks and discrete tasks and which proposes separate methodologies for each. Both methodologies emphasise capturing operators’ physical, perceptual, and cognitive skills, however, they fundamentally differ in their approach. The continuous task analysis recognises the non-arbitrary nature of operation ordering and that identifying suitable cues for subtask is a vital component of the skill. Discrete task analysis is a more traditional, chronologically ordered methodology and is intended to increase the resolution of skill classification and be practical for assessing complex tasks involving multiple unique subtasks through the use of taxonomy of generic actions for physical, perceptual, and cognitive actions

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management