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El mundo al instante

DARPA wants photonic integrated circuits

http://www.GPSWORLD.com
Miércoles 12 de Septiembre del 2018

The benefits of the multi-GNSS future

Multi-GNSS paves the way for complete exploitation of new signals and constellations in navigation, surveying, geodesy and remote sensing.

What exactly are the benefits of multi-GNSS, and how can you access them? For a start, download the multi-GNSS signal schema, and follow that up by attending a free webinar, “Multi-GNSS: Advantages, Challenges and Test Solutions.

The free 1-hour webinar, which will take place at 1 p.m. Eastern [10 a.m. Pacific,  7 p.m. (1900h) Central European Time] on Thursday, Sept. 20, will review advantages of using multi-GNSS for the end-user and challenges in obtaining maximum efficiency when combining multiple constellations and signals. It will also discuss different approaches of testing GNSS receivers against jamming and spoofing attacks.

You will learn:

Advantages of using multi-GNSS
Challenges when combining multiple constellations
Robustness of multi-GNSS receivers to jamming and spoofing
Test solutions for GNSS receivers.

The webinar presents sponsored content by Skydel and Talen-X. Register for it here.

http://www.GPSWORLD.com
Miércoles 12 de Septiembre del 2018

Digital Twinning and the Complexity of Reality -

Digital twinning is based on the idea that a digital informational construct of a physical system can be created as a separate entity. The digital information is a ‘twin’ of the information embedded within the physical system itself, and is linked to it throughout the entire system life cycle. Rapid development of the digital twin approach offers mining companies the promise of optimising processes and automating decision-making – if they are brave enough to tackle the complexity of reality. 

The basic concept of the Digital Twin model has remained fairly stable from its inception in 2002 when it was introduced by the University of Michigan’s then-new Product Lifecycle Management Centre.

The premise driving the model is that each system consists of two subsystems - the physical system that has always existed and a new virtual system that contains all of the information about the physical system. This means that there is a mirroring or twinning of systems between what exists in reality to what exists in virtual space and vice versa. In most cases the virtual system will contain many sub-virtual systems.

Transforming data

Digital twin systems are about the traditional transformation of Big Data into information, knowledge and mining wisdom. The reality in today’s competitive mining world is that an operations team must consider global factors even even at the local level.

The digital twin must work under such pressures and exploit this dynamic, big data environment. Data collected by itself, unanalysed, is essentially an expensive and fruitless exercise, even if the twin is a small entity with few parameters. Data MUST be transformed into meaning and insight.

An important difference between the digital twin concept and the traditional simulation concept is that the digital twin is always at work and always active. In contrast, older style simulators are fed old data or generated disturbance data, with the results observed and a conclusion created. The digital twin workflow is dynamic and fundamentally different.

The operator typically sees data filtered through the digital twin, which could be performing many functions that may not need operator intervention or clarification. The digital twin interacts with big data or AI tools which help shape the digital twin, and could be optimising (and disturbing) digital twin behaviour. Operators have the option to bypass the digital twin analysis and access direct data, access big data analytics information or access AI information.

Intuition plays a big part in operator decision making. While this can be supplanted by deep learning techniques, there is still a chasm between autonomy and intuition especially in the face of complexity. One of the concerns in digital twin proposals is that of interoperability. In future worlds, systems, equipment, modelling, analytical, and automation processes do not live in isolation. The cyberphysical systems (machines and equipment) communicate with each other and with the mining staff around them (operators, planners, managers) so interoperability must be assured and seamless.

A digital twin adds value to an operation

It needs to operate in a ‘plug and play’ environment. To say that this is a revolutionary concept, is an understatement. The digital twin promises to optimise processes, effect quick decision making, and potentially so much more.

In the future, we could see a short cycle, where an autonomous decision-making process leads to changes in the digital twin in a strategic sense with wide implications, as well as a real-time effect within a mining operation. While it is early days, we are seeing a rapid development in this technology, suggesting possibilities that we cannot even imagine.

The complexity of reality

The digital twin is a reflection of a perceived reality, in physical characteristics as well as in planning and management. However, collecting data is not enough. Various levels of analytics, visual representations, machine learning and AI techniques now enable us to see and reflect on reality in novel and potentially powerful ways.

How capable are we to handle these new frontiers? In creating a digital twin, do we proceed with traditional, lengthy, purely functional analysis of the whole operational environment, or do we embark on a more epistemological approach? The latter is harder, but importantly, promises a better understanding of the complex reality of a mining operation.

Recent history shows that we are, en masse, incapable of thinking beyond simple cause and effect systems analysis. Such linear thinking is well entrenched in society, politics and management philosophies.

Where does this leave digital twin philosophy?

The obvious answer is to vaguely suggest that the digital twin concept is an evolving field. Mining companies must take baby steps toward a vision of future mining, knowing that they need to expand their understanding of complex reality, non-linear systems, advanced decision theory concepts and the power of deep learning and Bayesian technologies. Success is there for the adventurous, collaborative open-minded pioneers.

This article has been written by Chris Green, Maptek Core Technologies Product Manager, and was originally published in Forge, the free quarterly Maptek newsletter that includes case studies, product and corporate news.

http://www.GPSWORLD.com
Miércoles 5 de Septiembre del 2018

5 Questions about BIM and GIS

fc717f7a454b0ad181340a2a8b6ae222387f7ebbQ&A with Chris Andrews, senior product manager 3D at Esri

BIM and GIS integration is often presented as the optimal solution for the movement of data throughout the asset lifecycle, but it is not so easy in practice. What are the biggest misconceptions?

One of the most obvious misconceptions when customers ask for the ability to use their design and construction data (BIM data) with GIS is that BIM models contain ‘all’ the data about assets. Typically, a BIM model is only a representation of what was built. Even the most accurate as-built BIM model will rarely contain the furniture and other unfixed assets that actually exist in the operational real-world structure. Another misconception is that BIM contains facility management information. For example, in architecture models we rarely see rooms, spaces or even a footprint of the building. These geometric properties are useful for building-asset management and space allocation, but they aren’t necessary for building construction. From this perspective, the BIM is often missing information. Lastly, everyone – including many BIM practitioners – forgets that BIM applies to multiple, diverse industries. Architecture, civil transportation and utilities all use BIM processes, but have divergent data and construction needs.

What is needed to further enhance the combination of BIM and GIS?

Critical to more efficient, resilient data interoperability between BIM and GIS will be the establishment of lightweight exchange formats and interfaces for access to data across domains. GIS is largely open. There is a robust geospatial open standards community. Esri publishes interface and format specifications for access to just about anything a system integrator could want to access in ArcGIS Online, ArcGIS Enterprise or a geodatabase. While the BIM world has some similarities, we find that open-standard BIM exchange formats can be complicated and incomplete. There are also many proprietary BIM model formats that are black-box data stores with little or no ability to access their content. We understand that BIM content is complex, diverse and often contains proprietary algorithms or techniques, but the pressure to better enable use of BIM data in asset lifecycles is enormous and demands better access to BIM content.

Which steps could software vendors take in order to support GIS and BIM integration?

Software vendors on both sides can work together to provide better access to data, more transparent interfaces to connect systems, and even to use common access and authentication patterns so that customers can more easily combine and use the data that they already own. Customers want to do their work using the tools and platforms that were designed for specific tasks, and more open access to data eliminates attempts to do tasks using the wrong tools. We want customers to be successful with the right data in the right tools, knowing that ‘the right data’ is a task-appropriate view on the geospatial context and the design and construction detail that makes up our customers’ assets.

Data is at the core of the digital transition, but BIM data is usually much more detailed than GIS data. How can BIM data be integrated into GIS data workably?

BIM is perceived to be more detailed than GIS because, to construct a building or bridge, the details have to be specified in the design documentation. With the emergence of 3D as a core capability of GIS, customers are now discovering that 3D technology enables them to have more accurate geospatial models of plans, proposals and the real world around us. Although what we find is that not all BIM information needs to be captured in a GIS for design and construction data to be used for mapping and spatial analysis, GIS technology also needs to be improved to support many orders of magnitude higher-density spatial information than was necessary in the past. We are not simply working on filters or better translations of BIM to get it into GIS, but – as an industry – we are inventing new technologies to support high-density 3D information about the built and natural worlds around us.

As two essential pillars of smart cities, how will BIM and GIS shape smart city-related developments in the coming years?

The preponderance of data about cities and their inhabitants presents an overwhelming problem for planning, analysis, monitoring and response to world events, environmental change and economic pressures. The key to enabling access to data for any urban problem in the future will be to identify the specific location, things and timing of events and programmes in cities related to the people who will be affected. Simplistically, GIS supplies location and BIM processes supply details about things. A more seamless flow of information about location and spatial characteristics and the design and behaviour of things will be essential to enable government leaders to manage the timing and impact of events and programmes on citizens in our increasingly densifying cities.

About Chris Andrews

Chris Andrews is the senior product manager for 3D across the ArcGIS platform at Esri, based in Southern California, USA. He has focused on strategic innovation projects that have significant market impact in response to customer demand, such as the ArcGIS Earth effort, the Indexed 3D Scene Layer open standard and the Autodesk alliance. He leads a team of product managers focused on customers in the defence, urban and AEC domains.

Last updated: 05/09/2018
http://www.GPSWORLD.com
Miércoles 5 de Septiembre del 2018

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