The “Blockchain” is the technology supporting Bitcoin, the infamous cryptocurrency known for being the first widely used and reportedly used in some criminal activities. Blockchain is also the technology underlying Ethereum, which is also a means to implement smart contracts. There is an increasing interest around Blockchain because it promises disruptive changes in banking, insurance and other sectors narrowly involved in everyday life. In this blog entry, I will try to explain what is Blockchain and how it works. In the next entry, I will present some uses in the energy sector.
Blockchain is an account book, a ledger. It contains the transaction records made between two parties, like “On April 3, John sold 3 potatoes kilos to Anthony and paid 1.05 Euro”. The way Blockchain works avoid any malicious change in the records. This feature is not granted by a supervisor, but is a consequence of the consensus reached by all peers participating in the Blockchain. This has consequences of paramount importance. For instance, when Blockchain is used to implement a payment system, like Bitcoin, it is not needed a bank supervising and facilitating the transaction anymore. Even it would not be necessary to have a currency as we currently have.
The blockchain is a decentralised application running on a peer-to-peer protocol, like the well-known BitTorrent, which implies all the nodes in the Blockchain have connections among them. The ledger is stored in all the nodes, so every node stores a complete copy of it. The last component is a decentralised verification mechanism.
The verification mechanism is the most important part because it is in charge of assuring the integrity of the ledger. It is based on consensus among nodes and there are several ways to implement it. The most popular ones are the proof-of-work and the proof-of-stake.
The proof-of-work is the most common verification mechanism. It is based on solving a problem that requires certain amount of computing effort. In a nutshell, the problem is to find out a code called hash using the block content (a block is a set of recent ledger inputs). The hash is unique for a given block, and two different blocks will always have different hashes. The majority of the nodes must agree in the hash value, and if some of them find a different hash, i.e. if there is no consensus, the transactions in the block are rejected.
Applications based on Blockchain can be classified into three different categories according to their development status. Blockchain 1.0 are the virtual cryptocurrencies like Bitcoin and Ether. Blockchain 2.0 are the smart contracts. A smart contract is a contract with the ability to execute by itself the agreements contained in it. This is done with no need for a supervisor who verifies the contract compliance. Finally, Blockchain 3.0 develops smart contract concept further to create decentralised autonomous organisational units that rely on their own laws and operate with a high degree of autonomy.
Machine vision is behind many of the great advances in the automation of the industry since it allows the control of quality of 100% of the production in processes with high cadences.
A non-automated process can be inspected by the operators themselves in the production process. However, in a highly automated process, inspecting the total production manually is a really costly process. Sampling inspection, i.e. determining the quality of a lot by analyzing a small portion of the production, has been used as a compromise solution, but due to the increasingly demanding quality demands of the final product, sampling inspection is not the solution.
It is in this context that the need to incorporate automatic systems for quality control arises, among which stands out the visual inspection through machine vision. The human ability to interpret images is very high, adapting easily to new situations. However, repetitive and monotonous tasks cause fatigue and therefore the performance and reliability of the operator’s inspection decline rapidly. One must also consider the inherent human subjectivity that makes two different people provide different results in the same situation. It is precisely these problems that can best address a machine, because it never tires, is fast and results are constant over time.
It is logical to think that the aim of a machine vision system is to emulate the virtues of people’s vision. For this, the first thing we must ask ourselves is, “what do we see with?” A simple question that common mortals would answer without hesitation “with the eyes”. However, the people who dedicate ourselves to machine vision would answer in a quite different way and say “with the brain”. Similarly, it can be thought that cameras are in charge of “seeing” in a machine vision system, when really that process is carried out by the image processing algorithms.
Obviously, in both cases it is a simplification of the problem, since the process of vision, natural or artificial, cannot be carried out without involving both eyes / cameras and brain / processing, without forgetting another key factor, illumination.
Many efforts have been made to try to emulate the human capacity to process images. This is why in the 1950s the term Artificial Intelligence (AI) was used to refer to the ability of a machine to display human intelligence. Among those capacities is that of interpreting images. Unfortunately, our knowledge about the functioning of the brain is still very limited, so the possibility of imitating such functioning is too. The development of this idea in the field of machine vision has been carried out by means of what is called Machine Learning (ML) popularized in recent years with the techniques of Deep Learning (DL) applied to the understanding of scenes. However, these techniques do not really have intelligence behind them, but rather are based on feeding them with a huge amount of images previously labeled by people. The processing that allows to classify the images as expected is considered like a black box and really, in most cases, we do not know why it works or not.
When machine vision is applied to the industry for the quality control there is usually not enough data to apply these techniques and it is required that the behavior of the system is always very predictable, so these techniques have not yet been popularized in the industry. That is why, when developing applications of machine vision for the industry, the objective is to solve well-defined problems in which cameras and lighting are selected to enhance the characteristics that are desired to be inspected in the image and subsequently endowed the system with the capacity of interpreting the acquired images with really low error levels.
Finally, the inspection results are stored and used in the production process, both to discard the units that do not meet the quality requirements before adding them a new value or to improve the manufacturing process and therefore reduce the production of defective units. This information is also used to ensure that the product met the quality conditions when it was delivered to the customer.
Among the different applications in which these techniques can be use are geometric inspection, surface finish inspection, the detection of imperfections in manufacturing, product classification, packaging control, color and texture analysis… and so on.
At CARTIF we have carried out numerous installations of machine vision systems such as cracking and pore detection in large steel stamped pieces for bodyworks, detecting the presence, type and correct placement of car seat parts, the detection and classification of surface defects in rolled steel, inspection of brake disks, detection of the position of elements for their depalletising, quality control of plastic parts or the inspection of the heat sealing of food packaging.
“It is April 21, 2011. SKYNET, the Superintelligence artificial system who became self-aware 2 days earlier has launched a nuclear attack on us humans. The April 19, SKYNET system, formed by millions of computer severs all across the world, initiated a geometric self-learning process. The new artificial intelligence concluded that all of humanity would attempt to destroy it and impede its capability to continue operating”
It seems the apocalyptic vision of Artificial Intelligence depicted in Terminatorscience fiction movies is still far from being a reality, yet. SKYNET, our nemesis in the films, was a collection of servers, drones, military satellites, war-machines, and Terminator robots to perform a relevant task: safeguarding the world.
Today’s post is focused on a different but relevant task: manufacturing the products of the future. In our previous posts, we reviewed the Industry 4.0 key ingredients, the so-called digital enablers. The last key ingredient, Cyber Physical Systems, can be seen as the “SKYNET” of manufacturing, and we defined it as a mixture of different technologies. Now it is time to be more specific.
The term “cyber-physical” itself is the compound name to designate of mixture of virtual and physical systems to perform a complex task. The rapid evolution of Information and Communication Technologies (ICT) is enabling the development of services no longer contained into the shells of the devices we buy. Take for example, digital personal assistants like Sirifrom Apple, Alexa from Amazon or Cortana from Microsoft. These systems provide us help with everyday tasks but are not mere programs inside our smartphones. They are a mixture of hardware devices (our phones and internet servers) that take signals (our voice) and communicates with software in the cloud that makes the appropriate processing and answers after some milliseconds with an appropriate and in-context answer. The algorithms integrated into the servers are able to process the speech using sophisticated machine learning algorithms and create the appropriate answer. The combination of user phones, tablets, Internet servers (physical side) and processing algorithms (cyber side) conform a CPS. It evolves and improves over time thanks the millions of requests and interactions (10 billion a week according Apple) between the users and intelligent algorithms. Other example of CPS can be found in the energy sector where the electrical network formed by smart meters, transformers, transmission lines, power stations and control centers conform the so called “Smart Grid”.
The same philosophy can be applied at industrial environments where IT technologies are deployed at different levels of complexity. The fast deployment of IoT solutions together with cloud computing solutions connected through Big Data Analytics open the door to the so-called Industrial analytics. Better than providing theoretical explanations, some examples of the CPS applications at manufacturing environment will be more illustrative:
CPS for OEM manufacturers where the key components (e.g. industrial robots) will be analyzed in real time measuring different internal signals. The advantages will be multiple. The OEM manufacturer will be able to analyze each robot usage and compare it with other robots in the same or different factories. They will be able to improve the next generation of robots or give advice for maintenance and upgrades (both hardware and software).
CPS for operators: a company providing subcontracted services (e.g. maintenance) will be able to gather information on-field through smart devices to optimize their operations like for example controlling spare parts stock in a centralized way instead of having to maintain multiple local stocks across different sites.
CPS for factories: gathering on-field information from manufacturing lines (e.g. time cycle) it is possible to build virtual models of the factories and create off-line simulations to aid in decision support (e.g. process optimization) or study the impact of changes in the production lines (e.g. building a new car model in the same line) before deciding new investments.
The combination of physical and virtual solutions open the door to limitless possibilities of factories’ optimization.
Last November I attend third Big Data Value Association (BDVA) Summit in Valencia. The BDVA is a fully self-financed non-profit organization under Belgium law that represents the ‘private’ side in Big Data Value Public Private Partnership (Big Data Value PPP), while the European Commission represents the ‘public’ side. The Big Data Value PPP is operational since January 2015, and its main objective is to boost European Big Data Value research, development and innovation. In particular BDVA aims at:
strengthening competitiveness and ensuring industrial leadership of providers and end users of Big Data Value technology-based systems and services;
promoting the widest and best uptake of Big Data Value technologies and services for professional and private use;
establishing the excellence of the science base of creation of value from Big Data.
BDVA has around 150 members from 27 different countriesworking in 9 Task Forces: Programme, Impact, Community, Communication, Policy & Society, Technical, Application, Business, Skills and Education.
In 2016 the first PPP calls have been launched inside H2020 programme and in January 2017 the approved project will celebrate the kick-off meetings. CARTIF is a partner in one of this project titled Transforming Transport. As part of CARTIF’s tasks, we will in charge of Big Data approach inside one of the pilots, including Data Analytics.
Data Analytics and Computational Intelligence is not new to CARTIF. During last years, projects like OPTIRAIL, Development of a Smart Framework based on Knowledge to support Infrastructure Maintenance decisions in Railway networks, PREFEX Advanced techniques for the prediction of the excavation front or GEOMAF, New Maintenance Operations Management Tool for Railway superstructure and infrastructure, have tried to make valuable for the companies of the transport sector the information, knowledge, and experience the have gathered along the way, which are not systematically put into good use for multiple reasons.
At a more technical level the process is developed starting from the data (monitoring, historic information, etc.) and knowledge (experience) from an expert on the field. A proper use, based on Computational Intelligence methods and similar techniques, make possible to extract, model, and transfer knowledge that will make the involved companies able to give a higher added value to their activity and services.
Even so the use of data analytics techniques in real industrial environment is lower than expected. It is necessary to continue disseminating the benefits that techniques of this type can bring both in the social field, and in the industrial and services environment. Thanks to the BDVA and to events such as the one held in Valencia, this much-needed dissemination is increasingly being heard by a greater number of companies.
From the reproduction of vital organs to the construction of shelters in space, so revolutionary is the future of 3D printing. With this perspective, it is not surprising that many people ensure that this way of materializing objects will change our lives to unsuspected levels. We can say that a new industrial and technological revolution is taking place in the same way that when Internet appeared in our lives, a network of which many of us doubted in its beginnings and that has changed our world.
Currently, 3D printing, also called additive manufacturing, is fully deployed in the aerospace industry, in the engineering, architecture, defense, automotive and medicine. Its main applications are the reproduction of 3D scans and the printing of objects designed with three-dimensional modeling programs (CAD), which allow reducing the time of development of new products or even launch them to the market.
Its implications are endless. It is not already necessary to wait months to have a huge quantity of the first model of an object to launch a product, due to three-dimensional model can be sent hundreds or thousands of kilometers away to become an object in any place. In this way, in the future, a lot of industrial production will be on demand and will travel online, and will completely change the idea of consuming products, because every person has the ability to customize their own products with a great advantage: the exclusivity of each article.
Although the majority of current 3D printings are not able to produce very tough, economical and even useful pieces enough to replace traditional production ways, they have a very relevant application nowadays; educate in the use of technology.
In relation to the future, from the social point of view, I think that the true revolution of printing will not be a specific application or use, but the speed with which this technology, which today it looks like magic, will turn into something essential for our lives. From the technical point of view, at the same time that technologies will be capable to depositing materials, we will see a growing emergence of functional parts that fully exploit the capabilities of additive manufacturing.
Something very revolutionary will be the 3D printing applied to medicine, reconstructive, maxillofacial, or orthodontic traumatology, where is already being investigated with biompatible materials that will give the possibility of making organs accepted by humans and surgeons will have in a few days of objects to solve the problems of each person in a specialized way.
In short: 3D printing has come to say and change the way we consume and produce forever.
Internet of Things(IoT) are becoming common. These are the objects that connect to Internet by themselves to carry out their duties with no human intervention. One possible application that can help us to save money and to reduce greenhouse gasses emissions is the remote control of domestic devices featuring thermostats. These devices are the conditioning air, electric heaters, fridges, heat pumps and heating. While heat pumps are not common in many European countries, gas heating is widespread. Although the latter is not electricity driven, the same ideas can be applied because it relies on a thermostat. The important feature shared by all those devices is that they have thermal inertia, which means there is no significant effect if they are switched off for a reasonable period.
The first step is to connect to Internet the devices. There is technology available in the market to do this, like the Siemens’ Synco Living series or the devices manufactured by Greenwave Systems. This technology enables users to remotely access the aforementioned devices.
The next step is to allow the electricity company to control the thermostats, so they will be able to change the temperature set-point when some conditions are hold. For instance, in the case of the air conditioning, it means they will be able to increase the set-point up to certain threshold or for a certain amount of minutes every hour. In return for allowing changing set-points, the customer will have discount in his electricity bill.
Dryer connected to a smart switch
We have to consider companies do not participate in this scheme for the love of humankind, but because of the benefits they gain. What the company is really doing is to buy the customer’s flexibility. The flexibility is the energy the customer is willing to save if there is a return. When the company aggregates the flexibility of many clients, they find they do not need to produce or to buy a huge amount of energy which leads to big economic savings, in particular under unforeseen circumstances like some weather events.
But these programs that are profitable both for the companies and the customers have an even more interesting side; they foster the integration of renewable energies in the grid. The problem with renewable energy is that it cannot be scheduled, as it occurs with conventional sources. As a result, we have energy when there is no demand or the demand can concentrate when the wind does not blow. Demand response programs, this is the name for the described scheme, enables companies to use the aggregated customer’s flexibility to reduce energy demand when renewable sources are weak. In this way there is no need to build CO2 emitting reserve power stations, which are very expensive because they are not continuously running.
Demand response programs can be seen as a case of Internet of Things (IoT) and they are not common in Europe, at least among domestic customers, as it occurs in the USA. These programs allow citizens to be directly engaged in the promotion of renewable energies and in the reduction of greenhouse gasses production. They are a kind of everyday life perturbation, and some people could perceive it as an intolerable intromission. However, we have to consider almost all of us have a product called flexibility we can sell to the electricity companies and, at the same time, it is a personal involvement in climate change mitigation.
