Honeywell and Foghorn Leverage Android for Smart Industrial Devices (Cloud Next '19)

Honeywell and Foghorn Leverage Android for Smart Industrial Devices (Cloud Next '19)

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Thanks. For being here I'm Anthony. Pass smart I lead the product management team for cloud IOT at Google and I'm, joined, by a few. Folks here so Sastry the CTO from foghorn, remya a VP of Product and Scott Bracken from Honeywell so we're gonna talk a little bit about some. Of the stuff we've been doing with with, foghorn, and Honeywell, so, a bit of an agenda little, overview of Google, and how we see IOT, fairly, high-level and generic view of IOT, for us and, then for corn welcome on, stage and talk about their edge solution, and there, is a core integration, which is pretty cool, you'll. See some of the platform, and they're gonna talk about a use, case they've done with Honeywell and they, actually have a nice demo here to show you what they've done which is really impressive, and I think. Adds a lot of value so, we'll have the demo and Q&A at the end so I hope you'll enjoy it so. You're probably here because you all know that IOT, can and probably. Is already bringing value to your to your businesses, and whether it's to solve something specific, or to really. Being. Part of a broader digital. Transformation. It's really central, to a lot, of know, creating, differentiation, for your businesses, and across, the industries. We, see that in agriculture, really know increasing, crop yield or lowering. Energy consumption. In oil and gas doing, predictive, maintenance or, security, in oil and gas since, all those use cases we're. Seeing logistics, to do high-value. Asset, tracking or, better, driving, behaviors, for fleets reducing. Risk for. The drivers so we see a lot of those use cases almost, across every industry and, we're seeing our two use cases. What. We've seen also that's a study, that came out this is a IOT, analytics, company. That does does. A bunch of competitive. Intelligence on, IOT, and in. Different sectors what. They're saying is about no. In 2021, about 50%, that company will have IOT, projects. And will have seen value, so it's more than having a projects actually seen value, for my ot in, 2021 that's about know only two years from now, but what's staggering, is different from 2018, to 2021, there's a massive difference and. Massive, growth in the, realization. Of value, through IOT, which is the big change here. I'm. Going to talk, a little bit about real-time. In, some of the talk because we're seeing we're seeing that, speed, is really critical, in IOT. You, know we've seen analytics. Done on both data on data base and that's great to build your models but what we're seeing from customers, is really the demand for.

Real-time, Insights, and real-time. Actionable. Insight, actually, and what's. Important it we're seeing that data by 2025. A. Quarter. Of the data will be a real-time in nature so, that's quite a lot but, as part of that quarter of data a 95%, of is actually IOT, data that's real-time, IOT, data, from. The global data sphere they call it it's kind of funky, word but interesting, so. Real-time. Nature being able to take decision. Right there on the spot either locally, or in the cloud is super important for an, IOT platform, and. It. Really it's it's, it's. More, important. Even because you want to get that accuracy, you know to reduce cost dry, efficiency. On a manufacturing. Line to simplify, your, logistics and reduce risk and. Make. Your environment work, more safe so that's something we've seen you know in and gas in particular you. Know to track workers, with their heart and know being in zones they're supposed to be and things like that this is vision, intelligence apply to, to. A work environment for safety, use cases that has to happen in real time you can't wait for the next day to say hey you should have been there the, accident happens too late. One. Thing that we've noticed and, I've been in the in that in that cloud IOT, space for quite some time now and I've seen it going through a lot of cycles of POC, II and pilots, and testing out stuff I, think. That's changing now. I'm seeing the, POCs and the pilots go to production and, that's a big shift that we're seeing we're seeing those growth you. Know people the comedies, I've been testing IOT for a while now, they realize what they can do with it and now it's going in production, let, a big shift we're seeing and the problem. Is you really have to think about okay, am I am i doing my IT project in isolation, am I using the right PI for I'm not limited by the platform using, am. I restricting, my possibilities, with a platform I chose so, choosing, choosing, they are the, provider. Of your IT platform is actually really important, and you. Often time, forget. About the delays of managing, complex infrastructure, when you put IT platform, in place, the. Specialized, tech and learning curve you have to have for hardware, communication. Ai a data, ingestion and all of that there's, no real standard, protocols it's pretty messy there's, a lot of hidden cost, that come with with a without. Your T deployments so having a platform that's simple flexible and, can. Really help you to get to value faster is, critical, for the success. We. See IOT from Google sign what we see our T as a big data challenge. Almost. More than anything else and big data in real time and having. That ability to, gain real-time insight, as I said we'll. Bring the competitiveness, that you're looking for for your business. So. In terms of platform, the. Google Cloud IOT platform is, a serverless platform, from. Connectivity, processing. Storage and analyze server, less means that you never have to worry about scaling. Up or down or you know pre provisioning, resources, and overusing. Resources, it's, just gonna scale, from one, or a few device to millions of devices, very. Easily and. That's really a core, differentiation. Of our platform, from connectivity.

To An analysis. If. We look a little bit deeper into the cloud iot platform itself. You're. Gonna see several components, here the, main one is cloud IOT core that's something we launched GA, last year in March so about, a year ago. That's been pretty successful this, is a global front-end, for your devices one, you're well globally and we, use all the Google front end the. GFE, will call them to ingest data from anywhere in the world you don't really have to think about where. Is my device physically, in which region. Does it have to connect you to have the lowest latency that will happen in a matter this. Is served by the same front-end that serve your Google search or your YouTube it's, the same throne and so it's really global really, scalable all across the globe once. The data flows into IOT, core it flows into pot Sub Pop says is this messaging, bus, global. Again also, realized you not have to worry about resources, here pops, up is available, globally that, means that if you have data ingested, in Australia, in, Asia in, North. America, they, all fall into a topic of your choice if, you want to get that air just query that topic and get the data out you. Don't have to think about oh where is my device again it's all global, infrastructure. Automatically. Scalable. Pub/sub. Stores data for seven days you can do snapshot, replays, and. Have, several subscribers, to it and get the data the data can go into a crowd function, a server, let's go compute service to trigger actions. Or do. Any kind of request or can go to cloud data fusion a cloud, data fusion is the managed version of sadef sadef is. An open-source, data. Processing, pipeline, it is, you. May know it as cask dot, IO so, that's open source and you, can create your workflow, with drag and drop little nodes and then, do, your transformation. Along the way plug that plug, the data out pops up to, your transformation, and drop it in some database if you, want to do longer, processes. Like. Windowing, analysis. In. Real time you can use data flow data. Flow is also scalable on its own and has, an Apache beam open source equivalent, all those are kind of have managed. But have their open source equivalents. Pretty cool and, then you learn in BigTable if you want to do time series if, you want to do big data analytics you. Have your bigquery you have cloud machine learning to do your training, send, those models, back and send them back to the device because podiatric, core is bi-directional and, then, you have some nice visualization, tools, so that's what we call the cloud IOT platform putting. That together is fairly simple, doesn't. Require much code if any code it's also realized and we're. Pretty happy with some of the some, of the early customers, that we've had you. Can see some of this customer at the spotlight session on Thursday, we. Have some other customer on stage one. Key element of our strategy with, Google Cloud IRT platform is we can't do it on our own we, have to have partners, working with us and I'm. Really happy today to have for corn as one of our partners if. Sastra wants to join me for corn. We've been working with them for over a year at the edge in particular, and solving. Some of our edge challenges, and doing machine learning at the edge so thank. You I'll, leave it up to my sister Thank You aunty he's, no makeup yeah, you run. Good. Evening or afternoon everyone, I know I'm between you and food so I'll try to be as interesting, as possible so, we are a. Google partner as you mentioned and we are an edge computing, platform provider company. We. Are a valley based company I'm going to give you a snapshot of who we are we're based in Sunnyvale right, here in the valley we provide. Software. That runs on small, devices, edge, devices, whether they are existing. Plcs. Gateways. Or embedded. Systems close, to the, machines, close to the assets and the. Key here is to do live. Data processing, analytics, and machine learning on the, data that's flowing through the edge to derive actionable. Insights we've, got customers. Across the globe hundreds, of them we've got lots of analyst coverage and so on, everyone, that you see here in the logos these are already investors, Honeywell, is the investor Honeywell, is actually going to talk today about. How. They're leveraging our technology, for some of the use cases as well you, can see all of the big technology, and industrial names, there as well that, all.

Our Investments. Traditionally. We have been shipping, our products, on. Gateways. PLC's, raspberry. PI's things, of that nature where you have a flavor of Linux or Windows or, a real-time operating system, what. We have recently also done is release. The same product, on handheld. Devices which is actually the cool part of the demo and the session today on Android. Based devices, so. The way the product, works is this. Is how it looks like you have a to here in the cloud which you used to manage all of the edge devices, the, software runs on the edge device itself we have a very flexible, micro-services, based architecture. In the, traditional, Linux world we have used containers, obviously, an Android there are no container so we have the, all of this as an app so, the notion that you. Can have all kinds of data that fed into our system, through a data, bus that we have the. Two core key components, here or the analytics, engine and, the edge ml the machine learning engine the idea being that as you, are ingesting a live data from all different sources you, can apply analytics. And machine learning on the on the data to derive the insights and then, you can take. Closed-loop, control actions by either using our SDK, there, is an SDK using, which you can build applications or, you. Can also visualize it, and send that information into IOT core in other places as, well. So. Maybe, let's, step back a second, a lot of you I'm sure many, of you are familiar with edge computing, but let's step back for a second to say why, is it actually important, right so, in a typical industrial. Environment, where there's no oil and gas manufacturing, transportation. Smart. Cities buildings doesn't matter all of these different sectors you've, got data coming in I might, add lots, of data especially if you're doing video audio, and acoustic type, of data and. There. Is a high cost latency. And security associated, with it by able by directly. Connecting these machines into, some kind of a central or. Cloud location, what, Falken really does is. To help with that solve the problem by. Introducing this. Edge, device. Right next to the asset on many cases on the asset itself where. The data is ingested, into our software running here and then, you, derive the insights and then send the results in insights back into the cloud where.

For Additional processing for, function your models, for, fleet-wide visibility. And, and, so on this, obviously. Has the benefits of low, latency, here because, there is no latency involved between these two and then, there is low cost of data transport, and then, security. Is also. Eliminated. Or minimized when, you're communicating with that. Another. Way to look at it a lot of the times when you're processing data in this, example, right here this. Is a sensor, signal, for one of the signals, you see here how the blips. Are in the signal this is a suction form when, you typically send that information to. Downsampled. Environment, whether it's cloud datacenter this. Signal, actually looks like that you actually pretty much missed our little point of water days so the fidelity of deriving. The insights, actionable, insights at the edge is a lot higher is really why, it's so important in addition to all of the three things that I talked about security cost, and of, course latencies. This. Is the high level product, solution which is our traditional. Solution. That we've been shipping on Linux it's the same solution that I showed you on the first slide has, been ported on to Android then, the idea, here is being that you ingest the data from all these different types of sources enrich. Them because a lot of the times in the industrial world the, data quality, is not good that layer fixes, all of that cleanses, normalizes, decodes, it and then. Does processing. Through these two layers that I talked about and then, the information itself can then be published to a cloud environment we have pretty, deep integration, with IOT, core as Anthony. Mentioned and, I'll mention, that in a second in the next slide how that is done as well and, using. The - DK you can then program. And programmatically, subscribe, to those insights and take closed-loop actions. So. One concept, that I want to bring into this picture is this notion of what is called education, I know, it's a new word so what is education a, lot of the times when you build data, science models machine learning models to run in, in.

A Server environment. Cloud environment, you. May or may not necessarily pay, attention to the amount of compute resources or, you may, be actually working on batch. Data rather than working on live data and you, may also not pay attention to the number of layers on the size of the model because, those, are not necessarily the constraints, you worry about in a cloud environment but. When you bring the same model to run on an edge environment, in, order to run efficiently and. Also perform. At the rate that you would expect you, have to deal with those things and that process is what we call edification, how, do you take your model that typically, works off of batch data and make, it run on real time live data how, do you then optimize, and reduce the number of layers and the weights and still. With, the same level of accuracy and finally, if you look at the anatomy of a machine learning model it. Has really. Three things one, is we call a pre-processing. Code which, is really feature extraction, let's say looking at a video or are you looking at some sensor signals from the machines before. You can apply a machine learning model an algorithm, you got to extract some features and that's typically what we call pre-processing, and that is the most compute-intensive, part, of it and then. Followed, by you've, passed those features into your machine learning algorithm or the equation which itself is not really, that computationally. Intensive and then. Followed by what a post-processing, which is how do you then take the results of that equation do something with it what, we do as part of the certification process, is, to extract, out all of the pre-processing, the post-processing, into, our highly, efficient complex. Event processing engine. That we built from ground up which, runs in really really small fitment. Few megabytes of memory runs, really fast and, then, leave the machine learning part equation to it into, your engine of course. You also have the ability to in fact do the machine learning part in the CEP engine itself as we move forward here are some examples, where we took a video based flare, modeling. Use case how, you can actually optimize when. You build take, a model that's built here you fire it you got almost a 10x or higher improvement. And yet, much, higher fidelity much, higher accurate. Results. Now. Even. When you build a machine learning model to, derive some insights, and predict some things and you deploy this in the edge environment, or any amount before that matter and. It's accurately, predicting, the results it does not necessarily mean over. Time it's going to continue to predict with the same level of accuracy because. Of data drifts, because of changes, in machine, behavior, the. Same model, that once used to accurately predict, the results, may no longer do that so. How do we how do we address that how do we fix that to address that we came up with this notion of automated. Closed, loop machine learning so what does that really mean so. As you see here in our architecture once. A model is deployed we, built this thing called the, prediction. Model degradation, detector. What, that module does is, that it continuously, measures, several, different factors whether it's an f1 score whether, it's a data distribution collection, how things are and tries to see if the, accuracy, the model that you're predicting is deviating. From what it was before and when it does what. It does is automatically, sends, that information to the Google IOT core here from, Google IIT core as Anthony, explained it's a pub/sub system you, can send that information to dataflow and then which can then be read, and configured.

Into Bigquery and the cloud ml engine where, you retrain. Incrementally. Your model with, this incremental, data that's coming in and then, push that model back on onto. On, to the edge engineer so this is all automated, of course you have an option to not, automate, it but this is done an automated way until. The accuracy, comes back to what it was before and this, is what we've been calling the closed loop I know different, terminology have been used this. Is truly revolutionizing. The, edge computing. Because you're actually introducing, the notion of AI artificial. Intelligence onto. The judge devices, on how do you self correct how do you self correct the models deployed on to this environment in order for you to continue to predict this. With the same level of accuracy. As. You, can see we are our, solution, is a generic platform we, have been deploying this across many many use, cases across many different verticals, our top verticals. Actually include manufacturing, a lot of big-name customers, oil, and gas, transportation. Smart, cities buildings we have done other things as well like mining and so on what, I want to do is just mention. A couple of use cases quickly here in the one, in the manufacturing, one in oil and gas that actually involves different. Paradigms, this, is a factory, I can publicly reference it's a GE factory, they, manufacture, industrial-grade. Capacitors. That look like this this is a highly expensive. Capacitors. That are used in power plants the, material cost alone is several thousand dollars and the, way the processing, in fluids was they, take an aluminum foil they winded through what is called a winding machine Kollmorgen warning machine and the. Winding machine itself has hundreds of sensors that are continuously. Measuring several, things winder, tension with the aspect ratio and so on once. They winded they press it into packs and then inserted, these are those packs and they run it through what is called a treat oven to take out all the moisture content and finally, they fill oil, into, the capacitor to test the capacitor. At this point if, the capacitor, fail slide is not working or seeing. Dielectric, failures it's too late in the process and that's exactly what was happening in the factory as they walked into the factory I see a lot of pink slips on the factory floor costing. Them a lot of scrap millions of dollars so, the challenge for us was how, do we connect our red solution, to, the sensors directly into this assembly line in the machine connected. And identify, exactly. What was causing these dielectric, failures more. Importantly, before, it is too late so, that the operator actually has a chance to go take the units offline and then fix it and that's what we've been able to successfully do that and reduce, all of their, pain.

Points On this crap the. Second use case I want to talk about is, interesting, this is an island gas use case we have Saudi. Aramco it's one of our customers many other customers as well we've actually publicly, talked about this in the PRC, conference, a couple of months ago this. Is as some of you may be familiar with like you know oil gas refineries, gas, is being refined you've got hundreds of compressors, and because. Of various reasons excessive. Pressure sometimes. Other reasons, compressor, failures the, gas gets released in through what, are called oil stacks these are called oil stocks and then, the gas gets burnt when, gas gets burned you see a fire what's. Worse you. Can see smoke sometimes fight. Itself is bad smoke, is even worse dark smoke is even worse right so, how, to identify up, until before this edge solution came up typically, they install video cameras, sometimes sometimes. They don't even do that and a, human being is able to monitor 24, by 7 which is not really practical all they can do is to see all that is file there is more what. We have done is installed, our software, on a controller. Box attached, to this these oil stacks many, many many of them and they'll, directly, take the video feed from, this combine. It with the audio feed of the compressor and identify. And correlate, when, there is in fact a floor flare, or a smoke, or and, also compute, the volumetric, flow of the gas that is born and things of that nature so human being and monitoring. Of this is eliminated, but more importantly, operator, gets an alert when there is a real problem and we get to the root cause and why the problem was occurring this was being widely deployed, in a very popular use case. One. Last thing I want to say is that this, particular. Session the rest of the talk, and the demo remember, as we talked about is our product that was released on the handheld. Devices. Mobile, edge platforms, we, have noticed that there is obviously a lot of traction in this in this field because you these, are all battery-powered devices which, makes it even more important, for our edge computing, to consume. Less, and less power less and less CPU and memory and we ported our software onto these devices and deploying. That with. The Honeywell especially, initially working with an evil device where, we have done a couple of use cases which we'll demonstrate today as well with, that I'd like to invite Scott breakn director, of advanced technology from Honeywell to speak about their, technology take, great thank. Thank. You Cerie. Hello. Everyone my name is Scott Bracken, as sastra mentioned and I lead the advanced technology, group for. The productivity. Products, team within Honeywell, and. I'd. Like to introduce you to the corporation, of Honeywell a little bit to explain. Where the prototypic, products group sits, within Honeywell, it's, an exciting time to be an engineer, at HUD Honeywell, because, there's a significant, amount of growth in being. Driven by technology in these. Four business segments, that Honeywell exists. Aerospace. Poland, Building Technologies. Safety. And productivity solutions, which is the group I'm from and I'll explain a little more about that in a moment and the. Performance of materials group the, one common, thing that that connects all of these systems are all of these business, groups is the fact that connected.

Solutions, Are what's driving the growth in all these business areas and therefore, the technology, development within Honeywell, it, is able to develop common platforms, across these different businesses, and. As. I mentioned, we've. Been growing. Significantly in, the technology, area and most specifically, transforming. The Honeywell Corporation, to a software, industrial. Business. The point of this slide is to point out that we, have already invested, heavily in software engineering talent. Within, our corporation. To build that capability. Now. Diving a little bit deeper into, the safety and productivity solutions, group you, can see that there's a lot of areas, within the industrial sector. Where. Technology. Can help our, be more productive first. In the connected, worker, many. Wearable. Devices that a an, employee can wear in a in a industrial, setting. Can, help monitor, for safety and to, help that employee. Be more productive in the. Supply chain in the logistics, area there's a tremendous amount of data that is important to our end customers for, managing. The efficiency, of their operations, in the. Distribution, center themselves, we have a lot of automation, that we've introduced, and provide to our customer, base again. For reducing their cost of operations, and being more efficient and finally. We also have a very wide. Portfolio, of sensors, primarily, for since for. Safety applications, pressure, sensors, and gas detection type sensors are quite, prominent in that portfolio, but, we have sensors that support all the different IOT applications, across, this suite. So. Once, again diving, a little bit deeper into. The Honeywell technology, area, specifically. In productivity, products, what am I talking about in, that realm there's, several different solutions and, many, customers use. This technology, in different ways but, we can provide common. Platforms, across these use cases to. Provide. Those customers, with the unique, needs that they have yet, with the power and the of having. A very robust, and a solution underneath things, such as vehicle mounted computers, all. Types of asset tracking, technologies. Particularly. RFID, sensing. Is is seeing a growth, spurt. In recent years this. Is not just limited to some of the industrial settings but also spills, over into retail quite. Prominently, and then. Looking, at the solutions on the right-hand of this chart, you. Know scanning. Is really at the core of what we've what we do in our productivity, products group right product. Identification, primarily. With barcodes, is what our scanning devices is centered around and then we provide value on top of that with other solutions, such as voice, solutions to. Allow, for hands-free operation of. Product, identification the. Mobile computers, which I'll go into in a little more detail in a moment and then, of course different tagging, that we can use for sensing.

The Devices, in any, kind of inventory setting. That all gets tied together obviously, in a cloud environment for, data management and inventory tracking purposes. And. Most. Recently, in fact in the last year our, flagship, device was recently, introduced, we, call it our mobility edge platform. And its unified, platform for mobile computing, it's, based on the Android or you know it's an Android based device, and it's, purpose-built. For these industrial, applications. The ruggedized design, of not only the form factor being. Very familiar to employees. But, also the, allowing. For a very, heavy use during a workday. You know as. I mentioned product, identification and, barcode scanning is the core operation, of these devices but, that can take place thousands, of times a day or thousands, of times in a single shift so, building a device that can handle that kind of workload, survive. The entire shift is a very critical aspect of our our customer, requirements, so, withstanding. You know the harsh environment whether. It's you. Know. Impact. Or whether it's the elements, even a. Moisture. And say a a, rain. Rainy environment, you can also withstand. We've. Used. An Android operating, system. Obviously for easy field deployment and giving. Us very, a, seamless. Operation with. The cloud connectivity, and. We. Built this device with multiple form factors. But. Underneath the hood is a single, common hardware platform, and I, particularly like this slide myself because I'm surrounded by software, engineers but actually I'm a hardware engineer at my, roots, and so, I have to at least show at least one slide with a with, a processor. Module, on, it and we're, quite proud of this processor, module that we introduced with the mobility edge platform, last year because it is common throughout all the different form factors, of the different mobile devices that we provide to our customer. Base and. Particularly. I'm excited, today to be on stage with Google, and with Foghorn, because, now, that we've deployed, this edge device, this platform we. We now can build a tremendous amount of value within the software, on top of that device and the, and the first place we starter, one first place we started is looking, for, this. Very thin layer that, Sastri's. Has already, described that, allows us to build the, AI and, machine learning toolset.

And Capability. On top of these on. Top of these mobility, edge devices and and, foghorns. Lightning. Offering. Meets, all of those requirements, for our needs across our entire, customer. Set. So. Now the last couple of slides before. I hand it off to the demo are just, describing the use cases that we are first investing, in the. First one is, quite an interesting one to, us because we have a rich history of. Designing. A very, sophisticated, software. Tool decoder, tool to, extract, from, an image a barcode, and decode, it for that customer's application, but. On the, edge of that application, and during different use cases many. Of the images we receive from our device on our imager, come. In very blurry, or very noisy, it's very common, new. Operators, that haven't operated as scanner before you know don't. Necessarily. Aim the device accurately, or I'm, sure many of you have been at a retail store when when. A particular packaging, maybe a shrink. Wrap packaging, that has some ripple in it is is. Causing, the scan. To be difficult, difficult to read for that device and an, operator. Naturally. Many times wants to go closer to that barcode, to pick up that scan and in fact they're, making it more blurry by going closer so. Again, there's, several different, environments where, a blurry barcode, or being, able to decode. A blurry barcode, would, be. Much more efficient, for our customer, base and as what you see here is a, data set so we created a synthetic data set of 50,000, images barcode, images. Were, they were image pairs clean images, and, blurry. Or damaged images, and we. Use that for training our machine, learning model that then we deployed through the foghorn system, onto our mobile led edge device and these was just some snippets of the images, that were part of our training data set so. That's our first. Use. Case. And our second one is very is it's quite basic but very important, to our customers so. These devices are owned by the customer, they're not owned by the employee they're not their personal devices so, the, typical method. For these being deployed is at the beginning of a shift the, operator will take the device use it throughout the shift and then docket, for charging overnight the. Very simple, requirement, is is that device, must operate continuously throughout. That entire shift, that, shift can vary in time it can vary in in, operation. And what what. Modes are being used and so. By. Applying, the machine learning type of approach, to this and working with the data scientists, at Foghorn, we're able to create a real-time, operating. Model. On on the mobility edge device that, can monitor and optimize to. Extend the life of that battery for, that device during the shift so. With that I'd like to invite Rami up from, Foghorn, VP. Of products to take. Us through the demo all, right I'm here to tell you that it's real. So. This is, the. Honeywell device it's a CT 60. And, our. Falcon lightning mobile is actually running on this so, following up from Scott's talk what, I will demo here are the two apps one. Is the battery, insight, app. And, the other one is the barcode optimization. Alright. But. Before we jump to the app what, I do want to do I am a product person is go. Through our Foghorn, manager. Here so what you see on this, side on the, left side here is our Foghorn manager portal, so think of it as our remote management.

Configuration. Monitoring. Console, and let you see all the edge devices, that have the Lightning mobile, or the Lightning stack installed, on it well one, of the things we do at Foghorn is be very focused. On the OT persona, right we understand, the users of our product our operators, in the manufacturing, floor your refinery, technician. And so, a lot of what we put in here is focused. On that personnel, so. Especially. In manufacturing great the, idea is to create this one gold, configuration. That can work across massive. Volumes of devices, and so this Fargo and manager portal lets you create that configuration, whether, it's to you know add a new sensor, define. Analytic, expressions, that, do complex event processing, or. Come up with machine learning models all of that is packaged, up really nicely into, a configuration, and deployed, onto the device and so, that's what we've done here so you can see the two solutions. Which is the barcode enhancer, and the battery insights there, are the two models that are part of this configuration and now that's, what's deployed on this device so. Let me pull up the foghorn app. So. You can see the four hand app here. That's. Installed, on this device and. Immediately. We have the two solutions, pop out which is the barcode enhancer, and the battery insights now. Scott, did talk about the. The goal of using battery insights and I'll, talk a little bit about the the metrics that show up here so, both, of these apps represent, two, classes, of learning, so to speak right, in the battery. Insights, app it's, an adaptive, learning model, that's that's, going on on this device right, so, it's, learning, on the fly based, on the pattern of usage how, often is the battery charged, how, long is the work shift like how does the user actually use it what we are building is a very unique, fingerprint. Of device usage, that, is specific, to this unit, so, the metric here is saying yeah it's okay for the shift right it's gonna last this battery's gonna last for the next 16 hours oh by the way also in for you that your work shift is around 8 hours so, this model is going to get better and better as the device gets used over time. Now. If you move on to the next one which is the barcode enhancer. It. Happens, behind the scenes right so this is the operator he has the scanner he's scanning the barcode and the, the goal is to have that first, can be successful, and, in. The event that scar talked about the image is grainy what happens is that gets passed to the for one stack right and we running this mural net model so the difference in the model is that's a New Relic model, using tensorflow, running on tensorflow light and the, influencing, is happening on the device so, to help demonstrate how, the barcodes, actually look before, and after the neural net model runs through it I'll actually go through a barcode, viewer enhancer, app here.

So. On, the top here you see the, before picture like this is what the operator is actually seen when they scan the barcode and once. It, passes through the lightning mobile stack here. This. Is the result. There. Is definitely much. More clarity, and more importantly, there's, no loss of productivity when, the operator tries to do that first scan there's, no manual, reentry, of the code so. Let me go through a couple of more images. This. Is an example of a full bar code sample. And. This. Is the clarified, one through our stack. This. Is one of my favorites it's a piece of the barcode I didn't, I didn't think I'd ever see I have a favorite bar code but here it is. There. We go. And. I, think now to do that is the last one so. The. Point of this presentation is, to say that we. Have life in mobile and we have the ability to now run on Honeywell, devices. With. The Google Cloud, technique. Of doing building, AI models, on their data science platform, what. Does it do for us as industrial operators. As industrial users, right it opens up this whole universe. This whole expanse, of use-cases eos we talked a little bit about sensor fusion in the past that is it's. The ability to combine video, audio, structured. Data and. What are the newer insights for example if you are from oil and gas and you have a technician, walking, around in your refinery he could take an Android, mobile device point, at a valve and really, get inside of it should this valve be on or off right. If, the question masked oneself is what is the use case today that's tethering, my operator, to, maybe a location. Instead, with the of Android devices is now more liberated, and therefore. The ability to get me really sites.

2019-04-14 01:19

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