Analysis At Large: Dedicated To The Life And Wo...
Frida Kahlo participated in the "International Exhibition of Surrealism" in 1940 at the Galeria de Arte, Mexicano. There, she exhibited her two largest paintings: The Two Fridas and The Wounded Table (1940). Surrealist Andrew Breton considered Kahlo a surrealistic, a label Kahlo rejected, saying she just painted her reality. However, In 1945, when Don Jose Domingo Lavin asked Frida Kahlo to read the book Moses and Monotheism by Sigmund Freud - whose psychoanalysis works Surrealism is based on - and paint her understanding and interpretation of this book. Frida Kahlo painted Moses, and this painting was recognized as second prize at the annual art exhibition in the Palacio de Bellas Artes.Kahlo did not sell many paintings in her lifetime, although she painted occasional portraits on commission. She had only one solo exhibition in Mexico in her lifetime, in 1953, just a year before her death at the age of 47.Today, her works sell for very high prices. In May 2006, Frida Kahlo self-portrait, Roots, was sold for $5.62 million at a Sotheby's auction in New York, sets a record as the most expensive Latin American work ever purchased at auction, and also makes Frida Kahlo one of the highest-selling woman in art.
Analysis at Large: Dedicated to the Life and Wo...
Industrial IoT (IIoT) is a subset of the Internet of Things (IoT) revolution that refers to the application of IoT principles, technology, and approaches, specifically in industry, manufacturing, energy and similar sectors. For all industries, IIoT ultimately aims first at gathering and analyzing data from factory sensors and devices, and then secondly to make intelligent responses based on data-driven insights. Automated real-time responses can be implemented to significantly streamline performance.\r\nIIoT concepts are similar to other IoT concepts, in particular the networking together of numerous small devices, sensors, instruments, and actuators, to create the \u201cinternet of things\u201d, a convergence of networking and device technology. However, IIoT differs from common IoT examples like smart homes, in both the degree and scale of technologies that are connected. Smart home sensors can monitor temperature, and send mobile device notifications in emergencies. Comparatively, in larger industrial settings, IIoT may orchestrate the operations and interactions of tens of thousands of devices, sensors, and robots. This difference requires more complex implementation methods, including using IIoT platforms, sophisticated device management software, and custom integrated automation tools. \r\nIndustrial applications have become the perfect testing ground for upcoming IoT technologies, including the following:\r\n\r\nDigital Twins \u2014 Digital twins is an innovative way using computer modeling, the digital twin, to predict behaviors and anticipate problems through the life of a real world object, process, or ecosystem. Simulations using real-world data can be applied to the digital twin model and reveal predictive insights, which can be used in pre-designs, or to monitor existing machine performance, and for example, suggest maintenance schedules to maximize ROI.\r\nElectronic Logging Device (ELD) \u2014 IIoT extends beyond factory settings, demonstrated by vehicle sensors like electronic logging devices (ELD) used in commercial fleets. ELDs connect a series of on-vehicle sensors to monitor trip mileage, time, gas usage, and even brake deterioration. Firstly, these devices help replace error-prone paper logs, and secondly capture essential vehicle, trip, and driving data that can be used to optimize fleet operations.\r\nIntelligent Edge \u2014 \u201cThe Edge\u201d is a frame of reference where compute and storage resources are positioned in relation to the data generated by a IoT and mobile devices. The general IoT analytics model is that when data is generated from sensors it is communicated to a central server for processing and insights. Today, sensors are generating huge volumes of data, but require an almost instantaneous computation response to that data. In many instances, a central server, even if located on-premises in the next building, may introduce lag times that hamper performance. To solve this problem, computational and storage resources are placed closer to the sensor devices, or closer to \u201cthe edge\u201d. For example, on-board computers work with networked GPS systems in self-driving trucks to not only control the vehicle in the moment, but to coordinate with others on the road. Now, sensor data can be processed instantly where the data is created, at an intelligent edge.\r\nRadio-Frequency Identification (RFID) \u2014 RFID technology significantly enables the tracking and tagging of items with identification and detailed object information. RFID is a commonly known technology, with a long existence of over 70 years, but as the technologies in the RFID ecosystem have become cheaper, standard, and widely adopted, it has become a way for entire value chains to integrate and provide greater efficiencies, and innovative new services such as omnichannel customer engagements. \r\n\r\n"}},"@type":"Question","name":"How is IIOT used?","acceptedAnswer":"@type":"Answer","text":"IIOT has found many applications, most notable in smart factories that can monitor and control hundreds of variables on the factory floor. The following examples illustrate the variety of uses for IIoT.\r\n\r\nIndustrial Production Lines \u2014 The most common application for IIoT is production and manufacturing where IIoT machines and devices can self-monitor, analyze, optimize, and prevent problems to achieve greater throughput and less downtime.\r\nSupply Chain Integrations \u2014 IIoT combined with RFID technologies enables a new revolution of global supply chain cooperation. For participants, IIoT tracking at the item-level allows for waste reduction and time savings. Supply chain participants can now integrate and machine level between each other, and reap significant efficiencies.\r\nSmart Building Management \u2014 IIoT can combine with building management software to unify, simplify, and automate building management. With data from various system sensors, like climate controls, energy, water, the building can optimize systems to reduce consumption during down times. By layering data, like current building occupancy data drawn from security entry points, building usage can be mapped with granularity.\r\nHealthcare Patient and Inventory Management \u2014 Healthcare efficiency is a key concern especially in modern times when demand continues to challenge healthcare capacity. IIoT helps to alleviate pressure by providing a platform for monitoring nearly everything within a clinical environment from tools, inventory, patients, healthcare staff, operating rooms, etc. With a precise picture of all these elements, managing for efficiency becomes a possible game-changer.\r\nIn-store Retail Management and Omnichannel Marketing \u2014 Retail IIoT supply chains coupled with in-store IoT applications has opened up fertile fields of innovation in customer omnichannel engagements. Because of the exceptional ability to track products and coordinate how they are presented throughout the retail value chain, customers now can expect to engage with customers in a seamless manner from any touchpoint, whether that is digital, mobile, in-store, etc.\r\n\r\n","@type":"Question","name":"IOT vs. IIOT","acceptedAnswer":"@type":"Answer","text":"IoT is an acronym for Internet of Things, and refers to appliances, sensors, and otherwise common devices, like thermostats, lights, heating and cooling, that have been network enabled. Further, these devices can communicate amongst themselves, machine-to-machine (M2M), to perform simple tasks, like adjusting lighting to conserve energy. IoT fits most consumer models, and aims to add some layer of \u201cintelligence\u201d to common \u201cdumb\u201d devices.\r\nIndustrial Internet of Things (IIoT) refers to IoT devices and machines within industries like manufacturing, energy, or transportation. In the industrial context, robots, vehicles, and industrial lines can all be IIoT enabled. Unlike consumer IoT, more sophisticated IIoT orchestrates device automations to ensure that production matches real-time demands.\r\nThe table below illustrates a comparison of common aspects shared by both IIoT and IoT.\r\n\r\nAspect\r\n\r\nIndustrial IoT (IIoT)\r\n\r\nInternet of Things (IoT)\r\n\r\nApplication\r\n\r\nIndustrial applications, manufacturing, power and energy, etc.\r\n\r\nGeneral appliances, wearables, robots, sensors\r\n\r\nScale\r\n\r\nComplex and wide area networks, with thousands of connected devices\r\n\r\nSmall networks, with a handful to many devices\r\n\r\nData\r\n\r\nMassives volumes and velocity of data generation requires special storage and compute setups\r\n\r\nLow to high volumes of data dependent on application\r\n\r\nProgrammability\r\n\r\nRemote and on-site programming\r\n\r\nOff-site programming\r\n\r\nSecurity\r\n\r\nIndustrial sensitivity requires robust data security features\r\n\r\nPersonal data requires identity and access management\r\n\r\nLife cycle\r\n\r\nDesigned for long life cycles\r\n\r\nDesigned for consumer life cycles\r\n\r\n\r\n \r\n","@type":"Question","name":"IIOT in manufacturing","acceptedAnswer":"@type":"Answer","text":"Industrial internet of things empower manufacturers to leverage streaming data from all their connected devices and use powerful analytics to operate smarter.\r\nIIoT connected devices create a platform to be able to rapidly roll out new features. IIoT grants the ability to remotely monitor, manage, and analyze device data. From a product perspective, this allows designers to understand how products are being used, and engaged with, delivering incredible insight into new product features, and importantly product shortcomings. From a marketing perspective, this insight can be turned into innovative offers, bundles, and value-adding opportunities.\r\nReal-time performance monitoring is key to optimizing manufacturing processes. Traditionally, KPIs are used to baseline performance, and analytics can determine when deviations are detected from normal operations. Newer approaches now exist, like creating a virtual model of a physical asset to simulate its performance, known as Digital Twins.\r\nIIoT enables the integration of digital supply networks by connecting systems between suppliers, distributors, and eventually customers. Technologies like RFID, which enables item-level tracking throughout the supply chain, help extend the capabilities of IIoT.\r\nPredictive analytics uses the help of AI to process the massive volume of IIoT data and deliver forecasts that can ensure optimal maintenance thereby extending the lifetime of devices. In business scenarios, like expedited shipping, data from delivery vehicles can be used to forecast safety maintenance and keep vehicles roadworthy.\r\n","@type":"Question","name":"IIOT vs. manufacturing execution systems (MES)","acceptedAnswer":"@type":"Answer","text":"Some confusion develops around the industrial internet of things (IIoT) and manufacturing execution systems. MES applications have been in use since the 1990s, and in the wave of modern industrial smart technology adoption, IIoT has become synonymous with industrial automation, and Industry 4.0, spurring the notion that MES has been replaced. To clarify, the Industrial Internet of Things (IIoT) and manufacturing execution systems (MES) work together to provide a complete picture of a manufacturer\u2019s floor operations.\r\nFor many business benefits, data and its analysis has been recognized as a fundamental requirement in modern manufacturing. For instance, to perform tasks like root cause analysis, large quantities of data are needed. IIoT gathers much of this data, like contextual details from devices, sensors, machines and controllers. The MES combines the data from IIoT with its own context, like customer details, orders, products, recipes, billings, etc., to complete the total picture of operations within the wall of the factory.\r\nWhile both IIoT and MES collect data, the MES, in conjunction with team members, owns analysis and determination of situational changes in the ongoing manufacturing process. This means, installing just an IIoT will provide a communications backbone between and among devices, but it will not include the analytical brain to compile the context.\r\n","@type":"Question","name":"IIOT platforms","acceptedAnswer":"@type":"Answer","text":"IIoT Platforms are, like IIoT, also often confused with manufacturing execution systems (MES), however, they can both work together. In the sequence that leads data from where it is generated, gathered, analyzed, and eventually turned into actionable insights that leads to changes in the industrial process, IIoT platforms lie before the stage of turning insight into action. That stage belongs to the MES. The line is not clear cut though.\r\nIIoT platforms manage edge device connectivity, collect data, validate data, and can also perform analytics on data to a degree, sometimes employing AI and machine learning techniques. The MES can overlap, or integrate with an IIoT platform to provide the additional layer of orchestrating multiple disparate systems into one unified and optimized factory floor.\r\nAn end-to-end IIoT platform is capable of the following:\r\n\r\nApp development support for custom applications\r\nData collection and aggregation capabilities of edge devices\r\nIoT data modeling, advanced analytics, data visualization\r\nIIoT device management\r\nIndustrial security standards\r\nMachine-to-machine communication (M2M)\r\n\r\n"] } DOWNLOAD PENTAHODownloadPentaho READ OUR BLOGSRead OurBlogs GET SUPPORTGetSupport FIND TRAININGFindTraining FIND A PARTNERFind aPartner EXPLORE
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