Future industrial automation systems will execute a number of control and monitoring functions in central data centers. The cloud computing paradigm will reduce IT costs and enable small companies to flexibly automate production processes. Centralized control and monitoring across companies and domains will facilitate a novel smart ecosystem for industrial automation connecting both embedded devices and information systems. To realize this vision, a number of technical, economical, and social challenges need to be solved. This talk focuses on software architecture challenges for cloud-connected automation systems. It points out the architectural impact of critical non-functional properties, such as latency, security, and multi-tenancy.
Software Architecture in Process Automation: UML & the "Smart Factory"Heiko Koziolek
Distributed control systems are currently evolving towards Industrial Internet-of-Things (IIoT) systems. Still, they still suffer from complex commissioning processes that incur high costs. Researchers have proposed several so-called ''Plug and Produce'' (PnP) approaches, where commissioning shall be largely automated, but they have suffered from semantic ambiguities and usually rely on proprietary information models. We propose a novel reference architecture for PnP in IIoT systems, which is based on OPC UA and PLCopen standards and can reduce industrial device commissioning times across vendor products to a few seconds. Our proof-of-concept implementation can handle more than 500 signals per millisecond during runtime, sufficient for most application scenarios.
AI firsts: Leading from research to proof-of-conceptQualcomm Research
AI has made tremendous progress over the past decade, with many advancements coming from fundamental research from many decades ago. Accelerating the pipeline from research to commercialization has been daunting since scaling technologies in the real world faces many challenges beyond the theoretical work done in the lab. Qualcomm AI Research has taken on the task of not only generating novel AI research but also being first to demonstrate proof-of-concepts on commercial devices, enabling technology to scale in the real world. This presentation covers:
The challenges of deploying cutting-edge research on real-world mobile devices
How Qualcomm AI Research is solving system and feasibility challenges with full-stack optimizations to quickly move from research to commercialization
Examples where Qualcomm AI Research has had industrial or academic firsts
Solutions for ADAS and AI data engineering using OpenPOWER/POWER systemsGanesan Narayanasamy
The ultimate goal of ADAS feature development is to make our roads safer and better suited for fully autonomous vehicles in the long run. Still, manufacturers and buyers shouldn’t underestimate the importance of ADAS for meeting current automotive challenges. The most significant impact of advanced driver assistance systems is in providing drivers with essential information and automating difficult and repetitive tasks. This increases safety for everyone on the road
IoT is reshaping the manufacturing and industrial processes, effectively changing the paradigm from one of repair and replace to more of predict and prevent. Using data streaming from connected equipment and machinery, organizations can now monitor the health of their assets and effectively predict when and how an asset might fail. However, without the right data management strategy and tools, investments in IoT can yield limited results. Join Cloudera and Tata Consultancy Services (TCS) for a joint webinar to learn more about how organizations are using advanced analytics and machine learning to drive IoT enabled predictive maintenance.
Software Architecture in Process Automation: UML & the "Smart Factory"Heiko Koziolek
Distributed control systems are currently evolving towards Industrial Internet-of-Things (IIoT) systems. Still, they still suffer from complex commissioning processes that incur high costs. Researchers have proposed several so-called ''Plug and Produce'' (PnP) approaches, where commissioning shall be largely automated, but they have suffered from semantic ambiguities and usually rely on proprietary information models. We propose a novel reference architecture for PnP in IIoT systems, which is based on OPC UA and PLCopen standards and can reduce industrial device commissioning times across vendor products to a few seconds. Our proof-of-concept implementation can handle more than 500 signals per millisecond during runtime, sufficient for most application scenarios.
AI firsts: Leading from research to proof-of-conceptQualcomm Research
AI has made tremendous progress over the past decade, with many advancements coming from fundamental research from many decades ago. Accelerating the pipeline from research to commercialization has been daunting since scaling technologies in the real world faces many challenges beyond the theoretical work done in the lab. Qualcomm AI Research has taken on the task of not only generating novel AI research but also being first to demonstrate proof-of-concepts on commercial devices, enabling technology to scale in the real world. This presentation covers:
The challenges of deploying cutting-edge research on real-world mobile devices
How Qualcomm AI Research is solving system and feasibility challenges with full-stack optimizations to quickly move from research to commercialization
Examples where Qualcomm AI Research has had industrial or academic firsts
Solutions for ADAS and AI data engineering using OpenPOWER/POWER systemsGanesan Narayanasamy
The ultimate goal of ADAS feature development is to make our roads safer and better suited for fully autonomous vehicles in the long run. Still, manufacturers and buyers shouldn’t underestimate the importance of ADAS for meeting current automotive challenges. The most significant impact of advanced driver assistance systems is in providing drivers with essential information and automating difficult and repetitive tasks. This increases safety for everyone on the road
IoT is reshaping the manufacturing and industrial processes, effectively changing the paradigm from one of repair and replace to more of predict and prevent. Using data streaming from connected equipment and machinery, organizations can now monitor the health of their assets and effectively predict when and how an asset might fail. However, without the right data management strategy and tools, investments in IoT can yield limited results. Join Cloudera and Tata Consultancy Services (TCS) for a joint webinar to learn more about how organizations are using advanced analytics and machine learning to drive IoT enabled predictive maintenance.
This presentation was made on June 11, 2020.
Recording from the presentation can be viewed here: https://youtu.be/02Gb062U_M4
The manufacturing industry is adopting artificial intelligence (AI) at a fast rate. This century-old industry is complex but has seen constant transformation across all of its facets.
Led by big data analytics, miniaturization of sensors enabling the Internet of Things (IoT), and, now, AI machine learning (ML), manufacturers everywhere have embarked on an AI transformation that is opening up potential new revenue streams as well taking costs and time out of existing processes.
This talk will walk through a use case for enterprise AI solutions within the manufacturing sector. We will discuss the challenges, motivation, and tool selection process, then cover the solution development in detail.
Speaker Bio:
eRic is armed with the technical know-how of Data Science, Machines Learning, and Big Data Analytics. He. is equipped with skill-sets to value-add businesses exploring into areas of Artificial Intelligence (AI) with an AI consultation approach. Translating BDA, Machine Learning, and AI into Business Values.
eRic CHOO had spent the last 8 years in the IT industry from integration of Infrastructure (Storage and Back-up) solutions to Advance Analytics Software specializing in BDA, Machines Learning, and AI. Before joining the IT industry, he had vast experience in the Semiconductor industry, thus a deep understanding in advance manufacturing processes.
SIONG Jong Hang works as a Solutions Engineer/Data Scientist at H2O.ai based in Singapore where he helps business, government, academia, and non-profit organizations in their transformation into AI. Prior to H2O.ai, he has worked at the Quant Group at Bank of America Merrill Lynch in Hong Kong and Teradata in Singapore as a data scientist. He has completed data science projects for various verticals in Europe and Asia. After hours, he’s an avid learner and has attained 100 MOOC certificates in various fields such as AI, science, engineering, and maths. He has also authored articles to instill interest in science, technology as well as AI.
industry overview of technology (A.I, C-V2X, 5G/6G) and application (DMS/ADAS, A.V, A.R, ...) trends being deployed or coming in near future, to adress the challenge of the Decade for Road Safety: cutting deaths and injuries by 50% by 2030. Taking this opportunity to identify some 5G/B5G/6G promising features to support the vision.
Necessity of the Digital Twin and Digital ThreadMarc Lind
As products move to include connectivity, sensors and intelligence many people are working on the infrastructure to support the data streaming back from the field. Big data clouds, data lakes and analytics initiatives have become the focus in many cases. Yet, without accurate context – Digital Twin – time series data generated during production and ongoing operation is difficult or even impossible to understand and analyze. In addition, the ability to interpret and act upon these data often require traceability to prior information from related revisions – Digital Thread. To complicate matters further as artificial intelligence / cognitive computing is introduced the necessity becomes even greater.
Artificial Intelligence Can Now Copy Your Voice: What Does That Mean For Humans?Bernard Marr
Artificial intelligence (AI) is a powerful tool. It has become so sophisticated that it can create artificial voices that sound like real voices. It is being used in many ways today to create audio for products, digital assistants and more. It now requires just seconds of real audio to train to make the reality of misuse very real.
To enable industrial companies to identify startups that can emerge as capable technology partners for the long term, we have compiled a catalogue of Industrial Deep Tech startups from the Forge Portfolio, to serve as a valuable resource.
Startups in the Forge Portfolio categorised under Industrial Deep Tech address manufacturing as a sector - apart from other core industrial sectors such as power, energy, resources, transportation, logistics, defence, aerospace, space etc. These startups are broadly categorised under Digital Technology (DT) and Operations Technology (OT).
Digital Technology addresses process innovations augmenting capabilities in the areas of digitisation, digitalisation, automation (robotics), analytics, autonomy, and intelligence. Operations Technology relates to innovations in the areas of design (product engineering services), materials (carbon composites), production & processing (additive manufacturing suppliers for new end product categories that traditional fabrication factories don't serve), and business models (manufacturing aggregator platform etc.).
This catalogue of 20 Industrial Tech startups is further organized into 7 Industrial Digital Transformation Themes which are further split into 11 sub-themes - that broadly outline operational capabilities, emerging/futuristic technology domains, or product categories in specific market segments that help organise and prioritise the various opportunities for value creation by industrial companies.
Gemini is the outcome of large-scale collaborative efforts by teams across Google and Google DeepMind 🧠, who built from the ground up to be multimodal.
Gemini is now available in Gemini Nano and Gemini Pro sizes on Google products- Google Pixel 8 and Google Bard respectively.
Unlike other generative AI multimodal models, Google's Gemini appears to be more product-focused, which is either integrated into the company's ecosystem or better plans to be.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/12/vitis-and-vitis-ai-application-acceleration-from-cloud-to-edge-a-presentation-from-xilinx/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Vinod Kathail, Fellow and Chief Architect at Xilinx, presents the “Vitis and Vitis AI: Application Acceleration from Cloud to Edge” tutorial at the September 2020 Embedded Vision Summit.
Xilinx SoCs and FPGAs provide significant advantages in throughput, latency, and energy efficiency for production deployments of compute-intensive applications when compared to CPUs and GPUs. Over the last decade, FPGAs have evolved into highly configurable devices that provide on-chip heterogeneous multi-core CPUs, domain-specific programmable accelerators and “any-to-any” interface connectivity.
Today, the Xilinx Vitis Unified Software Platform supports high-level programming in C, C++, OpenCL, and Python, enabling developers to build and seamlessly deploy applications on Xilinx platforms including Alveo cards, FPGA instances in the cloud, and embedded devices. Moreover, Vitis enables the acceleration of large-scale data processing and machine learning applications using familiar high-level frameworks, such as TensorFlow and SPARK. This presentation provides an overview of the Vitis Software platform and the accelerated Vitis Vision Library, which enables customizable functions such as image signal processing, adaptable AI inference, 3D reconstruction and motion analysis.
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/tools-for-creating-next-gen-computer-vision-apps-on-snapdragon-a-presentation-from-qualcomm/
Judd Heape, Vice President of Product Management for Camera, Computer Vision and Video Technology at Qualcomm, presents the “Tools for Creating Next-Gen Computer Vision Apps on Snapdragon” tutorial at the May 2022 Embedded Vision Summit.
The Snapdragon Mobile Platform powers the world’s best smartphones, XR headsets, PCs, wearables, cars and IoT products. Thanks to Snapdragon, these products feature powerful computer vision technologies that you can tap into to build next-gen apps. Inside Snapdragon is a hardware engine dedicated to computer vision–the Engine for Visual Analytics (EVA). EVA hardware acceleration gives developers access to high-performance, low-power computer vision functions to enhance apps that rely on advanced camera or video processing.
The EVA includes a motion processing unit, a feature descriptor unit, a depth estimation unit, a geometric correction unit and an object detection unit. These blocks power high-level functions such as electronic image stabilization, multi-frame HDR, face detection and real-time bokeh. In this presentation, Heape does a deep-dive into EVA’s Software Developer Kit (SDK) and available APIs, such as Optical Flow and Depth from Stereo, and explores how these features can be integrated into your apps.
AI model efficiency is crucial for making AI ubiquitous, leading to smarter devices and enhanced lives. Besides the performance benefit, quantized neural networks also increase power efficiency for two reasons: reduced memory access costs and increased compute efficiency.
The quantization work done by the Qualcomm AI Research team is crucial in implementing machine learning algorithms on low-power edge devices. In network quantization, we focus on both pushing the state-of-the-art (SOTA) in compression and making quantized inference as easy to access as possible. For example, our SOTA work on oscillations in quantization-aware training that push the boundaries of what is possible with INT4 quantization. Furthermore, for ease of deployment, the integer formats such as INT16 and INT8 give comparable performance to floating point, i.e., FP16 and FP8, but have significantly better performance-per-watt performance. Researchers and developers can make use of this quantization research to successfully optimize and deploy their models across devices with open-sourced tools like AI Model Efficiency Toolkit (AIMET).
Presenters: Tijmen Blankevoort and Chirag Patel
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/07/the-data-driven-engineering-revolution-a-presentation-from-edge-impulse/
Zach Shelby, Co-founder and CEO of Edge Impulse, presents the “Data-Driven Engineering Revolution” tutorial at the May 2021 Embedded Vision Summit.
In this talk, IoT industry pioneer and Edge Impulse co-founder Zach Shelby shares insights about how machine learning is revolutionizing embedded engineering. Advances in silicon and deep learning are enabling embedded machine learning (TinyML) to be deployed where data is born, from industrial sensor data to audio and video.
Shelby explains the new paradigm of data-driven engineering with ML, showing how developers are using data instead of code to drive algorithm innovation. To support widespread deployment, ML workloads need to run on embedded computing targets from MCUs to GPUs, with MLOps processes to support efficient development and deployment. Industrial, logistics and health markets are particularly ripe to deploy this data-driven approach, and Shelby highlights several exciting case studies.
Advantages of Artificial Intelligence - Avantika UniversityAvantika University
The advantages of Artificial intelligence are uncountable in the professional sector. Have a look at some of the advantages.
To know more details, visit us at : https://www.avantikauniversity.edu.in/engineering-colleges/advantages-of-artificial-intelligence.php
This presentation is present the history and invention of the artificial intelligence . This presentation is express the power of artificial intelligence in present and future. In this presentation we explain the natural language programming, speech recognition, computer vision,robotic, automatic programming, quantum computing.This presentation also express the power of neural network power in present and future
This presentation was made on June 11, 2020.
Recording from the presentation can be viewed here: https://youtu.be/02Gb062U_M4
The manufacturing industry is adopting artificial intelligence (AI) at a fast rate. This century-old industry is complex but has seen constant transformation across all of its facets.
Led by big data analytics, miniaturization of sensors enabling the Internet of Things (IoT), and, now, AI machine learning (ML), manufacturers everywhere have embarked on an AI transformation that is opening up potential new revenue streams as well taking costs and time out of existing processes.
This talk will walk through a use case for enterprise AI solutions within the manufacturing sector. We will discuss the challenges, motivation, and tool selection process, then cover the solution development in detail.
Speaker Bio:
eRic is armed with the technical know-how of Data Science, Machines Learning, and Big Data Analytics. He. is equipped with skill-sets to value-add businesses exploring into areas of Artificial Intelligence (AI) with an AI consultation approach. Translating BDA, Machine Learning, and AI into Business Values.
eRic CHOO had spent the last 8 years in the IT industry from integration of Infrastructure (Storage and Back-up) solutions to Advance Analytics Software specializing in BDA, Machines Learning, and AI. Before joining the IT industry, he had vast experience in the Semiconductor industry, thus a deep understanding in advance manufacturing processes.
SIONG Jong Hang works as a Solutions Engineer/Data Scientist at H2O.ai based in Singapore where he helps business, government, academia, and non-profit organizations in their transformation into AI. Prior to H2O.ai, he has worked at the Quant Group at Bank of America Merrill Lynch in Hong Kong and Teradata in Singapore as a data scientist. He has completed data science projects for various verticals in Europe and Asia. After hours, he’s an avid learner and has attained 100 MOOC certificates in various fields such as AI, science, engineering, and maths. He has also authored articles to instill interest in science, technology as well as AI.
industry overview of technology (A.I, C-V2X, 5G/6G) and application (DMS/ADAS, A.V, A.R, ...) trends being deployed or coming in near future, to adress the challenge of the Decade for Road Safety: cutting deaths and injuries by 50% by 2030. Taking this opportunity to identify some 5G/B5G/6G promising features to support the vision.
Necessity of the Digital Twin and Digital ThreadMarc Lind
As products move to include connectivity, sensors and intelligence many people are working on the infrastructure to support the data streaming back from the field. Big data clouds, data lakes and analytics initiatives have become the focus in many cases. Yet, without accurate context – Digital Twin – time series data generated during production and ongoing operation is difficult or even impossible to understand and analyze. In addition, the ability to interpret and act upon these data often require traceability to prior information from related revisions – Digital Thread. To complicate matters further as artificial intelligence / cognitive computing is introduced the necessity becomes even greater.
Artificial Intelligence Can Now Copy Your Voice: What Does That Mean For Humans?Bernard Marr
Artificial intelligence (AI) is a powerful tool. It has become so sophisticated that it can create artificial voices that sound like real voices. It is being used in many ways today to create audio for products, digital assistants and more. It now requires just seconds of real audio to train to make the reality of misuse very real.
To enable industrial companies to identify startups that can emerge as capable technology partners for the long term, we have compiled a catalogue of Industrial Deep Tech startups from the Forge Portfolio, to serve as a valuable resource.
Startups in the Forge Portfolio categorised under Industrial Deep Tech address manufacturing as a sector - apart from other core industrial sectors such as power, energy, resources, transportation, logistics, defence, aerospace, space etc. These startups are broadly categorised under Digital Technology (DT) and Operations Technology (OT).
Digital Technology addresses process innovations augmenting capabilities in the areas of digitisation, digitalisation, automation (robotics), analytics, autonomy, and intelligence. Operations Technology relates to innovations in the areas of design (product engineering services), materials (carbon composites), production & processing (additive manufacturing suppliers for new end product categories that traditional fabrication factories don't serve), and business models (manufacturing aggregator platform etc.).
This catalogue of 20 Industrial Tech startups is further organized into 7 Industrial Digital Transformation Themes which are further split into 11 sub-themes - that broadly outline operational capabilities, emerging/futuristic technology domains, or product categories in specific market segments that help organise and prioritise the various opportunities for value creation by industrial companies.
Gemini is the outcome of large-scale collaborative efforts by teams across Google and Google DeepMind 🧠, who built from the ground up to be multimodal.
Gemini is now available in Gemini Nano and Gemini Pro sizes on Google products- Google Pixel 8 and Google Bard respectively.
Unlike other generative AI multimodal models, Google's Gemini appears to be more product-focused, which is either integrated into the company's ecosystem or better plans to be.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/12/vitis-and-vitis-ai-application-acceleration-from-cloud-to-edge-a-presentation-from-xilinx/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Vinod Kathail, Fellow and Chief Architect at Xilinx, presents the “Vitis and Vitis AI: Application Acceleration from Cloud to Edge” tutorial at the September 2020 Embedded Vision Summit.
Xilinx SoCs and FPGAs provide significant advantages in throughput, latency, and energy efficiency for production deployments of compute-intensive applications when compared to CPUs and GPUs. Over the last decade, FPGAs have evolved into highly configurable devices that provide on-chip heterogeneous multi-core CPUs, domain-specific programmable accelerators and “any-to-any” interface connectivity.
Today, the Xilinx Vitis Unified Software Platform supports high-level programming in C, C++, OpenCL, and Python, enabling developers to build and seamlessly deploy applications on Xilinx platforms including Alveo cards, FPGA instances in the cloud, and embedded devices. Moreover, Vitis enables the acceleration of large-scale data processing and machine learning applications using familiar high-level frameworks, such as TensorFlow and SPARK. This presentation provides an overview of the Vitis Software platform and the accelerated Vitis Vision Library, which enables customizable functions such as image signal processing, adaptable AI inference, 3D reconstruction and motion analysis.
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/tools-for-creating-next-gen-computer-vision-apps-on-snapdragon-a-presentation-from-qualcomm/
Judd Heape, Vice President of Product Management for Camera, Computer Vision and Video Technology at Qualcomm, presents the “Tools for Creating Next-Gen Computer Vision Apps on Snapdragon” tutorial at the May 2022 Embedded Vision Summit.
The Snapdragon Mobile Platform powers the world’s best smartphones, XR headsets, PCs, wearables, cars and IoT products. Thanks to Snapdragon, these products feature powerful computer vision technologies that you can tap into to build next-gen apps. Inside Snapdragon is a hardware engine dedicated to computer vision–the Engine for Visual Analytics (EVA). EVA hardware acceleration gives developers access to high-performance, low-power computer vision functions to enhance apps that rely on advanced camera or video processing.
The EVA includes a motion processing unit, a feature descriptor unit, a depth estimation unit, a geometric correction unit and an object detection unit. These blocks power high-level functions such as electronic image stabilization, multi-frame HDR, face detection and real-time bokeh. In this presentation, Heape does a deep-dive into EVA’s Software Developer Kit (SDK) and available APIs, such as Optical Flow and Depth from Stereo, and explores how these features can be integrated into your apps.
AI model efficiency is crucial for making AI ubiquitous, leading to smarter devices and enhanced lives. Besides the performance benefit, quantized neural networks also increase power efficiency for two reasons: reduced memory access costs and increased compute efficiency.
The quantization work done by the Qualcomm AI Research team is crucial in implementing machine learning algorithms on low-power edge devices. In network quantization, we focus on both pushing the state-of-the-art (SOTA) in compression and making quantized inference as easy to access as possible. For example, our SOTA work on oscillations in quantization-aware training that push the boundaries of what is possible with INT4 quantization. Furthermore, for ease of deployment, the integer formats such as INT16 and INT8 give comparable performance to floating point, i.e., FP16 and FP8, but have significantly better performance-per-watt performance. Researchers and developers can make use of this quantization research to successfully optimize and deploy their models across devices with open-sourced tools like AI Model Efficiency Toolkit (AIMET).
Presenters: Tijmen Blankevoort and Chirag Patel
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/07/the-data-driven-engineering-revolution-a-presentation-from-edge-impulse/
Zach Shelby, Co-founder and CEO of Edge Impulse, presents the “Data-Driven Engineering Revolution” tutorial at the May 2021 Embedded Vision Summit.
In this talk, IoT industry pioneer and Edge Impulse co-founder Zach Shelby shares insights about how machine learning is revolutionizing embedded engineering. Advances in silicon and deep learning are enabling embedded machine learning (TinyML) to be deployed where data is born, from industrial sensor data to audio and video.
Shelby explains the new paradigm of data-driven engineering with ML, showing how developers are using data instead of code to drive algorithm innovation. To support widespread deployment, ML workloads need to run on embedded computing targets from MCUs to GPUs, with MLOps processes to support efficient development and deployment. Industrial, logistics and health markets are particularly ripe to deploy this data-driven approach, and Shelby highlights several exciting case studies.
Advantages of Artificial Intelligence - Avantika UniversityAvantika University
The advantages of Artificial intelligence are uncountable in the professional sector. Have a look at some of the advantages.
To know more details, visit us at : https://www.avantikauniversity.edu.in/engineering-colleges/advantages-of-artificial-intelligence.php
This presentation is present the history and invention of the artificial intelligence . This presentation is express the power of artificial intelligence in present and future. In this presentation we explain the natural language programming, speech recognition, computer vision,robotic, automatic programming, quantum computing.This presentation also express the power of neural network power in present and future
This presentation provides a brief overview of APM solutions for the Azure cloud computing platform. We identify three challenges unique to cloud computing which APM can address, and we summarize which APM techniques can be applied in IaaS, PaaS, and SaaS application architectures. To illustrate APM techniques for IaaS and PaaS we look at a variety APM offers in the Azure marketplace, including Riverbed AppInternals, Microsoft Application Insights, and NewRelic. To illustrate APM techniques for SaaS, we look at how SharePoint Online can be instrumented using JavaScript injection. This presentation was prepared and delivered by Ian Downard to the Portland Azure User Group on March 28th, 2016.
Why and How to Monitor App Performance in AzureIan Downard
This presentation provides a brief overview of APM solutions for the Azure cloud computing platform. We discuss three challenges unique to cloud computing which APM can address, and we summarize which APM techniques can be applied in IaaS, PaaS, and SaaS application architectures. To illustrate APM techniques for IaaS and PaaS we look at a variety APM offers in the Azure marketplace, including Riverbed AppInternals, Microsoft Application Insights, and New Relic. To illustrate APM techniques for SaaS, we look at how SharePoint Online can be instrumented using JavaScript injection. This presentation was prepared and delivered by Ian Downard to the Portland Azure User Group on March 28th, 2016, in Portland Oregon.
2011.10.13 - Annonces IBM pour Cloud Builders - Loic SimonClub Alliances
Deck exploité par Loic Simon et Patrice Fontaine d'IBM le 13 octobre 2011 dans le cadre de l'atelier du Best'Event de Best'Ware sur les Annonces Cloud IBM de la veille pertinentes pour les Cloud Builders.
Flexible and Scalable Integration in the Automation Industry/Industrial IoTconfluent
Speaker: Kai Waehner, Technology Evangelist, Confluent
Kafka-Native, End-to-End IIoT Data Integration and Processing with Kafka Connect, KSQL, and PLC4X
IIoT / Industry 4.0 with Apache Kafka, Connect, KSQL, Apache PLC4X Kai Wähner
Data integration and processing is a huge challenge in Industrial IoT (IIoT, aka Industry 4.0 or Automation Industry) due to monolithic systems and proprietary protocols. Apache Kafka, its ecosystem (Kafka Connect, KSQL) and Apache PLC4X are a great open source choice to implement this integration end to end in a scalable, reliable and flexible way.
This blog post covers a high level overview about the challenges and a good, flexible architecture. At the end, I share a video recording and the corresponding slide deck. These provide many more details and insights.
Apache Kafka is the De-facto Standard for Real-Time Event Streaming. It provides
Open Source (Apache 2.0 License)
Global-scale
Real-time
Persistent Storage
Stream Processing
PCL4X allows vertical integration and to write software independent of PLCs using JDBC-like adapters for various protocols like Siemens S7, Modbus, Allen Bradley, Beckhoff ADS, OPC-UA, Emerson, Profinet, BACnet, Ethernet.
Github example: https://github.com/kaiwaehner/iiot-integration-apache-plc4x-kafka-connect-ksql-opc-ua-modbus-siemens-s7
More details: http://www.kai-waehner.de/blog/2019/09/02/iiot-data-integr…and-apache-plc4x/
Video Recording: https://youtu.be/RWKggid25ds
SCADA a gyakorlatban - Accenture Industry X.0 MeetupAccenture Hungary
Július utolsó délutánján az ipari SCADA rendszerről, annak fejlődéséről és jövőjéről beszélgettünk.
Meséltünk a jelenlegi piacvezető SCADA termékekről és felhasználási területeikről, bemutattunk egy tipikus SCADA rendszer felépítést, kitértünk az IT security és SCADA rendszerek integrációjára is. Szót ejtettünk a SCADA vs. MES vs. Connected platform versenyről, érintettük a Digital Twin és Thread rendszereket, melyekben a SCADA egy nagyon fontos alkotóelem lehet.
A meetup során láthattátok, hogyan épül fel egy SCADA project, sőt, egy példa kapcsán kötetlenül beszélgettünk jelen megoldásunkról, és a továbbfejlesztés lehetőségeiről. Néhány mondatot szenteltünk a SCADA jövőjének is, hiszen ebben a SCADA az AR/VR technológiákkal integrálva jelenik meg. Ez olyan új lehetőségekkel szolgál, mint hogy virtuális környezetben bejárhatjuk a technológiát és valós idejű adatokat láthatunk a berendezés mellett; vagy a berendezés meghibásodás estén a javítási instrukciók a szemünk előtt folyamatosan jelennek meg.
From the Gaming Scalability event, June 2009 in London (http://gamingscalability.org).
Simon will discuss some of the key components of a compute grid infrastructure and highlight some of the key challenges organisations have to meet as their compute grids expand. Simon will also discuss one organisation within the spread betting industry who has recently started using grid technology. Finally Simon will describe how compute grids within the capital markets are beginning to resemble private clouds, and how the underlying infrastructure needs to change to enable these organisation to support a much wider range of applications running on the grid.
Simon Waterer is a Senior Solutions Architect with Platform Computing, a leading provider of HPC software. Since joining Platform, Simon has worked with a number of clients within the capital markets and insurance industry to understand their grid computing requirements. Recently Simon has worked with leading organisations within the spread betting industry who also have distributed processing requirements. Prior to working with grid technology Simon has had experience working with a number of other middleware technologies including data caching, messaging middleware and event stream processing.
Bottleneck Identification and Performance Modeling of OPC UA Communication Mo...Heiko Koziolek
The OPC UA communication architecture is currently becoming an integral part of industrial automation systems, which control complex production processes, such as electric power generation or paper production. With a recently released extension for pub/sub communication, OPC UA can now also support fast cyclic control applications, but the bottlenecks of OPC UA implementations and their scalability on resource-constrained industrial devices are not yet well understood. Former OPC UA performance evaluations mainly concerned client/server round-trip times or focused on jitter, but did not explore resource bottlenecks or create predictive performance models. We have carried out extensive performance measurements with OPC UA client/server and pub/sub communication and created a CPU utilization prediction model based on linear regression that can be used to size hardware environments. We found that the server CPU is the main bottleneck for OPC UA pub/sub communication, but allows a throughput of up to 40,000 signals per second on a Raspberry Pi Zero. We also found that the client/server session management overhead can severely impact performance, if more than 20 clients access a single server.
Architectural Decision Forces at Work: Experiences in an Industrial Consultan...Heiko Koziolek
The concepts of decision forces and the decision forces viewpoint were proposed to help software architects to make architectural decisions more transparent and the documentation of their rationales more explicit. However, practical experience reports and guidelines on how to use the viewpoint in typical industrial project setups are not available. Existing works mainly focus on basic tool support for the documentation of the viewpoint or show how forces can be used as part of focused architecture review sessions. With this paper, we share experiences and lessons learned from applying the decision forces viewpoint in a distributed industrial project setup, which involves consultants supporting architects during the re-design process of an existing large software system. Alongside our findings, we describe new forces that can serve as template for similar projects, discuss challenges applying them in a distributed consultancy project, and share ideas for potential extensions.
OpenPnP: a Plug-and-Produce Architecture for the Industrial Internet of ThingsHeiko Koziolek
Industrial control systems are complex, software-intensive systems that manage mission-critical production processes. Commissioning such systems requires installing, configuring, and integrating thousands of sensors, actuators, and controllers and is still a largely manual and costly process. Therefore, practitioners and researchers have been working on ``plug and produce'' approaches that automate commissioning for more than 15 years, but have often focused on network discovery and proprietary technologies. We introduce the vendor-neutral OpenPnP reference architecture, which can largely automate the configuration and integration tasks for commissioning. Using an example implementation, we demonstrate that OpenPnP can reduce the configuration and integration effort up to 90 percent and scales up to tens of thousands of communicated signals per second for large Industrial Internet-of-Things (IIoT) systems. OpenPnP can serve as a template for practitioners implementing IIoT applications throughout the automation industry and streamline commissioning processes in many thousands of control system installations.
Tool-Driven Technology Transfer in Software EngineeringHeiko Koziolek
This talk presentst the tool-driven technology transfer process ABB Corporate Research applies in selected software engineering University collaborations. As an example, we have created an add-in to a popular UML tool and developed the tooling in close interaction with the target users. Centering the technology transfer around tool implementations brings many benefits such as the need to make conceptual contributions applicable and the ability to quickly benefit from the new concepts. A challenge to this form of technology transfer is the long-term commitment to the maintenance of the tooling, which we try to address by creating an open developer community. Tool-driven technology transfer projects have proven to be valuable a instrument of bringing advanced software engineering technologies into our organization.
Distributed control systems are currently evolving towards Industrial Internet-of-Things (IIoT) systems. Still, they still suffer from complex commissioning processes that incur high costs. Researchers have proposed several so-called ''Plug and Produce'' (PnP) approaches, where commissioning shall be largely automated, but they have suffered from semantic ambiguities and usually rely on proprietary information models. This talk introduces a novel reference architecture for PnP in IIoT systems, which is based on OPC UA and PLCopen standards and can reduce industrial device commissioning times across vendor products to a few seconds. Our proof-of-concept implementation can handle more than 500 signals per millisecond during runtime, sufficient for most application scenarios.
Distributed control systems for manufacturing are currently evolving towards Industrial Internet-of-Things (IIoT) systems. Sensors and actuators get equipped with internet connectivity, which allows them to interface with cloud platforms. This potentially enables a number of application cases. However, industrial "things" may be much more complex and more resource-constrained than typical consumer space "things". This talk provides an overview of Industrial IoT application cases and sketches various challenges for researchers and practitioners using the example of turning a level sensor for industrial tanks into an IoT device.
Plug-and-Produce based on Standardized Industrie 4.0 Asset Admin ShellsHeiko Koziolek
Engineering and commissioning field devices and production modules in typical manufacturing settings is today still a largely manual and often error-prone process. Most proposed Plug&Produce approaches rely on proprietary technologies, device descriptions, and device functionalities and thus cannot incorporate devices from different vendors. In this contribution, we propose a minimal, but expressive AAS structure that is fully based on industry standards and Namur recommendations. We show how this AAS structure can be mapped to different communication technologies, such as OPC UA and MQTT. As a proof-of-concept, we have implemented a prototype using the proposed AAS structure to realize a restricted device-level PnP scenario. Due to the use of standards, our results can be easily reproduced by researchers and practitioners, so that a broad applicability of our concepts is possible.
Despite significant scientific research, systematic performance engineering techniques are still hardly used in industry, as many practitioners rely on ad-hoc performance firefighting. It is still not well understood where more sophisticated performance modeling approaches are appropriate and the maturity of the existing tools and processes can be improved. While there have been several industrial case studies on performance modeling in the last few years, more experience is needed to better understand the constraints in practice and to optimize existing tool-chains.
I gave a talk summarizing six years of performance modeling at ABB. In three projects, different approaches to performance modeling were taken, and experiences on the capabilities and limitations of existing tools were gathered. The talk reports on several lessons learned from these projects, for example the need for more efficient performance modeling and the integration of measurement and modeling tools.
MORPHOSIS: A Case Study on Lightweight Architecture Sustainability AnalysisHeiko Koziolek
Managing the cost-effective evolution of industrial
software systems is a challenging task because of their complexity
and long lifetimes. Limited pro-active evolution planning
and software architecture erosion often lead to huge maintenance
costs in such systems. However, formerly researched
approaches for evolution scenario analysis and architecture
enforcement are only reluctantly applied by practitioners due
to their perceived overhead and high costs. We have applied
several recent sustainability evaluation and improvement approaches
in a case study to the software architecture of a large
industrial software system currently under development at
ABB. We combined our selection of approaches in a lightweight
method called MORPHOSIS, for which this paper presents
experiences and lessons learned. We found that reasonable
sustainability evaluation and improvement is possible already
with limited efforts.
Systematic decision support for architectural design decisions is a major concern for software architects of evolving service-oriented systems. In practice, architects often analyse the expected performance and reliability of design alternatives based on prototypes or former experience. Model-driven prediction methods claim to uncover the tradeoffs between different alternatives quantitatively while being more cost-effective and less error-prone. However, they often suffer from weak tool support and focus on single quality attributes. Furthermore, there is limited evidence on their effectiveness
based on documented industrial case studies. Thus, we have applied a novel, model-driven prediction method called Q-ImPrESS on a large-scale process control system consisting of several million lines of code from the automation domain to evaluate its evolution scenarios. This presentation reports our experiences with the method and lessons learned. Benefits of Q-ImPrESS are the good architectural decision support and comprehensive tool framework, while one drawback is the time-consuming data
collection.
Towards an Architectural Style for Multi-tenant Software ApplicationsHeiko Koziolek
Multi-tenant software applications serve different organizations from a single instance and help to save development, maintenance, and administration costs. The architectural concepts of these applications and their relation to emerging platform-as-a-service (PaaS) environments are still not well understood, so that it is hard for many developers to design and implement such an application. Existing attempts at a structured documentation of the underlying concepts are either technology-specific or restricted to certain details. We propose documenting the concepts as a new architectural style. This paper initially describes the architectural properties, elements, views, and constraints of this style. We illustrate how the architectural elements are implemented in current PaaS environments, such as Force.com, Windows Azure, and Google App Engine.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
18. ABB partnered with cloud provider GlobaLogix
to provide a hosted version of SCADAVantage (SaaS)
RTUs triggering fast, basic control on-site
High latency SCADA functionality hosted
in 53 data centers in North America, regional proximity
But: no horizontal scaling, no elasticity
Architecture Challenges
Latency
[http://www.abb.com/cawp/seitp202/cf46b46446b6f83985257b7a00488357.aspx]
Devices connected to web services (done today in some areas)
Internet-of-things, devices talking to each other
Higher level MES, SCADA, ERP in the cloud: fleet management, etc.Still most software on premises
Partially moving upper levels off premises
To realize this vision, a number of technical, economical, and social challenges need to be solved. This talk focuses on software architecture challenges for cloud-connected automation systems. It points out the architectural impact of critical non-functional properties.
Stuxnet/Duqu shock: currently lots of emphasis on cyber security„Closed world assumption“ in current system architecturesNo outside network connection from sensitive plantsLimited security measures for Internet-based attacksCustomers afraid/reluctant to store data outside their reach