Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Securing your Kubernetes cluster_ a step-by-step guide to success !
SPICE MODEL of RN1102FT in SPICE PARK
1. Device Modeling Report
COMPONENTS: BRT
PART NUMBER: RN1102FT
MANUFACTURER: TOSHIBA
Bee Technologies Inc.
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
2. PSpice
model Model description
parameter
IS Saturation Current
BF Ideal Maximum Forward Beta
NF Forward Current Emission Coefficient
VAF Forward Early Voltage
IKF Forward Beta Roll-off Knee Current
ISE Non-ideal Base-Emitter Diode Saturation Current
NE Non-ideal Base-Emitter Diode Emission Coefficient
BR Ideal Maximum Reverse Beta
NR Reverse Emission Coefficient
VAR Reverse Early Voltage
IKR Reverse Beta Roll-off Knee Current
ISC Non-ideal Base-Collector Diode Saturation Current
NC Non-ideal Base-Collector Diode Emission Coefficient
NK Forward Beta Roll-off Slope Exponent
RE Emitter Resistance
RB Base Resistance
RC Series Collector Resistance
CJE Zero-bias Emitter-Base Junction Capacitance
VJE Emitter-Base Junction Potential
MJE Emitter-Base Junction Grading Coefficient
CJC Zero-bias Collector-Base Junction Capacitance
VJC Collector-base Junction Potential
MJC Collector-base Junction Grading Coefficient
FC Coefficient for Onset of Forward-bias Depletion
Capacitance
TF Forward Transit Time
XTF Coefficient for TF Dependency on Vce
VTF Voltage for TF Dependency on Vce
ITF Current for TF Dependency on Ic
PTF Excess Phase at f=1/2pi*TF
TR Reverse Transit Time
EG Activation Energy
XTB Forward Beta Temperature Coefficient
XTI Temperature Coefficient for IS
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
3. Input voltage vs. output current (ON characteristics)
Circuit simulation result
Evaluation circuit
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
4. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Vce = 0.2 V
VI(ON) (V)
Ic(mA) Error (%)
Datasheet Simulation
0.1 1.15 1.1497 -0.0261
0.2 1.2 1.1910 -0.7500
0.5 1.25 1.2494 -0.0480
1 1.3 1.3011 0.0846
2 1.4 1.3655 -2.4643
5 1.5 1.4803 -1.3133
10 1.7 1.6210 -4.6471
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
5. Output current vs. input voltage (OFF characteristics)
Circuit simulation result
Evaluation circuit
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
6. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Vce = 5 V
VI(OFF) (V)
Ic(uA) Error (%)
Datasheet Simulation
30 1.09 1.0823 -0.7064
50 1.1 1.1089 0.8091
100 1.14 1.1483 0.7281
200 1.18 1.1880 0.6780
500 1.24 1.2457 0.4597
1000 1.29 1.2966 0.5116
2000 1.33 1.3596 2.2256
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
7. DC current gain vs. output current
Circuit simulation result
Evaluation circuit
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
8. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Vce = 5 V
hFE
Ic(mA) Error (%)
Datasheet Simulation
1 15 15.567 3.7800
2 28 29.301 4.6464
5 64 64.976 1.5250
10 110 113.625 3.2955
20 180 184.460 2.4778
50 260 258.966 -0.3977
100 100 99.756 -0.2440
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
9. Output voltage VS. output current
Circuit simulation result
Evaluation circuit
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
10. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ IC/IB = 20
VCE (sat) (mV)
Ic(mA) Error (%)
Datasheet Simulation
3 0.057 0.058947 3.4158
5 0.053 0.054940 3.6604
10 0.056 0.056905 1.6161
50 0.110 0.105166 -4.3945
100 0.2 0.202719 1.3595
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005