This document summarizes the test results of a digital transistor component. It provides the component's part number, manufacturer, and descriptions of its electrical characteristics including input/output voltage and current relationships, gain, and saturation voltage under various test conditions. Simulation results are shown to match the manufacturer's datasheet specifications with low errors.
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
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*
SPICE MODEL of DTC363EU in SPICE PARK
1. Device Modeling Report
COMPONENTS: Digital transistors (built-in resistors)
PART NUMBER: DTC363EU
MANUFACTURER: ROHM
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
U13
DTC363EU
I2 V1
0Adc 0.3Vdc
0
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
4. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Vo = 0.3 V
VI(ON) (V)
Io(A) Error (%)
Datasheet Simulation
1m 1.2 1.18 -1.666
2m 1.25 1.26 0.8
5m 1.4 1.42 1.428
10m 1.6 1.61 0.625
20m 1.85 1.9 2.702
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
5. Output current vs. input voltage (OFF characteristics)
Circuit simulation result
Evaluation circuit
U12
DTC363EU
V2
V1 5Vdc
0Vdc
0
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
7. DC current gain vs. output current
Circuit simulation result
Evaluation circuit
U12 Vsense
DTC363EU 0Vdc
V2
I1
0Adc 5Vdc
0
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
8. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Vo = 5 V
hFE
Ic(A) Error (%)
Datasheet Simulation
500u 5.7 5.7 0
1m 10.4 10.5 0.961
2m 19.5 19 -2.564
5m 40 39.17 -2.075
10m 66 64.4 -2.424
20m 100 99.74 -0.26
50m 160 157.81 -1.368
100m 195 197.25 1.153
200m 205 212 3.414
500m 175 174.5 -0.285
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
9. Output voltage VS. output current
Circuit simulation result
Evaluation circuit
U12
DTC363EU
F
GAIN = 0.05
F1
I1
10mAdc
0
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
10. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Io/II = 20
VCE (sat)(mV)
Ic(A) Error (%)
Datasheet Simulation
3m 0.03 0.0294 -2
5m 0.022 0.02263 2.863
10m 0.0235 0.02388 1.617
20m 0.027 0.028 3.703
50m 0.039 0.0387 -0.769
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005