This document summarizes the modeling parameters and performance of the uPC78N24H voltage regulator. It includes:
1) A list of model parameters for the regulator including reference voltage, emission coefficient, and capacitance values.
2) Simulation results showing the input-output voltage differential is within 0.2% of measurements.
3) Ripple rejection ratio simulation matching measurements within 5%.
4) Output voltage simulation matching measurements to within 0.05% under varying load and input conditions.
This document summarizes the modeling parameters and performance of the uPC78N24H voltage regulator. It includes:
1) A list of model parameters for the regulator including reference voltage, emission coefficient, and capacitance values.
2) Simulation results showing the input-output voltage differential is within 0.2% of measurements.
3) Ripple rejection ratio simulation matching measurements within 5%.
4) Output voltage simulation matching measurements to within 0.05% under varying load and input conditions.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and key electrical parameters used in the PSpice model. Simulation results show the input-output voltage differential is within 0.2% of measured, and ripple rejection ratio matches measured performance. The output characteristic under varying load and input conditions is also modeled within 0.2% accuracy.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and key electrical parameters used in the PSpice model. Simulation results show the input-output voltage differential is within 0.035% of measured, and ripple rejection ratio is within 1.808% of measured. The maximum output voltage error shown in simulation is 0.447% compared to measurement.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and modeling parameters. It then provides simulation results and comparisons to measurements for key characteristics like input-output voltage differential, ripple rejection ratio, and output voltage. The simulations show good agreement with measurements within 1% error for most test cases.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and key electrical parameters represented in the PSpice model. Simulation results show the input-output voltage differential is within 0.1% of measured, and ripple rejection ratio is within 0.9% of measured. The output characteristic comparison shows simulation within 0.65% of measured.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and key electrical parameters used in the PSpice model. Simulation results show the input-output voltage differential is within 0.2% of measured, and ripple rejection ratio matches measured performance. The output characteristic under varying load and input conditions is also modeled within 0.2% accuracy.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and key electrical parameters used in the PSpice model. Simulation results show the input-output voltage differential is within 0.035% of measured, and ripple rejection ratio is within 1.808% of measured. The maximum output voltage error shown in simulation is 0.447% compared to measurement.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and modeling parameters. It then provides simulation results and comparisons to measurements for key characteristics like input-output voltage differential, ripple rejection ratio, and output voltage. The simulations show good agreement with measurements within 1% error for most test cases.
This document summarizes the modeling parameters and performance of a voltage regulator component. It describes the manufacturer, part number, and key electrical parameters represented in the PSpice model. Simulation results show the input-output voltage differential is within 0.1% of measured, and ripple rejection ratio is within 0.9% of measured. The output characteristic comparison shows simulation within 0.65% of measured.
This document summarizes the test results of a voltage regulator component. It describes the manufacturer, part number, and PSpice model parameters. It then provides the results of simulating the input-output voltage differential characteristic, ripple rejection ratio, and output characteristic. The simulation results match well with measurements, with less than 1% error in most cases.
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This document provides an inventory update of 6,747 parts at Spice Park as of April 2024. It lists the part numbers, manufacturers, and quantities of various semiconductor components, including 1,697 Schottky rectifier diodes from 29 different manufacturers. It also includes details on passive components, batteries, mechanical parts, motors, and lamps in the inventory.
The document provides an inventory update from April 2024 of the Spice Park collection which contains 6,747 electronic components. It includes tables listing the types of semiconductor components, passive parts, batteries, mechanical parts, motors, and lamps in the collection along with their manufacturer and quantities. One of the semiconductor components, the general purpose rectifier diode, is broken down into a more detailed table with 116 entries providing part numbers, manufacturers, thermal ratings, and remarks.
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The document provides an inventory update from March 2024 of parts in the Spice Park warehouse. It lists 6,725 total parts across various categories including semiconductors, passive parts, batteries, mechanical parts, motors, and lamps. The semiconductor section lists 652 general purpose rectifier diodes from 18 different manufacturers with quantities ranging from 2 to 145 pieces.
This document provides an inventory list of parts at Spice Park as of March 2024. It contains 3 sections - Semiconductor parts (diodes, transistors, ICs etc.), Passive parts (capacitors, resistors etc.), and Battery parts. For Semiconductor parts, it lists 36 different part types and provides the quantity of each part. It then provides further details of Diode/General Purpose Rectifiers, listing the manufacturer and quantity of 652 individual part numbers.
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The document provides an inventory update from February 2024 of Spice Park, which contains 6,694 total pieces of electronic components and parts. It lists 36 categories of semiconductor devices, 11 categories of passive parts, 10 types of batteries, 5 mechanical parts, DC motors, lamps, and power supplies. It provides the most detailed listing for solar cells, with 1,003 total pieces from 51 manufacturers listed with part numbers.
The document provides an inventory update from February 2024 of Spice Park, which contains 6,694 electronic components. It lists the components by type (e.g. semiconductor), part number, manufacturer, thermal rating, and quantity on hand. For example, it shows that there are 621 general purpose rectifier diodes from manufacturers such as Fairchild, Fuji, Intersil, Rohm, Shindengen, and Toshiba. The detailed four-page section provides further information on the first item, general purpose rectifier diodes, including 152 individual part numbers and specifications.
This document discusses circuit simulations using LTspice. It describes driving a circuit simulation by inserting a 250 ohm resistor between the output terminals. It also describes simulating a 1 channel bridge circuit where the DUT1 and DUT2 resistors are both set to 100 ohms and the input voltage is set to either 1V or 5V.
This document discusses parametric sweeps of external and internal resistance values Rg for circuit simulation in LTspice. It also references outputting a waveform similar to a report on fall time characteristics for a device modeling report with customer Samsung.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
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UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
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What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
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My slides at Nordic Testing Days 6.6.2024
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Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
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The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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1. Device Modeling Report
COMPONENTS : VOLTAGE REGULATOR
PART NUMBER : uPC78M18A
MANUFACTURER : NEC Electronics Corporation
Panasonic
Bee Technologies Inc.
All Rights Reserved Copyright (c) Bee Technologies Inc. 2004
2. MODEL PARAMETER
Pspice
model Model description
parameter
VREF Reference Voltage
N Emission Coefficient
BETA Tranconductance of JFET Transistor
VAF Early Voltage of Output Pass Transistor
CPZ Output Impedance Zero Capacitor
RB2 Base Resistance of Output Limit Voltage Source
ESC1 Coefficient of Current Limit Voltage Source
ESC2 Coefficient of Current Limit Voltage Source
EFB1 Coefficient of Foldback Current Voltage Source
EFB2 Coefficient of Foldback Current Voltage Source
EFB3 Coefficient of Foldback Current Voltage Source
EB Non-ideal Base-Collector Diode Saturation Current
All Rights Reserved Copyright (c) Bee Technologies Inc. 2004
3. Input-Output Voltage Differential Characteristic
Evaluation Circuit
U1
1 3
IN OUT
GND UPC78M18A
2
V1 RL
27 Cout
51.42
0.1u
0
Simulation result
Input - Output
Input
Example
VIN - VOUT Measurement Simulation % Error
27 (V) – 18 (V) 9 (V) 9.0058 (V) 0.064
All Rights Reserved Copyright (c) Bee Technologies Inc. 2004
4. Ripple Rejection (RR) Characteristic
Evaluation Circuit
U1
Vin Vout
1 3
IN OUT
GND UPC78M18A
2
D1 D2
S1VBA S1VBA C1 Cout RL
0.47u 0.1u 51.42
V1 D3 D4
VOFF = 0
VAMPL = 1 S1VBA S1VBA
FREQ = 120
V2
27
0
Simulation result
Output
Input
Comparison Table
Measurement Simulation % Error
Ripple rejection ratio
(dB)
73 72.765 -0.322
All Rights Reserved Copyright (c) Bee Technologies Inc. 2004