SPICE MODEL of RB051L-40 (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of RB051L-40 (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of 5GWJZ47 (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of 5GWJZ47 (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of HN2S01FU (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of HN2S01FU (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
In an increasingly interconnected and human-modified world, decision makers face problems of unprecedented complexity. For example, world energy demand is projected to grow by a factor of four over the next century. During that same period, greenhouse gas emissions must be drastically curtailed if we are to avoid major economic and environmental damage from climate change. We will also have to adapt to climate change that is not avoided. Governments, companies, and individuals face what will be, in aggregate, multi-trillion-dollar decisions.
These and other questions (e.g., relating to food security and epidemic response) are challenging because they depend on interactions within and between physical and human systems that are not well understood. Furthermore, we need to understand these systems and their interactions during a time of rapid change that is likely to lead us to states for which we have limited or no experience. In these contexts, human intuition is suspect. Thus, computer models are used increasingly to both study possible futures and identify decision strategies that are robust to the often large uncertainties.
The growing importance of computer models raises many challenging issues for scientists, engineers, decision makers, and ultimately the public at large. If decisions are to be based (at least in part) on model output, we must be concerned that the computer codes that implement numerical models are correct; that the assumptions that underpin models are communicated clearly; that models are carefully validated; and that the conclusions claimed on the basis of model output do not exceed the information content of that output. Similar concerns apply to the data on which models are based. Given the considerable public interest in these issues, we should demand the most transparent evaluation process possible.
I argue that these considerations motivate a strong open source policy for the modeling of issues of broad societal importance. Our goal should be that every piece of data used in decision making, every line of code used for data analysis and simulation, and all model output should be broadly accessible. Furthermore, the organization of this code and data should be such that any interested party can easily modify code to evaluate the implications of alternative assumptions or model formulations, to integrate additional data, or to generate new derived data products. Such a policy will, I believe, tend to increase the quality of decision making and, by enhancing transparency, also increase confidence in decision making.
I discuss the practical implications of such a policy, illustrating my discussion with examples from the climate, economics, and integrated assessment communities. I also introduce the use of open source modeling with the University of Chicago's new Center on Robust Decision making for Climate and Energy Policy (RDCEP), recently funded by the US National Science Foundation.
SPICE MODEL of RB051L-40 (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of RB051L-40 (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of 5GWJZ47 (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of 5GWJZ47 (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of HN2S01FU (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of HN2S01FU (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
In an increasingly interconnected and human-modified world, decision makers face problems of unprecedented complexity. For example, world energy demand is projected to grow by a factor of four over the next century. During that same period, greenhouse gas emissions must be drastically curtailed if we are to avoid major economic and environmental damage from climate change. We will also have to adapt to climate change that is not avoided. Governments, companies, and individuals face what will be, in aggregate, multi-trillion-dollar decisions.
These and other questions (e.g., relating to food security and epidemic response) are challenging because they depend on interactions within and between physical and human systems that are not well understood. Furthermore, we need to understand these systems and their interactions during a time of rapid change that is likely to lead us to states for which we have limited or no experience. In these contexts, human intuition is suspect. Thus, computer models are used increasingly to both study possible futures and identify decision strategies that are robust to the often large uncertainties.
The growing importance of computer models raises many challenging issues for scientists, engineers, decision makers, and ultimately the public at large. If decisions are to be based (at least in part) on model output, we must be concerned that the computer codes that implement numerical models are correct; that the assumptions that underpin models are communicated clearly; that models are carefully validated; and that the conclusions claimed on the basis of model output do not exceed the information content of that output. Similar concerns apply to the data on which models are based. Given the considerable public interest in these issues, we should demand the most transparent evaluation process possible.
I argue that these considerations motivate a strong open source policy for the modeling of issues of broad societal importance. Our goal should be that every piece of data used in decision making, every line of code used for data analysis and simulation, and all model output should be broadly accessible. Furthermore, the organization of this code and data should be such that any interested party can easily modify code to evaluate the implications of alternative assumptions or model formulations, to integrate additional data, or to generate new derived data products. Such a policy will, I believe, tend to increase the quality of decision making and, by enhancing transparency, also increase confidence in decision making.
I discuss the practical implications of such a policy, illustrating my discussion with examples from the climate, economics, and integrated assessment communities. I also introduce the use of open source modeling with the University of Chicago's new Center on Robust Decision making for Climate and Energy Policy (RDCEP), recently funded by the US National Science Foundation.
SPICE MODEL of TRS8E65C (Professional Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of TRS8E65C (Professional Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
Mobile Marketing - Move with your Customers or Lose them!Skeeter Harris
Location based marketing provides insight into one very important question. Where & When are your customers? Using tools like foursquare, Facebook deals, QR Codes a marketer can invoke action and engage customer or find new
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
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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
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.
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.
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.
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.
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.
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.
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
SPICE MODEL of RN1314 in SPICE PARK
1. Device Modeling Report
COMPONENTS: BRT
PART NUMBER: RN1314
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
0.3 0.7 0.701843 0.2633
0.5 0.72 0.720154 0.0214
1 0.76 0.747803 -1.6049
2 0.79 0.779855 -1.2842
5 0.85 0.838041 -1.4069
10 0.95 0.911652 -4.0366
20 1.1 1.0748 -2.2909
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 0.65 0.628800 -3.2615
50 0.67 0.642835 -4.0545
100 0.68 0.663857 -2.3740
200 0.7 0.685628 -2.0531
500 0.73 0.716238 -1.8852
1000 0.75 0.742103 -1.0529
2000 0.78 0.771998 -1.0259
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 12 12.277 2.3083
2 22 21.646 -1.6091
5 43 42.250 -1.7442
10 64 64.075 0.1172
20 84 84.879 1.0464
50 81 79.782 -1.5037
100 42 43.701 4.0500
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
5 0.084 0.08700 3.5714
10 0.081 0.082247 1.5395
20 0.1 0.104543 4.5430
80 0.302 0.305359 1.1123
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005