Bottom-up time estimations techiques
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An overview of bottom-up time estimations techniques in project management.Download additional slides, videos, and resources at https://www.christiansonjs.com/ Signup for The Free-Range Technologist, a monthly newsletter filled with creative commons resources, useful apps, and lifehacks: https://mailchi.mp/f8f0219bc305/jscott
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The document discusses churn, which is a measure of customer retention and loyalty. It defines churn as the number of customers lost over a period divided by the total number of customers at the beginning of the period. Churn is used to calculate key metrics like customer lifetime value (CLV) and is an important factor for subscription-based businesses. The document provides formulas for calculating churn rate, customer retention rate, customer lifetime period, and CLV. It also discusses how churn impacts a company's growth potential and provides tips for reducing different types of churn.
Porters 5 forces
An overview of Porter's Five Forces model: Buyer Power, Supplier Power, Threat of New Entrants, Threat of Substitute Products, Rivalry Among Existing Competitors.
Turning to Teaching: Going from Industry Leader to Classroom Teacher.
Our small planet has some big problems. Luckily young adults want to tackle these challenges and make their own dent in the world. Now is the time to use your experience and expert judgment to help the next generation of project managers and business leaders develop and perform as highly as possible. Whether it is as an adjunct professor, a guest lecturer, or an online tutor, this session will jumpstart you on your journey to becoming a master teacher.
Meet the Learning Machine: How Artificial Intelligence is transforming our wo...
Meet the Learning Machine: How Artificial Intelligence is transforming our wo...J. Scott Christianson
Artificial Intelligence, in the form of Machine Learning (ML), has already transformed medicine, retail sales, and other industries. It is about to enter all our lives, whether we want it or not! In this session, we will quickly develop a basic understanding of AI, and its advantages, applications, and difficulties.Smart Homes, Smart Farms, Smart Cities and the Internet of Things.
Our world seems to be getting smarter, thanks to the Internet of Things (IoT). Cheap electronics, a robust set of tools, and growing libraries of projects have enabled our surroundings to become "smart" and have empowered everyday innovators. Modern-day Edisons can inexpensively and quickly create new inventions using IoT devices. Already several Missouri entrepreneurs have used this technology to create thriving businesses. Let's explore these tools are being used to shape the future!
The blockchain game
Teach your students blockchain basics with this hands-on exercise that you can play in class. Video walkthrough and complete materials can be found at https://www.instructables.com/id/The-Blockchain-Game/
Crossing the Chasm
The document discusses how startups can cross the chasm between early adopters and the mainstream market. It notes that many startups fail to cross this chasm, even if the technology is adopted, because they focus on building products before finding customers. The ideal startup process focuses on experimenting to find customers and determine if people want to buy the product or service, rather than repeatedly building products without feedback.
Technology Adoption LifeCycle
The document discusses the technology adoption lifecycle model which describes how new technologies are adopted by different groups over time. It identifies six categories of adopters - innovators, early adopters, early majority, late majority, and laggards. The model shows that new technologies are first adopted by a small group of innovators, then early adopters, before gaining broader acceptance by the early and late majority, with laggards being the last to adopt.
Gartner Hype-Cycle
The document discusses the hype cycle, which is a model that analyzes emerging technologies and measures their progress by tracking five phases from innovation trigger through peak of inflated expectations to plateau of productivity. It suggests that emerging technologies often face inflated expectations early on before more realistic assessments of applications and benefits are possible.
Drivers of Exponential Growth
Gordon Moore is an American businessman and co-founder of Intel Corporation. He authored Moore's Law, which observes that the number of transistors in a dense integrated circuit doubles approximately every two years. While this trend has continued for over half a century, Moore's Law should be considered a conjecture, not a physical law. Sources in 2005 expected it to continue until at least 2015 or 2020. Exponential growth results in much larger outcomes over time compared to linear growth. New technologies like quantum computing, molecular computing, 3D chip design, field-programmable gate arrays, and cloud computing may help extend Moore's Law and continued exponential technological progress even without continued transistor doubling. Cloud computing provides processing power and storage via remote servers accessed
The Six Ds of Exponential Growth
The document discusses the "Six Ds of Exponential Growth" which describes the progression of exponential technologies. It was founded by Peter Diamandis and Ray Kurzweil to focus on scientific progress and exponential technologies. The six stages are: Digitalization, where technologies become represented by 1s and 0s and experience exponential growth; Deception, where the true potential is underestimated early on; Disruption, where technologies upend existing industries; Demonetization, where the cost comes out of technologies; Democratization, where access is provided to everyone; and finally Decentralization.
Small Biz vs. Start-Up, Fast Follower vs. First Mover
Many people confuse small businesses with start-ups. Here are a couple of the differences. Also, is is better for a startup to be first to market or a fast follower?
Agile and Traditional Project Management
Are these two Project Management Methods Really that all that different? There are probably more similarities than you think!
Copy and Paste Hardware
The document discusses how the Maker movement is accelerating innovation through open source tools. It provides examples of how various simulation training tools were developed by copying designs from other open source projects and modifying them to meet specific needs. This allowed innovative solutions to be developed rapidly and at low cost. The Center for the Digital Globe (CDiG) is described as focusing on expanding international education and research through collaboration across academic disciplines.
Using Print Anything 3D Printing in Your Class
3D printing is an exciting way to rapidly prototype ideas, from medical devices to new consumer products. However, the actual printing process can be more art than science. Enter Print Anything at MU, a service of MU Libraries that handles the hard work of operating 3D printers. In this presentation, teaching professor Christianson will outline how to incorporate 3D printing projects in a wide range of lessons: team development, consumer testing, design, etc. Mr. Frier will discuss the range of services and tools that are available from Print Anything. Participants will also learn about tinkercad, a free and easy-to-use tool for 3D design.
Schedule Performance Index (SPI) in Project Management
How to calculate and interpret SPI as it is used in earned value analysis in project management. Download additional slides, videos, and resources at https://www.christiansonjs.com/
Signup for The Free-Range Technologist, a monthly newsletter filled with creative commons resources, useful apps, and lifehacks: https://mailchi.mp/f8f0219bc305/jscott
Cost Variance (CV) in Project Management
How to calculate and interpret CV as it is used in earned value analysis in project management. Download additional slides, videos, and resources at https://www.christiansonjs.com/
Signup for The Free-Range Technologist, a monthly newsletter filled with creative commons resources, useful apps, and lifehacks: https://mailchi.mp/f8f0219bc305/jscott
More from J. Scott Christianson (20)
The Future of Money: Central Bank Digital Currencies
The Future of Money: Central Bank Digital Currencies
Turning to Teaching: Going from Industry Leader to Classroom Teacher.
Turning to Teaching: Going from Industry Leader to Classroom Teacher.
Meet the Learning Machine: How Artificial Intelligence is transforming our wo...
Meet the Learning Machine: How Artificial Intelligence is transforming our wo...
Smart Homes, Smart Farms, Smart Cities and the Internet of Things.
Smart Homes, Smart Farms, Smart Cities and the Internet of Things.
Small Biz vs. Start-Up, Fast Follower vs. First Mover
Small Biz vs. Start-Up, Fast Follower vs. First Mover
Schedule Performance Index (SPI) in Project Management
Schedule Performance Index (SPI) in Project Management
Recently uploaded
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
For International shipping and maritime laws all wood must contain the ISPM 15 Stamp. Here is how and why.
International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Slides for the 4th Presentation on LLM Fine-Tuning with QLoRA Presented by Anant, featuring DataStax Astra
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Literature Review Basics and Understanding Reference Management.pptx
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning an...
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
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IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Literature Review Basics and Understanding Reference Management.pptx
Literature Review Basics and Understanding Reference Management.pptx
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
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Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
Bottom-up time estimations techiques
- 1. Bottom-Up (Micro) • Single-Point Estimation • Three-Point Estimates • Multiple Estimators • Delphi Technique
- 2. 1. Have people familiar with the tasks make the estimate. 2. Use several people to make estimates. 3. Base estimates on normal conditions, efficient methods, and a normal level of resources. 4. Use consistent time units in estimating task times. 5. Treat each task as independent, don’t aggregate. 6. Don’t make allowances for contingencies. 7. Adding a risk assessment helps avoid surprises to stakeholders. Estimating Guidelines for Times, Costs, and Resources
- 3. Single Point Estimation 30 Days to Complete How long will Task A take to complete? Bottom-up (Micro)
- 4. Three-Point Estimates How long will Task A take to complete? Optimistic (a) 20 days Most Likely (m) 30 days Pessimistic (b) 58 days
- 5. Three-Point Estimates Optimistic (a) 20 days Most Likely (m) 30 days Pessimistic (b) 58 days How long will Task A take to complete? 20 Days + 30 Days + 58 Days 3 Estimates = 36 days Triangular Distribution or simple average
- 6. Activity Time Calculations weighted average activity time =Te
- 7. Activity Time Calculations Te a + 4m + b 6 =
- 8. Three-Point Estimates How long will Task A take to complete? te 20+ 4*30 + 58 6 = Optimistic (a) 20 days Most Likely (m) 30 days Pessimistic (b) 58 days
- 9. Three-Point Estimates How long will Task A take to complete? te = 33.00 days Optimistic (a) 20 days Most Likely (m) 30 days Pessimistic (b) 58 days
- 10. Beta Distribution Optimistic (a) LikelinessorProbability—> Most Likely (m) Pessimistic (b) te
- 11. Beta Distribution Optimistic (a) LikelinessorProbability—> Most Likely (m) Pessimistic (b) 33.00 days
- 12. Three-Point Estimates Estimated Project Duration: 243 days Probably that project will be done in 250 days: 74% PERT Analysis
- 13. Multiple Estimators Bottom Up Estimating Estimator 1 Estimator 2 Estimator 33 20 Days 30 Days 50 Days Bottom-up (Micro)
- 14. Multiple Estimators Bottom Up Estimating Estimator 1 Estimator 2 Estimator 33 20 Days 30 Days 50 Days Bottom-up (Micro)
- 15. Multiple Estimators Bottom Up Estimating 20 Days Estimator 1 Estimator 2 Estimator 33 20 Days + 30 Days + 50 Days 3 Estimators = 33.3 Days Bottom-up (Micro) 30 Days 50 Days
- 16. Delphi Technique
- 17. Multiple Estimators Estimator 1 Estimator 2 Estimator 3 30 days 3 Bottom-up (Micro)
- 18. Bottom-Up (Micro) • Single-Point Estimation • Three-Point Estimates • Multiple Estimators • Delphi Technique