Transparent Latent GAN
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1. TL-GAN matches feature axes in the latent space to generate images without fine-tuning the neural network. 2. It discovers correlations between the latent vector Z and image labels by applying multivariate linear regression and normalizing the coefficients. 3. The vectors are then adjusted to be orthogonal, allowing different properties to be matched while labeling unlabeled data to add descriptions.
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Best KNow Hydrogen Fuel Production in the World The cost in USD kwh for H2
Best KNow Hydrogen Fuel Production in the World The cost in USD kwh for H2Daniel Donatelli
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The cost in USD/kwh for H2
Daniel Donatelli
Secure Supplies Group
Index
Introduction - Page 3
The Need for Hydrogen Fueling - Page 5
Pure H2 Fueling Technology - Page 7
Blend Gas Fueling: A Transition Strategy - Page 10
Performance Metrics: H2 vs. Fossil Fuels - Page 12
Cost Analysis and Economic Viability - Page 15
Innovations Driving Leadership - Page 18
Laminar Flame Speed Adjustment
Heat Management Systems
The Donatelli Cycle
Non-Carnot Cycle Applications
Case Studies and Real-World Applications - Page 22
Conclusion: Secure Supplies Leadership in Hydrogen Fueling - Page 27
Engineering at Lovely Professional University (LPU).pdf
Engineering at Lovely Professional University (LPU).pdfSona
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LPUs engineering programs provide students with the skills and knowledge to excel in the rapidly evolving tech industry, ensuring a bright and successful future. With world-class infrastructure, top-tier placements, and global exposure, LPU stands as a premier destination for aspiring engineers.
The Golden Gate Bridge a structural marvel inspired by mother nature.pptx
The Golden Gate Bridge a structural marvel inspired by mother nature.pptxAkankshaRawat75
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The Golden Gate Bridge is a 6 lane suspension bridge spans the Golden Gate Strait, connecting the city of San Francisco to Marin County, California.
It provides a vital transportation link between the Pacific Ocean and the San Francisco Bay.
Indian Soil Classification System in Geotechnical Engineering
Indian Soil Classification System in Geotechnical EngineeringRajani Vyawahare
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This PowerPoint presentation provides a comprehensive overview of the Indian Soil Classification System, widely used in geotechnical engineering for identifying and categorizing soils based on their properties. It covers essential aspects such as particle size distribution, sieve analysis, and Atterberg consistency limits, which play a crucial role in determining soil behavior for construction and foundation design. The presentation explains the classification of soil based on particle size, including gravel, sand, silt, and clay, and details the sieve analysis experiment used to determine grain size distribution. Additionally, it explores the Atterberg consistency limits, such as the liquid limit, plastic limit, and shrinkage limit, along with a plasticity chart to assess soil plasticity and its impact on engineering applications. Furthermore, it discusses the Indian Standard Soil Classification (IS 1498:1970) and its significance in construction, along with a comparison to the Unified Soil Classification System (USCS). With detailed explanations, graphs, charts, and practical applications, this presentation serves as a valuable resource for students, civil engineers, and researchers in the field of geotechnical engineering. US Patented ReGenX Generator, ReGen-X Quatum Motor EV Regenerative Accelerati...
US Patented ReGenX Generator, ReGen-X Quatum Motor EV Regenerative Accelerati...Thane Heins NOBEL PRIZE WINNING ENERGY RESEARCHER
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Preface: The ReGenX Generator innovation operates with a US Patented Frequency Dependent Load Current Delay which delays the creation and storage of created Electromagnetic Field Energy around the exterior of the generator coil. The result is the created and Time Delayed Electromagnetic Field Energy performs any magnitude of Positive Electro-Mechanical Work at infinite efficiency on the generator's Rotating Magnetic Field, increasing its Kinetic Energy and increasing the Kinetic Energy of an EV or ICE Vehicle to any magnitude without requiring any Externally Supplied Input Energy. In Electricity Generation applications the ReGenX Generator innovation now allows all electricity to be generated at infinite efficiency requiring zero Input Energy, zero Input Energy Cost, while producing zero Greenhouse Gas Emissions, zero Air Pollution and zero Nuclear Waste during the Electricity Generation Phase. In Electric Motor operation the ReGen-X Quantum Motor now allows any magnitude of Work to be performed with zero Electric Input Energy.
Demonstration Protocol: The demonstration protocol involves three prototypes;
1. Protytpe #1, demonstrates the ReGenX Generator's Load Current Time Delay when compared to the instantaneous Load Current Sine Wave for a Conventional Generator Coil.
2. In the Conventional Faraday Generator operation the created Electromagnetic Field Energy performs Negative Work at infinite efficiency and it reduces the Kinetic Energy of the system.
3. The Magnitude of the Negative Work / System Kinetic Energy Reduction (in Joules) is equal to the Magnitude of the created Electromagnetic Field Energy (also in Joules).
4. When the Conventional Faraday Generator is placed On-Load, Negative Work is performed and the speed of the system decreases according to Lenz's Law of Induction.
5. In order to maintain the System Speed and the Electric Power magnitude to the Loads, additional Input Power must be supplied to the Prime Mover and additional Mechanical Input Power must be supplied to the Generator's Drive Shaft.
6. For example, if 100 Watts of Electric Power is delivered to the Load by the Faraday Generator, an additional >100 Watts of Mechanical Input Power must be supplied to the Generator's Drive Shaft by the Prime Mover.
7. If 1 MW of Electric Power is delivered to the Load by the Faraday Generator, an additional >1 MW Watts of Mechanical Input Power must be supplied to the Generator's Drive Shaft by the Prime Mover.
8. Generally speaking the ratio is 2 Watts of Mechanical Input Power to every 1 Watt of Electric Output Power generated.
9. The increase in Drive Shaft Mechanical Input Power is provided by the Prime Mover and the Input Energy Source which powers the Prime Mover.
10. In the Heins ReGenX Generator operation the created and Time Delayed Electromagnetic Field Energy performs Positive Work at infinite efficiency and it increases the Kinetic Energy of the system.
Mathematics behind machine learning INT255 INT255__Unit 3__PPT-1.pptx
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Mathematics behind machine learning INT255 Multi objective genetic approach with Ranking
Multi objective genetic approach with Rankingnamisha18
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Multi objective genetic approach with Ranking 
Air pollution is contamination of the indoor or outdoor environment by any ch...
Air pollution is contamination of the indoor or outdoor environment by any ch...dhanashree78
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Air pollution is contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere.
Household combustion devices, motor vehicles, industrial facilities and forest fires are common sources of air pollution. Pollutants of major public health concern include particulate matter, carbon monoxide, ozone, nitrogen dioxide and sulfur dioxide. Outdoor and indoor air pollution cause respiratory and other diseases and are important sources of morbidity and mortality.
WHO data show that almost all of the global population (99%) breathe air that exceeds WHO guideline limits and contains high levels of pollutants, with low- and middle-income countries suffering from the highest exposures.
Air quality is closely linked to the earths climate and ecosystems globally. Many of the drivers of air pollution (i.e. combustion of fossil fuels) are also sources of greenhouse gas emissions. Policies to reduce air pollution, therefore, offer a win-win strategy for both climate and health, lowering the burden of disease attributable to air pollution, as well as contributing to the near- and long-term mitigation of climate change.
Introduction to Safety, Health & Environment
Introduction to Safety, Health & Environmentssuserc606c7
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Introduction to Safety, Health &EnvironmentTaykon-Kalite belgeleri
Taykon-Kalite belgeleriTAYKON
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Kalite Politikam脹z
Taykon elik i巽in kalite, hayallerinizi bizlerle paylat脹脹n脹z an balar. Proje 巽iziminden detaylar脹n 巽旦z端m端ne, detaylar脹n 巽旦z端m端nden 端retime, 端retimden montaja, montajdan teslime hayallerinizin ger巽ekletiini g旦rd端端n端z ana kadar ge巽en t端m aamalar脹, 巽al脹anlar脹, t端m teknik donan脹m ve 巽evreyi i巽ine al脹r KAL聴TE.How to Build a Maze Solving Robot Using Arduino
How to Build a Maze Solving Robot Using ArduinoCircuitDigest
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Learn how to make an Arduino-powered robot that can navigate mazes on its own using IR sensors and "Hand on the wall" algorithm.
This step-by-step guide will show you how to build your own maze-solving robot using Arduino UNO, three IR sensors, and basic components that you can easily find in your local electronics shop.Structural QA/QC Inspection in KRP 401600 | Copper Processing Plant-3 (MOF-3)...
Structural QA/QC Inspection in KRP 401600 | Copper Processing Plant-3 (MOF-3)...slayshadow705
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This presentation provides an in-depth analysis of structural quality control in the KRP 401600 section of the Copper Processing Plant-3 (MOF-3) in Uzbekistan. As a Structural QA/QC Inspector, I have identified critical welding defects, alignment issues, bolting problems, and joint fit-up concerns.
Key topics covered:
Common Structural Defects Welding porosity, misalignment, bolting errors, and more.
Root Cause Analysis Understanding why these defects occur.
Corrective & Preventive Actions Effective solutions to improve quality.
Team Responsibilities Roles of supervisors, welders, fitters, and QC inspectors.
Inspection & Quality Control Enhancements Advanced techniques for defect detection.
Applicable Standards: GOST, KMK, SNK Ensuring compliance with international quality benchmarks.
This presentation is a must-watch for:
QA/QC Inspectors, Structural Engineers, Welding Inspectors, and Project Managers in the construction & oil & gas industries.
Professionals looking to improve quality control processes in large-scale industrial projects.
Download & share your thoughts! Let's discuss best practices for enhancing structural integrity in industrial projects.
Categories:
Engineering
Construction
Quality Control
Welding Inspection
Project Management
Tags:
#QAQC #StructuralInspection #WeldingDefects #BoltingIssues #ConstructionQuality #Engineering #GOSTStandards #WeldingInspection #QualityControl #ProjectManagement #MOF3 #CopperProcessing #StructuralEngineering #NDT #OilAndGasUS Patented ReGenX Generator, ReGen-X Quatum Motor EV Regenerative Accelerati...
US Patented ReGenX Generator, ReGen-X Quatum Motor EV Regenerative Accelerati...Thane Heins NOBEL PRIZE WINNING ENERGY RESEARCHER
油
Transparent Latent GAN
- 2. TL GAN in one sec Matching feature axes to latent space Without NN finetuning!
- 3. Why we need TL-GAN? Matching feature axes to latent space Matching options(Renderers) to feature vector(Directors)
- 4. GAN? http://jaejunyoo.blogspot.com/2017/01/generative-adversarial-nets-2.html latent vector襯 given dataset 覿 襷れ広る 蟆
- 5. In short Z eigenvector / Generator eigenvalue Just for easy understanding
- 6. Generating with condition Style-transfer networks (Pix2Pix, CycleGAN, Stargan) - Required label & Image - Hard to control each conditional volume Conditional Generator (conditional GAN) - Required label for training - Need retraining
- 8. TL-GAN
- 9. TL-GAN 1h + (generating 8h) : extract image label
- 10. TL-GAN
- 11. TL-GAN Discover Correlation 1. Z Label螳 coefficient (2D) 襯 る れ 蠏襯 牛 蟲. 蠏碁Μ螻 螳 螻襯 normalize https://github.com/SummitKwan/transparent_latent_gan/blob/04439c24ec5b9 d2da458bb933c6a7296d6bed9dd/src/tl_gan/script_label_regression.py
- 12. TL-GAN Discover Correlation 2. Vectorれ orthogonal蟆 譟一
- 14. After orthogonal
- 15. るジ 煙 螳讌 vector襯 企至 襷豢蟾? Unlabeled 一危一 label襯 覿願鍵 語る
- 16. DANN DANN : Domain Adversarial neural net training螻 test 覿れ 襦 所 るジ(presence of shift) 蟆
- 17. DANN
- 18. GirlsMoe
- 19. GirlsMoe similar approach with tl-gan Label with pre-trained model (illustration2vec) To ALL training data & Train C-GAN