diff --git a/.obsidian/graph.json b/.obsidian/graph.json index acb1cad5..cce98473 100755 --- a/.obsidian/graph.json +++ b/.obsidian/graph.json @@ -67,6 +67,6 @@ "repelStrength": 12.5, "linkStrength": 1, "linkDistance": 140, - "scale": 0.3869620710498517, + "scale": 0.1719831426888229, "close": true } \ No newline at end of file diff --git a/1 Daily Notes/2024/10 October/2024-10-30.md b/1 Daily Notes/2024/10 October/2024-10-30.md index e69de29b..88da961d 100644 --- a/1 Daily Notes/2024/10 October/2024-10-30.md +++ b/1 Daily Notes/2024/10 October/2024-10-30.md @@ -0,0 +1,55 @@ +--- +date: 2024-10-30 +tags: +--- +# Yesterday | Tomorrow + << [[1 Daily Notes/2024/10 October/2024-10-29]] | [[1 Daily Notes/2024/10 October/2024-10-31 ]] >> +# This Week's Weekly Note +[[ Weekly Note 2024-10-23]] +# Tasks for today +## Plan 😎 +1. Capture stuff for Lauren and GSA Done! +2. Finish NUCE assignment +3. ME2016 Miniproject +4. QE Research approach, diffusion papers. +## Due +```dataview +task +where + due <= date(this.date) + and due + and !completed + and status != "-" +sort due asc +group by file.folder +``` +## Scheduled +```dataview +task +where + scheduled + and scheduled <= date(this.date) + and !completed + and status != "-" +sort due asc +group by file.folder +``` +## Tasks in Progress +```dataview +task +where +status != "-" +and status = "/" +sort due asc +group by file.folder +``` +## Completed +```dataview +task +where + completed + and completion = date(this.date) +sort due asc +group by file.folder +``` +# Calendar Tasks \ No newline at end of file diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index c33e90f0..e7bb15a7 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -649,6 +649,17 @@ Opportunities and Challenges toward Responsible AI.pdf} } +@video{artemkirsanovKeyEquationProbability2024, + entrysubtype = {video}, + title = {The {{Key Equation Behind Probability}}}, + editor = {{Artem Kirsanov}}, + editortype = {director}, + date = {2024-08-22}, + url = {https://www.youtube.com/watch?v=KHVR587oW8I}, + urldate = {2024-10-30}, + abstract = {Get 4 months extra on a 2 year plan here: https://nordvpn.com/artemkirsanov. It’s risk free with Nord’s 30 day money-back guarantee! Socials: X/Twitter: https://x.com/ArtemKRSV Patreon: ~~/~artemkirsanov~~ My name is Artem, I'm a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience). In this video, we explore the fundamental concepts that underlie probability theory and its applications in neuroscience and machine learning. We begin with the intuitive idea of surprise and its relation to probability, using real-world examples to illustrate these concepts. From there, we move into more advanced topics: 1) Entropy – measuring the average surprise in a probability distribution. 2) Cross-entropy and the loss of information when approximating one distribution with another. 3) Kullback-Leibler (KL) divergence and its role in quantifying the difference between two probability distributions. OUTLINE: 00:00 Introduction 02:00 Sponsor: NordVPN 04:07 What is probability (Bayesian vs Frequentist) 06:42 Probability Distributions 10:17 Entropy as average surprisal 13:53 Cross-Entropy and Internal models 19:20 Kullback–Leibler (KL) divergence 20:46 Objective functions and Cross-Entropy minimization 24:22 Conclusion \& Outro CREDITS: Special thanks to Crimson Ghoul for providing English subtitles! Icons by https://www.freepik.com/} +} + @book{arthoFormalTechniquesSafetyCritical2015, title = {Formal {{Techniques}} for {{Safety-Critical Systems}}}, author = {Artho, Cyrille and Ölveczky, Peter Csaba}, @@ -3527,6 +3538,246 @@ Artificial Intelligence Program.pdf} file = {/home/danesabo/Zotero/storage/H2IWZM6I/Ellison et al. - Extending AADL for Security Design Assurance of Cy.pdf} } +@online{ENGR2100Module, + title = {{{ENGR}} 2100 {{Module}} 7.1 - {{Point Kinetics}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486309?module_item_id=5008204}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/BZCBC5EA/ENGR 2100 Module 7.1 - Point Kinetics.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/VYKI2KYI/17486309.html} +} + +@online{ENGR2100Modulea, + title = {{{ENGR}} 2100 {{Module}} 5 {{Review}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486295?module_item_id=5008181}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/S3CN9V7B/ENGR 2100 Module 5 Review.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleaa, + title = {{{ENGR}} 2100 {{Module}} 2.1 - 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Nuclear Fission.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulez, + title = {{{ENGR}} 2100 {{Module}} 2.2 - {{Radiation Interactions}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486199?module_item_id=5008129}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/AGZDPXWJ/ENGR 2100 Module 2.2 - Radiation Interactions.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + @book{EnhancingEffectivenessTeam2015, title = {Enhancing the {{Effectiveness}} of {{Team Science}}}, date = {2015-07-15}, @@ -5294,6 +5545,13 @@ Regulatory Premises.pdf} urldate = {2024-08-08} } +@online{HttpsVbnaaudkWs, + title = {{{https://vbn.aau.dk/ws/portalfiles/portal/140575/fulltext}}}, + url = {https://vbn.aau.dk/ws/portalfiles/portal/140575/fulltext}, + urldate = {2024-10-30}, + file = {/home/danesabo/Zotero/storage/3LAD5CQX/fulltext.pdf} +} + @online{HttpsWwwWhitehouse, title = {{{https://www.whitehouse.gov/wp-content/uploads/2024/02/Final-ONCD-Technical-Report.pdf}}}, url = {https://www.whitehouse.gov/wp-content/uploads/2024/02/Final-ONCD-Technical-Report.pdf}, @@ -7270,6 +7528,17 @@ for defect classification of TFT–LCD panels.pdf} urldate = {2024-01-28} } +@video{matlabInfinityMuSynthesis2020, + entrysubtype = {video}, + title = {H {{Infinity}} and {{Mu Synthesis}} | {{Robust Control}}, {{Part}} 5}, + editor = {{MATLAB}}, + editortype = {director}, + date = {2020-05-19}, + url = {https://www.youtube.com/watch?v=kRt7H0k8A4k}, + urldate = {2024-10-30}, + abstract = {This video walks through a controller design for an active suspension system. Actually, we design two controllers. For the first, we use H infinity synthesis to design a controller for a nominal plant model that will guarantee performance but not necessarily be robust to variation in the system. Then we build an uncertain model like we did in the last video and design a robust controller using mu synthesis. Watch the first videos in this series: Robust Control, Part 1: What Is Robust Control? - ~~~•~What~Is~Robust~Control?~|~Robust~Cont...~~ Robust Control, Part 2: Understanding Disk Margin - ~~~•~Understanding~Disk~Margin~|~Robust~Co...~~ Robust Control, Part 3: Disk Margins for MIMO Systems - ~~~•~Disk~Margins~for~MIMO~Systems~|~Robus...~~ Robust Control, Part 4: Working with Parameter Uncertainty - ~~~•~Working~with~Parameter~Uncertainty~|~...~~ Check out these other references: Robust Control of an Active Suspension: https://bit.ly/3bt8VCE -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.} +} + @article{mattosLatentAutoregressiveGaussian2016, title = {Latent {{Autoregressive Gaussian Processes Models}} for {{Robust System Identification}}}, author = {Mattos, César Lincoln C. and Damianou, Andreas and Barreto, Guilherme A. and Lawrence, Neil D.}, @@ -7732,6 +8001,41 @@ Insights from the Social Sciences.pdf} urldate = {2024-07-10} } +@online{Module81Fission, + title = {Module 8.1 - {{Fission Heat Generation-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486316?module_item_id=5008211}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/DVCE48RB/Module 8.1 - Fission Heat Generation-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/PUILTK75/17486316.html} +} + +@online{Module82Decay, + title = {Module 8.2 - {{Decay Heat}}, {{Plant Parameters}}, {{Design-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486248?module_item_id=5008212}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/WCPGR2IY/Module 8.2 - Decay Heat, Plant Parameters, Design-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR.pdf} +} + +@online{Module83Heat, + title = {Module 8.3 - {{Heat Conduction-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486217?module_item_id=5008213}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/EAWWPR9P/Module 8.3 - Heat Conduction-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/RQ5ICY3R/17486217.html} +} + +@online{Module8Class, + title = {Module 8 {{Class Notes-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486215?module_item_id=5008215}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/LQN7X96B/Module 8 Class Notes-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/EU6QMXAZ/17486215.html} +} + +@online{Module8Review1pdf, + title = {Module 8 {{Review-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486300?module_item_id=5008214}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/JMCTU5LC/Module 8 Review-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/TRAWYPDA/17486300.html} +} + @inproceedings{mohanS3ASecureSystem2013, title = {{{S3A}}: Secure System Simplex Architecture for Enhanced Security and Robustness of Cyber-Physical Systems}, shorttitle = {{{S3A}}}, @@ -10923,6 +11227,15 @@ Subject\_term: Careers, Politics, Policy}, isbn = {1-4612-0577-8} } +@online{SoraCreatingVideo, + title = {Sora: {{Creating}} Video from Text}, + shorttitle = {Sora}, + url = {https://openai.com/index/sora/}, + urldate = {2024-10-30}, + langid = {american}, + file = {/home/danesabo/Zotero/storage/YUQHRZUS/sora.html} +} + @misc{sorensenLecturesCurryHowardIsomorphism, title = {Lectures on the {{Curry-Howard Isomorphism}}}, author = {Sorensen, Morten Heine B. and Urzyczyn, Pawel}, @@ -11570,6 +11883,17 @@ Subject\_term: Careers, Politics, Policy}, annotation = {Page Version ID: 1191589904} } +@article{toffner-clausenMuSynthesisMuSynthesis1995, + title = {Mu-{{Synthesis}}: {{Mu-Synthesis}}}, + shorttitle = {Mu-{{Synthesis}}}, + author = {Tøffner-Clausen, S. and Andersen, Palle}, + date = {1995}, + journaltitle = {Recent Results in Robust and Adaptive Control, EURACO Workshop Florence 11-14 September 1995}, + pages = {269--303}, + abstract = {This paper provides an introduction to mu-synthesis.}, + file = {/home/danesabo/Zotero/storage/MCTBWR4Y/fulltext.pdf} +} + @article{tomlinComputationalTechniquesVerification2003, title = {Computational Techniques for the Verification of Hybrid Systems}, author = {Tomlin, C.J. and Mitchell, I. and Bayen, A.M. and Oishi, M.}, @@ -12307,6 +12631,22 @@ Subject\_term: Careers, Politics, Policy}, pages = {681--688} } +@article{wenFeedbackLinearizationControl2024, + title = {Feedback Linearization Control for Uncertain Nonlinear Systems via Generative Adversarial Networks}, + author = {Wen, Nuan and Liu, Zhenghua and Wang, Weihong and Wang, Shaoping}, + date = {2024-03-01}, + journaltitle = {ISA Transactions}, + shortjournal = {ISA Transactions}, + volume = {146}, + pages = {555--566}, + issn = {0019-0578}, + doi = {10.1016/j.isatra.2023.12.033}, + url = {https://www.sciencedirect.com/science/article/pii/S001905782300592X}, + urldate = {2024-10-30}, + abstract = {This article presents a novel approach to leverage generative adversarial networks(GANs) techniques to learn a feedback linearization controller(FLC) for a class of uncertain nonlinear systems. By estimating uncertainty through the adversarial process, where ground truth samples are exclusively obtained from a predefined integral model, the feedback linearization controller, learned through a minimax two-player optimization framework, enhances the reference tracking performance of the input-output uncertain nonlinear system. Furthermore, we provide theoretical guarantee of convergence and stability, demonstrating the safe recovery of robust FLC. We also address the common challenge of mode collapse in GANs training through the strict convexity of our synthesized generator structure and an enhanced adversarial loss. Comprehensive simulations and practical experiments are conducted to underscore the superiority and efficacy of our proposed approach.}, + keywords = {Convex optimization,Feedback linearization,Generative adversarial networks,Nonlinear systems} +} + @online{wengAutoencoderBetaVAE2018, title = {From {{Autoencoder}} to {{Beta-VAE}}}, author = {Weng, Lilian}, @@ -12795,6 +13135,24 @@ Subject\_term: Careers, Politics, Policy}, file = {/home/danesabo/Zotero/storage/7RV26D7X/Zhao et al. - 2021 - Neural Lyapunov Control for Power System Transient.pdf} } +@article{zhaoRobustVoltageControl2020, + title = {Robust {{Voltage Control Considering Uncertainties}} of {{Renewable Energies}} and {{Loads}} via {{Improved Generative Adversarial Network}}}, + author = {Zhao, Qianyu and Liao, Wenlong and Wang, Shouxiang and Pillai, Jayakrishnan Radhakrishna}, + date = {2020-11}, + journaltitle = {Journal of Modern Power Systems and Clean Energy}, + volume = {8}, + number = {6}, + pages = {1104--1114}, + issn = {2196-5420}, + doi = {10.35833/MPCE.2020.000210}, + url = {https://ieeexplore.ieee.org/abstract/document/9275598}, + urldate = {2024-10-30}, + abstract = {The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network. In this paper, a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network (IGAN). Firstly, both real and predicted data are used to train the IGAN consisting of a discriminator and a generator. The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction. Then, a new improved wolf pack algorithm (IWPA) is presented to solve the formulated robust voltage control model, since the accuracy of the solutions obtained by traditional methods is limited. The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads, which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods. Furthermore, IWPA has a better performance than traditional methods in terms of convergence speed, accuracy, and stability for robust voltage control.}, + eventtitle = {Journal of {{Modern Power Systems}} and {{Clean Energy}}}, + keywords = {Distribution networks,Gallium nitride,generative adversarial network,Generative adversarial networks,Load modeling,Power system stability,Robust voltage control,uncertainty,Uncertainty,Voltage control,wolf pack algorithm}, + file = {/home/danesabo/Zotero/storage/2MACG5H9/Zhao et al. - 2020 - Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Genera.pdf} +} + @article{zhaoStabilityL2gainControl2008, title = {On Stability, {{L2-gain}} and {{H}}∞ Control for Switched Systems}, author = {Zhao, Jun and Hill, David J.}, diff --git a/4 Qualifying Exam/2 Writing/2. QE State of the Art.md b/4 Qualifying Exam/2 Writing/2. QE State of the Art.md index 511bfc9f..20ac27aa 100644 --- a/4 Qualifying Exam/2 Writing/2. QE State of the Art.md +++ b/4 Qualifying Exam/2 Writing/2. QE State of the Art.md @@ -45,4 +45,3 @@ This is useful for us. If we can find an uncertainty transfer function $W_2$ tha $\Delta$ is almost always considered a free variable transfer function. Since $||\Delta||_\infty < 1 \text{ } \forall \omega$, $\Delta$ will not decrease the minimum robustness margin. This is fine for developing a controller, but when it comes to actually verifying robustness of a controller implementation, $\Delta$ cannot be a variable. To create a plant to simulate a perturbed plant, $\Delta$ must have an expression. **Limitation**: *There is no current method for creating random examples of $\Delta$.* Because of this, it is not currently possible to test implementations of controllers against unstructured perturbations. - diff --git a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md index 10d10aa3..64728d00 100644 --- a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md +++ b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md @@ -39,5 +39,27 @@ Something to justify, why diffusion model as opposed to other generative AI 16. Well we train a neural network as a denoiser. 17. Because the diffusion model forward steps are small and gaussian, we can know the reverse step is also a gaussian distribution. 18. So for our neural network, what we're trying to learn is the mean and standard deviation of the reverse steps for a given timestep. +19. What does this mean for perturbed plants? +20. Well, we can use a diffusion model to generate new perturbed plants that are similar to the original plant +21. How? We train a diffusion model on a structured set. +22. We need to pick a uncertainty function W_2 +23. Do this using the usual means. What is the worst we expect to handle? +24. We create a structured set by picking random scalar gains for $\Delta$ +25. This is our training data. We train the diffusion model to learn to this data +26. Then, we take the nominal plant, and take some amount of steps forward. +27. We heuristically go a certain amount forward to introduce a certain amount of noise +28. then we go backwards. The diffusion model will try to remove the noise, but not knowing the orignial nominal plant will introduce a perturbation. +29. **This is outcome number 3 and number 2** +30. We can use this frequency response data to find the 'worst case' distance to the critical point -1. We plot this location in the complex plane. +31. Why do we care about the complex plane? +32. This is where the nyquist robust stability and performance criterion live. +33. Our 'valid' perturbations live inside this circle of radius -1 +34. We repeat this several times, and only include examples in our set of unstructured perturbations that are within or robustness circle +35. **This is outcome number 1** +36. We generate enough examples to populate this circle until we're comfortable. ## Writin some stuff + +The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models [@sohl-dicksteinDeepUnsupervisedLearning2015]. Diffusion generative models are the state of the art for image and video generation, and have demonstrated promise for audio generation and noise removal [@kongDiffWaveVersatileDiffusion2020] [@SoraCreatingVideo]. A diffusion generative model, AlphaFold 3, won the Nobel Prize in Chemistry [@AlphaFold3Predicts2024] Diffusion models do this through a forward noise-inducing process, and a learned backwards noise-removing process. + +The forward diffusion process works by introducing small amounts of noise into \ No newline at end of file