This is my presentation slides at the American Control Conference (ACC), at May 2023, in San Diego. This was one of the talks in an invited session on "Resiliency and Privacy Throughout Networked Cyber-Physical Systems", that we co-organized and co-chaired during ACC 2023. I this invited sessions, great scholars presented their work on the intersection of resilient estimation, control and learning, as well as designing privacy-preserving mechanisms in multi-agent cyber-physical systems.
In my presentation, I discussed our progress on designing distributed interval-valued input and state observers for multi-agent LTI systems that are subject to bounded noise, as well as adversarial unknown inputs on both sensors and actuators. We introduced the notion of min-max consensus as a counterpart to average consensus in bounded-error settings. Moreover, we developed structural conditions for the stability of the proposed observers for different classes of dynamics and networks.
Controller synthesis for piecewise affine slab differential inclusions: A dua...Behzad Samadi
This document outlines a presentation on controller synthesis for piecewise affine systems. It introduces piecewise affine slab differential inclusions as a model for systems with hard nonlinearities. The objective is to propose a method for synthesizing piecewise affine controllers for stability and L2-gain performance of such systems using convex optimization. Key points covered include:
- Defining the dual parameter set for piecewise affine slab systems.
- Developing sufficient linear matrix inequality conditions for stability and L2-gain analysis using the dual parameter set.
- Formulating the controller synthesis problem based on the dual stability conditions, though additional work is needed to address non-convex terms.
- Providing examples of practical systems that can
This 3 sentence summary provides the key details from the document:
The document describes using a 1-D engine simulation model in GT-POWER to develop and test a model predictive control strategy for an internal combustion engine, where predictive models of the engine were identified using the LOLIMOT algorithm and incorporated into a model-based predictive controller, and this control strategy was first tested in a model-in-the-loop simulation and then later validated through hardware-in-the-loop experiments on a real engine testbed.
This document provides an introduction to deep neural networks (DNNs) by a Dr. Liwei Ren. It defines DNNs from both technical and mathematical perspectives. DNNs are composed of three main elements - architecture, activity rule, and learning rule. The architecture determines the network's capability and is typically a directed graph with weights, biases, and activation functions. Gradient descent and backpropagation are commonly used as the learning rule to update weights and minimize error. Universal approximation theorems show that both shallow and deep neural networks can approximate functions, with deep networks potentially being more efficient. Examples of DNN applications include image recognition. Security issues are also briefly mentioned.
1. The document discusses the integration of system identification (SYSID) methods with model predictive control (MPC).
2. It describes how SYSID can be used to estimate process models, which are then used for prediction in MPC. The model estimates are also regularly updated using new process data to adapt the MPC predictions over time.
3. However, the document notes that while the components of SYSID and MPC are established individually, fully integrating them in software in a systematic way remains a challenge, particularly for complex multi-variable systems.
Convex Optimization Modelling with CVXOPTandrewmart11
An introduction to convex optimization modelling using cvxopt in an IPython environment. The facility location problem is used as an example to demonstrate modelling in cvxopt.
Lecture on the use of Deep Learning in Optimization. It explains in detail the backpropagation algorithm. Various techniques like the Newton method, Gradient Descent, and Conjugate Direction are explained.
This document summarizes key concepts related to PID control design:
- A PID controller uses proportional, integral, and derivative feedback to generate a control action based on tuning the gains for each component.
- The proportional term reacts to current error, the integral term accounts for past errors to eliminate steady-state error, and the derivative term accounts for future error based on the rate of change of error.
- Increasing the proportional gain can make the system unstable if too high, while a low gain may not provide enough control action. The integral term eliminates steady-state error but can destabilize the system if not properly tuned. The derivative term speeds up transients but has little effect on steady-state
This document discusses fuzzy expert systems and the Mamdani fuzzy inference technique. It provides an example of a simple two-input, one-output fuzzy system to classify project risk. The Mamdani fuzzy inference process involves four steps: (1) fuzzification of the inputs, (2) rule evaluation, (3) aggregation of the rule outputs, and (4) defuzzification to produce a crisp output. The example shows each step applied to a system with three rules and illustrates how fuzzy logic handles rule evaluation and output aggregation.
Controller synthesis for piecewise affine slab differential inclusions: A dua...Behzad Samadi
This document outlines a presentation on controller synthesis for piecewise affine systems. It introduces piecewise affine slab differential inclusions as a model for systems with hard nonlinearities. The objective is to propose a method for synthesizing piecewise affine controllers for stability and L2-gain performance of such systems using convex optimization. Key points covered include:
- Defining the dual parameter set for piecewise affine slab systems.
- Developing sufficient linear matrix inequality conditions for stability and L2-gain analysis using the dual parameter set.
- Formulating the controller synthesis problem based on the dual stability conditions, though additional work is needed to address non-convex terms.
- Providing examples of practical systems that can
This 3 sentence summary provides the key details from the document:
The document describes using a 1-D engine simulation model in GT-POWER to develop and test a model predictive control strategy for an internal combustion engine, where predictive models of the engine were identified using the LOLIMOT algorithm and incorporated into a model-based predictive controller, and this control strategy was first tested in a model-in-the-loop simulation and then later validated through hardware-in-the-loop experiments on a real engine testbed.
This document provides an introduction to deep neural networks (DNNs) by a Dr. Liwei Ren. It defines DNNs from both technical and mathematical perspectives. DNNs are composed of three main elements - architecture, activity rule, and learning rule. The architecture determines the network's capability and is typically a directed graph with weights, biases, and activation functions. Gradient descent and backpropagation are commonly used as the learning rule to update weights and minimize error. Universal approximation theorems show that both shallow and deep neural networks can approximate functions, with deep networks potentially being more efficient. Examples of DNN applications include image recognition. Security issues are also briefly mentioned.
1. The document discusses the integration of system identification (SYSID) methods with model predictive control (MPC).
2. It describes how SYSID can be used to estimate process models, which are then used for prediction in MPC. The model estimates are also regularly updated using new process data to adapt the MPC predictions over time.
3. However, the document notes that while the components of SYSID and MPC are established individually, fully integrating them in software in a systematic way remains a challenge, particularly for complex multi-variable systems.
Convex Optimization Modelling with CVXOPTandrewmart11
An introduction to convex optimization modelling using cvxopt in an IPython environment. The facility location problem is used as an example to demonstrate modelling in cvxopt.
Lecture on the use of Deep Learning in Optimization. It explains in detail the backpropagation algorithm. Various techniques like the Newton method, Gradient Descent, and Conjugate Direction are explained.
This document summarizes key concepts related to PID control design:
- A PID controller uses proportional, integral, and derivative feedback to generate a control action based on tuning the gains for each component.
- The proportional term reacts to current error, the integral term accounts for past errors to eliminate steady-state error, and the derivative term accounts for future error based on the rate of change of error.
- Increasing the proportional gain can make the system unstable if too high, while a low gain may not provide enough control action. The integral term eliminates steady-state error but can destabilize the system if not properly tuned. The derivative term speeds up transients but has little effect on steady-state
This document discusses fuzzy expert systems and the Mamdani fuzzy inference technique. It provides an example of a simple two-input, one-output fuzzy system to classify project risk. The Mamdani fuzzy inference process involves four steps: (1) fuzzification of the inputs, (2) rule evaluation, (3) aggregation of the rule outputs, and (4) defuzzification to produce a crisp output. The example shows each step applied to a system with three rules and illustrates how fuzzy logic handles rule evaluation and output aggregation.
The document discusses the general robustness problem in control systems. It defines key terms like uncertainty and error, and describes the main sources of uncertainty as neglected dynamics, model reduction errors, parameter variations, and neglected nonlinearities. The main objectives of robust control are to provide satisfactory performance in the presence of these uncertainties. Common characterizations of uncertainty are additive and multiplicative errors. Representing uncertainties in a multiplicative form allows them to be analyzed using classical stability margin techniques from frequency domain robustness analysis.
Uniformity in mechanical properties of the slab affects quality of subsequent rolling process. One of the most important factors deciding quality of the slab is fluctuation of the molten steel level in the mould. That is, smoothing pouring without fluctuating in the mould level means improvement in quality of the slab and protects break-out problem and allows high speed casting process. If molten steel surface fluctuates severely, the forming oscillation marks on the slab is unstable, solidification of molten steel is not uniform and there will be entrapment of mould powder in the solidified cast strand. It makes quality of the slab inferior and generates defects on the slab.
This document contains 13 examples of exercises related to control systems. The examples involve tasks such as deriving state space models, bringing feedback control systems into generalized standard form, designing controllers, computing norms, and parametrizing stabilizing controllers. The examples cover topics including disturbance decoupling, observer design, state feedback, and coprime factorizations.
Digital Signal Processing[ECEG-3171]-Ch1_L03Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
The document provides an overview of backpropagation for neural networks. It begins by defining the loss function and discussing gradient descent. It then walks through the computational graph of a simple perceptron and derives the gradients for each operation using the chain rule. This allows computing the gradient of the loss with respect to the weights and biases, which are then updated using gradient descent. It discusses computing gradients for different activation functions like sigmoid, ReLU, and max pooling. Finally, it notes that backpropagation allows estimating parameters across stacked neural network layers.
The document proposes a distributed algorithm for network size estimation. Each node in the network runs simple first-order dynamics that exchanges information only with neighbors. The dynamics are designed such that the individual solutions of all nodes will converge to the total number of nodes N in the network. The algorithm provides a deterministic estimate of N and does not require initialization, making it "plug-and-play ready" for dynamic networks where nodes can join or leave over time. It is proven that if the gain k is larger than N^3, the estimates will converge to the true value N within a finite settling time.
Design and Implementation of Parallel and Randomized Approximation AlgorithmsAjay Bidyarthy
This document summarizes the design and implementation of parallel and randomized approximation algorithms for solving matrix games, linear programs, and semi-definite programs. It presents solvers for these problems that provide approximate solutions in sublinear or near-linear time. It analyzes the performance and precision-time tradeoffs of the solvers compared to other algorithms. It also provides examples of applying the SDP solver to approximate the Lovasz theta function.
Practical and Worst-Case Efficient ApportionmentRaphael Reitzig
Proportional apportionment is the problem of assigning seats to parties according to their relative share of votes. Divisor methods are the de-facto standard solution, used in many countries.
In recent literature, there are two algorithms that implement divisor methods: one by Cheng and Eppstein (ISAAC, 2014) has worst-case optimal running time but is complex, while the other (Pukelsheim, 2014) is relatively simple and fast in practice but does not offer worst-case guarantees.
This talk presents the ideas behind a novel algorithm that avoids the shortcomings of both. We investigate the three contenders in order to determine which is most useful in practice.
Read more over here: http://reitzig.github.io/publications/RW2015b
Dynamic programming is a technique for solving problems with overlapping subproblems and optimal substructure. It works by breaking problems down into smaller subproblems and storing the results in a table to avoid recomputing them. Examples where it can be applied include the knapsack problem, longest common subsequence, and computing Fibonacci numbers efficiently through bottom-up iteration rather than top-down recursion. The technique involves setting up recurrences relating larger instances to smaller ones, solving the smallest instances, and building up the full solution using the stored results.
Seminar on "Verification of Relational Data-Centric Systems with External Services" at the KRDB Research Centre for Knowledge and Data, Faculty of Computer Science, Free University of Bozen-Bolzano (Italy), 03/05/2012.
Distributed Parallel Process Particle Swarm Optimization on Fixed Charge Netw...Corey Clark, Ph.D.
The document presents a dynamically distributed binary particle swarm optimization (BPSO) approach for solving fixed-charge network flow problems. The approach distributes the BPSO algorithm across a cluster of devices using a distributed accelerated analytics platform. Testing showed the distributed BPSO approach found better solutions faster than serial BPSO and optimization approaches for various problem sizes, demonstrating the benefits of dynamic distributed computing for difficult mixed integer programs.
Planning Under Uncertainty With Markov Decision Processesahmad bassiouny
This document summarizes techniques for abstraction in Markov decision processes (MDPs) using logical and structured representations. It discusses decision-theoretic regression, which produces a logical description of the Q-function given a description of the value function. Decision-theoretic regression can be done propositionally or in first-order logic. It allows for structured value and policy iteration algorithms that exploit the logical and probabilistic structure of MDPs. Approximate decision-theoretic regression can provide more compact representations by pruning value distinctions.
This document discusses optimization techniques and provides examples to illustrate key concepts in optimization problems. It defines optimization as finding extreme states like minimum/maximum and discusses how it is applied in various fields. It then covers basic definitions like design variables, objective functions, constraints, convexity, local vs global optima. Examples are given to show unconstrained vs constrained problems and illustrate active, inactive and violated constraints. Optimization techniques largely depend on calculus concepts like derivatives and hessian matrix.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
This document provides an overview of signals and systems in digital signal processing. It defines what a signal and system are, provides examples of common discrete-time signals like impulse functions and exponential functions. It also discusses signal operations such as addition, delaying, time reversing and rate changing. The document classifies signals as periodic/aperiodic, even/odd, energy/power signals. It also classifies systems as continuous/discrete-time, time-variant/invariant, linear/non-linear, stable/unstable systems. In addition, it provides representations of systems using impulse response, difference equations and transfer functions.
New Design Architecture of Chaotic Secure Communication System Combined with ...ijtsrd
In this paper, the exponential synchronization of secure communication system is introduced and a novel secure communication design combined with linear receiver is constructed to ensure the global exponential stability of the resulting error signals. Besides, the guaranteed exponential convergence rate of the proposed secure communication system can be correctly calculated. Finally, some numerical simulations are offered to demonstrate the correctness and feasibility of the obtained results. Yeong-Jeu Sun "New Design Architecture of Chaotic Secure Communication System Combined with Linear Receiver" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38214.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/38214/new-design-architecture-of-chaotic-secure-communication-system-combined-with-linear-receiver/yeongjeu-sun
The document describes techniques for image texture analysis and segmentation. It proposes a methodology using constraint satisfaction neural networks to integrate region-based and edge-based texture segmentation. The methodology initializes a CSNN using fuzzy c-means clustering, then iteratively updates the neuron probabilities and edge maps to refine the segmentation. Experimental results demonstrate improved segmentation by combining region and edge information.
This lecture covers stability analysis techniques including Nyquist plots, Bode plots, and stability margins. Key points discussed include:
1) Nyquist plots can be used to determine stability by checking for encirclements of the critical point -1. Gain scaling affects the Nyquist plot.
2) Bode plots allow calculating gain and phase margins, which indicate how close a system is to instability.
3) Stability margins provide a measure of how far a system is from the threshold of instability, beyond just gain and phase margins.
4) Non-minimum phase systems with right half plane poles require special consideration in stability analysis.
This document describes a recursive pseudo-exhaustive two-pattern generator for built-in self-test of circuits. The generator recursively generates all two-pattern (n,k)-adjacent bit pseudo-exhaustive tests for values of k from 1 to n. It consists of a generic counter, carry generator, control logic, and 1's complement adder. The generator is used to test a 4-bit Wallace tree multiplier and cryptographic circuit in parallel. Simulation waveforms are provided for the generator components and circuits under test, verifying the generator's operation. Compared to prior approaches, the proposed generator requires fewer gates to implement.
The document discusses the general robustness problem in control systems. It defines key terms like uncertainty and error, and describes the main sources of uncertainty as neglected dynamics, model reduction errors, parameter variations, and neglected nonlinearities. The main objectives of robust control are to provide satisfactory performance in the presence of these uncertainties. Common characterizations of uncertainty are additive and multiplicative errors. Representing uncertainties in a multiplicative form allows them to be analyzed using classical stability margin techniques from frequency domain robustness analysis.
Uniformity in mechanical properties of the slab affects quality of subsequent rolling process. One of the most important factors deciding quality of the slab is fluctuation of the molten steel level in the mould. That is, smoothing pouring without fluctuating in the mould level means improvement in quality of the slab and protects break-out problem and allows high speed casting process. If molten steel surface fluctuates severely, the forming oscillation marks on the slab is unstable, solidification of molten steel is not uniform and there will be entrapment of mould powder in the solidified cast strand. It makes quality of the slab inferior and generates defects on the slab.
This document contains 13 examples of exercises related to control systems. The examples involve tasks such as deriving state space models, bringing feedback control systems into generalized standard form, designing controllers, computing norms, and parametrizing stabilizing controllers. The examples cover topics including disturbance decoupling, observer design, state feedback, and coprime factorizations.
Digital Signal Processing[ECEG-3171]-Ch1_L03Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
The document provides an overview of backpropagation for neural networks. It begins by defining the loss function and discussing gradient descent. It then walks through the computational graph of a simple perceptron and derives the gradients for each operation using the chain rule. This allows computing the gradient of the loss with respect to the weights and biases, which are then updated using gradient descent. It discusses computing gradients for different activation functions like sigmoid, ReLU, and max pooling. Finally, it notes that backpropagation allows estimating parameters across stacked neural network layers.
The document proposes a distributed algorithm for network size estimation. Each node in the network runs simple first-order dynamics that exchanges information only with neighbors. The dynamics are designed such that the individual solutions of all nodes will converge to the total number of nodes N in the network. The algorithm provides a deterministic estimate of N and does not require initialization, making it "plug-and-play ready" for dynamic networks where nodes can join or leave over time. It is proven that if the gain k is larger than N^3, the estimates will converge to the true value N within a finite settling time.
Design and Implementation of Parallel and Randomized Approximation AlgorithmsAjay Bidyarthy
This document summarizes the design and implementation of parallel and randomized approximation algorithms for solving matrix games, linear programs, and semi-definite programs. It presents solvers for these problems that provide approximate solutions in sublinear or near-linear time. It analyzes the performance and precision-time tradeoffs of the solvers compared to other algorithms. It also provides examples of applying the SDP solver to approximate the Lovasz theta function.
Practical and Worst-Case Efficient ApportionmentRaphael Reitzig
Proportional apportionment is the problem of assigning seats to parties according to their relative share of votes. Divisor methods are the de-facto standard solution, used in many countries.
In recent literature, there are two algorithms that implement divisor methods: one by Cheng and Eppstein (ISAAC, 2014) has worst-case optimal running time but is complex, while the other (Pukelsheim, 2014) is relatively simple and fast in practice but does not offer worst-case guarantees.
This talk presents the ideas behind a novel algorithm that avoids the shortcomings of both. We investigate the three contenders in order to determine which is most useful in practice.
Read more over here: http://reitzig.github.io/publications/RW2015b
Dynamic programming is a technique for solving problems with overlapping subproblems and optimal substructure. It works by breaking problems down into smaller subproblems and storing the results in a table to avoid recomputing them. Examples where it can be applied include the knapsack problem, longest common subsequence, and computing Fibonacci numbers efficiently through bottom-up iteration rather than top-down recursion. The technique involves setting up recurrences relating larger instances to smaller ones, solving the smallest instances, and building up the full solution using the stored results.
Seminar on "Verification of Relational Data-Centric Systems with External Services" at the KRDB Research Centre for Knowledge and Data, Faculty of Computer Science, Free University of Bozen-Bolzano (Italy), 03/05/2012.
Distributed Parallel Process Particle Swarm Optimization on Fixed Charge Netw...Corey Clark, Ph.D.
The document presents a dynamically distributed binary particle swarm optimization (BPSO) approach for solving fixed-charge network flow problems. The approach distributes the BPSO algorithm across a cluster of devices using a distributed accelerated analytics platform. Testing showed the distributed BPSO approach found better solutions faster than serial BPSO and optimization approaches for various problem sizes, demonstrating the benefits of dynamic distributed computing for difficult mixed integer programs.
Planning Under Uncertainty With Markov Decision Processesahmad bassiouny
This document summarizes techniques for abstraction in Markov decision processes (MDPs) using logical and structured representations. It discusses decision-theoretic regression, which produces a logical description of the Q-function given a description of the value function. Decision-theoretic regression can be done propositionally or in first-order logic. It allows for structured value and policy iteration algorithms that exploit the logical and probabilistic structure of MDPs. Approximate decision-theoretic regression can provide more compact representations by pruning value distinctions.
This document discusses optimization techniques and provides examples to illustrate key concepts in optimization problems. It defines optimization as finding extreme states like minimum/maximum and discusses how it is applied in various fields. It then covers basic definitions like design variables, objective functions, constraints, convexity, local vs global optima. Examples are given to show unconstrained vs constrained problems and illustrate active, inactive and violated constraints. Optimization techniques largely depend on calculus concepts like derivatives and hessian matrix.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
This document provides an overview of signals and systems in digital signal processing. It defines what a signal and system are, provides examples of common discrete-time signals like impulse functions and exponential functions. It also discusses signal operations such as addition, delaying, time reversing and rate changing. The document classifies signals as periodic/aperiodic, even/odd, energy/power signals. It also classifies systems as continuous/discrete-time, time-variant/invariant, linear/non-linear, stable/unstable systems. In addition, it provides representations of systems using impulse response, difference equations and transfer functions.
New Design Architecture of Chaotic Secure Communication System Combined with ...ijtsrd
In this paper, the exponential synchronization of secure communication system is introduced and a novel secure communication design combined with linear receiver is constructed to ensure the global exponential stability of the resulting error signals. Besides, the guaranteed exponential convergence rate of the proposed secure communication system can be correctly calculated. Finally, some numerical simulations are offered to demonstrate the correctness and feasibility of the obtained results. Yeong-Jeu Sun "New Design Architecture of Chaotic Secure Communication System Combined with Linear Receiver" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38214.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/38214/new-design-architecture-of-chaotic-secure-communication-system-combined-with-linear-receiver/yeongjeu-sun
The document describes techniques for image texture analysis and segmentation. It proposes a methodology using constraint satisfaction neural networks to integrate region-based and edge-based texture segmentation. The methodology initializes a CSNN using fuzzy c-means clustering, then iteratively updates the neuron probabilities and edge maps to refine the segmentation. Experimental results demonstrate improved segmentation by combining region and edge information.
This lecture covers stability analysis techniques including Nyquist plots, Bode plots, and stability margins. Key points discussed include:
1) Nyquist plots can be used to determine stability by checking for encirclements of the critical point -1. Gain scaling affects the Nyquist plot.
2) Bode plots allow calculating gain and phase margins, which indicate how close a system is to instability.
3) Stability margins provide a measure of how far a system is from the threshold of instability, beyond just gain and phase margins.
4) Non-minimum phase systems with right half plane poles require special consideration in stability analysis.
This document describes a recursive pseudo-exhaustive two-pattern generator for built-in self-test of circuits. The generator recursively generates all two-pattern (n,k)-adjacent bit pseudo-exhaustive tests for values of k from 1 to n. It consists of a generic counter, carry generator, control logic, and 1's complement adder. The generator is used to test a 4-bit Wallace tree multiplier and cryptographic circuit in parallel. Simulation waveforms are provided for the generator components and circuits under test, verifying the generator's operation. Compared to prior approaches, the proposed generator requires fewer gates to implement.
Similar to Distributed Resilient Interval Observers for Bounded-Error LTI Systems Subject to False Data Injection Attacks (20)
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
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%.
Home security is of paramount importance in today's world, where we rely more on technology, home
security is crucial. Using technology to make homes safer and easier to control from anywhere is
important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost,
AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
and saves images of friends and family members. The system scans this folder to recognize familiar faces
and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert,
ensuring a proactive response to potential security threats.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Pressure Relief valve used in flow line to release the over pressure at our d...
Distributed Resilient Interval Observers for Bounded-Error LTI Systems Subject to False Data Injection Attacks
1. Distributed Resilient Interval Observers for
Bounded-Error LTI Systems Subject to False Data
Injection Attacks
Mohammad Khajenejad, Scott Brown, and Sonia Martı́nez
Mechanical and Aerospace Engineering Department
University of California San Diego
mkhajenejad@eng.ucsd.edu
American Control Conference (ACC), June 2, 2023
1
2. 2
Introduction Observer design Stability Performance
Motivation
safety is especially critical in large
networked CPS
input reconstruction and state
estimation are important for fault
detection and attack mitigation
don’t always know distributions of
disturbances
Motivating question
Can we simultaneously estimate sets of states and inputs to identify and possibly
mitigate abnormal behavior?
Our contribution
Distributed interval observer design handling unknown adversarial inputs
3. 2
Introduction Observer design Stability Performance
Motivation
safety is especially critical in large
networked CPS
input reconstruction and state
estimation are important for fault
detection and attack mitigation
don’t always know distributions of
disturbances
Motivating question
Can we simultaneously estimate sets of states and inputs to identify and possibly
mitigate abnormal behavior?
Our contribution
Distributed interval observer design handling unknown adversarial inputs
4. 2
Introduction Observer design Stability Performance
Motivation
safety is especially critical in large
networked CPS
input reconstruction and state
estimation are important for fault
detection and attack mitigation
don’t always know distributions of
disturbances
Motivating question
Can we simultaneously estimate sets of states and inputs to identify and possibly
mitigate abnormal behavior?
Our contribution
Distributed interval observer design handling unknown adversarial inputs
5. 3
Introduction Observer design Stability Performance
Problem formulation
Target system, x ∈ Rn
xk+1 = Axk + Bwk + Gdk
wk ∈ [w, w], dk ∈ Rp
dk is unknown and arbitrary
Sensor network, i ∈ V = {1, . . . , N}
yi
k = Ci
xk + Di
vi
k + Hi
dk
vi
k ∈ [vi
, vi
]
6. 3
Introduction Observer design Stability Performance
Problem formulation
Target system, x ∈ Rn
xk+1 = Axk + Bwk + Gdk
wk ∈ [w, w], dk ∈ Rp
dk is unknown and arbitrary
Sensor network, i ∈ V = {1, . . . , N}
yi
k = Ci
xk + Di
vi
k + Hi
dk
vi
k ∈ [vi
, vi
]
7. 4
Introduction Observer design Stability Performance
Problem formulation (cont.)
Objective: distributed interval observer
a distributed system that generates xi
k , xi
k , di
k , and d
i
k such that
xi
k ≤ xk ≤ xi
k and di
k ≤ dk ≤ d
i
k ∀i ∈ V, k ≥ 0 (framer property)
the framers are uniformly bounded (stability)
8. 5
Introduction Observer design Stability Performance
Dealing with the unknown input
under some mild technical assumptions, we
1 Decompose the outputs into components
zi
1, affected by unknown input
zi
2, not affected by unknown input
2 Eliminate the input using feedback
Final reduced form
x+ = Āi
x + S̄i
zi
1 + T̄i
zi
2,+ + B̄i
w̃i
d = Āi
d x + S̄i
d zi
1 + T̄i
d zi
2,+ + Fi
w̃
9. 5
Introduction Observer design Stability Performance
Dealing with the unknown input
under some mild technical assumptions, we
1 Decompose the outputs into components
zi
1, affected by unknown input
zi
2, not affected by unknown input
2 Eliminate the input using feedback
Final reduced form
x+ = Āi
x + S̄i
zi
1 + T̄i
zi
2,+ + B̄i
w̃i
d = Āi
d x + S̄i
d zi
1 + T̄i
d zi
2,+ + Fi
w̃
10. 6
Introduction Observer design Stability Performance
Observer design
[Efimov.ea.2013]
x ∈ [x, x] ⇒ A+
x − A−
x ≤ Ax ≤ A+
x − A−
x
i) Propagation and measurement update
xi,0
k+1 = (Ãi
)+
xi
k − (Ãi
)−
xi
k + z̃i
k+1
+ (L̃i
)+
w̃i
− (L̃i
)−
w̃
i
xi,0
k+1 = (Ãi
)+
xi
k − (Ãi
)−
xi
k + z̃i
k+1
+ (L̃i
)+
w̃
i
− (L̃i
)−
w̃i
where Ãi
, Ti
Āi
− Li
Ci
2 and Ti
= I − Γi
Ci
2
Ti
, Li
, Γi
: observer gains (to-be-designed)
A+
ij , max{Aij , 0}, A−
= A+
− A
11. 7
Introduction Observer design Stability Performance
Observer design
Ni : Neighbors of node i
ii) Network update (state)
xi,t
k = max
j∈Ni
xj,t−1
k xi,t
k = min
j∈Ni
xj,t−1
k
xi
k = xi,tx
k xi
k = xi,tx
k
12. 8
Introduction Observer design Stability Performance
Observer design
Recall
d = Āi
d x + S̄i
d zi
1 + T̄i
d zi
2,+ + Fi
w̃
iii) Unknown input calculation
di,0
k = (Ãi
d )+
xi
k − (Ãi
d )−
xi
k + ξi
k+1
+ (Fi
)+
w̃i
− (Fi
)−
w̃
i
d
i,0
k = (Ãi
d )+
xi
k − (Ãi
d )−
xi
k + ξi
k+1
+ (Fi
)+
w̃
i
− (Fi
)−
w̃i
iv) Network update (input)
di,t
k = max
j∈Ni
dj,t−1
k d
i,t
k = min
j∈Ni
d
j,t−1
k
di
k = di,td
k d
i
k = d
i,td
k
13. 9
Introduction Observer design Stability Performance
Necessary and Sufficient Stability Condition
Definition (Individual errors)
ei
k ,
xk − xi
k
xi
k − xk
≥ 0
Collective error system
ek+1 = Mk Âek + Mk (Wk + Vk )
Mk ∈ M ⊂ {0, 1}2Nn×2Nn
is a state dependent switching signal encoding
xi
k = max
j∈N tx
i
xj,0
k xi
k = min
j∈N tx
i
xj,0
k
Theorem
The error system is ISS ⇐⇒ ∃M∗ ∈ M such that ρ(M∗Â) 1
14. 9
Introduction Observer design Stability Performance
Necessary and Sufficient Stability Condition
Definition (Individual errors)
ei
k ,
xk − xi
k
xi
k − xk
≥ 0
Collective error system
ek+1 = Mk Âek + Mk (Wk + Vk )
Mk ∈ M ⊂ {0, 1}2Nn×2Nn
is a state dependent switching signal encoding
xi
k = max
j∈N tx
i
xj,0
k xi
k = min
j∈N tx
i
xj,0
k
Theorem
The error system is ISS ⇐⇒ ∃M∗ ∈ M such that ρ(M∗Â) 1
15. 9
Introduction Observer design Stability Performance
Necessary and Sufficient Stability Condition
Definition (Individual errors)
ei
k ,
xk − xi
k
xi
k − xk
≥ 0
Collective error system
ek+1 = Mk Âek + Mk (Wk + Vk )
Mk ∈ M ⊂ {0, 1}2Nn×2Nn
is a state dependent switching signal encoding
xi
k = max
j∈N tx
i
xj,0
k xi
k = min
j∈N tx
i
xj,0
k
Theorem
The error system is ISS ⇐⇒ ∃M∗ ∈ M such that ρ(M∗Â) 1
16. 9
Introduction Observer design Stability Performance
Necessary and Sufficient Stability Condition
Definition (Individual errors)
ei
k ,
xk − xi
k
xi
k − xk
≥ 0
Collective error system
ek+1 = Mk Âek + Mk (Wk + Vk )
Mk ∈ M ⊂ {0, 1}2Nn×2Nn
is a state dependent switching signal encoding
xi
k = max
j∈N tx
i
xj,0
k xi
k = min
j∈N tx
i
xj,0
k
Theorem
The error system is ISS ⇐⇒ ∃M∗ ∈ M such that ρ(M∗Â) 1
17. 10
Introduction Observer design Stability Performance
Tractable and Sufficient Stability Condition
What about detectability? ρ(A − LC) 1 ; ρ(|A − LC|) 1
Assumption 1 (Sufficient condition for stability, informal)
For every node i ∈ V and state dimension s ∈ {1, . . . , n}, there is a j ∈ Ntx
i
which, given estimates of the other elements of xk , can compute a “good”
estimate of the sth
entry of xk
18. 10
Introduction Observer design Stability Performance
Tractable and Sufficient Stability Condition
What about detectability? ρ(A − LC) 1 ; ρ(|A − LC|) 1
Assumption 1 (Sufficient condition for stability, informal)
For every node i ∈ V and state dimension s ∈ {1, . . . , n}, there is a j ∈ Ntx
i
which, given estimates of the other elements of xk , can compute a “good”
estimate of the sth
entry of xk
19. 11
Introduction Observer design Stability Performance
Designing Stabilizing Gains
each node i ∈ V solves a local linear program and examines the rows of Ei
Theorem (Stabilizing gain design)
under Assumption 1, Li
∗, Ti
∗, and Γi
∗ solving
min
Ei ,Li ,Ti ,Γi
Pn
j=1
Pn
t=1 Ei
jt
subject to −Ei
≤ Ti
Āi
− Li
Ci
2 ≤ Ei
Ti
= In − Γi
Ci
2
guarantee that the observer is ISS.
solution identifies state dimensions which the node can estimate well
20. 11
Introduction Observer design Stability Performance
Designing Stabilizing Gains
each node i ∈ V solves a local linear program and examines the rows of Ei
Theorem (Stabilizing gain design)
under Assumption 1, Li
∗, Ti
∗, and Γi
∗ solving
min
Ei ,Li ,Ti ,Γi
Pn
j=1
Pn
t=1 Ei
jt
subject to −Ei
≤ Ti
Āi
− Li
Ci
2 ≤ Ei
Ti
= In − Γi
Ci
2
guarantee that the observer is ISS.
solution identifies state dimensions which the node can estimate well
21. 12
Introduction Observer design Stability Performance
Designing for Performance
seeks to zero-out the dynamics
sensitive to noise and leads to large errors (wide intervals)
can we do better while maintaining stability?
minimize a (linear) performance criteria similar to H∞ design
Error minimizing design
min
Zi ,Li ,Ti ,Γi
k|L̃i
|(w̃
i
− w̃i
)k∞
s.t. Ti
= I − Γi
Ci
2
Pn
t=1 Zi
jt 1, ∀j ∈ Ji
−Zi
≤ Ti
Ãi
− Li
Ci
2 ≤ Zi
Ji
: states for which the node is “responsible”
relies on the previous solution to verify assumption and identify Ji
22. 12
Introduction Observer design Stability Performance
Designing for Performance
seeks to zero-out the dynamics
sensitive to noise and leads to large errors (wide intervals)
can we do better while maintaining stability?
minimize a (linear) performance criteria similar to H∞ design
Error minimizing design
min
Zi ,Li ,Ti ,Γi
k|L̃i
|(w̃
i
− w̃i
)k∞
s.t. Ti
= I − Γi
Ci
2
Pn
t=1 Zi
jt 1, ∀j ∈ Ji
−Zi
≤ Ti
Ãi
− Li
Ci
2 ≤ Zi
Ji
: states for which the node is “responsible”
relies on the previous solution to verify assumption and identify Ji
24. 14
Introduction Observer design Stability Performance
Conclusions Future Work
proposed a novel distributed state and input interval observer
determined stability conditions
provided tractable methods for computing stabilizing gains
optimized the performance of the observer
future work: extensions to nonlinear, switched and hybrid systems
27. 17
Appendix: min/max consensus
We use min/max consensus to share estimates between nodes
xi,t
k = max
j∈Ni
xj,t−1
k xi,t
k = min
j∈Ni
xj,t−1
k
Simple static example of min consensus
Fast convergence compared to average consensus (finite time)
Iterations acquire information from further neighbors (Nt
i )
28. 18
Multidimensional intervals
Definition
an interval Ix , [x, x] ⊆ Rn
: the set of all x ∈ Rn
that satisfy x ≤ x ≤ x
[?, Lemma 2]
A ∈ Rp×n
, x ≤ x ≤ x ∈ Rn
A+
x − A−
x ≤ Ax ≤ A+
x − A−
x
A+
ij , max{Aij , 0}, A−
= A+
− A