This document contains lecture notes on introduction to control systems from Dr. Huynh Thai Hoang of Ho Chi Minh City University of Technology. The notes cover mathematical models of continuous control systems, including the concept of transfer functions. Chapter 2 discusses different mathematical models like transfer functions, block diagram algebra, signal flow graphs, and state space equations. It also covers linearized models of nonlinear systems.
This chapter provides complete solution of different circuits using Laplace transform method and also provides information about applications of Laplace transforms.
The document discusses several key points about derivatives:
1) The definition of a derivative directly from limits is not usually the most efficient way to find derivatives except for simple functions. General rules like the power rule and sum rule make derivatives easier to find.
2) Derivatives can be represented with different notations like f'(x) or dy/dx.
3) Functions being derived are assumed to be differentiable. Examples of finding derivatives using the power rule are shown for simple polynomial functions.
This document discusses the derivation of transfer functions for electrical systems. It begins by defining a transfer function as the ratio of the Laplace transform of the output variable to the Laplace transform of the input variable, assuming all initial conditions are zero. Laplace transforms are introduced as a method to convert differential equations into algebraic equations using a complex variable s. Input, output, and other signals are represented using Laplace transforms to allow analysis of systems. Methods for obtaining transfer functions include using block diagrams and signal flow graphs. An example circuit is provided to demonstrate calculating a transfer function by taking the ratio of the Laplace transform of the output current to the Laplace transform of the input voltage.
Control system introduction for different applicationAnoopCadlord1
The document provides an overview of control systems design. It begins by describing the general process for designing a control system, which involves modeling interconnected system components to achieve a desired purpose. Examples of early control systems are discussed to illustrate fundamental feedback principles still used today. Modern applications of control engineering are then briefly mentioned. The document notes that a design gap exists between complex physical systems and their models. An iterative design process is used to effectively address this gap while meeting performance and cost objectives.
This document discusses convolution and linear time-invariant (LTI) systems. It begins by defining the impulse response of a system and representing convolution using integral forms. Properties of convolution like time-invariance and linearity are described. The step response of LTI systems is introduced. Examples are provided to demonstrate calculating the convolution of inputs and impulse responses by breaking it into cases. Properties of LTI systems discussed include causality, stability, invertibility, and interconnecting multiple LTI systems.
This document discusses system modeling and properties of linearity and time invariance. It provides examples of modeling a resistor, square-law system, delay operator, and time compressor to illustrate these properties. A model is a set of mathematical equations relating the output and input signals of a physical system. A system is linear if the response to a sum of inputs is the sum of the responses. It is time invariant if its behavior does not change over time. Developing an accurate but simplified model is important for understanding system behavior and designing controllers.
This chapter provides complete solution of different circuits using Laplace transform method and also provides information about applications of Laplace transforms.
The document discusses several key points about derivatives:
1) The definition of a derivative directly from limits is not usually the most efficient way to find derivatives except for simple functions. General rules like the power rule and sum rule make derivatives easier to find.
2) Derivatives can be represented with different notations like f'(x) or dy/dx.
3) Functions being derived are assumed to be differentiable. Examples of finding derivatives using the power rule are shown for simple polynomial functions.
This document discusses the derivation of transfer functions for electrical systems. It begins by defining a transfer function as the ratio of the Laplace transform of the output variable to the Laplace transform of the input variable, assuming all initial conditions are zero. Laplace transforms are introduced as a method to convert differential equations into algebraic equations using a complex variable s. Input, output, and other signals are represented using Laplace transforms to allow analysis of systems. Methods for obtaining transfer functions include using block diagrams and signal flow graphs. An example circuit is provided to demonstrate calculating a transfer function by taking the ratio of the Laplace transform of the output current to the Laplace transform of the input voltage.
Control system introduction for different applicationAnoopCadlord1
The document provides an overview of control systems design. It begins by describing the general process for designing a control system, which involves modeling interconnected system components to achieve a desired purpose. Examples of early control systems are discussed to illustrate fundamental feedback principles still used today. Modern applications of control engineering are then briefly mentioned. The document notes that a design gap exists between complex physical systems and their models. An iterative design process is used to effectively address this gap while meeting performance and cost objectives.
This document discusses convolution and linear time-invariant (LTI) systems. It begins by defining the impulse response of a system and representing convolution using integral forms. Properties of convolution like time-invariance and linearity are described. The step response of LTI systems is introduced. Examples are provided to demonstrate calculating the convolution of inputs and impulse responses by breaking it into cases. Properties of LTI systems discussed include causality, stability, invertibility, and interconnecting multiple LTI systems.
This document discusses system modeling and properties of linearity and time invariance. It provides examples of modeling a resistor, square-law system, delay operator, and time compressor to illustrate these properties. A model is a set of mathematical equations relating the output and input signals of a physical system. A system is linear if the response to a sum of inputs is the sum of the responses. It is time invariant if its behavior does not change over time. Developing an accurate but simplified model is important for understanding system behavior and designing controllers.
Modern Control - Lec 02 - Mathematical Modeling of SystemsAmr E. Mohamed
This document provides an overview of mathematical modeling of physical systems. It discusses how to derive mathematical models from physical systems using differential equations based on governing physical laws. The key steps are: (1) defining the physical system, (2) formulating the mathematical model using differential equations, and (3) solving the equations. Common model types include differential equation, transfer function, and state-space models. The document also discusses modeling various physical elements like electrical circuits, mechanical translational/rotational systems, and electro-mechanical systems using differential equations. It covers block diagram representation and reduction of mathematical models. The overall goal is to realize the importance of deriving accurate mathematical models for analyzing and designing control systems.
1) The lecture discusses the time domain analysis of continuous time linear and time-invariant systems. It covers topics such as impulse response, convolution, and how the output of an LTI system can be determined from its impulse response and the input signal.
2) An example of analyzing the voltage response of an RC circuit to an arbitrary input is presented. The output is the sum of the zero-input response, due to initial conditions, and zero-state response, which is a convolution of the impulse response and input signal.
3) Detectors of high energy photons can be modeled as having an exponential decay impulse response. Examples of characterizing real detectors through measurements of energy resolution, timing resolution, and coincidence point spread
The document provides information about state variable models and transfer functions. It discusses:
- Modeling systems using state variables and representing them with first-order differential equations in matrix form.
- Obtaining transfer functions from state variable models by taking the Laplace transform of the state equations.
- Examples of modeling an RLC circuit and calculating its transfer function from the state equations.
- Using state variable models and feedback to design state variable feedback control systems. This involves estimating unmeasured states with observers and connecting the observer to the full-state feedback control law.
Optimal control of multi delay systems via orthogonal functionsiaemedu
This document summarizes an approach for computing the optimal control law of linear time-invariant and time-varying systems with time delays using orthogonal functions. The approach uses block-pulse functions and shifted Legendre polynomials to represent system variables and express delay differential equations as algebraic equations. This allows the optimal control problem to be solved as an algebraic equation problem. Numerical examples are provided to demonstrate the approach. The approach provides a unified method for computing optimal control of multi-delay systems using orthogonal functions.
1) The document discusses complex numbers and the Laplace transform, which can convert differential equations describing dynamic systems into algebraic equations.
2) The Laplace transform represents functions in the complex s-domain and allows converting between the time and frequency domains.
3) The Laplace transform is a useful mathematical tool for control systems analysis and design by representing systems as transfer functions and enabling calculation of time-domain responses from frequency-domain inputs.
This document contains lecture notes on signals and systems for a course at Chadalawada Ramanamma Engineering College. It includes:
1. An introduction to signals, systems, and some common elementary signals like the unit step, unit impulse, ramp, sinusoid, and exponential signals.
2. A classification of signals as continuous/discrete, deterministic/non-deterministic, even/odd, periodic/aperiodic, energy/power, and real/imaginary.
3. A discussion of basic operations on signals like amplitude scaling, addition, and subtraction.
The document introduces complex systems and defines a system as a set of interacting parts within a single structure. It provides examples of real-world systems and discusses describing a system from different points of view using models. The document outlines functional and oriented models, abstraction in models, and analyzing dynamic and stationary systems. It discusses representing stationary systems using transition and transformation functions and modeling economic systems with relevant variables and equations. Finally, it presents a case study modeling a school system as a linear, stationary system to calculate total student population over time under changing retention rates.
Differential equations can be powerful tools for modeling data. New methods allow estimating differential equations directly from data. As an example, the author estimates a differential equation model from simulated data from a chemical reactor. The estimated parameters are close to the true values, demonstrating the method works well on simulated data.
This document provides an overview of a control systems engineering course. It discusses key topics that will be covered, including modeling dynamic systems using differential equations, obtaining accurate plant models, designing controllers to meet performance specifications, handling uncertainties, and bridging the gap between linear control theory and real-world industrial problems. The course aims to address issues like robust stability, robust performance, nonlinearities, and designing controllers when plant models are difficult to stabilize.
This document provides an overview of modeling systems using Laplace transforms. It discusses:
1) Converting time functions to the frequency domain using Laplace transforms and inverse Laplace transforms
2) Finding transfer functions (TF) from differential equations to model systems
3) Using partial fraction expansions to simplify transfer functions for inverse Laplace transforms
4) Examples of using Laplace transforms to solve differential equations and model various mechanical and electrical systems.
1) The document analyzes the boundedness and domain of attraction of a fractional-order wireless power transfer (WPT) system.
2) It establishes a fractional-order piecewise affine model of the WPT system and derives sufficient conditions for boundedness using Lyapunov functions and inequality techniques.
3) The results provide a way to estimate the domain of attraction of the fractional-order WPT system and systems with periodically intermittent control.
Quantifying Information Leaks via Model Counting Modulo TheoriesQuoc-Sang Phan
The 41st CREST Open Workshop - Software Engineering And Computer Science Using Information
http://crest.cs.ucl.ac.uk/cow/the_41st_cow_27_and_28_april_2015/
This document discusses dynamic systems and their analysis using transfer functions. It begins by defining dynamic systems as those whose output depends on both current and previous inputs/outputs. It then covers:
- Transfer function representations of linear time-invariant (LTI) systems using Laplace transforms.
- Key properties of transfer functions including poles, zeros and zero-pole-gain form.
- MATLAB representations of transfer functions.
It also defines important concepts for analyzing dynamic systems like time and frequency response, stability, system order, and the effects of pole locations. Specific discussions are included on analyzing first and second order systems.
- The document provides an overview of mathematical concepts for control systems including complex variables, complex functions, differential equations, Laplace transforms, and their applications.
- It introduces complex numbers and variables, complex functions including poles and zeros. Basic concepts of differential equations and Laplace transforms are reviewed.
- Methods for solving differential equations using Laplace transforms are described, including taking the inverse Laplace transform using partial fraction expansion.
The finite time turnpike phenomenon for optimal control problemsMartinGugat
Often in dynamic optimal control problems with a long time horizon, in a large neighborhoodof the middle of the time interval the optimal control and the optimal state are very close to the solution of a static control problem that is derived form the dynamic optimal control problems by omitting the information about the initial state and possibly a desired terminal state.We show that for problems with a non-smooth tracking term in the objective function that is multiplied with a sufficiently large penalty-parameter in some cases the optimal state and the optimal controlreach the solution of the static control problem (the so-called turnpike) exactly after finite-time and remain there during a certain time-interval, until close to the end of the time interval possibly the state leaves the turnpike.This can be shown in different situations, for example under exact controllability assumptionsor with the assumption of nodal profile exact controllability, as studied byTatsien Li and his group.
transformada de lapalace universidaqd ppt para find eañoluis506251
The document discusses the Laplace transform and its applications. It defines the Laplace transform and provides examples of transforms for typical functions like constants, step functions, exponentials, derivatives and trigonometric functions. It then discusses using Laplace transforms to solve differential equations by taking the transform of both sides of an equation and using properties to find the inverse transform and solution. The document also covers other Laplace transform properties like the final value theorem, initial value theorem and their applications in dynamic analysis.
State space analysis provides a powerful modern approach for modeling and analyzing control systems. It represents a system using state variables and state equations. This allows incorporating initial conditions, applying to nonlinear/time-varying systems, and providing insight into the internal state of the system. A state space model consists of state equations describing how the state variables change over time, and output equations relating the outputs to the states and inputs. Common applications include modeling physical dynamic systems using energy-storing elements as states, and obtaining linear models for linear time-invariant systems. State space analysis provides advantages over traditional transfer function methods.
Differential equations model real-world phenomena involving continuously changing quantities and their rates of change. Some examples include:
1) Population growth modeled by an exponential growth differential equation where the rate of change of population is proportional to the current population.
2) The motion of a falling object modeled by a differential equation where acceleration due to gravity relates the rate of change of velocity to the rate of change of height over time.
3) Newton's law of cooling modeled by a differential equation where the rate of change of temperature is proportional to the difference between the temperature of an object and its environment.
4) The electric current in an RL circuit modeled by a differential equation relating the rate of change of current to
This document summarizes key concepts about transfer functions and convolution:
1. Convolution represents linear time-invariant (LTI) systems, where the output is the impulse response convolved with the input. In the frequency domain, the transfer function is the frequency response.
2. Properties of convolution systems are that they are linear, causal, and time-invariant. Composition of convolution systems corresponds to multiplication of transfer functions.
3. Examples of convolution systems and calculating their transfer functions are presented for circuits, mechanical systems, and communication channels. Interpreting the impulse response provides insight into how past inputs affect current system outputs.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Modern Control - Lec 02 - Mathematical Modeling of SystemsAmr E. Mohamed
This document provides an overview of mathematical modeling of physical systems. It discusses how to derive mathematical models from physical systems using differential equations based on governing physical laws. The key steps are: (1) defining the physical system, (2) formulating the mathematical model using differential equations, and (3) solving the equations. Common model types include differential equation, transfer function, and state-space models. The document also discusses modeling various physical elements like electrical circuits, mechanical translational/rotational systems, and electro-mechanical systems using differential equations. It covers block diagram representation and reduction of mathematical models. The overall goal is to realize the importance of deriving accurate mathematical models for analyzing and designing control systems.
1) The lecture discusses the time domain analysis of continuous time linear and time-invariant systems. It covers topics such as impulse response, convolution, and how the output of an LTI system can be determined from its impulse response and the input signal.
2) An example of analyzing the voltage response of an RC circuit to an arbitrary input is presented. The output is the sum of the zero-input response, due to initial conditions, and zero-state response, which is a convolution of the impulse response and input signal.
3) Detectors of high energy photons can be modeled as having an exponential decay impulse response. Examples of characterizing real detectors through measurements of energy resolution, timing resolution, and coincidence point spread
The document provides information about state variable models and transfer functions. It discusses:
- Modeling systems using state variables and representing them with first-order differential equations in matrix form.
- Obtaining transfer functions from state variable models by taking the Laplace transform of the state equations.
- Examples of modeling an RLC circuit and calculating its transfer function from the state equations.
- Using state variable models and feedback to design state variable feedback control systems. This involves estimating unmeasured states with observers and connecting the observer to the full-state feedback control law.
Optimal control of multi delay systems via orthogonal functionsiaemedu
This document summarizes an approach for computing the optimal control law of linear time-invariant and time-varying systems with time delays using orthogonal functions. The approach uses block-pulse functions and shifted Legendre polynomials to represent system variables and express delay differential equations as algebraic equations. This allows the optimal control problem to be solved as an algebraic equation problem. Numerical examples are provided to demonstrate the approach. The approach provides a unified method for computing optimal control of multi-delay systems using orthogonal functions.
1) The document discusses complex numbers and the Laplace transform, which can convert differential equations describing dynamic systems into algebraic equations.
2) The Laplace transform represents functions in the complex s-domain and allows converting between the time and frequency domains.
3) The Laplace transform is a useful mathematical tool for control systems analysis and design by representing systems as transfer functions and enabling calculation of time-domain responses from frequency-domain inputs.
This document contains lecture notes on signals and systems for a course at Chadalawada Ramanamma Engineering College. It includes:
1. An introduction to signals, systems, and some common elementary signals like the unit step, unit impulse, ramp, sinusoid, and exponential signals.
2. A classification of signals as continuous/discrete, deterministic/non-deterministic, even/odd, periodic/aperiodic, energy/power, and real/imaginary.
3. A discussion of basic operations on signals like amplitude scaling, addition, and subtraction.
The document introduces complex systems and defines a system as a set of interacting parts within a single structure. It provides examples of real-world systems and discusses describing a system from different points of view using models. The document outlines functional and oriented models, abstraction in models, and analyzing dynamic and stationary systems. It discusses representing stationary systems using transition and transformation functions and modeling economic systems with relevant variables and equations. Finally, it presents a case study modeling a school system as a linear, stationary system to calculate total student population over time under changing retention rates.
Differential equations can be powerful tools for modeling data. New methods allow estimating differential equations directly from data. As an example, the author estimates a differential equation model from simulated data from a chemical reactor. The estimated parameters are close to the true values, demonstrating the method works well on simulated data.
This document provides an overview of a control systems engineering course. It discusses key topics that will be covered, including modeling dynamic systems using differential equations, obtaining accurate plant models, designing controllers to meet performance specifications, handling uncertainties, and bridging the gap between linear control theory and real-world industrial problems. The course aims to address issues like robust stability, robust performance, nonlinearities, and designing controllers when plant models are difficult to stabilize.
This document provides an overview of modeling systems using Laplace transforms. It discusses:
1) Converting time functions to the frequency domain using Laplace transforms and inverse Laplace transforms
2) Finding transfer functions (TF) from differential equations to model systems
3) Using partial fraction expansions to simplify transfer functions for inverse Laplace transforms
4) Examples of using Laplace transforms to solve differential equations and model various mechanical and electrical systems.
1) The document analyzes the boundedness and domain of attraction of a fractional-order wireless power transfer (WPT) system.
2) It establishes a fractional-order piecewise affine model of the WPT system and derives sufficient conditions for boundedness using Lyapunov functions and inequality techniques.
3) The results provide a way to estimate the domain of attraction of the fractional-order WPT system and systems with periodically intermittent control.
Quantifying Information Leaks via Model Counting Modulo TheoriesQuoc-Sang Phan
The 41st CREST Open Workshop - Software Engineering And Computer Science Using Information
http://crest.cs.ucl.ac.uk/cow/the_41st_cow_27_and_28_april_2015/
This document discusses dynamic systems and their analysis using transfer functions. It begins by defining dynamic systems as those whose output depends on both current and previous inputs/outputs. It then covers:
- Transfer function representations of linear time-invariant (LTI) systems using Laplace transforms.
- Key properties of transfer functions including poles, zeros and zero-pole-gain form.
- MATLAB representations of transfer functions.
It also defines important concepts for analyzing dynamic systems like time and frequency response, stability, system order, and the effects of pole locations. Specific discussions are included on analyzing first and second order systems.
- The document provides an overview of mathematical concepts for control systems including complex variables, complex functions, differential equations, Laplace transforms, and their applications.
- It introduces complex numbers and variables, complex functions including poles and zeros. Basic concepts of differential equations and Laplace transforms are reviewed.
- Methods for solving differential equations using Laplace transforms are described, including taking the inverse Laplace transform using partial fraction expansion.
The finite time turnpike phenomenon for optimal control problemsMartinGugat
Often in dynamic optimal control problems with a long time horizon, in a large neighborhoodof the middle of the time interval the optimal control and the optimal state are very close to the solution of a static control problem that is derived form the dynamic optimal control problems by omitting the information about the initial state and possibly a desired terminal state.We show that for problems with a non-smooth tracking term in the objective function that is multiplied with a sufficiently large penalty-parameter in some cases the optimal state and the optimal controlreach the solution of the static control problem (the so-called turnpike) exactly after finite-time and remain there during a certain time-interval, until close to the end of the time interval possibly the state leaves the turnpike.This can be shown in different situations, for example under exact controllability assumptionsor with the assumption of nodal profile exact controllability, as studied byTatsien Li and his group.
transformada de lapalace universidaqd ppt para find eañoluis506251
The document discusses the Laplace transform and its applications. It defines the Laplace transform and provides examples of transforms for typical functions like constants, step functions, exponentials, derivatives and trigonometric functions. It then discusses using Laplace transforms to solve differential equations by taking the transform of both sides of an equation and using properties to find the inverse transform and solution. The document also covers other Laplace transform properties like the final value theorem, initial value theorem and their applications in dynamic analysis.
State space analysis provides a powerful modern approach for modeling and analyzing control systems. It represents a system using state variables and state equations. This allows incorporating initial conditions, applying to nonlinear/time-varying systems, and providing insight into the internal state of the system. A state space model consists of state equations describing how the state variables change over time, and output equations relating the outputs to the states and inputs. Common applications include modeling physical dynamic systems using energy-storing elements as states, and obtaining linear models for linear time-invariant systems. State space analysis provides advantages over traditional transfer function methods.
Differential equations model real-world phenomena involving continuously changing quantities and their rates of change. Some examples include:
1) Population growth modeled by an exponential growth differential equation where the rate of change of population is proportional to the current population.
2) The motion of a falling object modeled by a differential equation where acceleration due to gravity relates the rate of change of velocity to the rate of change of height over time.
3) Newton's law of cooling modeled by a differential equation where the rate of change of temperature is proportional to the difference between the temperature of an object and its environment.
4) The electric current in an RL circuit modeled by a differential equation relating the rate of change of current to
This document summarizes key concepts about transfer functions and convolution:
1. Convolution represents linear time-invariant (LTI) systems, where the output is the impulse response convolved with the input. In the frequency domain, the transfer function is the frequency response.
2. Properties of convolution systems are that they are linear, causal, and time-invariant. Composition of convolution systems corresponds to multiplication of transfer functions.
3. Examples of convolution systems and calculating their transfer functions are presented for circuits, mechanical systems, and communication channels. Interpreting the impulse response provides insight into how past inputs affect current system outputs.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
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.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.