Introduction to Simulation and Modeling.ppt
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This document defines key concepts related to systems, models, and computer simulation. It explains that a system has boundaries and relationships between parts, a model abstractly represents a system to understand it, and a simulation executes a model over time to emulate the system's dynamics. Computer simulation is well-suited when a model can be expressed mathematically and logically as computers perform those functions. Examples are provided of systems, models, and simulations.
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This document discusses systems, models, simulations, and their applications. It defines a system as a part of reality with boundaries and internal relationships. A model abstractly represents a system to help understand it, and can take mathematical, diagrammatic, or physical forms. A simulation uses a model to emulate a system's dynamic behavior over time. Computer simulation is useful when a model can be expressed mathematically, as computers can easily store and process such models. The key steps in simulation are to design, implement, execute, and analyze a model to validate understanding of the system.
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This document provides an introduction and overview of simulation. It discusses what a simulation is and the key components of modeling a system, including entities, attributes, activities, states, and events. It also describes discrete event simulation and the main strategies of activity-oriented, event-oriented, and process-oriented simulation. Finally, it briefly introduces some common network simulators used for wireless networks, including NS-2, GloMoSim, and QualNet.
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A system is an organized grouping of interdependent components designed to achieve a specific objective. Modeling and simulation involves simplifying a real or theoretical system into a model to understand its behavior over time. A simulation is an experiment conducted on a computer model that mimics the behavior of the real system. Modeling and simulation provides insights into systems that are difficult, hazardous or expensive to observe directly in reality.
Introduction to System, Simulation and Model
Introduction to System, Simulation and Model by
Md. Hasan Imam Bijoy: 182-15-11743
Md. Mahbubur Rahman: 182-15-11742
Aysha Siddika: 182-15-11639
An Overview of Performance Evaluation & Simulation
Overview of Performance Evaluation
Intro & Objective
The Art of Performance Evaluation
Professional Organizations, Journals, and conferences.
Performance Projects
Common Mistakes and How to Avoid Them
Selection of Techniques and Metrics
SECh78
System modeling techniques are used during requirements engineering and design to represent different perspectives of a system. Context models show the system and its environment, while process models illustrate system processes. Behavioral models include data flow diagrams for data processing and state machine diagrams for event-driven behavior. Semantic data models describe logical data structures. Object models represent system entities and relationships. CASE tools support creating and analyzing various system models during development. Prototyping, through evolutionary or throw-away approaches, helps validate requirements by allowing users to interact with early versions of the system. Rapid prototyping techniques include visual programming and reusing components.
System Modeling & Simulation Introduction
System modeling and simulation involves creating simplified representations of real-world systems to understand and evaluate their behavior over time. A system is composed of interconnected parts designed to achieve specific objectives. A model abstracts and simplifies a system for analysis. Simulation executes a model over time to observe how a system operates. It allows experimenting with systems that may be too expensive, dangerous or complex to study directly. Simulation has many uses including analyzing systems before implementation, optimizing designs, training, and evaluating "what-if" scenarios. Key areas where simulation is applied include manufacturing, business, healthcare, transportation and the military.
01 Introduction to System Dynamics
This document provides an introduction to system dynamics. It defines a system as a collection of interacting components with defined boundaries and inputs/outputs. Dynamic systems change over time even if inputs are constant, while static systems only depend on current inputs. Common dynamic systems include mechanical, electrical, thermal, and fluid systems. System dynamics involves defining a system, creating a mathematical model, simulating the model's behavior, and making recommendations. Models allow studying systems without experimenting on real systems. Simulation uses models to compute how systems react to inputs over time.
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Minggu-1a(General).pptx
This document discusses systems, models, simulations, and their applications. It defines a system as a part of reality with boundaries and internal relationships. A model abstractly represents a system to help understand it, and can take mathematical, diagrammatic, or physical forms. A simulation uses a model to emulate a system's dynamic behavior over time. Computer simulation is useful when a model can be expressed mathematically, as computers can easily store and process such models. The key steps in simulation are to design, implement, execute, and analyze a model to validate understanding of the system.
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This document provides an introduction and overview of simulation. It discusses what a simulation is and the key components of modeling a system, including entities, attributes, activities, states, and events. It also describes discrete event simulation and the main strategies of activity-oriented, event-oriented, and process-oriented simulation. Finally, it briefly introduces some common network simulators used for wireless networks, including NS-2, GloMoSim, and QualNet.
System Modelling and Simulation course for CSE and Allied streams
A system is an organized grouping of interdependent components designed to achieve a specific objective. Modeling and simulation involves simplifying a real or theoretical system into a model to understand its behavior over time. A simulation is an experiment conducted on a computer model that mimics the behavior of the real system. Modeling and simulation provides insights into systems that are difficult, hazardous or expensive to observe directly in reality.
Introduction to System, Simulation and Model
Introduction to System, Simulation and Model by
Md. Hasan Imam Bijoy: 182-15-11743
Md. Mahbubur Rahman: 182-15-11742
Aysha Siddika: 182-15-11639
An Overview of Performance Evaluation & Simulation
Overview of Performance Evaluation
Intro & Objective
The Art of Performance Evaluation
Professional Organizations, Journals, and conferences.
Performance Projects
Common Mistakes and How to Avoid Them
Selection of Techniques and Metrics
SECh78
System modeling techniques are used during requirements engineering and design to represent different perspectives of a system. Context models show the system and its environment, while process models illustrate system processes. Behavioral models include data flow diagrams for data processing and state machine diagrams for event-driven behavior. Semantic data models describe logical data structures. Object models represent system entities and relationships. CASE tools support creating and analyzing various system models during development. Prototyping, through evolutionary or throw-away approaches, helps validate requirements by allowing users to interact with early versions of the system. Rapid prototyping techniques include visual programming and reusing components.
System Modeling & Simulation Introduction
System modeling and simulation involves creating simplified representations of real-world systems to understand and evaluate their behavior over time. A system is composed of interconnected parts designed to achieve specific objectives. A model abstracts and simplifies a system for analysis. Simulation executes a model over time to observe how a system operates. It allows experimenting with systems that may be too expensive, dangerous or complex to study directly. Simulation has many uses including analyzing systems before implementation, optimizing designs, training, and evaluating "what-if" scenarios. Key areas where simulation is applied include manufacturing, business, healthcare, transportation and the military.
01 Introduction to System Dynamics
This document provides an introduction to system dynamics. It defines a system as a collection of interacting components with defined boundaries and inputs/outputs. Dynamic systems change over time even if inputs are constant, while static systems only depend on current inputs. Common dynamic systems include mechanical, electrical, thermal, and fluid systems. System dynamics involves defining a system, creating a mathematical model, simulating the model's behavior, and making recommendations. Models allow studying systems without experimenting on real systems. Simulation uses models to compute how systems react to inputs over time.
Chapter1
Discrete event simulation allows users to model real-world systems over time through imitation. It generates artificial histories to draw inferences about system behavior and operating characteristics without disrupting the real system. Simulation is useful when analytical solutions are not possible due to complex, random factors or when experimentation and testing are needed. Key advantages include exploring changes safely, compressing or expanding time, and gaining insights into variable interactions. Common applications include manufacturing, transportation, healthcare, and business processes.
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This chapter introduces discrete-event simulation and outlines the key steps in a simulation study. It defines simulation as imitating the operation of a real-world process over time through a conceptual model. Simulation allows experimenting with "what if" scenarios to analyze the potential effects of changes. The chapter describes when simulation is an appropriate tool, its advantages and disadvantages, common application areas, and components of systems and models. It distinguishes between discrete and continuous systems and events, and outlines the development process for discrete-event simulation models.
Introduction to simulation.pdf
This chapter introduces discrete-event simulation and outlines the key steps in a simulation study. It defines simulation as imitating the operation of a real-world process over time through a conceptual model. Simulation allows experimenting with "what if" scenarios to analyze the potential effects of changes. The chapter describes when simulation is an appropriate tool, its advantages and disadvantages, common application areas, and components of systems and models. It distinguishes between discrete and continuous systems and events. The final sections outline the development of a discrete-event simulation model and the steps of verifying and validating the model.
Introduction to simulation and modeling
Introduction to simulation and modeling will describe what is simulation, what is system and what is model. It will give a brief overview of simulation and modeling in computer science.
Basics of modeling and simulation
Operating companies or professionals need to foresee the results of their decisions and steps, a representative model provides chance to simulate and see the results of their actions.
System Modelling
This document provides an overview of system modeling. It discusses that system modeling involves developing abstract models of a system from different perspectives, and is commonly done using the Unified Modeling Language (UML). It also describes various UML diagram types used in system modeling like use case diagrams, class diagrams, and state diagrams. Finally, it gives examples of modeling different views of a mental health case management system, including contextual models, interaction models, structural models, and behavioral models.
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A simulation imitates real-world processes or systems using models. There are three main types of simulation: discrete event simulation which models processes over time like factory operations; dynamic simulation which models processes over space like machine kinematics; and process simulation which models interactions between systems like in-manufacture product modelling. Simulation works by creating a visual model of a process in simulation software. It has advantages like reducing financial risk by allowing repeated testing without real-world costs and examining long-term impacts. Simulation is used to evaluate changes to systems and predict new system performance as an alternative to costly real-world testing.
lecture 1.pptx
This document discusses modeling and simulation. It defines a model as a representation of an object, system, or idea that is different from the actual entity. Models are used to test systems without creating real versions, predict future behavior, train users safely, and investigate systems in detail. The document outlines different types of modeling including physics-based, finite element, data-based, multi-scale, mathematical, and hybrid modeling. It also discusses conceptual modeling and creating block diagrams to represent systems as subsystems and connections. Criteria for separating systems into subsystems include anatomy, function, and measurability of inputs and outputs.
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1
MODULE 1: INTRODUCTION TO SIMULATION
Module outline:
• What is Simulation?
• Simulation Terminology
• Components of a System
• Models in Simulation
• Typical applications
• References
WHAT IS SIMULATION?
simulation may be defined as a technique that imitates the operation of a real world
system or processes as it evolves over time. It involves the generation of an artificial
history of the system and observation of that artificial history to obtain information and
draw inferences about the operating characteristics of the real system. Simulation
educates us on how a system operates and how the system might respond to changes. It
enables us to test alternative courses of action to determine their impact on system
performance. Before an alternative is implemented, it must be tested. Although
performing tests with the “real thing” would be ideal. This is seldom practically feasible.
The cost associated with changing/improving a system may be very high both in the
term of capital required to implement the change and losses due to interruption in
production operations and other losses. In most cases experimentation with the
proposed alternative is practically impossible. In addition, as the cost of proposed
changes (alternative solutions) increase, so does the cost of physically experimenting.
As an example, suppose a heavy-duty conveyor is being considered as an alternative to
the existing material handling method (by trucks) for improving productivity and
speeding up the production operations in a factory (seeFigre3). It is obvious that
installing the proposed conveyor on a test basis would probably not be cost effective.
Therefore, experimentation with alternative configurations would be practically
impossible. In stead, experimentation with a representative model of the system would
probably make more sense.
Simulation is a means of experimenting with a detailed model of a real system to
Determine how the system will respond to changes in its environment, structure, and its
underlying assumption [Harrel (1996)]. Management Scientist uses a wide variety of
analytical tools to model, analyze, and solve complex decision problems. These tool
include linear programming, decision analysis, forecasting, Queuing theory and
Alternative 1: Use lift-truck
2
Point A Point B
(Warehouse) (Factory)
Alternative 2: use a conveyor
Point A
(warehouse ) . . . . . . . . Point B
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Systemprocessing 160107234141
System modeling is the process of developing abstract models of a system using graphical notations like UML. System models present different views or perspectives of a system, such as external context, interactions, structure, and behavior. Common UML diagram types used in system modeling include use case diagrams, sequence diagrams, class diagrams, and state diagrams. System modeling helps analysts understand system functionality and communicate with customers.
Software engineering ,system modeing >>Abu ul hassan sahadvi
System modeling involves developing abstract models of a system from different perspectives to help understand its functionality. Common modeling techniques include use case diagrams, which show interactions between a system and external actors, and class diagrams, which define the system's classes and their relationships. Behavioral models depict how a system responds to stimuli over time through states and transitions. System modeling aids communication and ensures requirements and design match customer needs.
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Simulation is a technique used to model complex systems and estimate statistical measures by representing the system and modeling individual elements' behavior randomly over time. Systems can be discrete, changing at separate points, or continuous, changing continuously. Simulation involves defining the problem, conceptually modeling the system, collecting data, coding the model, validating it, experimenting and analyzing results. It allows studying systems that would be impossible, too expensive, or impractical to study directly.
Simulation and Modeling
Simulation is used to create models that represent real world systems and allow experimenting with different strategies without impacting the actual system. Models simplify real systems for analysis while maintaining key behaviors and results. Successful simulation models are easy to understand, represent the system accurately, produce fast results, and allow control and updating. Simulators are used when real experimentation is unsafe, too expensive, or when systems are still in development. Common uses of simulation include modeling systems in fields like military, education, healthcare, and engineering.
Simulation and Modelling Reading Notes.pptx
This document discusses simulation and modeling. It defines simulation as imitating real-life situations using computer models. Models represent systems using mathematical relationships. Simulation allows experimenting with models to understand system behavior under different conditions without changing the real system. The document outlines the modeling and simulation process and provides examples of applications in areas like business planning, drug development, and traffic analysis.
Week 5
System models abstractly describe systems being analyzed and are used to communicate with customers. Different models show the system from external, behavioral, and structural perspectives. Common system models include context models depicting system boundaries, data flow diagrams modeling data processing, state machine models representing system states and transitions, and object models describing the system in terms of object classes and relationships. The Unified Modeling Language provides standard notations for object-oriented modeling.
Modeling & simulation in projects
This document provides an overview of modeling and simulation. It defines modeling as representing a system to enable predicting the effects of changes. Simulation involves running experiments on a model. The key steps in modeling and simulation projects are: 1) identifying the problem, 2) formulating and developing the model, 3) validating the model, 4) designing simulation experiments, 5) performing simulations, and 6) analyzing and presenting results. Modeling and simulation can be used for a variety of purposes including education, design evaluation, forecasting, and risk assessment.
Simulation
This document provides an overview of discrete-event system simulation. It discusses what simulation is, when it is appropriate to use, its advantages and disadvantages. It also covers components of a system like entities, attributes, events. Different types of models are described - static vs dynamic and deterministic vs stochastic. Various application areas of simulation are listed like manufacturing, logistics, military etc.
Soumya
Computer simulation is the use of a computer program to model a real-world system. The program contains mathematical equations that replicate the system's functional relationships. Running the program produces data that shows how the modeled system would behave over time. Simulations can also take the form of computer graphics animations. Key features of computer simulation include an interactive computer model of a system and the ability to experiment by changing model inputs. Examples include modeling theories of cognition, modeling organic molecule reactions, and fluid dynamics simulations. Computer simulation allows studying systems without building them and enables experimenting with different conditions efficiently. However, simulations can be difficult and expensive to construct and may require expert help to interpret results.
Simulation
This document provides an overview of simulation and discrete event simulation. It discusses different types of models including static/dynamic, deterministic/stochastic, and discrete/continuous. It also describes three approaches to discrete event simulation: activity-oriented, event-oriented, and process-oriented. The document outlines several popular simulators including CSIM, GloMoSim, NS-2, and NCTU-NS. It concludes with references for further reading on simulation and these simulators. Mini-projects and projects are proposed for using GloMoSim and developing a MAC simulator using PARSEC, respectively.
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The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
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Chapter1
Discrete event simulation allows users to model real-world systems over time through imitation. It generates artificial histories to draw inferences about system behavior and operating characteristics without disrupting the real system. Simulation is useful when analytical solutions are not possible due to complex, random factors or when experimentation and testing are needed. Key advantages include exploring changes safely, compressing or expanding time, and gaining insights into variable interactions. Common applications include manufacturing, transportation, healthcare, and business processes.
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This chapter introduces discrete-event simulation and outlines the key steps in a simulation study. It defines simulation as imitating the operation of a real-world process over time through a conceptual model. Simulation allows experimenting with "what if" scenarios to analyze the potential effects of changes. The chapter describes when simulation is an appropriate tool, its advantages and disadvantages, common application areas, and components of systems and models. It distinguishes between discrete and continuous systems and events, and outlines the development process for discrete-event simulation models.
Introduction to simulation.pdf
This chapter introduces discrete-event simulation and outlines the key steps in a simulation study. It defines simulation as imitating the operation of a real-world process over time through a conceptual model. Simulation allows experimenting with "what if" scenarios to analyze the potential effects of changes. The chapter describes when simulation is an appropriate tool, its advantages and disadvantages, common application areas, and components of systems and models. It distinguishes between discrete and continuous systems and events. The final sections outline the development of a discrete-event simulation model and the steps of verifying and validating the model.
Introduction to simulation and modeling
Introduction to simulation and modeling will describe what is simulation, what is system and what is model. It will give a brief overview of simulation and modeling in computer science.
Basics of modeling and simulation
Operating companies or professionals need to foresee the results of their decisions and steps, a representative model provides chance to simulate and see the results of their actions.
System Modelling
This document provides an overview of system modeling. It discusses that system modeling involves developing abstract models of a system from different perspectives, and is commonly done using the Unified Modeling Language (UML). It also describes various UML diagram types used in system modeling like use case diagrams, class diagrams, and state diagrams. Finally, it gives examples of modeling different views of a mental health case management system, including contextual models, interaction models, structural models, and behavioral models.
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A simulation imitates real-world processes or systems using models. There are three main types of simulation: discrete event simulation which models processes over time like factory operations; dynamic simulation which models processes over space like machine kinematics; and process simulation which models interactions between systems like in-manufacture product modelling. Simulation works by creating a visual model of a process in simulation software. It has advantages like reducing financial risk by allowing repeated testing without real-world costs and examining long-term impacts. Simulation is used to evaluate changes to systems and predict new system performance as an alternative to costly real-world testing.
lecture 1.pptx
This document discusses modeling and simulation. It defines a model as a representation of an object, system, or idea that is different from the actual entity. Models are used to test systems without creating real versions, predict future behavior, train users safely, and investigate systems in detail. The document outlines different types of modeling including physics-based, finite element, data-based, multi-scale, mathematical, and hybrid modeling. It also discusses conceptual modeling and creating block diagrams to represent systems as subsystems and connections. Criteria for separating systems into subsystems include anatomy, function, and measurability of inputs and outputs.
1 MODULE 1 INTRODUCTION TO SIMULATION Module out.docx
1
MODULE 1: INTRODUCTION TO SIMULATION
Module outline:
• What is Simulation?
• Simulation Terminology
• Components of a System
• Models in Simulation
• Typical applications
• References
WHAT IS SIMULATION?
simulation may be defined as a technique that imitates the operation of a real world
system or processes as it evolves over time. It involves the generation of an artificial
history of the system and observation of that artificial history to obtain information and
draw inferences about the operating characteristics of the real system. Simulation
educates us on how a system operates and how the system might respond to changes. It
enables us to test alternative courses of action to determine their impact on system
performance. Before an alternative is implemented, it must be tested. Although
performing tests with the “real thing” would be ideal. This is seldom practically feasible.
The cost associated with changing/improving a system may be very high both in the
term of capital required to implement the change and losses due to interruption in
production operations and other losses. In most cases experimentation with the
proposed alternative is practically impossible. In addition, as the cost of proposed
changes (alternative solutions) increase, so does the cost of physically experimenting.
As an example, suppose a heavy-duty conveyor is being considered as an alternative to
the existing material handling method (by trucks) for improving productivity and
speeding up the production operations in a factory (seeFigre3). It is obvious that
installing the proposed conveyor on a test basis would probably not be cost effective.
Therefore, experimentation with alternative configurations would be practically
impossible. In stead, experimentation with a representative model of the system would
probably make more sense.
Simulation is a means of experimenting with a detailed model of a real system to
Determine how the system will respond to changes in its environment, structure, and its
underlying assumption [Harrel (1996)]. Management Scientist uses a wide variety of
analytical tools to model, analyze, and solve complex decision problems. These tool
include linear programming, decision analysis, forecasting, Queuing theory and
Alternative 1: Use lift-truck
2
Point A Point B
(Warehouse) (Factory)
Alternative 2: use a conveyor
Point A
(warehouse ) . . . . . . . . Point B
...
Systemprocessing 160107234141
System modeling is the process of developing abstract models of a system using graphical notations like UML. System models present different views or perspectives of a system, such as external context, interactions, structure, and behavior. Common UML diagram types used in system modeling include use case diagrams, sequence diagrams, class diagrams, and state diagrams. System modeling helps analysts understand system functionality and communicate with customers.
Software engineering ,system modeing >>Abu ul hassan sahadvi
System modeling involves developing abstract models of a system from different perspectives to help understand its functionality. Common modeling techniques include use case diagrams, which show interactions between a system and external actors, and class diagrams, which define the system's classes and their relationships. Behavioral models depict how a system responds to stimuli over time through states and transitions. System modeling aids communication and ensures requirements and design match customer needs.
Operations research lecture 5
Simulation is a technique used to model complex systems and estimate statistical measures by representing the system and modeling individual elements' behavior randomly over time. Systems can be discrete, changing at separate points, or continuous, changing continuously. Simulation involves defining the problem, conceptually modeling the system, collecting data, coding the model, validating it, experimenting and analyzing results. It allows studying systems that would be impossible, too expensive, or impractical to study directly.
Simulation and Modeling
Simulation is used to create models that represent real world systems and allow experimenting with different strategies without impacting the actual system. Models simplify real systems for analysis while maintaining key behaviors and results. Successful simulation models are easy to understand, represent the system accurately, produce fast results, and allow control and updating. Simulators are used when real experimentation is unsafe, too expensive, or when systems are still in development. Common uses of simulation include modeling systems in fields like military, education, healthcare, and engineering.
Simulation and Modelling Reading Notes.pptx
This document discusses simulation and modeling. It defines simulation as imitating real-life situations using computer models. Models represent systems using mathematical relationships. Simulation allows experimenting with models to understand system behavior under different conditions without changing the real system. The document outlines the modeling and simulation process and provides examples of applications in areas like business planning, drug development, and traffic analysis.
Week 5
System models abstractly describe systems being analyzed and are used to communicate with customers. Different models show the system from external, behavioral, and structural perspectives. Common system models include context models depicting system boundaries, data flow diagrams modeling data processing, state machine models representing system states and transitions, and object models describing the system in terms of object classes and relationships. The Unified Modeling Language provides standard notations for object-oriented modeling.
Modeling & simulation in projects
This document provides an overview of modeling and simulation. It defines modeling as representing a system to enable predicting the effects of changes. Simulation involves running experiments on a model. The key steps in modeling and simulation projects are: 1) identifying the problem, 2) formulating and developing the model, 3) validating the model, 4) designing simulation experiments, 5) performing simulations, and 6) analyzing and presenting results. Modeling and simulation can be used for a variety of purposes including education, design evaluation, forecasting, and risk assessment.
Simulation
This document provides an overview of discrete-event system simulation. It discusses what simulation is, when it is appropriate to use, its advantages and disadvantages. It also covers components of a system like entities, attributes, events. Different types of models are described - static vs dynamic and deterministic vs stochastic. Various application areas of simulation are listed like manufacturing, logistics, military etc.
Soumya
Computer simulation is the use of a computer program to model a real-world system. The program contains mathematical equations that replicate the system's functional relationships. Running the program produces data that shows how the modeled system would behave over time. Simulations can also take the form of computer graphics animations. Key features of computer simulation include an interactive computer model of a system and the ability to experiment by changing model inputs. Examples include modeling theories of cognition, modeling organic molecule reactions, and fluid dynamics simulations. Computer simulation allows studying systems without building them and enables experimenting with different conditions efficiently. However, simulations can be difficult and expensive to construct and may require expert help to interpret results.
Simulation
This document provides an overview of simulation and discrete event simulation. It discusses different types of models including static/dynamic, deterministic/stochastic, and discrete/continuous. It also describes three approaches to discrete event simulation: activity-oriented, event-oriented, and process-oriented. The document outlines several popular simulators including CSIM, GloMoSim, NS-2, and NCTU-NS. It concludes with references for further reading on simulation and these simulators. Mini-projects and projects are proposed for using GloMoSim and developing a MAC simulator using PARSEC, respectively.
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Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
AI assisted telemedicine KIOSK for Rural India.pptx
It gives the overall description of SIH problem statement " AI assisted telemedicine KIOSK for Rural India".
Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Slides for the 4th Presentation on LLM Fine-Tuning with QLoRA Presented by Anant, featuring DataStax Astra
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
CalArts毕业证学历书【微信95270640】CalArts毕业证’圣力嘉学院毕业证《Q微信95270640》办理CalArts毕业证√文凭学历制作{CalArts文凭}购买学历学位证书本科硕士,CalArts毕业证学历学位证【实体公司】办毕业证、成绩单、学历认证、学位证、文凭认证、办留信网认证、（网上可查，实体公司，专业可靠）
(诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
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（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！子小伍玩小伍比山娃小一岁虎头虎脑的很霸气父亲让山娃跟小伍去夏令营听课山娃很高兴夏令营就设在附近一所小学山娃发现那所小学比自己的学校更大更美操场上还铺有塑胶跑道呢里面很多小朋友一班一班的快快乐乐原来城里娃都藏这儿来了怪不得平时见不到他们山娃恍然大悟起来吹拉弹唱琴棋书画山娃都不懂却什么都想学山娃怨自己太笨什么都不会斟酌再三山娃终于选定了学美术当听说每月要交元时父亲犹豫了山娃也说爸算了吧咱学校一学期才转
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Null Bangalore | Pentesters Approach to AWS IAM
#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/)
BRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION
AND CLASSIFICATION USING
ARTIFICIAL INTELLIGENCE
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
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.
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Introduction to Simulation and Modeling.ppt
- 2. Definitions › System, Model, Simulation Computer Simulation › why? how? Example Applications
- 3. A system is a part of some potential reality where we are concerned with space-time effects and casual relationships among parts of the system Defining a system requires setting boundaries › separates system from rest of the universe › makes the system a closed world
- 4. Examples: › the energy flow among biological components in a pond › the physical motion of balls rebounding inside a closed space › customer service at a fast-food restaurant › Federal Express
- 5. A model is something that we use in lieu of the real system in order to understand something about that system A model of a system is an abstraction of that system Models give us comprehensible representations of systems › something to think about › something to communicate about
- 6. Models can have many forms › mathematical equations › diagrams › physical mock-ups Choice of form depends on the purpose of the model A model is a static representation of the system
- 7. A simulation uses a model to emulate the dynamic characteristics of a system A simulation is an execution of a model The quality of a simulation is (obviously) only as good as the quality of the model
- 8. System: a cannonball in flight Model: a tennis ball Simulation: throw the tennis ball across the parking lot
- 9. Simulations are used instead of real systems for many reasons: › cheaper › more configurable and controllable › safer › faster (or slower) › more accessible – easier to collect data
- 10. Computer simulation is ideal when a model can be expressed in terms of mathematics and logic (that’s what computers do) Computer simulations can easily store information about all attributes of the model, throughout the simulation run
- 13. December 26, 2004 › tsunami-v.wmv › tsunami-sphere.wmv Source: › http://www.wldelft.nl/gen/news/tsunami/
- 14. Howard Odum, Univ of Florida
- 15. Howard Odum, Univ of Florida
- 16. Howard Odum, Univ of Florida
- 17. We continued with a group discussion, comparing and contrasting Conway’s Game of Life with SimCity, in terms of underlying models and execution methods.