The document discusses state modeling and state diagrams. It defines states as representing intervals of time for objects and events as occurring at points in time. State diagrams graphically show the transitions between states caused by events. The document covers different types of events, states, activities, and advanced state modeling concepts like nested states, concurrency, and signal generalization.
The document discusses UML diagrams including state diagrams and activity diagrams. For state diagrams, it describes how they model the dynamic aspects and states of an object over time through states, transitions, and events. The key elements of a state diagram like states, transitions, and events are defined. It also provides an example state diagram. For activity diagrams, it describes how they model the flow of activities in a system through activity nodes and edges. The basic components of an activity diagram like activities, forks, joins, and decisions are outlined. It concludes with the main uses of activity diagrams to model workflows and business requirements.
Ch2 mathematical modeling of control system Elaf A.Saeed
Chapter 2 Mathematical modeling of control system From the book (Ogata Modern Control Engineering 5th).
2-1 introduction.
2-2 transfer function and impulse response function.
2-3 automatic control systems.
Ch5 transient and steady state response analyses(control)Elaf A.Saeed
Chapter 5 Transient and steady-state response analyses. From the book (Ogata Modern Control Engineering 5th).
5-1 introduction.
5-2 First-Order System.
5-3 second-order system.
5-6 Routh’s stability criterion.
5-8 Steady-state errors in unity-feedback control systems.
Improving predictability and performance by relating the number of events and...Asoka Korale
In this paper however we establish a probabilistic relationship between the number of events and the time over which to observe them. The total time over which to observe a certain number of events is equivalent to the sum of their event inter-arrival times, making the number of events and the number of inter-arrival times in the sum also equivalent. By this sum of random variables, we establish a stochastic relationship between the number of events and the total time interval over which to observe them, allowing greater flexibility in characterizing the relationships between the underlying distributions. We also use this relationship to estimate the uncertainty in the time interval taken to observe a certain number of events and relate it to an uncertainty in the average number of events observed in that interval.
The event inter-arrival times are thus modeled as a sequence of random variables drawn from a single distribution. These random variables could be drawn from a distribution estimated from historical data governing the particular arrival process or from a particular distribution used to model it. The subject of this paper is then to utilize this idea to model the behaviour of a queue and server system where each state and the state transition probabilities are also stochastic. Clearer insights in to the performance of such systems is also envisaged with this type of analysis.
The event inter-arrival times are thus modeled as a sequence of random variables drawn from a single distribution. These random variables could be drawn from a distribution estimated from historical data governing the particular arrival process or from a particular distribution used to model it. The subject of this paper is then to utilize this idea to model the behaviour of a queue and server system where each state and the state transition probabilities are also stochastic. Clearer insights in to the performance of such systems is also envisaged with this type of analysis.
1) A schedule orders the operations of transactions running concurrently in chronological order.
2) Types of schedules include serial, complete, recoverable, cascading, and strict schedules. A serial schedule runs transactions one at a time without interleaving. A complete schedule ensures all transactions abort or commit. A recoverable schedule prevents lost updates.
3) Concurrency control ensures transactions running concurrently maintain isolation. Common techniques include lock-based protocols where transactions must acquire locks before accessing shared data items.
Chapter 1 Introduction to Control Systems From the book (Ogata Modern Control Engineering 5th).
1-1 introduction to control systems.
1-2 examples of control systems.
1-3 open loop vs. close loop.
1-4 design and compensation of control systems.
ME-314 Introduction to Control Engineering is a course taught to Mechanical Engineering senior undergrads. The course is taught by Dr. Bilal Siddiqui at DHA Suffa University. This lecture is introduction to the field.
ME-314 Introduction to Control Engineering is a course taught to Mechanical Engineering senior undergrads. The course is taught by Dr. Bilal Siddiqui at DHA Suffa University. This lecture is about time response of systems derived by inspection of poles and zeros. First and second order systems are considered, along with higher order and nonminimum phase systems
The document discusses UML diagrams including state diagrams and activity diagrams. For state diagrams, it describes how they model the dynamic aspects and states of an object over time through states, transitions, and events. The key elements of a state diagram like states, transitions, and events are defined. It also provides an example state diagram. For activity diagrams, it describes how they model the flow of activities in a system through activity nodes and edges. The basic components of an activity diagram like activities, forks, joins, and decisions are outlined. It concludes with the main uses of activity diagrams to model workflows and business requirements.
Ch2 mathematical modeling of control system Elaf A.Saeed
Chapter 2 Mathematical modeling of control system From the book (Ogata Modern Control Engineering 5th).
2-1 introduction.
2-2 transfer function and impulse response function.
2-3 automatic control systems.
Ch5 transient and steady state response analyses(control)Elaf A.Saeed
Chapter 5 Transient and steady-state response analyses. From the book (Ogata Modern Control Engineering 5th).
5-1 introduction.
5-2 First-Order System.
5-3 second-order system.
5-6 Routh’s stability criterion.
5-8 Steady-state errors in unity-feedback control systems.
Improving predictability and performance by relating the number of events and...Asoka Korale
In this paper however we establish a probabilistic relationship between the number of events and the time over which to observe them. The total time over which to observe a certain number of events is equivalent to the sum of their event inter-arrival times, making the number of events and the number of inter-arrival times in the sum also equivalent. By this sum of random variables, we establish a stochastic relationship between the number of events and the total time interval over which to observe them, allowing greater flexibility in characterizing the relationships between the underlying distributions. We also use this relationship to estimate the uncertainty in the time interval taken to observe a certain number of events and relate it to an uncertainty in the average number of events observed in that interval.
The event inter-arrival times are thus modeled as a sequence of random variables drawn from a single distribution. These random variables could be drawn from a distribution estimated from historical data governing the particular arrival process or from a particular distribution used to model it. The subject of this paper is then to utilize this idea to model the behaviour of a queue and server system where each state and the state transition probabilities are also stochastic. Clearer insights in to the performance of such systems is also envisaged with this type of analysis.
The event inter-arrival times are thus modeled as a sequence of random variables drawn from a single distribution. These random variables could be drawn from a distribution estimated from historical data governing the particular arrival process or from a particular distribution used to model it. The subject of this paper is then to utilize this idea to model the behaviour of a queue and server system where each state and the state transition probabilities are also stochastic. Clearer insights in to the performance of such systems is also envisaged with this type of analysis.
1) A schedule orders the operations of transactions running concurrently in chronological order.
2) Types of schedules include serial, complete, recoverable, cascading, and strict schedules. A serial schedule runs transactions one at a time without interleaving. A complete schedule ensures all transactions abort or commit. A recoverable schedule prevents lost updates.
3) Concurrency control ensures transactions running concurrently maintain isolation. Common techniques include lock-based protocols where transactions must acquire locks before accessing shared data items.
Chapter 1 Introduction to Control Systems From the book (Ogata Modern Control Engineering 5th).
1-1 introduction to control systems.
1-2 examples of control systems.
1-3 open loop vs. close loop.
1-4 design and compensation of control systems.
ME-314 Introduction to Control Engineering is a course taught to Mechanical Engineering senior undergrads. The course is taught by Dr. Bilal Siddiqui at DHA Suffa University. This lecture is introduction to the field.
ME-314 Introduction to Control Engineering is a course taught to Mechanical Engineering senior undergrads. The course is taught by Dr. Bilal Siddiqui at DHA Suffa University. This lecture is about time response of systems derived by inspection of poles and zeros. First and second order systems are considered, along with higher order and nonminimum phase systems
The document discusses the static and dynamic characteristics of instruments. The main static characteristics are accuracy, sensitivity, reproducibility, drift, static error, dead zone, precision, threshold, linearity, stability, range, bias, tolerance and hysteresis. The dynamic characteristics include speed of response, fidelity, lag, and dynamic error. Dynamic inputs can be transient or steady state periodic like step, ramp, parabolic or sinusoidal. Lag causes a delay or retardation in the instrument's response to changing inputs.
This document discusses time domain analysis of control systems. It introduces standard test signals used to analyze dynamic systems, including impulse, step, ramp, and parabolic signals. These signals mimic characteristics of actual inputs like sudden shock, changes, constant velocity, and acceleration. The time response of a system has two components - transient response as it moves from rest to steady state, and steady-state response once settled. Standard signals are used to examine a system's transient response and steady-state response depends on both system dynamics and input type.
This document introduces Generalized Stochastic Petri Nets (GSPNs). [1] GSPNs combine features of Petri Nets and stochastic processes. They contain both timed transitions with exponentially distributed random firing delays and immediate transitions with zero delay. [2] Immediate transitions have priority over timed transitions. GSPNs can model systems with both deterministic and stochastic behavior.
This document discusses steady state error in control systems. It defines steady state error as the difference between the input and output of a system at infinite time. The type of a control system, from Type 0 to higher, determines its steady state error for different input types like steps, ramps, and parabolas. Higher type systems have lower steady state error but reduced stability. The document also introduces static error constants that quantify steady state error for different input types, like position (Kp) for steps, velocity (Kv) for ramps, and acceleration (Ka) for parabolas. These constants are used to calculate the expected steady state error for a given system and input.
This document discusses the static and dynamic characteristics of measurement systems. Static characteristics refer to a system's performance when the input is constant or changing slowly, and include accuracy, precision, resolution, and sensitivity. Dynamic characteristics refer to how a system responds when the input is changing rapidly over time, and include speed of response, fidelity, lag, and overshoot. The document provides definitions and explanations of these key static and dynamic terms.
The document discusses state modeling and state diagrams. It defines states as representations of intervals of time that describe an object's behavioral condition. Events trigger transitions between states. A state diagram uses a graph to represent an object's states and the transitions between them caused by events. It specifies the object's response to input events over time. The document provides examples of how to notationally represent states, transitions, events, and other elements in a state diagram.
The document discusses state modeling concepts including events, states, transitions, conditions, and state diagrams. It defines events as external stimuli that can be signal, change, or time-based. States represent object attribute values and have duration. Transitions are instantaneous changes between states caused by events. Conditions are Boolean expressions that must be true for a transition to occur. State diagrams graphically show states and transitions between them labeled with events.
Software Engineering :Behavioral Modelling - II State diagramAjit Nayak
This document discusses software engineering principles related to behavioral modeling using state diagrams and activity diagrams. It provides examples and explanations of key concepts in behavioral modeling including states, events, conditions, transitions, activities, actions, concurrency, and swimlanes. It also discusses implementing classes based on interaction and state diagrams and provides an example state diagram for the states of a CourseSection class.
State diagrams describe the behavior of objects by modeling their states and transitions between states based on events. Key elements of state diagrams include states, transitions, events, and actions. States represent conditions of an object, transitions are triggered by events, and actions occur on state entry/exit or during transitions. Together these elements specify the dynamic behavior of objects in response to events.
State machines model the different states an object can be in and the transitions between those states. A state represents a condition or situation during an object's lifetime. Transitions between states are triggered by events and may include actions. States can have substates that run either sequentially or concurrently. Sequential substates represent stages in a process, while concurrent substates run in parallel. Advanced state machine features like entry/exit actions, internal transitions, and history states help simplify complex models.
The document provides an overview of state modeling and interaction modeling techniques. It defines key concepts like events, conditions, states, and transitions that are used in state diagrams. It also discusses use case diagrams, which model user interactions with a system through actors and use cases. The document explains that state diagrams describe the behavior and life cycles of objects in response to events, while use case and interaction diagrams elaborate the functional requirements and interactions between users and a system.
This document discusses state diagrams and statecharts. It introduces key concepts such as states, transitions, events, actions, and activities. States represent conditions or situations of an object, and transitions occur between states in response to events. State diagrams can show nested substates and concurrent states using orthogonal components separated by dashed lines. The document provides examples and notation for drawing state diagrams to model the behavior of objects.
The document discusses dynamic modeling concepts including events, states, state transition diagrams, operations, nested state diagrams, and concurrency. It provides examples of state transition diagrams for a digital watch and booking object. It describes identifying events, building state diagrams, and constructing a dynamic model sample for a simple database application. The dynamic model shows control flows and object behavior over time in response to events.
The document discusses dynamic modeling concepts including events, states, state transition diagrams, operations, nested state diagrams, and concurrency. It provides examples of state transition diagrams for a digital watch and booking object. It describes identifying events, building state diagrams, and constructing a dynamic model sample for a simple database application. The dynamic model shows control flows and object behavior over time in response to events.
State chart diagrams describe the different states an object can be in, the transitions between states, and activities that occur during an object's lifetime. A state diagram models the transitions within a single class in response to events. Elements include initial and final states, states, transitions between states indicating triggers and guards, and pseudostates. Common pseudostates include choices, histories, junctions, entry/exit points, and terminate. State diagrams are useful for modeling workflows, document processing, real-time applications, and the behavior of a class over multiple use cases.
Programming models for event controlled programsPriya Kaushal
This document discusses programming models for event controlled programs using state machine models. It describes state machine models as having states and state transition functions that change the state based on inputs. An example of modeling a door or washing machine process is provided. Finite state machine models are also described as having a finite number of states, inputs, outputs, and state transition functions. The use of state machine models and finite state machine models to represent processes like timers and function calls is demonstrated. Finally, representing state machine models with state tables for software design and implementation is discussed.
The document discusses state machine diagrams and state modeling in object-oriented software design. It defines states as abstractions of an object's attribute values and links that affect its behavior. State machine diagrams show an entity's different states and how it responds to events by transitioning between states. The characteristics of states and various state modeling concepts and notations in UML like composite states, submachine states, history states, and transitions are explained.
Unit 3(advanced state modeling & interaction meodelling)Manoj Reddy
The document provides an overview of advanced state modeling and interaction modeling techniques in UML. It discusses nested state diagrams and concurrent state diagrams for controlling complexity in state diagrams. It also covers activity models, use case models, and sequence models for interaction modeling. The relationships between class models, state models, and interaction models are also briefly described.
Events in UML include signals, calls, the passing of time, and state changes. There are four main types of events: signal events, call events, time events, and change events.
Signal events represent asynchronous communications between objects, with signals serving as parameters. Call events represent synchronous operation dispatches. Time events occur with the passage of time, modeled using "after." Change events represent a change in state or condition, modeled using "when."
State machines specify an object's sequence of states in response to events. States are represented as rectangles, and transitions between states are lines. State machines model behavior for objects responding to asynchronous stimuli or those with behavior dependent on past states.
State chart diagrams define the different states an object can be in during its lifetime, and how it transitions between states in response to events. They are useful for modeling reactive systems by describing the flow of control from one state to another. The key elements are initial and final states, states represented by rectangles, and transitions between states indicated by arrows. State chart diagrams are used to model the dynamic behavior and lifetime of objects in a system and identify the events that trigger state changes.
The document discusses the static and dynamic characteristics of instruments. The main static characteristics are accuracy, sensitivity, reproducibility, drift, static error, dead zone, precision, threshold, linearity, stability, range, bias, tolerance and hysteresis. The dynamic characteristics include speed of response, fidelity, lag, and dynamic error. Dynamic inputs can be transient or steady state periodic like step, ramp, parabolic or sinusoidal. Lag causes a delay or retardation in the instrument's response to changing inputs.
This document discusses time domain analysis of control systems. It introduces standard test signals used to analyze dynamic systems, including impulse, step, ramp, and parabolic signals. These signals mimic characteristics of actual inputs like sudden shock, changes, constant velocity, and acceleration. The time response of a system has two components - transient response as it moves from rest to steady state, and steady-state response once settled. Standard signals are used to examine a system's transient response and steady-state response depends on both system dynamics and input type.
This document introduces Generalized Stochastic Petri Nets (GSPNs). [1] GSPNs combine features of Petri Nets and stochastic processes. They contain both timed transitions with exponentially distributed random firing delays and immediate transitions with zero delay. [2] Immediate transitions have priority over timed transitions. GSPNs can model systems with both deterministic and stochastic behavior.
This document discusses steady state error in control systems. It defines steady state error as the difference between the input and output of a system at infinite time. The type of a control system, from Type 0 to higher, determines its steady state error for different input types like steps, ramps, and parabolas. Higher type systems have lower steady state error but reduced stability. The document also introduces static error constants that quantify steady state error for different input types, like position (Kp) for steps, velocity (Kv) for ramps, and acceleration (Ka) for parabolas. These constants are used to calculate the expected steady state error for a given system and input.
This document discusses the static and dynamic characteristics of measurement systems. Static characteristics refer to a system's performance when the input is constant or changing slowly, and include accuracy, precision, resolution, and sensitivity. Dynamic characteristics refer to how a system responds when the input is changing rapidly over time, and include speed of response, fidelity, lag, and overshoot. The document provides definitions and explanations of these key static and dynamic terms.
The document discusses state modeling and state diagrams. It defines states as representations of intervals of time that describe an object's behavioral condition. Events trigger transitions between states. A state diagram uses a graph to represent an object's states and the transitions between them caused by events. It specifies the object's response to input events over time. The document provides examples of how to notationally represent states, transitions, events, and other elements in a state diagram.
The document discusses state modeling concepts including events, states, transitions, conditions, and state diagrams. It defines events as external stimuli that can be signal, change, or time-based. States represent object attribute values and have duration. Transitions are instantaneous changes between states caused by events. Conditions are Boolean expressions that must be true for a transition to occur. State diagrams graphically show states and transitions between them labeled with events.
Software Engineering :Behavioral Modelling - II State diagramAjit Nayak
This document discusses software engineering principles related to behavioral modeling using state diagrams and activity diagrams. It provides examples and explanations of key concepts in behavioral modeling including states, events, conditions, transitions, activities, actions, concurrency, and swimlanes. It also discusses implementing classes based on interaction and state diagrams and provides an example state diagram for the states of a CourseSection class.
State diagrams describe the behavior of objects by modeling their states and transitions between states based on events. Key elements of state diagrams include states, transitions, events, and actions. States represent conditions of an object, transitions are triggered by events, and actions occur on state entry/exit or during transitions. Together these elements specify the dynamic behavior of objects in response to events.
State machines model the different states an object can be in and the transitions between those states. A state represents a condition or situation during an object's lifetime. Transitions between states are triggered by events and may include actions. States can have substates that run either sequentially or concurrently. Sequential substates represent stages in a process, while concurrent substates run in parallel. Advanced state machine features like entry/exit actions, internal transitions, and history states help simplify complex models.
The document provides an overview of state modeling and interaction modeling techniques. It defines key concepts like events, conditions, states, and transitions that are used in state diagrams. It also discusses use case diagrams, which model user interactions with a system through actors and use cases. The document explains that state diagrams describe the behavior and life cycles of objects in response to events, while use case and interaction diagrams elaborate the functional requirements and interactions between users and a system.
This document discusses state diagrams and statecharts. It introduces key concepts such as states, transitions, events, actions, and activities. States represent conditions or situations of an object, and transitions occur between states in response to events. State diagrams can show nested substates and concurrent states using orthogonal components separated by dashed lines. The document provides examples and notation for drawing state diagrams to model the behavior of objects.
The document discusses dynamic modeling concepts including events, states, state transition diagrams, operations, nested state diagrams, and concurrency. It provides examples of state transition diagrams for a digital watch and booking object. It describes identifying events, building state diagrams, and constructing a dynamic model sample for a simple database application. The dynamic model shows control flows and object behavior over time in response to events.
The document discusses dynamic modeling concepts including events, states, state transition diagrams, operations, nested state diagrams, and concurrency. It provides examples of state transition diagrams for a digital watch and booking object. It describes identifying events, building state diagrams, and constructing a dynamic model sample for a simple database application. The dynamic model shows control flows and object behavior over time in response to events.
State chart diagrams describe the different states an object can be in, the transitions between states, and activities that occur during an object's lifetime. A state diagram models the transitions within a single class in response to events. Elements include initial and final states, states, transitions between states indicating triggers and guards, and pseudostates. Common pseudostates include choices, histories, junctions, entry/exit points, and terminate. State diagrams are useful for modeling workflows, document processing, real-time applications, and the behavior of a class over multiple use cases.
Programming models for event controlled programsPriya Kaushal
This document discusses programming models for event controlled programs using state machine models. It describes state machine models as having states and state transition functions that change the state based on inputs. An example of modeling a door or washing machine process is provided. Finite state machine models are also described as having a finite number of states, inputs, outputs, and state transition functions. The use of state machine models and finite state machine models to represent processes like timers and function calls is demonstrated. Finally, representing state machine models with state tables for software design and implementation is discussed.
The document discusses state machine diagrams and state modeling in object-oriented software design. It defines states as abstractions of an object's attribute values and links that affect its behavior. State machine diagrams show an entity's different states and how it responds to events by transitioning between states. The characteristics of states and various state modeling concepts and notations in UML like composite states, submachine states, history states, and transitions are explained.
Unit 3(advanced state modeling & interaction meodelling)Manoj Reddy
The document provides an overview of advanced state modeling and interaction modeling techniques in UML. It discusses nested state diagrams and concurrent state diagrams for controlling complexity in state diagrams. It also covers activity models, use case models, and sequence models for interaction modeling. The relationships between class models, state models, and interaction models are also briefly described.
Events in UML include signals, calls, the passing of time, and state changes. There are four main types of events: signal events, call events, time events, and change events.
Signal events represent asynchronous communications between objects, with signals serving as parameters. Call events represent synchronous operation dispatches. Time events occur with the passage of time, modeled using "after." Change events represent a change in state or condition, modeled using "when."
State machines specify an object's sequence of states in response to events. States are represented as rectangles, and transitions between states are lines. State machines model behavior for objects responding to asynchronous stimuli or those with behavior dependent on past states.
State chart diagrams define the different states an object can be in during its lifetime, and how it transitions between states in response to events. They are useful for modeling reactive systems by describing the flow of control from one state to another. The key elements are initial and final states, states represented by rectangles, and transitions between states indicated by arrows. State chart diagrams are used to model the dynamic behavior and lifetime of objects in a system and identify the events that trigger state changes.
Objects can change states in response to events or over time. A UML state diagram captures these state changes by showing the possible states of an object, the transitions between states, and the starting and ending points of state change sequences. It also models the entry and exit activities that occur when a system enters or leaves a state, as well as activities that occur within a state. Guard conditions can be used to specify when a transition takes place.
Systems can change states in response to events or over time. A UML state diagram captures these state changes by showing the possible states of an object, the transitions between states, and the starting and ending points of state sequences. It also models the entry and exit activities of each state, as well as any activities that occur within a state. Guard conditions can be used to specify when a transition takes place.
Systems can change states in response to events or over time. A UML state diagram captures these state changes by showing the possible states of an object, the transitions between states, and the starting and ending points of state sequences. It also models the entry and exit activities that occur when transitioning into or out of a state, as well as activities that occur within a state. Guard conditions can be used to specify when a transition takes place.
UML state machine diagrams illustrate the states and transitions of an object in response to events. A state represents the condition of an object at a moment in time between events. A transition between two states occurs when an event happens, which may cause actions to fire or require conditional guards. States can be nested, with substates inheriting transitions from the superstate. Pipelining is a technique that decomposes a process into sub-operations that execute concurrently on different data in specialized pipeline segments, overlapping computations to improve efficiency.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
2. Introduction
• The structure of objects and their relationships to each other in
a system described by its static structure i.e. the class model.
• Some objects in a system have complex temporal behaviors,
which must be carefully design.
• Temporal phenomena that occur over an interval of time are
properly modeled with a state
• The state model examine changes to the objects and their
relationships over time
• The state model describes the sequence of operations that
occur in response to events (external stimuli)
3. Events
• occurrence at a point in time
– instantaneous
– often corresponds to verb in past tense
• e.g., alarm set, powered on
– or onset of a condition
• e.g., paper tray becomes empty, temperature drops below freezing
• may logically precede or follow another or may
be unrelated
– e.g., Flight 123 must depart DDN before it can arrive in Delhi
(causally related)
– e.g., Flight 123 may depart before or after flight 345 departs DDN
(causally unrelated)
• Concurrent event: causally unrelated events;
have no effect on one another
4. Kinds of events
Signal event:
– the event of sending or receiving of a signal
• Signal: an explicit one-way transmission of
information from one object to another
• may be parameterized
– E.g., stringEntered(“Foo”)
– sending of a signal by one object is a distinct
event from its reception by another
– Difference between signal and signal event
– every signal transmission is a unique
occurrence but we group them into signal
classes to indicate common structure and
behavior.
• E.g., IA flight 123 departs from DDN on Jan 11, 2013 is an
instance of FlightDeparture
5. Signal class - UML notation
<< signal >>
FlightDeparture
airline
flightNum
city
date
keyword “signal” in << >>
name of signal class
attributes
6. Kinds of Events
• Change event
– Event caused by satisfaction of a Boolean expression
– Intent: Expression continually tested; when changes from
false to true, the event happens
– UML Notation: keyword when followed by parenthesized
boolean expression
• when(room temperature < heating set point)
• when(room temperature > cooling set point)
• when(battery power < lower limit)
• when(tire pressure < minimum pressure)
7. Kinds of Events
• Time event
– Event caused by the occurrence of an absolute time or the
elapse of a time interval
– for absolute time the UML Notation: keyword when
followed by parenthesized expression involving time
• when (date = Jan 1, 2013)
– for time interval the UML Notation: keyword after followed
that evaluate to a timeby parenthesized expression
duration
• after (n timeUnits)
• after(10 seconds)
8. States
• an abstraction of values and links of an object
• behavioral condition that persists in time
• according to gross behavior of objects, set of values
and links are grouped together into a state
• often corresponds to
– verbs with suffix of “-ing”
• e.g., Boiling, Waiting, Dialing
– the duration of some condition
• e.g., Powered, BelowFreezing
• UML Notation: a rounded box containing an
optional state name
Powered Waiting Dialing
9. Contd.
• In defining states
– ignore attributes that do not affect the behavior of the object
– lump together in a single state all combinations of values and links
with the same response to events
– E.g., except for leading 0’s & 1’s, the exact digits dialed do not affect
the control of the phone line, so we can define a state Dialing and
track the phone number as a parameter
• Objects in a class have a finite number of possible states
– Each object can only be in one state at a time
– At a given moment of time, the various objects for a class can exist
in a multitude of states
• A state specifies the response of an object to input events
– E.g., if a digit is dialed in state DialTone, the phone line drops the
dial tone and enters state Dialing;
– If the receiver is replaced in state DialTone, the phone line goes
dead and enters state Idle.
10. Symmetry between Events and States
• Events represent points in time
• State represent intervals of time. A state
corresponds to the interval between two events
received by an object
Power turned on Power turned off Power turned on
Powered Not Powered
11. Transitions and Conditions
• Transition: an instantaneous change in state
– triggered by an event
– Transition is said to fire upon the change from source to target
state
– Origin and target state of a transition are different states but
may be the same
– e.g., when a phone line is answered, the phone line transitions
from the Ringing state to the Connected state.
• Guard Condition:
– boolean expression that must be true for transition to occur
– checked only once, at the time event occurs; transition fires if
true
– E.g., when you go out in the morning (event), if the temperature
is below freezing (condition), then put on your gloves (next
state).
12. State Diagrams
• a graph whose nodes are states and whose
directed arcs are transitions between states
• specifies state sequences caused by event
sequences
• all objects in a class execute the state diagram for
that class; diagram models their common
behavior
– Note: state names are unique within the scope of
state diagram
– A class with more than one state has important
temporal behavior
– A class is temporarily important if it has a single state
with multiple responses to events
13. State diagrams
S T
States
Graphical state-modeling notation:
– States: labeled rounded box
– Transitions: directed arcs, labeled by triggering event,
optional guard condition, and/or effects
Specifies the response of an object to input events
- ignores events except those for which behavior is
prescribed
Example:
Transition
14. State diagrams
Graphical state-modeling notation:
– States: labeled rounded box
– Transitions: directed arcs, labeled by triggering
event, optional guard condition, and/or effects
Example:
S T
event(attribs) [condition] / effect
States
EventTransition
15. • State diagrams can represent
– Continuous loops
• Do not care, how the loop is started
– One-shot life cycles
16.
17. “One-shot” state diagrams
• represent objects with finite lives
– have initial and finite states
• initial state - entered on object creation
• final state - entry implies destruction of object
19. Example - entry and exit points
Chess game
White’s
checkmate
stalemate
stalemate
checkmate
turn
black white
moves moves
Black’s
turn
Black wins
Draw
White wins
20. State Model
• multiple state diagrams, one for each class
with important temporal behavior
– diagrams interact by passing events and through
side effects of guard conditions
– events and guard conditions must match across
diagrams in the model
21. Details
– if more than one transition leaves a state, then the
first event to occur causes the corresponding
transition to fire
– if an event occurs and no transition matches it,
the event is ignored
– if more than one transition matches an event, only
one transition will fire but the choice is non-
deterministic
23. Activity Effects
• effect = reference to a behavior executed in
response to an event
– can be attached to a transition or a state
– listed after a slash (“/”)
– multiple effects separated with a “,”and are
performed concurrently
24. Activity Effects
• Activity = behavior that can be invoked by any
number of effects
• May be performed upon:
– a transition
– entry to or exit from a state
– some event within a state
• Notation:
– event / resulting-activity
25. Activities
Often useful to specify an activity that is
performed within a given state
– E.g., while in PaperJam state, the warning light
should be flashing
– E.g., on entry into the Opening state, the motor
should be switched on
– E.g., upon exit of the Opening state, the motor
should be switched off
27. Do-Activities
• continue for an extended time
• can occur only within a state
• can not be attached to a transition
• include
• continuous operations, such as displaying a picture on a
television screen
• Sequential operations that terminate by themselves after an
interval of time
• may be performed for all or part of time that an object is in a state
•may be interrupted by event received during execution; event may
or may not cause state transition
PaperJam
do/ flash warning light
28. Entry and Exit Activities
Opening
entry / motor up
exit / motor off
• can bind activities to entry to/ exit from a state
•All transitions into a state perform the same activity, in
which case it is more concise to attach the activity to the state
29. Order of activities
1. activities on incoming transition
2. entry activities
3. do-activities
4. exit activities
5. activities on outgoing transition
Events that cause transitions out of the state can interrupt
do-activities. If a do-activity is interrupted, the exit
activity is still performed
30. • In general, any event can occur within a state
and cause an activity to be performed.
• Entry and exit are only two examples of events
that can occur
• Difference between an event within a state
and self-transition: only the self-transition
causes the entry and exit activities to be
executed but an event within a state does not
31. Completion Transition
• triggered by completion of activity in the source
state
• Often the sole purpose of a state is to perform a
sequential activity.
• When the activity is completed, a transition to
another state fires
• An arrow without an event name indicates an
automatic transition that fires
State 1
do / blah()
State 2
32. Contd.
• If a state has one or more completion transitions, but
none of the guard conditions are satisfied, then the state
remains active and may become ‘stuck’.
• The completion event does not occur a second time
• Therefore no completion transition will fire later to
change the state
• So if a state has completion transition leaving it, normally
guard condition should cover every possible outcome.
• Do not use guard condition on a completion transition to
model waiting for a change of value
33. Sending signals
• A object can perform the activity of sending a signal to
another object.
• A system of objects interact by exchanging signals
• The activity “send target.S(attributes)” sends a signal S with
the given attributes to the target object.
• E.g., the phone line sends a connect(phone number) signal to
the switcher when a complete phone number has been dialed.
• A signal can be directed to a set of objects or a single object.
• If the target is a set of objects, each of them receives a
separate copy of the signal concurrently and independently
process the signal and determines whether to fire a transition
or not
• If an object receive signals from more than one object, the
order in which concurrent signals are received may affect the
final state (race condition)
34. Advanced state modeling
• Conventional state diagrams are sufficient for
describing simple systems but need additional
power to handle large problems
• Model complex system by using
– Nested state diagrams
– Nested states
– Signal generalization
– Concurrency
35. Nested state diagram
• Problem with flat state diagram
– Consider an object with n independent Boolean attributes
that affect control
– representing such object with a single flat state diagram
would require 2n states
• Expanding state
– Organize the model by having high-level diagram with sub
diagrams expanding certain states
– Submachine: a state diagram that may be invoked as part
of another state diagram (lower-level state diagram).
– UML Notation for submachine: list a local name followed
by a colon and the submachine name.
36. Nested state
• Nest states to show their commonality and share
behavior
• Composite state: state that encloses the nested
states.
– Labels in the outer contour
• A nested state receives the outgoing transitions
of its composite states
39. Signal Generalization
• Organize signals into generalization hierarchy with
inheritance of signal attributes
• View every actual signal as a leaf on a generalization
tree of signals.
• Received signal triggers transitions that are defined
for any ancestor signal type.
– E.g., typing an ‘a’ would trigger a transition on signal
Alphanumeric as well as signal KeyboardCharacter.
• A signal hierarchy permits different levels of
abstraction to be used in a model.
– E.g., some state might handle all i/p characters the same;
other states might treat control characters differently
from printing characters .
40.
41. CONCURRENCY
• State model supports concurrency among
objects
• Object can act & change state independent of
one another.
• Sometime objects shares constraints that
causes their state changes
42. AGGREGATION CONCURRENCY
• State Aggregation means collection of state
diagrams , one for each part
• “and” relationship
• Aggregate state is one state from first diagram &
a state from second diagram & state from each
other diagram.
• Transition for one object depend on another
object that allows interaction between the state
diagram.
43.
44. Concurrency within an Object
• Some objects can be partitioned into subsets of
attributes or links.
• Each of the partitioned subset has its own subdiagram.
• The state of the object comprises one state from each
subdiagram.
• The sub diagrams need not be independent; the same
event can cause transitions in more than one
subdiagram
• UML Notation- partition the composite state into
regions with dotted lines.
45.
46. Synchronization of Concurrent
Activities
• Sometimes one object must perform two ( or
more) activities concurrently.
• The object must complete both activities
before it can progress to its next state.
47. Fork and Join
OR
Splitting control and Merging control
• FORK- A transition that forks indicates splitting
of control into concurrent parts.
• JOIN- Explicit merging of concurrent control
by a transition.
48. Relation of class model, state model
• A state diagram describes all or part of the
behavior of the objects of a given class.
• States = classes of values & link for an object
• State model of a class is inherited by its
subclasses. Subclass inherits both the state &
Transitions.
• It is also possible to refine an inherited state
diagram by expanding state into nested state or
concurrent sub diagrams.
49. Contd.
• State structure is related to and constrained
by class structure.
– A composite state is the aggregation of more than
one concurrent substate.
– Try to make the state diagrams of subclasses
independent of the state diagrams of their
superclasses.