The document discusses nonlinear compensation of strain gauges using soft computing techniques. The objectives are to study the nonlinearity problem of strain gauges and suggest methods to circumvent the effect. Nonlinearity arises due to environmental factors and construction limitations. Four techniques are proposed: ADALIN, MLP, RBFNN, and ANFIS networks. ADALIN and MLP models are trained using LMS and backpropagation algorithms respectively to minimize error between strain gauge output and desired displacement values, compensating for nonlinearity. RBFNN is used to model the data relationship with minimized error.
Discussion of Bioelectrodes, types of electrodes, their materials, bio potentials and their electrodes used. Special electrodes and their designs are discussed.
Transducer and instrumentation lab manual awais ahmad
This document contains information about a lab manual for a course on Transducers and Instrumentation. It includes:
- The course code, instructor details, and list of 10 experiments to be conducted in the lab.
- Descriptions of the objectives and procedures for each experiment, which involve exploring and characterizing different types of transducers like phototransistors, RTDs, potentiometers, and LVDTs.
- Background theory sections on the working principles of the transducers used in each experiment.
This lab report describes an experiment measuring resistance in series circuits. The student built circuits with two and three resistors connected in series and measured the total resistance. Calculations using Ohm's law and Kirchhoff's voltage law found the total resistance to be within 0.022-0.11% of measured values, showing the calculations accurately modeled the physical circuits. Tables in the report show the individual resistor values, calculated and measured total resistances, and percent differences between calculations and measurements.
Transducers convert one form of energy to another. They are defined as devices that convert an input signal of one form to an output signal of another form. Transducers can measure many quantities including energy, pressure, temperature, position, and more. Common transducers include thermocouples, thermistors, strain gauges, and magnetic pickups. Transducer parameters that are important to consider include sensitivity, range, span, linearity, hysteresis, accuracy, and precision.
This document summarizes an experiment to verify Ohm's law and analyze resistive circuits. The experiment has two parts: 1) Develop a voltage-current characteristic curve for a resistor to verify Ohm's law. Measure voltage and current at increasing voltage levels and plot the relationship. 2) Determine voltages and currents in series and parallel resistor circuits using voltage and current divider rules. Measure voltages across individual resistors in series to verify calculations match measurements.
Electronics measurements and instrumentation basicsAbhishek Thakkar
This document discusses electronic measurements and instruments. It covers units and standards used in measurement, as well as concepts like accuracy, precision, resolution, and error. It also describes common electrical and non-electrical units, temperature scales, and metric prefixes used in engineering notation. The document outlines measurement standards and statistical analysis techniques used to characterize measurements. Finally, it provides a basic overview of the components in an electronic measurement system, including transducers, signal conditioners, analog-to-digital converters, signal processors, and display units.
Discussion of Bioelectrodes, types of electrodes, their materials, bio potentials and their electrodes used. Special electrodes and their designs are discussed.
Transducer and instrumentation lab manual awais ahmad
This document contains information about a lab manual for a course on Transducers and Instrumentation. It includes:
- The course code, instructor details, and list of 10 experiments to be conducted in the lab.
- Descriptions of the objectives and procedures for each experiment, which involve exploring and characterizing different types of transducers like phototransistors, RTDs, potentiometers, and LVDTs.
- Background theory sections on the working principles of the transducers used in each experiment.
This lab report describes an experiment measuring resistance in series circuits. The student built circuits with two and three resistors connected in series and measured the total resistance. Calculations using Ohm's law and Kirchhoff's voltage law found the total resistance to be within 0.022-0.11% of measured values, showing the calculations accurately modeled the physical circuits. Tables in the report show the individual resistor values, calculated and measured total resistances, and percent differences between calculations and measurements.
Transducers convert one form of energy to another. They are defined as devices that convert an input signal of one form to an output signal of another form. Transducers can measure many quantities including energy, pressure, temperature, position, and more. Common transducers include thermocouples, thermistors, strain gauges, and magnetic pickups. Transducer parameters that are important to consider include sensitivity, range, span, linearity, hysteresis, accuracy, and precision.
This document summarizes an experiment to verify Ohm's law and analyze resistive circuits. The experiment has two parts: 1) Develop a voltage-current characteristic curve for a resistor to verify Ohm's law. Measure voltage and current at increasing voltage levels and plot the relationship. 2) Determine voltages and currents in series and parallel resistor circuits using voltage and current divider rules. Measure voltages across individual resistors in series to verify calculations match measurements.
Electronics measurements and instrumentation basicsAbhishek Thakkar
This document discusses electronic measurements and instruments. It covers units and standards used in measurement, as well as concepts like accuracy, precision, resolution, and error. It also describes common electrical and non-electrical units, temperature scales, and metric prefixes used in engineering notation. The document outlines measurement standards and statistical analysis techniques used to characterize measurements. Finally, it provides a basic overview of the components in an electronic measurement system, including transducers, signal conditioners, analog-to-digital converters, signal processors, and display units.
This document discusses transducers, which convert one form of energy to another. It defines transducers as devices that convert a physical quantity into an equivalent electrical signal. It then describes different types of transducers, such as resistive, capacitive, electromagnetic, and piezoelectric transducers. The document also distinguishes between sensors and transducers, and discusses factors to consider when selecting transducers, such as operating principle, sensitivity, accuracy, and environmental compatibility. It concludes by classifying transducers as active/passive, analog/digital, and primary/secondary transducers.
Performance Analysis of ANN Training Algorithms to Detect the Magnetization L...IOSR Journals
This document analyzes and compares different artificial neural network (ANN) training algorithms for detecting the magnetization level in the magnetic core of a welding transformer. Three algorithms are evaluated: Resilient Backpropagation, Levenberg-Marquardt, and Gradient Descent. Resilient Backpropagation and Levenberg-Marquardt produced similar results in terms of root mean square error but Resilient Backpropagation is preferred due to its faster computational time and simpler algorithm. Gradient Descent was found not suitable for this application. The document concludes that Resilient Backpropagation is the best algorithm for detecting the magnetization level based on the evaluation factors.
This document provides an overview of transducers. It defines a transducer as a device that converts a non-electrical physical quantity into an electrical signal. Transducers contain a sensing element that produces a measurable response to physical changes and a transduction element that converts the sensor output into an electrical form. Transducers are classified based on their output signal type (analog or digital), application method (primary or secondary), energy conversion method (active or passive), and transduction principle used (resistive, capacitive, inductive, etc.). Examples of common transducers discussed include thermocouples, strain gauges, thermistors, and linear variable differential transformers. Selection factors and applications of transducers
Voltage Regulators Placement in Unbalanced Radial Distribution Systems for Lo...paperpublications3
Abstract: The Automatic Voltage Regulators (AVRs) help to reduce energy loss and improve the power quality of electric utilities. This paper presents selection of optimal location and tap setting for voltage regulators in Unbalanced Radial Distribution Systems (URDS). Power loss index (PLI) is used for the selection of optimal location of voltage regulators which will first found at each branch except source bus and the bus that has the highest power loss index are picked as the best location for the voltage regulators placement. Particle swarm optimization (PSO), is used for selecting the tap position of voltage regulator in an unbalanced radial distribution system. This algorithm makes the initial selection and tap position setting of the voltage regulators to minimize power losses and provide a good voltage profile along the distribution network and then reduce the total cost to obtain the maximum net savings. The effectiveness of the proposed method is illustrated on a test system of IEEE 33 bus unbalanced radial distribution systems.
Keywords:Unbalanced Radial Distribution Systems (URDS), Load Flow, Power loss index(PLI),Particle swarm optimization(PSO), Voltage Regulator placement, Loss minimization, cost saving.
This document provides a laboratory manual for experiments in a Microwave and Digital Communication lab. It includes 12 experiments covering topics like the characteristics of reflex klystron tubes, Gunn diodes, directional couplers, standing wave ratio measurements, and digital modulation techniques including time division multiplexing, frequency shift keying, phase shift keying, and differential phase shift keying. The manual provides the objectives, theoretical background, experimental procedures, observations tables and questions for each experiment.
Converting Capacitance Into Controller CountsFieldscale
There are several capacitive touch controller groups that utilize different acquisition and measurement techniques.
Most famous techniques are Charge Transfer, E-field Sensing, Relaxation Oscillator, Capacitance-to-Digital Conversion (CDC), Dual-Ramp and Sigma-Delta Modulator.
In most cases, the controller obtains a sample from the touch sensor, which is translated into raw data, called Counts.
Counts usually have a direct relation to the Capacitance of the touch sensor.
The document discusses different types of transducers. It defines a transducer as a device that converts one form of energy or information to another. There are several classifications of transducers discussed, including mechanical vs electrical transducers, primary vs secondary transducers, active vs passive transducers, and analog vs digital transducers. Common examples of transducers mentioned include thermometers, microphones, and strain gauges. The document provides detailed information on different types of electrical transducers.
The document discusses various types of sensors and transducers. It defines sensors as devices that measure physical quantities and produce a corresponding signal, while transducers are elements that experience a related change when subject to some input change. Common physical quantities that can be measured include temperature, pressure, light, current, and weight. Performance characteristics of sensors like range, error, accuracy, sensitivity, hysteresis, nonlinearity, repeatability, and resolution are also described. The document then discusses different types of displacement, position, velocity and motion sensors like potentiometers, strain gauges, capacitive, inductive, Hall effect, incremental encoders and tachogenerators.
This document describes the steps to calculate the charge time of a capacitive touch sensor. It involves simulating the sensor design in SENSE to extract resistance (R) and capacitance (C) values. An RC equivalent circuit is created for a single cell and the whole sensor. SPICE analysis is then used to simulate the circuit with a controller and measure the voltage output at different nodes to determine the charge time.
A direct brain to-brain interface in humansHamed Abdi
1) Researchers created the first direct brain-to-brain interface between humans using non-invasive EEG and TMS technologies.
2) In an experiment, one participant (the sender) viewed a computer game and their brain signals were decoded to transmit information via the internet to a TMS machine stimulating another participant (the receiver).
3) The receiver was then able to use the received brain signals to perform motor actions in the game, demonstrating basic information transmission between human brains located over 1 mile apart.
The definition of the capacitive transducer is to measure the displacement (how much distance it covers), pressure and other several physical quantities, these transducers are preferred. In these transducers, the capacitance between the plates is varied because of the distance between the plates, overlapping of plates, due to dielectric medium change, etc.
Static characteristics in mechanical measurements & metrologyChirag Solanki
The document discusses various static characteristics involved in mechanical measurement and metrology. It defines static characteristics as those that are constant or slowly varying over time, such as static calibration, sensitivity, error, linearity, threshold, resolution, hysteresis, drift, span and range. It provides examples of measuring pressure, temperature and sensitivity. It also explains concepts such as error, types of errors, linearity, and other static characteristics.
Filtering Electrocardiographic Signals using filtered- X LMS algorithmIDES Editor
This document presents a study on using a filtered-X least mean square (FXLMS) algorithm to remove various types of noise from electrocardiogram (ECG) signals. The FXLMS algorithm is an adaptive noise cancellation technique that is shown to outperform a standard least mean square (LMS) algorithm in terms of signal-to-noise ratio when removing noise such as baseline wander, powerline interference, muscle artifacts, and motion artifacts from real ECG signals based on simulations using a publicly available ECG database. The key aspects of the FXLMS algorithm and its application to adaptive noise cancelation in ECG signals are discussed.
This article describes the operational principles, construction and other features of the four most basic transducers viz. Strain Gauge, Potentiometer, Load Cell and LVDT. Also this article describes the characteristic features of different material transduction properties.
The document reports on an experiment to verify Ohm's Law by measuring the current and voltage in circuits with known resistors. Two resistors were tested (R1 = 11.2Ω, R2 = 21.1Ω). Measurements were taken using a voltmeter, ammeter, and ohmmeter. The data was plotted and linear fits confirmed Ohm's Law. Slopes from the plots matched the resistor values to within measurement error, verifying Ohm's Law.
Pe 4030 ch 2 sensors and transducers part 1 final sept 20 2016Charlton Inao
The document discusses various types of sensors and transducers. It defines sensors as devices that produce an output signal in response to a physical input. Transducers are defined as devices that convert a signal from one form of energy to another. Common transducers include temperature sensors, pressure sensors, and position sensors. The document provides examples of different types of position sensors such as potentiometers, strain gauges, linear variable differential transformers (LVDTs), and optical encoders. It also discusses important specifications for sensors like sensitivity, accuracy, resolution, and hysteresis.
This document presents information on the characteristics of instruments. It discusses both static and dynamic characteristics. The main static characteristics described are accuracy, sensitivity, reproducibility, drift, static error, dead zone, precision, threshold, linearity, stability, range/span, bias, tolerance, and hysteresis. The dynamic characteristics covered are speed of response, fidelity, lag, and dynamic error. The document was created by five students and guided by a professor to provide an overview of important instrument characteristics.
Transducers are devices that convert one form of energy or signal to another. They take an input signal in one form and produce an output signal in a different form. Examples include microphones, which convert sound waves to electrical signals, and speakers, which convert electrical signals back to sound waves. Transducers are classified based on what they measure, their output signal type, whether they are active or passive, their method of sensing input, and other factors. Common transducers include thermocouples for temperature, light dependent resistors for light intensity, and piezoelectric devices for pressure or vibration measurements.
Important slideshow for the students of XII vocational bifocal electronics. This slideshow covers 3rd chapter of their syllabus. Very useful for self preparation.
Control of Uncertain Hybrid Nonlinear Systems Using Particle FiltersLeo Asselborn
This paper proposes an optimization-based algorithm for the control of uncertain hybrid nonlinear systems. The considered system class combines the nondeterministic evolution of a discrete-time Markov process with the deterministic switching of continuous dynamics which itself contains uncertain elements. A weighted particle filter approach is used to approximate the uncertain evolution of the system by a set of deterministic runs. The desired control performance for a finite time horizon is encoded by a suitable cost function and a chance-constraint, which restricts the maximum probability for entering unsafe state sets. The optimization considers input and state constraints in addition. It is demonstrated that the resulting optimization problem can be solved by techniques of conventional mixed-integer nonlinear programming (MINLP). As an illustrative example, a path planning scenario of a ground vehicle with switching nonlinear dynamics is presented.
Newton method based iterative learning control for nonlinear systemsTian Lin
The document proposes a Newton-method based iterative learning control (ILC) method for nonlinear systems. It decomposes the nonlinear ILC problem into a sequence of linear time-varying ILC problems that can be solved using existing linear ILC algorithms. This approach converges semi-locally and avoids calculating inverse systems. The algorithm is demonstrated in a simulation of a single link manipulator model that successfully tracks the reference signal across trials. Future work aims to improve global convergence, stability, and monotonic convergence properties.
This document discusses transducers, which convert one form of energy to another. It defines transducers as devices that convert a physical quantity into an equivalent electrical signal. It then describes different types of transducers, such as resistive, capacitive, electromagnetic, and piezoelectric transducers. The document also distinguishes between sensors and transducers, and discusses factors to consider when selecting transducers, such as operating principle, sensitivity, accuracy, and environmental compatibility. It concludes by classifying transducers as active/passive, analog/digital, and primary/secondary transducers.
Performance Analysis of ANN Training Algorithms to Detect the Magnetization L...IOSR Journals
This document analyzes and compares different artificial neural network (ANN) training algorithms for detecting the magnetization level in the magnetic core of a welding transformer. Three algorithms are evaluated: Resilient Backpropagation, Levenberg-Marquardt, and Gradient Descent. Resilient Backpropagation and Levenberg-Marquardt produced similar results in terms of root mean square error but Resilient Backpropagation is preferred due to its faster computational time and simpler algorithm. Gradient Descent was found not suitable for this application. The document concludes that Resilient Backpropagation is the best algorithm for detecting the magnetization level based on the evaluation factors.
This document provides an overview of transducers. It defines a transducer as a device that converts a non-electrical physical quantity into an electrical signal. Transducers contain a sensing element that produces a measurable response to physical changes and a transduction element that converts the sensor output into an electrical form. Transducers are classified based on their output signal type (analog or digital), application method (primary or secondary), energy conversion method (active or passive), and transduction principle used (resistive, capacitive, inductive, etc.). Examples of common transducers discussed include thermocouples, strain gauges, thermistors, and linear variable differential transformers. Selection factors and applications of transducers
Voltage Regulators Placement in Unbalanced Radial Distribution Systems for Lo...paperpublications3
Abstract: The Automatic Voltage Regulators (AVRs) help to reduce energy loss and improve the power quality of electric utilities. This paper presents selection of optimal location and tap setting for voltage regulators in Unbalanced Radial Distribution Systems (URDS). Power loss index (PLI) is used for the selection of optimal location of voltage regulators which will first found at each branch except source bus and the bus that has the highest power loss index are picked as the best location for the voltage regulators placement. Particle swarm optimization (PSO), is used for selecting the tap position of voltage regulator in an unbalanced radial distribution system. This algorithm makes the initial selection and tap position setting of the voltage regulators to minimize power losses and provide a good voltage profile along the distribution network and then reduce the total cost to obtain the maximum net savings. The effectiveness of the proposed method is illustrated on a test system of IEEE 33 bus unbalanced radial distribution systems.
Keywords:Unbalanced Radial Distribution Systems (URDS), Load Flow, Power loss index(PLI),Particle swarm optimization(PSO), Voltage Regulator placement, Loss minimization, cost saving.
This document provides a laboratory manual for experiments in a Microwave and Digital Communication lab. It includes 12 experiments covering topics like the characteristics of reflex klystron tubes, Gunn diodes, directional couplers, standing wave ratio measurements, and digital modulation techniques including time division multiplexing, frequency shift keying, phase shift keying, and differential phase shift keying. The manual provides the objectives, theoretical background, experimental procedures, observations tables and questions for each experiment.
Converting Capacitance Into Controller CountsFieldscale
There are several capacitive touch controller groups that utilize different acquisition and measurement techniques.
Most famous techniques are Charge Transfer, E-field Sensing, Relaxation Oscillator, Capacitance-to-Digital Conversion (CDC), Dual-Ramp and Sigma-Delta Modulator.
In most cases, the controller obtains a sample from the touch sensor, which is translated into raw data, called Counts.
Counts usually have a direct relation to the Capacitance of the touch sensor.
The document discusses different types of transducers. It defines a transducer as a device that converts one form of energy or information to another. There are several classifications of transducers discussed, including mechanical vs electrical transducers, primary vs secondary transducers, active vs passive transducers, and analog vs digital transducers. Common examples of transducers mentioned include thermometers, microphones, and strain gauges. The document provides detailed information on different types of electrical transducers.
The document discusses various types of sensors and transducers. It defines sensors as devices that measure physical quantities and produce a corresponding signal, while transducers are elements that experience a related change when subject to some input change. Common physical quantities that can be measured include temperature, pressure, light, current, and weight. Performance characteristics of sensors like range, error, accuracy, sensitivity, hysteresis, nonlinearity, repeatability, and resolution are also described. The document then discusses different types of displacement, position, velocity and motion sensors like potentiometers, strain gauges, capacitive, inductive, Hall effect, incremental encoders and tachogenerators.
This document describes the steps to calculate the charge time of a capacitive touch sensor. It involves simulating the sensor design in SENSE to extract resistance (R) and capacitance (C) values. An RC equivalent circuit is created for a single cell and the whole sensor. SPICE analysis is then used to simulate the circuit with a controller and measure the voltage output at different nodes to determine the charge time.
A direct brain to-brain interface in humansHamed Abdi
1) Researchers created the first direct brain-to-brain interface between humans using non-invasive EEG and TMS technologies.
2) In an experiment, one participant (the sender) viewed a computer game and their brain signals were decoded to transmit information via the internet to a TMS machine stimulating another participant (the receiver).
3) The receiver was then able to use the received brain signals to perform motor actions in the game, demonstrating basic information transmission between human brains located over 1 mile apart.
The definition of the capacitive transducer is to measure the displacement (how much distance it covers), pressure and other several physical quantities, these transducers are preferred. In these transducers, the capacitance between the plates is varied because of the distance between the plates, overlapping of plates, due to dielectric medium change, etc.
Static characteristics in mechanical measurements & metrologyChirag Solanki
The document discusses various static characteristics involved in mechanical measurement and metrology. It defines static characteristics as those that are constant or slowly varying over time, such as static calibration, sensitivity, error, linearity, threshold, resolution, hysteresis, drift, span and range. It provides examples of measuring pressure, temperature and sensitivity. It also explains concepts such as error, types of errors, linearity, and other static characteristics.
Filtering Electrocardiographic Signals using filtered- X LMS algorithmIDES Editor
This document presents a study on using a filtered-X least mean square (FXLMS) algorithm to remove various types of noise from electrocardiogram (ECG) signals. The FXLMS algorithm is an adaptive noise cancellation technique that is shown to outperform a standard least mean square (LMS) algorithm in terms of signal-to-noise ratio when removing noise such as baseline wander, powerline interference, muscle artifacts, and motion artifacts from real ECG signals based on simulations using a publicly available ECG database. The key aspects of the FXLMS algorithm and its application to adaptive noise cancelation in ECG signals are discussed.
This article describes the operational principles, construction and other features of the four most basic transducers viz. Strain Gauge, Potentiometer, Load Cell and LVDT. Also this article describes the characteristic features of different material transduction properties.
The document reports on an experiment to verify Ohm's Law by measuring the current and voltage in circuits with known resistors. Two resistors were tested (R1 = 11.2Ω, R2 = 21.1Ω). Measurements were taken using a voltmeter, ammeter, and ohmmeter. The data was plotted and linear fits confirmed Ohm's Law. Slopes from the plots matched the resistor values to within measurement error, verifying Ohm's Law.
Pe 4030 ch 2 sensors and transducers part 1 final sept 20 2016Charlton Inao
The document discusses various types of sensors and transducers. It defines sensors as devices that produce an output signal in response to a physical input. Transducers are defined as devices that convert a signal from one form of energy to another. Common transducers include temperature sensors, pressure sensors, and position sensors. The document provides examples of different types of position sensors such as potentiometers, strain gauges, linear variable differential transformers (LVDTs), and optical encoders. It also discusses important specifications for sensors like sensitivity, accuracy, resolution, and hysteresis.
This document presents information on the characteristics of instruments. It discusses both static and dynamic characteristics. The main static characteristics described are accuracy, sensitivity, reproducibility, drift, static error, dead zone, precision, threshold, linearity, stability, range/span, bias, tolerance, and hysteresis. The dynamic characteristics covered are speed of response, fidelity, lag, and dynamic error. The document was created by five students and guided by a professor to provide an overview of important instrument characteristics.
Transducers are devices that convert one form of energy or signal to another. They take an input signal in one form and produce an output signal in a different form. Examples include microphones, which convert sound waves to electrical signals, and speakers, which convert electrical signals back to sound waves. Transducers are classified based on what they measure, their output signal type, whether they are active or passive, their method of sensing input, and other factors. Common transducers include thermocouples for temperature, light dependent resistors for light intensity, and piezoelectric devices for pressure or vibration measurements.
Important slideshow for the students of XII vocational bifocal electronics. This slideshow covers 3rd chapter of their syllabus. Very useful for self preparation.
Control of Uncertain Hybrid Nonlinear Systems Using Particle FiltersLeo Asselborn
This paper proposes an optimization-based algorithm for the control of uncertain hybrid nonlinear systems. The considered system class combines the nondeterministic evolution of a discrete-time Markov process with the deterministic switching of continuous dynamics which itself contains uncertain elements. A weighted particle filter approach is used to approximate the uncertain evolution of the system by a set of deterministic runs. The desired control performance for a finite time horizon is encoded by a suitable cost function and a chance-constraint, which restricts the maximum probability for entering unsafe state sets. The optimization considers input and state constraints in addition. It is demonstrated that the resulting optimization problem can be solved by techniques of conventional mixed-integer nonlinear programming (MINLP). As an illustrative example, a path planning scenario of a ground vehicle with switching nonlinear dynamics is presented.
Newton method based iterative learning control for nonlinear systemsTian Lin
The document proposes a Newton-method based iterative learning control (ILC) method for nonlinear systems. It decomposes the nonlinear ILC problem into a sequence of linear time-varying ILC problems that can be solved using existing linear ILC algorithms. This approach converges semi-locally and avoids calculating inverse systems. The algorithm is demonstrated in a simulation of a single link manipulator model that successfully tracks the reference signal across trials. Future work aims to improve global convergence, stability, and monotonic convergence properties.
Nonlinear Structural Dynamics: The Fundamentals TutorialVanderbiltLASIR
This presentation from Dr. Douglas Adams, Chairman of Civil & Environmental Engineering at Vanderbilt University, and Director of the Laboratory for Systems Integrity and Reliability (LASIR), introduces the fundamental concepts of nonlinear structure dynamics.
This document discusses nonlinear systems and their behavior. Nonlinear systems are represented by nonlinear differential equations and do not obey the principle of superposition. Their response depends on both the input amplitude and initial state. Nonlinear systems can exhibit phenomena like jump resonance, limit cycles, and asynchronous quenching. Nonlinearities can be incidental, inherently present in systems, or intentional, deliberately inserted. Examples of nonlinearities include saturation, dead zones, relays, and multivariable nonlinearities.
This document discusses nonlinear optics and the dynamical Berry phase. It introduces nonlinear optics and summarizes early experiments. It then discusses how the Berry phase is related to nonlinear optical effects like second harmonic generation (SHG). Computational methods are presented for calculating SHG and other nonlinear optical properties from first principles using time-dependent density functional theory and the dynamical Berry phase. Examples of applying these methods to study SHG in semiconductors are provided.
This document discusses nonlinear optics and summarizes key topics covered:
- It describes the difference between linear and nonlinear optics, where linear optics involves weak light that is unchanged and nonlinear optics involves intense light that can induce effects and be manipulated.
- Nonlinear optics allows changing light properties like color and shape, and has applications in telecommunications and creating ultrashort events.
- Phenomena like sum and difference frequency generation are examples of second-order nonlinear optical effects. Phase matching is important for efficient nonlinear optical processes.
- Applications of nonlinear optics include optical phase conjugation, optical parametric oscillators, optical computing, optical switching, and optical data storage.
1) The document describes different types of nonlinearities that can occur in systems. It classifies nonlinearities based on their magnitude (incidental or intentional) and frequency (limit cycles, jump resonance, etc.).
2) Some common types of nonlinearities described include saturation, dead zones, backlash, relays, harmonics, and chaotic behavior.
3) Nonlinearities can cause issues like degradation of system performance, limit cycles, and even destabilization of systems. Understanding different nonlinear effects is important for analyzing system behavior.
This document provides an overview of a presentation on bridge measurement systems. It discusses bridge sensors, including load cells and strain gauges. It introduces the Wheatstone bridge circuit and how it is adapted for bridge sensors. Specific circuits for measuring bridge resistance are presented. Key parameters like sensitivity, accuracy, and errors in bridge sensors are defined. Typical system components like instrumentation amplifiers, ADCs, and microcontrollers are discussed. Examples of calibrating load cells and basic connection diagrams are also provided.
CHARACTERISTICS OF INSTRUMENTATION,STRAIN GAUGE,DIFFERENTIATE TRANSDUCERkaushal boghani
The document summarizes the characteristics of instrumentation systems and strain gauges. It describes static characteristics such as accuracy, precision, reproducibility, and resolution, and dynamic characteristics such as speed of response, measuring lag, fidelity, and dynamic error. It then discusses different types of strain gauges including unbonded metal, bonded metal wire, bonded metal foil, thin film, semiconductor, and diffused metal strain gauges. The key information provided relates to the performance criteria used to evaluate instruments and the different technologies used in strain gauges.
This is an Introductory material for those who want to understand the basic difference between linear and nonlinear analysis in the context of civil and structural engineering.
Sensors for Biomedical Devices and systemsGunjan Patel
This document provides an overview of sensors used in biomedical devices and systems. It begins by defining key terms like sensor, transducer, and actuator. It then discusses different types of sensors like active and passive sensors. Examples of commonly used biomedical sensors are presented. Sources of sensor error and important sensor terminology are explained. The document provides details on displacement transducers, piezoelectric transducers, and strain gauges. It also describes the Wheatstone bridge circuit configuration often used with biomedical sensors.
This document discusses measurement and instrumentation in mechanical systems. It begins by defining measurement and classifying instruments as absolute or secondary. It then describes the generalized components of a measurement system including the sensing element, signal conditioning, and output display. Different types of inputs like desired, interfering, and modifying inputs are discussed. Examples of half, quarter, and full Wheatstone bridge circuits used with strain gauges are provided. Key characteristics like linearity, accuracy, precision, and hysteresis that are evaluated during static calibration of instruments are also summarized.
This document discusses the mechanical properties of viscoelastic materials. It covers topics like stress/strain behavior, creep, toughness, reinforcement, and modifiers. It explains how polymer chemistry, structures, and properties influence product performance. Key factors that determine a plastic's mechanical response are intermolecular forces, temperature, time under load, degree of crystallinity, and molecular weight. A plastic can behave as an elastic solid, viscoelastic solid, viscoelastic fluid, or viscous fluid depending on these factors. Tests like tensile testing, impact testing, and dynamic mechanical analysis are used to characterize mechanical properties.
The document discusses various mechanical properties of materials including stress-strain relationships, hardness, and the effect of temperature on properties. It describes common tests used to evaluate these properties such as tensile, compression, bending, and hardness tests. The tensile test is used to generate a stress-strain curve and determine properties like elastic modulus, yield strength, ultimate tensile strength, and ductility. The shape of the stress-strain curve provides information about the material's behavior and properties.
An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.
This document reviews methods for enhancing the linearity of different types of transducers using artificial neural network (ANN) models. It identifies several ANN models that can be used for nonlinearity correction in resistive, capacitive, and inductive transducers, including functional link artificial neural network (FLANN), radial basis function neural network, multilayer perceptron, and backpropagation network. It finds that FLANN has advantages over these other models as it has lower computational complexity and is easier to implement. The document then provides specific examples of using backpropagation networks, FLANN, and multilayer perceptron models to improve the linearity of thermocouples, linear variable differential transformers, and capacitive pressure sensors, respectively.
The document compares different algorithms for detecting voltage sags and swells in power systems. It describes RMS voltage detection, peak voltage detection, and discrete Fourier transform (DFT) methods. RMS detection uses historical data so it can be slow for mitigation. Peak detection requires only single-phase values but DFT is best for steady signals and not fast transients. The paper proposes a novel real-time algorithm to rapidly detect voltage sags and compares it to existing methods.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Analysis of optimal avr placement in radial distribution systems usingAlexander Decker
The document discusses optimal placement of voltage regulators in radial distribution systems using discrete particle swarm optimization. It first describes how voltage regulators work and their benefits in maintaining voltage levels and reducing losses. It then summarizes previous related work on determining optimal locations and settings of voltage regulators. The proposed method uses power loss index to initially select voltage regulator nodes and then applies discrete particle swarm optimization to determine the optimal tap settings, minimizing the number of regulators and overall costs while providing a smooth voltage profile. The method is tested on standard 15-node and 33-node radial distribution test systems.
Comparison and analysis of orthogonal and biorthogonal wavelets for ecg compr...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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Uncertainty and sensitivity analysis applied to a voltage series operational...nooriasukmaningtyas
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3. OBJECTIVE:
Brief Introduction
To study the nonlinearity problem of STRAIN GAUGE and
suggest novel methods of circumventing the effect of this
problem.
Nonlinear compensation of STRAIN GAUGE using different
ANN techniques
4. INTRODUCTION
SENSORS:- The sensors are devices which, for the purpose of measurement, turn physical
input quantities into electrical output signals, their output-input and output-time
relationship being predictable to a known degree of accuracy at specified environmental
conditions. It can also be defined as “a device which provides a usable output in response to
a specified measurand”.
It can also be defined as an element that senses a variation in input energy to
produce a variation in another or same form of energy is called a
sensor, whereas, transducer involves a transduction principle which converts a specified
measurand into an usable output.
Sensor Characteristics
Two general characteristics
STATIC DYNAMIC
Accuracy Fidelity
Precision (Repeatability) Speed of response
Resolution
Minimum detectable signal
Nonlinearity
Hysteresis
(Involves determination of transfer
function, frequency response)
Threshold
5. The sensor consists of several elements or blocks such as sensing element, signal
conditioning element, signal processing element and data presentation element.
General structure of sensor
Accuracy: Degree of closeness of measured value by instruments to the exact value of
the quantity.
Precision: Degree of reproducibility of measurements .
Resolution: minimum change in input value that can be detected by instruments.
Threshold: This is the smallest input change that produces a detectable output at zero
value condition of the measured.
Sensitivity: This is defined as the ratio of the incremental output (Δy) to incremental
input (Δx), i.e
6. Nonlinearity: The deviation from linearity, which itself is defined in terms of
superposition principles, is expressed as a percentage of the full scale output
at a given value of the input. Nonlinearity can, however, be specified here.
• deviation from a straight line joining the end points of the scale.
7. The nonlinearity problem gives rise to the following difficulties:
(i) Non accuracy in measurement
(ii) Limitation of dynamic range (linearity region)
(iii) Full potentiality of the sensor cannot be utilized.
The nonlinearity problem arises due to:
(i) Environmental changes such as change in temperature, humidity and
atmospheric pressure
(ii) Aging
(iii) Constructional limitations.
FIDELITY: it is the degree to which the instrument indicates the change in the
measured value without dynamic error.
SPEED OF RESPONSE: it is the rapidity with which an instrument responds to
change in the measured quantity.
8. Adaptive and intelligent methods for compensation of nonlinearities have
been proposed
These methods are based on the following structures:
(i) ADALIN : Single Neural Network
(ii) MLP : Multi Layer Perceptron
(iii) RBFNN : Radial Basis Function based Neural Network
(iv) ANFIS : Adaptive Neuro Fuzzy Inference System
The learning algorithms employed in the thesis are:
(i) LMS algorithm in ADALIN and ANFIS
(ii) BP algorithm in MLP
(iii) RBF learning algorithm
9. Strain gauge
STRAIN GAUGE is a passive electrical transducer. It gives variation in electrical resistance
between its two terminals as effect of strain on sensor(gage) on application of external force.
A metal conductor if stretched or compressed,a change in its resistance occurs due to change
In Its diameter and length. A change in its resistivity can be observed if subjected to strain,
this Property is called as piezoresistive effect. Thus resistive strain gage is also known as
piezoresistive gage. Since resistance of a conductor is directly proportional to its length
Therefore the resistance of gage increases with positive strain.
Usually, the wire gage consists of a resistance made of very
fine wire securely bonded to the member to be strained, in our case the balance. When a
force is applied to the balance, the wire then becomes either in tension or compression.
The resistance change of a wire when a force is applied is due in part to a change in
length and cross-section, and in part to an actual change in specific resistance.
10.
11. Principle of Strain Measurement
Strain-initiated resistance change is extremely small. Thus, for
strain measurement a Wheatstone bridge is formed to convert
the resistance change to a voltage change
12. The gage factor, K, of a strain gage, relates the change in resistance (DR) to the change in
length (DL). The gage factor is constant for a given strain gage, and R is the nondeformed
resistance of the strain gage, so
Gage factor is used to measure the sensitivity of a material to strain
Causes of Non linearities in strain gage
The major source of error comes from the fact that the resistances of most wires
Changes with temperature.
Error in designing resistance measurement circuit.
Wheatstone bridge circuit can provide solution to both effects. Wheatstone bridges are
Commonly used with strain gage set up to measure small changes in resistance. These
Bridges are inherently insensitive to supply voltage fluctuations.
13. Non linearity compensation of strain gage:
In the non linearity compensation scheme used in our project, here we are giving force
(strain) to a strip (whose strain is to be measured) by using micrometer manually.
This force will produce elongation on one side and compression on other side.
The differential voltage of the Strain Gage after being demodulated does not keep linear
relationship with the displacement.
The nonlinearity compensator can be developed by using different ANN techniques
like ADALIN, MLP, RBF-NN and ANFIS.
The output of the ANN based nonlinearity compensator is compared with the desired
signal (displacement signal given by using micrometer) to produce an error signal.
With this error signal, the weight vectors of the ANN model are updated. This process is
repeated till the mean square error (MSE) is minimized.
Once the training is complete, the Strain Gage together with the ANN model acts like a
linear Strain Gage with enhanced dynamic range.
In our project we have used RBFNN to compensate the non linearity of Strain Gage.
14. The ANN based adaptive nonlinear compensator can be developed by
MLP
FLANN
Cascaded FLANN (CFLANN)
ADALIN Based Non-linearity Compensation
The differential or demodulated voltage e at the output of LVDT is normalized by dividing each
value with the maximum value. The normalized voltage output e is subjected to functional
expansion and than input to the single neuron perceptron based nonlinearity compensator. The
output of the ADALIN based nonlinearity compensator is compared with the normalized input
displacement of the LVDT. The widrow-hoff algorithm, in which both the learning rates are
chosen as 0.07, is used to adapt the weights of the Neuron. Applying various input
patterns, the ANN weights are updated using the widrow-hoff algorithm. To enable complete
learning, 1000 iterations are made. Then, the weights of neurons are frozen and stored in the
memory. During the testing phase, the frozen weights are used in the ADALIN model.
15. The operation in a neuron involves the computation of the weighted sum of inputs and
threshold. The resultant signal is then passed through a nonlinear activation function.
This is also called as a perceptron, which is built around a nonlinear neuron; whereas the
LMS algorithm described in the preceding sections is built around a linear neuron. The
output of the neuron may be represented as,
Where is the threshold to the neurons at the first layer, wj(n) is the weight associated
with jth the input, N is the no. of inputs to the neuron and φ(.) is the nonlinear activation
function. Different types of nonlinear function are shown in Fig.
Different types of nonlinear activation function,
(a) Signum function or hard limiter,
(b) Threshold function,
(c) Sigmoid function,
(d) Piecewise Linear
16. Signum Function:
Threshold Function:
Sigmoid Function:
Where v is the input to the sigmoid function and a is the slope of the sigmoid function. For
the steady convergence a proper choice of a is required.
Piecewise-Linear Function:
17. MLP Based Non-linearity Compensation
The differential or demodulated voltage e at the output of LVDT is normalized by
dividing each value with the maximum value. The normalized voltage output e is
subjected to input to the MLP based nonlinearity compensator. In case of the MLP, we
used different neurons with different layers. However, the 1-30-50-1 network is
observed to perform better hence it is chosen for simulation. Each hidden layer and the
output layer contains tanh(.) type activation function. The output of the MLP based
nonlinearity compensator is compared with the normalized input displacement of the
LVDT. The BP algorithm, in which both the learning rate and the momentum rate are
chosen as 0.01 and 1 respectively, is used to adapt the weights of the MLP. Applying
various input patterns, the ANN weights are updated using the BP algorithm. To enable
complete learning, 400 iterations are made. Then, the weights of the various layers of
the MLP are frozen and stored in the memory. During the testing phase, the frozen
weights are used in the MLP model.
18. If P1 is the number of neurons in the first hidden layer, each element of the output vector of
first hidden layer may be calculated as,
where αj is the threshold to the neurons of the first hidden layer, N is the no. of inputs and φ(.)
is the nonlinear activation function in the first hidden layer of these type. The time index n has
been dropped to make the equations simpler. Let P2 be the number of neurons in the second
hidden layer. The output of this layer is represented as, fk and may be written as
19. where, αk is the threshold to the neurons of the second hidden layer. The output of the final
output layer can be calculated as
Where, αl is the threshold to the neuron of the final layer and P3 is the no. of neurons in the
output layer. The output of the MLP may be expressed as
2.2.3 Radial Basis Function Neural Network (RBF-NN)
The Radial Basis Function based neural network (RBFNN) consists of an input layer made up of
source nodes and a hidden layer of large dimension. The number of input and output nodes is
maintained same and while training, the same pattern is simultaneously applied at the input
and the output. The nodes within each layer are fully connected to the previous layer. The input
variables are each assigned to a node in the input layer and pass directly to the hidden layer
without weights. The hidden nodes contain the radial basis functions (RBFs) which are Gaussian
in characteristics. Each hidden unit in the network has two parameters called a center (μ), and a
width (σ) associated with it. The Gaussian function of the hidden units is radially symmetric in
the input space and the output of each hidden unit depends only on the radial distance
between the input vector x and the center parameter μ for the hidden unit.
20. Radial Basis Function neural network structure
The Gaussian function gives the highest output when the incoming variables are closest to
the center position and decreases monotonically as the distance from the center decreases.
Each node of the hidden layer contains the radial basis function:
21. Where μk is the center vector for the kth hidden unit and σk is the width of the Gaussian
function and denotes the Euclidean norm.
The parameters of the RBFNN are updated using the RBF algorithm. The RBF algorithm
is an exact analytical procedure for evaluation of the first derivative of the output error
with respect to network parameters.
24. Mean Square Error (MSE) Plot of RBFNN for Strain Gage non linearity compensation
The RBFNN based nonlinearity compensator improves the linearity quite appreciably about
0.0018, i.e. MSE is 0.0018.