Introduction to smart materials
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The document discusses smart materials and their classification and applications. It defines smart materials as materials that can sense and react to environmental stimuli in a controlled way. It provides a SMART acronym to describe the characteristics of smart materials and classifies common smart materials into types based on their input and output, including piezoelectric, electrostrictive, magnetostrictive, thermoelectric, and shape memory alloys. Examples of applications mentioned include smart fabric, aircraft, medical devices, robotics, and more.
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3
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Y
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Y
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Materials Testing,
Properties, Selection and
Modeling
FUNCTION
TRANSIT LOADS, HEAT,
CONTAIN PRESSURE, STORE
ENERGY, ETC; AT MAXIMUM
WEIGHT, COST, OR MAXIMUM
EFFICIENCY, SAFETY, ETC
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2
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area of the tube
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STIFFNESS
MAXIMISE STRENGTH
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E
Y
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21 /
E
32 /
Y
Column i.e. compressive
strut
21 /
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Y
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Y
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Y
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Y
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Y
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Modeling of wedm process for complex shape using multilayer perceptron and re...
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Wire cut electric discharge machining is the present days requirement in manufacturing the intricate and complex shape parts as required in the modern industrial products. The process being complex in nature for control over the machining process parameters. In the present study Multilayer perceptron (MLP) model is developed to predict the Material removal rate. The mathematical regression model is also developed by using Response Surface Methodology (RSM) to establish the relationshIp between the MRR and input process parameters like pulse on time (Ton), Pulse off time (Toff), Peak current (IP) and Servo voltage (SV). The predicted value by using MLP and RSM were compared with the experimental values. The average percentage errors are found to be 1.29 and -0.36527 for MLP and RSM respectively. It is observed that the predicted values with RSM are closer to experimental values as compared to MLP model. Key Words: WEDM, Response Surface Methodology, Neural Network, Multi Layer Perception, Material Removal Rate, Surface Roughness
MOCT (magneto optical current transformer)- presentation
Magneto Optical Current Transformer power point presentation. It is one of the nice and cool topics what you should choose in your college as a seminar topic. It is one of the best slides what you ever want to present in your college.It is one of the current topic of 2017.
Accurate characterizations of material using microwave T-resonator for solid ...
The topic of microwave sensors in enclosures is one of the most active areas in material characterization research today due to its wide applications in various industries. Surprisingly, a microwave sensor technology has been comprehensively investigated and there is an industry demand for an
accurate instrument of material characterization such as food industry, quality control, chemical composition analysis and bio-sensing. These accurate instruments have the ability to understand the properties of materials composition based on chemical, physical, magnetic, and electric characteristics. Therefore, a design of the T-resonator has been introduced
and investigated for an accurate measurement of material properties characterizations. This sensor is designed and fabricated on a 0.787 mm-thickness Roger 5880 substrate for the first resonant frequency to resonate at 2.4 GHz under unloaded conditions. Various standard dielectric of the sample under test (SUT) are tested to validate the sensitivity which making it a promising low-cost, compact in size, ease of fabrication and small SUT preparation for applications requiring novel sensing techniques inquality and control industries.
Magneto Optic Current Transformer Technology (MOCT)
An accurate electric current transducer is a key component of any power system instrumentation. To measure currents power stations and substations conventionally employ inductive type current transformers .For high voltage applications, porcelain insulators and oil-impregnated materials have to be used to produce insulation between the primary bus and the secondary windings. The insulation structure has to be designed carefully to avoid electric field stresses, which could eventually cause insulation breakdown. The electric current path of the primary bus has to be designed properly to minimize the mechanical forces on the primary conductors for through faults. The reliability of conventional high-voltage current transformers have been questioned because of their violent destructive failures which caused fires and impact damage to adjacent apparatus in the switchyards, electric damage to relays, and power service disruptions. In addition to the concerns, with the computer control techniques and digital protection devices being introduced into power systems, the conventional current transformers have caused further difficulties, as they introduce electromagnetic interference through the ground loop into the digital systems. Magneto-optical current transformer(MOCT)technology provides a solution for many of the above mentioned problems. The MOCT measures the electric current by means of Faraday Effect that is the orientation of polarized light rotates under the influence of the magnetic fields and the rotation angle is proportional to the strength of the magnetic field component in the direction of optical path. MOCT is a passive optical current transducer which uses light to accurately measure current on high voltage systems and determines the rotation angle & converts it into a signal of few volts proportional to the current
Modeling of photovoltaic system with maximum power point tracking control by ...
Modeling of photovoltaic system with maximum power point tracking control by ...International Journal of Power Electronics and Drive Systems
This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic module inputs (temperature and solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours. In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power. The entire system is implemented in the Matlab / Simulink environment where simulation results obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&O.Thermal resistance spectroradiometer system for led
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Parametric Optimization of Wire Electrical Discharge Machining (WEDM) Process...
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
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An Approach to Detecting Writing Styles Based on Clustering Techniques
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
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-Shweta Ambekar
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-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
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Conclusion and Future Work:
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Introduction to smart materials
- 4. TABLE: 1
- 6. SMART MATERIALS S SIGNIFICANT M MEASURABLE A APPROPRIATE R RESULT-ORIENTED T TIME-ORIENTED
- 7. CLASSIFICATION OF SMART MATERIALS Type of SMART Material Input Output Piezoelectric Deformation Potential Difference Electrostrictive Potential Difference Deformation Magnetostrictive Magnetic Field Deformation Thermoelectric Temperature Potential Difference Shape Memory Alloys Temperature Deformation Photochromic Radiation Colour Change Thermochromics Temperature Colour Change TABLE 2
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