169 research outputs found
Acoustic emission analysis of the effect of a 2D wedge shaped blade on the compact bone cutting process
No full textSurgeons may use a number of cutting instruments such as osteotomes and chisels to cut bone during an operative procedure. The initial loading of cortical bone during the cutting process results in the formation of microcracks in the vicinity of the cutting zone with main crack propagation to failure occuring with continued loading. When a material cracks, energy is emitted in the form of Acoustic Emission (AE) signals that spread in all directions, therefore, AE transducers can be used to monitor the occurrence and development of microcracking and crack propagation in cortical bone. In this research, number of AE signals (hits) and related parameters including amplitude, duration and absolute energy (abs-energy) were recorded during the indentation cutting process by a wedge blade on cortical bone specimens. The cutting force was also measured to correlate between load-displacement curves and the output from the AE sensor.
The results from experiments show AE signals increase substantially during the loading just prior to fracture between 90% and 100% of maximum fracture load. Furthermore, an amplitude threshold value of 64dB (with approximate abs-energy of 1500 aJ) was established to saparate AE signals associated with microcracking (41 – 64dB) from fracture related signals (65 – 98dB). The results also demonstrated that the complete fracture event which had the highest duration value can be distinguished from other growing macrocracks which did not lead to catastrophic fracture. It was observed that the main crack initiation may be detected by capturing a high amplitude signal at a mean load value of 87% of maximum load and unsteady crack propagation may occur just prior to final fracture event at a mean load value of 96% of maximum load. The author concludes that the AE method is useful in understanding the crack initiation and fracture during the indentation cutting process
Acoustic emission analysis of the effect of a 2D wedge shaped blade on the compact bone cutting process
No full textSurgeons may use a number of cutting instruments such as osteotomes and chisels to cut bone during an operative procedure. The initial loading of cortical bone during the cutting process results in the formation of microcracks in the vicinity of the cutting zone with main crack propagation to failure occuring with continued loading. When a material cracks, energy is emitted in the form of Acoustic Emission (AE) signals that spread in all directions, therefore, AE transducers can be used to monitor the occurrence and development of microcracking and crack propagation in cortical bone. In this research, number of AE signals (hits) and related parameters including amplitude, duration and absolute energy (abs-energy) were recorded during the indentation cutting process by a wedge blade on cortical bone specimens. The cutting force was also measured to correlate between load-displacement curves and the output from the AE sensor.\ud
The results from experiments show AE signals increase substantially during the loading just prior to fracture between 90% and 100% of maximum fracture load. Furthermore, an amplitude threshold value of 64dB (with approximate abs-energy of 1500 aJ) was established to saparate AE signals associated with microcracking (41 – 64dB) from fracture related signals (65 – 98dB). The results also demonstrated that the complete fracture event which had the highest duration value can be distinguished from other growing macrocracks which did not lead to catastrophic fracture. It was observed that the main crack initiation may be detected by capturing a high amplitude signal at a mean load value of 87% of maximum load and unsteady crack propagation may occur just prior to final fracture event at a mean load value of 96% of maximum load. The author concludes that the AE method is useful in understanding the crack initiation and fracture during the indentation cutting process
Operation Ajax : Studie om USA:s och Storbritanniens involvering i statskuppen, Iran 1953
No full textUniversity of Växjö, School of Social Sciences Course: PO 5363, Political Science, G3 Title: the Role of the USA’s and Great Britain in the Coup d'Etat, Iran 1953 Author: Ashkan Panahirad Supervisor: Lennart Bergfeldt The purpose of this study is to examine Great Britain’s and US’ motives and action alternatives in regards to the Coup d'état against the iranian regime under Mossadegh in 1953. The method used is motive analysis (investigates the actors motives). The theories used are Rational actors model and Governmental politics. Rational actor model allows states to choose among a set of alternatives displayed in a particular situation in order to achieve their goals. Governmental politics explains what happens in states as a result of bargaining games between important actors in the government. Analysis from the rational actor model shows that the motives behind the Coup d'état were oil, economical reasons, Iran and communism. Coup d'état was the most rational action for them to achieve their goals. Governmental politics reveal the shifting of policies from one administration to another. While Clement Attlee’s government and Harry Truman’s administration where more moderate, Winston Churchill’s and Eisenhower’s where more eager to replace Mossadegh, which finally lead to a Coup d'éta
Operation Ajax : Studie om USA:s och Storbritanniens involvering i statskuppen, Iran 1953
No full textUniversity of Växjö, School of Social Sciences Course: PO 5363, Political Science, G3 Title: the Role of the USA’s and Great Britain in the Coup d'Etat, Iran 1953 Author: Ashkan Panahirad Supervisor: Lennart Bergfeldt The purpose of this study is to examine Great Britain’s and US’ motives and action alternatives in regards to the Coup d'état against the iranian regime under Mossadegh in 1953. The method used is motive analysis (investigates the actors motives). The theories used are Rational actors model and Governmental politics. Rational actor model allows states to choose among a set of alternatives displayed in a particular situation in order to achieve their goals. Governmental politics explains what happens in states as a result of bargaining games between important actors in the government. Analysis from the rational actor model shows that the motives behind the Coup d'état were oil, economical reasons, Iran and communism. Coup d'état was the most rational action for them to achieve their goals. Governmental politics reveal the shifting of policies from one administration to another. While Clement Attlee’s government and Harry Truman’s administration where more moderate, Winston Churchill’s and Eisenhower’s where more eager to replace Mossadegh, which finally lead to a Coup d'éta
Hybrid emerging model predictive data-driven forecasting of three-phase electrical signals of photovoltaic systems using GBR-LSTM
No full textIn numerous industrial contexts, precise analysis and forecasting of electrical signals within three-phase systems are indispensable. As a result, this work presents DeepPhase, a hybrid framework that combines Long Short-Term Memory (LSTM) neural networks with gradient-boosted regression (GBR) to predict the current, voltage, and power of electrical signals. The performance of the model is evaluated in comparison to benchmark models, namely Bidirectional LSTM (BiLSTM), K-Nearest Neighbors (KNN), and LSTM, which utilize essential Key Performance Indicators (KPIs). As demonstrated by its highest Coefficient of Determination (R2) of 0.999, Mean Absolute Error (MAE) of 6.94 × 10−5, Mean Absolute Percentage Error (MAPE) of 0.07 %, and Root Mean Square Error (RMSE) of 0.000156, DeepPhase consistently exhibits predictive precision. For Three-Phase Current, MAE is 2.13 × 10−3, MAPE is 0.01 %, RMSE is 0.062432, and R2 is 0.960596; and for Three-Phase Voltage, MAE is 9.52E-03, MAPE is 0.03 %, RMSE is 0.014, and R2 is 0.999. The results of this study highlight the effectiveness of DeepPhase in analyzing the dynamics of complex Three-Phase electrical signals. This has significant implications for improving decision-making and control strategies in complex electrical systems
An Overview of Recent AI Applications in Combined Heat and Power Systems
No full textCombined heat and power (CHP) systems are among the important components for enhancing energy efficiency and sustainability by simultaneously generating electricity and useful thermal energy, reducing waste and costs. Consequently, the effective control of these systems is considered important. To that end, this paper provides a comprehensive review of the intelligent methodologies applied to CHP systems, emphasizing their prevalence in the USA and Europe through statistical insights. It outlines the mathematical foundations of CHP systems, analyzing the advancements in intelligent control methods for optimal planning, economic dispatch, and cost minimization. Artificial Intelligence (AI) models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Random Forest, are described and applied to a simulated CHP system. The Key Performance Indicators (KPIs) derived from these models demonstrate their efficacy for optimizing CHP performance. This paper also highlights the impact of AI-driven models for enhancing CHP system efficiency, while identifying the challenges in AI-CHP integration and envisioning CHP systems as important components of future sustainable energy systems
Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence
No full textAbstract This paper studies the effect of the number of switching (NOS) per day of capacitor banks on loss reduction in radial distribution systems. To this aim, the daytime (more precisely, 24 h) is divided into different numbers of time segments (equal to the same NOS) for capacitors’ size switching. The resulting non‐linear programming with discontinuous derivatives (called DNLP) model is solved subject to related constraints. The results reveal the impact of hourly switching of capacitor banks on further loss reduction (namely 118.4435, 83.7856, and 101.738 MWh for three IEEE systems) and higher net savings (i.e. k4.2772, and k$5.3542 for the same systems) of radial distribution systems compared to daily switching. Then, the hyper‐tuned Random Forest model is trained based on the IEEE 69‐bus network, fine‐tuned by the IEEE 10‐bus network, and fitted by the IEEE 33‐bus network to have an intelligent multi‐classification task with the highest accuracy. Numerical simulation, in both classic and intelligent parts, is presented to demonstrate the performance of DeepOptaCap. For the final step, DeepOptaCast is compared to other intelligent models of Light Gradient Boosting Method (LGBM), Decision Tree, and XGBoost, regarding KPIs of mean absolute percentage error, root mean squared percentage error, mean absolute error, root mean squared error, and coefficient of determination to demonstrate the model's superiority
Practical data connection between MATLAB and microcontrollers using virtual serial port and MicroPython Pyboard: A survey
No full textAbstract In this paper, a simple and practical method to hookup between Pyboard and computer using MicroPython and MATLAB is presented. With the presented way, MATLAB can connect to Pyboard with virtual serial port (VSP). This process is performed with a virtual port, without using MATLAB toolbox in all versions of this software and control prototyping is widely available on the hardware. This system can also be used in Simulink and widely be under the control of MATLAB to perform tasks. The system is based on (.py) file and (.m) file. One is made in MicroPython to perform analog to digital task and the second contains VSP source code to have a virtual connection with the proposed board and calculating codes to plot graphics. This way can cause the high speed of data sampling and data transfer in two different environments: Python interpretation environment and MATLAB environment. With the defined way, it is possible to make the devices that require calculation operations and the correlation of the computer and external environment with lower costs and fewer accessories. To validate the correctness of the proposed approach, an experimental prototype as a total harmonic distortion (THD) meter device has been built
Regression results for average number of articles per author model.
No full text<p>Regression results for average number of articles per author model.</p
Artificial Intelligence and Renewable Energy Integration in Optimizing Green Hydrogen Production
No full textIntegrating hydrogen production, electricity generation, and AI technologies in smart grids is considerable in developing sustainable and resilient energy systems, maximizing resource use, and improving grid efficiency. In this regard, the proposed chapter utilizes MATLAB/Simulink simulations and BiLSTM models to investigate and forecast hydrogen production, consumed electrical energy, and SoC levels in hydrogen-incorporated smart grid systems. The simulations spanned 24 hr and focused on generating hydrogen from wind and solar electricity by alkaline technology. The dataset is then examined using BiLSTM models to predict system characteristics. The BiLSTM model is assessed using KPIs of MAE, MSE, and R2. The results present accuracy in forecasting system dynamics, demonstrating the usefulness of the integrated method in optimizing energy production, consumption, and storage in hydrogen-based smart grid systems.</p
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