The significance and novelty of the present work are the preparation of the non-lead ceramic by the general formula of (1-x) K0.5Na0.5NbO3-xLa Mn0.5Ni0.5O3 (KNN-LMN) with different x (0 . . . > (HVL)(x=0.25) is rep...
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The significance and novelty of the present work are the preparation of the non-lead ceramic by the general formula of (1-x) K0.5Na0.5NbO3-xLa Mn0.5Ni0.5O3 (KNN-LMN) with different x (0algorithm and prediction. For this purpose, the radio isotopic Cf-252 neutron source is simulated by the HI-PROBE, RADDECAY and DCYSCORE cards using the FLUKA code. As a result, the neutron-gamma photon shielding ability of the KNN-LMN lead-free ceramics exposed to the Cf-252 neutron source is estimated and predicted. Findings show that by increasing the concentration of the x in (1-x) K0.5Na0.5NbO3-xLa Mn0.5Ni0.5O3 lead-free ceramics results in an ascending trend in density. In addition, the increment of the x rate (x refers to the concentration of La Mn0.5Ni0.5O3 in KNN-LMN non-lead ceramics) causes an increase in the value of the neutron attenuation parameter (Sigma), and a strong relationship is monitored between Sigma and density. Moreover, descending order of (HVL)(x=0.01). (HVL)(x=0.04). (HVL)(x=0.07) > . . . > (HVL)(x=0.25) is reported for half-value layer values against gamma photon. From the attained results, it can be concluded that increaisng the rate of x results in the better shielding proficiency in terms of neutron and gamma photon for chosen KNN-LMN-based lead-free ceramics. [GRAPHICS] .
Background: This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a linear quadratic reg...
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Background: This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a linear quadratic regulator (LQR) combined with an artificial neural network (ann) algorithm. A state representation of the Buck-boost converter is performed. Then the ann-LQR control strategy is compared to a classical control based on the proportional-integral controller combined with an ann algorithm. The ann algorithm generates the reference charging or discharging current based on a comparison between the power generated and the power consumed. In order to obtain an accurate comparison, two identical systems are designed, each consisting of a photovoltaic system optimized by an incremental conductance algorithm (INC) that powers a dynamic load and a backup storage system consisting of a lithium-ion battery. A management and protection algorithm is developed to protect the batteries from overcharge and deep discharge and to manage the load availability on the DC bus. The simulation results show an improvement in the performances of the storage system by the ann-LQR control compared to the ann-PI method and an increase in the stability, accuracy, efficiency of the system is observed. Photovoltaic (PV) energy is one of the most promising technologies for combating climate change and meeting the urgent need for green renewable energy and long-term development. PV energy generation has a number of advantages: Solar energy is limitless and available anywhere on the planet. However, photovoltaic energy is intermittent and depends on meteorological conditions;also, the energy consumed is unpredictable. For this reason, a storage system is necessary to overcome these problems. Objective: The objective of this study is to develop an optimal control using a Linear Quadratic Regulator (LQR) combined with a neural network algorithm (ann) to improve the performance of an electrical
Many design parameters affect the warpage behavior of the advanced micro electronics package, such as different type of die stack up, materiAI selection, substrate thickness and mold compound height etc. ThermAI Shado...
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ISBN:
(纸本)9798350329575
Many design parameters affect the warpage behavior of the advanced micro electronics package, such as different type of die stack up, materiAI selection, substrate thickness and mold compound height etc. ThermAI Shadow Moire (TSM) test is used to estimate warpage behavior of the advanced package warpage. However, optimizing, the design parameters through TSM method is time consuming and needs more resources, reducing the number of experiments becomes a criticAI issue. Currently, researchers and engineers are adopted finite element design-on-simulation (DOS) technology for the risk assessment of package warpage. DOS technology can effectively shorten the design cycle, reduces resource, and effectively optimize the package structure. However, TSM simulation anAIysis requires more number of iterations to do the risk assessment of package warpage which leads to more consumption of time. ArtificiAI Intelligence (AI) can help researchers avoid the above shorting and it can give the quick risk assessment of package warpage. Therefore, this study is mainly focused on AI-assisted DOS technology by combining artificiAI intelligence and simulation technology to predict the package warpage. In order to ensure warpage prediction accuracy, the simulation model was vAIidated with the TSM experiment prior to establishment of design on experiment (DOE) matrix and generate the AI training database. An ArtificiAI NeurAI Network (ann) machine learning AIgorithm combined with AI training database to build the AI training model. Once, the AI model built with proper vAIidation, we can immediately predict the package warpage by using the criticAI design parameters. The results of this investigation demonstrate good accuracy, with a difference between AI prediction warpage and finite element simulation of less than 8%, and a reduction in solving time from a few days to a few minutes.
Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications int...
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Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSannOM (Network and Device Resources Utilization through Smart ann -based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSannOM employs Artificial Neural Networks (ann) for precise decision-making by validating real -world datasets, underscoring ann's superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ann model for the offloading decision-making algorithm achieves a training accuracy of 97 % and a validation accuracy of 99 % . The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10-50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.
This paper focuses on the radiation shielding, structural and specifically mechanical features of the Hydroxyapatite (HAP) bio-composites which can be used instead of the bone and tooth tissues in the human body via F...
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This paper focuses on the radiation shielding, structural and specifically mechanical features of the Hydroxyapatite (HAP) bio-composites which can be used instead of the bone and tooth tissues in the human body via FLUKA Monte Carlo Code (FMCC), Phy-X: PSD software, and an analytical method. Since HAP bio-composites are so brittle, their use is limited instead of bone in the human body. This challenging issue persuaded the scientists and researchers to solve the problem by inserting various oxide or dioxide materials into HAP bio-composites. Thus, in this work, TiO2 and CeO(2 )with different ratios of x = 0, 3, 7.5, 10 wt.% and y = 0, 2, 6, 7.5 wt.% are inserted into HAP bio-composites and thus eight types of S (S1, S2, S3, and S4) and B (B1, B2, B3, and B4) samples are produced. Using Artificial Neural Network (ann), this study predicts and demonstrates the system's behavior. Outcomes reveal that increasing the TiO(2 )and CeO2 concentrations in the (100-x) HAP + xTiO2 and (100-y) HAP + yCeO(2) bio-composites will improve the gamma photon shielding performance of the S and B samples. Furthermore, the photon and electron spatial maps for simulated geometries related to the S4 sample are extracted by the FLUKA Monte Carlo Code and are represented graphically. The produced electrons with the highest energy are monitored in lead volumes due to various interactions of gamma photons with lead shields. In addition, sharp peaks are reported for Z(eff )curves related to the B samples which may be due to the K-edge absorption of the Ce in HAP samples. The FLUKA results are in full agreement with the predicted targets via the ann algorithm. Moreover, increasing the CeO2/TiO2 concentrations in HAP bio-composites will enhance the rigidity of the chosen S and B samples. The rising percentage of the mechanical moduli related to the S and B series vary between 30% and 90% which may be due to the relationship between the density of the selected HAP bio-composites and mechanical mod
Renewable energy sources (RESs) are increasingly used to meet the world's growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale rene...
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Renewable energy sources (RESs) are increasingly used to meet the world's growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale renewable energy sources, controllable loads, energy storage devices, and other nonrenewable sources are effectively integrated to form a virtual power plant (VPP). Uncertainty in forecasting renewable energy generation due to the intermittent nature of renewable energy sources is one of the biggest challenges in VPPs. Power generation by RESs changes with the day of the week, season, location, climate, and resource availability. Also, load demand and utility price vary with time and need to be forecasted for proper energy management of VPPs. However, the dispatching and planning of VPPs are significantly impacted by the volatile nature of RESs, load demand, and utility price. Predicting these uncertainties with high accuracy is essential to balance the electrical power generation and the load demand. In this article, a decision tree (DT) algorithm is proposed, to predict the uncertainty parameters, such as the day-ahead power from the RES, load demand, and utility prices of VPPs. The efficiency of the proposed model and the predicted results are compared with other complex models, such as the artificial neural network (ann) and auto-regressive integrated moving average (ARIMA) algorithms. Root-mean square error (RMSE), mean square error (MSE), coefficient of determination (R-2), and mean absolute error (MAE) are the statistical metrics used to evaluate the accuracy of the prediction. One-year meteorological data of the Chennai zone in India is considered for predicting the uncertainty parameters. IEEE 16-bus and 33-bus test systems are used to validate the forecasting model. It is evident from the results that the proposed DT algorithm can predict the uncertainty parameters more accurately and use lesser time than the ann and ARIMA algorithms.
Due to the low power generation performance caused by the unreasonable regulation of the circulating cooling water system (CCWS), a method for increasing the net power of a thermal power plant by optimizing the CCWS o...
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Due to the low power generation performance caused by the unreasonable regulation of the circulating cooling water system (CCWS), a method for increasing the net power of a thermal power plant by optimizing the CCWS operation points was proposed. First, an iterative solver was developed to calculate the stable heat transfer parameters of water circulation processes. Then, the net power model of power plants was established according to the relationship between the CCWS operating points and steam turbine power. Considering the constraints of water flow and the number of pumps in operation, an optimization model with the objective of maximum net power was established. A joint program for solving the objective considering environmental parameters was developed based on artificial neural networks and a hybrid improved particle swarm algorithm. The joint solver could obtain the stable operating temperature of cooling water, the optimal operating point of CCWSs, and the maximum net power of power plants. In addition, the influence of steam load and environmental parameters on optimal solutions was summarized. After executing the operation optimization for a CCWS, the results showed that the net power of typical operating conditions in different seasons increased by 35.85-1423.64 kW, indicating a remarkable effect.
Cetane number (CN) is one of the key factors of biodiesels and other diesel fuels. It is an indicator of ignition speed and required compression for ignition. CN estimation of biodiesel based on fatty acid methyl este...
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Cetane number (CN) is one of the key factors of biodiesels and other diesel fuels. It is an indicator of ignition speed and required compression for ignition. CN estimation of biodiesel based on fatty acid methyl esters (FAME) composition was the main goal of this work. Application of artificial neural network (ann) combined with particle swarm optimization (PSO) and teaching-learning based optimization (TLBO) is discussed in this communication. A number of 232 fuel samples was derived from the literature as the raw data for the models development. Different evaluative factors prove the satisfactory performance of the proposed ann models. The obtained values of R-squared and mean square of errors are 0.973 & 3.538 and 0.951 & 6.324 for the proposed TLBO-ann and PSO-ann, respectively. Based on the outcome of this study, ann coupled with PSO and TLBO algorithms can be suitable tools, especially TLBO algorithm to estimate CN of biodiesels.
Maximum power point tracking using artificial neural network model has been implemented. Maximum Power point tracking is used to track the maximum power from a PV panel under different conditions. This model is tested...
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ISBN:
(纸本)9781728134468
Maximum power point tracking using artificial neural network model has been implemented. Maximum Power point tracking is used to track the maximum power from a PV panel under different conditions. This model is tested for a variety of irradiance from the light source. The proposed ann-based algorithm does not have any sensor for prediction purpose. In order to cope with real life maximum disturbance, it is tested under non-linear time domain and non-linear condition state as well. That shows noteworthy performance in estimating maximum power point (MPP). It has the competency to predict irradiance and maximum power point (MPP) with a very tiny amount of error using two series neural networks. Simulation results and experimental studies manifest that proposed sensorless ann-based model has a minor amount of undesired oscillations and much more effective in tacking maximum power under swiftly changing condition.
In this paper, the artificial neural network (ann) algorithm for solving system of linear equations is considered and the algorithm convergence theorem is derived. Some numerical tests are given to demonstrate the eff...
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ISBN:
(纸本)9780769536453
In this paper, the artificial neural network (ann) algorithm for solving system of linear equations is considered and the algorithm convergence theorem is derived. Some numerical tests are given to demonstrate the effectiveness of our results.
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