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Simulation and prediction of the attenuation behaviour of the KNN-LMN-based lead-free ceramics by FLUKA code and artificial neural network (ann)-based algorithm

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ENVIRONMENTAL TECHNOLOGY 2023年第11期44卷 1592-1599页

作者： Akkurt, Iskender Boodaghi Malidarreh, Parisa Boodaghi Malidarre, Roya Suleyman Demirel Univ Dept Phys Isparta Turkey Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Hadaf Inst Higher Educ Sari Iran Payame Noor Univ Dept Phys Tehran Iran

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] .

关键词： FLUKA simulation radioisotope source ann algorithm HI-PROBE card

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Multiple-page-mapping backpropagation neural network for constant tension control

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IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS 1998年第3期145卷 239-245页

作者： Luo, FL Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Constant tension control is widely required in industrial applications. Because of random tension interference, motor running speed vibration usually occurs. Tension control is a way to minimise the effect of random tension interference, however, the tension modification reference is another interference to the speed control system. The rewinding roll drive of a metal-film coating machine is a system with multiple closed loops and multiple input and output variables. Desired speed and tension responses are difficult to achieve by implementing conventional analogue proportional-plus-integral (PI) control. The paper introduces an artificial neural network algorithm that can successfully isolate cross coupling between the speed and tension control loops, and both loops can operate quasi-independently. It overcomes the disadvantages of traditional PI control systems. To handle the variation of the rewinding roll diameter, multiple pages of the network are applied. This technique can treat a dynamic nonlinear system as a quasistatic linear control system. Therefore this method decouples the speed and tension-control paths, and both control paths can independently operate in a quasistatic state. Simulation results show the effectiveness of this control algorithm.

关键词： ann algorithm film coating machine speed-control systems tension control

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Improved estimation of Cetane number of fatty acid methyl esters (FAMEs) based biodiesels using TLBO-NN and PSO-NN models

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FUEL 2018年 232卷 620-631页

作者： Baghban, Alireza Kardani, Mohammad Navid Mohammadi, Amir H. Amirkabir Univ Technol Dept Chem Engn Mahshahr Campus Mahshahr Iran Univ Tehran Dept Petr Engn Tehran Iran Univ KwaZulu Natal Sch Engn Discipline Chem Engn Howard Coll CampusKing George V Ave ZA-4041 Durban South Africa IRGCP Paris France

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.

关键词： Biodiesel FAME Cetane number ann algorithm Evolutionary algorithm

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A Comparative Study of the Performances of the LQR Regulator versus the PI Regulator for the Control of a Battery Storage System

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2023年第8期16卷 884-894页

作者： Nouri, Achraf Lachheb, Aymen El Amraoui, Lilia Univ Carthage Lab Smart Elect Natl Engn Sch Carthage Carthage Tunisia Univ Carthage ICT Natl Engn Sch Carthage SEICTLR18ES44 Carthage Tunisia

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

关键词： Linear Quadratic Regulator (LQR) PI regulator ann algorithm batteries energy storage system incremental conductance algorithm (INC) management and protection algorithm

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Investigation and ann-based prediction of the radiation shielding, structural and mechanical properties of the Hydroxyapatite (HAP) bio-composite as artificial bone

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RADIATION PHYSICS AND CHEMISTRY 2022年 197卷 1页

作者： Malidarre, Roya Boodaghi Akkurt, Iskender Malidarreh, Parisa Boodaghi Arslankaya, Seher Hadaf Inst Higher Educ Sari Iran Payame Noor Univ Phys Dept Tehran Iran Suleyman Demirel Univ Phys Dept Isparta Turkey Iran Univ Sci & Technol Elect Engn Dept Tehran Iran Sakarya Univ Ind Engn Dept Sakarya Turkey

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

关键词： Hydroxyapatite (HAP) FLUKA Monte Carlo Code Mechanical properties ann algorithm

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Neural Network algorithm for Solving System of Linear Equations

Neural Network Algorithm for Solving System of Linear Equati...

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International Conference on Computational Intelligence and Natural Computing

作者： Zhou, Zhigang Chen, Lihong Wan, Li Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430072 Hubei Peoples R China Wuhan Univ Sci & Engn Nonlinear Res Ctr Wuhan 430072 Hubei Peoples R China

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... 详细信息

ISBN: (纸本)9780769536453

关键词： ann algorithm linear equations

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Integration of Artificial Neural Network and Finite Element Simulation for Package Warpage Prediction 25

Integration of Artificial Neural Network and Finite Element ...

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25th Electronics Packaging Technology Conference (EPTC)

作者： Panigrahy, Sunil Kumar Che, Fa Xing Ong, Yeow Chon Me, Prasad Nagavenkata N. Ng, Hong Wan Micron Technol Operat India LLP Floor 7thAquila Phoenix Bl B Survey 115-35 Hyderabad 500032 Telangana India Micron Semicond Asia Operat Pte Ltd 990Bendemeer Rd Singapore 339942 Singapore

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.

关键词： package warpage finite element modelling AI ann algorithm regression model TSM simulation

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Forecasting Uncertainty Parameters of Virtual Power Plants Using Decision Tree algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第16期51卷 1756-1769页

作者： Krishna, Raji Sathish, Hemamalini Zhou, Ning Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India SUNY Binghamton ECE Dept New York NY USA

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.

关键词： forecasting uncertainty parameters renewable energy sources distributed generators machine learning decision tree algorithm ann algorithm ARIMA algorithm statistical metrics

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An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC-MCC environments

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2024年 225卷

作者： Khan, Sheharyar Zheng, Jiangbin Irfan, Muhammad Ullah, Farhan Khan, Sohrab Northwestern Polytech Univ Sch Software Xian Peoples R China Univ Haripur Dept Informat Technol Haripur Pakistan

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.

关键词： MEC MCC Computation offloading ann algorithm Machine learning Optimization

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Method for increasing net power of power plant based on operation optimization of circulating cooling water system

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ENERGY 2023年第1期282卷

作者： Wang, Huijie Qiu, Baoyun Zhao, Fangling Yan, Tianxu Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225000 Peoples R China Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou 225000 Peoples R China Jiangsu Univ Natl Res Ctr Pumps Zhenjiang 212000 Peoples R China

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.

关键词： Thermal power plant Net power Circulating cooling water system Operation optimization ann algorithm SA-IPSO algorithm

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