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An optimization Scheme Based on Fuzzy Logic Control for Efficient Energy Consumption in Hydroponics Environment

ENERGIES 2020年第2期13卷 289-289页

作者： Khudoyberdiev, Azimbek Ahmad, Shabir Ullah, Israr Kim, DoHyeun Jeju Natl Univ Dept Comp Engn Jeju 63243 South Korea Virtual Univ Pakistan Dept Comp Sci Lahore 54000 Pakistan

As the world population is increasing rapidly, food and water demands are the most crucial problem for humanity. In some areas of the world, water or environment is unsuitable for plant growth;hydroponic systems can provide a suitable environment for crop production with effective management of natural resources. Internet of Things paradigm based automated systems has been creating an excellent opportunity for monitoring and controlling agriculture by minimizing the cost and maximizing the profit significantly over the past decade. The reduction of the cost can be achieved by sufficient usage of resources and setting up optimum operational parameters for agricultural devices. This paper presents an optimization scheme with novel objective function for hydroponics environment parameters management with efficient energy consumption. The proposed approach provides optimal energy and resource utilization in the hydroponics system with setting up a working level and operational duration to the actuators. We have developed an optimization scheme with objective function for optimal humidity and water level control based on fuzzy logic, which can support the optimal measurement for crop growth with energy efficiency. Fuzzy logic control is applied for the compromise between actuators working level and operational duration. A real hydroponics environment has been implemented and presented to evaluate the effectiveness of the proposed approach. It can be assessed through the simulation results that the optimization module achieves a signification reduction (18%) in energy consumption as compared to the other scheme.

关键词： hydroponics system internet of things (IoT) optimization algorithms efficient energy consumption fuzzy logic control (FLC)

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Simplified Power Allocation method for AF-Based Multi-Antennas Multiple Relays Networks 4

Simplified Power Allocation method for AF-Based Multi-Antenn...

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4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Soleimanpour-Moghadam, Mohadese Askarizade, Mohammad Talebi, Siamak Higher Educ Complex Bam Dept Mech Engn Bam Iran Shahid Bahonar Univ Kerman Dept Math & Comp Sci Kerman Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9781728175539

Multi-relay networking is known to have remarkable ability to increase network capacity and transmission quality. However, the rate maximization of amplify-and-forward multi-antenna in cooperative networks poses a nonlinear and nonconvex optimization challenge with nonlinear constraints that requires a highly complex solution. Based on mathematical principles, we unveil a novel method that reduces the complexity of the multi-antenna multi-relay rate maximization problem to an equivalent linear scalar expression. Having simplified the problem, we then apply the Branch and Bound algorithm as a known global optimization method to solve the expression.

关键词： Multi-relay networks resource allocation non-convex problem optimization algorithms

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Comparison of Local and Global optimization Methods for Calibration of a 3D Morphodynamic Model of a Curved Channel

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WATER 2020年第5期12卷 1333-1333页

作者： Shoarinezhad, Vahid Wieprecht, Silke Haun, Stefan Univ Stuttgart Inst Modelling Hydraul & Environm Syst Pfaffenwaldring 61 D-70569 Stuttgart Germany

In curved channels, the flow characteristics, sediment transport mechanisms, and bed evolution are more complex than in straight channels, owing to the interaction between the centrifugal force and the pressure gradient, which results in the formation of secondary currents. Therefore, using an appropriate numerical model that considers this fully three-dimensional effect, and subsequently, the model calibration are substantial tasks for achieving reliable simulation results. The calibration of numerical models as a subjective approach can become challenging and highly time-consuming, especially for inexperienced modelers, due to dealing with a large number of input parameters with respect to hydraulics and sediment transport. Using optimization methods can notably facilitate and expedite the calibration procedure by reducing the user intervention, which results in a more objective selection of parameters. This study focuses on the application of four different optimization algorithms for calibration of a 3D morphodynamic numerical model of a curved channel. The performance of a local gradient-based method is compared with three global optimization algorithms in terms of accuracy and computational time (model runs). The outputs of the optimization methods demonstrate similar sets of calibrated parameters and almost the same degree of accuracy according to the achieved minimum of the objective function. Accordingly, the most efficient method concerning the number of model runs (i.e., local optimization method) is selected for further investigation by setting up additional numerical models using different sediment transport formulae and various discharge rates. The comparisons of bed topography changes in several longitudinal and cross-sections between the measured data and the results of the calibrated numerical models are presented. The outcomes show an acceptable degree of accuracy for the automatically calibrated models.

关键词： 3D hydro-morphodynamic model automatic calibration optimization algorithms curved channel SSIIM PEST

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Application of Routing algorithms in Automated Distribution Network Planning 3

Application of Routing Algorithms in Automated Distribution ...

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3rd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Cibis, Kevin Wruk, Julian Zdrallek, Markus Univ Wuppertal Inst Power Syst Engn Wuppertal Germany

ISBN: (纸本)9781728147017

This contribution focuses on the presentation of an optimization tool for the automated planning of smart low-voltage (LV) networks. Furthermore, the methods developed in the scope of this paper for the determination of optimal cable routes are presented and explained. First of all the methodologies of the automated network planning tool and the different methods to determine cable paths are described. Afterwards the routing methods are applied to a typical LV network to analyze differences and usability considering different input data. Finally, the calculated routes are applied to the automated network planning process, followed by an analysis of the results. Existing routes form a sufficient basis for the automated implementation of future expansion plans. The optimal outcome of the usage of OpenRouteService can be produced for areas with many newly build roads, since these can lead to shorter courses within the network.

关键词： Automated Network Planning Distance Routing optimization algorithms Smart Grids

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Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms

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RESOURCES POLICY 2021年 73卷 1页

作者： Zhang, Hong Hoang Nguyen Bui, Xuan-Nam Pradhan, Biswajeet Ngoc-Luan Mai Diep-Anh Vu Changsha Univ Dept Econ & Management Changsha Peoples R China Hanoi Univ Min & Geol Min Fac Dept Surface Min Duc Thang Ward 18 Vien St Hanoi 100000 Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res Duc Thang Ward 18 Vien St Hanoi 100000 Vietnam Univ Technol Ctr Adv Modelling & Geospatial Informat Syst CAMG Sydney NSW 2007 Australia Sejong Univ Dept Energy & Mineral Resources Engn 209 Neungdong Ro Seoul 05006 South Korea Visagio Australia 6-189 St Georges Terrace Perth WA Australia Hanoi Univ Min & Geol Fac Econ & Business Adm Basic Econ Dept Duc Thang Ward 18 Vien St Hanoi 100000 Vietnam

The focus of this study aims at developing two novel hybrid intelligence models for forecasting copper prices in the future with high accuracy based on the extreme learning machine (ELM) and two meta-heuristic algorithms (i.e., particle swarm optimization (PSO) and genetic algorithm (GA)), named as PSO-ELM and GA-ELM models. Accordingly, the time series datasets of the copper price for thirty years were collected based on the influencing parameters, such as crude oil, iron ore, gold, silver, and natural gas prices. Furthermore, the exchange rate of the four largest countries in copper-producing, including Chile (USD/CLP), China (USD/CNY), Peru (USD/PEN), and Australia (USD/AUD), were also considered to evaluate the copper prices. The GA and PSO algorithms then optimized the weights and biases of the ELM model to reduce the error of the ELM model for forecasting copper price. The traditional ELM model (without optimization), and artificial neural networks (ANN) were also developed as the comparative models for resulting in convincing experimental results in the proposed PSO-ELM and GA-ELM models. The results indicated that the proposed hybrid PSO-ELM and GA-ELM models could forecast copper price with higher accuracy and reliability over the traditional ELM and ANN models. Of those, the PSOELM yielded the most dominant accuracy with a root-mean-squared error (RMSE) of 304.943, mean absolute error (MAE) of 241.946, mean absolute percentage error (MAPE) of 0.037, and mean absolute scaled error (MASE) of 0.933. The t-test and Wilcoxon test also demonstrated the statistical significance of the proposed models and the best 95% confident interval of the PSO-ELM model with the range of $177.046 to $67.054 with pvalue = 2.589e-05. Whereas, the GA-ELM model provided the forecasted copper price higher $137.233 than the actual copper price, and the 95% confidence interval is from $189.672 to $84.793 with p-value = 1.027e-06.

关键词： Copper price Forecasting price Natural resources Extreme learning machine optimization algorithms Hybrid models

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Advanced cuttlefish optimizer-random decision forest (ACORDF) based design of fractional order PID controller for higher-order time-delay system

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2021年

作者： George, Thomas Ganesan, V. Mt Zion Coll Engn Kadammanitta India Sathyabama Univ Dept Elect & Telecommun Engn Chennai Tamil Nadu India

Purpose The purpose of this manuscript, a state feedback gain depends on the optimal design of fractional order PID controller to time-delay system is established. In established optimal design known as advanced cuttlefish optimizer and random decision forest that is combined performance of random decision forest algorithm (RDFA) and advanced cuttlefish optimizer (ACFO). Design/methodology/approach The proposed ACFO uses the concept of crossover and mutation operator depend on position upgrading to enhance its search behavior, calculational speed as well as convergence profile at basic cuttlefish optimizer. Findings Fractional order proportional-integrator-derivative (FOPID) controller, apart from as tuning parameters (kp, ki and kd) it consists of two extra tuning parameters lambda and mu. In established technology, the increase of FOPID controller is adjusted to reach needed responses that demonstrated using RDFA theory as well as RDF weight matrices is probable to the help of the ACFO method. The uniqueness of the established method is to decrease the failure of the FOPID controller at greater order time delay method with the help of controller maximize restrictions. The objective of the established method is selected to consider parameters set point as well as achieved parameters of time-delay system. Originality/value In the established technique used to evade large order delays as well as reliability restrictions such as small excesses, time resolution, as well as fixed condition defect. These methods is implemented at MATLAB/Simulink platform as well as outcomes compared to various existing methods such as Ziegler-Nichols fit, curve fit, Wang method, regression and invasive weed optimization and linear-quadratic regression method.

关键词： Control systems optimization algorithms FOPID State feedback gain Three controller parameters Time-delay system Reliability constraints and small overruns

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An efficient hybrid tunicate swarm algorithm and radial basis function searching technique for maximum power point tracking in wind energy conversion system

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2021年

作者： Rajesh, Paulthurai Shajin, Francis H. Cherukupalli, Kumar Anna Univ Chennai Tamil Nadu India Pradad V Potluri Siddhartha Inst Technol Dept Elect & Elect Engn Vijayawada India

Purpose The purpose of this paper is to track the maximal power of wind energy conversion system (WECS) and enhance the search capability for WECS maximum power point tracking (MPPT). Design/methodology/approach The hybrid technique is the combination of tunicate swarm algorithm (TSA) and radial basis function neural network. Findings TSA gets input parameters from the rectifier outputs such as rectifier direct current (DC) voltage, DC current and time. From the input parameters, it enhances the reduced fault power of rectifier and generates training data set based on the MPPT conditions. The training data set is used in radial basis function. During the execution time, it produces the rectifier reference DC side voltage that is converted to control pulses of inverter switches. Originality/value Finally, the proposed method is executed in MATLAB/Simulink site, and the performance is compared with different existing methods like particle swarm optimization algorithm and hill climb searching technique. Then the output illustrates the performance of the proposed method and confirms its capability to solve issues.

关键词： Control systems Power systems Electrical engineering optimization algorithms Electrical machines and energy generation from renewable resources Distributed generation of electric power

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How to place your apps in the fog: State of the art and open challenges

How to place your apps in the fog: State of the art and open...

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Software - Practice and Experience

作者： Brogi, Antonio Forti, Stefano Guerrero, Carlos Lera, Isaac Department of Computer Science University of Pisa Pisa Italy Department of Computer Science University of the Balearic Islands Balearic Islands Spain

Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency-sensitive and bandwidth-hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open-source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog. © 2019 John Wiley & Sons, Ltd.

关键词： Fog computing Data handling Fog Quality of service Application deployment Application placements Models and algorithms Multiservice Applications Non functional requirements optimization algorithms Service placements State of the art

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A Systematic Literature Review on Support Vector Machines Applied to Classification

A Systematic Literature Review on Support Vector Machines Ap...

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2020 IEEE Engineering International Research Conference, EIRCON 2020

作者： Cano Lengua, Miguel Angel Papa Quiroz, Erik Alex Facultad de Ingenieria de Sistemas e Intormatica Universidad Nacional Mayor de San Marcos Lima Peru Universidad Nacional Mayor de San Marcos Universidad Privada Del Norte Lima Peru

ISBN: (纸本)9781728183671

This paper aims to identify the current state of the art of the latest research related to support vector machines through a literature review system according to the methodology proposed by Kitchenham and Charter, in order to answer the following research questions: Q1: In which research areas are they used? Q2: What are the main applications related with classification? Q3: What optimization methods or algorithms are used in SVMs? © 2020 IEEE.

关键词： classification hyperplane linear and nonlinear separation optimization algorithms Vector Support Machines

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Fast and stable MAP-Elites in noisy domains using deep grids

Fast and stable MAP-Elites in noisy domains using deep grids

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2020 Conference on Artificial Life, ALIFE 2020

作者： Flageat, Manon Cully, Antoine Imperial College London Department of Computing Adaptive & Intelligent Robotics Lab. LondonSW7 2AZ United Kingdom

Quality-Diversity optimisation algorithms enable the evolution of collections of both high-performing and diverse solutions. These collections offer the possibility to quickly adapt and switch from one solution to another in case it is not working as expected. It therefore finds many applications in real-world domain problems such as robotic control. However, QD algorithms, like most optimisation algorithms, are very sensitive to uncertainty on the fitness function, but also on the behavioural descriptors. Yet, such uncertainties are frequent in real-world applications. Few works have explored this issue in the specific case of QD algorithms, and inspired by the literature in Evolutionary Computation, mainly focus on using sampling to approximate the"true" value of the performances of a solution. However, sampling approaches require a high number of evaluations, which in many applications such as robotics, can quickly become impractical. In this work, we propose Deep-Grid MAP-Elites, a variant of the MAP-Elites algorithm that uses an archive of similar previously encountered solutions to approximate the performance of a solution. We compare our approach to previously explored ones on three noisy tasks: a standard optimisation task, the control of a redundant arm and a simulated Hexapod robot. The experimental results show that this simple approach is significantly more resilient to noise on the behavioural descriptors, while achieving competitive performances in terms of fitness optimisation, and being more sample-efficient than other existing approaches. © 2020 Massachusetts Institute of Technology.

关键词： optimization algorithms

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