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Deterministic and stochastic modelling of greenhouse microclimate

SYSTEMS SCIENCE & CONTROL ENGINEERING 2019年第3期7卷 65-72页

作者： Yang, Hua Liu, Qi-Fang Yang, Huai-Qing Shanxi Agr Univ Coll Informat Sci & Engn Taigu Shanxi Peoples R China

In order to improve the yield and quality of greenhouse crops, it is necessary to develop a reliable model to predict and control the microclimate of greenhouse. In this paper, the problem of deterministic and stochastic modelling for greenhouse microclimate defined by the variables of temperature and humidity is considered. Experiments were conducted in a naturally ventilated single-sloped greenhouse without crops in north China. Firstly, a mechanism model is adopted and the assumed unknown parameters are derived by using increased convergence factor particle swarm optimization algorithm. Secondly, considered the disturbance is independent identically distributed white noise, a stochastic dynamic model is constructed and the parameters are obtained by using maximum likelihood estimate. Finally, a comparison of measured and simulated data is given to show that the proposed models can reasonably forecast internal greenhouse microclimate.

关键词： Modelling greenhouse particle swarm optimization algorithm maximum likelihood estimate

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Low-energy PSO-based node positioning in optical wireless sensor networks

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OPTIK 2019年 181卷 378-382页

作者： Yan, Ziwei Goswami, Pratik Mukherjee, Amrit Yang, Lixia Routray, Sidheswar Palai, G. Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Jiangsu Peoples R China Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Guntur Andhra Pradesh India GITA Dept Elect & Commun Engn Bhubaneswar Odisha India

In this paper, particle swarm optimization (PSO) is introduced into Optical Wireless Sensor Networks. We consider the impact of node location on energy consumption when communicating over the network. The algorithm use PSO to optimize the position of nodes with the lowest energy consumption. The proposed method converges faster. By optimizing the positioning of node, energy consumption of the node is effectively reduced and the overall performance of the network is improved. An energy-saving optical wireless sensor network is obtained.

关键词： Optical wireless sensor networks Node positioning particle swarm optimization algorithm Clusters Energy consumption

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Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China

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ATMOSPHERIC POLLUTION RESEARCH 2019年第5期10卷 1588-1600页

作者： Liu, Hui Jin, Kairong Duan, Zhu Cent South Univ Sch Traff & Transportat Engn Lair Key Lab Traff Safety Track Minist EducIAIR Changsha 410075 Hunan Peoples R China

Exposure to particulate matter (PM2.5) with high concentrations can increase the risk of human illness and mortality. Consequently, it is meaningful to build an accurate model for PM2.5 forecasting and provide reference for air pollution management and short-term warning. This paper develops a novel hybrid model called WPDPSO-BP-Adaboost, based on WPD (Wavelet Packet Decomposition), the PSO (particle swarm optimization) algorithm, BPNN (Back Propagation Neural Network) and Adaboost algorithm. In the proposed structure, to obtain better performance of PM2.5 forecasting, the novel hybrid model can be describe as: the WPD is utilized to decompose the raw PM2.5 data into several sub-layers with low frequency and high frequency;optimized by PSO and Adaboost algorithm, the BPNN is employed to compete the three-step prediction for every single subseries. To investigate the three-step forecasting performance of the proposed models, there are three experiments involving eleven models for the comparisons, including the BP model, BP-Adaboost model, WPD-BP model, PSO-BP model, WPD-BP-Adaboost model, WPD-PSO-BP model, PSO-BP-Adaboost model, WPD-PSO -BP-Adaboost model, EEMD-GRNN model, CEEMDAN-ICA-ELM model and WPD-CEEMD-PSOGSA -SVM model. The experiments results show that (1) the WPD is useful in improving the forecasting performance;(2) the PSO and Adaboost algorithm can enhance the precision of forecasting significantly;(3) in all models, the WPD-PSO-BPAdaboost model performs best in multi-step forecasting.

关键词： Air pollution Early warning system Wavelet packet decomposition Neural network particle swarm optimization algorithm Adaboost algorithm PM2.5 forecasting

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Estimating soot volume fraction and temperature in flames using stochastic particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2011年第1-3期54卷 217-224页

作者： Sun, Yi-Peng Lou, Chun Zhou, Huai-Chun Huazhong Univ Sci & Technol State Key Lab Coal Combust Wuhan 430074 Hubei Peoples R China

A simulation investigation for simultaneous reconstruction of distributions of temperature and soot volume fraction from multi-wavelength emission in a sooting flame using the stochastic particle swarm optimizer (PSO) algorithm is presented. The self-absorption of the flame is considered. The selection of parameters of the stochastic PSO algorithm and detection wavelengths is analyzed. The effects of measurement errors and optical thickness of the flame on the accuracy of the reconstruction are investigated. It proved that the stochastic PSO algorithm is robust and can obtain accurate distributions of temperature and soot volume fraction from line-of-sight intensities in only several wavelengths, especially in the flame with large optical thickness, while other methods neglecting self-attenuation of the flame will take some errors. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Inverse radiation problem particle swarm optimization algorithm Flame emission spectrum Soot volume fraction Temperature reconstruction

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Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm

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ATMOSPHERIC POLLUTION RESEARCH 2019年第5期10卷 1482-1491页

作者： Liu, Wei Guo, Geng Chen, Fuji Chen, Yihui Fuzhou Univ Sch Econ & Management Fuzhou 350116 Fujian Peoples R China

Daily PM2.5 level has significant influence on human health, which is attracting increasing attention. The prediction of PM2.5 grades has thus become an important factor closely related to social development. In the past decades, many prediction methodologies for PM2.5 have been developed, including regression analysis, neural network model, and support vector machine model. Despite these progresses, it still remains a great challenge to predict the PM2.5 grades more accurately and efficiently. In this work, we applied meteorological pattern analysis to assist the support vector machine (SVM) model for PM2.5 class prediction. Cosine similarity was first used to extract three most relevant ones from six common meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed, wind direction, cumulative precipitation) to give the needed meteorological pattern for SVM model. Higher prediction accuracy was then obtained with the selected pattern composed by relative humidity, wind speed and wind direction. Moreover, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm were investigated for optimizing the parameters in the process of SVM classification, with PSO-SVM presenting the highest accuracy and efficiency (forecasting time significantly reduced by 25%). We further introduced the criteria of precision, recall and F1-score to evaluate the prediction results of PSO-SVM in each PM2.5 grade. Meanwhile, comparative studies confirmed that PSO-SVM displayed better performance than Adaboost and ANN models for the applied meteorological pattern analysis assisted PM2.5 grades prediction. These obtained results indicate the validity of meteorological pattern analysis for efficient air quality forecasting.

关键词： PM2.5 grades Meteorological pattern analysis SVM particle swarm optimization algorithm Air pollution

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Simulation optimization using particle swarm optimization algorithm with application to assembly line design

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APPLIED SOFT COMPUTING 2011年第1期11卷 605-613页

作者： Kuo, R. J. Yang, C. Y. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

Assembly line design is an important part of production system. Some processes need to undergo changes in order to increase in efficiency. Computer simulation has been applied on process design for many decades. Traditionally, simulation had to run all possible alternatives of assembly line and was not considered as an optimization technique. Thus, this study employs particle swarm optimization (PSO) algorithm which is with mutation based on similarity for simulation optimization in order to optimize the managerial parameters in production system. Through experimentation designs and statistics tests, the simulation results show that the proposed method is better than other algorithms, like genetic algorithm and conventional PSO algorithm for solving assembly line design problem. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Simulation particle swarm optimization algorithm Simulation optimization Assembly line

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An Improved particle swarm optimization algorithm with Harmony Strategy for the Location of Critical Slip Surface of Slopes

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China Ocean Engineering 2011年第2期25卷 357-364页

作者：李亮褚雪松 School of Civil Engineering Qingdao Technological University

The determination of optimal values for three parameters required in the original particle swarm optimization algorithm is very difficult. It is proposed that two new parameters simulating the harmony search strategy can be adopted instead of the three parameters which are required in the original particle swarm optimization algorithm to update the positions of all the particles. The improved particle swarm optimization is used in the location of the critical slip surface of soil slope, and it is found that the improved particle swarm optimization algorithm is insensitive to the two parameters while the original particle swarm optimization algorithm can be sensitive to its three parameters.

关键词： slope stability analysis limit equilibrium method particle swarm optimization algorithm harmony strategy

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Evolutionary dynamics in spatial threshold public goods game with the asymmetric return rate mechanism

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CHAOS SOLITONS & FRACTALS 2020年 136卷

作者： Wang, Xianjia Chen, Wenman Wuhan Univ Econ & Management Sch Wuhan 430070 Peoples R China Wuhan Univ Inst Syst & Engineer Wuhan 430070 Peoples R China

In reality, cooperators often are provided a higher return rate for their contributions. Inspired by the reality, this paper introduces the asymmetric return rate mechanism, where the return rate is asymmetric between cooperators and defectors. This paper mainly studies how the asymmetric return rate mechanism influences the evolutionary dynamics in spatial threshold public goods game on two different complex networks, the namely square lattice and Barabási-Albert scale-free network. The simulation results show that increasing the sensitivity for the spread of cooperation is more effective than increasing that for the spread of defection not only to promote cooperation, but also to elevate the provision of the public goods. In addition, a moderate value of threshold is the best to elevate both the promotion of cooperation and the provision of the public goods.

关键词： Asymmetric return rate Evolutionary dynamics particle swarm optimization algorithm Spatial threshold public goods game

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An application of particle swarm optimization algorithm to clustering analysis

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SOFT COMPUTING 2011年第3期15卷 533-542页

作者： Kuo, R. J. Wang, M. J. Huang, T. W. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

particle swarm optimization algorithm (PSOA), which maintains a population of particles, where each particle represents a potential solution to an optimization problem, is a population-based stochastic search process. This study intends to integrate PSOA with K-means to cluster data. It is shown that PSOA can be employed to find the centroids of a user-specified number of clusters. The proposed PSOA is evaluated using four data sets, and compared to the performance of some other PSOA-based methods and K-means method. Computational results show that the proposed method has much potential. A real-world problem for order clustering also illustrates that the proposed method is quite promising.

关键词： Clustering analysis particle swarm optimization algorithm K-means

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Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model

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AGRICULTURAL WATER MANAGEMENT 2020年 227卷

作者： Jin, Xiuliang Li, Zhenhai Feng, Haikuan Ren, Zhibin Li, Shaokun Chinese Acad Agr Sci Inst Crop Sci Minist Agr Key Lab Crop Physiol & Ecol Beijing 100081 Peoples R China Beijing Acad Agr & Forestry Sci Beijing Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China

The accurate and timely estimation of temporal and spatial changes in crop growth and yield before harvesting is essential for ensuring global food security. The integration of remote sensing data and crop models is a potential approach for the estimation of key crop growth parameters and crop yields. Therefore, the aim of this study was to assimilate biomass and canopy cover (CC) derived from vegetation indices into the AquaCrop model using the particle swarm optimization (PSO) algorithm in order to obtain a more accurate estimation of CC, biomass, and yield for maize. The results show that, compared to other vegetation indices, the enhanced vegetation index (EVI) and the three-band water index (TBWI) can be used to obtain a better estimation of CC (R-2 = 0.78 and root-mean-square error (RMSE) =9.84%) and biomass (R-2 = 0.76 and RMSE = 2.84 ton/ha), respectively. Additionally, it was found that the data assimilation approaches in which only CC was used as a state variable (scheme SVcc) and only biomass was used as a state variable (scheme SVbio) can be used to obtain more accurate estimations of CC (R-2 = 0.83 and RMSE = 8.12%) and biomass (R-2 = 0.81 and RMSE = 2.51 ton/ha), respectively;however, larger differences were found between the measured and estimated values of one variable (i.e., CC or biomass) when the other variable (i.e., biomass or CC) was used as the only state variable during the data assimilation. The data assimilation approach in which both CC and biomass were used as state variables (scheme SVcc+bio) produced a robust result, with the estimation accuracy being fairly close to that obtained using the single-variable (SVcc or SVbio) data assimilation approaches. The estimation accuracy for maize yield was slightly better when using a double-variable data assimilation approach (R-2 = 0.78 and RMSE = 1.44 ton/ha) than when using a single-variable data assimilation approach. In summary, this study presents a robust approach for increasing the estimat

关键词： Maize yield Canopy cover Biomass particle swarm optimization algorithm Double-state variables Data assimilation

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