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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 105846-000页

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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A Multi-Producer Group-Search-optimization Method-Based Maximum-Power-Point-Tracking for Uniform and Partial Shading Condition

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IEEE ACCESS 2020年 8卷 184688-184696页

作者： Li, Lijuan Chen, Yongdong Liu, Hongliang Tang, Wenbing Liu, Mengni Wu, Jun Li, Zeyu Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Xiangtan Univ Coll Math & Computat Sci Xiangtan 411105 Peoples R China Changde Power Supply Co State Grid Hunan Elect Power Co Changde 415001 Peoples R China

The power-voltage (P-U) curve of PV array shows multiple power points, which brings challenges to fast and accurately tracking of the global maximum power point. Considering the nonlinearity and the multi-peak characteristics of PV array output curve under the condition of partial shadow, a multi-producer group search optimization (MGSO) method for maximum power point tracking (MPPT) is proposed in this paper. In the MGSO, the characteristics and operations of three categories of members, including producers, scroungers, and rangers, are set according to the P-U characteristics. The number of producers is determined by the number of peaks. The initial position of each producer locates dispersedly to each peak region which makes the producers not fall into local optimum. The search strategy is simplified by the proposed angle transformation function of scroungers and omitting the ranger. The results of simulation and comparison demonstrate that the proposed MGSO method can effectively track the maximum power point under the uniform irradiance or partially shaded conditions, and also increase the utilization of solar energy.

关键词： Maximum power point trackers optimization Lighting Software algorithms particle swarm optimization Photovoltaic systems Licenses PV utilization group search optimization algorithm maximum power point tracking particle swarm optimization algorithm photovoltaic system

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optimization of Sports Fitness Management System Based on Internet of Health Things

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IEEE ACCESS 2020年 8卷 209556-209569页

作者： Tang, Yongquan Wang, Dehua Xinyang Normal Univ Sch Phys Educ Xinyang 464000 Peoples R China Sangmyung Univ Grad Sch Dept Phys Educ Seoul 03016 South Korea

Due to the huge scale of the Internet of Health Things (IoHT), various research methods currently applied in the field of Internet of Health Things are limited to the technical problems themselves, which lack a macro grasp of the entire Internet of Things (IoT) system. The verification of the proposed technical scheme requires the purchase of a large number of hardware devices, but as a result, only relatively limited research models can be constructed, which makes the entire system long management cycle and high management cost. Based on the research of particle swarm optimization algorithm, this paper finds its shortcomings and makes corresponding improvements. A new improved method of particle swarm optimization algorithm is proposed. By introducing the method of average particle distance and population distribution entropy in the algorithm search process, the inertia weight is dynamically changed. This is conducive to the improvement of the optimization efficiency of the particle swarm optimization algorithm, and has a good optimization accuracy. A IoHTs modeling method for component collaboration is proposed, and the IoHTs component library is designed according to the proposed modeling method. At the same time, this article discusses the XModel modeling simulation platform, and builds a IoHTs component model based on this platform, and has completed the verification of the validity of the proposed modeling method and the feasibility of the IoHTs IPv6 communication scheme. The component library designed in the XModel platform is based on the functions of IoT devices, and the components have more complete network communication features. The XModel platform specifically customizes the CMIoT component library, and supports the continuous expansion of the component library and the continuous refinement of component functions, providing a new method for communication research in the Internet of Health Things field.

关键词： Internet of Things Licenses Resource management Data models Sports Solid modeling optimization Internet of Health Things sports fitness management system XModel platform particle swarm optimization algorithm

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Prior Recognition of Flash Floods: Concrete Optimal Neural Network Configuration Analysis for Multi-Resolution Sensing

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IEEE ACCESS 2020年 8卷 210006-210022页

作者： Khan, Talha Ahmed Alam, Muhammad Mansoor Shahid, Zeeshan Su'ud, Mazliham Mohd Univ Kuala Lumpur British Malaysian Inst Kuala Lumpur 53100 Malaysia Usman Inst Technol Karachi 75300 Pakistan Univ Kuala Lumpur Malaysia Inst Informat & Technol MIIT Kuala Lumpur 50250 Malaysia Inst Business Management Karachi 75190 Pakistan Univ Kuala Lumpur Malaysian France Inst MFI Kuala Lumpur 43650 Malaysia

Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forwa

关键词： Flash floods sensors multi-resolution sensing early forecasting system artificial intelligence modified multi-layer feed forward neural network particle swarm optimization algorithm multi-layer perceptron

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The effect of formation thickness on the performance of deterministic and machine learning models for rate of penetration management in inclined and horizontal wells

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2020年 191卷 107160-107160页

作者： Najjarpour, Mohammad Jalalifar, Hossein Norouzi-Apourvari, Saeid Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran

Rate of penetration (ROP) is one of the most important parameters in reducing drilling expenditure. In this paper, a ROP management study has been conducted for a well in Southwest of Iran. As a part of this study, the best approach for ROP prediction was determined by comparing the performance of several methods and particle swarm optimization (PSO) algorithm was implemented for optimization of ROP. This paper highlights the effects of formation thickness and the magnitude of working data-set on the performance of different algorithms which are being used for ROP management studies, especially in horizontal and inclined wells. This topic has special importance, when the results show a massive difference in thin and thick formations for some ROP prediction methods. Comparing the results of different ROP prediction methods showed that hybrid Bingham model has the best performance in ROP prediction;only if a powerful mathematical tool like trust-region method is being used for the determination of its unknown coefficients. This superiority was not generalized in all individual formations and there were different results in the cases of thin and thick formations;so, application of the bag-of-algorithms strategy by using the most accurate method in each formation is suggested for the prediction of ROP. Performing this ROP management project resulted in the prediction of ROP with a total relative error of 13.59% and also 48.30% improvement in ROP.

关键词： Rate of penetration Machine learning Deterministic models Data-driven methods Trust-region method particle swarm optimization algorithm

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Determining biomass chemical exergy using a novel hybrid intelligent approach to promote biomass-based biorefineries

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JOURNAL OF CLEANER PRODUCTION 2020年 277卷 124089-124089页

作者： Aghbashlo, Mortaza Tabatabaei, Meisam Nadian, Mohammad Hossein Soltanian, Salman Ghasemkhani, Hamid Shafizadeh, Alireza Lam, Su Shiung Henan Agr Univ Henan Prov Engn Res Ctr Forest Biomass Value Adde Sch Forestry Zhengzhou 450002 Peoples R China Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Karaj Iran Univ Malaysia Terengganu Inst Trop Aquaculture & Fisheries AKUATROP Terengganu 21030 Malaysia Biofuel Res Team BRTeam Terengganu Malaysia Agr Res Extens & Educ Org AREEO Microbial Biotechnol Dept Agr Biotechnol Res Inst Iran ABRII Karaj Iran Inst Res Fundamental Sci IPM Brain Engn Res Ctr POB 19395-5531 Tehran Iran Univ Jiroft Fac Agr Dept Mech Engn Biosyst Jiroft Iran

The issue of sustainability has become a strategic imperative for researchers attempting to address energy and environmental concerns using biorefinery approach. Exergy-based methods have shown significant promises in terms of their ability to reliably locate the hotspots of resource degradation in biorefineries. The key step in analyzing biorefineries exegetically is to calculate biomass chemical exergy which is a very computationally-intensive task. Interestingly, proximate and ultimate analysis methods show potential to reflect the chemical exergy content of biomass. Hence, the present study was devoted to introducing a novel hybrid intelligent approach to determine the chemical exergy content of biomass based on both the composition analysis methods. In the developed hybrid models, input score variables in each inner loop of partial least square (PLS) approach were correlated with its output score variables using hybrid adaptive neuro-fuzzy inference system and particle swarm optimization algorithm (ANFIS-PSO). Both the developed modeling systems showed acceptable accuracy in determining the chemical exergy values of biomass materials. The model derived from ultimate analysis was slightly more accurate than that from proximate analysis (mean absolute percentage error of 0.207 vs. 0.506, respectively). Nevertheless, simple and inexpensive character of proximate analysis can facilitate real-world applications of the respective model. Overall, the developed model can pave the way for developing sustainable biorefineries by computing the chemical exergy of biomass more accurately than complex thermodynamic models. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Adaptive neuro-fuzzy inference system Biomass chemical exergy Partial least square particle swarm optimization algorithm Proximate analysis Ultimate analysis

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VIRTUAL MACHINE DEPLOYMENT STRATEGY BASED ON IMPROVED PSO IN CLOUD COMPUTING

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COMPUTING AND INFORMATICS 2020年第1-2期39卷 83-104页

作者： Pang, Shanchen Dong, Dekun Wang, Shuyu China Univ Petr Coll Comp Sci & Technol Qingdao 266580 Peoples R China

Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.

关键词： Cloud computing Pareto optimal solution particle swarm optimization algorithm resource reservation virtual machine deployment

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Logistics Distribution Location Based on particle swarm optimization algorithm

Logistics Distribution Location Based on Particle Swarm Opti...

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World Automation Congress

作者： Xichun CHEN Junli WANG School of Traffic and Transportation Engineering Lanzhou Jiaotong University Lanzhou 730070 China Gansu Province Supervision and Inspection Center of Product Quality Lanzhou 730030 China

ISBN: (纸本)9781467344975

The aim of logistics distribution center location is to improve the logistics service quality and reduce the transportation cost of goods. The problems are related to both the classical location problem and the vehicle routing problem. This paper proposes logistics distribution center location based on particle swarm optimization algorithm. It models the logistics distribution center location problem considering the user demand and operating cost, then it uses modified particle swarm optimization algorithm to give solution. Experimental result shows that the proposed model and algorithm is effective.

关键词： distribution center location particle swarm optimization algorithm optional point

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Prediction and analysis of bearing vibration signal with a novel gray combination model

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ADVANCES IN MECHANICAL ENGINEERING 2020年第5期12卷

作者： Yuan, Qiang Sun, Yu Zhou, Rui-ping Wen, Xiao-fei Dong, Liang-xiong Wuhan Univ Technol Sch Energy & Power Engn Wuhan Peoples R China Zhejiang Ocean Univ Sch Port & Transportat Engn Zhoushan 316000 Zhejiang Peoples R China

Bearings are the core components of ship propulsion shafting, and effective prediction of their working condition is crucial for reliable operation of the shaft system. Shafting vibration signals can accurately represent the running condition of bearings. Therefore, in this article, we propose a new model that can reliably predict the vibration signal of bearings. The proposed method is a combination of a fuzzy-modified Markov model with gray error based on particle swarm optimization (PGFM (1,1)). First, particle swarm optimization was used to optimize and analyze the three related parameters in the gray model (GM (1,1)) that affect the data fitting accuracy, to improve the data fitting ability of GM (1,1) and form a GM (1,1) based on particle swarm optimization, which is called PGM (1,1). Second, considering that the influence of historical relative errors generated by data fitting on subsequent data prediction cannot be expressed quantitatively, the fuzzy mathematical theory was introduced to make fuzzy corrections to the historical errors. Finally, a Markov model is combined to predict the next development state of bearing vibration signals and form the PGFM (1,1). In this study, the traditional predictions of GM (1,1), PGM (1,1), and newly proposed PGFM (1,1) are carried out on the same set of bearing vibration data, to make up for the defects of the original model layer by layer and form a set of perfect forecast system models. The results show that the predictions of PGM (1,1) and PGFM (1,1) are more accurate and reliable than the original GM (1,1). Hence, they can be helpful in the design of practical engineering equipment.

关键词： particle swarm optimization algorithm gray system theory fuzzy mathematical theory Markov data forecasting shafting vibration

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Joint Power and Channel Allocation Based on Caching with User Preferences in D2D HetNets

Joint Power and Channel Allocation Based on Caching with Use...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Lyu, Xinpeng Wang, Ying Liu, Man Li, Zhendong Fei, Zixuan Ning, Weichen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781728107325

Device-to-device (D2D) and caching can effectively solve the problem of repeated transmission of plentiful short videos in wireless networks. This paper considers a downlink video transmission process among cellular users and D2D users with interference between co-channel users. Caching with user preferences in D2D users is also taken into consideration to improve the system capacity. Joint channel and power allocation is optimized to maximize the sum rate of all users. The problem is a mixed integer nonlinear programming (MINLP) and is hard to solve. Firstly, the concept of cache similarity distance based on user preferences and video popularity is introduced to divide users into different groups. Then, the optimization problem is decoupled into two sub-problems. And a joint channel and power allocation algorithm based on caching with user preferences is proposed. Besides, a penalty function is introduced to handle constraints and a speed update function is modified to optimize binary variables and continuous variables simultaneously. Finally, an improved particle swarm optimization algorithm (PSO) is proposed to solve the problem. Simulation results show that the proposed algorithm can effectively improve the system capacity of the network.

关键词： Device-to-device user preferences channel power particle swarm optimization algorithm

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