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Design of improved four-coil structure with high uniformity and effective coverage rate

HELIYON 2023年第4期9卷 e15193页

作者： Zhu, Xuehua Liu, Chuangchuang Su, Hao Miao, Yutong Cheng, Hongde AnHui Polytech Univ AnHui Key Lab Detect Technol & Energy Saving Devic Wuhu 241000 Peoples R China

Helmholtz coils have extensive applications in biological medicine, aerospace, and other in-dustries depending on the simple structure and miraculous magnetic field characteristics. How-ever, the uniform zone generated by them is not appropriate for scientific experiments with large devices. Due to the limitations of Helmholtz coils in application, a novel design technique is proposed to improve the homogeneity and region of magnetic field. The main approach is to add an auxiliary coil on each side of Helmholtz coils to compensate for the magnetic field that exists farther out from the center point. To analyze the size relationship between the auxiliary coil and the main coil to obtain the best magnetic field distribution, the traditional Maclaurin expansion method and particle swarm optimization (PSO) algorithm are used to research and discuss. The magnetic field distribution and the corresponding effective coverage rate (ECR) of the improved schemes with different structural parameters are calculated under the relative deviations of 0.1%, 0.5% and 1%, respectively. The results obtained by the above optimization methods are verified by the finite element software COMSOL and specific experiments. Both optimization methods manifest that the maximum effective coverage rate can be achieved when the size of the auxiliary coil is consistent with that of the main coil. In addition, we compare the improved four-coil structure proposed in this paper with the existing four-coil square structure under the same volume. The data show that the improved structure has certain advantages in the spatial magnetic field distribution. The corresponding tri-axial coil system is established by adopting the param-eters on the single axis, which can achieve a constant magnetic field in arbitrary directions by controlling the magnitude and direction of current on each axis. This provides a theoretical basis for the application of magnetic navigation technology.

关键词： Uniform magnetic field Maclaurin expansion method particle swarm optimization algorithm Effective coverage rate Finite element simulation

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A new deterministic PSO algorithm for real-time systems implemented on low-power devices

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2023年第1期429卷

作者： Dlugosz, Zofia Rajewski, Michal Dlugosz, Rafal Talaska, Tomasz Pedrycz, Witold Bydgoszcz Univ Sci & Technol Fac Telecommun Comp Sci & Elect Engn ul Al Prof S Kaliskiego 7 PL-85796 Bydgoszcz Poland Aptiv Serv Poland Podgorki Tynieckie 2 PL-30399 Krakow Poland Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye King Abdulaziz Univ Comp Engn Fac Engn Dept Elect Jeddah 21589 Saudi Arabia

The paper presents a novel, deterministic particle swarm optimization (PSO) algorithm, facilitating its implementation in low-power application specific integrated circuits (ASIC) realized in CMOS technology. The PSO algorithms are commonly used in a variety of optimization problems. They allow to search for a global extreme in a selected fitness function, which is used for numerical modeling of specific optimized problems. The conventional version of the algorithm is relatively difficult to implement directly in hardware, especially when each particle is represented by a separate circuit working in parallel with other particles. In such a situation, each particle has to be equipped with its own copy of each function. One of the problems is, for example, an efficient realization of the random function block that provides for each particle some factors used in computing a cognitive and a social velocity components. In this paper we shows a novel way to work around this problem. In the proposed approach the random function does not need to be used. It has been replaced by a simple computationally deterministic mechanism, which at the level of the overall swarm allows for effective emulation of the former random mechanism. We proposed such simplifications that reduce the formulas describing the algorithm to only basic arithmetic and logic operations. Comprehensive investigations have shown that performance of the proposed approach is comparable with its original version or in may situations even better. The proposed algorithm was tested with numerous test functions (50 runs for each tested function), to verify its flexibility and adaptation abilities to different problems.(c) 2023 Elsevier B.V. All rights reserved.

关键词： particle swarm optimization algorithm Parallel computing Deterministic approach Hardware implementation Low power circuits CMOS technology

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Forecasting the seasonal natural gas consumption in the US using a gray model with dummy variables

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 108002-108002页

作者： Wang, Zheng-Xin He, Ling-Yang Zhao, Yu-Feng Zhejiang Univ Finance & Econ Sch Econ Hangzhou 310018 Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300350 Peoples R China

To forecast the seasonal fluctuations of US natural gas consumption accurately, a novel gray model based on seasonal dummy variables and its derived model are separately established. Then, the approximate time response formula in the proposed seasonal forecasting model is optimally calculated by using particle swarm optimization algorithm. On this basis, empirical analysis is conducted using data pertaining to natural gas consumption in the US during 2010-2019. The results show that gray model based on seasonal dummy variables and its derived model can recognize seasonal fluctuations in US natural gas consumption, whose prediction performances are much better than that of a traditional gray model, autoregressive integrated moving average (ARIMA), support vector regression (SVR), recurrent neural network (RNN), transformer, and fuzzy time series forecasting models. The mean absolute percentage errors (MAPEs) of the proposed seasonal gray forecasting model and its derived model, the classic gray model, ARIMA, SVR, RNN, Transformer, and Fuzzy time series forecasting models are 3.46%, 2.37%, 12.23%, 3.39%, 2.38%, 3.08%, 3.84%, and 5.02% in the training set, while those are 4.57%, 4.42%, 12.44%, 7.9%, 8.09%, 11.33%, 35.19% and 13.19% in the test set, respectively. The predicted and empirical results obtained by utilizing the proposed gray model implied that natural gas consumption in the US from 2020 to 2022 will maintain its seasonal growth and periodic changes, with the highest and the lowest values in the first and second quarters, respectively. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Gray model Seasonal dummy variables Natural gas consumption particle swarm optimization algorithm

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Hybrid QPSO-NNIA2 algorithm for Multi-Objective optimization Problem

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第8期33卷

作者： Zhang, Lan Northwestern Polytech Univ Dept Appl Math Xian Shaanxi Peoples R China Xian Aeronaut Polytech Inst Basic Dept Xian Shaanxi Peoples R China

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

关键词： Multi-objective optimization problem Pareto optimal solution vicinity distance Quantum particle swarm optimization algorithm particle swarm optimization algorithm

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Reservoir production prediction with optimized artificial neural network and time series approaches

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartA期215卷

作者： Li, Weirong Wang, Linjun Dong, Zhenzhen Wang, Ruifei Qu, Bochao Shiyou Univ Xi'an Shaanxi Peoples R China

Numerical simulation of oil reservoirs is one of the most commonly used methods for reservoir production prediction, but its accuracy is based on accurate geological modeling and high-quality history matching. Therefore, numerical simulation is time-consuming and costly and requires extensive information. Traditional back-propagation neural networks and their improved algorithms are widely used for production prediction, but they are not suitable for time-series prediction problems. Based on variations in oil production, this study proposes a reservoir production prediction model based on a combined convolutional neural network (CNN) and a long short-term memory (LSTM) neural network model optimized by the particle swarm optimization (PSO) algorithm. First, the model extracts important temporal data features through the upper CNN, which is next imported to the lower LSTM network to further extract correlation features in the time dimension;then, it iteratively optimizes the key hyperparameters in the CNN-LSTM model through the PSO algorithm;finally, it uses the trained model for reservoir prediction. Compared with the training results of the LSTM neural network and CNN model, the PSO-CNN-LSTM model has higher prediction accuracy in time-series production prediction. Our proposed hybrid model is a data-driven method and is based on routinely available production data. Quick and accurate production prediction can lead to better informed operational decisions and optimization of recovery and economics.

关键词： Convolutional neural network Long short-term memory network particle swarm optimization algorithm Hybrid network model Production prediction Time series forecast

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optimization of the Local Split-Window algorithm for FY-4A Land Surface Temperature Retrieval

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REMOTE SENSING 2019年第17期11卷 2016-2016页

作者： Wang, Lijuan Guo, Ni Wang, Wei Zuo, Hongchao Lanzhou Univ Coll Atmospher Sci Lanzhou 730000 Gansu Peoples R China China Meteorol Adm Key Lab Drought Climate Change & Disaster Reduct Key Lab Drought Climate Change & Disaster Reduct Inst Arid Meteorol Lanzhou 730020 Gansu Peoples R China

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

关键词： land surface temperature split-window algorithm FY-4A particle swarm optimization algorithm

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How to realize the power demand side actively matching the supply side? --A virtual real-time electricity prices optimization model based on credit mechanism

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APPLIED ENERGY 2023年第1期343卷

作者： Guo, Zhilong Xu, Wei Yan, Yue Sun, Mei Jiangsu Univ Ctr Energy Dev & Environm Protect Zhenjiang 212013 Jiangsu Peoples R China

Intermittence and fluctuation of renewable energy increase the cost of real-time balance of power supply and demand, which will affect the stability of the power grid. How to design the incentive mechanism to increase the flexibility of the distributed energy system and reduce the cost of real-time supply-demand balance is an urgent problem to be solved. In this study, from the perspective of the demand side actively matching the supply side, a virtual real-time electricity price optimization model based on the credit mechanism is proposed. We use the linear decreasing inertia weight particle swarm optimization algorithm to optimize the user's electricity price and realize the minimization of the total electricity cost. The influence of the virtual real-time electricity price incentive mechanism on the matching degree between the power load curve and the distributed energy output curve is also studied. Moreover, a novel index is put forward to quantitatively measure the matching degree between users' power load curve and distributed energy output curve. Taking a multi-microgrid system in a China's industrial park as the case, we have found the results that the virtual real-time electricity price optimization model can not only reduce users' total power cost by 7.8108%, but also improve the matching degree between the demand side and the supply side by 2.4643%. The new demand response incentive mechanism we have presented in this paper may promote the effective utilization of distributed energy and reduce the impact of renewable energy on power grid.

关键词： Distributed energy Virtual real-time electricity price Demand side matches supply side particle swarm optimization algorithm

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Energy consumption prediction method based on LSSVM-PSO model for autonomous underwater gliders

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OCEAN ENGINEERING 2021年 230卷 108982-108982页

作者： Song, Yang Xie, Xudong Wang, Yanhui Yang, Shaoqiong Ma, Wei Wang, Peng Tianjin Univ Sch Mech Engn Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300350 Peoples R China Tianjin Univ Qingdao Inst Ocean Engn Qingdao 266237 Shandong Peoples R China Pilot Natl Lab Marine Sci & Technol Joint Lab Ocean Observing & Detect Qingdao 266237 Shandong Peoples R China

Currently, most autonomous underwater gliders (AUGs) operate on primary lithium batteries. As the state of charge of a primary lithium battery and the influence of marine environment on the glider are difficult to measure, it is hard to forecast the energy consumption of a glider accurately, which has caused the failure of many glider missions. For the purpose of safely deploying the AUG mission and effectively optimizing the motion parameters to increase the endurance, it is very important to make an accurate energy consumption prediction model of the AUG. In this paper, a novel model based on the least squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithm, namely the LSSVM-PSO model, is proposed to forecast the energy consumption of the AUG. Considering that the kernel function and the LSSVM related parameters have a great influence on the performance of the prediction model, several LSSVM models based on different kernel functions for energy consumption prediction are established, and the parameters are optimized by the PSO algorithm. The performance of LSSVM-PSO models with different kernel functions are compared based on the sea trial data. The results indicate that the LSSVM-PSO model with a radial basis kernel function has a higher accuracy than other models for energy consumption prediction. Moreover, the performance of the LSSVM-PSO model trained by different sample sizes and that of the conventional mathematical energy consumption prediction model are compared. The results demonstrate that the LSSVM-PSO model is superior with a large enough training sample size.

关键词： Autonomous underwater gliders Energy consumption prediction Least squares support vector machines 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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PSO-BASED PID CONTROLLER DESIGN FOR AN ENERGY CONVERSION SYSTEM USING COMPRESSED AIR

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第3期24卷 671-679页

作者： Atacak, Ismail Kucuk, Bayram Gazi Univ Technol Fac Comp Eng Dept TR-06500 Ankara Turkey Gazi Univ Inst Informat TR-06500 Ankara Turkey

In this study, an optimal control algorithm is proposed to overcome low performance problems arising from the non-linear characteristics of pneumatic motor in compressed air-based energy conversion systems. The effectiveness of the proposed algorithm is tested on an energy conversion system which includes a compressor, a proportional valve, a pneumatic motor (PM), a permanent magnet direct current (PMDC) generator and a control card. The control function of the system is carried out by driving the proportional valve with the control signals which is obtained depending on the PMDC generator output voltage error. In this structure, an optimal proportional-integral-derivative (PID) controller which tunes on-line its own gain parameters by particle swarm optimization (PSO) algorithm according to the operating conditions of the system used. In order to observe the effects of PSO-based PID controller on the system performance, the energy conversion system is also controlled by a discrete time PID controller. The experimental results show that PSO-based PID controller provides more robust control performance than discrete time PID controller under various operating conditions.

关键词： compressed air-based energy conversion optimal PID controller particle swarm optimization algorithm pneumatic motor

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