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Research on the collaborative mechanism between site selection optimization model and control of green space planning under the background of urban renewal

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DISCOVER SUSTAINABILITY 2025年第1期6卷 1-22页

作者： Li, Xiangyun Dai, Desheng Dong, Jingwen Hu, Kaili Nanjing Municipal Design & Res Inst Co Ltd Nanjing 210018 Peoples R China Nanjing Inst Technol Sch Civil Engn & Architecture Nanjing 211167 Peoples R China Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210024 Peoples R China

As a part of integrated urban ecosystems, green space is an important target of urban renewal planning. In the process of urban renewal of green space, it is difficult to control the special planning for green space systems, and the scale of urban public space is insufficient. From the perspective of spatial governance, it is of great practical importance to study the relationship between site selection optimization and the control of special planning for green space systems. This paper proposes a method of green space evaluation and site selection optimization based on Geographic Information System (GIS) modelling and particle swarm optimization algorithm. It optimizes the green space allocation by using the model constraints. Taking the special planning for green space systems in Gaochun District, Nanjing City as an example, it analyzes the constraints of the special planning for green space systems in the site selection optimization model from two perspectives: indicator conditional constraints and space requirement constraints. The GIS model of optimal site selection is introduced into the special planning for green space systems' adjustment process in the context of urban renewal. This approach can bring a variety of positive benefits to meet the needs of green space sharing and people's well-being.

关键词： Green space planning Site selection optimization Urban renewal particle swarm optimization algorithm

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Gene Regulatory Network Construction Based on a particle swarm optimization of a Long Short-term Memory Network

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CURRENT BIOINFORMATICS 2020年第7期15卷 713-724页

作者： Tang, Zhenhao Chai, Xiangying Wang, Yu Cao, Shengxian Northeast Elect Power Univ Sch Automat Engn Jilin Jilin Peoples R China

Background: The Gene Regulatory Network (GRN) is a model for studying the function and behavior of genes by treating the genome as a whole, which can reveal the gene expression mechanism. However, due to the dynamics, nonlinearity, and complexity of gene expression data, it is a challenging task to construct a GRN precisely. And in the circulating cooling water system, the Slime-Forming Bacteria (SFB) is one of the bacteria that helps to form dirt. In order to explore the microbial fouling mechanism of SFB, constructing a GRN for the fouling-forming genes of SFB is significant. Objective: Propose an effective GRN construction method and construct a GRN for the fouling-forming genes of SFB. Methods: In this paper, a combination method of Long Short-Term Memory Network (LSTM) and Mean Impact Value (MIV) was applied for GRN reconstruction. Firstly, LSTM was employed to establish a gene expression prediction model. To improve the performance of LSTM, a particle swarm optimization (PSO) was introduced to optimize the weight and learning rate. Then, the MIV was used to infer the regulation among genes. In view of the fouling-forming problem of SFB, we have designed electromagnetic field experiments and transcriptome sequencing experiments to locate the fouling-forming genes and obtain gene expression data. Result: In order to test the proposed approach, the proposed method was applied to three datasets: a simulated dataset and two real biology datasets. By comparing with other methods, the experimental results indicate that the proposed method has higher modeling accuracy and it can be used to effectively construct a GRN. And at last, a GRN for fouling-forming genes of SFB was constructed using the proposed approach. Conclusion: The experiments indicated that the proposed approach can reconstruct a GRN precisely, and compared with other approaches, the proposed approach performs better in extracting the regulations among genes.

关键词： Gene regulatory network long short-term memory particle swarm optimization algorithm mean impact value slime-forming bacteria yeast gene data machine learning

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Research Review of Ship Power Load Forecasting Based on Intelligent optimization algorithms

Research Review of Ship Power Load Forecasting Based on Inte...

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2024 IEEE International Conference on Cognitive Computing and Complex Data

作者： Meng, Ningjia Mei, Jiahao Shen, Kang Liang, Tianqin Zhang, Yuanyuan Xie, Jijin Beibu Gulf Univ Coll Mech & Marine Engn Qinzhou Peoples R China Beibu Gulf Univ Eastern Michigan Joint Coll Engn Qinzhou Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Amidst the swift advancements of the global shipping industry and the escalating demand for energy conservation and emission reduction, ship power load forecasting has emerged as a pivotal research domain to guarantee the stable operation of ship power systems. Precise power load forecasting not only optimizes the scheduling and management of ship power systems but also markedly enhances fuel utilization efficiency and mitigates emissions. In recent years, intelligent optimization algorithms, renowned for their notable advantages in addressing complex nonlinear problems, have been increasingly applied to the study of ship power load forecasting. This paper delves into ship power load forecasting methodologies predicated on intelligent optimization algorithms, with a primary focus on the application of genetic algorithms, particle swarm optimization algorithms, and artificial neural networks. These algorithms emulate natural evolution, swarm intelligence, and neural system mechanisms, thereby effectively addressing complex power load forecasting challenges. In conclusion, ship power load forecasting founded on intelligent optimization algorithms holds substantial theoretical significance and extensive application prospects. With the advancement and application of related technologies, the operational efficiency and reliability of ship power systems will be further augmented, thereby furnishing robust support for attaining green shipping and sustainable development.

关键词： intelligent optimization algorithms ship power load forecasting genetic algorithm particle swarm optimization algorithm

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SCP model based on the new energy vehicle collaborative detection configuration method research

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Zhou, Mingyang Qiu, Chengcheng Chongqing Jiaotong Univ Coll Aviat Chongqing 400000 Peoples R China Nanjing Audit Univ Sch Accounting Nanjing 210000 Jiangsu Peoples R China

To address the problem of inaccurate positioning of new energy vehicles during cooperative detection, this paper investigates the positioning accuracy of different combinations of radar and infrared sensors. In order to meet the control requirements of the vehicle co-detection formation, a distributed control method considering the vehicle dynamics characteristics is adopted for this purpose. And considering the cost of sensors, the optimal parameters are calculated, and an optimal aircraft co-detection configuration design scheme based on the SCP model (structure-behavior-performance model) is proposed. At the working level of multi-craft cooperative detection guidance, a two-layer cooperative guidance structure is designed based on the basic framework. Research on vehicle configuration design, vehicle data transmission, processing technology, and cooperative detection and guidance is carried out. The basic principles of the particle swarm optimization algorithm and the algorithmic flow of the discrete particle swarm algorithm are applied to calculate the assignment of targets by the particle swarm optimization algorithm. The GDOP values are analyzed according to the simulation results, and it can be concluded that: the change rate of 2 similar to 5 sensors accuracy can reach 71.1%, 71.6%, and 71.5%, respectively, when the infrared sensor spacing is 1km similar to 3km. 5 similar to 10 sensors accuracy change rate is only 53.3%, 52.2%, and 49.7%, respectively, which shows that the value is improved by 20%, indicating that the collaborative sensing accuracy of the aircraft has been effectively improved, which is of great help to the collaborative detection of new energy vehicles based on SCP model.

关键词： SCP model particle swarm optimization algorithm Radar and infrared sensor Cooperative vehicle detection configuration Two-layer cooperative guidance

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PSO-Based System Identification on Cement Vertical Mill System Using Real-World Data

PSO-Based System Identification on Cement Vertical Mill Syst...

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2024 International Conference on Guidance, Navigation and Control

作者： Xu, Yanxin Xu, Yang Min, Cheng Zhu, Jing Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

ISBN: (纸本)9789819622504;9789819622481;9789819622474

Despite the rapid development of cement industry, the production control of cement vertical mills continues to heavily rely on traditional expert experience-based methods. In order to enhance production efficiency, this paper meticulously analyzes the dynamic structure and production processes inherent in cement vertical mill systems. It divides the process from the furnace to the mill outlet into three distinct subsystems and carefully selects mathematical process models to construct a comprehensive system model. Field operation data from the Tangshan Plant of Tianjin Cement Research Institute serve as the basis for identifying model parameters. The paper utilizes the particle swarm optimization algorithm to optimize and determine the most appropriate model parameters for the vertical mill system, ultimately achieving an impressive fitting degree of approximately 90% for the optimized model.

关键词： System Identification particle swarm optimization algorithm Data Preprocessing

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A Improved particle swarm optimization algorithm for the Application with Boundary-layer Problems of Power-law Fluids

A Improved Particle Swarm Optimization Algorithm for the App...

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2013 International Conference on Mechanical Engineering and Instrumentation(ICMEI 2013)

作者： Jianhui Zhou Lixi Yue Yannan Lu College of Qian'an Hebei United University

In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm(ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Ordinary differential equations Parameter estimation boundary-layer problems

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Hybrid Noise Reduction Filter Using the Gaining–Sharing Knowledge-Based optimization and the Whale optimization algorithms

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SN Computer Science 2024年第4期5卷 417页

作者： Nabahat, Mehrdad Khiyabani, Farzin Modarres Navmipour, Nima Jafari Department of Mathematics Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran

Noise reduction is one of the main challenges for researchers. Classical image de-noising methods reduce the image noise but sometimes lose image quality and information, such as blurring the edges of the image. To solve this challenge, this work proposes two optimal filters based on a generalized Cauchy (GC) distribution and two different nature-inspired algorithms that preserve image information while decreasing the noise. The generalized Cauchy filter and the bilateral filter are two parameter-based filters that significantly remove image noise. Parameter-based filters require proper parameter selection to remove the noise and maintain the edge details. To this end, two filters are considered. In the previous works, the parameters of the mask that was made with the GC function were optimized and the mask size was considered fixed. By studying different noisy images, we find that the selected mask size significantly impacts the designed filter performance. Therefore in this paper, a mask is designed using the GC function to formulate the first filter, and despite the optimization of the filter parameters, the selected mask size is also optimized using the peak signal-to-noise ratio (PSNR) as a fitness function. In most metaheuristic-based bilateral filters, only the domain and range parameters, which are based on Gaussian distribution, are optimized and the neighboring radius is a constant value. Filter results on different noisy images show that the neighboring radius has a major effect on the filter performance. Since the filter designed with the GC function causes significant noise removal, this function is effective, and on the other hand, it’s almost similar behavior with the Gaussian function has caused it to be combined with the bilateral filter to design the second filter in this paper. The kernel of the domain and range is considered to be the GC function instead of the Gaussian function. The domain and range parameters and the neighboring radius are opti

关键词： Bilateral filter Gaining sharing knowledge-based optimization algorithm Generalized Cauchy filter Image de-noising Image noise particle swarm optimization algorithm Whale optimization algorithm

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A Design Method of Biplanar Uniform Field Coils Based on Magnetic Shielding Rooms 21

A Design Method of Biplanar Uniform Field Coils Based on Mag...

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IEEE 21st Biennial Conference on Electromagnetic Field Computation (IEEE CEFC)

作者： Yuan, Shuai Shi, Minxia Zhang, Leran Yang, Jianzhi Li, Teng Ma, Yuzheng Beihang Univ Sch Instrumentat & Optoelect Engn Minist Educ Key Lab Ultra Weak Magnet Field Measurement Techn Beijing Peoples R China

ISBN: (纸本)9798350348958;9798350348965

The digest presents a design method of biplanar coils to realize precise compensation for residual magnetic fields inside magnetic shielding rooms (MSR). The magnetic field generated by the biplanar coils inevitably couples with the magnetic shielding material, thereby deteriorating the uniformity of the magnetic field in the target region. Based on the coupling principle of the coils, the mirror image method is introduced into the traditional target field method. An improved particle swarm optimization (PSO) algorithm is used to obtain the coils' parameters, thus determining the shapes of the coils. The simulation results show that the B-x coil designed by this method can generate a more uniform magnetic field inside MSR.

关键词： biplanar coils magnetic shielding rooms magnetic shielding material target field method particle swarm optimization algorithm

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Two-Stage Adaptive Constrained particle swarm optimization Based on Bi-Objective Method

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IEEE ACCESS 2020年 8卷 150647-150664页

作者： Feng, Qian Li, Qing Wang, Heng Feng, Yongfeng Pan, Yichen Univ Sci & Technol Sch Automat & Elect Engn Beijing 100083 Peoples R China North China Univ Sci & Technol Coll Mech Engn Tangshan 063009 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China

For the sake of better balancing the relationship between diversity and convergence when handling constrained optimization problems, a two-stage adaptive constrained particle swarm optimization algorithm based on bi-objective method (TABC-PSO) is proposed. In accordance with different phases of the constraint process, the target-constraint space derived from the angle is partitioned adaptively, and simultaneously the global best particle is selected and the external archive set is safeguarded. In the first stage, the whole space is divided adaptively in term of the angular distribution of individual, and the feasible region is explored comprehensively. In the second stage, local regions are adaptively compartmentalized and in-depth exploitation is carried out. Primary and secondary external archive sets are established to maintain population diversity and accelerate convergence. The two phases are switched adaptively in light of the storage status of the two external archive sets. We evaluated TABC-PSO algorithm on the benchmark functions in CEC 2006 and CEC 2017. The experimental results show that TABC-PSO algorithm compared with other state-of-the-art algorithms can be superior to applied to test functions with different types of constraints and possesses a competitive search capability.

关键词： optimization particle swarm optimization Convergence Sociology Statistics Linear programming Heuristic algorithms Constrained optimization particle swarm optimization algorithm bi-objective optimization adaptive

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Multi-objective optimization of printed circuit heat exchanger with airfoil fins based on the improved PSO-BP neural network and the NSGA-II algorithm

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第6期55卷 2125-2138页

作者： Wang, Jiabing Zeng, Linlang Yang, Kun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objec-tive functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.(c) 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Multi -objective optimization Printed circuit heat exchanger Airfoil fin Design of experiment BP neural network particle swarm optimization algorithm

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