检索结果-内蒙古大学图书馆

Infrared small target detection based on the dynamic particle swarm optimization

INFRARED PHYSICS & TECHNOLOGY 2021年 117卷 103837-103837页

作者： Shahraki, Hadi Aalaei, Shokoufeh Moradi, Saed Univ Sistan & Baluchestan Fac Ind & Min Dept Comp Engn Khash Iran Mashhad Univ Med Sci Sch Med Dept Med Informat Mashhad Razavi Khorasan Iran Univ Isfahan Fac Engn Dept Elect Engn Esfahan Iran

In this paper, a new method is introduced for detecting small targets in infrared images. The proposed method is based on the particle swarm optimization algorithm (PSO). Considering the nature of the small target detection problem, a dynamic optimization algorithm is developed to detect targets. The proposed algorithm is called the dynamic particle swarm detector (DPSD). Unlike common small target detection algorithms, the computational complexity of the proposed algorithm is not linear to number of pixels in the input image. Therefore, it is capable of operating on a large-scale image in a relatively small timeslot. Many experiments are carried out to evaluate the effectiveness of the proposed method, where the DPSD is compared to seven well-known methods in terms of quantitative detection metrics. The results show that the proposed detector outperforms the baseline algorithms.

关键词： Artificial intelligence Infrared search and track Dynamic optimization algorithm particle swarm optimization algorithm Real-time small target detection

来源：评论

学校读者我要写书评

暂无评论

A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater

引用

ENERGY 2012年第1期38卷 406-413页

作者： Siddhartha Sharma, Naveen Varun Natl Inst Technol Dept Mech Engn Hamirpur 177005 India

This work is undertaken with an objective to develop and implement a trained particle swarm optimization (PSO) algorithm for prediction of an optimized set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The simulation is carried out based on the basis of the algorithm developed for three different cases using the climatic condition data of the city Hamirpur, India situated between (latitude) 31 degrees 25'-31 degrees 52'N and (longitude) 76 degrees 18' to 76 degrees 44' E. The final results obtained from this algorithm are compared with experimental results and found to be satisfactory as far as flexibility, speed and global convergence are concerned. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Thermal performance Solar air heater particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Study on the optimization of Support Parameters of Metro Station Constructed by Arch Cover Method

引用

GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2022年第8期40卷 4147-4157页

作者： Guo, Xinping Jiang, Hongren Jiang, Annan Dalian Maritime Univ Sch Transportat Engn Dalian 116026 Peoples R China Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China

The arch is the main stress structure of metro station in the construction of arch cover method. The preliminary geological survey has some limitations, and the arch structure design based on the survey results is usually too conservative, which increases the investment cost. Therefore, it is necessary to optimize the design parameters of arch structure. In this paper, based on particle swarm optimization (PSO) algorithm, the engineering cost is taken as the optimization objective, and the monitoring control values of displacement are taken as the constraint condition. The scheme optimization is carried out for the thickness of outer primary lining and inner primary lining and removal length of temporary support. The final optimization values of parameters obtained by PSO algorithm are that the removal length of temporary support is 18 m, the thickness of the outer primary lining is 22 cm, and the thickness of the inner primary lining is 26 cm. Compared with the original design scheme, the engineering cost of the optimized scheme is reduced by 8.79%. The optimized parameters can not only meet the safety requirements of the project, but also effectively reduce the project cost, which has guiding significance to the actual project construction.

关键词： Arch cover method Temporary support Primary lining particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Hybrid particle swarm and Grey Wolf Optimizer and its application to clustering optimization

引用

APPLIED SOFT COMPUTING 2021年 101卷 107061-107061页

作者： Zhang, Xinming Lin, Qiuying Mao, Wentao Liu, Shangwang Dou, Zhi Liu, Guoqi Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang Henan Peoples R China

Grey Wolf Optimizer (GWO) and particle swarm optimization (PSO) algorithm are two popular swarm intelligence optimization algorithms and these two algorithms have their own search mechanisms. Based on their unique search mechanisms and their advantages after the improvements on them, this paper proposes a novel hybrid algorithm based on PSO and GWO (Hybrid GWO with PSO, HGWOP). Firstly, GWO is simplified and a novel differential perturbation strategy is embedded in the search process of the simplified GWO to form a Simplified GWO with Differential Perturbation (SDPGWO) so that it can improve the global search ability while retaining the strong exploitation ability of GWO. Secondly, a stochastic mean example learning strategy is applied to PSO to create a Mean Example Learning PSO (MELPSO) to enhance the global search ability of PSO and prevent the algorithm from falling into local optima. Finally, a poor-for-change strategy is proposed to organically integrate SDPGWO and MELPSO to obtain an efficient hybrid algorithm of GWO and PSO. HGWOP can give full play to the advantages of these two improved algorithms, overcome the shortcomings of GWO and PSO and maximize the whole performance. A large number of experiments on the complex functions from CEC2013 and CEC2015 test sets reveal that HGWOP has better optimization performance and stronger universality compared with quite a few state-of-the-art algorithms. Experimental results on K-means clustering optimization show that HGWOP has obvious advantages over the comparison algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Intelligence optimization algorithm Grey Wolf Optimizer particle swarm optimization algorithm Hybrid algorithm K-means clustering

来源：评论

学校读者我要写书评

暂无评论

Two-dimensional variational mode decomposition for seismic record denoising

引用

JOURNAL OF GEOPHYSICS AND ENGINEERING 2022年第3期19卷 433-444页

作者： Zhang, Xingli Chen, Yan Jia, Ruisheng Lu, Xinming Shandong Univ Sci & Technol Coll Comp Sci & Engn Qianwan Port Rd Qingdao 266590 Peoples R China

Seismic signal denoising is the main task of seismic data processing. This study proposes a novel method for the denoising seismic record on the basis of a two-dimensional variational mode decomposition (2D-VMD) algorithm and permutation entropy (PE). 2D-VMD is a recently introduced adaptive signal decomposition method in which K and a are important decomposing parameters to determine the number of modes, and have a predictable effect on the nature of detected modes. We present a novel method to address the problems of selecting appropriate K and a values and apply these values to the proposed method. First, for a 2D seismic signal, the 2D-VMD method can decompose it into K modes with specific direction and vibration characteristics. Next, the PE value of each mode is calculated. Random noise components are eliminated according to the PE value. Finally, the signal components are reconstructed to acquire the denoised seismic signal. Experimental and simulation results indicate that the proposed method has remarkable denoising effect on synthetic and real seismic signals. We hope that this new method can inspire and help evaluate new ideas in this field.

关键词： seismic signal noise reduction two-dimensional variational mode decomposition particle swarm optimization algorithm permutation entropy

来源：评论

学校读者我要写书评

暂无评论

Modeling and Joint optimization of Security, Latency, and Computational Cost in Blockchain-based Healthcare Systems

Modeling and Joint Optimization of Security, Latency, and Co...

引用

IEEE International Conference on Communications (IEEE ICC)

作者： Li, Zukai Tian, Wei Wu, Jingjin BNU HKBU United Int Coll Dept Stat & Data Sci Zhuhai Guangdong Peoples R China Guangdong Prov Key Lab Interdisciplinary Res & Ap Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350333077

In the era of the Internet of Things (IoT), blockchain is a promising technology for improving the efficiency of healthcare systems, as it enables secure storage, management, and sharing of real-time health data collected by the IoT devices. As the implementations of blockchain-based healthcare systems usually involve multiple conflicting metrics, it is essential to balance them according to the requirements of specific scenarios. In this paper, we formulate a joint optimization model with three metrics, namely latency, security, and computational cost, that are particularly important for IoT-enabled healthcare. However, it is computationally intractable to identify the exact optimal solution of this problem for practical sized systems. Thus, we propose an algorithm called the Adaptive Discrete particle swarm algorithm (ADPSA) to obtain near-optimal solutions in a low-complexity manner. With its roots in the classical particle swarm optimization (PSO) algorithm, our proposed ADPSA can effectively manage the numerous binary and integer variables in the formulation. We demonstrate by extensive numerical experiments that the ADPSA consistently outperforms existing benchmark approaches, including the original PSO, exhaustive search and Simulated Annealing, in a wide range of scenarios.

关键词： IoT-enabled healthcare blockchain joint optimization integer programming particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimum displacement profile for the direct displacement-based design of steel moment-resisting frames

引用

STRUCTURES 2022年 44卷 323-342页

作者： Arab, Mohammadamin Riahi, Hossein Tajmir Daei, Maryam Univ Isfahan Dept Civil Engn Esfahan Iran

The direct displacement-based design (DDBD) procedure is well established for designing reinforced concrete and steel moment-resisting frames (SMRFs). However, a limited number of researches is available on optimum DDBD of SMRFs. Furthermore, the nonlinear time-history analysis response of structures designed based on this procedure is inconsistent with its initial assumptions. Design displacement profile is one of the most essential and influential parameters in the DDBD method because it can impress other design parameters. In this paper, an optimum displacement profile is proposed to improve the results of the DDBD procedure via using the particle swarm optimization (PSO) algorithm. In this regard, nine SMRFs with a different number of stories have been designed using the DDBD procedure. Nonlinear time history analyses are performed for these frames using OpenSees software. The models are subjected to a set of 20 ground motion records. Then, PSO algorithm has been used to optimize the displacement profile of the DDBD procedure to achieve uniform drift distribution along with the height of the frames. Finally, based on regression analysis, the optimum design displacement profile for SMRFs with a different number of stories is formulated. The proposed equations show an average reduction of about 20% and 40% in steel usage and design base shear of the frames, respectively, while they have uniform drift along their heights.

关键词： Direct Displacement Based Design Nonlinear Time History Analysis Design Displacement Profile particle swarm optimization algorithm Steel Moment-resisting Frame

来源：评论

学校读者我要写书评

暂无评论

Application of PSO-GWO optimization algorithm for PI optimization in permanent magnet synchronous motor 7

Application of PSO-GWO optimization algorithm for PI optimiz...

引用

2023 7th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2023

作者： Xu, Xiao Peng, Qian School of Mechanical and Automotive Engineering Xiamen University of Technology Fujian Xiamen China

ISBN: (纸本)9798400708305

A PI (proportional integral) control parameter optimization method based on particle swarm optimization improved grey wolf algorithm is proposed to address the issues of insufficient parameter adjustment accuracy, slow response speed, and poor stability in traditional PI control for permanent magnet synchronous motors (PMSM). To improve the issue of the traditional grey wolf optimization algorithm easily falling into local optima and causing a decrease in accuracy, the position update strategy of the particle swarm algorithm is applied to the position update of the grey wolf algorithm, effectively enhancing the global optimization capacity of the grey wolf algorithm. By establishing a simulation model of the PMSM vector control system and conducting hardware-in-the-loop experimental tests, the motor system response of the improved grey wolf algorithm and traditional PI control is compared and analyzed. The results show that compared to optimizing PI control parameters using traditional methods, the improved grey wolf algorithm significantly enhances the response speed, anti-interference capability, and robustness of the PMSM control system. © 2023 ACM.

关键词： Improve the Grey Wolf algorithm particle swarm optimization algorithm Permanent magnet synchronous motor PI control paramet

来源：评论

学校读者我要写书评

暂无评论

Test Case Generation for Modified Code using a Variant of particle swarm optimization (PSO) algorithm

Test Case Generation for Modified Code using a Variant of Pa...

引用

10th International Conference on Information Technology - New Generations (ITNG)

作者： Tiwari, Shailesh Mishra, K. K. Misra, A. K. MNNIT Allahabad CSED Allahabad Uttar Pradesh India

ISBN: (纸本)9780769549675

In this paper, a variant of particle swarm optimization (PSO) algorithm using modified time varying acceleration coefficients (PSO-TVAC) has been proposed and applied in creation of new test cases for modified code in regression testing. The performance of the proposed algorithm is compared with other existing PSO algorithms on five well known benchmark test functions. The experiments prove that the proposed algorithm has better performance. The test cases generated by the proposed PSO-TVAC algorithm have greater code coverage capability over the initial test cases.

关键词： regression testing particle swarm optimization algorithm test case generation

来源：评论

学校读者我要写书评

暂无评论

Optimal scheduling of wind-photovoltaic power-generation system based on a copula-based conditional value-at-risk model

引用

Clean Energy 2022年第4期6卷 550-556页

作者： Xin Ju Xiaomin Liu Shangke Liu Yangli Xiao State Grid Ningxia Economic and Technological Research Institute YinchuanNingxiaChina

Increasing the application of renewable energy in the power system is an effective way to achieve the goal of‘Dual Carbon’.At the same time,the high proportion of renewable energy connected to the grid endangers the safe operation of the power *** solve this problem,this paper proposes the application of a copula function to describe the correlation between wind power and photovoltaic power,and reduce the uncertainty of power-system operation with a high proportion of renewable *** order to increase the robustness of the model,this paper proposes the application of the conditional value-at-risk theory to construct the objective function of the model and effectively control the tail risk of power-system operation *** case analysis,it is found that the model proposed in this paper has strong practicality and economy.

关键词： renewable energy ‘Dual Carbon’targets copula-CVaR particle swarm optimization algorithm optimized scheduling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：