检索结果-内蒙古大学图书馆

2nd International Conference on Function Materials and Nanotechnology (FMN 2013)

作者： Shan, Huilin Zhang, Yinsheng Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China

ISBN: (纸本)9783037858059

This paper presents principles of a down-converted mixer for four sub-harmonic and proposes a particle swarm optimization algorithm as a global search algorithm, and the performance equation is used as the assessment of the mixer circuit optimization method. Dielectric substrate adopts Electronic Materials with RF/Duroid 5880 whose dielectric constant is 2.20 and 5mil in thickness. The optimization algorithm can quickly get optimal results. The simulation results show that this mixer achieves higher 1 dB compression point, loss of frequency conversion which is less than 15 dB and good linearity.

关键词： particle swarm optimization algorithm Sub-harmonic mixer down-converted mixer

来源：评论

学校读者我要写书评

暂无评论

Research on Optimal Hydroelectric Power Generation Balance Problem Based on particle swarm optimization 3

Research on Optimal Hydroelectric Power Generation Balance P...

引用

3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Zhang, Xinyu Huaqiao University School of Information Science and Engineering Xiamen China

ISBN: (纸本)9798350318180

In this paper, we established an optimization model based on particle swarm optimization and calculated the hydroelectric conversion efficiency of Lake Mead and Lake Powell with the height of water level under the constraint conditions by particle swarm optimization to provide a reference for developing a reasonable water allocation plan. © 2023 IEEE.

关键词： particle swarm optimization algorithm Water allocation water level

来源：评论

学校读者我要写书评

暂无评论

Parameter Estimation of Photovoltaic Cell Model Based on Improved particle swarm optimization 3

Parameter Estimation of Photovoltaic Cell Model Based on Imp...

引用

2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Yang, Bofei Zhao, Shuke Zhi, Haijie Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China Karamay Karamay China

ISBN: (纸本)9798350341546

In the process of photovoltaic system converting solar energy into electric energy, the PV model is established. The parameters of the model have a large impact on the conversion performance and efficiency. For the purpose of accurate identification of circuit model parameters, an improved optimization algorithm based on particle swarm optimization is adopted, which combines the optimization strategy and hybrid algorithm proposed in recent years, It makes up for the fact that traditional particle swarm algorithms tend to be stuck in local optimal solutions and search efficiency decreases in multi-dimension. such as easy to fall into the local optimal solution and the decline of search efficiency in multi-dimensional. The improved algorithm is applied to the parameter identification of PV models with a single diode, and the accuracy of the algorithm is verified. The original particle swarm optimization algorithm and the optimization algorithm are simulated and the difference between the simulation data and the experimental data is calculated. The experiment shows that the Root-mean-square deviation (RMSE) of the optimized algorithm is far less than that of the original particle swarm optimization algorithm, the measurement results of current and voltage are closer to the true values, and the parameter identification is more accurate. Satisfied the needs of practical applications. © 2023 IEEE.

关键词： Parameter identification particle swarm optimization algorithm Single diode model

来源：评论

学校读者我要写书评

暂无评论

Hybrid Lithology Identification Method Based on Isometric Feature Mapping Manifold Learning and particle swarm optimization-Optimized LightGBM

引用

PROCESSES 2024年第8期12卷 1593页

作者： Wang, Guo Deng, Song Xu, Shuguo Li, Chaowei Wei, Wan Zhang, Haolin Li, Changsheng Gong, Wenhao Pan, Haoyu SINOPEC Res Inst Petr Engn Co Ltd Beijing 100000 Peoples R China Changzhou Univ Coll Petr Engn Changzhou 213000 Peoples R China

Accurate identification of lithology in petroleum engineering is very important for oil and gas reservoir evaluation, drilling decisions, and petroleum geological exploration. Using a cross-plot to identify lithology only considers two logging parameters, causing the accuracy of lithology identification to be insufficient. With the continuous development of artificial intelligence technology, machine learning has become an important means to identify lithology. In this study, the cutting logging data of the Junggar Basin were collected as lithologic samples, and the identification of argillaceous siltstone, mudstone, gravel mudstone, silty mudstone, and siltstone was established by logging and logging parameters at corresponding depths. Aiming at the non-equilibrium problem of lithologic data, this paper proposes using equilibrium accuracy to evaluate the model. In this study, manifold learning is used to reduce logging and logging parameters to three dimensions. Based on balance accuracy, four dimensionality reductions including isometric feature mapping (ISOMAP), principal component (PCA), independent component (ICA), and non-negative matrix factorization (NMF) are compared. It is found that ISOMAP improves the balance accuracy of the LightGBM model to 0.829, which can effectively deal with unbalanced lithologic data. In addition, the particle swarm optimization (PSO) algorithm is used to automatically optimize the super-parameters of the lightweight gradient hoist (LightGBM) model, which effectively improves the balance accuracy and generalization ability of the lithology identification model and provides strong support for fast and accurate lithology identification.

关键词： lithology identification manifold learning lightweight gradient hoist particle swarm optimization algorithm disequilibrium

来源：评论

学校读者我要写书评

暂无评论

Design of College Community Management and Service System Founded on particle swarm optimization Arithmatic in Cloud Platform 3

Design of College Community Management and Service System Fo...

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wang, Ke Liaoning Normal University Dalian Department of Education Liaoning China College of Food Industry Shandong Institute of Commerce and Technology Jinan Shandong China

ISBN: (纸本)9798350317022

College student community has increasingly become an important force to promote and prosper campus culture, and student community has a good appeal among the student masses. The quality of student community work directly determines the level of student education management. College student community leaders need high ability and level to make good use of the community and serve the community well. The management service system of student community based on particle swarm optimization algorithm can effectively improve the management level and service level of student community. The positioning of the college student community should not only be limited to the cultivation of students' knowledge and ability exercise, but should be scientifically positioned according to its own characteristics, combined with the needs of society and reality, and actively expand new functions. Provide necessary financial and technical support for associations, provide necessary guarantees for student associations, and encourage associations to actively obtain financial support by means of awards and allocations. The college can proceed from the actual situation, gradually plan and build the activity venue of student associations, equip corresponding activity equipment, use existing resources, provide services for associations, and improve resource utilization. © 2023 IEEE.

关键词： campus culture college community particle swarm optimization algorithm resource utilization service system

来源：评论

学校读者我要写书评

暂无评论

The particle swarm optimization multi-kernel relevance vector machine for remaining useful life prediction of lithium-ion batteries 6

The particle swarm optimization multi-kernel relevance vecto...

引用

6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Tian, Qiancheng Liu, Xiping Ding, Shuai Chen, Haitao Huang, Jun Yang, Ziwei State Key Lab Space Power Sources Technol Shangha Peoples R China Shanghai Inst Space Power Sources Shanghai Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

The long life of spacecraft has put forward higher requirements for the prediction of the remaining useful life (RUL) of the lithium-ion battery, and the prediction method based on relevance vector machine (RVM) has also received extensive attention. The kernel function is the main factor affecting the prediction of the RVM. Single-kernel RVM has only one kernel function, and the RUL prediction effect is ordinary. Multi-kernel RVM has many types of kernel functions, but the weight coefficient of the kernel function is relatively difficult to determine. Therefore, this paper proposes a multi-kernel RVM based on a particle swarm optimization (PSO) algorithm to predict the RUL of the lithium-ion battery. Firstly the capacity degradation data of the lithium-ion battery is reconstructed in phase space. Then Gaussian, polynomial, Sigmoid, and linear kernel functions are used to establish a multi-kernel RVM model. Finally, the particle swarm optimization algorithm is used to perform parameter self-optimization. Taking the lithium-ion battery capacity degradation data set as an example, according to the prediction evaluation index, the PSO multi-kernel RVM prediction method is better than the single-kernel and multi-kernel RVM prediction method. This method increases the types of kernel functions, solves the problem of difficulty in determining the weight coefficients of the kernel function, and improves the accuracy of predicting the RUL of the lithium-ion battery.

关键词： Relevance vector machine particle swarm optimization algorithm The phase of space reconstruction Lithium-ion battery Remaining useful life prediction

来源：评论

学校读者我要写书评

暂无评论

Research on identification of unbalance parameters of rotor with multi-plane using improved particle swarm optimization

引用

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2021年第2期43卷 1-12页

作者： Yun, Xialun Pang, Zhekai Jiang, Gedong Mei, Xuesong Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710048 Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Intelligent Robots Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Based on Bentley RK4 test rig, the method of nonlinear particle swarm optimization (PSO) is used to solve inverse problem instead of previous methods, which reduces identification complexity and avoids the ill posed problem caused by the singular equation in the process of solving. This paper realized model-based standard particle swarm optimization (SPSO) algorithm after modelling of the finite element model of rotor system and creating the optimization variables and individual fitness function, and then established asynchronous adaptive particle swarm optimization with optimizing speed weight and search scope. In order to further improve algorithm accuracy, chaos weighted particle swarm optimization and double chaos particle swarm optimization (DCPSO) are established. The simulation results of four methods show that DCPSO can realize accurate identification of unbalanced parameters with an average error of 2.86%. The effectiveness and rationality of this algorithm are verified from the speed of 240-3000 r/min. Particularly, at 2040 r/min, the percentage of amplitude decrease in two measuring points is 94.07% and 95.93%, which has achieved excellent vibration suppression effect.

关键词： Multi-plane rotor system Finite element model particle swarm optimization algorithm Identification of unbalanced parameters

来源：评论

学校读者我要写书评

暂无评论

ADVANTAGES OF THERMAL INDUSTRY CLUSTER AND APPLICATION OF particle swarm optimization MODEL

引用

THERMAL SCIENCE 2021年第2期25卷 977-987页

作者： Chen, Yiwei Song, Xiaoli Bai, Yakai Hu, Chunyu Southeast Univ Sch Econ & Management Nanjing Peoples R China Zhejiang Agr & Forestry Univ Jiyang Coll Sch Humanities Hangzhou Zhejiang Peoples R China Chinese Acad Sci Beijing Peoples R China Southeast Univ Coll Civil Engn Nanjing Peoples R China

This paper first analyzes that the waste heat before the flue gas of thermal power company enters the desulfurizationwer can be heated by the low pressure economizer, and then the heated condensate can be incorporated into the regenerative system, so as to recover the waste heat and improve the unit efficiency. In this paper, the possible lay-out scheme of the low pressure economizer in the thermal system is constructed firstly, and then the mathematical model of the equivalent enthalpy drop increment of the unit is established with the water partition coefficient as the independent variable. particle swarm optimization and co-ordinate rotation are used to optimize the model, and the results show that particle swarm optimization is more effective in solving the problem. After the low pressure economizer is added to the unit, the heat consumption of the whole plant of the unit is reduced by 61.131, the unit efficiency is increased by 0.783%, and the coal consumption of the whole plant is reduced by 2.293. Finally, from the perspective of industrial agglomeration, industrial diversification and economic resilience, this paper analyzes the advantages of diversified agglomeration of thermal power companies.

关键词： flue gas waste heat low pressure economizer economic resilience particle swarm optimization algorithm industrial agglomeration industrial diversification

来源：评论

学校读者我要写书评

暂无评论

Contactless Heart Rate and Heart Rate Variability Estimation Using Palm Images with particle swarm optimization and Independent Component Analysis algorithms

引用

SENSORS AND MATERIALS 2024年第11期36卷 4757-4774页

作者： Su, Te-Jen Lin, Wei-Hong Yang, Wen-Rong Hung, Ya-Chung Zhuang, Qian-Yi Wang, Shih-Ming Tseng, Li-chin Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung 80782 Taiwan Cheng Shiu Univ Dept Comp Sci & Informat Engn Kaohsiung 833301 Taiwan

Since the coronavirus disease 2019 (COVID-19) pandemic, some patients with COVID-19 have experienced abnormal heart rates, posing potential health risks. In this study, we develop a noncontact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in achieving accurate diagnosis and treatment. In this research, we collected data from 20 experimental testers based on palm images captured from photoplethysmography signals and measuring HR and HRV data by combining intelligent algorithms, namely, separation methods by particle swarm optimization and independent component analysis signal. The proposed method's new contactless measurement performance can effectively eliminate infection concerns and obtain HR and HRV rapidly and handily. Moreover we provide higher accuracies for physiological parameters, namely, root mean square error (2.00 bpm), mean absolute percentage error (1.5%), and measurement time (8 s), than those in recently published literature.

关键词： particle swarm optimization algorithm heart rate heart rate variability independent component analysis contactless measurement

来源：评论

学校读者我要写书评

暂无评论

Prediction of yarn strength based on an expert weighted neural network optimized by particle swarm optimization

引用

TEXTILE RESEARCH JOURNAL 2021年第23-24期91卷 2911-2924页

作者： Zhang, Baowei Song, Jiuxiang Zhao, Suna Jiang, Hao Wei, Jingdian Wang, Yonghua Zhengzhou Univ Light Ind Zhengzhou 450002 Henan Peoples R China

Aiming at solving the problem that existing artificial neural networks (ANNs) still have low accuracy in predicting yarn strength, this study combines traditional expert experience and an ANN to propose a hybrid network, named the expert weighted neural network. Many studies have shown that it is reliable to predict yarn strength based on ANN technology. However, most ANN training models face with problems of low accuracy and easy trapping into their local minima. The strength prediction of traditional yarns relies on expert experience. Obvious expert experience can help the model perform preliminary learning and help the algorithm model achieve higher accuracy. Therefore, this study proposes a neural network model that combines expert weights and particle swarm optimization (PSO). The model uses PSO to optimize the weights of experts and investigates its effectiveness in yarn strength prediction.

关键词： Yarn strength expert weights artificial neural network expert weighted neural network particle swarm optimization algorithm

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：