检索结果-内蒙古大学图书馆

Ultra-broadband low scattering metasurface utilizing mixed-elements based on phase cancellation

JOURNAL OF PHYSICS D-APPLIED PHYSICS 2020年第2期53卷 025102-025102页

作者： Yang, Jianing Huang, Cheng Song, Jiakun Ji, Chen Luo, Xiangang Chinese Acad Sci Inst Opt & Elect State Key Lab Opt Technol Nanofabricat & Microeng Chengdu 610209 Sichuan Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Recently, phase cancellation metasurface has caused much attention due to its great capability of realizing radar cross section (RCS) reduction. However, owing to the intrinsic resonance property of the meta-atom, it is still challenging to expand the RCS reduction bandwidth. Here, we present a novel strategy to design ultra-broadband low-scattering metasurface by using the mixed meta-atoms combined with the evolutionary algorithm. The mixed meta-atoms are composed of single-layer or double-layer reflected elements, and the six different unit configurations are adopted to generate various reflection phases over a wide band. The planar array theory combined with particle swarm optimization (pso) algorithm is employed to obtain the optimal mixed meta-atoms and their arrangement. The proposed low-scattering metasurface can achieve more than 10 dB RCS reduction between 5 and 18 GHz with a fractional bandwidth of 113% under normal incidence for both polarizations. The experimental results agree well with the simulated ones, and its bandwidth of the 10 dB RCS reduction reaches about 105.3%. In addition, our metasurface is still demonstrated to have the wide-angle stability for low-scattering property. The proposed design provides a feasible way to achieve ultra-broadband RCS reduction, which may find potential applications in electromagnetic fields.

关键词： low scattering ultra-broadband mixed elements pso algorithm metasurface

来源：评论

学校读者我要写书评

暂无评论

Time-energy Optimal Trajectory Planning for Collaborative Welding Robot with Multiple Manipulators

引用

Procedia Manufacturing 2020年 43卷 527-534页

作者： Xuemei Liu Chengrong Qiu Qingfei Zeng Aiping Li Nan Xie School of Mechanical Engineering Tongji University Shanghai 210804 China

In order to minimize energy consumption and running time while ensuring the smoothness of collaborative welding robot with multiple manipulators, a methodology for optimal trajectory planning is proposed. The trajectory is interpolated in each joint space by means of cubic B-spline and motion time nodes are optimized based on particle swarm optimization(pso) algorithm. The simulation results show that the proposed trajectory planning method provides an ideal trajectory for the joint controller, so that the robot can smoothly complete the welding task with less energy and time consumption.

关键词： Multiple manipulators Time-energy optimal trajectory Cubic B-spline pso algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

引用

SENSORS 2020年第21期20卷 6378.-6378.页

作者： Karim, Ahmad M. Kaya, Hilal Guzel, Mehmet Serdar Tolun, Mehmet R. Celebi, Fatih V. Mishra, Alok AYBU Comp Engn Dept TR-06830 Ankara Turkey Ankara Univ Comp Engn Dept TR-06830 Ankara Turkey Konya Food & Agr Univ Comp Engn Dept TR-42080 Konya Turkey Molde Univ Coll Specialized Univ Logist Fac Logist N-6402 Molde Norway Atilim Univ Software Engn Dept TR-06830 Ankara Turkey

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (pso) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The pso algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.

关键词： deep sparse auto-encoders medical diagnosis linear model data classification pso algorithm

来源：评论

学校读者我要写书评

暂无评论

Modeling and sensitivity analysis of the alkylphenols removal via moving bed biofilm reactor using artificial neural networks: Comparison of levenberg marquardt and particle swarm optimization training algorithms

引用

BIOCHEMICAL ENGINEERING JOURNAL 2020年 161卷 107685-107685页

作者： Mohammadi, Farzaneh Bina, Bijan Karimi, Hossein Rahimi, Somayeh Yavari, Zeynab Isfahan Univ Med Sci Sch Hlth Dept Environm Hlth Engn Esfahan Iran Isfahan Univ Med Sci Environm Res Ctr Res Inst Primordial Prevent Noncommunicable Dis Esfahan Iran Islamic Azad Univ Dept Environm Hlth Firoozabad Branch Firoozabad Iran Shahid Sadoughi Univ Med Sci Genet & Environm Adventures Res Ctr Sch Abarkouh Paramed Yazd Iran

Alkylphenols (APs) are nonionic surfactants with toxic and estrogenic properties. APs from municipal and industrial wastewater are frequently detected in surface waters. Therefore, a broadly accepted method for the treatment of APs is needed. The moving-bed bioreactor (MBBR) is an effective process for micropollutant elimination. In this study, the modeling of 4-nonylphenol (4-NP) and 4-tert-octylphenol (44-OP) removal from synthetic wastewater using MBBR was performed. Also, a comparison was made between the multilayer perceptron artificial neural network (MLPNN) trained with the traditional Levenberg Marquardt (LM) and the particle swarm optimization (pso) algorithms. The performance of MBBR in removing chemical oxygen demand (COD) and APs was predicted using the COD surface area loading rate (SALR), COD volumetric loading rate (VLR), hydraulic retention time (HRT), and the initial concentration of APs. The results showed that the best transfer functions are Tan-sigmoid in the hidden layer and Purelin in the output layer. The number of optimal neurons was 5:9:3 for LM and 5:11:3 for pso. Moreover, the network trained with pso algorithm was slightly more predictive (R = 0.9997 MSE = 2.526e-5, MAE = 0.0041) than the traditional LM algorithm (R = 0.9989, MSE = 2.582e-5, MAE = 0.0043), especially by increasing the number of neurons. Finally, a sensitivity analysis was performed using ANN-pso and Pearson correlation, and the results were completely compatible.

关键词： Alkylphenols Wastewater Artificial neural networks pso algorithm

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of IoT-Based Health and Environment WSN Deployment

引用

SENSORS 2020年第20期20卷 5923.-5923.页

作者： Shakeri, Maryam Sadeghi-Niaraki, Abolghasem Choi, Soo-Mi Islam, S. M. Riazul KN Toosi Univ Technol Fac Geodesy & Geomat Engn Geoinformat Technol Ctr Excellence Tehran 19697 Iran Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees algorithm (BA) and Particle Swarm Optimization (pso) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the pso algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for pso and 0.0375 for BA. The pso also had lower complexity than BA. Therefore, the pso algorithm obtained better performance for IoT-based sensor network deployment.

关键词： wireless sensor network deployment health and environment applications IoT Bees algorithm pso algorithm coverage lifetime Minimum Spanning Tree

来源：评论

学校读者我要写书评

暂无评论

Prediction of Air Quality Index Based on Wavelet Transform Combination Model 11

Prediction of Air Quality Index Based on Wavelet Transform C...

引用

11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Zhu, Yiwendi Zhou, Xiaofeng Hohai Univ Coll Comp & Informat Nanjing Peoples R China

ISBN: (纸本)9781728118598

In the prediction of air quality index, atmospheric space is a large and complex system with many interfering factors, which leads to the nonlinearity, time-variability and uncertainty of AQI time series. This paper takes the data provided by Nanjing environmental protection testing center as the research ***, wavelet analysis was used to preprocess the AQI time *** ARIMA model and LS-SVM model are used to predict the linear autocorrelation part and the nonlinear part. pso algorithm is used to optimize the parameters of LS-SVM model. Simulation experiments show that, after selecting the appropriate wavelet decomposition function and parameters of the combined model, the combined model does reduce errors compared with the traditional ARIMA model and LS-SVM model, and has higher accuracy. It provides a new way to forecast the actual air quality index.

关键词： AQI time series wavelet transform ARIMA pso algorithm LS-SVM

来源：评论

学校读者我要写书评

暂无评论

pso (Particle Swarm Optimization) for Interpretation of Magnetic Anomalies Caused by Simple Geometrical Structures

引用

PURE AND APPLIED GEOPHYSICS 2018年第10期175卷 3539-3553页

作者： Essa, Khalid S. Elhussein, Mahmoud Cairo Univ Dept Geophys Fac Sci PO 12613 Giza Egypt

A new efficient approach to estimate parameters that controlled the source dimensions from magnetic anomaly profile data in light of pso algorithm (particle swarm optimization) has been presented. The pso algorithm has been connected in interpreting the magnetic anomaly profiles data onto a new formula for isolated sources embedded in the subsurface. The model parameters deciphered here are the depth of the body, the amplitude coefficient, the angle of effective magnetization, the shape factor and the horizontal coordinates of the source. The model parameters evaluated by the present technique, generally the depth of the covered structures were observed to be in astounding concurrence with the real parameters. The root mean square (RMS) error is considered as a criterion in estimating the misfit between the observed and computed anomalies. Inversion of noise-free synthetic data, noisy synthetic data which contains different levels of random noise (5, 10, 15 and 20%) as well as multiple structures and in additional two real-field data from USA and Egypt exhibits the viability of the approach. Thus, the final results of the different parameters are matched with those given in the published literature and from geologic results.

关键词： Magnetic anomaly pso algorithm the depth RMS

来源：评论

学校读者我要写书评

暂无评论

A New Optimization algorithm and Its Comparison on Traditional Optimization algorithms 38

A New Optimization Algorithm and Its Comparison on Tradition...

引用

38th Chinese Control Conference (CCC)

作者： Yin Yumin Liao Bolin Li Shuai Jishou Univ Coll Informat Sci & Engn Jishou 416000 Hunan Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9789881563972

Bio-inspired algorithm is a kind of algorithm proposed by simulating biological evolution or social behavior in nature. This kind of algorithm can solve many complex optimization problems, such as assignment problem, knapsack problem, travel path problem and so on, which are difficult to solve by traditional methods. Presently, bio-inspired algorithms have received a significant amount of attention in both academic and engineering societies. This paper presents a new bio-inspired algorithm, termed beetle antennae search (BAS) algorithm. First, inspired by the foraging behavior of beetles, the principle of BAS algorithm is presented. Then, two application examples are conducted to evaluate the effectiveness of BAS algorithm. Importantly, to substantiate the advantage of BAS algorithm, abundant comparative experiments are provided. Experiment results reveal that the BAS algorithm is superior to the other algorithms.

关键词： Bio-inspired algorithm Optimization solution BAS algorithm pso algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Placement of Capacitor Using Particle Swarm Optimization 13th

Optimal Placement of Capacitor Using Particle Swarm Optimiza...

引用

13th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS)

作者： Shahir, Farzad Mohammadzadeh Babaei, Ebrahim Sadikoglu, Fahreddin Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Near East Univ Fac Engn TR-99138 Nicosia North Cyprus Turkey

ISBN: (纸本)9783030041649

In this paper, a research regarding finding an optimal place for installing the capacitor under particle swarm optimization (pso) has been presented in radial networks by considering the size and price of different capacitors. In this paper, the calculations and results for a typical radial network have been calculated by considering harmonic distortion calculation (HDF) and its results have been stated. Based on this, all the losses have been calculated and compared too. The results of this study have been calculated by MATLAB software.

关键词： Capacitor Placing capacitor pso algorithm Radial network

来源：评论

学校读者我要写书评

暂无评论

Segmentation of Tissues from MRI Biomedical Images Using Kernel Fuzzy pso Clustering Based Level Set Approach

引用

CURRENT MEDICAL IMAGING 2018年第3期14卷 389-400页

作者： Ramudu, Kama Babu, Tummala Ranga Acharya Nagarjuna Univ Dept Elect & Commun Engn Guntur India Acharya Nagarjuna Univ RVR&JC Coll Engn Dept ECE Guntur India

Background: We propose a novel global region based method for segmentation of biomedical images with a fast level set model using Kernel Fuzzy Particles Swarm Optimization (KFpso) clustering. In the field of medical study, segmentation of biomedical images is one of the most important approaches for the diagnosis of a patient. Segmentation of these images is still a tedious task and cumbersome due to weak contrast and poor resolution of images etc. The automatic segmentation of such images is very difficult. The main reason is a large amount of inhomogeneity present in the background and foreground of real world image. The conventional methods like C-V model and Distance Regularized Level Set (DRLS) method lead to getting improper segmentation with unconvinced results. Methods: We proposed an efficient segmentation method for MRI biomedical images with level set approach using KFpso clustering. In the pre-processing step, Kernel Fuzzy C-Means (KFCM) clustering is combined with pso Initialization algorithm called KFpso clustering for improving the clustering efficiency. In KFCM clustering algorithm, the initial cluster center is chosen randomly, and with the help of pso, the optimal cluster centers are chosen. Results: The Membership Function (MF) of this algorithm is less sensitive to noise and consideration of the spatial information. In the post processing step, the resulting Kernel fuzzy pso clustering is modified to the level set model for faster and accurate segmentation. Conclusion: The Proposed segmentation results are effective, superior and accurate compared to conventional methods. This new approach is very helpful for accurate detection of the white matter and gray matter, cancerous cells in brain and bone images.

关键词： Biomedical images image segmentation KFCM clustering level sets pso algorithm DRLS

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：