检索结果-内蒙古大学图书馆

A Single-Image Noise Estimation algorithm Based on Pixel-Level Low-Rank Low-Texture Patch and Principal Component Analysis

引用

SENSORS 2022年第22期22卷 8899页

作者： Li, Yong Liu, Chenguang You, Xiaoyu Liu, Jian Harbin Inst Technol Res Ctr Adv Microscopy & Instrumentat Harbin 150001 Peoples R China Harbin Inst Technol Res Ctr Basic Space Sci Harbin 150001 Peoples R China

Noise level is an important parameter for image denoising in many image-processing applications. We propose a noise estimation algorithm based on pixel-level low-rank, low-texture subblocks and principal component analysis for white Gaussian noise. First, an adaptive clustering algorithm, based on a dichotomy merge, adaptive pixel-level low-rank matrix construction method and a gradient covariance low-texture subblock selection method, is proposed to construct a pixel-level low-rank, low-texture subblock matrix. The adaptive clustering algorithm can improve the low-rank property of the constructed matrix and reduce the content of the image information in the eigenvalues of the matrix. Then, an eigenvalue selection method is proposed to eliminate matrix eigenvalues representing the image to avoid an inaccurate estimation of the noise level caused by using the minimum eigenvalue. The experimental results show that, compared with existing state-of-the-art methods, our proposed algorithm has, in most cases, the highest accuracy and robustness of noise level estimation for various scenarios with different noise levels, especially when the noise is high.

关键词： image processing image denoising noise level white Gaussian noise clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Free-Form Surface Partitioning and Simulation Verification Based on Surface Curvature

引用

MICROMACHINES 2022年第12期13卷

作者： Liu, Hongwei Zhang, Enzhong Sun, Ruiyang Gao, Wenhui Fu, Zheng Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

To address the problem of low overall machining efficiency of free-form surfaces and difficulty in ensuring machining quality, this paper proposes a MATLAB-based free-form surface division method. The surface division is divided into two stages: Partition area identification and area boundary determination. In the first stage, the free-form surface is roughly divided into convex, concave, and saddle regions according to the curvature of the surface, and then the regions are subdivided based on the fuzzy c-means clustering algorithm. In the second stage, according to the clustering results, the Voronoi diagram algorithm is used to finally determine the boundary of the surface patch. We used NURBS to describe free-form surfaces and edit a set of MATLAB programs to realize the division of surfaces. The proposed method can easily and quickly divide the surface area, and the simulation results show that the proposed method can shorten machining time by 36% compared with the traditional machining method. It is proved that the method is practical and can effectively improve the machining efficiency and quality of complex surfaces.

关键词： regional division free-form surface surface curvature clustering algorithm Voronoi diagrams

来源：评论

学校读者我要写书评

暂无评论

Collaborative Evaluation Method of Multiple Team Membership: Case Study of Engineering Design Company

引用

JOURNAL OF MANAGEMENT IN ENGINEERING 2022年第6期38卷

作者： Jiang, Qi Qiang, Maoshan Lin, Chen Tsinghua Univ State Key Lab Hydrosci & Engn Inst Project Management & Construct Technol Beijing 100084 Peoples R China Beijing Inst Water Dept Technol & Qual Management Landscape Architecture Beijing 100048 Peoples R China

Team members' project collaborative process evaluation is the key link to improve the management effectiveness of an organization. However, there is a lack of evaluation methods from the perspective of multiple team membership (MTM). Based on the data in the project management system (PMS) of engineering design enterprises, this article studies the project participation evaluation method of team members from the perspective of MTM. Using a clustering algorithm, this article developed and verified the participation evaluation index of single projects and multiple projects, a classification and statistics method, and the centrality evaluation method of team members. The research revealed the distribution law of MTM quantity and the influence of team members' knowledge and ability on it. We found that this index can evaluate objectively team members' project participation, which can divided into three levels: high, medium, and low. Collaborative activities that contribute significantly to the project account for 20% of the total. The reasonable range for team members to invest in high participation projects is from 2 to 4. For medium participation projects, the number is from 6 to 8. For low participation projects, the number is from 18 to 25, or even unlimited. This study found four positive and four negative effects of personal characteristics on work efficiency. When individuals have these characteristics, team members can better play to their abilities under the project management mechanism, which provides a reference for enterprises to identify and cultivate team members.

关键词： Multiple team membership (MTM) Project participation Collaborative evaluation method clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Solution of Fire Coordination Scheme of Equipment System Based on Fuzzy clustering-auction Mechanism 39

Solution of Fire Coordination Scheme of Equipment System Bas...

引用

39th Chinese Control Conference (CCC)

作者： Xu, Ke-hu Wang, Guo-sheng Army Acad Armored Force Dept Weapon & Control Beijing 100072 Peoples R China

ISBN: (纸本)9789881563903

Aiming at the problem of the large-scale weapon target distribution model, the conventional algorithms are low efficient to obtain the solution and can not obtain the fire optimization scheme untimely. Thus a new two-level task optimization distribution model is constructed, which can obtain a distribution scheme in a shorter time by combining the fuzzy clustering algorithm with the auction algorithm. The numerical example shows that the established collaborative task allocation model and the corresponding solution method can get the task allocation scheme in time and effectively, and solve the problem of the fire power optimal allocation of the large-scale weapon-target.

关键词： Collaborative task allocation clustering algorithm auction algorithm

来源：评论

学校读者我要写书评

暂无评论

Partitioning and hierarchical based clustering: a comparative empirical assessment on internal and external indices, accuracy, and time

引用

International Journal of Information Technology (Singapore) 2020年第4期12卷 1377-1384页

作者： Hassan, Syed Imtiyaz Samad, Afreen Ahmad, Omair Alam, Afshar Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard (Deemed to be University) New Delhi India

clustering is an unsupervised data mining technique where exploration is done with little knowledge of data classes. Its aim is to recognize the hidden information from the data for effective decision-making. Though many clustering algorithms has already been implemented till date, still it is an active topic of research for data mining. Researcher’s attempts to explore, compare, evaluate, and improve the different clustering algorithms available, for specialized situation and context. The purpose of all these efforts are to refine and propose improved version of algorithm after statistical evaluation by different metrices. The present research is an attempt to analysis empirically, the partitioning based clustering algorithms and hierarchical based clustering algorithm;by conducting extensive experiments. Both algorithms effectiveness has been measured through external and internal validity indices and Pearson’s correlation distance function using anatomized experiments. The parameters of evaluation that have been taken into consideration;for Internal Indices: Silhouette Index, Davies-Bouldin Validity Index and Calinski-Harabasz index;for external indices: Jaccard index, Rand Index, Entropy and Normalized Mutual Information. The other parameters of evaluation are accuracy and time of execution. Based on the experiments it may be concluded that K-means algorithm produces more promising result than hierarchical algorithm except in accuracy. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： clustering algorithm Data mining Data science Hierarchical algorithm K-means Machine learning Pearson’s correlation distance Validation indices

来源：评论

学校读者我要写书评

暂无评论

Energy and task completion time minimization algorithm for UAVs-empowered MEC SYSTEM

引用

SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2022年 35卷

作者： Asim, Muhammad Mashwani, Wali Khan Abd El-Latif, Ahmed A. Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Kohat Univ Sci & Technol Inst Numer Sci Kohat Pakistan Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci Lab Riyadh 11586 Saudi Arabia Menoufia Univ Fac Sci Dept Math & Comp Sci Al Minufiyah 32511 Egypt

This paper presents an energy and task completion time minimization scheme for the unmanned aerial vehicles (UAVs)-empowered mobile edge computing (MEC) system, where several UAVs are deployed to serve large-scale users' equipment (UEs). The aim is to minimize the weighted sum of energy consumption and task completion time of the system by planning the trajectories of UAVs. The problem is NP-hard, non-convex, non-linear, and mixed-decision variables. Therefore, it is very challenging to be solved by conventional optimization techniques. To handle this problem, this paper proposes an energy and task completion time minimization algorithm (ETCTMA) that solves the above problem in three steps. In the first step, the deployment updation of stop points (SPs) is handled by adopting a differential evolution algorithm with a variable population size. Then, in the second step, the association between SPs and UAVs is determined. Specifically, a clustering algorithm is proposed to associate SPs with UAVs. Finally, in the third step, a low-complexity tabu search algorithm is adopted to construct the trajectories of all UAVs. The performance of the proposed ETCTMA is tested on seven instances with up to 700 UEs. The results reveal that our proposed algorithm ETCTMA outperforms other variants in terms of minimizing the weighted sum of energy consumption and task completion time of the system.

关键词： Mobile edge computing Unmanned aerial vehicle Differential evolution clustering algorithm Tabu search algorithm

来源：评论

学校读者我要写书评

暂无评论

On representative day selection for capacity expansion planning of power under extreme conditions

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 137卷

作者： Li, Can Conejo, Antonio J. Siirola, John D. Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Ohio State Univ Dept Integrated Syst Engn 1971 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn 2015 Neil Ave Columbus OH 43210 USA Sandia Natl Labs Ctr Res Comp PO 5800 Albuquerque NM 87185 USA

Capacity expansion planning (CEP) of power systems determines the optimal future generation mix and/or transmission lines. Due to the increasing penetration of renewables, CEP has to capture the hourly variations of renewable generator outputs and load demand. Since CEP problems typically involve planning horizons of several years, solving the fullspace models where the operating decisions corresponding to all the days is intractable. Therefore, some "representative days"are selected as a surrogate to the fullspace model. We present an input-based and a cost-based approach in combination with the k-means and the k-medoids clustering algorithms for representative day selection. The mathematical properties of the proposed algorithms are analyzed, including an approach to calculate the "optimality gap"of the investment decisions obtained from the representative day model to the fullspace model, and the relationship between the clustering error and the optimality gap. To capture the extreme operating conditions, two novel approaches, i.e., a "load shedding cost'' approach and a "highest cost'' approach, are proposed to identify the "extreme days". We conclude with a case study based on the Electric Reliability Council of Texas (ERCOT) region, which compares the different approaches and the effects of adding the extreme days.

关键词： Power systems Capacity expansion planning Renewable units Representative day selection clustering algorithm Extreme operating conditions

来源：评论

学校读者我要写书评

暂无评论

clustering-based particle detection method for digital holography to detect the three-dimensional location and in-plane size of particles

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第5期32卷

作者： Huang, Jianqing Li, Shen Zi, Yabo Qian, Yong Cai, Weiwei Alden, Marcus Li, Zhongshan Shanghai Jiao Tong Univ Sch Mech Engn Key Lab Educ Minist Power Machinery & Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China Lund Univ Div Combust Phys POB 118 S-22100 Lund Sweden

Digital holography (DH) has been extensively applied in particle field measurements due to its promising ability to simultaneously provide the three-dimensional location and in-plane size of particles. Particle detection methods are crucial in hologram data processing to determine particle size and particle in-focus depth, which directly affect the measurement accuracy and robustness of DH. In this work, inspired by clustering algorithms, a new clustering-based particle detection (CBPD) method was proposed for DH. To the best of our knowledge this is the first time that clustering algorithms have been applied in processing holograms for particle detection. The results of both simulations and experiments confirmed the feasibility of our proposed method. This data-driven method features automatic recognition of particles, particle edges and background, and accurate separation of overlapping particles. Compared with seven conventional particle detection methods, the CBPD method has improved accuracy in measuring particle positions and displacements.

关键词： digital holography clustering algorithm particle detection data-driven approach 3D imaging

来源：评论

学校读者我要写书评

暂无评论

Cooperative caching strategy based on cluster and social interest in mobile edge network

引用

DIGITAL SIGNAL PROCESSING 2022年 127卷

作者： Wang, Weiguang Li, Hui Liu, Yang Cheng, Wei Liang, Rui Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China

Mobile edge caching can deliver contents directly without the backhaul link, which can effectively solve the problem of spectrum scarcity caused by huge mobile data traffic. In this paper, different from the existing user-centric clustering algorithms, a distributed caching algorithm is proposed based on content providers (CPs), which can form a CPs cluster as large as possible to satisfy the UE requirements. The cache capacity in the cluster formed by this algorithm is collectively used to provide higher content hit probability and diversity. Furthermore, considering the impact of social interests on the performance of caching strategies, a closed-form expression of the network hit ratio of the entire cache is derived on the basis of random geometry theory. Then, a network hit ratio maximization optimization problem is constructed and solved. The simulation results show that the proposed strategy has superior data offloading performance than other cooperative caching strategies. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Mobile edge caching network Cooperative caching clustering algorithm Social interest

来源：评论

学校读者我要写书评

暂无评论

Electricity-consuming forecasting by using a self-tuned ANN-based adaptable predictor

引用

ELECTRIC POWER SYSTEMS RESEARCH 2022年 210卷

作者： Baba, Abdullatif Univ Turkish Aeronaut Assoc Comp Engn Dept Bahcekapi Quarter Okul St 11 TR-06790 Ankara Turkey

Accurate forecasting of power consumption is an essential and modern approach for planning smart infrastructural projects that are required to overcome the future challenges of power markets. In this context, a new design of a self-tuned ANN-based adaptable predictor is presented in this paper. At first, the main design of the original adaptable predictor is clarified including its new architecture that partially relies on the Hebbian law, its training process is also explained as well as the dataset which is used for training. Then, all the details of the self-tuning-based technique are explained with all the relevant results that prove its high capability to produce more accurate forecasting outcomes. The impact of our suggested approach is introduced by explaining two different practical examples that employ the K-means clustering algorithm, and the genetic algorithm when it is used to optimize the operating/outage schedule for a group of local solar units, respectively.

关键词： Self-tuning Adaptable predictor ANN model Smart grids clustering algorithm Evolutionary algorithms

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：