检索结果-内蒙古大学图书馆

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

A New Method Of Wind Farm clustering Based On Improved Fuzzy C-Means clustering and MPSO 5

A New Method Of Wind Farm Clustering Based On Improved Fuzzy...

引用

IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Ye, Chao Peng, Qi Xu, Huawei Hu, Jianyao Shao, E. Yang, Lin MIIT Elect Res Inst 5 Cambridge MA 02139 USA

ISBN: (纸本)9781728143231;9781728143224

Affected by many factors such as irregular cluster distribution, complex terrain and wake effect, the operation conditions of each unit in large-scale doubly fed wind farm are different. In order to improve the accuracy of equivalent output model of wind farm, an improved equivalent modeling method of doubly fed wind farm is proposed. Firstly, the characteristic state variable matrix which can represent the operation state of each unit is selected as the clustering index, and the improved fuzzy c-means clustering algorithm is used to divide the cluster, then the parameters of the peer check-in model are identified based on the global optimal position variation particle swarm optimization algorithm, and finally the same group of units is equivalent to a fan. DIgSILENT simulation software is used for modeling, two dynamic conditions of wind speed step and three-phase short circuit fault. The simulation results show that the dynamic characteristics of the equivalent model are basically the same as that of the detailed model. Compared with the traditional single machine equivalent model, the accuracy of this method is higher.

关键词： Wind farm clustering clustering algorithm The particle swarm Parameter identification

来源：评论

学校读者我要写书评

暂无评论

Histogram Methods for Unsupervised clustering 20

Histogram Methods for Unsupervised Clustering

引用

ACM Southeast Conference (ACMSE)

作者： Benzel, Steven Stanescu, Ana Univ West Georgia Carrollton GA 30118 USA

ISBN: (纸本)9781450371056

A new algorithm is presented to find clusters in a dataset of points in R-n with no prior knowledge of possible clustering. The algorithm detects clusters in a top down fashion by testing modality of density functions generated from the dataset and splitting the set accordingly. Results on synthetic and text datasets demonstrate that the method is comparable to other established unsupervised learning algorithms, which do in fact require the number of clusters ab initio. The method proves to be particularly suitable for certain distributions and offers a valid alternative in situations where most of the well-known algorithms do not produce consistent results.

关键词： clustering algorithm Unsupervised Learning Density Functions Modality Testing

来源：评论

学校读者我要写书评

暂无评论

Big Data and IoT for real-time miscarriage prediction A clustering comparative study

引用

Procedia Computer Science 2021年 191卷 200-206页

作者： Hiba Asri LIMA Laboratory Computer Science Departement Ibn Zohr University Agadir Morocco

Sensors and mobile phones are becoming very useful to gather a huge amount of data rapidly and to understand the behaviour of the human. Today, the real challenge is how to benefit from the use of Big Data performant tools and machine learning algorithms to get the desired value. The present paper presents a comparative study of clustering algorithms (Kmeans, Bisecting Kmeans and Gaussian mixture) for a real time miscarriage prediction. Wearable healthcare sensors ((heart rate sensor, temperature sensor and activity sensor) and mobile phone are used for gathering real time data about the pregnant women. Sensors are managed using IoT technologies such as Raspberry Pi to collect and process data in real time. Prediction’s results are sent to the doctor through a mobile phone created and the pregnant woman receives recommendations based on her behaviour. Our study compares the performance and the efficiency of the predictive models created by the three algorithms, including time to build the model, clusters distribution and centres definition. We evaluate models using the Internal clustering validation silhouette method. The dataset generated and analyzed during the current study is available on GitHub platform via the following link: https://***/hibaasri/Miscarriage-Prediction . Databricks platform and Spark are used to analyze data and build models.

关键词： Big Data clustering algorithm Machine Learning Miscarriage Prediction

来源：评论

学校读者我要写书评

暂无评论

A Case Study for clustering Characteristics Evaluation and clustering Dimension Selection by Using UCI News Data 20

A Case Study for Clustering Characteristics Evaluation and C...

引用

4th International Conference on algorithms, Computing and Systems (ICACS) / 2nd African Electronics, Computer and Communication Conference (AECCC)

作者： Huang Chih-Chien Hsu Chung-Chian Pon YuShun Liao WenWei Natl Yunlin Univ Sci & Technol Dept Informat Management 123 Univ Rd Sect 3 Touliu Yunlin Taiwan Univ Colorado Boulder Cooperat Inst Res Environm Sci 325 Broadway Boulder CO 80305 USA

ISBN: (纸本)9781450377324

The purpose of this study is to research and explore the clustering algorithm, and provide methods for clustering characteristics evaluation and clustering dimension selection, in order to help the user to understand the impact and meaning of clustering parameters and data dimensions on clustering, thereby strengthening the use of clustering algorithm. In previous studies, many scholars have proposed various types of clustering algorithms. Most of these algorithms need to set the clustering parameters, and the selection of clustering parameters will affect the results after clustering. Therefore, the user must fully understand the meaning of clustering parameters for clustering and select appropriate clustering parameters for clustering, then the clustering algorithm can be effectively used to help solve decision-making problems. Based on the above factors, this study focus on doing further analysis and description on the meaning of the clustering data distribution & the meaning of parameters to the clusters, and the relationship among the clusters, find out the important clustering feature and propose a new clustering evaluation formula, and expect to assist the decision-maker to find appropriate clustering parameters effectively.

关键词： K-Means DBSCAN Hierarchical clustering Dendrogram clustering 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

A new Query Reformulation Approach using Web Result clustering and User Profile

引用

Procedia Computer Science 2021年 192卷 1180-1189页

作者： Abd El Heq Silem Hajer Taktak Faouzi Moussa University of Tunis El Manar Faculty of Sciences of Tunis LIPAH LR11ES14 2092 Tunis Tunisia Université de Lorraine Laboratoire LCOMS France

Query reformulation techniques are essential for information retrieval systems. These techniques eliminate the bad queries (short and ambiguous queries) and minimize users search time. There are two kinds of techniques in literature; the first uses the query logs with the click-through data to identify relevant queries and propose them to the user. The second group uses relevant terms extracted from different sources such as Wikipedia, Wordnet, and pseudo-relevant documents to expand the initial query and increase the likelihood with relevant documents. Both groups generate false queries because they do not consider the user interests and due to the assumption that the user clicks only on relevant results (first group) and that the top-k retrieved documents (top 5 to 15) are relevant (second group).In this paper, we propose a novel approach to reformulate the user queries using his profile (which contains the user interests). The approach retrieves documents related to the query from four different data sources (≈ 1000 doc). Then, it extracts the topics of these documents using the Lingo clustering algorithm (cluster the document with the same topic) and Text razor API (extract the potential topics of each cluster). Finally, it generates queries based on these topics and the user profile. The results show that the proposed approach outperforms the existing solution in [email protected] , [email protected] , and MAP.

关键词： Query expansion Query reformulation Information retrieval clustering algorithm User interests Human-computer interaction

来源：评论

学校读者我要写书评

暂无评论

An assignment-based decomposition approach for the vehicle routing problem with backhauls

引用

Multimodal Transportation 2024年第4期3卷

作者： Parviziomran, Irandokht Mahmoudi, Monireh School of Packaging College of Agriculture and Natural Resources Michigan State University East Lansing 48824 MI United States

In the context of the Vehicle Routing Problem with Backhauls, which involves delivering to linehaul and picking up from backhaul customers, we propose a novel mathematical model that can decompose the main problem into three sub-problems: two Open Vehicle Routing Problems and one Assignment Problem. In our proposed model, Open Vehicle Routing Problems optimize routes for homogeneous vehicles serving linehaul/backhaul customers, while the Assignment Problem matches linehaul and backhaul routes. We utilize Lagrangian decomposition approach and solve subproblems in parallel and sequential layouts. We measure the performance of the foregoing arrangements (in terms of solution quality and computational efficiency) by testing our model on two benchmark datasets, proposed by Goetschalckx and Jacobs-Blecha (1989) and Toth and Vigo (1997) and are known as GJ and TV datasets in the extant literature, respectively. Our model matches best known solutions in 35 % and 33 %, with most within 2 % deviation, for GJ and TV instances, respectively. We also showcase our model on a real-world transportation network containing 100, 250, and 500 customers and geographically located in Lansing, Michigan. To reduce the computational burden of solving the Vehicle Routing Problem with Backhauls on the Lansing dataset, we present a cluster first-route second algorithm and then analyze the impact of vehicle capacity on the solution quality of our proposed algorithm. © 2024 The Authors

关键词： clustering algorithm Lagrangian decomposition Open vehicle routing problem Routing Vehicle routing problem with backhauls

来源：评论

学校读者我要写书评

暂无评论

Study of Procrastination in Higher Vocational Education Based on Online Learning Data 22

Study of Procrastination in Higher Vocational Education Base...

引用

Proceedings of the 6th International Conference on Digital Technology in Education

作者： Fang Feng Mengxin Tang Wen Lei School of Information Technology in Education South China Normal University China

ISBN: (纸本)9781450398091

Academic procrastination is a common phenomenon in China's higher vocational education. Due to the weakening of the role of teacher supervisors and the lack of students' self-control, the academic procrastination of students in online learning is more likely to occur. At present, it has become a trend to use educational data mining and artificial intelligence technology to evaluate, predict and intervene in online learning, so as to solve the problem of practical teaching lag and improve the teaching effect of vocational education. In this paper, the data of "Computer Application Foundation" course of higher vocational students on Chaoxing platform is used to process the data by using K-means and DBSCAN clustering algorithms, and the performance of the two algorithms is evaluated by using the contour coefficient. The results show that the K-means algorithm has better performance. The students were divided into active learners, mild procrastinators and severe procrastinators by K-means clustering algorithm. Then, combined with decision tree (DT), neural network (NN) and Naive Bayes (NB) algorithm to verify the accuracy of K-means clustering algorithm in identifying the classification of students' procrastination tendency, this paper hopes to provide some advises for online learning procrastinators and encourage students to keep learning initiative and enthusiasm.

关键词： Academic procrastination Classification algorithm clustering algorithm Educational data mining

来源：评论

学校读者我要写书评

暂无评论

Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks

引用

JOURNAL OF SYSTEMS AND SOFTWARE 2018年 146卷 196-214页

作者： Wang, Tianshu Zhang, Gongxuan Yang, Xichen Vajdi, Ahmadreza Nanjing Univ Chinese Med Sch Informat Technol Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Normal Univ Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China

Wireless sensor networks have been employed widely in various fields, including military, health care, and manufacturing applications. However, the sensor nodes are limited in terms of their energy supply, storage capability, and computational power. Thus, in order to improve the energy efficiency and prolong the network life cycle, we present a genetic algorithm-based energy-efficient clustering and routing approach GECR. We add the optimal solution obtained in the previous network round to the initial population for the current round, thereby improving the search efficiency. In addition, the clustering and routing scheme are combined into a single chromosome to calculate the total energy consumption. We construct the fitness function directly based on the total energy consumption thereby improving the energy efficiency. Moreover, load balancing is considered when constructing the fitness function. Thus, the energy consumption among the nodes can be balanced. The experimental results demonstrated that the GECR performed better than other five methods. The GECR achieved the best load balancing with the lowest variances in the loads on the cluster heads under different scenarios. In addition, the GECR was the most energy-efficient with the lowest average energy consumed by the cluster heads and the lowest energy consumed by all the nodes. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Wireless sensor networks clustering algorithm Genetic algorithm Energy-efficiency Network life cycle Routing

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：