检索结果-内蒙古大学图书馆

Research on Adaptive Intelligent Decision algorithm in Industry 4.0 Digital Economy and Management Transformation Based on clustering algorithm

引用

TEHNICKI VJESNIK-TECHNICAL GAZETTE 2024年第3期31卷 870-880页

作者： Zhang, Yanzhi Zhuang, Guoping Lv, Haiyang Li, Qinghua Yantai Nanshan Univ Coll Technol & Data Yantai 265713 Peoples R China Fushan 1 Senior High Yantai 265599 Peoples R China Yantai Nanshan Univ Coll Econ & Management Longkou 265713 Shandong Peoples R China

How to transform the business model of enterprises and make it more in line with the new requirements of industrial upgrading is an important part of the government's supply-side reform in the era of Industry 4.0. For the current research on the good management transformation of digital economy in the fourth Industrial revolution, linear function and four commonly used nonlinear function models are first selected to test the good management transformation of digital economy in different technologies, and the key success factors of the strategic transformation from traditional industry to Industry 4.0 business model are explored based on fuzzy hierarchical analysis. Logistic and Gompertz models were used to judge the life cycle stage of the hot technologies of the fourth Industrial revolution, and based on their development trend clustering, the hot technology groups of the fourth Industrial revolution were explored. Secondly, k-means clustering algorithm based on the optimal class center perturbation is proposed, and simulation experiments are carried out. A clustering algorithm based on k-means algorithm is designed, and the moving mode of k-means in the algorithm is changed, and a disturbance strategy is added to strengthen the adaptive intelligent decision-making ability of the algorithm. K-means clustering algorithm based on the optimal class center disturbance is proposed, and simulation experiments are conducted. At the same time, the values of step size factors in the algorithm are compared experimentally. Finally, it explores the function mechanism of digital economy, management transformation and management transformation in the operation of supply chain node enterprises. By analyzing the current situation and problems of digital operation of logistics and supply chain enterprises, it constructs the digital operation system of supply chain enterprises, and proposes the path of digital transformation and upgrading of supply chain enterprises, so as to promot

关键词： adaptive intelligent decision making clustering algorithm digital economy industry 4.0 management transformation

来源：评论

学校读者我要写书评

暂无评论

Improvement and application of information communication technology in wireless routing protocol based on adaptive K-means clustering algorithm

引用

WIRELESS NETWORKS 2024年第6期30卷 5997-6009页

作者： Chen, Zili Pan, Yanyan Fujian Chuanzheng Commun Coll Inst Gen Aviat Ind Fuzhou 350007 Peoples R China

With the continuous development of information and communication network technology and data mining technology, an ICT routing algorithm based on improved k-means clustering algorithm and gray wolf optimization is proposed to improve the energy efficiency and extend the life cycle of wireless communication networks. Firstly, we model the atmospheric channel, design the topology of the tolerant network and optimize the routing algorithm to achieve the interoperability between multiple networks and break through the traditional network requirements for time and space constraints. Then we implement an IPv6-based soft router supporting Anycast routing protocol under Linux environment and propose a new Anycast routing implementation scheme. Finally, an ICT routing algorithm (KGRA algorithm) based on improved k-means clustering and gray wolf optimization is designed, which first clusters the sensor nodes in the monitoring area using the improved k-means clustering algorithm. The clusters formed by the improved k-means clustering algorithm are optimized, and then the grey wolf optimization algorithm with improved convergence factor is used to select the cluster heads in the optimized cluster area. Finally, the nodes in the monitoring area will send data to the aggregation nodes using single-hop intra-cluster and multi-hop inter-cluster transmission, thus increasing the survival period of the network and greatly reducing energy consumption while balancing the energy consumption of the network.

关键词： Information communication K-means clustering algorithm Wireless routing To improve the application

来源：评论

学校读者我要写书评

暂无评论

Security Improvement of UAV Group IoT in Polygonal Area Based on Similarity clustering algorithm

引用

JOURNAL OF TESTING AND EVALUATION 2024年第3期52卷 1775-1788页

作者： Yan, Xiang Xu, Jia Northwest Univ Sch Informat Sci & Technol 1 Xuefu AveGuodu Educ Technol Ind Pk Xian 710000 Peoples R China Northwestern Polytech Univ Sch Elect & Informat 1 Xuefu AveGuodu Educ Technol Ind Pk Xian 710000 Peoples R China

Currently, unmanned aerial vehicle (UAV) group Internet of Things (IoT) has poor stability, poor endurance, and low security. In this regard, we report a polygon area UAV group IoT clustering algorithm for large-scale, high-speed mobile IoT environment. First, the UAV group IoT was divided into a polygon area and a few cluster heads were used to cover the polygon area uniformly. Then, the clustering index in the maximum speed similarity clustering algorithm was induced into the weighted clustering algorithm. Simultaneously, the link retention rate, relative node degree, and node residual energy ratio were improved to select the IoT node with the maximum weight as the cluster head. Finally, the IoT security was improved by reputation calculation. The results show that the clustering algorithm in this paper can reduce the number of clusters and lower the handover rate between clusters, improve the stability of clustering, prolong the survival time of the minimum node, improve the overall endurance of the IoT, and have high security.

关键词： polygon area unmanned aerial vehicle Internet of Things clustering algorithm weighting

来源：评论

学校读者我要写书评

暂无评论

Optimization Technology of Combustion Engine Control Based on Swarm Intelligent Optimization algorithm and Improved clustering algorithm

引用

IEEE ACCESS 2024年 12卷 121596-121609页

作者： Wang, Jiadong Zhang, Ming Li, Jinfeng Guoneng Beijing Gas Fired Cogenerat Co Ltd Beijing 100000 Peoples R China

Since control instructions are the fundamental component of thermal power generation, the quality and effectiveness of implementation directly affect the efficiency of the energy system. In order to improve the efficiency of internal combustion engine control, an optimization method of internal combustion engine control based on enhanced clustering algorithm and swarm intelligence optimization algorithm is proposed. The process simplifies the main structure of the gas turbine, divides the combustion engine model into multi-input single-output systems, and introduces the artificial bee colony algorithm to optimize the parameters. A new nectar search formula is constructed by using the global optimal nectar, and the control parameters are calculated by fuzzy logic clustering. The experimental results showed that the modeling error of the load model of internal combustion engine was in the range of -0.47 MW similar to 0.51 MW. When the training iteration speed was tested, the loss value of the research method dropped rapidly in the first 10 iterations. When analyzing the change of the control quantity during load change, if the exhaust flow rate was taken as the control quantity, the control results of the research method was always kept within 5lbm/s error. It demonstrates that this research method can effectively improve the running quality of the internal combustion engine and has a good running efficiency. The research can provide certain technical reference for gas turbine control in thermal power generation.

关键词： Combustion engine control swarm intelligence optimization algorithm clustering algorithm fuzzy logic fireworks algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on Multidimensional Power Big Data clustering algorithm Based on Graph Mode

引用

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2025年第2期29卷 358-364页

作者： Han, Xue Zhang, Yue Gao, Sheng State Grid East Inner Mongolia Informat & Telecomm Hohhot 010010 Inner Mongolia Peoples R China Chinese Acad Sci SICT Shenyang Inst Comp Technol Co Ltd Shenyang 110000 Liaoning Peoples R China

Power system data possess many characteristics and indicators, having certain high dimensions and redundant information, which can easily increase the calculation and storage overhead. To reduce the dimension of power data, eliminate redundant information, and reduce the delay time, a data clustering algorithm is proposed. Firstly, an algorithm based on PCA and kernel local Fisher identification is used to reduce the dimension of large multidimensional samples and enhance the accuracy of subsequent clustering. Thereafter, the redundant data are processed after dimension reduction is processed to optimize the data quality by introducing a bloom filter structure. In the graph model, data clustering is completed based on the parallel processing of redundant data. Simulation results show that the correctness and stability of this method are over 85%, and the delay time is decreased, representing good application prospects.

关键词： graph model multidimension power big data clustering algorithm fisher discriminant

来源：评论

学校读者我要写书评

暂无评论

Linear Diophantine Fuzzy clustering algorithm Based on Correlation Coefficient and Analysis on Logistic Efficiency of Food Products

引用

IEEE ACCESS 2024年 12卷 34889-34902页

作者： Kannan, Jeevitha Jayakumar, Vimala Saeed, Muhammad Alballa, Tmader Khalifa, Hamiden Abd El-Wahed Gomaa, Heba Ghareeb Alagappa Univ Dept Math Karaikkudi 630003 India Univ Management & Technol Dept Math Lahore 54000 Punjab Pakistan Princess Nourah bint Abdulrahman Univ Coll Sci Dept Math POB 84428 Riyadh 11671 Saudi Arabia Qassim Univ Coll Sci Dept Math Buraydah 51452 Saudi Arabia Cairo Univ Fac Grad Studies Stat Res Dept Operat & Management Res Giza 12613 Egypt Inst Management Informat Syst Suez 43332 Egypt

The significance of clustering algorithms lies in their ability to distinguish problems and devise customized solutions. In the broader context of clustering, fuzzy clustering is one of the crucial aspects. In response to the real-world clustering problems, this research suggests a new fuzzy cluster scheme of data under the linear diophantine fuzzy set(LDFS) framework. More precisely, LDF clustering is initiated with the aid of the correlation coefficient( $\mathcal {CC}$ ) and weighted correlation coefficient( $\mathcal {WCC}$ ) for LDFS. Due to their ability to quantify the degree of similarity between two elements, $\mathcal {CC}$ are valuable in clustering problems. The LDF- clustering algorithm comprises a well-integrated algorithm for managing uncertainty and $\mathcal {CC}$ among LDFS. Also, our approach to LDF clustering is compared to existing fuzzy clustering studies to assess its effectiveness. Since LDFS broadens the score space, the experimental evaluation of our proposed scheme enables Decision makers(DM) to freely select their score values. The theme of this study is to impart the commencement of LDF-clustering analysis and attempt to apply $\mathcal {CC}$ to the clustering problem. An interpretative example provides the analysis of the logistic efficiency of food products by employing an LDF-clustering algorithm.

关键词： LDFS clustering algorithm correlation coefficient logistics food products optimization decision making algorithms

来源：评论

学校读者我要写书评

暂无评论

SRG: a clustering algorithm based on scale division and region growing

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 137-157页

作者： Jia, Yunwei Lu, Keke Li, Xia Hao, Chenxiang Tianjin Univ Technol Sch Mech Engn Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin 300384 Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin 300384 Peoples R China

clustering is an important technique for data mining and machine learning. In this paper, a novel clustering algorithm, scale division and region growing (SRG), is proposed, which is based on scale division and region growing. This method uses the division-and-merge strategy, which consists of two primary phases, namely the division phase and the merging phase. In the division phases, the data is mapped to a two-dimensional space, and the clustering problem is transformed to an image processing problem. Then the image is divided into different scales to adaptively find the appropriate scale. After finding the appropriate scale, the region growing is carried out under this scale and the multiple sub-clusters are created. In the merging phases, the sub-clusters are evaluated and merged to form the final clusters in the dataset. The proposed algorithm is tested on 15 benchmark datasets including both the synthetic datasets and the real datasets, and it is found that SRG can obtain better clustering results and has higher accuracy than other typical clustering algorithms. Moreover, the experimental results on noisy datasets show that the proposed algorithm has good robustness.

关键词： clustering algorithm Scale division Region growing Merging

来源：评论

学校读者我要写书评

暂无评论

A Novel Crowding clustering algorithm for Unsupervised and Supervised Filter Feature Selection Problem

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年 1-39页

作者： Ghanem, Khadoudja Layeb, Abdesslem Univ Constantine 2 Fac Informat & Commun Technol Dept Comp Sci & Its Applicat MISC Lab Constantine Algeria Univ Constantine 2 Fac Informat & Commun Technol Dept Comp Sci & Its Applicat LISIA Lab Constantine Algeria

Feature selection is a problem that continues to be explored by researchers until an exact and fast method is found. A variety of criteria based on statistics, consistency, information, distance and similarity were used to propose new feature selection methods. In this paper, a new criterion inspired by the crowding distance used in multi-objective optimization problems is introduced. A crowding distance is computed for each feature based on its neighbors. All computed distances are sorted to assess the variability of each feature. Features with the highest values exhibit more diversity and capture some pattern in the data;these features are the most relevant ones. Based on this criterion, a simple novel supervised and unsupervised filter feature selection method called Crowding clustering algorithm (CCA) is proposed. Mainly, crowding distance is used to determine relevant features, and clustering is used to eliminate redundant features. Several variants of each method are also proposed in order to allow user to select the most suitable method based on specific needs and constraints of the application. We conduct an extensive comparative study on diverse datasets to evaluate the effectiveness and efficiency of the proposed methods. Our proposed unsupervised filter method achieved the best performance on 18 out of 26 datasets, with a mean accuracy above 81%, significantly outperforming five state-of-the-art methods, which range from 67 to 73%. This reflects an 8-14% accuracy improvement. Similarly, our supervised filter methods achieved the highest accuracy on 18 out of 26 datasets, exceeding 87%, representing a 2-5% improvement over six state-of-the-art methods (82-85% accuracy). In terms of processing time, both our unsupervised and supervised methods show a slight increase compared to state-of-the-art methods, with differences ranging from 0.02 to 0.2 s, which are typically negligible. Overall results demonstrate that the proposed methods outperform or achieve com

关键词： Feature selection Supervised filter feature selection Unsupervised filter feature selection Crowding distance clustering algorithm High-dimensional data

来源：评论

学校读者我要写书评

暂无评论

Customer Segmentation Marketing Strategy Based on Big Data Analysis and clustering algorithm

引用

JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2024年第1期26卷 1页

作者： Li, Xiaotong Lee, Young Sook Tongmyong Univ Sch Design Busan South Korea

Traditional customer segmentation methods cannot obtain more effective information from massive customer data, which affects the formulation of marketing strategies. Based on this, this study constructs a customer segmentation marketing strategy model that integrates support vector machines and clustering algorithms. This model first utilizes support vector machines to segment existing customer data, and then integrates support vector machines and clustering algorithms to construct a customer segmentation model. Finally, simulation experiments are conducted using the dataset. The results show that the model algorithm obtains the optimal solution when the quantity of iterations is 50. Meanwhile, the average error rate of the model algorithm in the customer segmentation process is 6.82%, the average recall rate is 91.28%, and the average profit predicted by the impact strategy developed by the segmentation model is 29.88%, which is 2.53% different from the true value.

关键词： Big Data Analysis clustering algorithm Customer Segmentation Marketing Strategy Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Iterative Multi-Attribute clustering algorithm and Its Application in Port Congestion Estimation

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第11期24卷 12026-12037页

作者： Bai, Xiwen Ma, Zhongjun Hou, Yao Li, Yiliang Yang, Dong Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Univ Int Business & Econ Sch Int Trade & Econ Beijing 100029 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hong Kong 999077 Peoples R China

Container port congestion threatens the effectiveness and sustainability of the global supply chain because it stagnates cargo flows and triggers ripple effects across connected, multimodal freight transport networks. This study aims to develop a novel and tangible method to measure port congestion by investigating ship behaviors between different zones in port waters. Different port zones have varying ship densities because ships moor in the anchorage area randomly but dock at berths in an orderly and close fashion. This observation leads us to apply the density-based clustering method for port zone identification and differentiation. In order to ensure the method is globally applicable and accurate, we develop a new clustering algorithm, an iterative, multi-attribute DBSCAN (IMA-DBSCAN), which incorporates an iterative process, together with both spatial information and domain knowledge. The necessary input data for the algorithm is extracted from the Automatic Identification System (AIS), a satellite-based tracking system with real-time ship positioning and sailing data. An illustrative case suggests that our algorithm can rapidly and precisely identify anchorage areas and individual berths (even in a port with complicated geographic features), while other methods cannot. The algorithm is applied to measure congestion at 20 major container ports in the world. The results show a significant increase in congestion at the Port of Los Angeles from August to December 2020, which matches the realistic statistics and proves the efficiency and practical applicability of the proposed algorithm.

关键词： Index Terms-Port congestion ship trajectory clustering algorithm AIS domain knowledge

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：