检索结果-内蒙古大学图书馆

MOSS SOFTWARE: A NEW TOOL FOR MULTI-OBJECTIVE GREEN SUPPLIER SELECTION

INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2022年第2期29卷 244-266页

作者： Toktas-Palut, Peral Onan, kivanc Gurbuz, Mustafa Zahid Gulden-Ozdemir, Birsen Dogus Univ Dept Ind Engn Istanbul Turkey Dogus Univ Dept Comp Engn Istanbul Turkey

Competition between companies is getting more intense by the day. Corporations need to decrease costs while improving the quality and reliability of their deliveries. Meanwhile, the demand for environmentally sustainable products and operations is increasing. These issues are all compounded by the supplier relations between companies and their need to develop better economic and environmental supply chain contracts. This research thus aims to propose a tool, i.e., the MOSS Software, concerning the total cost, rate of defected materials, rate of late deliveries, and environmental improvement potential for suppliers. MOSS is a two-stage software. First, the objectives are included in a multi-objective evolutionary mathematical model, and the model is solved using the NSGA-III algorithm. In the second stage, as a post-Pareto analysis approach, the k means algorithm is used for selecting representative solutions among Pareto sets by comparing silhouette values for different k values. An application of the MOSS software is also presented in this paper.

关键词： Green supplier selection Lot-sizing Quantity discount Multi-objective optimization Genetic algorithm k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Increasing flexibility of self-piercing riveting by reducing tool-geometry combinations using cluster analysis in the application of multi-material design

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS 2022年第6期236卷 1310-1320页

作者： kappe, Fabian Schadow, Luca Bobbert, Mathias Meschut, Gerson Paderborn Univ Lab Mat & Joining Technol LWF D-33098 Paderborn Germany

A frequently used mechanical joining process that enables the joining of dissimilar materials is self-piercing riveting. Nevertheless, the increasing number of materials as well as material-thickness combinations leads to the need for a large number of rivet-die combinations as the rigid tool systems are not able to react to changing boundary conditions. Therefore, tool changes or system conversions are needed, resulting in longer process times and inflexibility of the joining processes. In this investigation, the flexibility of the self-piercing riveting process by reducing the required tool-geometry combinations is examined. For this purpose, various joints consisting of similar as well as dissimilar materials with different material thickness are sampled and analysed. Subsequently, a cluster algorithm is used to reduce the number of rivet-die combinations required. Finally, the effect of the changed tool geometries on both the joint formation and the joint load-bearing capacity is investigated. The investigation showed that a reduction by 55% of the required rivet-die combinations was possible. In particular, the rivet length influences the joint formation and the joint load-bearing capacity. An exclusive change of the die (e.g. die depth or die diameter) did not show a significant influence on these parameters.

关键词： Self-piercing riveting flexibility cluster analysis k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Software Technology Immersive Education Research and Practice Based on Spark Platform 3

Software Technology Immersive Education Research and Practic...

引用

3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Genrang, Zheng Chunping, Huang Zhongshan Polytechnic Department of Information Engineering Guangdong Zhongshan China

ISBN: (纸本)9798350380989

From the perspective of education informatization, a teaching resource sharing system with huge storage space and computation amount is established. When the user accesses the teaching materials, it constructs the database with a large number of users through instant access to the data. This project intends to study an optimization scheme that does not require any intermediary output based on Hadoop and combined with Spark's parallel computing mode, so that it can make better use of Spark's iterative computing capability. It realizes the efficient recommendation of teaching resources. A hierarchical resource model based on collaborative filtering is proposed. The unrated resources predicted in this way are imprecise. A time function is added to the prediction process for non-rated resources to reflect the change in user interest over time. The index function is used to describe the change of time. The practicability of the Spark-based in the recommendation of teaching resources are verified by the research. © 2024 IEEE.

关键词： collaborative filtering Educational resources recommendation k-means clustering algorithm recommendation system spark

来源：评论

学校读者我要写书评

暂无评论

Research and application of k-means clustering based on the color characteristics of woodblock prints of Zhuxianzhen 25

Research and application of K-means clustering based on the ...

引用

Proceedings of the 2025 2nd International Conference on Generative Artificial Intelligence and Information Security

作者： Minghai Sun Yufan Cheng Hubei University of Technology Wuhan Hubei China

ISBN: (纸本)9798400713453

Objective This study aims to quantitatively analyze the color composition of woodblock prints of Zhuxianzhen, explore its color combination patterns, and provide a theoretical basis for the color design of cultural and creative products, so as to more accurately inherit and present the visual characteristics of this traditional Chinese folk art. Methods In this study, k-means clustering algorithm is used to extract color features from selected sample images of woodblock prints of Zhuxianzhen, and a color association network model based on the priority of the main color is constructed by calculating the proportion of color distribution and its combination frequency. Based on the results of the analysis of the color matching law of this model, the practice of innovative visual design and cultural and creative product development of Zhuxianzhen woodblock prints is carried out. Conclusion The results of the research present the color matching and color matching law of Zhuxianzhen woodblock prints in the form of icons, which can reflect the color characteristics of Zhuxianzhen woodblock prints in a more objective way, provide the designers with scientific and rich color matching solutions, improve the efficiency of designers' work, and at the same time, promote the re-design application of the traditional Chinese folk art.

关键词： k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Research into Data Mining algorithms Based on Cloud Computing Environments

Research into Data Mining Algorithms Based on Cloud Computin...

引用

2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Song, Yu Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

Focusing on the Hadoop architecture in a cloud computing environment and based on the distributed Hadoop system, the MapReduce model and the HBase database, an improved k-means clustering algorithm based on the Hadoop framework was developed, the original cluster analysis designer was improved, data elements were extracted and an analysis model for hot topics in the network was created, and simulation experiments with different databases were completed. The simulation experiments on different datasets and the data mining results in terms of the sampling interval and system accuracy with the improved k-means clustering algorithm suggest that the algorithm is more suitable for data mining in cloud computing and that it has some advantages. © 2023 IEEE.

关键词： cloud computing environment data mining k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Rule Reduction for EBRB Classification Based on clustering 1

引用

18th Web Information Systems and Applications Conference (WISA)

作者： Chen, Longjiang Fu, Yanggeng Chen, Nannan Ye, Jifeng Liu, Genggeng Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China

ISBN: (数字)9783030875718

ISBN: (纸本)9783030875718;9783030875701

The extended belief rule base (EBRB) system has been successfully applied to classification problems in various fields. However, the existing EBRB generation method converts all data into extended belief rules, which leads to the large scale of rule base and affects the efficiency and accuracy of subsequent inference. In view of this, this paper proposes an EBRB rule reduction method based on the adaptive k-means clustering algorithm (RC-EBRB). In the rule generation process, the k-means clustering algorithm is applied to obtain the rule cluster centers, which are used to generate new rules. In the end, these new rules form a reduced EBRB. Moreover, in order to determine the initial cluster centers and the number of clusters in the k-means clustering algorithm, the algorithm idea of k-means++ is introduced and a reduction granularity adjustment algorithm with threshold is proposed, respectively. Finally, four datasets on commonly used classification datasets from UCI are used to verify the performance of the proposed method. The experimental results are compared with the existing EBRB methods and the traditional machine learning methods, which prove the effectiveness of the method.

关键词： Extended belief rule base Rule reduction Data-driven k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Prenatal Detection of Ventricular Septal Defects by VGG -16 Model Using Ultrasound Images 3

Prenatal Detection of Ventricular Septal Defects by VGG -16 ...

引用

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

作者： kavitha, D. Lakshmi, C.R. CMR Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru India Cambridge Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350317022

Congenital heart defect is one of the important anomaly. The deformed heart can be differentiated from the normal heart based on several parameters such as the size of auricles, ventricles, valve and position of heart, area, circumference, and perimeter. One of the methods to detect the anomalies in fetal heart is by applying advanced image pprocessing techniques and artificial intelligence algorithms. This proposed system is the primary framework for diagnosing the prenatal ventricular septal defects (PVSD). The first step is to denoise the US images using enhanced anisotropic diffusion Enhanced Perona Malik Filter (EPMF), followed by k-means clustering segmentation method as the second step, and finally, VGG-16 architecture was implemented with the pre-trained weights from the database. The original image is compared with the reference image in terms of different parameters using a VGG16 deep learning algorithm to predict PVSD anomalies at the early stage of pregnancy. The experimental results shows that PSNR reach to high value of 33.45, Dice coefficient and jacquard index and conformity coefficient as 0.88-0.86. VGG-16 is the first attempt to diagnose prenatal ventricular septal defects to achieve an accuracy of 90%. This proposed system will give a second opinion for the doctors in diagnosing the abnormalities at the early stages. © 2023 IEEE.

关键词： Enhanced Perona Malik filter k-means clustering algorithm VGG-16 CNN model

来源：评论

学校读者我要写书评

暂无评论

Detecting Semantic Similarity of Diseases based Machine Learning

Detecting Semantic Similarity of Diseases based Machine Lear...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qumsiyeh, Emma Yousef, Malik Salah, Zaidoun Jayousi, Rashid Al-Quds University Department of Computer Science and Information Technology Jerusalem Palestine Zefat Academic College Galilee Digital Health Research Center Department of Information Systems Zefat Israel Arab American University Molecular Genetics and Genetic Toxicology Ramallah Palestine

ISBN: (纸本)9798350337488

Identifying disease-disease associations (DDAs) is critical in medicine and systems biology, providing valuable insights into the complex relationships among different diseases. Recent advances in systems biology and the increasing availability of diverse biological data have provided opportunities for such research. However, the heterogeneity of the data makes it difficult to identify disease associations. To develop effective methods for predicting DDAs, it is necessary to leverage data at the molecular level. In this study, utilizing the Grouping-Scoring-Modeling approach, we have developed a statistical technique to compute a semantic similarity metric between the disease under study and other diseases. We have also used Jaccard similarity to compute similarity among disease groups. We have considered GediNETPro, using machine learning techniques, to record, over the Monte Carlo cross-validation, all the ranks based on the scores assigned by the S component to each disease group. The k-means clustering algorithm is applied to these recorded ranks for each disease group to detect patterns of similar diseases by placing them into coherent clusters. A cluster score (named as cluster-score) is assigned for each cluster by averaging the ranks of its members. Notably, the lowest cluster score is the more significant one. The semantic approach is applied to the top-ranked clusters to detect a semantic relationship between the disease under investigation and other diseases (groups). In addition, we have applied diversity metrics based on the Jaccard measurement to each disease cluster to support the semantic findings. A high value of diversity indicates a greater likelihood of detecting distinct groups with semantic relationships. This study enhances our understanding of disease associations and increases our ability to improve treatment strategies. This is especially important in a context where disease associations go beyond the scope of genetic factors. © 2023 IEEE.

关键词： disease-disease associations diversity metrics GediNETPro Grouping-Scoring-Modeling approach Jaccard index Jaccard similarity k-means clustering algorithm machine learning Monte Carlo cross-validation semantic similarity

来源：评论

学校读者我要写书评

暂无评论

Stochastic optimization models for the solar system design and energy management problem in an off-grid residential house

引用

JOURNAL OF ENERGY STORAGE 2024年 100卷

作者： Hooshmand, Farnaz Tehran Polytech Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran

Solar energy is clean, eco-friendly, abundant, inexpensive, and sustainable, and using it as a source of electricity generation can significantly reduce environmental pollution. For residential houses located in isolated areas without access to the national electricity grid, photovoltaic-battery systems (PBSs) are good choices for electricity generation. This paper addresses the optimization of PBS sizing and energy managing for isolated off-grid standalone houses, considering uncertainties in solar irradiation. We propose a novel approach by formulating the problem as a two-stage stochastic programming model, enhanced with a k-means clustering algorithm for scenario generation. Additionally, we extend the model by incorporating worst-case (WC) and conditional-value-at-risk (CVaR) measures to enhance robustness against uncertainty. To address the variability in scenario occurrence probabilities, we develop a distributionally robust optimization (DRO) model, further contributing to the system reliability. Computational experiments on real-world datasets reveal that our modeling frameworks outperform existing stochastic optimization models in terms of computational efficiency. Moreover, the risk-based and DRO models improve system reliability, resulting in lower unmet demand and reduced diesel generator (DG) usage. Specifically, compared to the expected-value-based model, the WC- and CVaR-based models, despite increasing construction costs by 20 % and 10 % respectively, achieve approximately 85 % and 50 % reductions in unmet demand and 57 % and 30 % reductions in DG usage across different seasons, on average. Furthermore, although the DRO model incurs a 9.8 % higher construction cost than the expected-value-based model, it demonstrates approximately 46 % and 28 % reductions in unmet demand and DG usage, respectively, across various seasons, on average.

关键词： Photovoltaic-battery system size Energy consumption management Scenario-based stochastic programming k-means clustering algorithm Risk measures Distributionally robust optimization

来源：评论

学校读者我要写书评

暂无评论

Outdoor Location Fingerprint Based on Optimized k-means clustering

Outdoor Location Fingerprint Based on Optimized K-means Clus...

引用

IEEE/CIC International Conference on Communications in China (ICCC)

作者： Liu, Jingjing Chuai, Gang Gao, Weidong Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing Peoples R China

ISBN: (纸本)9781665439442

In the indoor scene, location fingerprint has been developed to improve the locating speed by using clustering algorithm to process the fingerprint database. In the outdoor scene, location fingerprint is still at the stage of constructing a fingerprint database due to the vast region and its large data, so its locating speed needs to be faster. In the view of the slow speed, this paper proposes optimized k-means algorithm by I-CFSFDP algorithm to process the outdoor fingerprint database, aiming to greatly increase the locating speed without sacrificing the accuracy of locating. In order to verify the performance of the algorithm, the standard propagation model is applied to calculate the sampling point's reference signal received power (RSRP), and universal kriging algorithm is used to interpolate the database, ensuring the authenticity of outdoor environment simulation. The result shows that the locating speed of outdoor fingerprint database can be greatly improved without decreasing accuracy, after being processed by I-CFSFDP optimized k-means.

关键词： outdoor locating location fingerprint RSRP universal kriging interpolation algorithm k-means clustering 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：