检索结果-内蒙古大学图书馆

IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT)

作者： Srinivas, M. Satya Lakshmi, P. Vijaya kumar, V. kalyan Durga Shyam Balaji, V. Siva Sai Sasi Inst Tech & Engn Comp Sci & Engn Tadepalligudem AP India

ISBN: (纸本)9781665404167

This research provides a novel method for improving clustering efficiency in blood banks. Blood transfusions are used to save the lives of billions of people throughout the world. Every day, about 2 million people are involved in accidents throughout the world, resulting in an emergency scenario in which we require adequate blood for the patient to cover up the blood loss and ensure his survival. Blood banks play a significant role in this situation;a blood bank is a spot where blood is collected and stored for future use;nevertheless, locating a suitable blood bank in the surrounding area is a common task for patient caretakers. In this research, we presented an effective clustering or grouping of blood banks to tackle this problem. Our primary objective here is to first cluster blood banks using an advanced version of the k means clustering algorithm, namely the Entropy weighted k means clustering algorithm while taking into account factors such as longitudinal and latitudinal points of blood banks, blood bank category (Government, Charity, and Private), and blood component availability. We use the sum of the square error to assess the effectiveness of this clustering algorithm. In this process, we also identify the region where a new blood bank is needed based on longitudinal and latitudinal information.

关键词： Blood Banks Entropy Weighted k-means clustering algorithm Machine Learning Data Analysis

来源：评论

学校读者我要写书评

暂无评论

Construction of Risk Prediction Models for Enterprise Finance Sharing Operations Using k-means and C4.5 algorithms

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 208-208页

作者： Pan, Chun Changjiang Inst Technol Fac Econ & Management Wuhan 430212 Peoples R China

The evaluation of financial sharing centres in enterprises typically relies on outdated financial data, lacks comprehensive assessment, and presents risks such as employee misconduct. To address these challenges, we propose a risk prediction model for enterprise financial sharing operations based on the k-means clustering algorithm for performance evaluation and the C4.5 algorithm for managing employee risks. Our approach enhances the accuracy and objectivity of performance evaluation while improving the efficiency of personnel risk management. Results indicate that the k-means algorithm classifies employee performance into five levels, facilitating comprehensive performance evaluation. Furthermore, through risk management optimisation, accuracy and recall rates increase to 0.905 and 0.890, respectively. The proposed risk prediction model achieves high accuracy rates of 90.5% and 92.4% in the training and test sets, respectively. Practical application of our methodology and model in A Group's financial sharing centre demonstrates their effectiveness and potential for enhancing the operation and management of enterprise financial sharing centres.

关键词： Enterprise financial sharing Risk prediction models k-means clustering algorithm C4.5 algorithm

来源：评论

学校读者我要写书评

暂无评论

kM-Mask RCNN: A Lightweight Instance Segmentation algorithm for Strawberries With Multiple Growth Cycles

引用

IEEE ACCESS 2024年 12卷 169291-169309页

作者： Cong, Peichao Xu, Yutao Li, Tianheng Li, Shanda Feng, Hao Zhang, Xin Guangxi Univ Sci & Technol Sch Mech & Automot Engn Liuzhou 545006 Peoples R China

Accurate and efficient detection of multi-growth-cycle strawberry fruits can improve automated harvesting. However, the small size and unbalanced distribution of strawberry fruits make accurate identification of multi-growth-cycle strawberries difficult using the existing detection models. Herein, local enhancement technology is adopted for preprocessing when compiling the dataset to ensure the balance of the number of samples in each category to solve the above problems. Second, a new instance segmentation algorithm called kM-Mask RCNN is developed, which optimally adjusts the size of the anchor frame and the anchor ratio based on the k-means clustering algorithm to improve the recognition accuracy of the algorithm on small targets and uses MobileNet V3 to replace the Resnet50 structure in the Mask RCNN backbone network to reduce the complexity of the algorithm and realize lightweight operation. Finally, the experimental results reveal five strawberry growth stages in the homemade dataset(based on StrawDI_Db1 database): 'Green ripe stage', 'White ripe stage', 'Turning stage', 'Mature', and 'Deformed', for which the kM-Mask RCNN yields mAPs of 91.19%, 88.09%, 93.70%, 93.19%, and 87.13%, respectively. The P, R, and F1-score values of this algorithm are 93.9%, 94.2%, and 94.05%, respectively. Additionally, the number of parameters, FLOPs, and fps of this algorithm are 27M, 12G, and 22.32, respectively, satisfying the real-time requirements for strawberry detection. The findings provide important theoretical support for the automated harvesting of strawberries with multiple growth cycles.

关键词： Feature extraction Accuracy Residual neural networks Robots clustering algorithms Classification algorithms Transformers Prediction algorithms Instance segmentation Manuals Convolutional neural networks k-means clustering algorithm lightweighting mask RCNN strawberry

来源：评论

学校读者我要写书评

暂无评论

Indirect solution modeling of melting behavior of SiO2 based on the image processing technology

引用

ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2024年第2期19卷 e3028-e3028页

作者： Lu, Cunhao Zhang, Yi Zhang, Jiayi Sun, Weixiang Xia, Anying Zhang, Mingli Chen, Jian Yangzhou Univ Sch Mech Engn Huayang Weststr 196 Yangzhou 225127 Jiangsu Peoples R China Shandong Jinyu Elect Technol Co LTD Innovat Workshop Jinan Shandong Peoples R China McGill Univ Montreal Neurol Inst McGill Ctr Integrat Neurosci Montreal PQ Canada Shandong Technol & Business Univ Sch Comp Sci Yantai Shandong Peoples R China

The utilization of tempered blast-furnace slag through the direct fiber forming process to produce high-value thermal insulation materials offers a dual benefit: it efficiently utilizes the latent heat in the unused slag and significantly increases the value of blast-furnace slag utilization. However, measuring the melting properties of iron slag at high temperatures is challenging. In this study, the melting behavior of SiO2 in a high-temperature molten pool was investigated. We employ dynamic visual data (video stream) captured via a non-contact charge coupled device video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines the centroid position of SiO2. Given that SiO2 is proportionate to crucible pixels, the area of the SiO2 is calculated through pixel statistics within these contours. A new indirect method is then proposed to process image information to obtain SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, this indirect method has been compared with shape from shading, quantitative characterization, and dimensional analysis techniques. Besides, the strengths and limitations of each method have been discussed. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements, which provides theoretical and technical support for the direct fiber forming process of blast-furnace slag.

关键词： image processing indirect solution method k-means clustering algorithm traversal algorithm

来源：评论

学校读者我要写书评

暂无评论

Fracture behavior and acoustic emission characteristics of layered sandstone with a bedding-parallel crack

引用

THEORETICAL AND APPLIED FRACTURE MECHANICS 2024年 131卷

作者： Liu, kewei Zou, Liansong Guo, Tengfei Guo, Can Yang, Jiacai Zhang, Yi Cent South Univ Sch Resources & Safety Engn Changsha Peoples R China

Bedding planes and cracks are widely found in natural rock mass, exerting a substantial effect on the stability and security of layered rock structures. Cracked straight-through Brazilian disc (CSTBD) tests were conducted to analyze the fracture mechanics of layered sandstone. Meanwhile, acoustic emission (AE) monitoring system was applied to reveal AE characteristics. Test results indicate that bedding planes substantially influence the strength and crack extension behaviors. Tensile failure along bedding planes corresponds to the lowest strength, and the highest strength is observed when failure appears perpendicular to the beddings. Three failure modes are recognized: bedding planes failure (theta = 0 degrees), mixed failure (theta = 15 degrees, 30 degrees, 45 degrees, 60 degrees, and 75 degrees), and matrix failure (theta = 90 degrees). The b-value can be considered as a predecessor index of rock engineering instability, and fracture will occur when b-value achieves the minimum value. Comparing the proportions of tensile-shear crack obtained from four different classification methods, it is found that the average frequency (AF) and rise angle (RA) distribution acquired through the k-means clustering algorithm provides optimal results for identifying the tensile and shear crack types. Furthermore, the study reveals that the absolute value of mode I fracture toughness declines first and then increases with the theta growth, while mode II fracture toughness exhibits the opposite trend. Finally, the mixed-mode fracture mechanism of layered sandstone was analyzed, and results showed that the generalized maximum tangential stress (GMTS) criterion could produce better projections for fracture toughness and crack initiation orientations.

关键词： CSTBD Layered sandstone AE characteristics k-means clustering algorithm Fracture characteristics

来源：评论

学校读者我要写书评

暂无评论

Cluster Analysis of Trip Purpose Based on Residents' Travel Characteristic 7

Cluster Analysis of Trip Purpose Based on Residents' Travel ...

引用

IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Wei, Huang Lei, Gong Qin, Luo Tian, Lei Shenzhen Technol Univ Coll Urban Transportat & Logist Shenzhen Peoples R China

ISBN: (纸本)9781665460071

In the era of big data and artificial intelligence, individual generated traffic data has gradually become the key data source for extracting personal behavior information and travel purpose prediction. Based on the Person Trip survey of residents' travel in Tokyo, this paper uses Spearman correlation coefficient to explore the correlation between residents' travel attribute patterns. In the situation of unsupervised learning, k-means clustering algorithm is used to analyze the differences of travel purpose caused by various attributes of residents' travel. This paper explores the possible reasons for the low accuracy of travel purpose prediction by supervised machine learning methods. Through the visual display of clustering results, it puts forward guiding suggestions for the method system of obtaining residents' travel patterns based on traffic big data, which is of great significance to the development of new technologies and academic theories in the field of urban transportation.

关键词： Resident trip pattern Trip purpose Correlation analysis k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Partition harvesting of a column-comb litchi harvester based on 3D clustering

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 197卷 1页

作者： Li, Can Lin, Jiaquan Li, Boyang Zhang, Sheng Li, Jun South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China Guangdong Lab Lingnan Modern Agr Guangzhou 510642 Peoples R China Guangdong Ruobo Intelligent Co Ltd Foshan 528000 Peoples R China

Accurate recognition of litchi fruits in orchard environments and acquisition of their coordinate position information are key for realizing successful harvesting using litchi harvesters. However, the existing detection methods are often aimed at large and relatively sparse fruits and thus are inappropriate for small and densely distributed litchi fruits. Therefore, at present, litchi fruit are typically manually harvested, resulting in a low harvesting efficiency that cannot meet the needs of growers. To improve the efficiency of litchi harvesting, this study proposes a column-comb litchi harvesting method based on k-means 3D clustering partitioning, which includes four main steps: (1) Litchi image acquisition and labeling methods are developed. (2) An improved version of the present YOLOv3-tiny network model structure is developed named the YOLOv3-tiny-Litchi network model, and the litchi fruit detection results of five kinds of neural networks, namely, YOLOv3-tiny, YOLOv3-tiny-Litchi, YOLOv4, YOLOv5x and Faster R-CNN, are compared. (3) A depth camera is used to obtain the 3D coordinates of litchi fruits, and the k-means clustering algorithm is used to divide the litchi harvesting area to obtain the optimal partitioning results. (4) Field experiments on litchi harvesting are reported. The experimental results show that the improved YOLOv3-tiny-Litchi model can recognize litchi fruits more accurately;the recall rate is 78.99%, the precision rate is 87.43%, and the F1 score is 0.83. The results of 3D clustering partitioning show that when k is equal to 6, the optimal harvesting rate is 90.03%, which satisfies the theoretical requirements of litchi harvesters. The field experiments show that when the theoretical number of partitions is 6, the average harvesting rate of the litchi harvester is 91.15% and the recognition rate is 88.39%, and the harvesting efficiency of mechanical partition harvesting is 1.4 times that of mechanical harvesting without partitioning and 2

关键词： Litchi k-means clustering algorithm Object detection Column-comb Partition harvest

来源：评论

学校读者我要写书评

暂无评论

A New Porosity Evaluation Method Based on a Statistical Methodology for Granular Material: A Case Study in Construction Sand

引用

APPLIED SCIENCES-BASEL 2024年第16期14卷 7379页

作者： Wang, Binghui Xin, Shuanglong Jin, Dandan Zhang, Lei Wu, Jianjun Guo, Huiyi Jiangsu Univ Sci & Technol Fac Architecture & Civil Engn Zhenjiang 212100 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable.

关键词： porosity observed porosity target particles digital image k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

On the Deployment Problem of Multiple Drones in Millimeter Wave Systems

引用

JOURNAL OF NETWORk AND SYSTEMS MANAGEMENT 2024年第4期32卷 71-71页

作者： Shakhatreh, Hazim Al-Dagamseh, Wa'ed Albasheer, Samah Bani-Hani, khaled Hayajneh, khaled Yarmouk Univ Hijjawi Fac Engn Technol Dept Telecommun Engn Irbid 21163 Jordan Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait

Millimeter wave (mm-Wave) frequency bands have paved the way for wide drone applications to support future wireless networks. This research studies the integration of drone-assisted wireless networks with 5G mm-Wave communications. Two scenarios are considered: the single and multiple drone scenarios. In the first scenario, we aim to find an effective 3D positioning of a single drone that minimizes the overall transmit power required to serve the users. Therefore, we utilize the particle swarm optimization (PSO) algorithm to solve the optimization problem. In the second scenario, a drone's low transmission power may be inefficient in utilizing a single drone to cover a set of users. To that end, we aim to minimize the number of drones and find efficient 3D placements for the drones required to serve the users. Minimizing the number of drones will reduce operational costs during drones' wireless network planning and operations. Moreover, it helps operators utilize unused drones to extend wireless coverage. Therefore, we propose the clustering algorithm to solve this optimization problem. Finally, the results of simulations are used to verify the efficacy of the suggested algorithms in our scenarios.

关键词： Drones Millimeter wave communications 5G and beyond wireless applications PSO algorithm k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

A Framework for Adaptive Façade Optimization Design Based on Building Envelope Performance Characteristics

引用

BUILDINGS 2024年第9期14卷 2646页

作者： Chen, Ping Tang, Hao Shandong Jianzhu Univ Sch Architecture & Urban Planning SAU Jinan 250101 Peoples R China

The adaptive fa & ccedil;ades serve as the interface between the indoor and outdoor energy of the building. Adaptive fa & ccedil;ade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks often neglect the influence of building envelope performance characteristics on adaptive fa & ccedil;ade optimization design. This paper aims to reveal the potential functional relationship between building fa & ccedil;ade performance characteristics and adaptive fa & ccedil;ade design. It proposes an adaptive fa & ccedil;ade optimization design framework based on building envelope performance characteristics. The method was then applied to a typical office building in northern China. This framework utilizes a k-means clustering algorithm to analyze building envelope performance characteristics, establish a link to adaptive fa & ccedil;ade design, and use the optimization algorithm and machine learning to make multi-objective optimization predictions. Finally, Pearson's correlation analysis and visual decision tools were employed to explore the optimization potential of adaptive fa & ccedil;ades concerning indoor daylighting performance, view performance, and solar energy utilization. The results showed that the optimized adaptive fa & ccedil;ade design enhances useful daylight illuminance (UDI) by 0.52%, quality of view (QV) by 5.36%, and beneficial solar radiation energy (BSR) by 14.93% compared to traditional blinds. In addition, each office unit can generate 309.94 kWh of photovoltaic power per year using photovoltaic shading systems. The framework provides new perspectives and methods for adaptive fa & ccedil;ade optimization design, which helps to achieve multiple performance objectives for buildings.

关键词： adaptive fa & ccedil ades building envelope performance daylighting performance quality of view solar energy utilization k-means clustering algorithm multi-objective optimization

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

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

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：