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Intelligent analysis technology of bamboo structure. Part II: The variability of radial distribution of fiber volume fraction

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INDUSTRIAL CROPS AND PRODUCTS 2021年 174卷 114164-114164页

作者： Xu, Haocheng Li, Jing Ma, Xinxin Yi, Wukun Wang, Hankun Int Ctr Bamboo & Rattan Inst New Bamboo & Rattan Based Biomat Key Lab Natl Forestry & Grassland Adm Beijing Bam Beijing 100102 Peoples R China Chinese Acad Forestry Res Inst Wood Ind Key Lab Natl Forestry & Grassland Adm Wood Sci & Beijing 100091 Peoples R China Xiaoxiang Res Inst Big Data Changsha 410000 Peoples R China

Scientific and reasonable utilization of bamboo is strongly related to the radial distribution of the fiber volume fraction. In this paper, the k-means clustering algorithm was applied to set separate thresholds for individual vascular bundles, to solve the problem that the bamboo ring cannot be binarized well. We introduced a layered model based on the WEB coordinate system, programmed via JavaScript, to layer the cross-section within the entire bamboo conveniently and accurately. It allowed uniform layering of sample areas of any thickness, any width, and any number of layers. Furthermore, this research systematically studied the bamboo rings at the breast height of Moso bamboo [Phyllostachys edulis (Carr)H. de Lebaie] grown in 12 major producing areas of China and the 50-internode sections from base to top of one bamboo with the method mentioned above. The results indicated that the thickness of a single layer should not be higher than 0.4 mm while examining the gradient structure of bamboo. The radial distribution of fiber volume fraction from 12 areas all followed the ExpDec1 function, and the mean goodness of fit (R2) was 0.988. The radial distribution of fiber volume fraction in the first 45 sections from the base to the top corresponded to the ExpDec1 function. It provided a theoretical basis for the functional improvement of bamboo and the development of new bamboo-based smart materials.

关键词： Fiber volume fraction Radial distribution k-means clustering algorithm Regional differences Axial variation ExpDec1 function

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Research on glass classification and identification based on heritage samples

Research on glass classification and identification based on...

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作者： Zhiyuan Liu Institute of electric power South China University of Technology

The study of ancient glass has been intensified in archaeological circles today,but its susceptibility to weathering can affect archaeologists' ability to classify *** order to classify glass artefacts,this paper establishes a 'subclass classification model based on the sum of the absolute values of relative changes' and uses the k-means clustering algorithm to classify the *** order to identify different classes of glass objects,the chemical composition of the glass is extracted from the centroids of the different subclasses,and the Euclidean distance between the class centres is solved for,and the class represented by the class centre with the lowest Euclidean distance is taken as the class corresponding to the unknown object.

关键词： k-means clustering algorithm Euclidean distance glass artefacts

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Chemical composition analysis of ancient glass products based on decision tree

Chemical composition analysis of ancient glass products base...

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作者： Zhihao He Weiduo Qin Changping Duan School of Food Science and Bioengineering Tianjin Agriculture University School of Humanities Tianjin Agriculture University School of Mechanical and Electrical Engineering Lanzhou University of Technology

Due to the effects of prolonged burial,freshly unearthed ancient glass is often weathered to varying degrees,and it is difficult to identify the type of *** introduce machine learning into the composition analysis and type identification of ancient glass *** objective is to build a reliable ancient glass classification model based on decision trees and two different k-means clustering *** performance of the decision tree is measured by the ROC *** performance of its clustering algorithm was evaluated by the Calinski-Harabasz *** results show that the area of AUC in the decision tree is 1 and the highest Calinski-Harabasz index of the two clustering algorithms is *** predictive ability of the model was verified well.

关键词： Machine learning Decision tree k-means clustering algorithm Mini-batch k-means

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An Efficient and Accurate Object Detection algorithm And Its Application 5

An Efficient and Accurate Object Detection Algorithm And Its...

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

作者： Jiang Libiao Li Xiaojun South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China

ISBN: (纸本)9781728143231;9781728143224

In order to solve the problem that the recognition accuracy and recognition speed of the current deep learning target detection algorithm are not compatible, an efficient and accurate object detection algorithm was proposed and applied to the inspection of small-sized body stamping parts of an auto parts enterprise. Firstly, image data sets of 10 small-sized automobile body stampings were produced. The transfer learning method was used to train YOLO V3, YOLO V3-tiny and the model proposed in this paper. Then, three models were used for small-sized body stamping parts detection and recognition experiment. The experimental results show that our model can detect 37 images per second in the same sample and test environment, which is 208.33% higher than the YOLO V3 model;the average detection precision is 96.50%, compared with The YOLO V3-tiny model is improved by 4.40%, and the average detection precision is improved up to 22.58% on a single smaller size object. This study can provide visual navigation support for the small-size body stamping robot automatic sorting system.

关键词： machine vision deep learning object detection transfer learning k-means clustering algorithm

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Improved Tabu Search algorithm for Solving the Vehicle Routing Problem with Soft Time Windows in B2C Environment 32

Improved Tabu Search Algorithm for Solving the Vehicle Routi...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Li, Lin Chen, Ying Meng, Jiao Shenyang Aerosp Univ Coll Sci Shenyang 110136 Peoples R China

ISBN: (纸本)9781728158556

With the development of E-commerce, the B2C E-commerce develop rapidly. Customers put forward higher requirements for delivery speed and accitracy. This paper studied the vehicle routing problem with soft time windows in B2C environment. It improved the tabu search (TS) algorithm, and used k-means clustering algorithm to determine the initial solution to improve the algorithm's convergence speed. The algorithm was tested by using the data in literature [7]. The computing results were compared with those results in the literature [7] to verify the effectiveness of the algorithm.

关键词： B2C E-commerce Soft time window k-means clustering algorithm Tabu search

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S-DDoS: Apache spark based real-time DDoS detection system

S-DDoS: Apache spark based real-time DDoS detection system

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5th International Symposium on Intelligent Systems Technologies and Applications (ISTA) / International Conference on Applied Soft Computing and Communication Networks (ACN)

作者： Patil, Nilesh Vishwasrao krishna, C. Rama kumar, krishan Panjab Univ Natl Inst Tech Teachers Training & Res NITTTR Dept Comp Sci & Engn Chandigarh India Panjab Univ Univ Inst Engn & Technol UIET Dept Informat Technol Chandigarh India

A Distributed Denial of Service (DDoS) attack is the biggest threat to Internet-based applications and consumes victim service by sending a massive amount of attack traffic. In the literature, numerous approaches are available to protect the victim from the DDoS attacks. However, the attack incidents are increasing year by year. Further, several issues exist in the traditional framework based detection system such as itself becoming a victim, slow detection, no real-time response, etc. Therefore, the traditional framework based system is not capable of processing live traffic in the big data environment. This paper proposes a novel Spark streaming-based distributed and real-time DDoS detection system called S-DDoS. The proposed S-DDoS system employs the k-means clustering algorithm to recognize the DDoS attack traffic in real-time. The proposed detection model designed on the Apache Hadoop framework using highly scalable H2O sparkling water. The detection model deployed on the Spark framework to classify live traffic flows. The results show that the proposed S-DDoS detection system efficiently detects the DDoS attack from network traffic flows with higher detection accuracy (98%).

关键词： Distributed denial of service (DDoS) k-means clustering algorithm big data entropy network security apache spark

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Segmentation of Rapeseed Color Drone Images Using k-means clustering 19

Segmentation of Rapeseed Color Drone Images Using K-Means Cl...

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3rd International Conference on Computer Science and Application Engineering (CSAE)

作者： Yang, kang Liu, Changhua Wu, Xiaoming Li, Hao Wuhan Polytech Univ Wuhan Peoples R China Chinese Acad Agr Sci Oil Crops Res Inst Wuhan Peoples R China

ISBN: (纸本)9781450362948

Aiming at the problem of unsatisfactory segmentation effect of aerial image of rapeseed flowers in the process of florescence recognition. This paper proposes a method combining k-means algorithm and color segmentation algorithm to segment rapeseed image. Firstly, the k-means algorithm is used to first process the rapeseed image in Lab space. Then, the clustering results were processed once again in HSV space using color segmentation algorithm. Finally, the segmented rapeseed was subjected to morphological treatment to complete the effective segmentation of rapeseed and rapeseed flowers. Sixty different aerial rapeseed images were selected for segmentation experiments. The results show that this method can not only segment rapeseed well, but also effectively avoid the influence of illumination. The results of this experiment can provide reference for the later study of the flowering period of rapeseed.

关键词： Drone rapeseed image k-means clustering algorithm Color segmentation algorithm Image segmentation

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Energy-efficient trajectory planning for amulti-UAV-assisted mobile edge computing system

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Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1713-1725页

作者： Pei-qiu HUANG Yong WANG ke-zhi WANG School of Automation Central South UniversityChangsha 410083China Department of Computer and Information Sciences Northumbria UniversityNewcastle NE 18STUK

We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things *** goal is to minimize the energy consumption of this system by planning the trajectories of *** problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and *** tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three *** the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same *** the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same *** the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each *** with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales.

关键词： Multiple unmanned aerial vehicles Mobile edge computing Trajectory planning Differential evolution k-means clustering algorithm Greedy method

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An Improved clustering algorithm Based on k-means and Artificial Bee Colony Optimization for Datasets that Contain Outliers

An Improved Clustering Algorithm Based on k-Means and Artifi...

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International Conference on Computing, Power and Communication Technologies

作者： Anu Balachandran k.A. Abdul Nazeer Department of Computer Science and Engineering National Institute of Technology

ISBN: (纸本)9781538644928;9781538644911

k-means clustering algorithm is the most widely used algorithm in clustering. It is most popular because of its simplicity. There are a lot of issues faced by k-means algorithm such as, low quality of clusters formed, inability to detect outliers and solutions that can be local optimal solution. In this paper a simple outlier detection algorithm that makes use of Mean and Standard Deviation, is applied on datasets. These datasets are then given as input to an already existing hybrid clustering algorithm of k-means and Artificial Bee Colony (ABC) algorithm. By applying the outlier detection algorithm, it ensures that the clusters formed are of better quality.

关键词： clustering Data mining k-means clustering algorithm Artificial bee colony (ABC) algorithm Outlier detection ARI score

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Research on Improved k-means clustering algorithm

Research on Improved K-means Clustering Algorithm

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Zhang, Yinsheng Shan, Huilin Li, Jiaqiang Zhou, Jie Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China

ISBN: (纸本)9783037853122

The traditional k-means clustering algorithm prematurely plunges into a local optimum because of sensitive selection of the initial cluster center. Hierarchical clustering algorithm can be used to generate the initial cluster center of k-means clustering algorithm. The geometric features of input data can achieve a good distribution by means of pretreatment and feature extraction and selection. In the learning of fuzzy neural network, Java language is used to write source code of the algorithm. The experimental results show that new algorithm has improved the clustering quality effectively.

关键词： Pattern Recognition Hierarchical clustering k-means clustering algorithm Java

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