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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Jeny, J. R., V Sowmya, R. Kiran, G. Sai Babu, M. Kiran Arjun, Ch Vignan Inst Technol & Sci Dept Comp Sci & Engn Hyderabad India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

The shilling attack detection methods or approaches that are previously proposed keep emphasis on identifying the shillers or the attackers that act individually. There is a lack of focus on the attackers or shillers that act as a group. A shilling attack is an attack in which fake profiles are injected. When such profiles are present in an online recommendation system, the output that is generated by the online recommendation system will be biased. This results in inaccuracy of the online recommendation system. Here, we are proposing a shilling attack detection system in which we can not only identify individual shillers but also group shillers. In this system, the candidate groups are generated with an interval of time and are then grouped according to a degree called group suspicion degree. Then by making use of the dbscan algorithm and clustering of the groups which are suspicious is done. These groups are the shilling groups.

关键词： dbscan clustering algorithm Detecting Group Shilling attacks Group Shilling attacks Recommender System Online recommendation system

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Automatic Extraction of Forest Inventory Variables at the Tree Level by Using Smartphone Images to Construct a Three-Dimensional Model

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FORESTS 2023年第6期14卷 1081-1081页

作者： Song, Jiayin Huang, Qiqi Zhao, Yue Song, Wenlong Fan, Yiming Lu, Chao Northeast Forestry Univ Coll Comp & Control Engn Harbin 150040 Peoples R China

This paper focuses on the current urgent demand for the accurate measurement of forest inventory variables in the fields of forestry carbon sink measurement, ecosystem research, and forest resource conservation, and proposes the use of images to construct a three-dimensional measurement model of forest inventory variables, which is a new method to realize the automatic extraction of forest inventory variables. This method obtains sample site information by using high-definition images taken in the forest by a smartphone, which significantly improves the field operation efficiency and simple operation, and effectively alleviates the problems of long field operation times, complicated operations, and expensive equipment used by current methods for obtaining forest inventory variables. We propose to optimize the Eps parameters of the dbscan algorithm based on the MVO algorithm for point cloud clustering to obtain single wood point clouds, which improves the accuracy of the model and can effectively solve the problem of large interference from human factors. The scale coefficients of the image and the actual model are obtained by the actual measurement of tree height and diameter at breast height to complete the construction of the three-dimensional measurement model of the stand and are then combined with the AdQSM algorithm to realize the automatic extraction of forest inventory variables, which provides a new interdisciplinary method for the comprehensive extraction of forest inventory variables. The accuracy of the model measured in the experimental sample site of Fraxinus mandshurica Rupr was as follows: the absolute error of tree height measurement ranged from 0.05 to 0.37 m, the highest relative error of measurement was 2.03%, and the average relative error was 1.53%;for the absolute error of diameter at breast height, measurement ranged from 0.007 to 0.057 m, the highest relative error of measurement was 7.358%, and the average relative error was 3.616%. The met

关键词： dbscan clustering algorithm forest inventory variables 3D measurement model point cloud filtering point cloud segmentation

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An Optimization Method for the Calculation of SCADA Main Grid's Theoretical Line Loss Based on dbscan

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2019年第5期15卷 1156-1170页

作者： Cao, Hongyi Ren, Qiaomu Zou, Xiuguo Zhang, Shuaitang Qian, Yan Nanjing Agr Univ Coll Engn Nanjing Jiangsu Peoples R China

In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in density-based spatial clustering of applications with noise (dbscan) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and dbscan clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the dbscan algorithm outperforming other methods in processing effect. The experimental verification shows that the dbscan clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid's theoretical line loss.

关键词： Boxplot Method dbscan clustering algorithm Main Grid SCADA Theoretical Line Loss

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Lane Detection algorithm Based on Density clustering and RANSAC 30

Lane Detection Algorithm Based on Density Clustering and RAN...

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30th Chinese Control and Decision Conference (CCDC)

作者： Wang, Jitong Hong, Wei Gong, Lei Jilin Univ Coll Commun Engn Changchun 130022 Peoples R China

ISBN: (纸本)9781538612446

In order to improve the accuracy, real-time and robustness of video lane detection, in this paper, we propose a fast lane detection algorithm based on dbscan (Density Based Spatial clustering of Applications with Noise) clustering algorithm and improved RANSAC (random sample consensus). Firstly, the image is preprocessed by graying and binarizing in the inverse perspective mapping image. Secondly, eliminating noise points and smoothing lane boundaries by using morphological erosion and opening operation in the binary image, then traversing the binary image and extracting the feature points. Then, the feature points are clustered by dbscan clustering algorithm, which are then divided into clusters. Finally, the points in every class was fitted a straight line or curve line by using improved RANSAC algorithm based on parabola. The experimental results show that the improved RANSAC algorithm accelerates the speed of lane detection. The proposed algorithm has good real-time, robustness and accuracy.

关键词： detection dbscan clustering algorithm Improved RANSAC fitting

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Short-Term Power Load Forecasting Model Based on Fuzzy Neural Network using Improved Decision Tree

Short-Term Power Load Forecasting Model Based on Fuzzy Neura...

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Sustainable Power and Energy Conference (iSPEC)

作者： Zhenxue Xie Ruogu Wang Zihao Wu Tong Liu State Grid Shaanxi Electric Power Research Institute Xi’an China

ISBN: (纸本)9781728149318

Aiming at the low accuracy of short-term power load forecasting, Short-Term power Load Forecasting Model Based on Fuzzy Neural Network using Improved Decision Tree is proposed. High accuracy power load forecasting for a specific area, specific date and specific time period in the smart grid environment can be implemented for power dispatching. The short-term power load forecasting model preprocesses the historical data of each region and quantifies each feature, then classifies the training data using dbscan clustering algorithm and decision tree algorithm MID3, and finally sends the classified data to the fuzzy neural network for training and prediction. Based on the original fuzzy neural network, the short-term power load forecasting model introduces the decision tree to classify the historical data. The simulation results show that the short-term power load forecasting model improves the prediction accuracy, reduces the relative error, and the model is more effective.

关键词： smart grid short-term power load forecast dbscan clustering algorithm decision tree algorithm MID3 fuzzy neural network Fuzzy neural networks Decision Trees Smart grids electric loads forecasting

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A new approach for solution of vehicle routing problem with hard time window: an application in a supermarket chain

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2017年第12期42卷 2067-2080页

作者： Comert, Serap Ercan Yazgan, Harun Resit Sertvuran, Irem Sengul, Hanife Sakarya Univ Dept Ind Engn Sakarya Turkey

In this study, a vehicle routing problem with hard time windows (VRPHTW) that appears to meet demands of customers' service within time intervals in a supermarket chain is solved. In VRPHTW, to reach a solution by an exact method is quite difficult and sometimes impossible if number of constraints is large enough (i.e., NP-hard), and solution time of a VRPHTW grows exponentially. As the size of the problem grows, a near optimal solution can be found using a heuristic method. A hierarchical approach consisting of two stages as "cluster-first route-second" is proposed. In the first stage, customers are assigned to vehicles using three different clustering algorithms (i.e., K-means, K-medoids and dbscan). In the second stage, a VRPHTW is solved using a MILP. The main contribution of the article is that the proposed hierarchical approach enables us to deal with a large size real problem and to solve it in a short time using the exact method. Finally, the proposed approach is employed on a supermarket chain. An instance of the algorithm is demonstrated to illustrate the applicability of the proposed approach and the results obtained are highly favourable.

关键词： Vehicle routing problem with hard time windows K-means clustering algorithm K-medoids clustering algorithm dbscan clustering algorithm cluster first route second approach

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Lane Detection algorithm Based on Density clustering and RANSAC

Lane Detection Algorithm Based on Density Clustering and RAN...

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第30届中国控制与决策会议

作者： Jitong Wang Wei Hong Lei Gong College of communication Engineering Jilin University

In order to improve the accuracy,real-time and robustness of video lane detection,in this paper,we propose a fast lane detection algorithm based on dbscan(Density Based Spatial clustering of Applications with Noise) clustering algorithm and improved RANSAC(random sample consensus).Firstly,the image is preprocessed by graying and binarizing in the inverse perspective mapping ***,eliminating noise points and smoothing lane boundaries by using morphological erosion and opening operation in the binary image,then traversing the binary image and extracting the feature ***,the feature points are clustered by dbscan clustering algorithm,which are then divided into ***,the points in every class was fitted a straight line or curve line by using improved RANSAC algorithm based on *** experimental results show that the improved RANSAC algorithm accelerates the speed of lane *** proposed algorithm has good real-time,robustness and accuracy.

关键词： Lane detection dbscan clustering algorithm Improved RANSAC fitting

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The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier

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KNOWLEDGE-BASED SYSTEMS 2015年 76卷 200-218页

作者： Salehi, Saber Selamat, Ali Mashinchi, M. Reza Fujita, Hamido Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Univ Teknol Malaysia IRDA Digital Media Ctr Johor Baharu 81310 Johor Malaysia Iwate Prefectural Univ Takizawa Iwate 0200193 Japan

Granulation extracts a bundle of similar patterns by decomposing universe. Hyperboxes are granular classifiers to confront the uncertainties in granular computing. This paper proposes a granular classifier to discover hyperboxes in three phases. The first phase of the proposed model uses the set calculus to build the hyperboxes;where, the means of the dbscan clustering algorithm constructs the structure. The second phase develops the geometry of hyperboxes to improve the classification rate. It uses the Particle Swarm Optimization (PSO) algorithm to optimize the seed_points and expand the hyperboxes. Finally, the third phase identifies the noise points;where, the patterns in the second phase did not belong to any hyperboxes. We have used the capability of membership function of a fuzzy set to improve the geometry of classifier. The performance of a proposed model is carried out in terms of coverage, mis-classification error and accuracy. Experimental results reveal that the proposed model can adaptively choose an appropriate granularity. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Hyperbox geometry of classifiers Granular classifier Membership functions Particle swarm optimization dbscan clustering algorithm

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Counting crowd flow based on feature points

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NEUROCOMPUTING 2014年 133卷 377-384页

作者： Liang, Ronghua Zhu, Yuge Wang, Haixia Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

A counting approach for crowd flow based on feature points is proposed. The objective is to obtain the characteristics of the crowd flow in a scene, including the crowd orientation and numeric count. For the feature point detection, a three-frame difference algorithm is used to obtain a foreground containing only the moving objects. Therefore, after the SURF feature point detection, only the feature points of the foreground are retained for further processing. This greatly reduces the time complexity of the SURF algorithm. For feature point clustering, we present an improved dbscan clustering algorithm in which the non-motion feature points are further eliminated and only the remaining feature points are clustered. For the calculation of the crowd flow orientation, the feature points are tracked based on a local Lucas Kanade optical flow with Hessian matrix algorithm. In the crowd flow number counting, the crowd eigenvectors are constructed based on the SURF feature points and are trained using a support vector regression machine. The experimental results show that the proposed crowd orientation and counting method are more robust and provide crowd flow statistics with higher accuracy than previous approaches. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.

关键词： Three-frame difference SURF feature point dbscan clustering algorithm Hessian matrix Optical flow Support vector regression

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Applicability of clustering Techniques on Masquerade Detection 3

Applicability of Clustering Techniques on Masquerade Detecti...

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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Raveendran, Reshma Dhanya, K. A. SCMS Sch Engn & Technol Dept Comp Sci & Engn Ernakulam Kerala India

ISBN: (纸本)9781479930807

In masquerade attack, attacker impersonates legitimate user. Most of the masquerade detection techniques done so far are based on supervised learning techniques. But here in this paper masquerade detection based on unsupervised learning techniques are used. Various clustering algorithms used are K-Means, K-Medoid, Agglomerative clustering algorithm and dbscan. A comparative study is done based on the detection capability of these four clustering algorithms. The experiment is conducted on Schonlau data set [1]. From the experiment it was found that K-Medoid algorithm, agglomerative clustering algorithm and dbscan algorithm outperforms K-means clustering.

关键词： Masquerade detection unsupervised learning techniques clustering Schonlau data set k- means clustering k- medoid clustering agglomerative clustering dbscan clustering algorithm

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