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6th International Conference on Natural Language Processing (ICNLP)

作者： Li, Yan Fan, Jiulun Han, Gang Xian Univ Posts & Telecommun Natl Engn Res Ctr Secured Wireless Xian 710121 Peoples R China

ISBN: (纸本)9798350349122;9798350349115

The PBFT algorithm, a classic Byzantine Fault Tolerance consensus mechanism, is designed to mitigate node failures and malicious behavior within distributed systems. However, its effectiveness is hindered when confronted with large-scale networks and heightened latency. In response to these challenges, we proposed the K-DPBFT algorithm, a novel approach based on Byzantine Fault Tolerance principles, aimed at overcoming the aforementioned *** dataset is initially partitioned through multiple iterations, merging clustering results from different partitions. Subsequently, the data is clustered using the dbscan algorithm. Finally, two rounds of the PBFT consensus algorithm are conducted to achieve node consensus, aiming to strike a balance between efficiency and performance. Experimental results demonstrate that, in a consensus process involving a significant number of participating nodes, the K-DPBFT algorithm, while possessing Byzantine Fault Tolerance capability, reduces communication interactions by over tenfold compared to the traditional PBFT algorithm. Moreover, the consensus duration is shortened by more than 20%, significantly enhancing consensus efficiency.

关键词： Practical byzantine fault tolerance Blockchain dbscan algorithm Consensus algorithm

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Revdbscan and Flexscan-O(n log n) Clustering algorithms 28th

Revdbscan and Flexscan-O(n log n) Clust...

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28th International Conference on Neural Information Processing

作者： Jankowski, Norbert Nicolaus Copernicus Univ Dept Informat Torun Poland

ISBN: (纸本)9783030923068;9783030923075

The goal of this paper is to present two new algorithms conceptually close to density-based clustering. Both algorithms deal with problems no worse than the dbscan algorithm, and additionally, flexscan deals with nonuniform distributions of data. The complexity of both algorithms is O(n log n) in contrary to the well-known dbscan algorithm which complexity is O(n(2)). Additionally, we show that the complexity of dbscan cannot be reduced to O(n log n) just by using locality sensitive hashing trees (or either r-trees or kd-trees). In the final part of the paper, we present results on benchmark datasets. Results clearly show the superiority of the proposed algorithms.

关键词： Clustering Cluster analysis Unsupervised learning Machine learning Locality sensitive hashing dbscan algorithm

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China City Planning Review 2021年第2期30卷 33-42页

作者： Zhou Huiquan Wang Li Liang Sisi(Proofread) School of Geography and Planning Sun Yat-Sen UniversityGuangzhouP.R.China School of Geographical Sciences Southwest UniversityChongqingP.R.China 不详

The international catering industry is a way to represent the international lifestyle of a *** study of the spatial distribution of international restaurants can reveal the sociological characteristics of the globalization-intensive areas within the city,and it is also the external manifestation of the degree of urban globalization and social *** the international restaurants of Chongqing as its research object,this paper adopts the dbscan algorithm to investigate the spatial distribution and social-cultural significance in respect of urban living in the context of *** is found that there are mainly four types of international restaurants in Chongqing,namely Western,Japanese,Korean,and Southeast Asian ***,the spatial distribution of international restaurants showcases that the restaurants mostly agglomerate in the urban core,and alongside Jichang Road(also the rail transit line 3).The spatial distribution of the international restaurants overlaps with local daily life space,but is separated from local industrial spaces;the clusters of fine dining restaurants and fast food restaurants are isolated from each *** restaurants are adjacent to commercial areas,transportation stations,universities,and tourist ***,the street corners,surroundings of banks,and shopping malls are considered micro-location factors for international restaurants.

关键词： international restaurant dbscan algorithm catering space customer services globalization

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Landslide Susceptibility Assessment Based on Clustering Methods

Landslide Susceptibility Assessment Based on Clustering Meth...

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作者： DEBORAH SIMON MWAKAPESA 江西理工大学

学位级别：硕士

The aim of this study is to assess and compare two clustering algorithms,namely,the Wave-cluster and Density Based Spatial Clustering of Application with Noise(dbscan)algorithms,for landslide susceptibility assessment in Baota District,*** on historical reports,interpretation of aerial photographs and field survey reports 293 were identified in the study *** factors associated with landslide occurrence were prepared and used as inputs into the models for landslide susceptibility *** models divided the study area grids into 478 and 465 subclasses respectively,which were further grouped into five susceptibility levels using K-means algorithm,based on the landslide density values of the obtained *** resulting two models were validated and compared using area under the receiver operating characteristics(AUC-ROC)curve and statistical measures including sensitivity,specificity and *** analysis demonstrated that the Wave-cluster model outperformed the dbscan model with sensitivity,specificity and accuracy values of 88.05%,89.2% and 88.54% respectively,while dbscan had sensitivity,specificity and accuracy values of 86.69%,86.38% and 86.56% *** addition,the Wave-cluster model had higher value AUC of 0.823 than the dbscan model(0.808).This indicates that,the landslide susceptibility maps(LSMs)generated in this study had good performance for assessing landslide susceptibility in the study area and can be useful tools in assessing landslides,as the foundation for preventing and reducing landslide consequences for suitable social and economic ***,this is the first time these models are assessed and compared,especially in landslide susceptibility assessment.

关键词： Landslide Landslide susceptibility Wave-cluster dbscan algorithm

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A Research on Dynamic Identification of Main Route of River Estuary Segment Based on AIS

A Research on Dynamic Identification of Main Route of River ...

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3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Pei YiLou Chen DeJun Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781538678312

The dynamic uncertainty caused by tidal current in river estuary is affected, A method for dynamic identification of main route of river estuary segment based on AIS (Automatic Identification System) is proposed by using the AIS navigation trajectory of the most recent typical ship and the time point of large Ocean currents. Firstly, clustering algorithm is used to analyze the navigation trajectory of typical ships in the estuary recently, and the track of the estuary channel is identified, then, cubic spline interpolation algorithm is used to fit the center trajectory points obtained by dbscan (Density-Based Spatial Clustering of Applications with Noise), and get the main route, combined with the current characteristics of large ocean current, the main route is modified appropriately, therefore, the effect of frequent changes on the channel of river estuary is solved, and the validity of this method is verified through the analysis of the measured data.

关键词： AIS dbscan algorithm Main course of the estuary

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Subset simulation with adaptable intermediate failure probability for robust reliability analysis: an unsupervised learning-based approach

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2022年第6期65卷 172-172页

作者： Zhao, Yinghao Wang, Zeyu South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510641 Peoples R China Guangzhou Inst Bldg Sci Grp Co Ltd Guangzhou 510440 Peoples R China Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China

Subset simulation (SS) was known for its computational efficiency in estimating small failure probabilities as well as reducing emulation demands. The main idea behind SS lies in decomposing the original reliability problem into sub-reliability problems with more frequent probabilities. By taking advantage of Markov Chain Monte Carlo-based sampling technique (MCMC), this innovative strategy enables the computational feasibility for estimating small failure probability based on the prescribed number of simulations. However, SS still has several limitations that can potentially decrease its computational efficiency. It is mainly attributed to the fact that the estimated failure probability can be inconsistent due to insufficient samples in each subset. Moreover, SS requires empirical definition for MCMC proposal sampling to guarantee a good acceptance rate. However, the inherent shortcoming of MCMC brings the correlations among the samples in each subset, which can inevitably cause the biased estimate for failure probability. To address these limitations, a new method, called ULSS, that combines SS, importance sampling (IS), and dbscan algorithm is proposed to overcome these limitations. Specifically, the batch size of samples in each subset is adaptively increased until the prescribed threshold is satisfied, which facilitates the adjustment of intermediate failure probabilities. To enable the process of adaptive sampling, MCMC is substituted with IS to draw samples located in the effective sampling regions defined through dbscan. Computational performance of ULSS is demonstrated by investigating three examples with one for illustrative interpretation and the other two for engineered paradigm. Results indicate the computational consistency, unbias, and robustness of ULSS in terms of the statistical properties of the estimated failure probability.

关键词： Reliability analysis Monte carlo simulation Subset simulation Importance sampling Unsupervised learning dbscan algorithm

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A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

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PEERJ COMPUTER SCIENCE 2021年 7卷 e580页

作者： Azhir, Elham Navimipour, Nima Jafari Hosseinzadeh, Mehdi Sharifi, Arash Darwesh, Aso Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Yunlin Taiwan Gachon Univ Pattern Recognit & Machine Learning Lab 1342 Seongnamdaero Sujeonggu Seongnam South Korea Univ Human Dev Dept Informat Technol Sulaymaniyah Iraq

Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.

关键词： Query optimization Access plan recommendation Cluster computing Parallel Processing MapReduce dbscan algorithm

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Railway Overhead Contact System Point Cloud Classification

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SENSORS 2021年第15期21卷 4961-4961页

作者： Chen, Xiao Chen, Zhuang Liu, Guoxiang Chen, Kun Wang, Lu Xiang, Wei Zhang, Rui Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl & Local Joint Engn Lab Safe Space Informat T Chengdu 611756 Peoples R China Sichuan Vocat & Tech Coll Commun Dept Rd & Bridge Engn Chengdu 611130 Peoples R China

As the railway overhead contact system (OCS) is the key component along the high-speed railway, it is crucial to detect the quality of the OCS. Compared with conventional manual OCS detection, the vehicle-mounted Light Detection and Ranging (LiDAR) technology has advantages such as high efficiency and precision, which can solve the problems of OCS detection difficulty, low efficiency, and high risk. Aiming at the contact cables, return current cables, and catenary cables in the railway vehicle-mounted LiDAR OCS point cloud, this paper used a scale adaptive feature classification algorithm and the dbscan (density-based spatial clustering of applications with noise) algorithm considering OCS characteristics to classify the OCS point cloud. Finally, the return current cables, catenary cables, and contact cables in the OCS were accurately classified and extracted. To verify the accuracy of the method presented in this paper, we compared the experimental results of this article with the classification results of TerraSolid, and the classification results were evaluated in terms of four accuracy indicators. According to statistics, the average accuracy of using this method to extract two sets of OCS point clouds is 99.83% and 99.89%, respectively;the average precision is 100% and 99.97%, respectively;the average recall is 99.16% and 99.42%, respectively;and the average overall accuracy is 99.58% and 99.69% respectively, which is overall better than TerraSolid. The experimental results showed that this approach could accurately and quickly extract the complete OCS from the point cloud. It provides a new method for processing railway OCS point clouds and has high engineering application value in railway component detection.

关键词： railway OCS point cloud scale adaptive feature algorithm dbscan algorithm classification

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A Regionalization Approach Based on the Comparison of Different Clustering Techniques

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10563页

作者： Colmenero, Jose Luis Aguilar Garcia-Miguel, Javier Portela Univ Complutense Madrid UCM Grp Anal Secur & Syst GASS Dept Software Engn & Artificial Intelligence DISIA Fac Comp Sci & Engn Madrid 28040 Spain Univ Complutense Madrid UCM Fac Stat Studies Dept Stat & Data Sci Madrid 28040 Spain

For biodiversity conservation and the development of protected areas, it is essential to create strategic plans that ensure the preservation and sustainable use of natural resources. Biogeography plays a crucial role in supporting these efforts by identifying and categorizing geographic areas (regionalization) that represent different biotas, as well as recognizing patterns in biodiversity distribution. Another application of regionalization is in planning species sampling and inventories. Developing a species list is vital for monitoring and understanding diversity patterns. This study focuses on the Palearctic region, specifically the areas between Morocco, the Iberian Peninsula, and France. Its aim is to compare different clustering algorithms-such as K-means++, dbscan, PD-clustering, Infomap, and federated heuristic optimization based on fuzzy clustering-with a reference regionalization, using environmental and soil data. Various spatial contiguity approaches were applied, including the third-degree polynomial model and principal coordinates. The results demonstrated that the hybrid approach offers a robust solution in the construction of the regions and that K-means++ and PDC produced regions with strong spatial similarity to the reference regionalization, closely aligning with the expected number of regions, especially at the biome level. Our study shows that a purely statistical regionalization can approximate a global reference regionalization, making it reproducible.

关键词： biodiversity biogeography K-means algorithm dbscan algorithm PD clustering algorithm regionalization Infomap fuzzy c-means

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Controlled Islanding Based on the Coherency of Generators and Minimum Electrical Distance

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IEEE ACCESS 2021年 9卷 146830-146840页

作者： Aghamohammadi, M. R. Mahdavizadeh, S. Fazel Shahid Beheshti Univ Elect Engn Dept Tehran *** Iran

Inter-area oscillations and cascading failures are the most serious threats to the security of the electric power system. Uncontrolled islanding will occur in the event of an unstable inter-area oscillation or a progressive cascading failure. The establishment of uncontrolled islands with a deficiency in load-generation balance is the main reason for system blackout. Controlled islanding has been proposed as a preventive strategy for reducing the risk of blackout in this regard. A new algorithm for applying the controlled islanding strategy is proposed in this paper, based on load coherency and nearest electrical distances between coherent groups of generators. Coherent generators as the main core for controlled islands are identified in this method, which is based on the correlation coefficients between generators and the dbscan clustering algorithm. The sub-networks are then created by applying mixed-integer linear programming to each coherent group. Once this is accomplished, non-linear programming is used to construct the stable sub-networks associated with islands that meet the requirements of load-generation balance, voltage limitations, and transmission limits. The proposed scheme is implemented on the small-scale IEEE 39-bus system and a large-scale realistic power system which is the Iran power grid. The results demonstrate that the proposed method is capable of being implemented in a real power system.

关键词： Intentional islanding generator coherency electrical distances dbscan algorithm Dijkstra algorithm non-linear programming

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