Automatic identification of fracture using density-based clustering algorithm with time-spatial constraints

2017 ◽  
Author(s):  
Qingfeng Xue ◽  
Yibo Wang ◽  
Hongyu Zhai ◽  
Xu Chang
Keyword(s):  
Clustering Algorithm ◽  
Automatic Identification ◽  
Spatial Constraints ◽  
Density Based Clustering

scholarly journals Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining

2021 ◽  
Vol 13 (3) ◽  
pp. 1089
Author(s):  
Hailin Zheng ◽  
Qinyou Hu ◽  
Chun Yang ◽  
Jinhai Chen ◽  
Qiang Mei
Keyword(s):  
Clustering Algorithm ◽  
Pattern Mining ◽  
Transportation Networks ◽  
Automatic Identification ◽  
Identification System ◽  
Classification Models ◽  
Transmission Path ◽  
The Hierarchical Structure ◽  
Hierarchical Clustering Algorithm ◽  
And Control

Since the spread of the coronavirus disease 2019 (COVID-19) pandemic, the transportation of cargo by ship has been seriously impacted. In order to prevent and control maritime COVID-19 transmission, it is of great significance to track and predict ship sailing behavior. As the nodes of cargo ship transportation networks, ports of call can reflect the sailing behavior of the cargo ship. Accurate hierarchical division of ports of call can help to clarify the navigation law of ships with different ship types and scales. For typical cargo ships, ships with deadweight over 10,000 tonnages account for 95.77% of total deadweight, and 592,244 berthing ships’ records were mined from automatic identification system (AIS) from January to October 2020. Considering ship type and ship scale, port hierarchy classification models are constructed to divide these ports into three kinds of specialized ports, including bulk, container, and tanker ports. For all types of specialized ports (considering ship scale), port call probability for corresponding ship type is higher than other ships, positively correlated with the ship deadweight if port scale is bigger than ship scale, and negatively correlated with the ship deadweight if port scale is smaller than ship scale. Moreover, port call probability for its corresponding ship type is positively correlated with ship deadweight, while port call probability for other ship types is negatively correlated with ship deadweight. Results indicate that a specialized port hierarchical clustering algorithm can divide the hierarchical structure of typical cargo ship calling ports, and is an effective method to track the maritime transmission path of the COVID-19 pandemic.

Distance Density Based Clustering Algorithm in Wireless Sensor Network

2011 ◽  
Vol 291-294 ◽  
pp. 344-348
Author(s):  
Lin Lin ◽  
Shu Yan ◽  
Yi Nian
Keyword(s):  
Clustering Algorithm ◽  
Distribution Density ◽  
Simulation Experiment ◽  
Clustering Algorithms ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Cluster Heads ◽  
Hierarchical Topology ◽  
Energy Factors ◽  
Density Based Clustering

The hierarchical topology of wireless sensor networks can effectively reduce the consumption in communication. Clustering algorithm is the foundation to realize herarchical structure, so it has been extensive researched. On the basis of Leach algorithm, a distance density based clustering algorithm (DDBC) is proposed, considering synthetically the distribution density of around nodes and the remaining energy factors of the node to dynamically banlance energy usage of nodes when selecting cluster heads. We analyzed the performance of DDBC through compared with the existing other clustering algorithms in simulation experiment. Results show that the proposed method can generare stable quantity cluster heads and banlance the energy load effectively.

An Efficient Density-based Clustering Algorithm for Face Groping

2021 ◽  
Author(s):  
Shenfei Pei ◽  
Feiping Nie ◽  
Rong Wang ◽  
Xuelong Li
Keyword(s):  
Clustering Algorithm ◽  
Density Based Clustering

An improved OPTICS clustering algorithm for discovering clusters with uneven densities

2021 ◽  
Vol 25 (6) ◽  
pp. 1453-1471
Author(s):  
Chunhua Tang ◽  
Han Wang ◽  
Zhiwen Wang ◽  
Xiangkun Zeng ◽  
Huaran Yan ◽  
...  
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Substantial Improvement ◽  
Experimental Results ◽  
High Time ◽  
Parameter Setting ◽  
K Nearest Neighbor ◽  
Density Based Clustering

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.

scholarly journals A new method for unveiling open clusters in Gaia

2018 ◽  
Vol 618 ◽  
pp. A59 ◽  
Author(s):  
A. Castro-Ginard ◽  
C. Jordi ◽  
X. Luri ◽  
F. Julbe ◽  
M. Morvan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Dimensional Space ◽  
Open Clusters ◽  
Machine Learning Algorithms ◽  
New Era ◽  
Use Of Data ◽  
Manual Intervention ◽  
Density Based Clustering ◽  
Artificial Neural Network Ann ◽  
Astrometric Data

Context. The publication of the Gaia Data Release 2 (Gaia DR2) opens a new era in astronomy. It includes precise astrometric data (positions, proper motions, and parallaxes) for more than 1.3 billion sources, mostly stars. To analyse such a vast amount of new data, the use of data-mining techniques and machine-learning algorithms is mandatory. Aims. A great example of the application of such techniques and algorithms is the search for open clusters (OCs), groups of stars that were born and move together, located in the disc. Our aim is to develop a method to automatically explore the data space, requiring minimal manual intervention. Methods. We explore the performance of a density-based clustering algorithm, DBSCAN, to find clusters in the data together with a supervised learning method such as an artificial neural network (ANN) to automatically distinguish between real OCs and statistical clusters. Results. The development and implementation of this method in a five-dimensional space (l, b, ϖ, μα*, μδ) with the Tycho-Gaia Astrometric Solution (TGAS) data, and a posterior validation using Gaia DR2 data, lead to the proposal of a set of new nearby OCs. Conclusions. We have developed a method to find OCs in astrometric data, designed to be applied to the full Gaia DR2 archive.

scholarly journals Modelling the Risk of Imported COVID-19 Infections at Maritime Ports Based on the Mobility of International-Going Ships

2022 ◽  
Vol 11 (1) ◽  
pp. 60
Author(s):  
Zhihuan Wang ◽  
Chenguang Meng ◽  
Mengyuan Yao ◽  
Christophe Claramunt
Keyword(s):  
Clustering Algorithm ◽  
Infection Risk ◽  
Data Driven ◽  
Risk Level ◽  
Automatic Identification ◽  
Identification System ◽  
Prevention Measures ◽  
Increase Rate ◽  
Spatio Temporal ◽  
Imported Infections

Maritime ports are critical logistics hubs that play an important role when preventing the transmission of COVID-19-imported infections from incoming international-going ships. This study introduces a data-driven method to dynamically model infection risks of international ports from imported COVID-19 cases. The approach is based on global Automatic Identification System (AIS) data and a spatio-temporal clustering algorithm that both automatically identifies ports and countries approached by ships and correlates them with country COVID-19 statistics and stopover dates. The infection risk of an individual ship is firstly modeled by considering the current number of COVID-19 cases of the approached countries, increase rate of the new cases, and ship capacity. The infection risk of a maritime port is mainly calculated as the aggregation of the risks of all of the ships stopovering at a specific date. This method is applied to track the risk of the imported COVID-19 of the main cruise ports worldwide. The results show that the proposed method dynamically estimates the risk level of the overseas imported COVID-19 of cruise ports and has the potential to provide valuable support to improve prevention measures and reduce the risk of imported COVID-19 cases in seaports.

scholarly journals Lane Formation Beyond Intuition Towards an Automated Characterization of Lanes in Counter-flows

2020 ◽  
Vol 5 ◽  
Author(s):  
Luca Crociani ◽  
Giuseppe Vizzari ◽  
Andrea Gorrini ◽  
Stefania Bandini
Keyword(s):  
Clustering Algorithm ◽  
Variable Density ◽  
Automatic Identification ◽  
Computing Power ◽  
Dbscan Clustering ◽  
Significant Difference ◽  
Lane Formation ◽  
Human Coder ◽  
Behavioural Dynamics

Pedestrian behavioural dynamics have been growingly investigated by means of (semi)automated computing techniques for almost two decades, exploiting advancements on computing power, sensor accuracy and availability, computer vision algorithms. This has led to a unique consensus on the existence of significant difference between unidirectional and bidirectional flows of pedestrians, where the phenomenon of lane formation seems to play a major role. The collective behaviour of lane formation emerges in condition of variable density and due to a self-organisation dynamic, for which pedestrians are induced to walk following preceding persons to avoid and minimize conflictual situations. Although the formation of lanes is a well-known phenomenon in this field of study, there is still a lack of methods offering the possibility to provide an (even semi-) automatic identification and a quantitative characterization. In this context, the paper proposes an unsupervised learning approach for an automatic detection of lanes in multi-directional pedestrian flows, based on the DBSCAN clustering algorithm. The reliability of the approach is evaluated through an inter-rater agreement test between the results achieved by a human coder and by the algorithm.

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