scholarly journals Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1638 ◽  
Author(s):  
Kun Xia ◽  
Hanyu Wang ◽  
Menghan Xu ◽  
Zheng Li ◽  
Sheng He ◽  
...  
Keyword(s):  
Spatial Clustering ◽  
Mesh Network ◽  
User Friendliness ◽  
Hybrid Clustering ◽  
Racquet Sports ◽  
Clustering Model ◽  
Dbscan Algorithm ◽  
Multiple Devices ◽  
Share Information ◽  
Data Calibration

Racquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed to achieve reliable activity recognition and perform number counting. Additionally, a Bluetooth mesh network enables communication between a phone and wristband, and sets-up the connection between multiple devices. This allows users to track their exercise through the phone and share information with other players and referees. Considering the complexity of the classification algorithm and the user-friendliness of the measurement system, the improved multi-layer hybrid clustering model applies three-level K-means clustering to optimize feature extraction and segmentation and then uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to determine the feature center of different movements. The model can identify unlabeled and noisy data without data calibration and is suitable for smartwatches to recognize multiple racquet sports. The proposed system shows better recognition results and is verified in practical experiments.

scholarly journals Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam

2021 ◽  
Vol 10 (4) ◽  
pp. 198
Author(s):  
Sevim Sezi Karayazi ◽  
Gamze Dane ◽  
Bauke de Vries
Keyword(s):  
Geographically Weighted Regression ◽  
Spatial Clustering ◽  
Least Square ◽  
Geospatial Data ◽  
Evidence Based ◽  
Ordinary Least Square ◽  
Mass Tourism ◽  
Dbscan Algorithm ◽  
National Monuments ◽  
Combined Data

Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam.

scholarly journals Object-Independent Grasping in Heavy Clutter

2020 ◽  
Vol 10 (3) ◽  
pp. 804 ◽  
Author(s):  
HyunJun Jo ◽  
Jae-Bok Song
Keyword(s):  
Spatial Clustering ◽  
Computational Time ◽  
Success Rates ◽  
Computational Costs ◽  
Difference Image ◽  
Cluttered Environment ◽  
Depth Difference ◽  
Dbscan Algorithm ◽  
Bin Picking ◽  
Object Region

When grasping objects in a cluttered environment, a key challenge is to find appropriate poses to grasp effectively. Accordingly, several grasping algorithms based on artificial neural networks have been developed recently. However, these methods require large amounts of data for learning and high computational costs. Therefore, we propose a depth difference image-based bin-picking (DBP) algorithm that does not use a neural network. DBP predicts the grasp pose from the object and its surroundings, which are obtained through depth filtering and clustering. The object region is estimated by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and a depth difference image (DDI) that represents the depth difference between adjacent areas is defined. To validate the performance of the DBP scheme, bin-picking experiments were conducted on 45 different objects, along with bin-picking experiments in heavy clutters. DBP exhibited success rates of 78.6% and 83.3%, respectively. In addition, DBP required a computational time of approximately 1.4 s for each attempt.

scholarly journals Methodology for Extracting Potential Customized Bus Routes Based on Bus Smart Card Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2224 ◽  
Author(s):  
Jing Li ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Qi Ouyang
Keyword(s):  
Smart Card ◽  
Traffic Congestion ◽  
Spatial Clustering ◽  
Extraction Process ◽  
Methodological Framework ◽  
Transit Service ◽  
Dbscan Algorithm ◽  
Smart Card Data ◽  
Division Process

To alleviate traffic congestion and traffic-related environmental pollution caused by the increasing numbers of private cars, public transport (PT) is highly recommended to travelers. However, there is an obvious contradiction between the diversification of travel demands and simplification of PT service. Customized bus (CB), as an innovative supplementary mode of PT service, aims to provide demand-responsive and direct transit service to travelers with similar travel demands. But how to obtain accurate travel demands? It is passive and limited to conducting online surveys, additionally inefficient and costly to investigate all the origin-destinations (ODs) aimlessly. This paper proposes a methodological framework of extracting potential CB routes from bus smart card data to provide references for CB planners to conduct purposeful and effective investigations. The framework consists of three processes: trip reconstruction, OD area division and CB route extraction. In the OD area division process, a novel two-step division model is built to divide bus stops into different areas. In the CB route extraction process, two spatial-temporal clustering procedures and one length constraint are implemented to cluster similar trips together. An improved density-based spatial clustering of application with noise (DBSCAN) algorithm is used to complete these procedures. In addition, a case study in Beijing is conducted to demonstrate the effectiveness of the proposed methodological framework and the resulting analysis provides useful references to CB planners in Beijing.

Multi-Radius Density Clustering Algorithm Based on Outlier Factor

2014 ◽  
Vol 472 ◽  
pp. 427-431
Author(s):  
Zong Lin Ye ◽  
Hui Cao ◽  
Li Xin Jia ◽  
Yan Bin Zhang ◽  
Gang Quan Si
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Similar Process ◽  
The Core ◽  
Dbscan Algorithm ◽  
Proposed Model ◽  
Density Clustering ◽  
Relationship Of ◽  
Core Points ◽  
The Relationship

This paper proposes a novel multi-radius density clustering algorithm based on outlier factor. The algorithm first calculates the density-similar-neighbor-based outlier factor (DSNOF) for each point in the dataset according to the relationship of the density of the point and its neighbors, and then treats the point whose DSNOF is smaller than 1 as a core point. Second, the core points are used for clustering by the similar process of the density based spatial clustering application with noise (DBSCAN) to get some sub-clusters. Third, the proposed algorithm merges the obtained sub-clusters into some clusters. Finally, the points whose DSNOF are larger than 1 are assigned into these clusters. Experiments are performed on some real datasets of the UCI Machine Learning Repository and the experiments results verify that the effectiveness of the proposed model is higher than the DBSCAN algorithm and k-means algorithm and would not be affected by the parameter greatly.

scholarly journals A Novel Recursive Non-Parametric DBSCAN Algorithm for 3D Data Analysis with an Application in Rockfall Detection

2021 ◽  
Vol 16 (4) ◽  
pp. 579-587
Author(s):  
Pitisit Dillon ◽  
Pakinee Aimmanee ◽  
Akihiko Wakai ◽  
Go Sato ◽  
Hoang Viet Hung ◽  
...  
Keyword(s):  
Point Cloud ◽  
Crack Detection ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Divide And Conquer ◽  
Dbscan Algorithm ◽  
3D Data ◽  
Data Points ◽  
Clustering Data ◽  
Better Than

The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a well-known algorithm for spatial-clustering data point clouds. It can be applied to many applications, such as crack detection, rockfall detection, and glacier movement detection. Traditional DBSCAN requires two predefined parameters. Suitable values of these parameters depend upon the distribution of the input point cloud. Therefore, estimating these parameters is challenging. This paper proposed a new version of DBSCAN that can automatically customize the parameters. The proposed method consists of two processes: initial parameter estimation based on grid analysis and DBSCAN based on the divide-and-conquer (DC-DBSCAN) approach, which repeatedly performs DBSCAN on each cluster separately and recursively. To verify the proposed method, we applied it to a 3D point cloud dataset that was used to analyze rockfall events at the Puiggcercos cliff, Spain. The total number of data points used in this study was 15,567. The experimental results show that the proposed method is better than the traditional DBSCAN in terms of purity and NMI scores. The purity scores of the proposed method and the traditional DBSCAN method were 96.22% and 91.09%, respectively. The NMI scores of the proposed method and the traditional DBSCAN method are 0.78 and 0.49, respectively. Also, it can detect events that traditional DBSCAN cannot detect.

scholarly journals An Empirical Recommendation Framework to Support Location-Based Services

2020 ◽  
Vol 12 (9) ◽  
pp. 154 ◽  
Author(s):  
Animesh Chandra Roy ◽  
Mohammad Shamsul Arefin ◽  
A. S. M. Kayes ◽  
Mohammad Hammoudeh ◽  
Khandakar Ahmed
Keyword(s):  
Global Positioning System ◽  
Recommendation System ◽  
Spatial Clustering ◽  
Location Based Services ◽  
Point Of Interest ◽  
Dbscan Algorithm ◽  
Global Positioning ◽  
Similarity Calculation ◽  
Single User ◽  
Interest Profile

The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users.

scholarly journals VisHive: Supporting web-based visualization through ad hoc computational clusters of mobile devices

2018 ◽  
Vol 18 (2) ◽  
pp. 195-210 ◽  
Author(s):  
Zhe Cui ◽  
Shivalik Sen ◽  
Sriram Karthik Badam ◽  
Niklas Elmqvist
Keyword(s):  
Ad Hoc ◽  
Spatial Clustering ◽  
Principal Component ◽  
Text Analytics ◽  
Web Based ◽  
Web Technologies ◽  
Multiple Devices ◽  
Noise Clustering ◽  
Client Side ◽  
Visual Interface

Current web-based visualizations are designed for single computers and cannot make use of additional devices on the client side, even if today’s users often have access to several, such as a tablet, a smartphone, and a smartwatch. We present a framework for ad hoc computational clusters that leverage these local devices for visualization computations. Furthermore, we present an instantiating JavaScript toolkit called VisHive for constructing web-based visualization applications that can transparently connect multiple devices—called cells—into such ad hoc clusters—called a hive—for local computation. Hives are formed either using a matchmaking service or through manual configuration. Cells are organized into a master–slave architecture, where the master provides the visual interface to the user and controls the slaves and the slaves perform computation. VisHive is built entirely using current web technologies, runs in the native browser of each cell, and requires no specific software to be downloaded on the involved devices. We demonstrate VisHive using four distributed examples: a text analytics visualization, a database query for exploratory visualization, a density-based spatial clustering of applications with noise clustering running on multiple nodes, and a principal component analysis implementation.

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