Visual Analysis of Large Multivariate Scattered Data using Clustering and Probabilistic Summaries

Rapp, Tobias; Peters, Christoph; Dachsbacher, Carsten

doi:10.1109/tvcg.2020.3030379

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Therefore, in this paper, we propose Gaussian distribution density points of obstacles to achieve dynamic step size. Gaussian distribution is widely used in data clustering in data science (Rapp et al, 2021;Zhou and Wang, 2021). By using the Gaussian distribution function to generate points concentrated around their mean values, as shown in Figure 3, the random points are uniformly distributed around the desired path to reduce the sampling space.…”

Section: Dynamic Variable Step Samplingmentioning

confidence: 99%

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Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

Shang

Liu

Qin

et al. 2023

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PurposeThe goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.Design/methodology/approachThe path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.FindingsThe algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.Originality/valueAn improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

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Section: Dynamic Variable Step Samplingmentioning

confidence: 99%

“…Gaussian distribution is widely used in data clustering in data science (Rapp et al. , 2021; Zhou and Wang, 2021).…”

Section: Qgd-rrt Algorithm Frameworkmentioning

confidence: 99%

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

Shang

Liu

Qin

et al. 2023

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“…Particle simulations are used across a plethora of scientific fields. Analyzing and understanding this kind of data is a hot topic, as evidenced by the fact that 2015, 2016 and 2019 scientific visualization contests all focused on particle datasets [6], [10], [11]. For each of these datasets, the tasks of feature extraction and tracking were critical for understanding the development of the simulated phenomena over time.…”

Section: The Proposed Systemmentioning

confidence: 99%

Novel extraction and tracking method used in multiplelevel for computer vision

Akram

Abdulateef

2023

IJEECS

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Various scientific applications, including cosmological simulation, fluid simulation, and molecular dynamics, depend heavily on the analysis of particle data. Although there are techniques for feature extraction and tracking regarding volumetric data, it is more difficult to do such tasks for particle data due to the lack of explicit connectivity information. Even though one could transform the particle data to volume beforehand, doing so runs the risk of incurring error and growing the data size. In order to facilitate feature extraction and tracking for scientific particle data, we adopt a deep learning (DL) method in this research. In order to capture the relation between physical features and spatial locations in a neighborhood, we use a DL model that generates latent vectors. Through clustering the latent vectors, characteristics could be retrieved from the vectors. The Cam-shift tracking algorithm, which just needs inference of the latent vector for chosen regions of interest, is implemented in the feature space to accomplish quick feature tracking. With the use of two datasets, we test our approach and contrast it with other approaches already in use.

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“…The proposed approach entails a hybrid algorithm combining a scatter plot and visual geometric group for identifying gearbox fault types, which included no-fault (Type A), rust (Type B), chipped (Type C), gearbox gear worn (Type D), and gearbox aged (Type E). Initially, the accelerometer was installed on the gearbox for signal capture, followed by signal processing to generate a scatter plot [20]. Subsequently, the scatter plot was utilized as a feature map input into VGG 19 for training and fault type identification [21].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Gearbox Using Vibration Scatter Plot and Visual Geometric Group Network

Wang,

Hung,

et al. 2024

Processes

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This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor was integrated with a NI-9234 DAQ card. Digital signal processing techniques were employed to actively filter out noise from the captured signals. Gaussian white noise was incorporated into the training data to enhance the noise resistance of the network model, which was then utilized for scatter plot generation. The VGG technique was subsequently applied to identify faults. The testing data were collected at two different speeds, with 1500 samples taken at each speed, totaling 3000 samples. For both training and testing, 400 samples of each fault type were employed for training, while 200 samples were allocated for testing. The test results demonstrated an overall identification accuracy of 97.7% for both the no-fault gearbox and the four-fault states, underscoring the effectiveness of the proposed methodology.

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Visual Analysis of Large Multivariate Scattered Data using Clustering and Probabilistic Summaries

Cited by 8 publications

References 44 publications

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

Novel extraction and tracking method used in multiplelevel for computer vision

Fault Diagnosis of Wind Turbine Gearbox Using Vibration Scatter Plot and Visual Geometric Group Network

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