Improved Reduced‐Order Fault Detection Filter Design for Polytopic Uncertain Discrete‐Time Markovian Jump Systems with Time‐Varying Delays

Rong, Lihong; Peng, Xiuyan; Liu, Liangliang; Zhang, Biao

doi:10.1155/2018/9489620

Cited by 6 publications

(4 citation statements)

References 32 publications

(37 reference statements)

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“…The local-density ρ i and distance δ i values of each data point are output according to the above formula, and then the corresponding decision graph can be generated to determine and select the clustering center point. The clustering centroids often have large local density ρ i and distance δ i values at the same time, and based on this feature, the clustering centroids can be circled in the decision graph [23][24][25]. Step 1…”

Section: Density Peaks Clustering Algorithmmentioning

confidence: 99%

A Wolf Pack Optimization Theory Based Improved Density Peaks Clustering Approach

et al. 2020

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In view of the problem that the Density Peaks Clustering (DPC) algorithm needs to manually set the parameter cut-off distance (dc) we propose a Wolf Pack optimization theory based Density Peaks Clustering approach (WPA-DPC). Firstly, we introduce dc parameter into the Wolf Pack Algorithm (WPA) to speed up the search. Secondly, we introduce the WPA into the DPC algorithm; the cut-off distance is used as the location of the wolf group. Finally, we make silhouette index in the search process as the fitness value, and the optimal location of the wolf group is the parameter value at the end. The simulation results show that compared with the traditional Density Peaks Clustering algorithm, the proposed algorithm is closer to the true clustering number. According to the evaluation results of silhouette and f-measure, the quality of clustering and the accuracy are greatly improved.

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Section: Density Peaks Clustering Algorithmmentioning

confidence: 99%

A Wolf Pack Optimization Theory Based Improved Density Peaks Clustering Approach

et al. 2020

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“…Cam-shift algorithm is the commonly used gesture tracking algorithm, which is good for tracking solid objects in a black-and-white background. However, the contrast between the background color and the target is not obvious, and the tracking effect is poor [15][16][17][38][39][40]. To verify the effectiveness of the switching federated filter algorithm, a gesture tracking and positioning experiment based on the 3D interactive software is presented.…”

Section: Gesture Tracking and Positioning Experiments Based On D Imentioning

confidence: 99%

Large‐Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

Hou

2018

Complexity

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Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated filtering algorithm work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction.

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“…Hand gesture data are also collected using optics camera [13][14][15] or radar. [16][17][18][19] Optical-based gesture recognition mainly uses cameras to capture gesture images and then applies machine-learning methods 20,21 for feature extraction and recognition. Coelho et al 14 used Kinect to capture RGB and depth images of hand gestures.…”

Section: Introductionmentioning

confidence: 99%

Hand gesture recognition algorithm combining hand-type adaptive algorithm and effective-area ratio for efficient edge computing

Zhang

Xiao

et al. 2021

J. Electron. Imag.

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Most existing gesture recognition algorithms have low recognition rates under rotation, translation, and scaling of hand images as well as different hand types. We propose a new hand gesture recognition algorithm that combines the hand-type adaptive algorithm and effective-area ratio based on feature matching. Samples are divided into several groups according to the subjects' palm shapes and the algorithm is trained using self-collected data. The user's hand type is paired with one of the sample libraries by the hand-type adaptive algorithm. To further improve the accuracy, the effective-area ratio of the gesture is calculated based on the minimum bounding rectangle, and the preliminary gesture is recognized by the effective-area ratio feature method. The results of experiments demonstrate that the proposed algorithm could accurately recognize gestures in real time and exhibits good adaptability to different hand types. The overall recognition rate is over 94%. The recognition rate still exceeds 93% when hand gesture images are rotated, translated, or scaled.

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Improved Reduced‐Order Fault Detection Filter Design for Polytopic Uncertain Discrete‐Time Markovian Jump Systems with Time‐Varying Delays

Cited by 6 publications

References 32 publications

A Wolf Pack Optimization Theory Based Improved Density Peaks Clustering Approach

A Wolf Pack Optimization Theory Based Improved Density Peaks Clustering Approach

Large‐Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

Hand gesture recognition algorithm combining hand-type adaptive algorithm and effective-area ratio for efficient edge computing

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