New Stabilization Results for Semi‐Markov Chaotic Systems with Fuzzy Sampled‐Data Control

Wu, Tao; Cao, Jinde; Xiong, Lianglin; Zhang, Haiyang

doi:10.1155/2019/7875305

Cited by 51 publications

(19 citation statements)

References 51 publications

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“…e kernel-related filter tracking algorithm converts the target tracking into a linear regression model and then completes the training through the multichannel histogram feature, which can realize the tracking and recognition of the research object [20][21][22]. If the corresponding input of the research target image is z, the corresponding weight coefficient is w, and the corresponding output is f(z) � w T z. us, the key to using this method for target tracking is to find the corresponding numerical results of f (z).…”

Section: Filter Tracking Algorithm Based On Swimming Targetmentioning

confidence: 99%

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

et al. 2021

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This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.

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Section: Filter Tracking Algorithm Based On Swimming Targetmentioning

confidence: 99%

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

et al. 2021

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“…A low frequency underwater metastructure composed by helix metal and viscoelastic damping rubber systems are developed by Gao and Zhang [14]. New Stabilization Results for Semi-Markov Chaotic Systems with Fuzzy Sampled-Data Control is developed by Wu et al [15]. Differential received signal strength based RFID positioning for construction equipment tracking is in investigated by Wu et al [16].…”

Section: -Review Of Research Work Analyzing and Minimizing The Surface Roughness In The Machining Operationsmentioning

confidence: 99%

WITHDRAWN: Surface Roughness Prediction and Minimization in 5-Axis Milling Operations of Gas Turbine Blades

Eyvazian

Musharavati

Khan

et al. 2021

Preprint

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To enhance the quality of machined parts, virtual machining systems are presented in this study. In the turbine blades, the minimization of the surface roughness of the blades can decrease the Reynolds number to decrease the loss of energy in power generation. Due to difficulties of polishing process in minimizing the surface roughness of machined blades, the optimized machining parameters for minimizing the surface roughness is an effective solution for the problem. In this study, a virtual machining system is developed to predict and minimize the surface roughness in 5-Axis machining operations of gas turbine blades. To minimize the surface roughness, the machining parameters were optimized by the Genetic algorithm. To validate the developed system, the turbine blades were machined using a 5-Axis CNC machine tool and the machined blades were measured using the CMM machine to obtain the surface roughness of machined parts. So, a 41.29% reduction in the measured surface roughness and a 42.09% reduction in the predicted surface roughness are obtained using the optimized machining parameters. The developed virtual machining system can be applied in the machining process of turbine blades to enhance the surface quality of machined blades and thus improve the efficiency of gas turbines.

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“…In recent years, new techniques and models have been developed by researchers worldwide [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. During the last 6 years, several studies have been individualized regarding the flash-flood susceptibility investigations, which were carried out through the integration of GIS techniques with bivariate statistical models such as: frequency ratio [ 36 ], weights of evidence [ 37 ], statistical index [ 38 ], evidential belief function [ 39 ], certainty factor [ 40 ], or index of entropy [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

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New Stabilization Results for Semi‐Markov Chaotic Systems with Fuzzy Sampled‐Data Control

Cited by 51 publications

References 51 publications

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

WITHDRAWN: Surface Roughness Prediction and Minimization in 5-Axis Milling Operations of Gas Turbine Blades

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

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