The Experts below are selected from a list of 6036 Experts worldwide ranked by ideXlab platform
Ming Chen - One of the best experts on this subject based on the ideXlab platform.
-
FSKD (4) - A Fuzzy Self-Tuning Algorithm for Depth-Axis Control in Image-Based Visual Servo Control
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Xiao Jing Shen, Ming ChenAbstract:This paper is concerned with choosing image features for image-based visual servo control and how a fuzzy self- Tuning PI control Algorithm is designed to track a planar target with motion along the depth-axis. In this paper, we study a specific class of IBVS problem, in which a camera is constrained to move in the depth-axis(or Z-axis), since depth control is very important in IBVS system. Such a constraint condition makes it possible to find image moments reflecting target depth, and thus leads to a relative simple PI depth controller. The fuzzy self Tuning Algorithm is introduced to improve the performance of the PI depth controller And the significant properties of the simulation results show that the fuzzy self Tuning Algorithm is shown by simulation.
-
A fuzzy self-Tuning Algorithm for depth-axis control in image-based visual servo control
Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, 2007Co-Authors: Xiao Jing Shen, Ming ChenAbstract:This paper is concerned with choosing image features for image-based visual servo control and how a fuzzy self- Tuning PI control Algorithm is designed to track a planar target with motion along the depth-axis. In this paper, we study a specific class of IBVS problem, in which a camera is constrained to move in the depth-axis(or Z-axis), since depth control is very important in IBVS system. Such a constraint condition makes it possible to find image moments reflecting target depth, and thus leads to a relative simple PI depth controller. The fuzzy self Tuning Algorithm is introduced to improve the performance of the PI depth controller And the significant properties of the simulation results show that the fuzzy self Tuning Algorithm is shown by simulation.
Zhizhuo Zhao - One of the best experts on this subject based on the ideXlab platform.
-
research on a secondary Tuning Algorithm based on svd stft for fid signal
Measurement Science and Technology, 2016Co-Authors: Haobin Dong, Jian Ge, Zhiwen Yuan, Zhizhuo ZhaoAbstract:The Tuning precision of a Proton precession magnetometer's sensor is the key to getting the best signal to noise ratio (SNR) of free induction decay (FID) signals. By analyzing the noises of the magnetometer's sensor and conditioning circuit, this paper introduces the principle of Tuning and proposes a secondary Tuning Algorithm based on the singular value decomposition (SVD) and short-time Fourier transform (STFT), targeting the current lack of a Tuning method. Moreover, the STFT for an FID signal's feature analysis is applied for the first time. First, the space matrix is constructed by the acquisition of ADC for the untuned FID signal, and then the SVD is performed to eliminate the noise and obtain the useful signal. Finally, the STFT technique is applied to the denoised signal to extract the time-frequency feature. By theory analysis, simulation modeling and the testing of an actual FID signal, the results show that, compared with general Tuning methods such as peak detection and fast Fourier transform (FFT), the proposed Algorithm improves the sensor's Tuning precision, and the time of the Tuning process is no more than one second. Furthermore, the problem of misTuning in strong-disturbance environments is solved. Thus, the secondary Tuning Algorithm based on the SVD and STFT is more practical.
-
Research on a secondary Tuning Algorithm based on SVD & STFT for FID signal
Measurement Science and Technology, 2016Co-Authors: Haobin Dong, Jian Ge, Zhiwen Yuan, Zhizhuo ZhaoAbstract:The Tuning precision of a Proton precession magnetometer's sensor is the key to getting the best signal to noise ratio (SNR) of free induction decay (FID) signals. By analyzing the noises of the magnetometer's sensor and conditioning circuit, this paper introduces the principle of Tuning and proposes a secondary Tuning Algorithm based on the singular value decomposition (SVD) and short-time Fourier transform (STFT), targeting the current lack of a Tuning method. Moreover, the STFT for an FID signal's feature analysis is applied for the first time. First, the space matrix is constructed by the acquisition of ADC for the untuned FID signal, and then the SVD is performed to eliminate the noise and obtain the useful signal. Finally, the STFT technique is applied to the denoised signal to extract the time-frequency feature. By theory analysis, simulation modeling and the testing of an actual FID signal, the results show that, compared with general Tuning methods such as peak detection and fast Fourier transform (FFT), the proposed Algorithm improves the sensor's Tuning precision, and the time of the Tuning process is no more than one second. Furthermore, the problem of misTuning in strong-disturbance environments is solved. Thus, the secondary Tuning Algorithm based on the SVD and STFT is more practical.
Le Gruenwald - One of the best experts on this subject based on the ideXlab platform.
-
An SLA and operation cost aware performance re-Tuning Algorithm for cloud databases
IEEE International Conference on Cloud Computing CLOUD, 2017Co-Authors: Liangzhe Li, Le GruenwaldAbstract:Cloud database, also called Database as a Service (DbaaS), is defined as a pay-per-use model for enabling ondemand access to reliable and configurable services. Tenants pay to the cloud service provider only for the services they use through a service level agreement (SLA). Due to changes in queries and/or databases, tenants' performance SLA may be violated at some point in time. This paper proposes a performance Tuning Algorithm, called AutoClustC, which estimates the costs of resource provisioning and database repartitioning and chooses the lower cost approach to tune the system in order to re-guarantee the performance SLA when a performance violation occurs. When database partitioning is chosen, the Algorithm takes both performance SLA and operation cost into consideration, and uses data mining techniques to automatically and dynamically re-partition the databases for a tenant to generate better partitioning solutions. © 2016 IEEE.
-
CLOUD - An SLA and Operation Cost Aware Performance Re-Tuning Algorithm for Cloud Databases
2016 IEEE 9th International Conference on Cloud Computing (CLOUD), 2016Co-Authors: Liangzhe Li, Le GruenwaldAbstract:Cloud database, also called Database as a Service (DbaaS), is defined as a pay-per-use model for enabling on-demand access to reliable and configurable services. Tenants pay to the cloud service provider only for the services they use through a service level agreement (SLA). Due to changes in queries and/or databases, tenants' performance SLA may be violated at some point in time. This paper proposes a performance Tuning Algorithm, called AutoClustC, which estimates the costs of resource provisioning and database re-partitioning and chooses the lower cost approach to tune the system in order to re-guarantee the performance SLA when a performance violation occurs. When database partitioning is chosen, the Algorithm takes both performance SLA and operation cost into consideration, and uses data mining techniques to automatically and dynamically re-partition the databases for a tenant to generate better partitioning solutions.
Xiao Jing Shen - One of the best experts on this subject based on the ideXlab platform.
-
FSKD (4) - A Fuzzy Self-Tuning Algorithm for Depth-Axis Control in Image-Based Visual Servo Control
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Co-Authors: Xiao Jing Shen, Ming ChenAbstract:This paper is concerned with choosing image features for image-based visual servo control and how a fuzzy self- Tuning PI control Algorithm is designed to track a planar target with motion along the depth-axis. In this paper, we study a specific class of IBVS problem, in which a camera is constrained to move in the depth-axis(or Z-axis), since depth control is very important in IBVS system. Such a constraint condition makes it possible to find image moments reflecting target depth, and thus leads to a relative simple PI depth controller. The fuzzy self Tuning Algorithm is introduced to improve the performance of the PI depth controller And the significant properties of the simulation results show that the fuzzy self Tuning Algorithm is shown by simulation.
-
A fuzzy self-Tuning Algorithm for depth-axis control in image-based visual servo control
Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, 2007Co-Authors: Xiao Jing Shen, Ming ChenAbstract:This paper is concerned with choosing image features for image-based visual servo control and how a fuzzy self- Tuning PI control Algorithm is designed to track a planar target with motion along the depth-axis. In this paper, we study a specific class of IBVS problem, in which a camera is constrained to move in the depth-axis(or Z-axis), since depth control is very important in IBVS system. Such a constraint condition makes it possible to find image moments reflecting target depth, and thus leads to a relative simple PI depth controller. The fuzzy self Tuning Algorithm is introduced to improve the performance of the PI depth controller And the significant properties of the simulation results show that the fuzzy self Tuning Algorithm is shown by simulation.
Haobin Dong - One of the best experts on this subject based on the ideXlab platform.
-
research on a secondary Tuning Algorithm based on svd stft for fid signal
Measurement Science and Technology, 2016Co-Authors: Haobin Dong, Jian Ge, Zhiwen Yuan, Zhizhuo ZhaoAbstract:The Tuning precision of a Proton precession magnetometer's sensor is the key to getting the best signal to noise ratio (SNR) of free induction decay (FID) signals. By analyzing the noises of the magnetometer's sensor and conditioning circuit, this paper introduces the principle of Tuning and proposes a secondary Tuning Algorithm based on the singular value decomposition (SVD) and short-time Fourier transform (STFT), targeting the current lack of a Tuning method. Moreover, the STFT for an FID signal's feature analysis is applied for the first time. First, the space matrix is constructed by the acquisition of ADC for the untuned FID signal, and then the SVD is performed to eliminate the noise and obtain the useful signal. Finally, the STFT technique is applied to the denoised signal to extract the time-frequency feature. By theory analysis, simulation modeling and the testing of an actual FID signal, the results show that, compared with general Tuning methods such as peak detection and fast Fourier transform (FFT), the proposed Algorithm improves the sensor's Tuning precision, and the time of the Tuning process is no more than one second. Furthermore, the problem of misTuning in strong-disturbance environments is solved. Thus, the secondary Tuning Algorithm based on the SVD and STFT is more practical.
-
Research on a secondary Tuning Algorithm based on SVD & STFT for FID signal
Measurement Science and Technology, 2016Co-Authors: Haobin Dong, Jian Ge, Zhiwen Yuan, Zhizhuo ZhaoAbstract:The Tuning precision of a Proton precession magnetometer's sensor is the key to getting the best signal to noise ratio (SNR) of free induction decay (FID) signals. By analyzing the noises of the magnetometer's sensor and conditioning circuit, this paper introduces the principle of Tuning and proposes a secondary Tuning Algorithm based on the singular value decomposition (SVD) and short-time Fourier transform (STFT), targeting the current lack of a Tuning method. Moreover, the STFT for an FID signal's feature analysis is applied for the first time. First, the space matrix is constructed by the acquisition of ADC for the untuned FID signal, and then the SVD is performed to eliminate the noise and obtain the useful signal. Finally, the STFT technique is applied to the denoised signal to extract the time-frequency feature. By theory analysis, simulation modeling and the testing of an actual FID signal, the results show that, compared with general Tuning methods such as peak detection and fast Fourier transform (FFT), the proposed Algorithm improves the sensor's Tuning precision, and the time of the Tuning process is no more than one second. Furthermore, the problem of misTuning in strong-disturbance environments is solved. Thus, the secondary Tuning Algorithm based on the SVD and STFT is more practical.
