Least squares support vector machine model. by Yang Guo (399435)

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”

AdaBoost training flowchart. by Yang Guo (399435)

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”

Schematic diagram of chiller units [25]. by Yang Guo (399435)

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”

Confusion matrix diagram. by Yang Guo (399435)

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”

Model comparison analysis results. by Yang Guo (399435)

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”

<i>AR</i> of the five fault diagnosis model. by Yang Guo (399435)

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”

<i>AR</i> of the seven benchmark fault diagnosis model. by Yang Guo (399435)

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”

S1 Data - by Hongping Wei (196994)

“…Experimental results confirmed that the proposed estimation algorithm exhibits optimal performance compared to EDR estimations based on PCE and VWE, and the estimated values have smaller errors relative to the true EDR values. …”

Loss curves of the multi-head mechanism. by Hongping Wei (196994)

“…Experimental results confirmed that the proposed estimation algorithm exhibits optimal performance compared to EDR estimations based on PCE and VWE, and the estimated values have smaller errors relative to the true EDR values. …”

Training flow chart of the multi-head mechanism. by Hongping Wei (196994)

“…Experimental results confirmed that the proposed estimation algorithm exhibits optimal performance compared to EDR estimations based on PCE and VWE, and the estimated values have smaller errors relative to the true EDR values. …”

Introduction of turbulence field related content. by Hongping Wei (196994)

“…Experimental results confirmed that the proposed estimation algorithm exhibits optimal performance compared to EDR estimations based on PCE and VWE, and the estimated values have smaller errors relative to the true EDR values. …”

Power spectrum diagram. by Hongping Wei (196994)

“…Experimental results confirmed that the proposed estimation algorithm exhibits optimal performance compared to EDR estimations based on PCE and VWE, and the estimated values have smaller errors relative to the true EDR values. …”

Quantitative results on WEDU dataset. by Dunlu Lu (19964225)

“…The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. …”

Counting results on DRPD dataset. by Dunlu Lu (19964225)

“…The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. …”

Quantitative results on RFRB dataset. by Dunlu Lu (19964225)

“…The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. …”

Main module structure. by Dunlu Lu (19964225)

“…The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. …”

Counting results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. …”

Quantitative results on DRPD dataset. by Dunlu Lu (19964225)

“…The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. …”

Architecture of MAR-YOLOv9. by Dunlu Lu (19964225)

“…The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. …”

Quantitative results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. …”