Noise decrease rate comparison. by Xianlin Ren (22783589)

“…This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. …”

SDAE network hyperparameters. by Xianlin Ren (22783589)

“…This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. …”

Signal-noise overlap ration comparison. by Xianlin Ren (22783589)

“…This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. …”

SDAE network training parameters. by Xianlin Ren (22783589)

“…This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. …”

Time cost comparison. by Xianlin Ren (22783589)

“…This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. …”

The principle of Partial Convolution. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Ablation experiments results of YOLOv5s. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Overall network architecture of FCMI-YOLO. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

The principle of MLCA mechanism. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Parameters of the dataset. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Comparison of mAP@0.5 for different ratios. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Primary training parameters for the model. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Distribution of the dataset. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Parameters of the FasterNext and C3. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

System diagram. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Schematic diagram of Inner-IoU. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Model train environment. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

The structure of FasterNext. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

The structure of MLCA mechanism. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Impact of class imbalance. by GaoXiang Zhao (21499525)