An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent

Liu, Miaomiao; Yao, Dan; Liu, Zhigang; Guo, Jingfeng; Chen, Jing

doi:10.1155/2023/4765891

Cited by 25 publications

(10 citation statements)

References 19 publications

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“…The input insect pest images and their corresponding labeled images are used to train DMSAU-Net. Five - fold cross-validation (fivefold CV) scheme and stochastic gradient descent (SGD) with an adaptive moment estimator (Adam) are often used to train all models [ 22 ].…”

Section: Dilated Multi-scale Attention U-net (Dmsau-net)mentioning

confidence: 99%

Crop insect pest detection based on dilated multi-scale attention U-Net

Wang,

Zhang,

Zhang

2024

Plant Methods

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Background Crop pests seriously affect the yield and quality of crops. Accurately and rapidly detecting and segmenting insect pests in crop leaves is a premise for effectively controlling insect pests. Methods Aiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the multi-scale features of insect pest images. An attention module is added to its decoder to focus on the edge of the insect pest image. Results The experiments on the crop insect pest image IP102 dataset are implemented, and achieved the detection accuracy of 92.16% and IoU of 91.2%, which is 3.3% and 1.5% higher than that of MSR-RCNN, respectively. Conclusion The results indicate that the proposed method is effective as a new insect pest detection method. The dilated Inception can improve the accuracy of the model, and the attention module can reduce the noise generated by upsampling and accelerate model convergence. It can be concluded that the proposed method can be applied to practical crop insect pest monitoring system.

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Section: Dilated Multi-scale Attention U-net (Dmsau-net)mentioning

confidence: 99%

Crop insect pest detection based on dilated multi-scale attention U-Net

Wang,

Zhang,

Zhang

2024

Plant Methods

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“…It estimates the first-order and second-order moments of the gradient for each parameter based on the objective function with exponential moving average. By keeping the feature scaling of each parameter's gradient unchanged, high noise and gradient dilution issues can be solved during the parameter space iteration process [22].…”

Section: Input Layermentioning

confidence: 99%

Lightweight Arc Fault Detection Method Based on Adam-Optimized Neural Network and Hardware Feature Algorithm

Chen,

Han,

Zhao

et al. 2024

Energies

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Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices.

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“…To optimize the balanced cross-entropy defined in Equation (1), the Adam optimizer [ 35 ] was used. The model was trained with a learning rate of 0.001 and for 100 epochs.…”

Section: Lightweight Semantic Segmentation Fcn-mobilenetv2mentioning

confidence: 99%

IRDC-Net: Lightweight Semantic Segmentation Network Based on Monocular Camera for Mobile Robot Navigation

Dang

Tran

Tan

2023

Sensors

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Computer vision plays a significant role in mobile robot navigation due to the wealth of information extracted from digital images. Mobile robots localize and move to the intended destination based on the captured images. Due to the complexity of the environment, obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement. This study offers a real-time solution to the problem of extracting corridor scenes from a single image using a lightweight semantic segmentation model integrating with the quantization technique to reduce the numerous training parameters and computational costs. The proposed model consists of an FCN as the decoder and MobilenetV2 as the decoder (with multi-scale fusion). This combination allows us to significantly minimize computation time while achieving high precision. Moreover, in this study, we also propose to use the Balance Cross-Entropy loss function to handle diverse datasets, especially those with class imbalances and to integrate a number of techniques, for example, the Adam optimizer and Gaussian filters, to enhance segmentation performance. The results demonstrate that our model can outperform baselines across different datasets. Moreover, when being applied to practical experiments with a real mobile robot, the proposed model’s performance is still consistent, supporting the optimal path planning, allowing the mobile robot to efficiently and effectively avoid the obstacles.

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An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent

Cited by 25 publications

References 19 publications

Crop insect pest detection based on dilated multi-scale attention U-Net

Crop insect pest detection based on dilated multi-scale attention U-Net

Lightweight Arc Fault Detection Method Based on Adam-Optimized Neural Network and Hardware Feature Algorithm

IRDC-Net: Lightweight Semantic Segmentation Network Based on Monocular Camera for Mobile Robot Navigation

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