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Full-Text Articles in Engineering
Image Semantic Segmentation Algorithm Based On Improved Deeplabv3+, Weiping Zhao, Yu Chen, Song Xiang, Yuanqiang Liu, Chaoyue Wang
Image Semantic Segmentation Algorithm Based On Improved Deeplabv3+, Weiping Zhao, Yu Chen, Song Xiang, Yuanqiang Liu, Chaoyue Wang
Journal of System Simulation
Abstract: Mainstream image semantic segmentation networks currently face problems such as incorrec segmentation, discontinuous segmentation, and high model complexity, which cannot be flexibly and efficiently deployed in practical scenarios. To this end, an image semantic segmentation network that optimizes the DeepLabv3+ model is designed by comprehensively considering the network parameters, prediction time, and accuracy. The lightweight EfficientNetv2 is adopted to extract backbone network features and improve parameter utilization. In the atrous spatial pyramid pooling module, the mixed strip pooling is utilized to replace the global average pooling, and a depthwise separable dilated convolution is introduced to reduce parameters and improve …
Detection Of Small Objects In Open-Pit Coal Mine Stopes Using Uav Remote Sensing Images, Liu Mingrui, Che Ben, Dong Hongbo, Zhu Ruoli
Detection Of Small Objects In Open-Pit Coal Mine Stopes Using Uav Remote Sensing Images, Liu Mingrui, Che Ben, Dong Hongbo, Zhu Ruoli
Coal Geology & Exploration
Open-pit coal mine stopes witness frequent vehicle accidents due to their complex terrains. Therefore, accurately positioning vehicles in the stopes is critical to safe coal mining. To overcome the inaccurate positioning of small targets in the mining areas using Unmanned aerial vehicle (UAV) remote sensing images, this study proposed an improved YOLOv7 model for object detection. First, to expedite the reasoning of the YOLOv7 network, the ELAN module in the original YOLOv7 model was improved by introducing partial convolution. Based on this, the eSE channel attention mechanism was combined to form the PConv-eSE convolution attention module. The purpose is to …
An Attention Mechanism-Based Cnn-Bilstm Real-Time Transient Electromagnetic Method, Gu Yao, Xie Haijun, Zhou Zipeng, Li Lu
An Attention Mechanism-Based Cnn-Bilstm Real-Time Transient Electromagnetic Method, Gu Yao, Xie Haijun, Zhou Zipeng, Li Lu
Coal Geology & Exploration
The one-dimensional transient electromagnetic (TEM) method is time-consuming and suffers other drawbacks such as difficult parameter adjustment and heavy dependence on the initial model. Therefore, this study proposed a real-time TEM inversion method—the Attention mechanism-based convolutional neural network (CNN) - bidirectional Long Short-Term Memory (BiLSTM) (AC-BiLSTM). By fully utilizing the time difference, the AC-BiLSTM performed model training in non-observation time and the real-time inversion of the collected data in observation time. With the measured data mixed with a certain proportion of data obtained from forward modeling as the dataset, the sampling time and apparent resistivity as the input features in …
Rgb-D Saliency Object Detection Based On Cross-Refinement And Circular Attention, Qingqing Dong, Hao Wu, Wenhua Qian, Fengling Kong
Rgb-D Saliency Object Detection Based On Cross-Refinement And Circular Attention, Qingqing Dong, Hao Wu, Wenhua Qian, Fengling Kong
Journal of System Simulation
Abstract: In order to solve the problems that the boundary of the saliency object detection area is vague, and the detection area is incomplete or inaccurate, an RGB-D saliency object detection method based on cross-refinement and circular attention is proposed. A cross-refinement module is designed at the stage of extracting features using encoders, which is used to supplement feature information of each other and improve the feature quality before fusion. It also suppresses the negative impact of poor-quality depth maps and addresses the issue that the edges of the saliency object are blurred. For the features after fusion, the circular …
Surface Defect Detection Of Power Equipment Using Adaptive Receptive Field Network, Hao Yu, Jinxia Jiang, Xiaohan Lai, Feng Mei
Surface Defect Detection Of Power Equipment Using Adaptive Receptive Field Network, Hao Yu, Jinxia Jiang, Xiaohan Lai, Feng Mei
Journal of System Simulation
Abstract: For the detection of defects such as icing, rust, and contamination of power equipment in substations, a novel adaptive receptive field network (ARFN) is proposed, in which an adaptive receptive field module (ARFM) combined with the attention mechanism can effectively fuse multi-scale features. Considering the small sample learning attribute of defect detection, a power equipment surface defect simulation data synthesis method based on real texture is also proposed. The experimental results on the simulation dataset show that the network has high detection accuracy for surface defects across devices, while having advantages such as small size and fast operation speed.
Semantic Segmentation Model Based On Adaptive Fusion And Attention Refinement, Yun Wei, Qi Luo, Yingzhi Zhao
Semantic Segmentation Model Based On Adaptive Fusion And Attention Refinement, Yun Wei, Qi Luo, Yingzhi Zhao
Journal of System Simulation
Aiming at the insufficient use of context information and loss of detail information of the existing semantic segmentation, a model based on adaptive fusion and attention refinement is proposed.The model introduces an adaptive fusion module in the process of coding, and solves the insufficient use of context information by fusing each feature map according to the corresponding weight. An attention thinning module is designed in the process of decoding, so that the low-order features and high-order features can guide and optimize each other to solve the loss of detail information.The experimental results show that the average intersection union …
Multi-Agent Cooperative Combat Simulation In Naval Battlefield With Reinforcement Learning, Ding Shi, Xuefeng Yan, Lina Gong, Jingxuan Zhang, Donghai Guan, Mingqiang Wei
Multi-Agent Cooperative Combat Simulation In Naval Battlefield With Reinforcement Learning, Ding Shi, Xuefeng Yan, Lina Gong, Jingxuan Zhang, Donghai Guan, Mingqiang Wei
Journal of System Simulation
Abstract: Due to the rapidly-changed situations of future naval battlefields, it is urgent to realize the high-quality combat simulation in naval battlefields based on artificial intelligence to comprehensively optimize and improve the combat effectiveness of our army and defeat the enemy. The collaboration of combat units is the key point and how to realize the balanced decision-making among multiple agents is the first task. Based on decoupling priority experience replay mechanism and attention mechanism, a multi-agent reinforcement learning-based cooperative combat simulation (MARL-CCSA) network is proposed. Based on the expert experience, a multi-scale reward function is designed, on which a naval …
Solving Turkish Math Word Problems By Sequence-To-Sequence Encoder-Decoder Models, Esi̇n Gedi̇k, Tunga Güngör
Solving Turkish Math Word Problems By Sequence-To-Sequence Encoder-Decoder Models, Esi̇n Gedi̇k, Tunga Güngör
Turkish Journal of Electrical Engineering and Computer Sciences
Solving math word problems (MWP) is a challenging task due to the semantic gap between natural language texts and mathematical equations. The main purpose of the task is to take a written math problem as input and produce a proper equation as output for solving that problem. This paper describes a sequence-to-sequence (seq2seq) neural model for automatically solving Turkish MWPs based on their semantic meanings in the text. It comprises a bidirectional encoder to comprehend the semantics of the problem by encoding the input sequence and a decoder with attention to extract the equation by tracking the semantic meanings of …
Research On Image Super-Resolution Reconstruction Based On Loss Extraction Feedback Attention Network, Hong Sun, Yuxiang Zhang, Yuelan Ling
Research On Image Super-Resolution Reconstruction Based On Loss Extraction Feedback Attention Network, Hong Sun, Yuxiang Zhang, Yuelan Ling
Journal of System Simulation
Abstract: Since the first application of convolutional neural network to the field of super-resolution image reconstruction (super-resolution convolutional neural network, SRCNN), a large number of studies have proved that deep learning can improve the effect of image reconstruction. Aiming at the too many parameters in the image super-resolution network and the insufficient utilization of image features resulting in less available high-frequency information, a loss extraction feedback attention network (LEFAN) is proposed to reuse parameters in a circular way and increase the reuse of low-resolution image features to capture more high-frequency information. The loss caused in the reconstruction process is extracted …