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Dual Aggregation Convolution for Image Super-Resolution 3

Dual Aggregation Convolution for Image Super-Resolution

引用

3rd International Conference on Cloud Computing, Big data application and Software Engineering, CBASE 2024

作者： Wang, Zhongxun Xie, Zheng School of Physics and Electronic Information Yantai University Shandong China Yantai University Shandong Data Open Innovation Application Laboratory of Smart grid Advanced Technology Shandong China

ISBN: (纸本)9798331506582

In recent years, numerous image super-resolution (SR) methods have been introduced, achieving significant advancements. However, many existing methods employ complex, task-specific operations that, while effective, introduce substantial computational overhead when directly stacked, thereby hampering their practical utility. In response, we introduce DACNet, an efficient SR model that innovatively integrates spatial and channel information through a collaborative framework. Specifically, we alternately apply the Aggregation Spatial Convolution Module (ASCM) and the Aggregation Channel Convolution Module (ACCM) to dynamically extract and integrate multi-level features. Furthermore, we employ a Convolution Compensation Module (CCM) to mitigate local pixel forgetting. Our comprehensive experiments demonstrate that DACNet not only achieves superior performance but also maintains minimal computational demands, making it highly effective in resource-constrained environments. © 2024 IEEE.

关键词： Convolution

来源：评论

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Experiment Study of Passive Radar Maritime Target Detection Based on Staring Radar 4

Experiment Study of Passive Radar Maritime Target Detection ...

引用

4th International Conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2024

作者： Wu, Zaiyuan Song, Jie Wang, Zhongxun Yantai University School of Physics and Electronic Information Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology Yantai China Naval Aviation University Yantai China

ISBN: (纸本)9798350378542

This paper introduces the characteristics of passive radar system and staring radar as well as briefly introduces the data processing method, uses gaze radar as a non-cooperative radiation source, carries out a series of maritime target detection experiments, and verifies the experimental results with AIS system. The results show that the passive radar system can accurately get the target speed, distance and other information. © 2024 IEEE.

关键词： Radar target recognition

来源：评论

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Cotton Field Pest Recognition Based on Gradient Descent Mechanism and Large Convolutional Kernel Network 4

Cotton Field Pest Recognition Based on Gradient Descent Mech...

引用

4th IEEE International Conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Liu, Qianlong He, Pengfei Zhao, Zhimin Li, Chenglin School of Physics and Electronic Information Yantai University Yantai China School of Physics and Electronic Information Yantai University Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology Yantai China

ISBN: (纸本)9798350359589

To assist farmers in efficiently managing cotton fields in industrial areas, we propose a pest image recognition solution. This solution is based on the GD Mechanism (Gather and Disturb Mechanism) and a large convolutional kernel network. It incorporates the FAM (Feature Alignment Module), IFM (Information Fusion Module), and Inject (Information Injection Module) to align, fuse, and inject features at different hierarchical levels. This enhances the feature fusion capability of the network structure. To boost the model's sensitivity to enlarge the receptive field, we employ the RepLKNet with a large convolutional kernel as an enhancement to the existing small convolutional kernel in the original CNN architecture. Experimental results indicate a significant improvement in recognition accuracy after modifying the model. The optimized model's average precision increases by 2.8%, reaching 96.2%. This provides strong support for preventing and identifying cotton pests at different stages. © 2024 IEEE.

关键词： convolution kernel feature layer GD mechanism pest detection in cotton field YOLOv5

来源：评论

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Passive radar multi-target detection experimental study at sea 9

Passive radar multi-target detection experimental study at s...

引用

9th International Symposium on Computer and Information Processing technology, ISCIPT 2024

作者： Wu, Zaiyuan Song, Jie Wang, Zhongxun Wang, Qian Yantai University Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology China Yantai University School of Physics and Electronic Information Yantai China Naval Aviation University Yantai China

ISBN: (纸本)9798350388404

The non-cooperative dual-base radar provides superior performance in terms of safety and robustness of target detection. This paper presents a series of experimental maritime target detections using a passive radar system and the collection of a large amount of data. The first is an introduction to the principle of passive radar and the key technologies involved, the next step is to describe the experimental scenario, finally, the experimental data were used for the plotting of a radar system diagram and the analysis of the resultant graphs. Experimental results showed that the system could succeed in detecting targets at sea. © 2024 IEEE.

关键词： Radar target recognition

来源：评论

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Passive Radar Weak Target Detection Algorithm Fused with Feature Pyramid Network 4

Passive Radar Weak Target Detection Algorithm Fused with Fea...

引用

4th International Conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2024

作者： Wang, Qian Wang, Zhongxun Song, Jie Wu, Zaiyuan Yantai University Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology Yantai China School of Physics and Electronic Information Yantai University Yantai China Naval Aviation University Yantai China

ISBN: (纸本)9798350378542

Target detection is an important application of passive radar, but due to the non-cooperative characteristics of the radiation source, the target signal-to-noise ratio is too low, and the existing detection algorithms are difficult to effectively detect the target from the echo with low signal-to-noise ratio, and the problem of false detection occurs, which poses a challenge to the weak target detection of passive radar. In order to solve the above problems, the data collected by field passive radar experiments were used to make datasets, and the weak target detection experiments were carried out based on deep learning, and a passive radar weak target detection algorithm based on feature pyramid network was proposed. Firstly, a two-layer routing dynamic sparse attention mechanism is added to provide more flexible computational allocation and content awareness, and secondly, feature fusion is carried out to replace the neck network with a BiFPN network, so that the neural network can better understand and interpret multi-scale information. then, auxiliary detection heads were added to help propagate gradients better, speed up training and prevent gradient disappearance problems. Finally, using the WIoU dynamic non-monotonic focusing mechanism, it is possible to focus on the anchor frame of normal quality and improve the overall performance of the detector. In order to verify the effectiveness of the network, ablation experiments and comparative experiments were carried out. Ablation experiments show that the detection performance is improved after each improvement, the recall and average precision are increased by 3.4% and 4% respectively, and the precision is increased to 89.6% and 3.8% respectively. Experimental results show that the improved algorithm significantly reduces false detection and missed detection in passive radar weak target detection. © 2024 IEEE.

关键词： Ablation

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Improved Gerstner Function Wave Model Based on Elfouhaily Spectrum 15

Improved Gerstner Function Wave Model Based on Elfouhaily Sp...

引用

15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Wang, Jianlong Wang, Zhongxun Chen, Yujie Ma, Ketao School of Physics and Electronic Information Yantai University and Shandong Data Open Innovation Application Laboratory of Smart grid Advanced Technology Yantai University Yantai China School of Intelligent Manufacturing Qingdao Huanghai University Qingdao China

ISBN: (纸本)9798350354010

Aiming at the limitations of a single wave simulation approach across various marine environments, and with the aim of enhancing the reliability and realism of wave 3D modelling in different scenarios, this paper proposes a wave formation approach ground on the combination of the Gerstner function and the Elfouhaily wave spectra, which achieves the modelling of waves with different wave strengths, and feasible application of the model to further the reliability and safety of sea surface modelling is also outlined. Firstly, the improved Gerstner's functional equation system is selected as the wave construction model, and it is extended into a three-dimensional form to simulate the details and realism of the waves;secondly, in order to control the wave model and achieve the stochasticity and realism of the waves, the Elfouhaily wave spectra are used to gain the parameters for the control of the wave model, and then to establish the reliable wave simulation model;Finally, wave simulations and model comparisons are carried out with respect to the spectral parameters of multiple wave spectra, and issues related to the safety of ocean engineering applications and offshore activities based on the model are briefly *** experimental results show that the proposed method is reliable and practical, with high realism, and is applicable to a variety of wave scenarios, which is of great significance in improving the prognosis and safety assessment of the marine environment. © 2024 IEEE.

关键词： Stochastic systems

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MCNN-TEAN based unbalanced HRRP target identification method 24

MCNN-TEAN based unbalanced HRRP target identification method

引用

7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Wu, Wenjing Dan, Bo Wang, Zhongxun Xing, Zijie School of Physics and Electronic Information Yantai University Yantai264005 China Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology Yantai University Yantai264005 China Naval Aviation University Yantai264001 China Shandong Technology and Business University Yantai264003 China

ISBN: (纸本)9798400717192

The High Resolution Range Profile (HRRP) contains rich target information and is commonly utilized in the field of radar automatic target recognition. Most of the key targets of concern in practical applications are non-cooperative, making it challenging to acquire data for such targets. This often results in an imbalance in target categories within datasets, thereby impacting the classification performance of models. To address these issues, this paper proposes the Weighted-SMOTE (Synthetic Minority Over-sampling Technique) algorithm. The algorithm assigns different weights to the number of synthetic new samples for each sample based on the Euclidean distance between minority class samples and the remaining samples. Furthermore, it utilizes the MCNN-TEAN (Multi-scale Convolutional Neural Network - TransEncoder Attention Network) model to extract multi-level features and capture long-term dependencies between feature dimensions, thereby improving the classification accuracy of targets. © 2024 ACM.

关键词： Convolutional neural networks

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Lightweight Mobile Phone Screen Flaw Detection Algorithm Based on Improved YOLOv8 15

Lightweight Mobile Phone Screen Flaw Detection Algorithm Bas...

引用

15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Chen, Wanxin Zhou, Wei Zhang, Yuyuan Yan, Kaibin Ma, Lixiao He, Pengfei Yantai University School of Physics and Electronic Information Yantai China Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology Yantai China Shandong Lanyang Intelligent Technology Co. Yantai China Yantai Taihai Manul Nuclear Power Equipment Co. Yantai China

ISBN: (纸本)9798350354010

Aiming at the demand for detecting small defects on the surface of cell phone screens with highly reflective characteristics in industrial production, a lightweight detection algorithm for small defects on the surface of highly reflective media based on improved YOLOv8 (MGND-YOLOv8n) is proposed, which realizes a great reduction of computational volume and model parameters under the premise of guaranteeing the accuracy and effectively improves the detection speed. This algorithm employs the lightweight structure of MobileNetV3 to substitute the backbone structure of YOLOv8, thereby enhancing portability and realizing the lightweight network model. Additionally, GSConv and Slim-Neck structures are incorporated at the neck end of YOLOv8 to mitigate redundancy and duplicate information. To maintain the detection speed and reduce the complexity of the model, a detection head based on group normalization convolution is devised. Finally, the WIoU-v3 loss function is utilized to account for the quality imbalance of defective samples. Compared with CIoU, this effectively enhances the gradient gain distribution, improves the convergence speed and regression accuracy. Experimental results show that, compared with the original YOLOv8 algorithm, the proposed algorithm improves the average accuracy of mobile phone screen defect detection results by 1.7% at mAP@0.5, reduces the model parameters and computational load by 56.8% and 67.6% respectively, and the detection speed reaches 134.8FPS. © 2024 IEEE.

关键词： Convolution

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Research on Expansion Neighborhood and Cost Function-Based A* Algorithm 14

Research on Expansion Neighborhood and Cost Function-Based A...

引用

14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Zhao, Zhimin He, Pengfei Sun, Caihui Li, Chenglin School of Physics and Electronic Information Yantai University Yantai China Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology Yantai University Yantai China Ludong University College of International Education Yantai China

ISBN: (纸本)9798350301359

The ENCF-A* (Expansion Neighborhood and Cost Function-Based A*) algorithm tackles challenges in the traditional A* method such as lengthy paths and numerous turns. It achieves this by expanding the search scope, elongating step distances, and minimizing path deflection angles. The algorithm's efficiency is enhanced through the incorporation of obstacle distribution and a cost function that encourages movement toward the target. Additionally, the introduction of a turn cost function and the removal of redundant nodes prevent unnecessary detours, resulting in more realistic motion paths. Experimental outcomes exhibit significant improvements: ENCF-A* reduces path nodes by 82.61%, decreases deflection angles by 71.96%, and shortens path distances by 9.71% Consequently, this algorithm notably enhances the efficiency of tracing Automated Guided Vehicles (AGVs) in intricate environments. © 2023 IEEE.

关键词： A* algorithm AGVs cost function Expansion Neighbourhood

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Random Noise Suppression of Seismic data with Generative Adversarial Network 5

Random Noise Suppression of Seismic Data with Generative Adv...

引用

5th International Conference on Artificial Intelligence and advanced Manufacturing, AIAM 2023

作者： Wang, Linlin Wang, Jiao Wang, Zhongxun Liu, Peixue School of Physics and Electronic Information Yantai University Yantai China College of Intelligent Manufacturing Qingdao Huanghai University Qingdao China Shandong Data Open Innovation Application Laboratory of Smart grid Advanced Technology Yantai University Yantai China

ISBN: (纸本)9781839539848

Due to the complex nature of the seismic exploration environment, there is a significant amount of noise in the data gathering process, resulting in various factors that can affect the quality of the data. Standard denoising techniques and models are often insufficient in meeting the demand for high-quality seismic data. However, with the advancement of artificial intelligence, Generative Adversarial Network (GAN) has emerged as an effective solution for seismic data denoising. The GAN-based algorithm for random noise reduction in seismic data involves a generator and discriminator. The generator uses noisy seismic data as input to produce denoised data, while the discriminator evaluates the authenticity of the data. Through game training between the generator and discriminator, this method has been proven to successfully remove random noise from synthetic seismic data. Furthermore, experiments comparing this method to the conventional CNN method on real seismic data using evaluation indices such as data visualization and signal-to-noise ratio have demonstrated its effectiveness. This method shows great potential for future research and application in seismic data denoising. © The Institution of Engineering & technology 2023.

关键词： Generative adversarial networks

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