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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Zhang, Dazhi Jiang, Mengyuan Fu, Yongjie Zhou, Huabing China Nuclear Power Operation Technology Corporation Ltd Cnnc Key Laboratory on Nuclear Industry Simulation Wuhan430040 China College of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9798350393224

In this study, we employ a Gated Recurrent Unit (GRU) neural network model to predict changes in coolant flow in small-scale nuclear power plants under Loss-of-Coolant Accident (LOCA) conditions with high precision and speed. We quantitatively compare this model with the traditional Long Short-Term Memory (LSTM) method to validate the accuracy of the GRU model. Additionally, we perform an interpretability analysis on the predictive model using the SHAP (SHapley Additive exPlanations) method for the first time, revealing the inherent influencing factors in L O C A accidents. This research aims to enhance trust in artificial intelligence models in nuclear power scenarios, facilitate a better understanding of prediction results and the current system state, and promote the practical application of artificial intelligence models in nuclear power systems. © 2023 IEEE.

关键词： GRU LOCA Model interpretability Neural networks Nuclear energy SHAP Time series forecasting

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Semantic Injection Infrared and Visible Image Fusion Via a Disentangled Representation Network 4

Semantic Injection Infrared and Visible Image Fusion Via a D...

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Lu, Changkun Zhou, Huabing Wuhan Institute of Technology College of Computer Science and Engineering Wuhan China Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

Image fusion is the task of generating images from the same scene with different sensors. Images obtained by different sensors have different expressions under the image target semantics. Good semantic aware images can better serve the subsequent advanced image processing tasks. Most of the existing fusion methods are based on the underlying logic of the image, without considering the high-level semantic fusion. We propose a semantic injection disentangled representation network SDR. The source image is decomposed into structural and modal features by the corresponding encoder, and a generator based on semantic injection is constructed to generate the fused image under Semantic injection. On the open data set, our fusion network is compared with eight most advanced methods. Qualitative and quantitative experiments show that our method is superior to the current most advanced fusion methods. The fusion result preserves the structural information of the infrared image and the texture details of the visible image. The model proposed in this paper combines the advantages of feature decoupling and semantic injection, and has a better effect on fused images. © 2022 IEEE.

关键词： Semantics

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Image-Category Aware Loss for Super-Multiple Category Classification on Chu Bamboo and Silk ancient Chinese characters 23

Image-Category Aware Loss for Super-Multiple Category Classi...

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Proceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security

作者： Shengnan Li Chi Zhou Kaili Wang Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology China

ISBN: (纸本)9798400716966

In classification problems, real-world data often exhibits a long-tailed distribution: a few major categories have a large number of samples, while numerous minority categories have only a small number of samples. However, accurately identifying Chinese text images remains being a challenging task due to the vast number of classes and the characteristic long-tailed distribution observed in ancient Chinese text images in real-world scenarios. And specially, the extremely uneven category distribution deteriorates the recognition performance in the insufficient of hard and few-shot sample feature learning. To address this problem above, we devise an integrated-weighted distribution balance loss that considers both the hard and few-shot samples, that mainly induce the feature distribution and prediction ability imbalances. Firstly Image-Aware (IA) loss is proposed to adaptively reweights of hard samples, corresponding the prediction distribution imbalance issue at the image-level by assigning adaptively weights to the samples that are challenging to accurately identification. And then Category-Aware (CA) loss is devised to adaptively rebalance of weights, tackling the long-tail uneven sample distribution issue of the dataset at the category level by readjusting weights and decision boundary for tail samples. Experimental results from the integrated Image-Category Aware (ICA) loss demonstrate a significant and effective performance improvement trained on many network models with this integrated-loss function on the dataset of the Chu Bamboo and Silk 730 (Chu730) compared to existing methods.

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Improvement of the spreading effect of atmospheric pressure microplasma jet treatment through shielding-gas-controlled focusing

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Plasma Science and technology 2022年第9期24卷 85-97页

作者： Li LV Jianhang CHEN Jiahao WANG Shengquan WANG Meng LI Deyu TU Liping SHI Tao WANG Anhui Province Engineering Laboratory of Intelligent Demolition Equipment Maanshan 243032People’s Republic of China School of Mechanical Engineering Anhui University of TechnologyMaanshan 243032People’s Republic of China Anhui Province Key Laboratory of Special Heavy Load Robot Anhui University of TechnologyMaanshan 243032People’s Republic of China Key Laboratory of Green Fabrication and Surface Technology of Advanced Metal Materials Ministry of EducationAnhui University of TechnologyMaanshan 243032People’s Republic of China

The spreading effect of atmospheric pressure microplasma jets(APμPJ)on the surface of materials will increase the etching area,and controlling the diameter of the jet can improve the precision of surface *** this work,a two-dimensional axisymmetric simulation model is established to analyze the effect of nitrogen(N_(2))shielding gas on helium(He)from gas *** addition,by etching the polyethylene terephthalate fllm,the relationship between the etching effect and aerodynamic analysis is *** simulation results are similar to the experimental results,indicating that N2 shielding gas has a focusing effect which is related to the N_(2)flow rate,distance difference between the inner and outer tubes,and outer tube nozzle *** is hoped that the results of this work can provide a certain reference for the use of shielding gas to control the jet flow of APμPJ.

关键词： microplasma jet nitrogen shielding gas focusing effect gas dynamic

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Drug ADMET Prediction Method Based on Improved Graph Convolution Neural Network 4

Drug ADMET Prediction Method Based on Improved Graph Convolu...

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Xiao, Haoliang Chen, Xiangyang Wuhan Institute of Technology College of Computer Science and Engineering Wuhan China Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

To improve the performance of the determination model for evaluating drug absorption, distribution, metabolism, exclusion, and toxicity (ADMET), a drug ADMET prediction model based on graph convolution network is proposed from the two chemical properties of drug solubility and toxicity. At the same time, a multilayer perceptron and an attention mechanism are introduced to improve the model by using special information such as features in molecular bonds and the difference in interaction strength between adjacent atoms. The error of the improved model is reduced by about 24% and 25% on average on two public data sets of drug solubility, and the accuracy of three public data sets of drug toxicity is increased by about 7.8% on average and 9.6 % at most. Experiments show that the good performance of this method can be used for early drug ADMET prediction, thus providing a reference for computer-aided drug design. © 2022 IEEE.

关键词： Forecasting

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Scene Text Image Super-Resolution via Anchor Point Guided and Efficient Transformer

Scene Text Image Super-Resolution via Anchor Point Guided an...

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Wenfei Xiong Xiaoqian Shi Hang Yang Huabing Zhou Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

Scene text image super-resolution (STISR) aims to restoring textual details within low-resolution (LR) text images, producing high-resolution (HR) images that closely align with human perception. However, current state-of-the-art methods lack a comprehensive approach to modeling text images, resulting in an unsatisfactory balance between improving image quality and enhancing downstream task performance in STISR. In this paper, we propose a novel STISR framework (ATSR) via Anchor Point Guided and Efficient Transformer. We comprehensively analyze text images from three perspectives: local features within characters, character-level features, and image-level global features. Specifically, we propose Anchor Point Guided Pixel Attention (APGPA) to establish the invariance characteristics of characters. To ensure accurate characters restoration, we introduce a text recognition operator as a prior constraint on the character features of the generated text image. Additionally, recognizing the significance of global features for image quality and subsequent text recognition tasks, we present the Efficient Text Super-Resolution Transformer (ETSRT), a module that efficiently reconstructs images by leveraging visual and semantic information from the context. Our approach outperforms the existing baseline as demonstrated by numerous experiments.

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Field Grape Berry Counting Algorithm Based on Spatial Attention Mechanism

Field Grape Berry Counting Algorithm Based on Spatial Attent...

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2022 Chinese Automation Congress, CAC 2022

作者： Tang, Yuling Li, Yanan Sun, Ming Qi, Yang Wuhan Institute of Technology School of Computer Science and Engineering School of Artificial Intelligence Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9781665465335

Automated grape yield estimation is one of the important aspects of intelligent vineyard management. In order to meet requirements of fast estimation and large-scale grape yield estimation in complex field environments, this paper proposes a Spatial Attention Grape Berry Counting Network (SAGBCNet). Firstly, the Spatial Feature Network (SFNet) is used to extract more spatial information of grape berries in image. Then attention mechanism is used to adaptively fit the long-distance context features of location and channel, so as to effectively distinguish the grape berry from background. Finally, through Feature Fusion Network, feature maps of different scales are fused to generate the predicted density map, and counting results of grape berries in the field are obtained. The experiment on the field grape counting dataset showed that SAGBCNet obtained a lower counting error for grape berries. The mean absolute error (MAE) and mean squared error (MSE) were 35.39 and 47.39 respectively. © 2022 IEEE.

关键词： Fruits

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A SAR Denoising Network Based on Generative Adversarial Learning

A SAR Denoising Network Based on Generative Adversarial Lear...

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robotics, Control and Automation Engineering (RCAE), International Conference on

作者： Niannian Yi Shengchang Pei Nianxin Ai Shuiping Zhang Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

Synthetic Aperture Radar (SAR) images are critical in microwave remote sensing and are used for resource exploration and target location. However, due to the principle of coherent imaging, SAR images are often interfered by speckle noise, which reduces image quality. It is necessary to remove noise but retain details. We are committed to improving the network structure to increase the diversity and authenticity of generated images. Inspired by Se-Res-Unet, we fuse generative adversarial networks for SAR image denoising and named the model SAR-RsuGan. In it, the generator and the discriminator compete with each other to generate high-quality images. We use synthetic SAR images for training and compare with other methods to obtain the best performance.

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Optimised design of cross-shaft parameters based on response surface optimisation model with MOGA

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International Journal of Wireless and Mobile Computing 2024年第2期26卷 198-206页

作者： Xiong, Sijie Xie, Yuanmin Zou, Chunlong Mao, Yanfeng Cao, Yongcheng Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Wuhan University of Science and Technology Hubei Wuhan China Hubei University of Automotive Technology Hubei Shiyan China Technology Research and Development Hubei Jingmen Wusan Machinery Equipment Manufacturing Co. Ltd. Jingmen Hubei China

Cross-shaft is the core component of the cross-type universal coupling and has a vital transmission function. This paper proposes Sparse Grid and the Kriging interpolation to construct a response surface model to solve the problem of long design cycles, low reliability and high susceptibility to cross-shaft fatigue deformation. The critical dimensions of the cross-shaft are used as design variables, and the maximum equivalent force and deformation are reduced as the optimisation objective. Then experimental points are obtained by Sparse Grid Initialisation and then the response surface model is obtained with high accuracy by Kriging interpolation, and finally, the optimised design of the cross-shaft is completed using MOGA in this paper. Compared with the original structural solution, the maximum deformation of the cross-shaft is reduced by 0.4717 mm (22.35%), the maximum equivalent force is reduced by 130.35 Mpa (17.21%) and the mass increased by only 4.17%. © 2024 Inderscience Enterprises Ltd.

关键词： Multiobjective optimization

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Traffic Sign Object Detection with the Fusion of SSD and FPN

Traffic Sign Object Detection with the Fusion of SSD and FPN

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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Fei Xie Gengsheng Zheng Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

To enhance traffic sign object detection further, this paper proposes a method that combines the SSD (Single Shot MultiBox Detector) model with FPN (Feature Pyramid Network) fusion for traffic sign identification. The SSD structure is leveraged to extract feature information from traffic sign images. Subsequently, the obtained feature information is fused using the FPN structure to enable more efficient object detection. Additionally, the attention mechanism is introduced to concentrate the fused FPN and SSD structures on target-relevant features, thereby improving accuracy, classification precision, and the ability to handle interference, ultimately enhancing overall performance. The post-processing step uses DIoU-NMS (Distance-IoU Non-Maximum Suppression) based on centroid distance. Validation is performed on the CCTSDB dataset, demonstrating a significant improvement in accuracy compared to traditional SSD. Experimental results confirm the effectiveness of the proposed method in enhancing detection efficiency and accuracy.

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