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5th Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2024

作者： Zhan, Feng Zhang, Zhi Wuhan University of Science and Technology College of Computer Science and Technology Hubei Wuhan430065 China Information Processing and Real-time Industrial System Hubei Province Key Laboratory of Intelligent Hubei Wuhan430065 China Information Processing and Real-time Industrial System Hubei Province Key Laboratory of Intelligent Hubei Wuhan30065 China

ISBN: (纸本)9798350374407

In the field of autonomous driving, 3D target detection is an important technology. In view of the shortcomings of existing monocular 3D detection algorithms in terms of accuracy and real-time performance, we propose a lightweight 3D target detection algorithm based on the attention mechanism. In order to ensure the real-time performance of the model, we use depthwise separable convolutions in the backbone network. The feature maps of each scale are then connected to an EMA module, which can fuse spatial attention and cross-dimensional Coordinate attention is used to enhance the feature extraction capability of the network, and BiFPN is subsequently used for multi-scale feature fusion. Finally, experiments were conducted on the KITTI dataset, and the results showed that our algorithm was superior to other methods in both accuracy and speed. © 2024 IEEE.

关键词： Object recognition

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Corundum production quality prediction based on support vector regression 12

Corundum production quality prediction based on support vect...

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12th IEEE Conference on industrial Electronics and Applications, ICIEA 2017

作者： Ju, Li Zhou, Ju Zhang, Xiaolong Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan430065 China

ISBN: (纸本)9781538621035

Aiming at the problem that the production process of corundum refractory is complex and the product quality cannot be predicted effectively, a quality forecasting model based on support vector regression is proposed in this paper. Firstly, an effective data set is extracted according to corundum production characteristics. The least angle regression method is applied to select feature. Then linear combination method is used to obtain a customized kernel function for support vector regression model, where the model is optimized with cross-validation. The experimental results indicate the proposed model can predict corundum quality accurately and has good fitting ability and practicality. © 2017 IEEE.

关键词： Forecasting

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Inferring DTIs Based on Similarity Clustering and CaGCN-DTI Model from Heterogeneous Network

Inferring DTIs Based on Similarity Clustering and CaGCN-DTI ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Li, Aoxing Lin, Xiaoli Yu, Haiping Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan China Huanggang Normal University Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science Wuhan China

ISBN: (纸本)9781665401265

Although much progress has been made in new drug development, it is still a costly, complicated and less efficient process. Therefore, drug repositioning studies are highly desirable. So far, many methods based on known drug-target interactions (DTIs) have been designed to detect potential DTIs, but there are many challenges for improving the performance of prediction DTIs. This paper proposes a new method (CaGCN-DTI) for DTIs prediction, which uses a heterogeneous network that incorporates a wide variety of biological data (drug, target, disease, and side effect) to discover potential interactions between drugs and targets. First, the drug-protein similarity network is preprocessed by Spectral clustering, then the graph convolutional network (GCN) combined with attention mechanism and random walk with restart (RWR) is used to aggregate message transmission to the network. Finally, the embedding vectors are used to discover potential DTIs by matrix decomposition. Experiments are performed based on ten-fold cross-validation, and the results show that CaGCN-DTI outperforms other previous methods in terms of AUC and AUPR. © 2021 IEEE.

关键词： Convolution

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Fine grained image recognition algorithm based on CNN-Transformer and paired interaction 4

Fine grained image recognition algorithm based on CNN-Transf...

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4th International Conference on Neural Networks, information and Communication Engineering, NNICE 2024

作者： Wang, Yu Zhang, Hong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350394375

A new fine-grained image recognition algorithm (P-ResViT, Paired Interaction Res50-ViT) based on CNN-Transformer is proposed to address the issue of existing fine-grained image recognition methods based on convolutional neural networks (CNN) lacking attention mechanisms and difficulty in accurately locating key regions, as well as weak ability to extract contextual information and global features. This algorithm introduces paired interaction attention mechanisms to supplement external attention. This algorithm can utilize the self attention mechanism of the Vision Transformer and the external attention mechanism formed by paired image inputs during training to achieve precise positioning, while preserving the inductive bias and local feature extraction capabilities of CNN. Firstly, the fine-tuning improved ResNet-50 completes local feature extraction and obtains high-resolution feature maps. Secondly, the Transformer encodes the tokenized image blocks of the feature map as input sequences for extracting global context. Finally, the Transformer completes global feature extraction and outputs the classification sequence. In addition, two external attention mechanisms were introduced by inputting image pairs during training, including collaborative attention mechanisms for similar images and cross attention mechanisms for heterogeneous images, fully utilizing the correlation information between images. After experimental evaluation, the P-ResViT proposed in this article has reached advanced levels in three fine-grained image datasets CUB, CAR, and AIR. © 2024 IEEE.

关键词： Image recognition

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Medical Entity and Attributes Extraction system Based on Relation Annotation

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Wuhan University Journal of Natural Sciences 2016年第2期21卷 145-150页

作者： ZOU Yuwei GU Jinguang FU Haidong College of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065HubeiChina Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan 430065HubeiChina

The abundant entities and entity-attribute relations in medical websites are important data resources for medical ***,the medical websites are usually characterized of storing entity and attribute values in different *** extract those data records efficiently,we propose an automatic extraction system which is related to entity and attribute relations（attributes and values）of separate *** system includes following modules：（1）rich-information interactive annotation page rendering;（2）separate storage attribute relations annotating;（3）annotated relations for pattern generating and data records *** paper presents the relations about the attributes which are stored in many pages by effective annotation,then generates rules for data records *** experiments show that the system can not only complete attribute relations of separate storage extraction,but also be compatible with regular relation extraction,while maintaining high accuracy.

关键词： relation annotation information extraction medical data relation extraction

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Corrections to “Adaptive Multitask Autoencoder-Based Hyperspectral Unmixing Exploiting Auxiliary Data via Graph Associations”

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Jia Chen Jun Li Paolo Gamba School of Computer Science and Technology and Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China School of Computer Science China University of Geosciences (Wuhan) Wuhan China Telecommunications and Remote Sensing Laboratory University of Pavia Pavia Italy

Presents corrections to the paper, (Corrections to “Adaptive Multitask Autoencoder-Based Hyperspectral Unmixing Exploiting Auxiliary Data via Graph Associations”).

关键词： Autoencoders Geoscience and remote sensing Hyperspectral imaging

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Semantic Representation of Evidence-Based Medical Guidelines and Its Use Cases

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Wuhan University Journal of Natural Sciences 2015年第5期20卷 397-404页

作者： HU Qing HUANG Zhisheng GU Jinguang College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Department of Computer Science Vrije University Amsterdam

Semantic representation of evidence-based medical guidelines provides the support for the data inter-operability and has been found many applications in the medical domain. In this paper, we describe a semantic representation approach of evidence-based medical guidelines, which is based on the Semantic Web Technology standards. We discuss several use cases of that semantic representation of evidence-based medical guideline, and show that they are potentially useful for medical applications.

关键词： evidence-based medical guidelines semantic repre- sentation semantic technology use cases

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Age estimation of Caenorhabditis elegans with label distribution learning

Age estimation of Caenorhabditis elegans with label distribu...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Zhao, Zi-Kang Liu, Jun Wang, Jun-Ji Chen, Gong Li, Chen-Qian Zhao, Zihao Jiang, Min Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Caenorhabditis elegans (C. elegans) is widely used in life research as a model organism. Accurate measurement of its age has practical implications for follow-up research. However, due to the similar characteristics of nematodes in different age groups, the general method is difficult to estimate accurately. Aiming at this problem, this paper proposes a method of label distribution learning, and uses fluorescence microscopy images to estimate the age of C. elegans. First, the logical age label are mapped to the label distribution. Secondly, the distribution learning loss function is used to make the neural network learn the relationship between the label distribution and corresponding image. Finally, the distribution learning loss function and the classification loss function are combined to strengthen the learning of the neural network. Experiments show that the accuracy and mean absolute error (MAE) of the method in this paper are improved compared with the baseline. In the experiment of estimating the age on different parts' data set, it was also concluded that the fluorescent image of the torso of C. elegans can better reflect the age characteristics than the head and tail. © 2022 IEEE.

关键词： Deep learning

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Exploiting Local and Global Characteristics for Contrast Based Visual Saliency Detection

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Journal of Shanghai Jiaotong university(Science) 2015年第1期20卷 14-20页

作者：徐新王英林张晓龙 School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology School of Information Management and Engineering Shanghai University of Finance and Economics

Visual saliency is an important cue in human visual system to identify salient region in the image;it can be useful in many applications including image retrieval,object recognition,image segmentation,*** contrast has been used as an effective feature to detect visual salient ***,the conventional contrast measures either in spectral domain or in spatial domain fail to give sufficient consideration towards the local and global characteristics of the *** paper presents a visual saliency detection algorithm based on a novel contrast *** measurement extracts the spectral information of image block using the 2D discrete Fourier transform(DFT),and combines with the total variation(TV)of image block in spatial *** proposed algorithm is used to perform salient region detection in the image,and compared with state-of-the-art *** experimental results from the MSRA dataset validate the effectiveness of the proposed algorithm.

关键词： visual saliency contrast measure multi-scale local contrast global contrast

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ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection

ResPocket: A Multi-Scale Feature Fusion Method for Improving...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Yang, Wenli Chen, Yaoyu Liao, Xiongwei Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology China

ISBN: (纸本)9798350386226

Accurate detection of protein binding sites is critical for facilitating drug design. Most existing models rely on features at a single scale, leading to the loss of crucial global or local structural information. Therefore, this paper proposes a novel 3D model (ResPocket), to fully capture the multi-scale features of proteins. First, Residual Block (ResB) based on U-Net is used to capture features from different scales, including global and local structural information. Then, an Enhanced Attention Module (EAM) is designed to encode semantic dependencies by effectively capturing global context information. In addition, ResPocket focuses on learning local information about protein pockets with Self-Guided Mask Block (SGMB), to increase the robustness of the overall model and reduce its dependence on external mask guidance. Experimental results show that ResPocket improves by an average of 4.89% on DCA Top-n, by an average of 1.48% and 2.25% on DCC and DVO metrics, respectively. © 2024 IEEE.

关键词： Binding sites

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