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Prescribed-Time Stabilization of Singularly Perturbed Systems

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 569-571页

作者： Yan Lei Yan-Wu Wang Xiao-Kang Liu Wu Yang the School of Artificial Intelligence and Automation Huazhong University of Science and Technology(HUST)Wuhan 430074 Key Laboratory of Image Processing and Intelligent Control Ministry of EducationWuhan 430074China the School of Electrical Engineering Guangxi UniversityNanning 530004China

Dear Editor, This letter investigates the prescribed-time stabilization of linear singularly perturbed systems. Due to the numerical issues caused by the small perturbation parameter, the off-the-shelf control design techniques for the prescribed-time stabilization of regular linear systems are typically not suitable here. To solve the problem, the decoupling transformation techniques for time-varying singularly perturbed systems are combined with linear time-varying high gain feedback design techniques.

关键词： prescribed stabilization perturbed

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Abstractive Text Summarization Model Based on BERT Vectorization and Bidirectional Decoding

Abstractive Text Summarization Model Based on BERT Vectoriza...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Teng, Keliang Qiang, Baohua Wang, Yufeng Yang, Xianyi Wang, Yuemeng Wang, Chen Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC Shijiazhuang050081 China

ISBN: (纸本)9781510666597

Facing the pressure of the extremely inflated network information and data overload surplus, it is extremely important to locate "valuable information" efficiently and accurately. Text summarization technology in the field of Natural Language processing (NLP) is an effective means to analyze and process network information. In this paper, we propose an abstractive text summarization model based on bidirectional encoder representations from transformers (BERT) vectorization and bidirectional decoding. The BERT is adopted to obtain a more global vector representation, which helps the subsequent encoder and decoder to fuse the full-text information to generate a summary with high generality. The decoding phase adopts a bidirectional decoding structure and combines the attention mechanism to maintain the bilateral decoding result to generate summaries. The bidirectional decoding structure can be fine-tuned according to the bidirectional results, which can overcome the tilt problem of the unidirectional structure, and the generated summaries are more consistent. The experimental results on the NLPCC2017 text summarization dataset show that the summaries generated by our model have the higher coherence at the word and sentence level, and the stronger generalization of the full text. © 2023 SPIE.

关键词： Decoding

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Portrait Matting via Semantic and Detail Guidance 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Wang, Deming Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Portrait matting is a challenging computer vision task that aims to estimate the per-pixel opacity of the foreground human regions. To produce high-quality alpha mattes, the majority of available methods employ a user-supplied trimap as an auxiliary input. However, obtaining trimap is difficult, which restricts the application of these methods. Recently, trimap-free approaches have been developed, but the alpha quality is significantly lower than that of trimap-based methods due to the lack of guidance. In this paper, we propose a portrait matting framework via semantic and detail guidance. We use the mask obtained from portrait segmentation as semantic guidance to improve semantic understanding of portrait matting, and we present a detail encoder block to capture the low-level feature around the subject as detail guidance, which helps to refine alpha details, such as hair. The proposed approach achieves remarkable results on both composition and real-world benchmarks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Moving Forward Together: A Multi-User Pattern Extraction Neural Model for Sequential Recommendations 6

Moving Forward Together: A Multi-User Pattern Extraction Neu...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Du, Xiangpeng Li, Xiaoge Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China

ISBN: (纸本)9798400707674

The sequential recommendation is a very important task in recommendation systems. The aim of it is to dynamic predict user's interests based on their historical behaviors. Despite recent progress, most of deep learning approaches focus on extracting the behavioral patterns of a single user. Pulling multiple users can increase the diversity of historical information. In this paper, we propose multi-user behavioral pattern extraction model (TMGN) via a combination of traditional behavioral pattern extraction and graph convolutional aggregation. Concretely, TMGN first learns the graph structure of users and items. Then it performs graph convolution aggregation to derive a representation of users to extract user interest features. We recall the users with the highest similarity by calculating the cosine similarity of each candidate user. Finally, we inject multiple users' sequence patterns and time information into the muti-head attention mechanism. Experiments on actual data (Movielens1m, Movielen20m and Amazon beauty) show that the proposed method outperforms current state-of-the-art sequential recommendation methods. TMGN achieves an average improvement of 5.67%, 5.75%, 7.32%, and 7.19% over the most robust baseline in Hit@5, Hit@10, NDCG@5, and NDCG@10, respectively. We provide extensive ablation studies to analyze the impact of each model component on TMGN. © 2023 ACM.

关键词： Convolution

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HTRNN: A New Protein Nuclear Localization Prediction Tool 7

HTRNN: A New Protein Nuclear Localization Prediction Tool

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Miao, Xinyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Current protein nuclear localization assays encounter multiple challenges that underscore the constraints of conventional biochemical assays and sequence-based procedures. This paper highlights the emerging interest in utilizing artificial intelligence to surmount these limitations. Specifically, a supervised deep learning algorithm, employing the HTRNN model, is presented to identify mechanisms responsible for the nuclear location of proteins. This methodology embraces an entire data-driven end-to-end structure, making expert experience or previous biological knowledge unnecessary. By utilizing amino acid sequence features from both the head and tail regions, the algorithm displays remarkable detection and generalization capabilities, which have been validated by experimental results on present tagged nuclear location protein datasets. Overall, the data-driven algorithm plays a crucial role in enhancing the identification of protein nuclear localization properties. © 2024 IEEE.

关键词： Proteins

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Research on the Detection of Abnormal Cervical Cells Based on Improved YOLOv7 9

Research on the Detection of Abnormal Cervical Cells Based o...

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9th International Conference on intelligent Computing and Signal processing, ICSP 2024

作者： Wang, Xuewei Yuan, Shuang Yang, Lidong Niu, Dawei Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia China

ISBN: (纸本)9798350376548

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. The algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. The experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively. © 2024 IEEE.

关键词： Convolution

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MATL: A deep neural network using multi-scale convolutions and transformer for transcription factor binding site prediction

MATL: A deep neural network using multi-scale convolutions a...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fan, Jinli Shen, Ruyi Huang, Xiaodi Zhang, Yanju Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China College of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (纸本)9798350386226

Transcription factors bind to specific sequences of DNA known as transcription factor binding sites (TFBSs) in order to regulate gene expression. Identifying TFBSs is crucial for deciphering gene expression mechanisms, understanding in vitro life characteristics, and drug design. In recent years, many deep learning methods that utilize only the sequence features or a combination of sequence and shape features have been developed to predict TFBSs. However, these methods have not fully considered the characteristics of TFBSs, and their encoding strategies are relatively simplistic, thereby failing to capture the complex dependencies hidden within TFBSs. To address these issues, we propose a novel deep neural network model called MATL, which integrates sequence and shape features for TFBSs prediction. The model employs techniques such as multi-scale convolution and Transformer, and combines different modules for feature extraction based on the distribution patterns of different feature matrices. Additionally, we introduce transfer learning to address the issue of unsatisfactory prediction results due to insufficient training data in some datasets. As the results indicate, MATL exhibits excellent predictive performance on 165 validated ChIP-seq datasets, surpassing state-of-the-art methods. We also conducted ablation experiments on sequence encoding, feature extraction, and shape modules used in this study, and demonstrated that these modules play a positive role in predicting TFBSs. Moreover, the results also indicated that transfer learning can improve prediction accuracy for some TFBSs datasets with small amounts of data. The source code of MATL is available at https://***/LittleWhiteKing/MATL. © 2024 IEEE.

关键词： Transcription

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Fusion-CAM: Fine-Grained and High-Faithfulness Explanations for Deep Convolutional Network via Hierarchical Fusion 7

Fusion-CAM: Fine-Grained and High-Faithfulness Explanations ...

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Zheng, Chen Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

The interpretability of the convolutional neural networks(CNNs) has become a research hotspot. A popular explanation method is based on Class Activation Mapping (CAM), which visualizes the salient regions most relevant to neural network decisions. However, many CAM methods use the feature maps produced by the final convolution layer to generate the class activation maps, which usually have a low spatial resolution and can only generate coarse-grained visual explanations that provide a rough spatial location of the target object. In this paper, we propose a novel CAM method named Fusion-CAM. It improves traditional CAM methods by combining final class activation map containing semantic information with intermediate layer class activation maps containing fine-grained details, to generate fine-grained visual explanations with high faithfulness. In order to obtain high-quality intermediate layer class activation maps, we utilize Layer-wise Relevance Propagation (LRP) to obtain the weighting components of each channel of the intermediate layer feature maps, and the intermediate layer class activation maps generated by weighted summation are less noisy and have clear fine-grained details, which help to improve the quality of the final class activation map. Qualitative and quantitative experiments show that Fusion-CAM can be easily attached to different CAM methods to improve their performance. © 2024 IEEE.

关键词： Convolution

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Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

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Computers, Materials & Continua 2024年第9期80卷 4813-4832页

作者： Tao Zhou Yunfeng Pan Huiling Lu Pei Dang Yujie Guo Yaxing Wang School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzuUniversityYinchuan750021China

Multimodal lung tumor medical images can provide anatomical and functional information for the same *** as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and *** to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key *** solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this ***,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate ***,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after ***,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic ***,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local *** proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,*** is of great significance for computer-aided diagnosis.

关键词： Medical image segmentation U-Net saliency feature guidance cross-modal feature enhancement cross-dimension feature enhancement

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A Dual-branch Model for Early Detection of Alzheimer's Disease Using Resting-State fMRI 7

A Dual-branch Model for Early Detection of Alzheimer's Disea...

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Wang, Yixuan Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Alzheimer's disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images. © 2024 IEEE.

关键词： Feature extraction

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