检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Yang, Xing Zhang, Jian Chen, Qijian Wang, Li Wang, Lihui Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

Automatic segmentation of breast tumors from the ultrasound images is essential for the subsequent clinical diagnosis and treatment plan. Although the existing deep learning-based methods have achieved significant progress in automatic segmentation of breast tumor, their performance on tumors with similar intensity to the normal tissues is still not pleasant, especially for the tumor boundaries. To address this issue, we propose a PBNet composed by a multilevel global perception module (MGPM) and a boundary guided module (BGM) to segment breast tumors from ultrasound images. Specifically, in MGPM, the long-range spatial dependence between the voxels in a single level feature maps are modeled, and then the multilevel semantic information is fused to promote the recognition ability of the model for non-enhanced tumors. In BGM, the tumor boundaries are extracted from the high-level semantic maps using the dilation and erosion effects of max pooling, such boundaries are then used to guide the fusion of low and high-level features. Moreover, to improve the segmentation performance for tumor boundaries, a multi-level boundary-enhanced segmentation (BS) loss is proposed. The extensive comparison experiments on both publicly available dataset and in-house dataset demonstrate that the proposed PBNet outperforms the state-of-the-art methods in terms of both qualitative visualization results and quantitative evaluation metrics, with the Dice score, Jaccard coefficient, Specificity and HD95 improved by 0.70%, 1.1%, 0.1% and 2.5% respectively. In addition, the ablation experiments validate that the proposed MGPM is indeed beneficial for distinguishing the non-enhanced tumors and the BGM as well as the BS loss are also helpful for refining the segmentation contours of the tumor. Copyright © 2023, The Authors. All rights reserved.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Neural network based on improved parallel bat algorithm and its application

Journal of Network Intelligence

引用

Journal of Network Intelligence 2021年第3期6卷 428-439页

作者： Zhao, Zhuo-Qiang Liu, Shi-Jian Xu, Lin Pan, Jeng-Shyang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Institute of Artificial Intelligence Fujian University of Technology Fuzhou350118 China STEM University of South Australia Australia College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The artificial neural network is a research hotspot emerging in the field of artificial intelligence. Back Propagation (BP) neural network plays an important role in neural network. The network has the disadvantages of slow convergence of learning algorithms and easy falling into a local minimum. In this paper, a parallel bat algorithm with a new communication strategy is proposed, which is used to optimize the weights and thresholds of the BP neural network and establish the improved PBA-BP model. It was applied to optimize the parameters of the PID controller. Finally, we verified the effectiveness of the proposed method through the simulations. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Three term control systems

来源：评论

学校读者我要写书评

暂无评论

Rgmef: A Lightweight Multi-Exposure Image Fusion Network Based on Retinex and Gradient Guidance

SSRN

引用

SSRN 2024年

作者： Feng, Yunfei Zhang, Mei Zhu, Jinhui School of Automation Science and Engineering South China University of Technology Guangzhou510641 China School of Software Engineering South China University of Technology Guangzhou510641 China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education Guangzhou510641 China

The goal of the multi-exposure image fusion (MEF) is to create a high dynamic range (HDR) image with balanced exposures and more details by extracting and enhancing valid information from multiple low dynamic range (LDR) images with varied exposures. In recent years, deep learning has provided many solutions for MEF, and they have achieved remarkable effects. However, because of the high network complexity, these approaches often have low efficiency and limit the potential improvement of the effect. In order to fuse valuable information from different images, we introduce a lightweight but robust MEF network, which focus on the image internal characteristics rather than long-distance information. In this study, we first design a decomposed network inspired by Retinex theory to extract the illumination map from an image and obtain the gradient map through Sobel operator. Subsequently, we introduce two lightweight feature extraction modules to extract image features with the guidance of illumination and gradient maps respectively, and then fuse these features to produce an HDR result through three fusion blocks. Furthermore, we design a loss function to obtain a more suitable brightness range for the current scene. Numerous comparative experiments demonstrate the superiority of our method in terms of image texture, pixel intensity, and color retention. Moreover, some ablation studies indicate that our approach has a significant adaptability to source images. © 2024, The Authors. All rights reserved.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

从信号到知识——基于人工智能的医学影像裸数据诊断价值初探

引用

engineering 2024年第3期34卷 60-69页

作者： Bingxi He Yu Guo Yongbei Zhu Lixia Tong Boyu Kong Kun Wang Caixia Sun Hailin Li Feng Huang Liwei Wu Meng Wang Fanyang Meng Le Dou Kai Sun Tong Tong Zhenyu Liu Ziqi Wei Wei Mu Shuo Wang Zhenchao Tang Shuaitong Zhang Jingwei Wei Lizhi Shao Mengjie Fang Juntao Li Shouping Zhu Lili Zhou Shuo Wang Di Dong Huimao Zhang Jie Tian Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology The First Hospital of Jilin UniversityChangchun 130021China Neusoft Medical Systems Co.Ltd. Shenyang 110167China Neusoft Research of Intelligent Healthcare Technology Co.Ltd. Shenyang 110167China School of Life Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi’an 710126China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Encouraging and astonishing developments have recently been achieved in image-based diagnostic *** medical care and imaging technology are becoming increasingly ***,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established *** this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing ***,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not *** results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

关键词： Computed tomography Diagnosis Deep learning Lung cancer Raw data

来源：评论

学校读者我要写书评

暂无评论

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

arXiv

引用

arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Research on Gas Emergency Disposal Method Based on Knowledge Graph

Research on Gas Emergency Disposal Method Based on Knowledge...

引用

第34届中国控制与决策会议

作者： Qi Zichen Hu Yuling Wang Yahui School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing Methods for Building Big Data Beijing University of Civil Engineering and Architecture

When a gas accident occurs,relevant departments need to quickly determine an effective decision-making and disposal ***,most of the current gas accident emergency response plans are stored in text ***-site disposal personnel need to consult a large number of texts to determine the corresponding disposal measures,and the timeliness is *** order to improve the reusability and flexibility of the gas emergency response plan stored in the form of text,and overcome the shortcomings of poor timeliness,this paper constructs a top-down and bottom-up gas emergency response knowledge *** to the classification of gas accident cases,a knowledge graph is constructed by combining TextRank keyword extraction,expert experience and semantic role labeling,and the feasibility of the method is verified by *** will be a promising decision-making method for gas emergency disposal.

关键词： Gas Emergency Disposal Method Knowledge Graph knowledge extraction TextRank extraction method

来源：评论

学校读者我要写书评

暂无评论

Fast Latent Factor Analysis via a Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm

arXiv

引用

arXiv 2023年

作者： Li, Jinli Yuan, Ye School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

A high-dimensional and incomplete (HDI) matrix can describe the complex interactions among numerous nodes in various big data-related applications. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm learns a latent factor relying on the stochastic gradient of current instance error only without considering past update information. To address this critical issue, this paper innovatively proposes a Fuzzy PID-incorporated SGD (FPS) algorithm with two-fold ideas: 1) rebuilding the instance learning error by considering the past update information in an efficient way following the principle of PID, and 2) implementing hyper-parameters and gain parameters adaptation following the fuzzy rules. With it, an FPS-incorporated LFA model is further achieved for fast processing an HDI matrix. Empirical studies on six HDI datasets demonstrate that the proposed FPS-incorporated LFA model significantly outperforms the state-of-the-art LFA models in terms of computational efficiency for predicting the missing data of an HDI matrix with competitive accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

AdaSemiCD: An Adaptive Semi-supervised Change Detection Method Based on Pseudo Label Evaluation

arXiv

引用

arXiv 2024年

作者： Ran, Lingyan Wen, Dongcheng Zhuo, Tao Zhang, Shizhou Zhang, Xiuwei Zhang, Yanning School of Computer Science Shaanxi Provincial Key Laboratory of Speech and Image Information Processing The National Engineering Laboratory for Integrated Aerospace-GroundOcean Big Data Application Technology The Ningbo Institute of Northwestern Polytechnical University Northwestern Polytechnical University Xi’an710072 China College of Information Engineering Northwest A&F University Yangling712100 China

Change detection(CD) is an essential field in remote sensing, with a primary focus on identifying areas of change in bitemporal image pairs captured at varying intervals of the same region. The data annotation process for CD tasks is both time-consuming and labor-intensive. To better utilize the scarce labeled data and abundant unlabeled data, we introduce an adaptive semi-supervised learning method, AdaSemiCD, to improve pseudo-label usage and optimize the training process. Initially, due to the extreme class imbalance inherent in CD, the model is more inclined to focus on the background class, and it is easy to confuse the boundary of the target object. Considering these two points, we develop a measurable evaluation metric for pseudo-labels that enhances the representation of information entropy by class rebalancing and amplification of ambiguous areas, assigning greater weights to prospective change objects. Subsequently, to enhance the reliability of samplewise pseudo-labels, we introduce the AdaFusion module, to dynamically identifying the most uncertain region and substituting it with more trustworthy content. Lastly, to ensure better training stability, we introduce the AdaEMA module, which updates the teacher model using only batches of trusted samples. Experimental results on ten public CD datasets validate the efficacy and generalizability of our proposed adaptive training framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

FaceBench: A Multi-View Multi-Level Facial Attribute VQA dataset for Benchmarking Face Perception MLLMs

arXiv

引用

arXiv 2025年

作者： Wang, Xiaoqin Ma, Xusen Hou, Xianxu Ding, Meidan Li, Yudong Chen, Junliang Chen, Wenting Peng, Xiaoyang Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China AIAC Xi’an Jiaotong-Liverpool University China Tsinghua University China The Hong Kong Polytechnic University Hong Kong City University of Hong Kong Hong Kong Sun Yat-sen University China

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely unexplored. To address this gap, we introduce FaceBench, a dataset featuring hierarchical multi-view and multi-level attributes specifically designed to assess the comprehensive face perception abilities of MLLMs. Initially, we construct a hierarchical facial attribute structure, which encompasses five views with up to three levels of attributes, totaling over 210 attributes and 700 attribute values. Based on the structure, the proposed FaceBench consists of 49,919 visual question-answering (VQA) pairs for evaluation and 23,841 pairs for fine-tuning. Moreover, we further develop a robust face perception MLLM baseline, Face-LLaVA, by training with our proposed face VQA data. Extensive experiments on various mainstream MLLMs and Face-LLaVA are conducted to test their face perception ability, with results also compared against human performance. The results reveal that, the existing MLLMs are far from satisfactory in understanding the fine-grained facial attributes, while our Face-LLaVA significantly outperforms existing open-source models with a small amount of training data and is comparable to commercial ones like GPT-4o and Gemini. The dataset will be released at https://***/CVI-SZU/FaceBench. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Radiation Pattern Decoupled Dipole Antennas with Shorted Stubs for Millimeter-Wave Applications

Radiation Pattern Decoupled Dipole Antennas with Shorted Stu...

引用

IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Jingchang Nan Kai Lu Jingtao Cao Pengfei Hu Nan Yang Mingming Gao School of electronic and Information Engineering and Liaoning Provincial Key Laboratory of Radio Frequency Big Data Intelligent Application Liaoning Technical University(LNTU) Huludao China School of Electronics and Information technology and Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University (SYSU) Guangzhou China

ISBN: (纸本)9781665490382

A pair of shorted stubs are presented in this paper to decouple $1 \times 2$ millimeter-wave printed dipole antennas and to obtain restored radiation patterns. The $1 \times 2$ printed dipole antennas are placed side by side with a spacing of about a half wavelength. By inserting shorted stubs between the dipole arms and the ground, the induced current on the coupled antenna arm is shorted to the ground. Since there is little current left on the dipole arm of the coupled antenna, the isolation between the two ports can be enhanced and the radiation patterns can be therefore restored. To verify the idea, a prototype operating at 37-40 GHz frequency band was designed, fabricated, and tested. Good agreement can be observed. Measured results show an average isolation ($-|S _{2}1|$) of 30 dB over the operating band, and the maximum radiation is corrected to the boresight direction simultaneously.

关键词： Antenna measurements Dipole antennas Current measurement Millimeter wave measurements Prototypes Directive antennas MIMO communication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：