检索结果-内蒙古大学图书馆

RGB-based combustion state classification and stability analysis of SOFC afterburner: effects of fuel utilization and steam-to-carbon ratio

引用

Fuel 2025年 402卷

作者： Jingjing Wang Shuyu Zhang Jingang Lai Jiashu Jin Chun Zou Jung-Sik Kim Zhonghua Deng Yuanwu Xu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 China Sino-French Engineer School/School of General Engineering Beihang University Beijing 100191 China Wuhan Huamao Automation Co. Ltd. Wuhan 430205 China School of Artificial Intelligence and Automation Wuhan University of Science and Technology Wuhan 430081 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518055 China

Solid oxide fuel cell (SOFC) systems efficiently convert chemical energy into electricity but produce high-temperature exhaust gases containing unreacted fuels. The afterburner combusts these residual fuels to enhance heat recovery and system efficiency, with its performance influenced by stack fuel composition variations. This study investigates how fuel utilization rate ( U f ) and steam-to-carbon ratio (S/C) affect afterburner combustion characteristics in a 1 kW methane-reformed SOFC system. Results showed maximum afterburner temperature decreased from 981.3 K to 835 K as U f increased to 0.6, while S/C minimally impacted temperature distribution. A dimensionless stability index was developed to quantitatively assess combustion performance. An image-based analysis approach extracted five key parameters describing flame characteristics under varying conditions. These parameters supported the development of a three-tier hierarchical classification framework to identify weak, stable, and unstable combustion states, achieving an overall recognition accuracy of 74.5 %. These findings provide insights into SOFC operating parameters, residual stack fuel compositions, and afterburner stability relationships. The proposed assessment methodology offers guidance for optimizing thermal management and improving energy recovery efficiency in SOFC systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis

引用

Chinese Medical Journal 2024年第4期137卷 421-430页

作者： Ming Cai Ke Zhao Lin Wu Yanqi Huang Minning Zhao Qingru Hu Qicong Chen Su Yao Zhenhui Li Xinjuan Fan Zaiyi Liu Department of Radiology Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application GuangzhouGuangdong 510080China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China Department of Pathology The Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalYunnan Cancer CenterKunmingYunnan 650118China The Second School of Clinical Medicine Southern Medical UniversityGuangzhouGuangdong 510515China Institute of Computing Science and Technology Guangzhou UniversityGuangzhouGuangdong 510006China Department of Pathology Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China Department of Pathology The Sixth Affiliated Hospital of Sun Yat-sen UniversityGuangzhouGuangdong 510655China

Background:Artificial intelligence(AI)technology represented by deep learning has made remarkable achievements in digital pathology,enhancing the accuracy and reliability of diagnosis and prognosis *** spatial distribution of CD3^(+)and CD8^(+)T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer(CRC).This study aimed to investigate CD3_(CT)(CD3^(+)T cells density in the core of the tumor[CT])prognostic ability in patients with CRC by using AI ***:The study involved the enrollment of 492 patients from two distinct medical centers,with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation *** facilitate tissue segmentation and T-cells quantification in whole-slide images(WSIs),a fully automated workflow based on deep learning was *** the completion of tissue segmentation and subsequent cell segmentation,a comprehensive analysis was ***:The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3_(CT) and CD3-CD8(the combination of CD3^(+)and CD8^(+)T cells density within the CT and invasive margin)in predicting mortality(C-index in training cohort:0.65 vs.0.64;validation cohort:0.69 vs.0.69).The CD3_(CT) was confirmed as an independent prognostic factor,with high CD3_(CT) density associated with increased overall survival(OS)in the training cohort(hazard ratio[HR]=0.22,95%confidence interval[CI]:0.12–0.38,P<0.001)and validation cohort(HR=0.21,95%CI:0.05–0.92,P=0.037).Conclusions:We quantify the spatial distribution of CD3^(+)and CD8^(+)T cells within tissue regions in WSIs using AI *** CD3_(CT) confirmed as a stage-independent predictor for OS in CRC ***,CD3_(CT) shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

关键词： Colorectal cancer Artificial intelligence Deep learning Digital pathology Prognosis Immune cells CD3 CD8 TME

来源：评论

学校读者我要写书评

暂无评论

A survey on negative transfer

arXiv

引用

arXiv 2020年

作者： Zhang, Wen Deng, Lingfei Zhang, Lei Wu, Dongrui the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China the School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

—Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces the learning performance in the target domain, has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to handle it. However, there does not exist a systematic survey on the formulation of NT, the factors leading to NT, and the algorithms that mitigate NT. This paper fills this gap, by first introducing the definition of NT and its factors, then reviewing about fifty representative approaches for overcoming NT, according to four categories: secure transfer, domain similarity estimation, distant transfer, and NT mitigation. NT in related fields, e.g., multi-task learning, lifelong learning, and adversarial attacks, are also discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

Data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

引用

SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Andre, Dekker Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Centralized model training faces privacy constraints, although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. In this work, we propose a novel federated semi-supervised learning framework for complex and realistic scenario, which can efficiently leverage the limited labeled data and more homogeneous unlabeled data from multiple clinical sites for collaborative model training. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining local models during each communication round to enhance the robustness and generalizability of the global model. Extensive comparative and ablation experiments confirm the efficacy of our proposed methodology, demonstrating improvement over recent state-of-the-art federated semi-supervised segmentation methods in scenarios with only a small amount of labeled data. This approach highlights the significant potential of multi-source and heterogeneous unlabeled data in facilitating the training of federated deep learning models and promotes clinical applications, assists radiologists in diagnosis and improves patient outcomes. The source code will be released on https://***/zhenweishi/FedPSR © 2024, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A visual kinematics calibration method for manipulator based on nonlinear optimization

arXiv

引用

arXiv 2020年

作者： Peng, Gang Wang, Zhihao Yang, Jin Li, Xinde Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China IEEE senior member School of Automation South East University Nanjing China

The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to the high cost of the measuring instruments but also not applicable to all manipulators. Another calibration method uses a camera, but the system error caused by the camera's parameters affects the calibration accuracy of the kinematics of the robot arm. Therefore, this paper proposes a method for calibrating the geometric parameters of a kinematic model of a manipulator based on monocular vision. Firstly, the classic Denavit-Hartenberg (D-H) modeling method is used to establish the kinematic parameters of the manipulator. Secondly, nonlinear optimization and parameter compensation are performed. The three-dimensional positions of the feature points of the calibration plate under each manipulator attitude corresponding to the actual kinematic model and the classic D-H kinematic model are mapped into the pixel coordinate system, and the sum of Euclidean distance errors of the pixel coordinates of the two is used as the objective function to be optimized. The experimental results show that the pixel deviation of the end pose corresponding to the optimized D-H kinematic model proposed in this paper and the end pose corresponding to the actual kinematic model in the pixel coordinate system is 0.99 pixels. Compared with the 7.9 deviation pixels between the pixel coordinates calculated by the classic D-H kinematic model and the actual pixel coordinates, the deviation is reduced by nearly 7 pixels for an 87% reduction in error. Therefore, the proposed method can effectively avoid system errors caused by camera parameters in visual calibration, can improve the absolute positioning accuracy of the end of the robotic arm, and has good economy and universality. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

引用

SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Dekker, Andre Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Background and Objective: Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Conducting centralized training to enhance model performance is impractical in real-world scenarios. Although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. Methods: In this work, we propose a novel federated semi-supervised learning framework (FSSL) for complex and realistic scenario, which can efficiently leverage the limited labeled data and abundant unlabeled data from multiple clinical sites. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining all local models during communication round. Results: We conducted extensive comparative and ablation experiments to validate the effectiveness of our proposed method. When only a limited amount of labeled data is available in the partially labeled sites, even without loading the pre-trained model, our approach consistently achieves the highest average Dice scores of 0.7383, 0.4768, and 0.6629 on the internal test sets, external unseen test sets, and pre-training test sets, respectively. Upon loading the pre-trained model, the scores on the three test sets further improve to 0.8205, 0.6185, and 0.7334, respectively. Conclusion: Our proposed method is capable of collaboratively training a global model with robust segmentation performance and generalizability by efficiently utilizing unlabeled data from different sites, even when only a limited amount of labeled da

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Multimodal data integration for biologically-relevant artificial intelligence to guide adjuvant chemotherapy in stage II colorectal cancer

引用

EBioMedicine 2025年 117卷 105789页

作者： Chenyi Xie Ziyu Ning Ting Guo Lisha Yao Xiaobo Chen Wanghong Huang Suyun Li Jiahui Chen Ke Zhao Xiuwu Bian Zhenhui Li Yanqi Huang Changhong Liang Qingling Zhang Zaiyi Liu Guangdong Cardiovascular Institute Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Guangzhou 510080 China Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080 China. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080 China Department of Pathology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou Guangdong 510080 China. Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing 400038 China. Department of Radiology The Third Affiliated Hospital of Kunming Medical University Yunnan Cancer Hospital Kunming 650000 China. Department of Pathology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou Guangdong 510080 China. Electronic address: zhangqingling@***. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080 China. Electronic address: liuzaiyi@***.

BACKGROUND:Adjuvant chemotherapy provides a limited survival benefit (<5%) for patients with stage II colorectal cancer (CRC) and is suggested for high-risk patients. Given the heterogeneity of stage II CRC, we aimed to develop a clinically explainable artificial intelligence (AI)-powered analyser to identify radiological phenotypes that would benefit from chemotherapy. METHODS:Multimodal data from patients with CRC across six cohorts were collected, including 405 patients from the Guangdong Provincial People's Hospital for model development and 153 patients from the Yunnan Provincial Cancer Centre for validation. RNA sequencing data were used to identify the differentially expressed genes in the two radiological clusters. Histopathological patterns were evaluated to bridge the gap between the imaging and genetic information. Finally, we investigated the discovered morphological patterns of mouse models to observe imaging features. FINDINGS:The survival benefit of chemotherapy varied significantly among the AI-powered radiological clusters [interaction hazard ratio (iHR) = 5.35, (95% CI: 1.98, 14.41), adjusted P = 0.012]. Distinct biological pathways related to immune and stromal cell abundance were observed between the clusters. The observation only (OO)-preferable cluster exhibited higher necrosis, haemorrhage, and tortuous vessels, whereas the adjuvant chemotherapy (AC)-preferable cluster exhibited vessels with greater pericyte coverage, allowing for a more enriched infiltration of B, CD4-T, and CD8-T cells into the core tumoural areas. Further experiments confirmed that changes in vessel morphology led to alterations in predictive imaging features. INTERPRETATION:The developed explainable AI-powered analyser effectively identified patients with stage II CRC with improved overall survival after receiving adjuvant chemotherapy, thereby contributing to the advancement of precision oncology. FUNDING:This work was funded by the National Science Fund of China (8192502

关键词： Imaging Multimodal artificial intelligence Predictive biomarker Prognosis Risk stratification Stage II colorectal cancer

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Recurrent Network for Single image Deraining 8

Multi-Scale Recurrent Network for Single Image Deraining

引用

8th International Conference on Digital Home, ICDH 2020

作者： Li, Youxing Lan, Rushi Sun, Long Li, Ji Luo, Xiaonan Pang, Cheng Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China

ISBN: (纸本)9781728192345

Rain streaks noise is one of the greatest influence of outdoor scene tasks. In order to improve the effect of the outdoor scene tasks, we need to reduce the impact of the rain streaks noise while ensuring that other important details are preserved. To handle this issue, we proposed a multi-scale recurrent network (MSReNet) for single image rain removal. By divided MSReNet into two stages: (i) removal rain streaks in the first stage and (ii) restoring background-truth details in the second stage, our network can availably remove huge rain streaks and restore some important background details. Extensive experiments on both synthetic and real images demonstrated that the proposed MSReNet prominently exceeds many recent state-of-the-art methods. Taking the proposed method effectiveness, it is also attractive as a preprocessing process for some visual tasks. © 2020 IEEE.

关键词： Rain

来源：评论

学校读者我要写书评

暂无评论

Make your choice for multimodal knowledge graph completion

引用

Knowledge-Based Systems 2025年 323卷

作者： Yifan Xue Shuoyan Ren Wanqiang Cai Yingyao Ma Lotfi Senhadji Huazhong Shu Jiasong Wu Laboratory of Image Science and Technology Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing 210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing 210096 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Univ Rennes INSERM LTSI - UMR 1099 F-35000 Rennes France Centre de Recherche en Information Biomédicale Sino-français (CRIBs) Univ Rennes INSERM F-35000 Rennes France

Knowledge graph completion (KGC) aims to predict missing entities in knowledge graphs by learning effective representations of entities and their relations. Recent advances have explored multimodal KGC by incorporating structural, textual, and visual information. However, two critical challenges remain unresolved: (1) modal heterogeneity, where significant differences in feature distributions across modalities hinder effective fusion; and (2) spatial heterogeneity, where embedding knowledge graphs in a single geometric space fails to capture their complex topological structures. To address these challenges, we propose ChoicE, a unified framework that leverages a mixture of experts (MoE) design for both encoding and decoding. In the encoder, the multimodal chooser preprocesses multiple modalities to derive embedding representations for each modality. These representations are then processed by distinct experts specialized for structural, textual, and visual features, facilitating effective fusion while preserving modality-specific information. In the decoder, the geometric chooser projects the unified multimodal embeddings into Euclidean, complex, or hyperbolic space, dynamically selecting the most appropriate space to model the inference patterns inherent to each query. Extensive experiments on multiple benchmark datasets demonstrate that ChoicE effectively overcomes these dilemmas and achieves state-of-the-art performance in multimodal KGC. The data and code are released at https://***/r/ChiocE-master/ .

关键词： Knowledge graph Knowledge graph completion Link prediction Multimodal Transformer

来源：评论

学校读者我要写书评

暂无评论

Parity space-based model mismatch detection for linear discrete time-invariant systems with unknown disturbances

Parity space-based model mismatch detection for linear discr...

引用

第三十九届中国控制会议

作者： Yi Tang Dan Ling Hong Zhang Yanewei Wang Ying Zheng the Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Electrical and Information Engineering Zhengzhou University of Light Industry School of Mechanical & Electrical Engineering Wuhan Institute of Technology

Model mismatch is one of the main factors of control performance degradation. In this paper, a new model mismatch detection approach with parity space-based methods is proposed for linear discrete time-invariant（LDTI） systems. This paper establishes a parity space-based residual evaluation function for the detection of model mismatch. The problem of parity space-based model mismatch is formulated on a state space model of a LDTI system. A standard evaluation function and its thresholds for the detection of model mismatch are proposed by providing the parity model-based residuals. It is shown the proposed approach results in an optimal fault detectability without any assumptions on the probability distribution of unknown disturbances. The effectiveness of the proposed methods is demonstrated by the simulation study of the paper machine head box control example.

关键词： Parity space Model mismatch detection LDTV systems model-based residual generation

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：