In breast cancer grading,the subtle differences between HE-stained pathological images and the insufficient number of data samples lead to grading inefficiency.With its rapid development,deep learning technology has b...In breast cancer grading,the subtle differences between HE-stained pathological images and the insufficient number of data samples lead to grading inefficiency.With its rapid development,deep learning technology has been widely used for automatic breast cancer grading based on pathological images.In this paper,we propose an integrated breast cancer grading framework based on a fusion deep learning model,which uses three different convolutional neural networks as submodels to extract feature information at different levels from pathological images.Then,the output features of each submodel are learned by the fusion network based on stacking to generate the final decision results.To validate the effectiveness and reliability of our proposed model,we perform dichotomous and multiclassification experiments on the Invasive Ductal Carcinoma(IDC)pathological image dataset and a generated dataset and compare its performance with those of the state-of-the-art models.The classification accuracy of the proposed fusion network is 93.8%,the recall is 93.5%,and the F1 score is 93.8%,which outperforms the state-of-the-art methods.展开更多
As the world faces increasing energy demands and concerns about climate change,nuclear power is experiencing a resurgence as a viable and sustainable energy source.This article explores the strategic initiatives conce...As the world faces increasing energy demands and concerns about climate change,nuclear power is experiencing a resurgence as a viable and sustainable energy source.This article explores the strategic initiatives concerning the advancement of nuclear technologies,highlighting the prompt adoption of SMRs(small modular reactors),the ongoing advancements in Generation IV reactors in the medium term,and the long-term aspirations linked to nuclear fusion.SMRs offer enhanced safety,economic viability,and flexible deployment alternatives,making them an attractive solution for meeting pressing energy demands.In the medium term,Generation IV reactors are anticipated to improve efficiency,sustainability,and safety,effectively tackling the challenges associated with conventional fission reactors.However,significant challenges lie ahead,including public perception,regulatory hurdles,financial barriers,and the need for a skilled workforce.By addressing these challenges,nuclear power can play a pivotal role in creating a sustainable and reliable energy future,contributing significantly to global efforts in climate change mitigation.展开更多
变电站室内无人机巡检可有效降低人工巡检作业强度。由于飞行精度要求高,搭载能力有限,仅依靠无人机搭载摄像头与惯性测量单元(inertial measurement unit, IMU)数据融合确定位姿无法满足精度要求,为此,提出基于变电站室内已有固定摄像...变电站室内无人机巡检可有效降低人工巡检作业强度。由于飞行精度要求高,搭载能力有限,仅依靠无人机搭载摄像头与惯性测量单元(inertial measurement unit, IMU)数据融合确定位姿无法满足精度要求,为此,提出基于变电站室内已有固定摄像头的泛在物联的多视觉-惯导融合框架,针对室内光线情况对无人机摄像头图像进行强化,并与IMU数据结合得到初步的无人机位置数据。进一步通过在无人机上布设二维码(quick response code,QR码),应用改进后的PnP(perspective-n-point)算法优化无人机位姿数据。飞行结束后在无人机机巢对IMU的累计误差进行校验。实验证明:该方法布设与维护的工作量小,相较仅依靠搭载摄像头与IMU数据融合算法,飞行精度有较大提高,可满足变电站内无人机巡检作业的需要。展开更多
针对低空经济发展涉及的安全管理问题,在总结低空经济相关技术路线原理及落地方案的运行经验,分析低空安防普适性的4个建设方案:雷达与通感一体技术融合方案、广播式自动相关监视技术方案、远程识别技术方案和基于TDOA(time difference ...针对低空经济发展涉及的安全管理问题,在总结低空经济相关技术路线原理及落地方案的运行经验,分析低空安防普适性的4个建设方案:雷达与通感一体技术融合方案、广播式自动相关监视技术方案、远程识别技术方案和基于TDOA(time difference of arrival)无线电技术的多源融合方案的基础上,构建无人飞行器探测技术评价指标体系,并建立了一种基于决策试验评估实验室(decision-making trial and evaluation laboratory, DEMATEL)和优劣解距离法(technique for order preference by similarity to an ideal solution, TOPSIS)的多属性评价方法。结果发现,以TDOA为基础的多源融合方案是构建城市低空安防体系的有效路径和普适性方案。研究表明,低空安防体系的建设是一个系统性工程,需要政府、企业和社会各方的共同努力,在技术、数据、运营等多个层面进行整合,以适应未来低空经济的发展需求。展开更多
Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as com...Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as complex defect morphology,texture similarity,and fuzzy edges,leading to poor accuracy and missed detections.In order to resolve these problems,we propose MSCM-Net(Multi-Scale Cross-Modal Network),a multiscale cross-modal framework focused on detecting rail surface defects.MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps,effectively capturing and enhancing features at different scales for each modality.To further enrich feature representation and improve edge detection in blurred areas,we propose a multi-scale void fusion module that integrates multi-scale feature information.To improve cross-modal feature fusion,we develop a cross-enhanced fusion module that transfers fused features between layers to incorporate interlayer information.We also introduce a multimodal feature integration module,which merges modality-specific features from separate decoders into a shared decoder,enhancing detection by leveraging richer complementary information.Finally,we validate MSCM-Net on the NEU RSDDS-AUG RGB-depth dataset,comparing it against 12 leading methods,and the results show that MSCM-Net achieves superior performance on all metrics.展开更多
文摘In breast cancer grading,the subtle differences between HE-stained pathological images and the insufficient number of data samples lead to grading inefficiency.With its rapid development,deep learning technology has been widely used for automatic breast cancer grading based on pathological images.In this paper,we propose an integrated breast cancer grading framework based on a fusion deep learning model,which uses three different convolutional neural networks as submodels to extract feature information at different levels from pathological images.Then,the output features of each submodel are learned by the fusion network based on stacking to generate the final decision results.To validate the effectiveness and reliability of our proposed model,we perform dichotomous and multiclassification experiments on the Invasive Ductal Carcinoma(IDC)pathological image dataset and a generated dataset and compare its performance with those of the state-of-the-art models.The classification accuracy of the proposed fusion network is 93.8%,the recall is 93.5%,and the F1 score is 93.8%,which outperforms the state-of-the-art methods.
文摘As the world faces increasing energy demands and concerns about climate change,nuclear power is experiencing a resurgence as a viable and sustainable energy source.This article explores the strategic initiatives concerning the advancement of nuclear technologies,highlighting the prompt adoption of SMRs(small modular reactors),the ongoing advancements in Generation IV reactors in the medium term,and the long-term aspirations linked to nuclear fusion.SMRs offer enhanced safety,economic viability,and flexible deployment alternatives,making them an attractive solution for meeting pressing energy demands.In the medium term,Generation IV reactors are anticipated to improve efficiency,sustainability,and safety,effectively tackling the challenges associated with conventional fission reactors.However,significant challenges lie ahead,including public perception,regulatory hurdles,financial barriers,and the need for a skilled workforce.By addressing these challenges,nuclear power can play a pivotal role in creating a sustainable and reliable energy future,contributing significantly to global efforts in climate change mitigation.
文摘针对低空经济发展涉及的安全管理问题,在总结低空经济相关技术路线原理及落地方案的运行经验,分析低空安防普适性的4个建设方案:雷达与通感一体技术融合方案、广播式自动相关监视技术方案、远程识别技术方案和基于TDOA(time difference of arrival)无线电技术的多源融合方案的基础上,构建无人飞行器探测技术评价指标体系,并建立了一种基于决策试验评估实验室(decision-making trial and evaluation laboratory, DEMATEL)和优劣解距离法(technique for order preference by similarity to an ideal solution, TOPSIS)的多属性评价方法。结果发现,以TDOA为基础的多源融合方案是构建城市低空安防体系的有效路径和普适性方案。研究表明,低空安防体系的建设是一个系统性工程,需要政府、企业和社会各方的共同努力,在技术、数据、运营等多个层面进行整合,以适应未来低空经济的发展需求。
基金funded by the National Natural Science Foundation of China(grant number 62306186)the Technology Plan Joint Foundation of Liaoning Province(grant number 2023-MSLH-246)the Technology Plan Joint Foundation of Liaoning Province(grant number 2023-BSBA-238).
文摘Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as complex defect morphology,texture similarity,and fuzzy edges,leading to poor accuracy and missed detections.In order to resolve these problems,we propose MSCM-Net(Multi-Scale Cross-Modal Network),a multiscale cross-modal framework focused on detecting rail surface defects.MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps,effectively capturing and enhancing features at different scales for each modality.To further enrich feature representation and improve edge detection in blurred areas,we propose a multi-scale void fusion module that integrates multi-scale feature information.To improve cross-modal feature fusion,we develop a cross-enhanced fusion module that transfers fused features between layers to incorporate interlayer information.We also introduce a multimodal feature integration module,which merges modality-specific features from separate decoders into a shared decoder,enhancing detection by leveraging richer complementary information.Finally,we validate MSCM-Net on the NEU RSDDS-AUG RGB-depth dataset,comparing it against 12 leading methods,and the results show that MSCM-Net achieves superior performance on all metrics.
