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An efficient schedulability analysis based on worst-case interference time for real-time systems

science China(Information sciences) 2024年第9期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of Computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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Semisupervised learning-based depth estimation with semantic inference guidance

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science China(Technological sciences) 2022年第5期65卷 1098-1106页

作者： ZHANG Yan FAN XiaoPeng ZHAO DeBin Department of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Depth estimation is a fundamental computer vision problem that infers three-dimensional(3D)structures from a given *** it is an ill-posed problem,to fit the projection function from the given scene to the 3D structure,traditional methods generally require mass amounts of annotated *** pixel-level annotation is quite labor consuming,especially when addressing reflective surfaces such as mirrors or *** widespread application of deep learning further intensifies the demand for large amounts of annotated ***,it is urgent and necessary to propose a framework that is able to reduce the requirement on the amount of *** this paper,we propose a novel semisupervised learning framework to infer the 3D structure from the given ***,semantic information is employed to make the depth inference more ***,we make both the depth estimation and semantic segmentation coarse-to-fine frameworks;thus,the depth estimation can be gradually guided by semantic *** compare our model with state-of-the-art *** experimental results demonstrate that our method is better than many supervised learning-based methods,which proves the effectiveness of the proposed method.

关键词： depth estimation semisupervised learning semantic information neural networks

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EdgeReg: Edge-assisted Unsupervised Medical Image Registration

EdgeReg: Edge-assisted Unsupervised Medical Image Registrati...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Jun Wang, Wenyi Wang, Wei Luo, Gongning Li, Yacong Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Institute of Technology Shenzhen School of Computer Science and Technology Shenzhen China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9798350386226

Medical image registration (MIR) is essential for various clinical diagnoses and treatments. Despite the rapid progress in deep learning-based MIR techniques, most methods focus on directly optimizing the raw image intensity information. In this paper, we explore the usage of edge information of anatomical structures associated with the spatial location of image intensities to assist in registration, termed EdgeReg. The intuition is that the edge information can provide additional rich boundary information to the raw images, enhancing the network's feature representation. Additionally, as the edge images are strictly spatially consistent with the raw images, additional supervised information can be added to network training. Specifically, we first extract the edge images from the raw moving and fixed images using the Sobel operator and feed these images into a lightweight feature extractor to merge the image intensity and edge information. The enriched features are subsequently input into established registration networks. Finally, similarity loss is applied to both the raw and edge images. Extensive experiments show that EdgeReg is compatible with various networks across diverse datasets and dimensions (2D and 3D), achieving superior registration performance. In particular, EdgeReg does not rely on segmentation labels and is trained in an unsupervised paradigm. Therefore, edge information is a beneficial assistance for unsupervised MIR. The code is available at https://***/PerceptionComputingLab/EdgeReg. © 2024 IEEE.

关键词： Image registration

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Low Gilbert damping in Bi/In-doped YIG thin films with giant Faraday effect

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Chinese Physics B 2024年第10期33卷 419-426页

作者： Jin Zhan Yi Wang Xianjie Wang Hanxu Zhang Senyin Zhu Lingli Zhang Lingling Tao Yu Sui Wenqing He Caihua Wan Xiufeng Han V.I.Belotelov Bo Song School of Physics Harbin Institute of TechnologyHarbin 150001China Beijing National Laboratory for Condensed Matter Physics and Institute of PhysicsChinese Academy of SciencesBeijing 100190China Russian Quantum Center Moscow 119991Russia National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of TechnologyHarbin 150001China Zhengzhou Research Institute Harbin Institute of TechnologyZhengzhou 450046China Frontiers Science Center for Matter Behave in Space Environment Harbin Institute of TechnologyHarbin 150001China

Magnetic films with low Gilbert damping are crucial for magnonic devices,which provide a promising platform forrealizing ultralow-energy *** this study,low Gilbert damping and coercive field were observed in Bi/In-dopedyttrium iron garnet(BiIn:YIG)thin *** BiIn:YIG(444)films were deposited onto different substrates using pulsedlaser *** coercivity(<1 Oe)with saturation magnetization of 125.09 emu/cc was achieved along the in-planedirection of BiIn:YIG *** values of Gilbert damping and inhomogeneous broadening of ferromagnetic resonance inBiIn:YIG films were obtained to be as low as 4.05×10^(-4)and 5.62 Oe,*** addition to low damping,the giantFaraday rotation angles(up to 2.9×10^(4)deg/cm)were also observed in the BiIn:YIG *** modifying the magneticstructure and coupling effect between Bi^(3+)and Fe^(3+)of Bi:YIG,doped In^(3+)plays a key role on variation of the *** low damping and giant Faraday effect made the BiIn:YIG film an appealing candidate for magnonic andmagneto-optical devices.

关键词： magnonic device Gilbert damping Faraday effect

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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Microstructure evolution of extruded Mg-RE-Ag alloy during short-time heat treatment

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Journal of Rare Earths 2024年第12期42卷 2350-2361,I0006页

作者： Zehua Li Jinghuai Zhang Shujuan Liu Jiaming Hao Liyuan Liu Yuying He Haodong Zhang Xingkai Jiao Xiaobo Zhang Ruizhi Wu Key Laboratory of Superlight Material and Surface Technology Ministry of EducationCollege of Material Science and Chemical EngineeringHarbin Engineering UniversityHarbin150001China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology Nanjing Institute of TechnologyNanjing211167China

Microstructure regulation via short-time heat treatment is conducive to the optimization in the microstructure and properties of precipitable magnesium(Mg)alloys,but there is currently a lack of relevant *** this work,the microstructure evolution of a Mg-RE-Ag alloy during different short-time heat treatments was characterized and *** results show that extreme short-time heat treatment(ESHT,e.g.,2 min)at 450-480℃can greatly increase solute concentration in Mg matrix through the rapid re-dissolution of the second-phase and simultaneously maintain fine grains,while the ESHT at a too high temperature(e.g.,510℃)is not suitable due to excessive grain growth and coarse second phase regenerated at grain *** is found that 480℃is the approximate critical temperature for appropriate ESHT,and further prolongation of the time will lead to excessive grain *** is suggested that in addition to grain boundary migration,grain rotation is activated,resulting in the annihilation of high-angle grain boundaries with relatively low misorientation,as well as the reduction in the ability of the residual second phase to pin grain *** addition,the reasons for the abnormal grain boundary segregation and grain boundary continuous phase were analyzed from the perspective of interfacial *** study provides a basis for effective microstructure regulation of Mg-RE alloys.

关键词： Magnesium alloy Rare earths Microstructure Heat treatment Precipitates

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Tunable VO_(2) cavity enables multispectral manipulation from visible to microwave frequencies

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Light(science & Applications) 2024年第3期13卷 510-518页

作者： Hang Wei Jinxin Gu Tao Zhao Zhiyuan Yan He-Xiu Xu Shuliang Dou Cheng-Wei Qiu Yao Li Center for Composite Materials and Structure Harbin Institute of TechnologyHarbin 150001China National University of Singapore Department of Electrical&Computer EngineeringSingapore 117583Singapore School of Chemical Engineering and Technology Harbin Institute of TechnologyHarbin 150001China Suzhou Laboratory Suzhou 215123China

Optical materials capable of dynamically manipulating electromagnetic waves are an emerging field in memories,optical modulators,and thermal ***,their multispectral design preliminarily attracts much attention,aiming to enhance their efficiency and integration of ***,the multispectral manipulation based on these materials is challenging due to their ubiquitous wavelength dependence restricting their capacity to narrow *** this article,we cascade multiple tunable optical cavities with selective-transparent layers,enabling a universal approach to overcoming wavelength dependence and establishing a multispectral platform with highly integrated *** on it,we demonstrate the multispectral(ranging from 400 nm to 3 cm),fast response speed(0.9 s),and reversible manipulation based on a typical phase change material,vanadium *** platform involves tandem VO_(2)-based Fabry–Pérot(F-P)cavities enabling the customization of optical responses at target bands *** can achieve broadband color-changing capacity in the visible region(a shift of~60 nm in resonant wavelength)and is capable of freely switching between three typical optical models(transmittance,reflectance,and absorptance)in the infrared to microwave regions with drastic amplitude tunability exceeding *** work represents a state-of-art advance in multispectral optics and material science,providing a critical approach for expanding the multispectral manipulation ability of optical systems.

关键词： spectral microwave visible

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A review on current development of thermophotovoltaic technology in heat recovery

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International Journal of Extreme Manufacturing 2024年第2期6卷 297-323页

作者： Shuni Chen Yanming Guo Qinghui Pan Yong Shuai Ministry of Industry and Information Technology Key Laboratory of Aerospace Thermophysics School of Energy Science and EngineeringHarbin Institute of TechnologyHarbin 150001People’s Republic of China

The burning of fossil fuels in industry results in significant carbon emissions,and the heat generated is often not fully *** high-temperature industries,thermophotovoltaics(TPVs)is an effective method for waste heat *** review covers two aspects of high-efficiency TPV systems and industrial waste heat *** the system level,representative results of TPV complete the systems,while selective emitters and photovoltaic cells in the last decade are *** key points of components to improve the energy conversion efficiency are further analyzed,and the related micro/nano-fabrication methods are *** the application level,the feasibility of TPV applications in high-temperature industries is shown from the world waste heat utilization *** potential of TPV in waste heat recovery and carbon neutrality is illustrated with the steel industry as an example.

关键词： thermophotovoltaic waste heat recovery micro/nano-fabrication carbon neutrality

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Generating Long Financial Report Using Conditional Variational Autoencoders With Knowledge Distillation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1669-1680页

作者： Wang, Ziao Ren, Yunpeng Zhang, Xiaofeng Wang, Yiyuan Harbin Institute of Technology Department of Computer Science Engineering Shenzhen518055 China

Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a conditional variational autoencoders (CVAEs)-based approach that distills external knowledge from a set of news-report data. Specifically, we design an encoder-decoder architecture to learn the latent variable distribution from this set of news-report data to provide background knowledge. Next, a teacher-student network is employed to distill knowledge to refine the output of the decoder component. To evaluate the model performance, extensive experiments have been performed on two public datasets using evaluation criteria like BLEU, ROUGE, METEOR, and human evaluation. Our promising experimental results demonstrate that our proposed approach outperforms existing state-of-the-art approaches. © 2020 IEEE.

关键词： Natural language processing systems

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Improving long-tail classification via decoupling and regularisation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 62-71页

作者： Shuzheng Gao Chaozheng Wang Cuiyun Gao Wenjian Luo Peiyi Han Qing Liao Guandong Xu School of Computer Science and Technology Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Harbin Institute of TechnologyShenzhenChina Faculty of Engineering and Information Technology University of Technology SydneySydneyNew South WalesAustralia

Real-world data always exhibit an imbalanced and long-tailed distribution,which leads to poor performance for neural network-based *** methods mainly tackle this problem by reweighting the loss function or rebalancing the ***,one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle *** this paper,the authors shed light on the significance of the angle distribution in achieving a balanced feature space,which is essential for improving model performance under long-tailed ***,it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature *** tackle these challenges,the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components:classifier norm(*** magnitude of the classifier vector),feature norm(*** magnitude of the feature vector),and cosine similarity between the classifier vector and feature *** this way,the authors analyse the change of each component in the training process and reveal three critical problems that should be solved,that is,the imbalanced angle distribution,the lack of feature discrimination,and the low feature *** from this analysis,the authors propose a novel loss function that incorporates hyperspherical uniformity,additive angular margin,and feature norm *** component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature *** authors conduct extensive experiments on three popular benchmark datasets including CIFAR-10/100-LT,ImageNet-LT,and iNaturalist *** experimental results demonstrate that the authors’loss function outperforms several previous state-of-the-art methods in addressing the challenges posed by imbalanced and longtailed datasets,t

关键词： computer vision image classification long-tailed data machine learning

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