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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Yan, Shiyu Yang, Xiaohua Lu, Zhongjian Li, Meng Gong, Helin Liu, Jie Computer School University of South China Hunan Hengyang China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China Cnnc Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Metamorphic Relation (MR) plays a crucial role in Metamorphic Testing (MT), which can be regarded as the implication relation from the input pattern to the output pattern. Based on MR's properties, the identification of Metamorphic Relations (MRs) can be from its input pattern and output pattern, respectively. However, the finding of the effective input pattern is very first and important. Especially for complicated mathematical function includes more than two input variables, we systematically analyze the structural model of input pattern in MR, propose the concept of the class of basic input pattern, as well as prove that any one input pattern can be expressed as a combination of basic input patterns in scientific computing programs. Moreover, the structural model of input pattern in MR is established based on previous proof. Finally, we put forward an identification algorithm framework and analysis its complexity. The core contribution of our method is to decompose the input pattern identification problem in multi-dimensional space into a single one-dimensional. For this reason, the examples show that the complexity of the algorithm is reduced significantly. © 2022 IEEE.

关键词： Computational complexity

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Mechanistic investigation of highly bendable magnesium alloy sheet fabricated by short-process manufacturing

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Journal of Magnesium and Alloys 2024年第10期12卷 4289-4310页

作者： Shiwei Xu Peijie Xiao Xiaofan Wu John Holmes Frédéric Mompiou Zhenyu Xiao Chen Jin Tianjiao Li Kuaishe Wang Zhuoran Zeng College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan UniversityChangsha 410082China Suzhou Research Institute of Hunan University Suzhou 215131China College of Engineering Computing and CyberneticAustralian National UniversityACT 2601Australia CEMES-CNRS Universitéde Toulouse29 rue J.MarvigToulouse 31055France Qinghai Salt Lake Teli Magnesium Co. Ltd.Xining 810000China International Joint Laboratory for Light Alloys(Ministry of Education) College of Materials Science and EngineeringChongqing UniversityChongqing 400044China School of Metallurgical Engineering Xi’an University of Architecture&TechnologyXi’an 710055China College of Materials Science and Engineering Hunan UniversityChangsha 410082China

In this study,a commercial magnesium alloy AZ31(Mg-3Al-1Zn-0.3Mn)sheet through a short manufacturing process was found to be ductile and highly formable in *** possessing a strong basal texture,the short-processed sheet without any annealing can be bent at a small radius,only 0.2 times its thickness in the 90°bending ***,it could withstand direct deformation by repeated *** in-situ microstructural characterization reveals that extension twin bands with strain localization appear in the bending *** subsequent flattening,these twin bands underwent detwinning,reducing local strain concentrations and enabling further bending *** outstanding bend formability originated from the significant-type dislocation loops slipping on the prismatic crystal planes within dynamic-recrystallized *** grains underwent a uniform refinement to several microns in the short manufacturing process and exhibited low residual *** active prismatic dislocation slip within refined grains was due to its much lower relative activation stress to basal slip(CRSSprism/CRSSbasal of only∼1.6)owing to the effective grain boundary ***,the prismatic dislocation activity was further enhanced when bypassing Al-Mn nano-particles during motion,leaving debris and loops that facilitated easy multiplication.

关键词： Magnesium Deformation Texture In-situ characterization Dislocation slip.

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ON THE SCALAR CURVATURE COMPACTNESS CONJECTURE IN THE CONFORMAL CASE

arXiv

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arXiv 2024年

作者： Allen, Brian Tian, Wenchuan Wang, Changliang Lehman College CUNY United States University of California Santa Barbara United States School of Mathematical Sciences Institute for Advanced Study Key Laboratory of Intelligent Computing and Applications Ministry of Education Tongji University Shanghai200092 China

Is a sequence of Riemannian manifolds with positive scalar curvature, satisfying some conditions to keep the sequence reasonable, compact? What topology should one use for the convergence and what is the regularity of the limit space? In this paper we explore these questions by studying the case of a sequence of Riemannian manifolds which are conformal to the n-dimensional round sphere. We are able to show that the sequence of conformal factors are compact in several analytic senses and are able to establish C0 convergence away from a singular set of small volume in a similar fashion as C. Dong [Don22]. Under a bound on the total scalar curvature we are able to show that the limit conformal factor has weak positive scalar curvature in the sense of weakly solving the conformal positive scalar curvature equation. Copyright © 2024, The Authors. All rights reserved.

关键词： Conformal mapping

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Self-Supervised Rotation Learning for 3D Segmentation on Nasopharyngeal Carcinoma MRI Images

Self-Supervised Rotation Learning for 3D Segmentation on Nas...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Changtai Jiang, Ruohui Yin, Shihua Yang, Jinzhu Ban, Xiaojuan University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering School of Intelligence Science and Technology Beijing China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing China Second Affiliated Hospital of Guangxi Medical University Nanning China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350337488

Segmenting the tumors of nasopharyngeal carcinoma (NPC) in Magnetic Resonance Imaging (MRI) images is critical for its diagnosis and treatment. In medical image segmentation, it is vital to exploit the rich information contained in 3D sliced images. However, the precise identification and location of lesion regions often require plenty of labels. To alleviate this situation, in this article, we propose 3DRotNPC, a framework based on a tailored Self-Supervised Learning (SSL) strategy, to accurately segment tumor regions for NPC under the circumstance of label limitation. To learn the rich 3D spatial and geometric nature of MRI images in a Self-Supervised way, the proxy task of randomly selecting and rotating images in consecutively sliced data is designed. After the SSL pre-training stage, the learned parameters of the Convolutional Neural Networks (CNNs) based model are transferred to adapt the downstream segmentation task. We verify the capability of 3DRotNPC on the NPC tumor dataset which is collected and curated from the clinical treatment in the representative hospital. Extensive experiments demonstrate that our approach delivers considerable gains in downstream 3D voxel segmentation, especially with a 10% to 50% number of labels. © 2023 IEEE.

关键词： 3D tumor segmentation MRI image nasopharyngeal carcinoma Self-Supervised Learning

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Performance Verification of State Feedback H∞ State Molecular Controller Via Chemical Reaction Networks

Performance Verification of State Feedback H∞ State Molecul...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Jingwang Yao Xing'An Wang Hui Lv Yang Li Wujie Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China College of Environmental and Chemical Engineering Dalian University Dalian China Dalian Chivy Biotechnology Co. Ltd Dalian PR China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

An important goal in synthetic biology is in the design and implementation of control strategies embedded in biochemical reaction systems to enable control of the process at the molecular level. The problem of set-point tracking and perturbation disturbances in the biochemical reaction process can be solved by a state feedback control strategy and a more stable and better dynamic performance of synthetic controllers. First, it is shown how a set of CRNs can provide state feedback to the biomolecular implementation of the H ∞ controller. Then, to facilitate the modularity of biochemical process design, CRNs of trigonometric functions and repetitive sequence step signals are defined for the first time as input signals to the controller. Finally, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from several perspectives, such as setpoint tracking and external perturbations. The results show that the state feedback H ∞ molecular controller outperforms the conventional linear controller because it has a faster tracking response and better perturbation rejection performance.

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Location IoU: A New Evaluation and Loss for Bounding Box Regression in Object Detection

Location IoU: A New Evaluation and Loss for Bounding Box Reg...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lu Yang Kai Zhang Jiaqi Liu Chongke Bi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Autobrain (Tianjin) Technology LTD Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the field of object detection bounding box regression, IoU (Intersection over Union) is a commonly used evaluation metric for measuring the overlap between predicted and ground truth bounding boxes. One limitation of IoU, however, is that the gradient goes to zero when there is no intersection between bounding boxes. Recent evaluation metrics have been developed to overcome this problem by incorporating penalty terms and other techniques for improving IoU. Despite these advances, existing methods still suffer to some degree from the problems of small gradients and slow rate of convergence. In this paper, a new evaluation method named LIoU (Location IoU) is proposed to address these issues. It can maximize the shared area of the intersection when used as the loss function and incorporate a variable gradient parameter to fit to datasets of different sizes. The experimental results indicate that we achieve 0.27% and 3.48% accuracy improvements respectively in SSD and YOLOv5 network on the VOC dataset, and 2.23% in the YOLOv5 network on the COCO dataset. The results of this study highlight the effectiveness of LIoU in improving the performance of object detection models.

关键词： YOLO Measurement Location awareness Accuracy Shape Neural networks Benchmark testing

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A Meta-learning-based Domain-invariant Method for Cuff-less Blood Pressure Estimation

A Meta-learning-based Domain-invariant Method for Cuff-less ...

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Smart World Congress (SWC), IEEE

作者： Jinpeng Guo Feiyi Fan Renju Liu Yuan Jin Wei Zhang Yiqiang Chen Yang Gu Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China AI Dream (Zhuhai) Intelligent Technology Co. Ltd. Shenzhen China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Cuff-less blood pressure estimation methods usually require personalized fine-tuning to achieve better estimation accuracy due to significant inter-individual data variability. The emerging domain generalization methods can extract blood pressure-related knowledge from diverse individuals and estimate readings for unseen subjects; however, these existing methods are less effective in aligning distributions for regression tasks on highly individual physiological time-series data. In this paper, we present a meta-learning-based approach for domainin-variant blood pressure estimation (MetaDIBP), which optimizes a temporal invariant representation learning module with a twostage meta-learning strategy. Specifically, we minimize differences between a prospective target domain and the rest of the source domains in the meta-training stage; update the model utilizing all source domains to ensure consistent and robust performance across various unseen target domains in the successive metatesting stage; and incorporate deep feature extraction, Correlation Alignment loss, and Maximum Mean Discrepancy loss into a temporal invariant representation learning module to serve as the network architecture for feature distribution discrepancy reduction. Our experimental results demonstrate that the proposed method outperforms traditional domain generalization and deep-learning-based blood pressure models, achieving a mean absolute error of $12.989 \mathbf{~ m m H g}$ for systolic blood pressure and 7.391 mmHg for diastolic blood pressure. With personalized finetuning, the mean absolute error further improves to 7.464 mmHg for systolic blood pressure and $4.509 \mathbf{~ m m H g}$ for diastolic blood pressure, showing advantages over existing methods.

关键词： Metalearning Representation learning MIMICs Estimation Network architecture Feature extraction Blood pressure Physiology Data mining Optimization

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Image encryption based on 2D-SAHM chaotic system and a novel DNA operation rule

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The European Physical Journal Special Topics 2023年第6期233卷 1311-1330页

作者： Huang, Lilian Ye, Youxin Liu, Yang College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Marine Communication and Information Technology Harbin Engineering University Ministry of Industry and Information Technology Harbin China National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Harbin China Sichuan Key Laboratory of Agile Intelligent Computing Southwest China Institute of Electronic Technology Chengdu China

With the continuous development of information technology, ensuring information security has become an important issue. As a widely used multimedia tool, images often face the risk of leakage. Therefore, this paper proposes an image encryption algorithm based on 2D-SAHM chaotic system and a new DNA operation rule. The algorithm first constructs a new chaotic system 2D-SAHM, which has better chaotic characteristics and can generate more random chaotic sequences than the traditional low-dimensional chaotic system. And then, on the basis of this system, the algorithm scrambles and diffuses the image. In the scrambling stage, the whole image is converted into a one-dimensional pixel matrix and divided into several sub-images by double Joseph scrambling. In the diffusion stage, to solve the problem that traditional DNA encryption relies too much on chaotic sequences, this paper proposes a new DNA operation rule. This rule determines the operation symbol of DNA by the type of amino acid, which improves the randomness of the algorithm. The final simulation results and performance analysis show that the algorithm has high security and strong anti-interference ability.

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A Digital Emergency Communication Method for Coal Mining Accident Based on Distributed Acoustic Sensor 22

A Digital Emergency Communication Method for Coal Mining Acc...

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22nd International Conference on Optical Communications and Networks, ICOCN 2024

作者： Li, Yazhuo Ma, Jiaqi Zhao, Xinying Li, Hongren Tong, Shuai Zou, Ningmu Zhang, Xuping Xiong, Fei Zhang, Yixin Liu, Huanhuan College of Engineering and Applied Sciences Nanjing University Nanjing210023 China Ministry of Education Nanjing University Key Laboratory of Intelligent Optical Sensing and Manipulation Nanjing210093 China School of Information Science and Engineer Shandong University Qingdao266237 China Inner Mongolia DT Electric Power Design Co. Ltd Hohhot010010 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9798350367652

Reliable emergency communication after coal mining accidents can ensure carrying out effective rescue work and reducing casualties. This paper presents a digital emergency communication method for coal mining accident based on distributed acoustic sensor (DAS). The rescue information such as number of trapped workers and injuries, oxygen content, gas consistency, food surplus and water surplus is encoded into digital signals and modulated into vibration signals, which can be generated by intrinsically safe vibrator and monitored by DAS. Experimental results demonstrate that this method can achieve simultaneous multi-point communication utilizing the existing optical cables under coal mine conditions, with a symbol error rate (SER) of less than 1.25%, showing enormous application potential. © 2024 IEEE.

关键词： Coal mines

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Pose Estimation for Non-Cooperative Spacecraft Based on Deep Learning

SSRN

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SSRN 2024年

作者： Gao, Xiang Liu, Chang Liu, Jia Hu, Weiduo Shenzhen Aerospace Dongfanghong Satellite Ltd Guangdong Shenzhen518052 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518052 China Beihang University Beijing100191 China Total Design Department Institute of Advanced Computing and Digital Engineering Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing China School of Astronautics

This paper proposes an end-to-end pose estimation framework for spacecraft. The framework can run on full-size images and jointly calculate translation and rotation parameters based on shared features of convolutional network. In addition, we introduce the method of label normalization. In the training stage, images and labels are normalized and then input into the neural network architecture for training. In the test stage, the normalized images are input to the trained framework, and the corresponding predicted pose is obtained after the output parameters are reversely normalized. Then we use the local optimization method to optimize the predicted pose. The effectiveness of our method is verified by a large number of experiments. © 2024, The Authors. All rights reserved.

关键词： Network architecture

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