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arXiv 2023年

作者： Liu, Feifan Zhang, Shuang Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No.2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in deep learning techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists' topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Optimization of Experiment Teaching in the Course of Mechanical Principle 2020

Optimization of Experiment Teaching in the Course of Mechani...

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2020 3rd International Conference on Education Technology Management

作者： Pujun Hao Jingna Liu Wenpeng Ma Ruiqin Wu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin and National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) China

ISBN: (纸本)9781450388757

Based on experience of former teaching reformation and philosophy of "Emerging Engineering Education", an experimental teaching reformation program that includes restructured knowledge system, modularization of experimental teaching process and diversified evaluation system is proposed. It is found that students have deeper and comprehensive understanding on knowledge and more enthusiasm in experimental process. Finally students have definite self-cognition about their division of labor in teamwork. Through gathering statistics, the effectiveness of reformation program is verified and this program can enrich practical experience of experimental teaching reformation.

关键词： experiment teaching diversified evaluation modular teaching process Mechanical principle

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Curriculum Reform of "Fundamentals of Mechanical design" for Non-Mechanical Majors Based on OBE Engineering Education Concept 2020

Curriculum Reform of "Fundamentals of Mechanical Design" for...

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2020 3rd International Conference on Education Technology Management

作者： Jingna Liu Pujun Hao Linwei Lyu Wenpeng Ma Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin and National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) China

ISBN: (纸本)9781450388757

Focusing on the non-mechanical students' weak mechanical knowledge, a new cultivation mode founded on the outcome-oriented education (OBE) is suggested to improve the teaching effect for "Fundamentals of mechanical design". The teaching course contents are optimized to make students' learning process proceed simultaneously with project case study. The teaching methods of innovative project driven and problem-oriented are adopted. Students can be guide to change the passive learning mode by autonomous learning mode with the online learning resources. The innovative methods can be recommended to the process of curriculum design to reform the content. The course experiment has been changed into online and offline mixed teaching, so as to improve students' experimental practice time and cultivate their ability of integrating theory with practice, analyzing problems and solving problems. Finally, the formative evaluation mode with the learning results as the assessment index is adopted to improve the students' comprehensive ability. Compared with the control class, the experimental class has greatly improved the excellent rate and pass rate. In the questionnaire survey, the experimental class students have a higher recognition for the curriculum reform.

关键词： curriculum reform learning outcomes oriented Engineering education concept fundamentals of mechanical design

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DQPS: An intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge computing Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Emergency Collision Avoidance Strategy for Autonomous Vehicles Based on Steering and Differential Braking

Research Square

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Research Square 2022年

作者： Li, Haiqing Li, Yongfu Zheng, Taixiong Ye, Qing School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing China China Merchants Chongqing Communications Technology Research&Design Institute Co. Ltd. Chongqing China

This paper develops a novel integrated collision avoidance strategy for autonomous vehicles in emergency based on steering and braking. Specifically, the framework of the collision avoidance strategy is composed of two parts: an up-level decision-making layer and a low-level controller layer. The purpose of the up-level is to select the appropriate control strategy based on the vehicle information, and the low-level is to drive the vehicle according to the instructions generated by the up-level. More concretely, a novel control strategy is proposed by integrating with four-wheel steering, active rear steering and differential braking with guaranteed path-tracking accuracy and driving stability by adaptive model predictive control (AMPC). Finally, extensive co-simulations in MATLAB/Simulink and CarSim are conducted to verify the effectiveness of the proposed collision avoidance strategy in terms of tracking error, yaw rate and roll angle. © 2022, CC BY.

关键词： Collision avoidance

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Humanoid Robot Control Based on Deep Learning 12th

Humanoid Robot Control Based on Deep Learning

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Guo, Bin Yi, Pengfei Zhou, Dongsheng Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China College of Computer Science and Technology Dalian University of Technology Dalian1160243 China

ISBN: (纸本)9783030237110

The direct control of humanoid robot by human motion is an important aspect of current research. Most of these methods are based on additional equipments, such as Kinect, which are usually not equipped on robot. In order to avoid using these external equipments, we explored a robot controlling method only using the low-resolution camera on robot. Firstly, a stacked hourglass network is employed to obtain the accurate 2D heatmap containing positions of human joints from RGB image captured by camera on robot. Then, 3D human poses including coordinates of human body joints are estimated from 2D heatmaps by a method aiming to reconstruct 3D human poses from 2D poses. Finally, the rotation angles of robot are computed according to these 3D coordinates and are transmitted to the robot to reconstruct the original human pose. Using the NAO robot as an example, the experimental results show that the humanoid robot can imitate motions of different human actors in different scenes well while applying our method. © 2019, Springer Nature Switzerland AG.

关键词： Anthropomorphic robots

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Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

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Procedia Computer Science 2023年 226卷 36-42页

作者： Tianhang Liu Hechen Li Long Bai Yanan Wu An Wang Mobarakol Islam Hongliang Ren Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong China College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Department of Biomedical Engineering National University of Singapore Singapore Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to accommodate the stages of nasal intubation. Automated landmark detection can provide accurate localization and quantitative evaluation. The Detection Transformer (DeTR) leads object detectors to a new paradigm with long-range dependence. However, current DeTR requires long iterations to converge, and does not perform well in detecting small objects. This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation. The semantics aligner can effectively align the semantics of object queries and image features in the same embedding space using the most discriminative features. To evaluate the performance of our solution, we utilize a publicly accessible glottis dataset and automatically annotate a nostril detection dataset. The experimental results demonstrate our competitive performance in detection accuracy. Our code can be accessible at https://***/ConorLTH/airway-intubation_landmarks-detection .

关键词： Bleeding regions segmentation Medical image segmentation Semi-supervised learning Video capsule endoscopy

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Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis

arXiv

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arXiv 2023年

作者： Liu, Yanyun Hua, Xiumeng Zhu, Shouping Wang, Congrui Chen, Xiao Shi, Yu Song, Jiangping Zhou, Weihua School of Life Science and Technology Xidian University Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'An China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment School of Life Science and Technology Xidian University Shaanxi Xi'An China Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of Technology Xidian University Guangdong Guangzhou China Beijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials Animal Experimental Centre Fuwai Hospital National Centre for Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences Peking Union Medical College 167A Beilishi Road Xi Cheng District Beijing China Department of Cardiovascular Surgery Fuwai Hospital National Center for Cardiovascular Diseases National Clinical Research Center of Cardiovascular Diseases State Key Laboratory of Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China The Cardiomyopathy Research Group Fuwai Hospital Beijing China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Background Given the dimensionality of WSIs and the increase in the number of potential cases, the manual histological diagnosis of myocarditis based on Dallas Criteria is time-consuming, experience-dependent. Objectives To overcome the limitations of manual diagnosis methods, this study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital hematoxylin and eosin (HE)-stained images to assist pathologists in diagnosing myocarditis. Methods 898 HE-stained whole slide images (WSIs) of myocardium from 154 heart transplant patients diagnosed with myocarditis or dilated cardiomyopathy (DCM) were included in this study. An automated deep-learning (DL)-based computational pathology approach was developed to identify nuclei and detect myocardial inflammatory infiltration, enabling the quantification of the lymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based on the quantification of LND was proposed to determine if the myocardial inflammatory infiltration was present. The performance of our approach was evaluated with a five-fold cross-validation experiment, tested with an internal test set from the myocarditis group, and confirmed by an external test from a double-blind trial group. Results An LND of 1.02/mm2 could distinguish WSIs with myocarditis from those without. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the five-fold cross-validation experiment were 0.899 ± 0.035, 0.971 ± 0.017, 0.728 ± 0.073 and 0.849 ± 0.044, respectively. For the internal test set, the accuracy, sensitivity, specificity, and AUC were 0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity, specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and 0.852, respectively. Conclusion Our new approach provides accurate and reliable quantification of the LND of myocardial WSIs bas

关键词： Pathology

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Base station network traffic prediction approach based on LMA-DeepAR

arXiv

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arXiv 2021年

作者： Zhang, Jiachen Zuo, Xingquan Xu, Mingying Han, Jing Zhang, Baisheng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing Uinversity of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing Uinversity of Posts and Telecommunications Beijing China Network Interface Virtualization Shanghai System Design Deptment Zhongxing Telecommunication Equipment Corporation Shanghai China

Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the network traffic nonlinear and non-stationary, which brings challenges to the long-term prediction of network traffic. In this paper, the traffic model LMA-DeepAR for base station network is established based on DeepAR. Acordding to the distribution characteristics of network traffic, this paper proposes an artificial feature sequence calculation method based on local moving average (LMA). The feature sequence is input into DeepAR as covariant, which makes the statistical characteristics of network traffic near a period of time in the past be considered when updating parameters, and the interference of non-stationary network traffic on model training will be reduced. Experimental results show that the proposed prediction approach (LMA-DeepAR) outperforms other methods in the overall long-term prediction performance and stability of multi cell network traffic. Copyright © 2021, The Authors. All rights reserved.

关键词： Base stations

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Cascaded adaptive aberration-eliminating multimode fiber imaging

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advanced Photonics 2025年第2期 135-143页

作者： Zhong Wen Qilin Deng Quanzhi Li Yizhou Tan Jingshan Zhong Chiming Zhang Jiahe Zhang Clemens F.Kaminski Ying Gu Xu Liu Qing Yang Zhejiang University College of Optical Science and Engineering State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University International Research Center for Advanced Photonics Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center Chinese PLA General Hospital The First Medical Center Department of Laser Medicine Chinese PLA General Hospital Hainan HospitalLaser Medicine Center Zhejiang Laboratory Research Center for Intelligent Manufacturing Computing University of Cambridge Department of Chemical Engineering and Biotechnology

In vivo microscopic imaging inside a biological lumen such as the gastrointestinal tract, respiratory airways, or within blood vessels has faced significant technological challenges for decades. A promising candidate technology is the multimode fiber(MMF) endoscope, which enables minimally invasive diagnostics at a resolution reaching the cellular level. However, for in vivo imaging applications deep inside a biological lumen, sample-induced aberrations and the dynamic dispersion in the MMF make the MMF endoscope a chaotic system with many unknowns, where multiple minor fluctuations can couple and compound into intractable problems. We introduce a dynamically encoding, cascaded, optical, and ultrathin polychromatic light-field endoscopy(DECOUPLE) to tackle this challenge. DECOUPLE includes an adaptive aberration correction that can accurately track and control MMF behavior in the spatial-frequency domain to compensate for chaos introduced during complex dynamic imaging processes. We demonstrate the flexibility and practicality of DECOUPLE for noninvasive volumetric imaging in two colors for light passing through various highly aberrating samples including 120-μm-thick onion epidermal slices and 80-μm-thick layers of fat emulsions. To summarize, we represent a significant step toward practical in vivo imaging deep within biological tissue.

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