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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Kaiwei Zhu Shihan Kong Fang Wu Jinan Sun Junzhi Yu Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China SPIC Nuclear Energy Co. Ltd. China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world.

关键词： Target tracking Observers Path planning software Real-time systems Reliability Vehicle dynamics

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Effect of molybdenum on interfacial properties of titanium carbide reinforced Fe composite

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Journal of Materials Science & Technology 2022年第12期107卷 252-258页

作者： Seungchan Cho Junghwan Kim Ilguk Jo Jae Hyun Park Jaekwang Lee Hyun-Uk Hong Bong Ho Lee Wook Ryol Hwang Dong-Woo Suh Sang-Kwan Lee Sang-Bok Lee Composites Research Division Korea Institute of Materials Science(KIMS)Changwon 51508South Korea Advanced Materials Engineering Dong-Eui UniversityBusan 47340South Korea Department of Aerospace and Software Engineering Gyeongsang National UniversityJinju 52828South Korea Department of Physics Pusan National UniversityBusan 46241South Korea Department of Materials Convergence and System Engineering Changwon National UniversityChangwon 51140South Korea Center for Core Research Facilities Daegu Gyeongbuk Institute of Science and TechnologyDaegu 42988South Korea School of Mechanical Engineering Gyeongsang National UniversityJinju 52828South Korea Graduate Institute of Ferrous Technology Pohang University of Science and TechnologyGyeongbuk 37673South Korea

This study shows that the mechanical strength of the composite of Fe matrix and titanium carbide(Ti C)ceramic particles is significantly enhanced with addition of molybdenum(Mo) atoms. Ti C reinforced Fe(Fe-0.2C-7Mn) composites with and without Mo were fabricated by a liquid pressing infiltration(LPI)process and the effect of Mo on interfacial properties of TiC–Fe composite was investigated using atomic probe tomography(APT) analysis, molecular dynamics(MD) simulations, first-principle density functional theory(DFT), and thermodynamic calculations. First, DFT calculations showed that total energies of the Mo-doped Ti C–Fe superlattices strongly depend on the position of Mo defects, and are minimized when the Mo atom is located at the TiC/Fe interface, supporting the probable formation of MoC-like interphase at the TiC/Fe interface region. Then, APT analysis confirmed the DFT predictions by finding that about6.5 wt.% Mo is incorporated in the Ti C–Fe(Mo) composite and that sub-micrometer thick(Ti,Mo)C interphase is indeed formed near the interface. The MD simulations show that Mo atoms migrate to the Mo-free TiC–Fe interface at elevated temperatures and the mechanical strength of the interface is considerably enhanced, which is in good agreement with experimental observations.

关键词： Metal matrix composites(MMCs) Titanium carbide Fe matrix composite Infiltration Molybdenum Interfacial property

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Attribute expansion relation extraction approach for smart engineering decision-making in edge environments

Attribute expansion relation extraction approach for smart e...

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作者： Cui, Mengmeng Zhang, Yuan Hu, Zhichen Bi, Nan Du, Tao Luo, Kangrong Liu, Juntong School of Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China School of Competitive Sports Shandong Sport University Jinan China School of Continuing Education and Training Shandong Sport University Jinan China Department of Physical Education Chengdu Technological University Chengdu China Sports Department Chengdu University of Information Technology Chengdu China

In sedimentology, the integration of intelligent engineering decision-making with edge computing environments aims to furnish engineers and decision-makers with precise, real-time insights into sediment-related issues. This approach markedly reduces data transfer time and response latency by harnessing the computational power of edge computing, thereby bolstering the decision-making process. Concurrently, the establishment of a sediment knowledge graph serves as a pivotal conduit for disseminating sediment-related knowledge in the realm of intelligent engineering decision-making. Moreover, it facilitates a comprehensive exploration of the intricate evolutionary and transformative processes inherent in sediment materials. By unveiling the evolutionary trajectory of life on Earth, the sediment knowledge graph catalyzes a deeper understanding of our planet's history and dynamics. Relationship extraction, as a key step in knowledge graph construction, implements automatic extraction and establishment of associations between entities from a large amount of sedimentary literature data. However, sedimentological literature presents multi-source heterogeneous features, which leads to a weak representation of hidden relationships, thus decreasing the accuracy of relationship extraction. In this article, we propose an attribute-extended relation extraction approach (AERE), which is specifically designed for sedimentary relation extraction scenarios. First, context statements containing sediment entities are obtained from the literature. Then, a cohesive hierarchical clustering algorithm is used to extend the relationship attributes between sediments. Finally, mine the relationships between entities based on AERE. The experimental results show that the proposed model can effectively extract the hidden relations and exhibits strong robustness in dealing with redundant noise before and after sentences, which in turn improves the completeness of the relations between deposits. Af

关键词： Knowledge graph

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Interlocking Strategy Confined in Multifunctional Rubber-Based Composites for Boosting Anti-Corrosion and Electromagnetic Interference Shielding

SSRN

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SSRN 2025年

作者： Sun, Jiahao Chen, Jianing Gao, Zhenliang Li, Yang Liu, Chuntai Li, Muhe Liu, Xianhu Shen, Changyu State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment National Engineering Research Center for Advanced Polymer Processing Technology Zhengzhou University Zhengzhou450002 China College of Material Science and Engineering Zhengzhou University Zhengzhou450001 China Zhengzhou University China

Addressing the corrosion issues arising from incorporating highly conductive fillers into the rubber matrix remains a significant challenge, particularly in the harsh marine corrosion environment and under the threat of early warning radar detection. Inspired by the principles of interface engineering in composites, an interlocking strategy for regulating the multiple interfacial interactions (including dipole-dipole, electrostatic and chemical bonding) between methyl vinyl silicone rubber (VMQ) and metal nanoparticles is proposed. This modification facilitates molecular conformational transformation and ordering within the VMQ-based composite, thereby inducing stable dipole distribution of silicone rubber at the composite interface. Consequently, this significantly enhanced the crosslinking degree of rubber vulcanization and interfacial adhesion between the filler and matrix. Compared to conventional rubber-based composites, the engineered VMQ/Ag@Al/ MWCNTs composite maintained an electromagnetic interference (EMI) shielding effectiveness exceeding 100 dB after a 240-hour copper accelerated salt spray (CASS) test. Furthermore, the modified rubber matrix composite exhibits a breaking strength of 2.5 MPa and an elongation at break of 120%, significantly higher than that of pure silicone rubber with a breaking strength of only 0.43 MPa. Additionally, the VMQ/Ag@Al/MWCNTs composite avoids a series of issues, including complex molecular orientation, interface instability, and performance degradation due to structural relaxation following 240-hour post-treatments such as ultraviolet aging, and high temperature and humidity. The findings offer a novel approach for multi-scale interface control, enhancing the performance and functional applications of rubber matrix composites. © 2025, The Authors. All rights reserved.

关键词： Seawater corrosion

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Archer: adaptive memory compression with page-association-rule awareness for high-speed response of mobile devices 25

Archer: adaptive memory compression with page-association-ru...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Changlong Li Zongwei Zhu Chao Wang Fangming Liu Fei Xu Edwin H.-M. Sha Xuehai Zhou School of Computer Science and Technology East China Normal University and Jianghuai Advance Technology Center Hefei China and MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application School of Software Engineering University of Science and Technology of China Hefei China and Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Huazhong University of Science and Technology and Peng Cheng Laboratory School of Computer Science and Technology East China Normal University

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate associationrule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art.

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Exploring Depth-based Perception Conflicts in Virtual Reality through Error-Related Potentials

Exploring Depth-based Perception Conflicts in Virtual Realit...

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IEEE Annual International Symposium Virtual Reality

作者： Haolin Gao Kang Yue Songyue Yang Yu Liu Mei Guo Yue Liu Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Human Computer Interactions Institute of Software Chinese Academy of Sciences Beijing China

Virtual Reality (VR) offers a valuable platform for real-life skills training. However, previous research has indicated that human’s perception of depth in VR differs from that of the real world. Such perceptual conflicts can impact immersion and the learning of skills, thus attracting widespread attention. Various methods have been proposed to enhance users’ depth perception, yet the underlying mechanisms of depth perception conflicts still require further research. In this paper, we used Error-Related Potentials (ErrPs) from electroencephalography (EEG) data to investigate the differences in participants’ perceptions at varying depths within the near-field. We designed a within-subjects experiment to successfully introduce depth perception conflicts. From participants exposed to three distinct depths, we collected questionnaire results, performance data, and EEG data. Our findings showed that EEG can effectively detect depth perception conflicts and, following each conflict, participants’ behavioral patterns showed significant changes. In situations with shallower depths, participants exhibited stronger responses to the designed conflicts. This increased sensitivity correlates with their accuracy in depth estimation. This study represents a novel approach to depth perception in VR using ErrPs, setting the stage for further use of physiological signals to measure the granularity of depth perception in VR/AR environments.

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Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification 33

Ranking Aggregation with Interactive Feedback for Collaborat...

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Huang, Ji Liang, Chao Zhang, Yue Wang, Zhongyuan Zhang, Chunjie National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China

Person re-identification (re-ID) aims to retrieve the same person from a group of networking cameras. Ranking aggregation (RA), a method to aggregates multiple ranking results, can further improve the retrieval accuracy in re-ID tasks. Existing RA work can be generally divided into unsupervised methods and fully-supervised methods. Unsupervised methods lack external supervision, can hardly achieve the optimal results. In contrast, fully-supervised methods need massive labeling data for training, which is prohibitively expensive in the practical application. This paper studies interactive RA (IRA) to address the above challenges in existing RA research. The core idea is to utilize a small amount of supervisory information, obtained from users' relevance feedback, to supervise RA method to produce better re-ranking results. Compared with unsupervised methods, IRA introduces supervisory information and thus has better aggregation accuracy. Compared with fully-supervision methods, the supervisory information of IRA is more readily available, and can be targeted to specific queries. Particularly, we propose two IRA implementations, based on ranking positions and scores respectively, to adapt to diverse application scenarios, where rankers only give rankings, or rankers give similarity scores. Experiments on three public re-ID datasets have shown that IRA significantly outperforms the-state-of-art unsupervised baselines, and achieves similar accuracy with less labeling cost than the fully-supervised RA method. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Computer vision

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ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by Distributed Ledger Technology and Internet of Things 23

ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Haoyu Li, Leixiao Lin, Hao Chang, Xiangyang Wan, Jianxiong Li, Jie Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Reg. Eng. and Technol. Research Center of Big Data Based Software Service China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

P2P Botnet is famous for the resilience against termination. However, its dependence on Neighbor List (NL) makes it susceptible to infiltration and poison, also leading to a dearth of adequate protection of Botmaster's identity. So as to overcome these weaknesses, the novel botnet infrastructure combined with blockchain emerges. But due to some flaws such like high latency and expensive transactions fees on blockchain, these new schemes can't satisfy the need of constructing hyper large-scale botnet in practice. We propose ZombieCoin3.0, a Botnet underlying overlay leveraging IoT devices to be maintainers and integrated with Distributed Ledger Technology (DLT), which is able to deploy bots fully on blockchain. And it is versatile for file transfer, data feedback and other command and control scenarios that many contemporaries are impotent to cope with. Our proposal eliminates the P2P botnets' dependence on NL and the bottlenecks that other blockchain combined botnets have. Experiments' results authenticated that the effectiveness of our proposal as well as low latency of communication and nearly zero overhead of cost other comparatives unable to attain are achieved. © 2021 IEEE.

关键词： Costs Distributed ledger Botnet Blockchains Proposals Internet of Things Low latency communication

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Improving Predictive Navigation Through the Optimization of Counterfactual Track Evaluation

Improving Predictive Navigation Through the Optimization of ...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Alexander Stringer Geoffrey Dolinger Timothy Sharp Debra Hogue Joseph Karch Lesya Borowska Justin G. Metcalf US Air Force's Oklahoma City Air Logistics Complex and the 76 Software Engineering Group School of Electrical and Computer Engineering and the Advanced Radar Research Center at the University of Oklahoma Department of Computer Science University of Oklahoma

Modern navigation applications are confronting the challenge of path identification and prediction. Because of the inherent noisiness of measurement techniques, positional data often contains errors. Over time, when attempting to predict future positions, these errors can compound, causing drift in the target's path. Filtering or regression techniques can use periodic high-fidelity measurements, like those from GPS sensors, to correct positional data and reduce historic errors. These techniques have been much less effective in specialized environments where GPS is intermittent or denied. At the same time, the development of semi- and fully-autonomous systems has increased the need for accurate predictive navigation. To address this problem, previous methods were proposed for repairing tracking data using causality-aware machine learning (ML). It combined a long short-term memory (LSTM) network that predicted target paths with the non-dominated sorting genetic algorithm II (NSGA-II) which identified and evaluated counterfactual paths. Here te authors expand on that work by improving the simulated data used to train and test the system, expanding the GA by adding additional first principles-based objective functions, improving upon the LSTM implementation, and utilizing Extended Kalman Filter (EKF) pre-processing. System testing is conducted on Matlab-generated navigation scenarios, and results are compared to both EKF correction and spline interpolation. The proposed counterfactual track repair (CTR) tool produces paths with less repeatability than traditional approaches. However, it is shown to consistently generate more realistic path corrections, lower error in predictions based on its corrected paths, and is highly configurable, demonstrating the value and utility of this approach.

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爆破型和聚能型装药水下爆炸载荷特性

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哈尔滨工程大学学报（英文版） 2024年第2期23卷 302-315页

作者：张之凡李海龙张竞元王龙侃张桂勇宗智 State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Naval Architecture EngineeringDalian University of TechnologyDalian 116024China China Ship Research and Development Academy Beijing 100192China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai 200240China

Blasting and shaped charges are the main forms of underwater weapons,and their near-field underwater explosions(UNDEX)can severely damage ***,it is of great importance to study underwater explosive load characteristics of different forms of *** full physical process of a typical underwater explosion of a sphere/column blasting charge and a shaped charge was simulated using the Eulerian *** loading characteristics of the underwater blast shock wave and bubble,as well as the projectile,were *** results show that the shock wave loads of spherical,cylindrical,and polygonal charges propagate outward in spherical,ellipsoidal–spherical and ellipsoidal–spherical wavefronts,*** the shock wave reaches 16 times the distance-to-diameter ratio,its surface is approximately *** addition,in the shaped charge underwater explosion,the shaped charge liner cover absorbs 30°–90°of the shock wave energy and some of the bubble energy to form a high-speed shaped ***,ellipsoidal,and ellipsoidal bubbles are generated by underwater explosions of spherical,cylindrical,and shaped charges,*** obtained results provide a reference for evaluating the power of underwater weapons.

关键词： Blasting charge Shaped charge Load characteristics Shock wave Bubble

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