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Gas-Liquid Separation of Liquid Hydrogen in Microgravity: Optimization of Structural Parameters, Angles, Rotational Speeds, and Gas-Liquid Ratios 4

Gas-Liquid Separation of Liquid Hydrogen in Microgravity: Op...

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4th International Conference on Defence technology, ICDT 2024

作者： Li, Zehao Xu, Wenlong Zhu, Honglai Gao, Yaru Chen, Chongwu Zhao, Haitao Institute for Advanced Technology Shandong University Jinan250061 China Beijing Institute of Control Engineering Beijing100190 China Institute of Software Chinese Academy of Sciences Beijing100190 China School of Energy and Power Engineering Shandong University Jinan250061 China

Liquid hydrogen (LH2) is a high-efficiency cryogenic propellant extensively used in the aerospace industry due to its superior specific impulse and energy density. Despite its advantages, managing LH2 in orbit presents significant challenges, particularly in microgravity, where fluid transport and gas-liquid interface stability are adversely affected. This study addresses these challenges by investigating the effects of different structural parameters, angles, rotational speeds, and gas-liquid ratios on LH2 gas-liquid separation through comprehensive numerical simulations validations. We analyze the impacts of various pore diameters and axial spacings, as well as the evolution of gas-liquid configurations at different angles and rotational speeds. Additionally, we explore the effects of different gas-liquid ratios on separation performance. Our findings identify optimal parameter combinations and elucidate key mechanisms influencing gas-liquid separation efficiency. The study employs high-precision models and microgravity simulation experiments to validate the numerical results, providing a robust foundation for optimizing LH2 management devices. This research contributes valuable insights into the management of liquid hydrogen (LH2) in microgravity environments and provides foundational knowledge that may benefit future deep-space exploration missions. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Aerospace industry

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Augmentation to Disentangling: A Novel Approach for Improving Feature Disentanglement in Occluded Person Re-Identification

Augmentation to Disentangling: A Novel Approach for Improvin...

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International Conference on Computers, Communications, and systems (ICCCS)

作者： Shaochen Han Da Li Huapeng Yang ZhangQin Huang Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Occluded person re-identification poses a significant challenge due to the obstruction of human body parts by various obstacles, requiring the use of incomplete or invisible body parts for matching. Existing methods often address occlusion through feature disentanglement. However, the absence of occlusion labels hampers accurate disentanglement, leading to challenges in precisely distinguishing occluded regions. In response to these challenges, we propose a novel approach that actively introduces occlusion to guide feature disentanglement. Our method incorporates an Occlusion Sample Augmentation module, actively introducing occlusion to person images and generating accurate occlusion labels. Additionally, we employ pose estimation to partition the human body into distinct semantic components, enabling selective matching of non-occluded parts. To address interference from occlusion, we introduce an Occlusion-augmented Push Loss. This loss function effectively separates human body features from occluded part features based on augmented occlusion location labels, guiding the learning of feature disentanglement. Experimental results on both the Occluded-Duke and DukeMTMC-reID datasets, encompassing occluded and holistic re-identification tasks, showcase the outstanding performance of our method, achieving state-of-the-art (SOTA) performance.

关键词： Accuracy Communication systems Image matching Semantics Pose estimation Interference Phase frequency detectors

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In-Plane Dynamic Crushing Behaviors of a Vertex-Based Hierarchical Auxetic Honeycomb

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Acta Mechanica Solida Sinica 2024年第1期37卷 53-62页

作者： Yichen Zan Xiuhui Hou Zichen Deng School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi'an 710072China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi'an 710072China School of Aeronautics Northwestern Polytechnical UniversityXi'an 710072China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China

Auxetic metamaterials,which exhibit the negative Poisson’s ratio(NPR)effect,have found wide applications in many engineering ***,their high porosity inevitably weakens their bearing capacity and impact *** improve the energy absorption efficiency of auxetic honeycombs,a novel vertex-based hierarchical star-shaped honeycomb(VSH)is designed by replacing each vertex in the classical star-shaped honeycomb(SSH)with a newly added self-similar *** analytical model is built to investigate the Young’s modulus of VSH,which shows good agreement with experimental results and numerical *** in-plane dynamic crushing behaviors of VSH at three different crushing velocities are investigated,and empirical formulas for the densification strain and plateau stress are *** results reveal more stable deformation modes for VSH,attributed to the addition of self-similar star-shaped ***,compared with SSH under the same relative densities,VSH exhibits better specific energy absorption and higher plateau ***,VSH is verified to be a better candidate for energy absorption while maintaining the auxetic *** study is expected to provide a new design strategy for auxetic honeycombs.

关键词： Vertex-based hierarchical star-shaped honeycomb Auxetic honeycomb Energy absorption Plateau stress Negative Poisson's ratio

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Sea Level Anomalies Prediction Via Two-Stage Spatiotemporal Transformer 21

Sea Level Anomalies Prediction Via Two-Stage Spatiotemporal ...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Fang, Shaoteng Zhao, Yuehao Wang, Kefan Tian, Chenyu Kang, Qi Tongji University Department of Control Science and Engineering Shanghai China The School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney Australia Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Software Technology Dalian University of Technology Dalian China

ISBN: (纸本)9798350365221

This study successfully applies the spatiotemporal transformer-based model to sea surface height anomaly prediction task. An improved ResNet autoencoder, named SW-GD Autoencoder, is proposed based on Sliced-Wasserstein distance and Gradient Difference loss functions to extract spatial feature information. This autoencoder demonstrates a reduced complexity and training difficulty compared to GAN-based architectures while maintaining high data generation quality. Additionally, the VidHRFormer model is employed for inference, introducing a large receptive field spatial feed-forward network module that enhances information transmission between adjacent patches in the feature map, thereby improving the observability of prediction results. To simplify training, we used a two-stage approach: first training the autoencoder separately, then combining it with VidHRFormer for final training. Compared to state-of-the-art ConvLSTM models, our method yields superior results. © 2024 IEEE.

关键词： Sea level

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Characterising Bicyclist Posture in Various Bicycle Types for Safety Evaluation

Characterising Bicyclist Posture in Various Bicycle Types fo...

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2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024

作者： Oikonomou, Maria Iraeus, Johan Lioras, Athanasios Mylonas, Vasileios Rorris, Lambros Mihailidis, Athanassios School of Mechanical Engineering Aristotle University of Thessaloniki Greece Division of Vehicle Safety Chalmers University of Technology Gothenburg Sweden Software Research and DevelopmentDepartment BETA CAE Systems SA Thessaloniki Greece School of Physical Education and Sport Science Aristotle University of Thessaloniki Greece Software Research and Development Department BETA CAESystems International Luzern Switzerland

The growing use of bicycles and the increasing frequency of accidents underscore the need for an in-depth investigation into bicyclists' kinematics and injuries. This study aims to collect and analyse bicyclists' posture data across three bicycle types to facilitate the positioning of Human Body Model (HBM) for implementation in crash simulations. Ten male volunteers, with characteristics similar to the 50th percentile male, were measured while riding in the city, racing, and mountain bikes. Characteristic angles of the volunteers' postures were calculated for each bicycle and for pedal angles ranging from 0 degrees to 360 degrees, with a 30-degree increment. This approach allowed for the examination of posture variations throughout the pedaling cycle and across different bicycle types. The findings indicate that, apart from the characteristic angles of the lower extremities, the pelvis and spine were most influenced through the pedaling for each bike. Notably, the differences in characteristic angles across the three bikes were greater in the pelvis, spine region, and upper extremities compared to those occurring in the lower extremities. The posture information obtained from this study can guide the future positioning of HBMs, thereby enhancing the accuracy of crash simulations. © 2024 International Research Council on the Biomechanics of Injury. All rights reserved.

关键词： Bicycles

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Research on intelligent roadside parking system based on multi-dimensional feature visualization technology

Research on intelligent roadside parking system based on mul...

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IEEE advanced Information technology, Electronic and Automation Control Conference (IAEAC)

作者： Min Ju Xiaobo Zhang Zhangqin Huang Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

With the rapid advancement of urban modernization and the sustainable development of economy, the problems of driving difficulty and parking difficulty are increasingly prominent in large, medium and small cities. In order to alleviate the contradiction between urban space and vehicle growth rate and improve the utilization efficiency of parking resources, based on the roadside parking data of Nanshan District in Shenzhen City, this paper combines the time series data visualization method and geospatial data visualization method in multi-dimensional data visualization, and adopts the mode of view collaborative interaction to design the intelligent roadside parking collaborative interactive visualization system. The system integrates multisource data, displays roadside parking data information in an all-round way, supports exploratory analysis of roadside parking data characteristics and laws, and helps to understand parking demand and parking distribution at different times and regions. It provides decision support for optimizing parking resource allocation and roadside parking management.

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Facial expression recognition method based on dual-branch fusion network with noisy labels

Facial expression recognition method based on dual-branch fu...

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IEEE advanced Information technology, Electronic and Automation Control Conference (IAEAC)

作者： Zhenhuai Lv Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

As an important problem in the field of computer vision, facial expression recognition has been widely used in many fields such as human-computer interaction and emotion recognition. This paper proposes a dual-branch parallel facial expression recognition network that integrates Convolutional Neural Network (CNN) and Transformer. By consolidating local and global information obtained from the dual branches, the network effectively combines subtle local variations and overall contextual features of facial expressions. The fused feature representation demonstrates enhanced representativeness and discriminative power, leading to a notable improvement in facial expression recognition accuracy. In addressing the challenges posed by inter-class similarity and annotation ambiguity, the Erasing Attention Consistency (EAC) mechanism is employed. This mechanism effectively prevents the model from memorizing noisy samples, thereby enhancing robustness against noisy labels. Additionally, the focal loss function is utilized to rectify imbalances within data samples. Experiments conducted on RAF-DB datasets with labels containing noise reveal that the proposed methodology achieves accuracies of 87.65% and 86.05% at 10% and 20% noise rate, respectively. This substantiates the effectiveness of the proposed approach.

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MQTT Subscription Tree Optimization and Performance Analysis

MQTT Subscription Tree Optimization and Performance Analysis

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IEEE advanced Information technology, Electronic and Automation Control Conference (IAEAC)

作者： Aodong Li Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

With the continuous development of the Internet of Things (IoT) technology, the large number of messages generated by the increasing number of IoT devices has brought new challenges to MQTT servers. Due to the need for timely forwarding of MQTT messages, the latency of message reception and forwarding will also increase when there are too many clients connected to the server and message publishing and subscription activities are too frequent. In response to this issue, this paper proposes an MQTT subscription tree optimization method that combines hash tables and trie trees. Then, this method is used to implement the subscription tree module of a simple MQTT server, and its performance is compared and analyzed with several other subscription tree implementation methods. The final experimental results show that when a large number of clients connect to the server and frequently publish messages or subscribe to topics, the method proposed in this paper can reduce message forwarding latency, achieve fast message routing, and improve the efficiency of server message forwarding.

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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CRAFTER: A Persona Generation Tool for Requirements engineering 19

CRAFTER: A Persona Generation Tool for Requirements Engineer...

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19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Karolita, Devi Grundy, John Kanij, Tanjila Obie, Humphrey McIntosh, Jennifer Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne Australia Department Informatics Engineering Faculty of Engineering Palangka Raya University Palangka Raya Indonesia Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Melbourne Australia

ISBN: (纸本)9789897586965

Personas, a user characterisation, have been widely used in requirements engineering (RE) to enhance the understanding of end-users and their needs. However, the persona generation process is time-consuming and demands familiarity with a user-centered approach. The central issue lies in existing tools for automatically generating personas, which are restricted to generating persona templates and provide limited user control to tailor personas according to their specific needs. This paper introduces CRAFTER, a persona generation tool that uses Large Language Models (GPT-3.5 model). This tool not only automates persona creation but also offers recommendations to users for generating personas tailored to their requirements. The study involved an online questionnaire with 19 respondents who utilised the tool, providing feedback that indicated the tool’s sufficiency for persona generation while identifying areas for improvement. Beyond its primary function, CRAFTER stands out by providing guidance to requirements engineers throughout the persona creation process. The tool grants users the flexibility to customise personas based on their specific requirements, acknowledging the crucial human subjectivity in persona development. Additionally, CRAFTER promotes persona reusability, allowing users to save and reuse generated personas for future projects. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Requirements engineering

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