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Pluvial Flood Emulation with Hydraulics-informed Message Passing 41

Pluvial Flood Emulation with Hydraulics-informed Message Pas...

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41st international conference on Machine Learning, ICML 2024

作者： Kazadi, Arnold Doss-Gollin, James Silva, Arlei Department of Computer Science Rice University HoustonTX United States Department of Civil and Environmental Engineering Rice University HoustonTX United States

Machine Learning (ML) has emerged as a promising alternative to numerical methods for physics-based simulation due to its flexibility and efficiency. Flood modeling is a key case study for ML-based simulation due to its relevance as a tool for supporting preventive and emergency measures to mitigate flood risks. However, the complexity of the topography or domain (ground elevation) and the sparsity of the time-evolving precipitations (external forcing) can be challenging for most existing ML approaches for simulating flooding processes in space and time. Another critical challenge is incorporating physics domain knowledge (hydraulics) into these data-driven models. This paper addresses these challenges by introducing a hydraulics-informed graph neural network for flood simulation. Given a (geographical) region and precipitation data, our model predicts water depths in an auto-regressive fashion. We propose a message-passing framework inspired by the conservation of momentum and mass expressed in the shallow-water equations, which describe the physical process of a flooding event. Empirical results on a dataset covering 9 regions and 7 historical precipitation events demonstrate that our model outperforms the best baseline, and can capture the propagation of water flow more effectively, especially at the very early stage of the flooding event when the amount of water in the domain is scarce. Differently from some of the most recent methods for ML-based simulation, which tend to work well only when the domain is a smooth surface (e.g., flat terrain), we show that our solution achieves accurate results for real ground elevation data. Copyright 2024 by the author(s)

关键词： Floods

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Developing and Implementing a Gamification Feature in a Virtual Reality Temporal Bone Surgical Simulator

Developing and Implementing a Gamification Feature in a Virt...

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international conference on Extended Reality (XR Salento)

作者： Nohr, Anders Mikkelsen, Peter Trier Sorensen, Mads Solvsten Andersen, Steven Arild Wuyts Rigshosp Copenhagen Hearing & Balance Ctr Dept Otorhinolaryngol Head & Neck Surg & Audiol Copenhagen Denmark

ISBN: (纸本)9783031717031;9783031717048

This paper explores the development and implementation of a gamification feature, progression bars, within the Visible Ear Simulator (VES) to enhance surgical training. VES is an advanced virtual reality temporal bone surgical simulator, incorporating automated final-product ratings based on a modified Welling Scale. By introducing progression bars, the simulator offers real-time feedback on trainee performance, aiming to improve both engagement and learning outcomes. Our gamification design is grounded in well-established motivational and educational theories, including Flow Theory, Self-Determination Theory, and Deliberate Practice Theory, to achieve a theoretically sound approach. Addressing a need in the gamification literature for clarification studies, we aim to scientifically test the effectiveness of this feature through randomized trials to evaluate its impact on skills acquisition and trainee motivation. This work-in-progress paper describes our current progress and research. Our goal is to develop a comprehensive, freely accessible gamified ear surgery simulator that better prepares trainees for real-world surgical procedures.

关键词： Gamification Surgical simulation Training Medical education Virtual Reality simulation Temporal Bone Surgery

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Work-in-Progress: Rapid Development of advanced Virtual Labs for In-Person and Online education 129

Work-in-Progress: Rapid Development of Advanced Virtual Labs...

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129th ASEE Annual conference and Exposition: Excellence Through Diversity, ASEE 2022

作者： Li, Yiyang Shen, Yuzhong Delacruz, Pauline Sukenik, Charles Sanders, Brian Mason, Justin

During the closure of K-12 schools and universities due to the COVID-19 pandemic, many educators turned to web conferencing tools such as Zoom and WebEx to deliver online lectures. For courses with labs, some teachers provide recorded videos of real labs. Watching recorded lab videos is a passive experience, as the procedures and point of view are fixed, and students do not have any control of the lab and thus miss the opportunity to explore different options, including making mistakes that is important part of the learning process. One approach that holds great potential to enhance laboratory experience for online education is the use of computer-based modeling and simulation tools. simulation based virtual laboratories emulate lab equipment and configurations in highly realistic 3D environments and can provide very effective learning experiences. While there exist limited interactive lab computer simulations for various subjects, their presentations are still very primitive and often lack realism and complexity. This paper presents methodologies and preliminary findings on rapid development of advanced virtual labs using modeling and simulation for in-person and online education. The importance of modeling and simulation has long been recognized by the scientific community and agencies such as DoD and NSF. However, high-quality simulations are not commonplace, and simulations have not been widely employed in education. Existing simulations for education lack interoperability and compatibility. While there are sporadic uses of computer-based simulations in education that were developed in a piecemeal fashion, there was never systematic development at an industry level for such purposes. Virtual lab development usually require substantial amount of effort and lack of systematic research on rapid virtual lab development hinders their wide use in education. This paper proposes a wholistic and systematic approach for addressing the issues in rapid lab simulation develo

关键词： Students

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Spin crossover nanomaterials as possible basis for modern electronic device 16

Spin crossover nanomaterials as possible basis for modern el...

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16th international conference on Correlation Optics, COR 2023

作者： Ivashko, Viktor Krulikovskyi, Oleh Haliuk, Serhii Safronov, Ihor Samila, Andrii Department of Radioengineering and Information Security Yuriy Fedkovych Chernivtsi National University Storozhynetska 101 Chernivtsi58012 Ukraine Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control Stefan Cel Mare University of Suceava University 13 Suceava720229 Romania Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58012 Ukraine

ISBN: (纸本)9781510671829

Present work includes a computer simulation of temperature- and pressure-induced spin transition phenomenon in a 2D spin crossover nanosystem. modeling was carried out within a framework of an Ising-like model with the help of Monte Carlo technique based on metropolis algorithm. All the obtained results are in accordance with the experimental ones. For a fixed values of temperature, a reverse spin transition of the system was obtained. Brief review of possible practical application of such type of a system is presented. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Nanosystems

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Enhancing Energy Efficiency and Security in Wireless Sensor Networks: A Survey and Proposed Methodology Integrating Prediction Algorithms and Deep Neural Networks 6

Enhancing Energy Efficiency and Security in Wireless Sensor ...

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6th international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Shafiuddin, Shaik Krishna, Konda Hari Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331505790

This research study provides a complete assessment of energy- and trust-aware techniques in IoT-WSNs, emphasizing the significant problems and limits of current methodologies. Traditional methodologies frequently face challenges such as excessive processing overhead, difficulty reaching real-time optimal solutions, and increased delay, especially in large-scale or resource-constrained contexts. To solve these issues, the paper suggests a new approach that combines powerful prediction algorithms, deep neural networks, and strong security frameworks. The process entails initializing exact inputs and simulation settings, modeling advanced prediction algorithms, developing deep neural networks, and constructing resilient sensor nodes, all while implementing extensive security measures. The suggested approach seeks to considerably improve energy efficiency, network lifetime, and security by focusing on mobile sink movement prediction and network operation optimization. This study not only covers existing methodologies, but also shows the promise of the proposed methodology through extensive analysis and preliminary results, demonstrating its ability to overcome the limits of present WSN deployments. © 2024 IEEE.

关键词： Prediction models

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research on Digital Portrait Matching System for Accurate Employment of Graduates by Integrating Professional Information 3

Research on Digital Portrait Matching System for Accurate Em...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Fu, Lijun E-commerce Industry Research Institute Taizhou Vocational & Technical College Zhejiang Taizhou318000 China

ISBN: (纸本)9798350380989

User profile is an important means for enterprises to understand and obtain their potential customers. However, the existing statistical analysis algorithms cannot effectively deal with unstructured text information, nor can they deeply dig out the characteristics of users from many aspects. Aiming at the personalized employment needs of college students, this project intends to carry out the research on employer characteristic modeling based on multi-source and heterogeneous job-hunting big data for college students' personalized job-hunting needs. The research and application of evaluation index and labeling system, multi-source data fusion method, evaluation labeling method are mainly carried out. This project intends to use LDA technology to mine network content and build topic modeling through cross-domain collection and classification of book resources. According to user characteristics and content, a service oriented personalized recommendation algorithm is designed. This project proposes a group behavior detection algorithm, which can effectively reduce the group query time and computation amount by classifying the group characteristics. In this way, the purpose of fast and efficient. simulation results show that the algorithm is effective. © 2024 IEEE.

关键词： computer system digital portrait Graduate employment precise employment user portrait

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Knowledge Representation Learning with Hyperboloid Models 16

Knowledge Representation Learning with Hyperboloid Models

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16th international conference on advanced computer Theory and Engineering, ICACTE 2023

作者： Li, Yu Gan, Jianhou Wang, Jun Han, Xiaoyu Wang, Mingjie Yunnan Normal University Key Laboratory of Education Informatization for Nationalities Ministry of Education Kunming China Yunnan Normal University Key Laboratory of Education Informatization for Nationalities Ministry of Education Yunnan Key Laboratory of Smart Education Kunming China

ISBN: (纸本)9798350317534

Knowledge graph describes the objective knowledge in the real world in a structured form. Knowledge representation learning aims to project the entities and relations in the knowledge graph into a low-dimensional, continuous vector space and learn vector representations of the semantic information of entities and relations. Moreover, link prediction and other tasks are carried out by vector calculation, and a variety of downstream tasks are achieved while retaining the structure of the knowledge graph itself. However, the existing knowledge representation learning models have certain defects when modeling complex relationships such as 1-N, N-1, and N-N, and the processing effect is not well. In order to better deal with complex relations, this paper proposes a knowledge representation learning model based on hyperboloid model, which emitters the knowledge graph into hyperbolic space for knowledge representation learning. Experiments are carried out on the public datasets FB15K, FB15K-237 and WN18RR. The experimental results show that the proposed model is superior to the baseline model in most evaluation indicators, which proves effectiveness of the proposed model. © 2023 IEEE.

关键词： Knowledge graph

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research on Glass Composition Classification Model Based on Random Forest and System Clustering

Research on Glass Composition Classification Model Based on ...

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2023 international conference on computer simulation and modeling, Information Security, CSMIS 2023

作者： Xiong, Shiwei Song, Minhui Shi, Tianyu Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798350343724

In order to study the classification rules of high-potassium glass and lead-barium glass in ancient glass products, this paper selects the data of question C of the 2022 National College Students ' Mathematical modeling Contest of the Higher education Council Cup for analysis. In this paper, the random forest classification model in machine learning is established, and the ancient glass relics are classified by using grid search to adjust the optimal parameters. At the same time, the importance value and threshold of weathering and chemical composition are calculated, and finally eight important indexes are selected. According to the eight important indicators and combined with the system clustering method, the high-potassium glass and the lead-barium glass are divided into three and four sub-categories respectively. At the same time, the polymerization coefficient line chart is drawn according to the elbow rule to verify the rationality of the clustering number. By constructing the control sample and fluctuating the chemical composition content by 10 %, it is found that the clustering model of high-potassium glass is more sensitive to the silica content, while the clustering model of lead-barium glass has strong robustness. Finally, the model is applied to predict the type and sub-category of the unknown type of glass relics according to the chemical composition of the glass relics, and the maximum correlation minimum redundancy algorithm is used to find that the binary classification results are sensitive to the four chemical components of weathering, magnesium oxide, lead oxide and barium oxide. © 2023 IEEE.

关键词： Cluster analysis

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Hierarchical Deep Learning Framework for Multi-Scale Energy modeling in Smart Building Environments 7

Hierarchical Deep Learning Framework for Multi-Scale Energy ...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Raj, J. Relin Francis Nikshya, J. Ebens Vinothini, S. Umesh, R. Krishnan, R. Santhana Gopikumar, S. Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Electronics And Communication Engineering Chennai India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India Psna College of Engineering And Technology Department of Computer Applications Dindigul India Velammal College of Engineering And Technology Department of Information Technology Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Civil Engineering Chennai Avadi India

ISBN: (纸本)9798350359299

In contemporary urban landscapes, the quest for sustainable energy consumption has propelled the evolution of smart building technologies. These structures are equipped with advanced sensors, actuators, and control systems to optimize energy utilization while ensuring occupant comfort and operational efficiency. Central to this paradigm is energy modeling, a process that entails the analysis and prediction of energy consumption patterns within building environments. Efficient energy modeling holds paramount significance for devising effective strategies to mitigate energy wastage, reduce operational costs, and minimize environmental footprints. This research presents a novel approach, the Hierarchical Deep Learning Framework for Multi-scale Energy modeling in Smart Building Environments, aimed at enhancing energy modeling efficiency. The Deep Energy Fusion (DEF) algorithm is introduced by integrating Deep Belief Networks (DBNs) with a hierarchical energy modeling architecture. DEF leverages DBNs' capability to learn complex hierarchical representations of energy data across various scales, facilitating a comprehensive understanding of energy consumption patterns. To evaluate DEF's performance, the simulation analysis are conducted by comparing it with existing algorithms. Metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R^2) are employed to assess prediction accuracy. Our results demonstrate that DEF outperforms traditional algorithms in capturing both local and global dependencies within the energy data, thereby improving energy modeling accuracy. Moreover, DEF showcases superior performance in terms of computational efficiency and scalability. This research contributes to advancing energy modeling techniques in smart buildings, offering a robust framework for optimizing energy management strategies © 2024 IEEE.

关键词： Energy efficiency

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simulation and optimization of inchworm-like pneumatic crawling actuator based on ABAQUS software 7

Simulation and optimization of inchworm-like pneumatic crawl...

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7th international conference on Robotics, Control and Automation Engineering, RCAE 2024

作者： Mu, Junqi Zong, Xiaofeng Wei, Yiyang Hou, Xuping China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan430074 China

ISBN: (纸本)9798350355642

The actuator, serving as the fundamental power source of a soft robot, functions as its central component. The field of soft robotics has garnered increasing research attention since its inception. Pneumatic crawling robots, being an essential branch within the realm of soft robotics, possess notable characteristics such as rapid response time, cost-effectiveness, and human-computer interaction safety. Consequently, they hold significant research value. Among various types of crawling robots that have garnered considerable interest from researchers is the inchworm crawling robot. This paper aims to address the design and development aspects pertaining to inchworm actuators. By utilizing ABAQUS simulation software and taking displacement as the reference parameter, an analysis was conducted to investigate the impact of various geometric parameters on actuator displacement. Additionally, optimization of these geometric parameters was performed. The values of aspect ratio, length-to-width ratio, vertex angle and gap depth suitable for inchworm-like actuator are obtained. These findings are presented for the first time in the field of crawling soft robots and provide a theoretical basis for the development of pneumatic inchworm-like actuators. © 2024 IEEE.

关键词： Aspect ratio

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