检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Krumpen, Stefan Klein, Reinhard Weinmann, Michael Institute of Computer Science II University of Bonn Endenicher Allee 19a Bonn53115 Germany

VR/AR technology is a key enabler for new ways of immersively experiencing cultural heritage artifacts based on their virtual counterparts obtained from a digitization process. In this paper, we focus on enriching VR-based object inspection by additional haptic feedback, thereby creating tangible cultural heritage experiences. For this purpose, we present an approach for interactive and collaborative VR-based object inspection and annotation. Our system supports high-quality 3D models with accurate reflectance characteristics while additionally providing haptic feedback regarding the object shape features based on a 3D printed replica. The digital object model in terms of a printable representation of the geometry as well as reflectance characteristics are stored in a compact and streamable representation on a central server, which streams the data to remotely connected users/clients. The latter can jointly perform an interactive inspection of the object in VR with additional haptic feedback through the 3D printed replica. Evaluations regarding system performance, visual quality of the considered models as well as insights from a user study indicate an improved interaction, assessment and experience of the considered objects. Copyright © 2021, The Authors. All rights reserved.

关键词： Reflection

来源：评论

学校读者我要写书评

暂无评论

Next-Generation Artificial Intelligence Solutions for Comprehensive Analysis and Improvement of Heat Dissipation in High-Intensity Boiling Environments

Next-Generation Artificial Intelligence Solutions for Compre...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： H. Manoj T. Gadiyar S Vinod Kumar Vijilius Helena Raj Dinesh Kumar Yadav Mortada Kareem Sabi Hasoni Sorabh Lakhanpal Department of Computer Science and Business System Canara Engineering College India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh AlTalb Collage of Nursing National University of Science and Technology Dhi Qar Iraq Lovely Professional University Phagwara India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This research delves into the realm of AI-driven heat dissipation, offering a suite of algorithms to address the challenges posed by high-intensity boiling environments. The proposed predictive modeling algorithm, rooted in linear regression, provides a systematic and robust approach through 20 meticulously designed steps. This algorithm, along with neural networks, reinforcement learning, generative adversarial networks (GANs), and genetic algorithms, forms a versatile toolbox for thermal management. Linear regression, through its step-by-step predictive modeling process, ensures accurate heat dissipation predictions. Neural networks, employing backpropagation, showcase adaptability and complexity, excelling in real-time adaptation. Reinforcement learning, specifically Q-Learning, introduces dynamic decision-making strategies, vital for navigating complex thermal scenarios. GANs contribute by generating realistic synthetic data, an invaluable asset for simulating high intensity boiling environments. Genetic algorithms, resembling natural selection, prove versatile and scalable in evolving optimal solutions. The research evaluates these algorithms comprehensively, demonstrating the proposed method’s superiority in prediction accuracy, energy efficiency, adaptability, and overall performance. The proposed predictive modeling algorithm stands out as a benchmark, showcasing its potential for practical implementation and optimization in diverse scenarios. This comprehensive exploration sets the stage for innovative solutions to the intricate challenges of thermal management, as continuous adaptation and fine-tuning remain pivotal in this dynamic landscape.

关键词： Heating systems Adaptation models Accuracy Heuristic algorithms Neural networks Linear regression Predictive models Prediction algorithms Optimization Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Aggregative Dynamics and Revenue Structuring in Energy Internet User-Prosumer Ecosystems

Aggregative Dynamics and Revenue Structuring in Energy Inter...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Anil Pratap Singh Ch Veena R J Anandhi Atul Singla Ashish Parmar Haider Alabdely Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal M.P India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh The Islamic university Najaf Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study explores income structure and group dynamics in complicated energy internet consumer-prosumer ecosystems.a DRORA (Dynamic Revenue Optimization and Aggregation) is proposed after studying energy consumers and prosumers throughout time. PDPA, EDOA, and RFO are in the system. These boost energy market efficiency, transparency, and fairness. As a way to promote fair pay and customer cost, the PDPA lets prosumers have a say in how prices are set. The EDOA makes sure that resources are used efficiently by distributing power in the best way possible to cut down on costs and improve system performance. With the RFOA’s accurate income predictions, stakeholders can make smart decisions that will help them make the most money. Simulations and real-world studies have shown that the suggested model is better than traditional methods at getting stable and fair income sharing in the Energy Internet environment, which is always changing.

关键词： Technological innovation Costs Regulators System performance Ecosystems Energy Internet Modulation

来源：评论

学校读者我要写书评

暂无评论

Assessing the Societal Impacts of Emerging Distributed Intelligence Technologies

Assessing the Societal Impacts of Emerging Distributed Intel...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Irfan Khan V Alekhya B Rajalakshmi Sorabh Lakhanpal Mohammed Ayad Alkhafaji K. Santhi Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science New Horizon College of Engineering Bangalore Lovely Professional University Phagwara College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Dept. of IT St. Martin’s Engineering College Secunderabad Telangana India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The Integrated Impact Assessment Framework (iiAF) is established in this study to examine how new distributed intelligence technologies (DIT) influence society. The framework comprises three powerful algorithms: SNDA, EIS, and EIAM. These illustrate DIT’s various benefits. SNDA examines the complex social web, identifying hubs, community dynamics, and future trends. EIS predicts economic repercussions and provides GDP, expenditures, and job losses. computer faults and privacy are important ethical considerations for EIAM. These strategies paint a comprehensive picture of the effect. Fake data suggests the new strategy outperforms six others. The recommended strategy scores higher on significance, economic indicators, and ethics. The iiAF’s DIT impact evaluation is more complete, educated, and responsible than other techniques. This work contributes to our understanding of the problem by proposing a flexible and resilient paradigm for dealing with the societal consequences of diverse technologies. The iiAF may demonstrate to politicians the immediate impacts of DIT adoption and discuss morality and society. The framework’s holistic approach helps us understand how technology and society interact and evolve.

关键词： Ethics Technological innovation Privacy Economic indicators Reviews Heuristic algorithms Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

From Internet of Things Data to Business Processes: Challenges and a Framework

arXiv

引用

arXiv 2024年

作者： Mangler, Jürgen Seiger, Ronny Benzin, Janik-Vasily Grüger, Joscha Kirikkayis, Yusuf Gallik, Florian Malburg, Lukas Ehrendorfer, Matthias Bertrand, Yannis Franceschetti, Marco Weber, Barbara Rinderle-Ma, Stefanie Bergmann, Ralph Serral Asensio, Estefanía Reichert, Manfred Department of Informatics Technical University of Munich Garching Germany Institute of Computer Science University of St.Gallen St.Gallen Switzerland Business Information Systems II University of Trier Trier Germany Branch University of Trier Germany Institute of Databases and Information Systems Ulm University Ulm Germany KU Leuven Leuven Belgium

The IoT and Business Process Management (BPM) communities co-exist in many shared application domains, such as manufacturing and healthcare. The IoT community has a strong focus on hardware, connectivity and data;the BPM community focuses mainly on finding, controlling, and enhancing the structured interactions among the IoT devices in processes. While the field of Process Mining deals with the extraction of process models and process analytics from process event logs, the data produced by IoT sensors often is at a lower granularity than these process-level events. The fundamental questions about extracting and abstracting process-related data from streams of IoT sensor values are: (1) Which sensor values can be clustered together as part of process events?, (2) Which sensor values signify the start and end of such events?, (3) Which sensor values are related but not essential? This work proposes a framework to semi-automatically perform a set of structured steps to convert low-level IoT sensor data into higher-level process events that are suitable for process mining. The framework is meant to provide a generic sequence of abstract steps to guide the event extraction, abstraction, and correlation, with variation points for plugging in specific analysis techniques and algorithms for each step. To assess the completeness of the framework, we present a set of challenges, how they can be tackled through the framework, and an example on how to instantiate the framework in a real-world demonstration from the field of smart manufacturing. Based on this framework, future research can be conducted in a structured manner through refining and improving individual steps. Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Advanced Simulation and Optimization Strategies in Thermo-Fluid Dynamics: A Deep Learning Approach to Enhancing Heat Transfer in Evaporative Cooling Systems

Advanced Simulation and Optimization Strategies in Thermo-Fl...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： B Rajalakshmi V Alekhya Sorabh Lakhanpal Irfan Khan Maha Chasib Munshid Ramya Maranan Department of Computer Science New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Collage of Nursing National University of Science and Technology Dhi Qar Iraq Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article describes a novel technique to study and improve evaporative cooling systems to move heat better. The system uses the GA, CNN, LSTM, and FVM algorithms. Each thermofluid dynamics study approach provides something unique. The CNN approach independently acquires spatial information from raw modeling data to study fluid movement. Temporal linkages in sequential data reveal heat’s movement over space and time via LSTM. GAN adds continuing antagonistic processes to the dataset, strengthening the model. GA adjusts GAN settings to improve data, whereas FVM simulates realistic fluid dynamics to improve heat transport. Ablation studies demonstrate each algorithm’s distinct contributions and importance. Performance comparisons reveal that the recommended framework outperforms others in accuracy, processing time, memory utilization, convergence rate, resilience, and adaptability. Bar charts, line charts, pie charts, and histograms show how the recommended method compares to others. The framework is precise, efficient, and versatile, making it ideal for improving thermo-fluid dynamics models in evaporative cooling systems. Researchers and engineers seeking to improve heat transfer in evaporative cooling systems can utilize it. The proposed architecture is full and adaptable to thermal-fluid utilization issues.

关键词： Heuristic algorithms Evaporative cooling Generative adversarial networks Data models Convolutional neural networks Long short term memory Optimization

来源：评论

学校读者我要写书评

暂无评论

Prediction of the final size for COVID-19 epidemic using machine learning:A case study of Egypt

引用

Infectious Disease Modelling 2020年第1期5卷 622-634页

作者： Lamiaa A.Amar Ashraf A.Taha Marwa Y.Mohamed Networks and Distributed Systems Department Informatics Research InstituteCity of Scientific Research and Technological ApplicationsSRTA-CITYEgypt Multimedia and Computer Graphics Department Informatics Research InstituteCity of Scientific Research and Technological ApplicationsSRTA-CITYEgypt Department of Mathematics and Computer Science Faculty of ScienceAlexandria UniversityEgypt

COVID-19 is spreading within the sort of an enormous epidemic for the *** epidemic infects a lot of individuals in *** World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory *** these days,Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this *** this paper displayed,the real database of COVID-19 for Egypt has been analysed from February 15,2020,to June 15,2020,and predicted with the number of patients that will be infected with COVID-19,and estimated the epidemic final *** regression analysis models have been applied for data analysis of COVID-19 of *** this study,we’ve been applied seven regression analysis-based models that are exponential polynomial,quadratic,thirddegree,fourth-degree,fifth-degree,sixth-degree,and logit growth respectively for the COVID-19 ***,the exponential,fourth-degree,fifth-degree,and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one *** addition,we have applied the well-known logit growth regression model and we obtained the following epidemiological insights:Firstly,the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September ***,the final total size for cases 1.6676Et05 *** action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.

关键词： COVID-19 Regression analysis model Epidemic model

来源：评论

学校读者我要写书评

暂无评论

EFaR 2023: Efficient Face Recognition Competition

arXiv

引用

arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Either Or: Interactive Articles or Videos for Climate science Communication

引用

computer graphics Forum 2025年

作者： Poehls, J. Meuschke, M. Carvalhais, N. Lawonn, K. Max Planck Institute for Biogeochemistry Department of Biogeochemical Integration Germany University of Magdeburg Department of Simulation and Graphics Germany University of Jena Institute of Computer Science Germany

Effective communication of climate science is critical as climate-related disasters become more frequent and severe. Translating complex information, such as uncertainties in climate model predictions, into formats accessible to diverse audiences is key to informed decision-making and public engagement. This study investigates how different teaching formats can enhance understanding of these uncertainties. This study compares two multimodal strategies: (1) a text-image format with interactive components and (2) an explainer video combining dynamic visuals with narration. Participants' immediate and delayed retention (one week) and engagement are assessed to determine which format offers greater saliency. Sample analysis (n = 622) displayed equivalent retention by viewers between both formats. Metrics assessing interactivity found no correlation between interactivity and information retention. However, a stark contrast was observed in the time viewers spent engaging with each format. The video format was 29% more efficient with information taught over a period of time vs. the article. Additionally, retention on the video format worsened with age (P = 0.004) while retention on the article format improved with education (P = 0.038). These results align with previous findings in literature. © 2025 The Author(s). computer graphics Forum published by Eurographics - The European Association for computer graphics and John Wiley & Sons Ltd.

关键词： Signaling

来源：评论

学校读者我要写书评

暂无评论

Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization

arXiv

引用

arXiv 2021年

作者： Thaler, Franz Payer, Christian Urschler, Martin Štern, Darko Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Austria School of Computer Science University of Auckland New Zealand

In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from large observer variabilities, which result in uncertain annotations. To model the annotation ambiguities of the training dataset, we propose to learn anisotropic Gaussian parameters modeling the shape of the target heatmap during optimization. Furthermore, our method models the prediction uncertainty of individual samples by fitting anisotropic Gaussian functions to the predicted heatmaps during inference. Besides state-of-the-art results, our experiments on datasets of hand radiographs and lateral cephalograms also show that Gaussian functions are correlated with both localization accuracy and observer variability. As a final experiment, we show the importance of integrating the uncertainty into decision making by measuring the influence of the predicted location uncertainty on the classification of anatomical abnormalities in lateral cephalograms. © 2021, CC BY-SA.

关键词： Anisotropy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：