检索结果-内蒙古大学图书馆

Estimating Canopy Interception Water Storage with GNSS-Transmissometry

学校读者我要写书评

暂无评论

Estimating Canopy Interception Water Storage with GNSS-Trans...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Konstantin Schellenberg Thomas Jagdhuber David Chaparro Oliver Binks Florian Hellwig Clémence Dubois Mehmet Kurum Adriano Camps Henrik Hartmann Christiane Schmullius Department for Earth Observation Friedrich Schiller University Jena Jena Germany Department Biogeochemical Processes Max Planck Institute for Biogeochemistry Jena Germany German Aerospace Center (DLR) Microwaves and Radar Institute (HR) Wessling Germany Institute of Geography University of Augsburg Augsburg Germany Centre for Ecological and Forestry Applications (CREAF) Cerdanyola del Vallès Spain German Aerospace Center (DLR) Institute of Data Science Jena Germany School of Electrical and Computer Engineering University of Georgia Athens GA USA Department of Signal Theory and Communications CommSensLab and Institut d’Estudis Espacials de Catalunya Universitat Politècnica de Catalunya Barcelona Spain Julius Kühn-Institute for Forest Protection Federal Research Institute for Cultivated Plants Quedlinburg Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Storage of interception water in the canopy (S c ) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (M g ) and S c . The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. S c -values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal M g cycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that S c maxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify S c and evaporation fluxes independently from EC measurements and field experiments.

关键词： Water storage Sensitivity Biomedical optical imaging Vegetation mapping Forestry Vegetation Optical variables measurement

Exploiting Network Information to Disentangle Spillover Effects in a Field Experiment on Teens' Museum Attendance

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Noirjean, Silvia Mariani, Marco Mattei, Alessandra Mealli, Fabrizia Department of Statistics Computer Science Applications 'G. Parenti' University of Florence Italy IRPET - Regional Institute for Economic Planning of Tuscany Italy Florence Center for Data Science Italy

A key element in the education of youths is their sensitization to historical and artistic heritage. We analyze a field experiment conducted in Florence (Italy) to assess how appropriate incentives assigned to high-school classes may induce teens to visit museums in their free time. Non-compliance and spillover effects make the impact evaluation of this clustered encouragement design challenging. We propose to blend principal stratification and causal mediation, by defining sub-populations of units according to their compliance behavior and using the information on their friendship networks as mediator. We formally define principal natural direct and indirect effects and principal controlled direct and spillover effects, and use them to disentangle spillovers from other causal channels. We adopt a Bayesian approach for inference. Copyright © 2020, The Authors. All rights reserved.

关键词： Bayesian networks

A novel performance enhancement of real-time iot applications using big data analytics

学校读者我要写书评

暂无评论

AIP Conference Proceedings 2024年第1期2742卷

作者： Chettipalayam Radhakrishnan Sakthivel Sundararajan Periyasamy Anvar Shathik Josephine Ruth Fenitha Department of Computer Science( SF) Sri Ramakrishna Mission Vidyalaya College of Arts and Science Coimbatore Tamil Nadu India. Department of MCA SNS College of Technology Coimbatore Tamil Nadu India. Department of Computer Science and Applications SRM Trichy Arts and Science College Trichy Tamil Nadu India Department of cloud technology & Data science srinivas university college of engineering and technology Mukka Mangalore India. Sri SaiRam Institute of Technology Sairam College Rd Sai Leo Nagar West Tambaram Chennai Tamil Nadu India.

In 2017, it was projected that there will be 285 million visually impaired people of all ages, with 31.9 million of them being blind, bringing the total number of visually impaired people to 285 million. There are a number of cutting-edge technological solutions available to make the transportation of these individuals more efficient. The next major challenge for technical professionals will be to provide solutions that are both efficient and cost-effective. The ability to navigate safely, recognise impediments, and move freely between places in new settings is one of the most difficult difficulties for individuals who have visual impairments. New mobile application solutions are being created, and the applications may be downloaded and installed on Android mobile devices. The software will depict the area using portable cameras and will allow people with vision impairment to be transported straight to the environment using the app. A large portion of the proposed system makes use of the YOLO3 software in order to detect and determine the distance between the objects and a camera. It also influences the direction in which the item will travel. Once this is completed, the system will provide a voice command that will instruct and educate visually impaired individuals on how to navigate the surroundings. It is a groundbreaking piece of work on a worldwide scale. The suggested method is both cost-effective and accessible to people from all socioeconomic backgrounds.

关键词：

Deep Reinforcement Learning based Indoor Air Quality Sensing by Cooperative Mobile Robots

学校读者我要写书评

暂无评论

Deep Reinforcement Learning based Indoor Air Quality Sensing...

IEEE Conference on Wireless Communications and Networking

作者： Zhiwen Hu Tiankuo Song Kaigui Bian Lingyang Song National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 101 Middle School Beijing China Department of Computer Science Peking University China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

Confronted with the severe indoor air pollution nowadays, we propose the usage of multiple robots to detect the indoor air quality (IAQ) cooperatively for fewer sensors and larger sensing area. To acquire the complete real-time IAQ distribution map, we exploit the real statistical data to construct the IAQ data model and adopt Kalman Filter to obtain the estimation of the unmeasured area. Since the movement of the robots affects the estimation accuracy, a proper movement strategy should be planned to minimize the total estimation error. To solve this optimization problem, we design a deep Q-learning approach, which provides sub-optimal movement strategies for real-time robot sensing. By simulations, we verify the adopted IAQ data model and testify the effectiveness of the proposed solution. For application considerations, we have deployed this system in Peking University since Dec. 2018 and developed a website to visualize the IAQ distribution.

关键词：

IoT-enabled social relationships meet artificial social intelligence

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Dhelim, Sahraoui Ning, Huansheng Farha, Fadi Chen, Liming Atzori, Luigi Daneshmand, Mahmoud The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing China The School of Computing Ulster University NewtownabbeyBT37 0QB United Kingdom The Department of Electrical and Electronic Engineering University of Cagliari piazza d'Armi Cagliari09123 Italy The Department of Business Intelligence and Analytics The Department of Computer Science Stevens Institute of Technology Hoboken United States

With the recent advances of the Internet of Things, and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This paper discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Yang, Fan Zhang, Hongyang R. Wu, Sen Ré, Christopher Su, Weijie J. Yau Mathematical Sciences Center Tsinghua University Beijing Institute of Mathematical Sciences and Applications China Northeastern University Boston United States Department of Computer Science Stanford University United States Department of Statistics and Data Science Department of Computer Science University of Pennsylvania United States

The problem of learning one task with samples from another task has received much interest recently. In this paper, we ask a fundamental question: when is combining data from two tasks better than learning one task alone? Intuitively, the transfer effect from one task to another task depends on dataset shifts such as sample sizes and covariance matrices. However, quantifying such a transfer effect is challenging since we need to compare the risks between joint learning and single-task learning, and the comparative advantage of one over the other depends on the exact kind of dataset shift between both tasks. This paper uses random matrix theory to tackle this challenge in a linear regression setting with two tasks. We give precise asymptotics about the excess risks of some commonly used estimators in the high-dimensional regime, when the sample sizes increase proportionally with the feature dimension at fixed ratios. The precise asymptotics is provided as a function of the sample sizes and covariate/model shifts, which can be used to study transfer effects: In a random-effects model, we give conditions to determine positive and negative transfers between learning two tasks versus single-task learning;the conditions reveal intricate relations between dataset shifts and transfer effects. Simulations justify the validity of the asymptotics in finite dimensions. Our analysis examines several functions of two different sample covariance matrices, revealing some estimates that generalize classical results in the random matrix theory literature, which may be of independent interest. Copyright © 2020, The Authors. All rights reserved.

关键词： Covariance matrix

Attention-Enhanced Neural Network Models for Turbulence Simulation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Yuan, Zelong Wang, Jianchun Peng, Wenhui Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Guangzhou511458 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen518055 China Department of Computer Engineering Polytechnique Montreal H3T1J4 Canada

Deep neural network models have shown a great potential in accelerating the simulation of fluid dynamic systems. Once trained, these models can make inference within seconds, thus can be extremely efficient. However, it becomes more difficult for neural networks to make accurate predictions when the flow becomes more chaotic and turbulent at higher Reynolds numbers. One of the most important reasons is that, existing models lack the mechanism to handle the unique characteristic of high-Reynolds-number turbulent flow: multi-scale flow structures are non-uniformly distributed and strongly nonequilibrium. In this work, we address this issue with the concept of visual attention: intuitively, we expect the attention module to capture the nonequilibrium of turbulence by automatically adjusting weights on different regions. We compare the model performance against a state-of-the-art neural network model as baseline, the Fourier Neural Operator (FNO), on two-dimensional (2D) turbulence prediction task. Numerical experiments show that the attention-enhanced neural network model outperforms existing state-of-the-art baselines, and can accurately reconstruct a variety of statistics and instantaneous spatial structures of turbulence at high Reynolds numbers flow. Furthermore, the attention mechanism provides 40% error reduction with 1% increase of parameters, at the same level of computational cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Turbulence

A survey of hybrid human-artificial intelligence for social computing

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wang, Wenxi Ning, Huansheng Shi, Feifei Dhelim, Sahraoui Zhang, Weishan Chen, Liming School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083 China Department of Software Engineering China University of Petroleum Qingdao266580 China School of Computing Ulster University United Kingdom

Along with the development of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been continuously extended. In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI. However, the conventional technologies of AI have drawbacks in dealing with more complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI) which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI in dealing with social problems shows the advantages that AI can’t surpass. This paper firstly introduces the concepts of H-AI. AI is the intelligence in the transition stage of H-AI, so the latest research progresses of AI in social computing are reviewed. Secondly, it summarizes typical challenges faced by AI in social computing, and makes it possible to introduce H-AI to solve these challenges. Finally, the paper proposes a holistic framework of social computing combining with H-AI, which consists of four layers: object layer, base layer, analysis layer, and application layer. It represents HAI has significant advantages over AI in solving social problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Social sciences computing

An efficient aggregate signature scheme for healthcare wireless sensor networks

学校读者我要写书评

暂无评论

Journal of Network Intelligence

Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks