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Real-time river level estimation based on variations of radar reflectivity—a case study of the Quitandinha River watershed, Petrópolis, Rio de Janeiro (Brazil)

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Bulletin of Atmospheric science and Technology 2021年第1-4期2卷 1-20页

作者： da Silva, Fabricio Polifke Rotunno Filho, Otto Corrêa Justi da Silva, Maria Gertrudes Alvarez Sampaio, Rafael João Pires, Gisele Dornelles de Araújo, Afonso Augusto Magalhães Civil Engineering Program Alberto Luiz Coimbra Institute for Postgraduate Studies and Research in Engineering – COPPE Universidade Federal do Rio de Janeiro RJ Rio de Janeiro 21945-970 Brazil Engineering and Society Group Exact Sciences and Technologies School Universidade Iguaçu UNIG Av. Abílio Augusto Távora 2134 Jardim Nova Era RJ Nova Iguaçu 26275-580 Brazil Meteorology Laboratory Science and Technology Center Universidade Estadual do Norte Fluminense Darcy Ribeiro – UENF Avenida Brenand s.n. Imboassica RJ Macaé 27925-535 Brazil Department of Water Resources and Environment Polytechnic School Universidade Federal do Rio de Janeiro – UFRJ Av. Athos da Silveira Ramos CT Escola Politécnica Cidade Universitária RJ Rio de Janeiro 21941-590 Brazil

The real-time warning for flash floods represents one of the major challenges for monitoring centers and civil defenses due to the short time frame in which such phenomena occur. That situation often takes place particularly in mountainous regions where high slope rate favors an increase in the velocity of the water flow and it might generate conditions to impact the water level due to potential for provoking erosion, landslides, and debris flow. An initial one new and unique prototype of hydrometeorological relationship for real-time, site-tunable, water level for flash flood prediction is presented. Time variations of the reflectivity measured by horizontal polarization radar are evaluated and correlated to generate the local estimate of the elevation of the Quitandinha River water level for the next hour. The proposal of this relationship is not intended to act as a simplified hydraulic model but to characterize if a critical elevation of the water level could be obtained from the radar reflectivity data and consequently whether the flash flooding event should be expected. We verified that the proposed relationship that tended to underestimate the water level peaks, however, qualitatively was initially able to indicate if a flood event could occur or not in the next hour. The expectation related to this initial relationship is to improve the obtained results in order to be used as a tool for decision-making in relation to the issuance of warnings delivered by environmental monitoring centers. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.

关键词： Nowcasting Reflectivity River level Weather radar reflectivity

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Reports of the AAAI 2010 spring symposia

Reports of the AAAI 2010 spring symposia

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作者： Barkowsky, Thomas Bertel, Sven Broz, Frank Chaudhri, Vinay K. Eagle, Nathan Genesereth, Michael Halpin, Harry Hamner, Emily Hoffmann, Gabe Hölscher, Christoph Horvitz, Eric Lauwers, Tom McGuinness, Deborah L. Michalowski, Marek Mower, Emily Shipley, Thomas F. Stubbs, Kristen Vogl, Roland Williams, Mary-Anne Cognitive Systems Group University of Bremen Research Center SFB/TR 8 Spatial Cognition Germany Human Factors Division Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign IL United States Adaptive Systems Research Group Computer Science Department University of Hertfordshire United Kingdom Artificial Intelligence Center at SRI International United States Txteagle Inc. MIT Media Laboratory United States Computer Science Department Stanford University United States University of Edinburgh United Kingdom Robotics Institute Carnegie Mellon University United States Palo Alto Research Center United States Center for Cognitive Science University of Freiburg Germany CREATE lab Carnegie Mellon Robotics Institute United States Rensselaer Polytechnic Institute United States University of Southern California United States Department of Psychology Temple University Spatial Intelligence and Learning Center United States IRobot Corporation United States Stanford Program in Law Science and Technology Stanford University Law School United States Innovation and Enterprise Research Laboratory University of Technology Sydney Australia

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of computer science, presented the 2010 Spring Symposium Series Monday through Wednesday, March 22-24, 2010, at Stanford University. The titles of the seven symposia were Artificial Intelligence for Development;Cognitive Shape Processing;Educational Robotics and Beyond: Design and Evaluation;Embedded Reasoning: Intelligence in Embedded Systems;Intelligent Information Privacy Management;It's All in the Timing: Representing and Reasoning about Time in Interactive Behavior;and Linked Data Meets Artificial Intelligence. Copyright © 2010.

关键词： Embedded systems

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Performance study of strongly coupled magnetic resonance

arXiv

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arXiv 2019年

作者： Wijerathna Basnayaka, Chathuranga M. Jayakody, Dushantha Nalin K. Sharma, Abhishek Wang, Hwang-Cheng Muthuchidambaranathan, P. Kaur, Kuljeet School of Postgraduate Studies and Research SRI LANKA Technological Campus Sri Lanka School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Dept. of Electronics and Communication Engineering LNM Institute of Information and Technology Jaipur India Department of Electronic Engineering National Ilan University Ilan Taiwan Dept. of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India Ecole de Technologie Superieure Universite du Quebec Montreal Canada

Strongly Coupled Magnetic Resonance (SCMR) uses electromagnetic resonance in order to efficiently transfer power wirelessly over mid-range distances. Since the energy exchange capability of resonant objects higher than non-resonant objects, strongly coupled systems are able to achieve more efficient energy transfer than other wireless power transfer systems. The paper presents detailed experimental and simulated analysis of the performance of the SCMR system. A prototype of the SCMR system was implemented and experiments were conducted to analyze the performance of the system. Finally, the resonant frequency of the system was experimentally verified and the factors influencing the wireless power transfer were also studied. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy transfer

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E-THeTA: An Efficient and Adaptive TDMA Approach for Wireless Body Area Networks

E-THeTA: An Efficient and Adaptive TDMA Approach for Wireles...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Jorge F. Herculano Luís O. S. de Andrade Willians De P. Pereira Alirio S. de Sá Distributed Systems Laboratory (LaSiD) Graduate Program in Mechatronics (PPGM) Computer Science Institute (IC) and Polytechnic School (EP) Federal University of Bahia (UFBA) Ondina Campus Salvador Bahia Brazil Federal University of Southern Bahia (UFSB) Itabuna Campus Bahia Brazil Sergipe Federal Institute of Education Science and Technology (IFS) Socorro Campus Sergipe Brazil Rondônia Federal Institute of Education Science and Technology (IFRO) Porto Velho Campus Rondônia Brazil

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

Several research efforts have proposed adaptive solutions for Time Division Multiple Access (TDMA) to satisfy time requirements and save energy on Wireless Body Area Networks (WBANs) affected by radiofrequency interferences and carrying dynamic traffic. Even with the benefits, most of these solutions waste network bandwidth, decreasing network performance and the ability to recover from some unexpected communication conditions. This paper introduces the E-THeTA, an adaptive and efficient approach based on TDMA crafted to enhance performance, save energy, and efficiently use WBAN bandwidth. Compared to IEEE 802.15.4 and IEEE 802.15.6 standards and other adaptive approaches, the proposed solution reduces message losses, consumes less energy, and maintains low communication latencies in most evaluated scenarios.

关键词： Wireless communication IEEE 802.15 Standard Radio frequency Time division multiple access Adaptive systems Bandwidth Body area networks Standards

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Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM And NB Methods

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Journal of Physics: Conference Series 2019年第1期1201卷

作者： Bryan Pratama Dedi Dwi Saputra Deny Novianti Endah Putri Purnamasari Antonius Yadi Kuntoro Hermanto Windu Gata Nia K Wardhani Sfenrianto Sfenrianto Sularso Budilaksono Master of Computer Science-Postgraduate Program STMIK Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Jakarta Indonesia Master Faculty of Computer Science-Postgraduate Program Universitas Mercubuana Jakarta Indonesia Information Systems Management Department BINUS Graduate Program–Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Graduate School Master's Degree Computer Science Universitas Persada Indonesia YAI. Jakarta Indonesia

The Brimob Corps is a special police force, just like the special military detachments held by the TNI such as Paskhas and so on. At present brigade corps police national is busy being discussed in the real world and cyberspace, especially on social media twitter. Many opinions about the brigade corps police national so there are positive and negative opinions. Social media twitter is now one places to spread information about brigade corps police national. This study cases uses text mining techniques with support vector machine (SVM) method which aims to classify public sentiments towards brigade corps police national on twitter. The dataset used is tweet in Indonesian with keyword 'Brimob' with a total dataset of 1000 tweets. Text mining, transform, tokenize, stemming, and classification, etc. techniques are useful for building classification and analysis of sentiment. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. Accuracy values obtained with support vector machine (SVM) approach 86,96%, with precision values of 86,96%, and recall values of 86,96%.

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Swarm intelligence: Past, present and future

arXiv

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arXiv 2018年

作者： Yang, Xin-She Deb, Suash Zhao, Yu-Xin Fong, Simon He, Xingshi School of Science and Technology Middlesex University LondonNW4 4BT United Kingdom IT & Educational Consultant Ranchi India Decision Sciences and Modelling Program Victoria University MelbourneVIC Australia College of Automation Harbin Engineering University Harbin China Department of Computer and Information Sciences University of Macau Macau China College of Science Xi'an Polytechnic University No. 19 Jinhua South Road Xi'an China

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.68T99, 90C30, 90C59 Copyright © 2018, The Authors. All rights reserved.

关键词： Swarm intelligence

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Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority Over Sampling Technique Method

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Journal of Physics: Conference Series 2020年第1期1471卷

作者： Dedi Dwi Saputra Windu Gata Nia Kusuma Wardhani Ketut Sakho Parthama Hendra Setiawan Sularso Budilaksono Dimas Yogatama Agus Hadiyatna Endah Putri Purnamasari Bryan Pratama Deny Novianti Master of Computer Science-Postgraduate Program Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri Jakarta Indonesia Master Faculty of Computer Science-Postgraduate Program University of Mercubuana Jakarta Indonesia Graduate School Master's Degree Computer Science University Paramita Indonesia Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Bani Saleh Jakarta Indonesia Graduate School Master's Degree Computer Science University of Persada Indonesia YAI Jakarta Indonesia Graduate School Master's Degree Computer Science University of Gadjah Mada Jakarta Indonesia

Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, 'transform case', 'tokenize', 'token filters by length', 'n-gram', 'stemming' were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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