检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Slimen, Iyed Ben Gueddana, Amor Lakshminarayanan, Vasudevan SysCom Lab National Engineering School of Tunis ENIT University of EL Manar 1002 Le Belvédère Tunis Tunisia Green & Smart Communication Systems Lab Gres’Com Engineering School of Communication of Tunis Sup’Com University of Carthage Ghazela Technopark Ariana2083 Tunisia Theoretical & Experimental Epistemology Lab TEEL School of Optometry and Vision Science University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada

We investigate the counterparts of random walk in universal quantum computing and their implementation using standard quantum circuits. Quantum walk have been recently well investigated for traversing graphs with certain oracles. We focus our study on traversing a 1-D graph, namely a circle, and show how to implement discrete circular quantum walk in quantum circuits built with universal CNOT and single quit gates. We review elementary quantum gates and circuit decomposition and propose a a generalized version of the all CNOT based quantum discrete circular walk. We simulated these circuits on an IBM quantum supercomputer London IBM-Q with 5 qubits. This quantum computer has non perfect gates based on superconducting qubits, therefore we analyze the impact of errors on the fidelity of the Walker circuit. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

引用

作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CEPC Technical design Report

引用

Radiation Detection Technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi Yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-Yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui University HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu University Institute of ScienceVaranasi Bandirma Onyedi Eylul University Bandirma Beihang University Beijing Beijing Computational Science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal University Beijing Brown University ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad University Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo University Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central China Normal University WuhanHubei Central South University ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan China academy of engineering physics MianyangSichuan China building materials academy Beijing China Center of Advanced Science and Technology Beijing China Institute of Atomic Energy Beijing China Jiliang University HangzhouZhejiang China Nuclear Instrument CO. LTDBeijing China University of Geosciences Beijing China University of Geosciences WuhanHubei China University of Mining and Technology XuzhouJiangsu China West Normal University NanchongSichuan Chongqing University Department of PhysicsChongqing Chulalongkorn University Bangkok City University of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell University IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu University DalianLiaoning Dalian University of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East China University Of Science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by China,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical design Report(TDR)follows the Preliminary Conceptual design Report(Pre-CDR,2015)and the Conceptual design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

关键词： initiated exceeding precise

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2021 Challenge on Image Deblurring

arXiv

引用

arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Breast mass regions classification from mammograms using convolutional neural networks and transfer learning.

引用

Optica Acta: International Journal of Optics 2023年第10期70卷

作者： Yuliana Jiménez-Gaona María José Rodríguez-Álvarez Diana Carrión-Figueroa Darwin Castillo-Malla Vasudevan Lakshminarayanan a Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja Loja Ecuadorb Instituto de Instrumentación para la Imagen Molecular i3M Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC) Valencia Spaind Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo Waterloo Canada b Instituto de Instrumentación para la Imagen Molecular i3M Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC) Valencia Spain c Hospital Carlos Andrade Marín IESS Quito Ecuador d Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo Waterloo Canadae Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo Waterloo Canada

This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance image resolution, thus leading to more precise breast tissue segmentation and subsequent classification utilizing convolutional neural networks (CNNs). Three recognized public mammography databases: CBIS-DDSM, Mini-MIAS, and Inbreast were used as pre-processing data. Our statistical findings revealed that the EDSR method (PSNR = 39.05 dB/ SSIM = 0.90) consistently outperformed the visual quality of images when compared to SR-RDN (PSNR = 32.68 dB/SSIM = 0.82). Similarly, UNet demonstrated superior performance over SegNet, boasting an average Intersection over Union (IoU) of 0.862, an average Dice coefficient of 0.991, and an accuracy rate of 0.947 in Region of Interest (RoI) segmentation results. In conclusion, the ResNet model contributed to enhanced accuracy compared to conventional machine learning algorithms. However, it did not surpass state-of-the-art deep CNN-based classifiers, achieving an accuracy rate of 75%.Abbreviations:AUC: Area under curve; CAD: computer aided system; CC: Cranio caudal; CNN: Convolutional neural network; DNN: Deep neural network; DDSM: Digital Database for Screening Mammography; DM: Digital mammography; DL: Deep learning; EDSR: Enhanced Deep Residual Network; E2E: End to End; ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks; ESPCN: Efficient sub-pixel convolutional neural network; GAN: Generative adversarial network; HR: High resolution; IoU: Intersection over Union; LR: Low resolution; MDSR: Multi-scale deep super-resolution; MLO: Mediolateral Oblique; PSNR: Peak signal to Noise Ratio; RoI: Region of interest; RDN: Residua Dense Network; RDB: Residual Dense Block; RNN: Recurrent Neural Network; ReLU: Rectified Linear Unit; SR-GAN: Super-Resolution Using a Generative Adversarial Network; SSIM: Structural Sim

关键词： Breast cancer classification convolutional neural network mammography images segmentation super resolution

来源：评论

学校读者我要写书评

暂无评论

Deep high-order supervised hashing

引用

Optik 2019年 180卷 847-857页

作者： Jing Dong Cheng Qiu Le Sun Jian Xin Zhang Christian Desrosiers Bin Liu Jian Lu Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian 116622 China School of Information and Communication Engineering Dalian University of Technology Dalian 116024 China Laboratory for Imagery Vision and Artificial Intelligence (LIVAI) École de technologie supérieure (ETS) Montreal H3C1K3 Canada International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China

Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised hashing (DHoSH) architectures based on point-wise labels (DHoSH-PO) and pair-wise labels (DHoSH-PA), respectively. The core of DHoSH is a trainable layer of bilinear features incorporated into existing deep convolutional neural network (CNN) for end-to-end learning. This layer captures the interaction of local features by bilinear pooling, using correlation to model dependencies of features within the same layer or cross-correlation for features across different layers of the CNN. Furthermore, DHoSH systematically employs the high-order statistics of features of multiple layers. Extensive experiments on commonly used image retrieval benchmarks show that our DHoSH-PO and DHoSH-PA models yield improved accuracy over its first-order counterparts and achieve effective performance for these benchmarks.

关键词： High-order statistics Supervised hashing Bilinear pooling CNN Image retrieval

来源：评论

学校读者我要写书评

暂无评论

Deep learning for LiDAR point clouds in autonomous driving: A review

arXiv

引用

arXiv 2020年

作者： Li, Ying Ma, Lingfei Zhong, Zilong Liu, Fei Cao, Dongpu Li, Jonathan Chapman, Michael A. Department of Geography and Environmental Management University of Waterloo 200 University Avenue West WaterlooN2L 3G1 Canada School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China Xilinx Technology Beijing Limited Beijing100083 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada Departments of System Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Civil Engineering Ryerson University TorontoONM5B 2K3 Canada

Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3D point clouds is a challenging and tedious task. In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification. Although several published research papers focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on deep learning applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this paper is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3D deep architectures, the remarkable deep learning applications in 3D semantic segmentation, object detection, and classification;specific datasets, evaluation metrics, and the state of the art performance. Finally, we conclude the remaining challenges and future researches. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Recent progress and trends in predictive visual analytics

引用

Frontiers of computer science 2017年第2期11卷 192-207页

作者： Junhua LU Wei CHEN Yuxin MA Junming KE Zongzhuang LI Fan ZHANG Ross MACIEJEWSKI State Key Lab of Computer Aided Design and Computer Graphics Zhejiang University Hangzhou 310058 China College of Science Zhejiang University of Technology Hangzhou 310023 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 USA

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem snurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the pre- diction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual ana- lytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summariza- tion of the predictive analytics workfiow.

关键词： predictive visual analytics visualization visual analytics data mining predictive analysis

来源：评论

学校读者我要写书评

暂无评论

The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

引用

LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Modified Ćuk Converter With Constant Switching Frequency Modulated Sliding Mode Control

A Modified Ćuk Converter With Constant Switching Frequency ...

引用

Compatibility in Power Electronics (CPE)

作者： Emre Ozsoy Sanjeevikumar Padmanaban Frede Blaabjerg Mahajan Sagar Bhaskar Fiaz Ahmad Research & Development Design Center Danfoss Drives A/S Denmark Department of Energy Technology Aalborg University Esbjerg Denmark Center of Reliable Power Electronics (CORPE) Department of Energy Technology Aalborg University Aalborg Denmark Renewable Energy Lab Department of Communications & Networks College of Engineering Prince Sultan University Riyadh Saudi Arabia Electrical Engineering National University of Computer & Emerging Science5 Peshawar Pakistan

High output gain ratio demand for DC-DC converters have been increasing in recent years due to diverse integration of various renewable sources and industrial applications. This article focused to provide a detailed analysis of a Cuk converter with increased voltage conversion ratio. The circuit only consists of two inductors, five capacitors and four diodes. In particular, configuration is composed of combination of two different cascaded switched capacitor voltage boost network. The voltage in the output is 29 times higher than the input voltage, if 0.9 duty ratio is used. Further, high gain DC-DC converter control is a challenging task due to higher order and nonlinear characteristics. Standard PI controllers cannot accomplish robust operation for all operation regions. Hence, Sliding Mode Controller (SMC) is a reliable and robust method for high order nonlinear systems due to its robustness and independence from system dynamics and parameter variations. Furthermore, this paper develops a constant switching frequency SMC approach to high gain modified Cuk converter. Comprehensive theoretical analysis is presented and numerically verified using Matlab/Simulink software.

关键词： DC-DC power converters Switches Switching frequency PI control Voltage control Hysteresis Inductors

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：