检索结果-内蒙古大学图书馆

Electrical design of a smart control and monitoring system for small horizontal-axis wind turbines

学校读者我要写书评

暂无评论

Renewable Energy and Power Quality Journal

Renewable Energy and Power Quality Journal 2022年 20卷 116-120页

作者： Vilà, J. Luo, N. Pacheco, L. Pujol, T. Gonzalez, J.R. Ferrer, I. Massaguer, A. Massaguer, E. Department of Electrical Engineering Electronics and Automation Polytechnic School University of Girona Girona17003 Spain Department of Mechanical Engineering and Industrial Construction Polytechnic School University of Girona Girona17003 Spain Department of Computer Architecture and Technology Polytechnic School University of Girona Girona17003 Spain AULA ECOGranja Vilà sustainable engineering Camí Vell de Figueres a Olot S/N Veïnat de Pols Girona17772 Spain

The popularity of small wind turbines would rise in future, because of electricity prices are still growing. Next generation of small wind-turbines can have higher efficiency values. Under wind gusts and turbulences, the system should be very sensitive to wind conditions. In this work, it is presented a control system that can improve the performance of commercial small-scale wind turbines by including active pitch and load control. We show the electrical design of a smart active pitch control to increase the energy generation of micro-wind turbines ( © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Horizontal axis wind turbine

A Bayesian Network-based Quantum procedure for Failure Risk Analysis

学校读者我要写书评

暂无评论

TechRxiv

TechRxiv 2021年

作者： de las Heras, Ginés Carrascal Botella, Guillermo del Barrio, Alberto A. Kremer, David IBM Consulting España Madrid28830 Spain Department of Informatic Systems and Computation Faculty of Informatics Complutense University of Madrid Madrid28040 Spain Department of Computer Architecture and Automation Faculty of Informatics Complutense University of Madrid Madrid28040 Spain

Studying the propagation of failure probabilities in interconnected systems like that of electrical distribution networks is traditionally performed by means of Monte Carlo simulations. In this paper, we propose a procedure to create a model of the system in a quantum computer using a restricted representation of Bayesian networks. Some examples of this implementation on sample models are presented using Qiskit and tested using both quantum simulators and IBM Quantum hardware. Results show a correlation in the precision of the results when considering the number of Monte Carlo iterations alongside the sum of shots in a single quantum circuit execution. © 2021, CC BY.

关键词： Bayesian networks

Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Hou, Jinbo Qiu, Kehai Zhang, Zitian Yu, Yong Wang, Kezhi Capolongo, Stefano Zhang, Jiliang Li, Zeyang Zhang, Jie The Department of Electronic and Electrical Engineering the University of Sheffield Sheffield United Kingdom The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom Brunel University London United Kingdom The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China The Design & Health Lab Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Italy The Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom The State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang China The Centre for Wireless Innovation Queen’s University of Belfast BelfastBT3 9DT United Kingdom

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building’s wireless performance can be significantly enhanced while ensuring indoor daylight performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Daylighting

Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Priego-Torres, Blanca Maria Lobato-Delgado, Barbara Atienza-Cuevas, Lidia Sanchez-Morillo, Daniel Biomedical Engineering and Telemedicine Research Group Department of Automation Engineering Electronics and Computer Architecture and Networks School of Engineering University of Cádiz Avda. Universidad de Cádiz 10 Cádiz Puerto Real11510 Spain Hospital Universitario Puerta del Mar Avda Ana de Viya 21 Cádiz11009 Spain Department of Pathology Hospital Universitario Puerta del Mar Avda. Ana de Viya 21 Cádiz11009 Spain

The quantification of biomarkers on immunohistochemistry breast cancer images is essential for defining appropriate therapy for breast cancer patients, as well as for extracting relevant information on disease prognosis. This is an arduous and time-consuming task that may introduce a bias in the results due to intra- and inter-observer variability which could be alleviated by making use of automatic quantification tools. However, this is not a simple processing task given the heterogeneity of breast tumors that results in non-uniformly distributed tumor cells exhibiting different staining colors and intensity, size, shape, and texture, of the nucleus, cytoplasm and membrane. In this research work we demonstrate the feasibility of using a deep learning-based instance segmentation architecture for the automatic quantification of both nuclear and membrane biomarkers applied to IHC-stained slides. We have solved the cumbersome task of training set generation with the design and implementation of a web platform, which has served as a hub for communication and feedback between researchers and pathologists as well as a system for the validation of the automatic image processing models. Through this tool, we have collected annotations over samples of HE, ER and Ki-67 (nuclear biomarkers) and HER2 (membrane biomarker) IHC-stained images. Using the same deep learning network architecture, we have trained two models, so-called nuclei- and membrane-aware segmentation models, which, once successfully validated, have revealed to be a promising method to segment nuclei instances in IHC-stained images. The quantification method proposed in this work has been integrated into the developed web platform and is currently being used as a decision support tool by pathologists. © 2023, CC BY-NC-ND.

关键词： Textures

Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

学校读者我要写书评

暂无评论

Power Efficient Video Super-Resolution on Mobile NPUs with...

17th European Conference on computer Vision, ECCV 2022

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Zürich Switzerland AI Witchlabs Zürich Switzerland University of Wuerzburg Würzburg Germany MediaTek Inc. Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Video Algorithm Group Camera Department Xiaomi Inc. Beijing China Audio & Video Technology Platform Department ZTE Corporation Shenzhen China Beijing China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China MIGU Video Co. Ltd. Beijing China BOE Technology Group Co. Ltd. Beijing China GenGenAI Seoul Korea Republic of North China University of Technology Beijing China Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Uppsala Sweden Northeastern University Shenyang China

ISBN: (纸本)9783031250651

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

INSTANCE SEGMENTATION AND TEETH CLASSIFICATION IN PANORAMIC X-RAYS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wang, Ran Zhao, Yifei Wang, Shipan Du, Kehan Tang, Mojie Li, Yiyao Chen, Qianhe Tao, Hongxi Wang, Yong School of Journalism and Information Communication Huazhong University of Science and Technology China School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Computer Science United Kingdom University of Bristol United Kingdom School of Architecture and Urban Planning Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China Computer Science Department University College London United Kingdom School of Computing and Information Systems Singapore Management University Singapore

With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual analytics system, RumorLens, to help them deal with the rumor efficiently and gain an in-depth understanding of the patterns of rumor spreading. RumorLens integrates natural language processing (NLP) and other data processing techniques with visualization techniques to facilitate interactive analysis and validation of suspected rumors. We propose well-coordinated visualizations to provide users with three levels of details of suspected rumors: an overview displays both spatial distribution and temporal evolution of suspected rumors;a projection view leverages a metaphor-based glyph to represent each suspected rumor and further enable users to gain a quick understanding of their overall characteristics and similarity with each other;a propagation view visualizes the dynamic spreading details of a suspected rumor with a novel circular visualization design, and facilitates interactive analysis and validation of rumors in a compact manner. By using a real-world dataset collected from Sina Weibo, one case study with a domain expert is conducted to evaluate RumorLens. The results demonstrated the usefulness and effectiveness of our approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Social networking (online)

FPGA Implementation of Post-Quantum DME Cryptosystem

学校读者我要写书评

暂无评论

FPGA Implementation of Post-Quantum DME Cryptosystem

Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Jose L. Imana Ignacio Luengo Department of Computer Architecture and Automation Faculty of Physics Complutense University Madrid Spain Faculty of Mathematics Complutense University Madrid Spain

ISBN: (数字)9781728158037

ISBN: (纸本)9781728158044

The rapid development of quantum computing constitutes a significant threat to modern Public-Key Cryptography (PKC). The use of Shor's algorithm with potential powerful quantum computers could easily break the two most widely used public key cryptosystems, namely, RSA and Elliptic Curve Cryptography (ECC), based on integer factorization and discrete logarithm problems. For this reason, Post-Quantum Cryptography (PQC) based on alternative mathematical features has become a fundamental research topic due to its resistance against quantum computers. The National Institute of Standards and Technology (NIST) has even opened a call for proposals of quantum-resistant PKC algorithms in order to standardize one or more PQC algorithms. Cryptographic systems that appear to be extremely difficult to break with large quantum computers are hash -based cryptography, lattice -based cryptography, code -based cryptography, and multivariate -quadratic cryptography. Furthermore, efficient hardware implementations are highly required for these alternative quantum -resistant cryptosystems.

关键词：

PLAM: A posit logarithm-approximate multiplier

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Murillo, Raul Del Barrio, Alberto A. Botella, Guillermo Kim, Min Soo Kim, HyunJin Bagherzadeh, Nader Department of Computer Architecture and Automation Complutense University of Madrid Madrid28040 Spain NGD Systems IrvineCA92618 United States School of Electronics and Electrical Engineering Dankook University Gyeonggi-do Yongin-si16890 Korea Republic of Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States

The Posit™ Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in Neural Network related tasks and produced some unit designs which are still far from being competitive with their floating-point counterparts. This paper proposes a Posit Logarithm-Approximate Multiplication (PLAM) scheme to significantly reduce the complexity of posit multipliers, the most power-hungry units within Deep Neural Network architectures. When comparing with state-of-the-art posit multipliers, experiments show that the proposed technique reduces the area, power, and delay of hardware multipliers up to 72.86%, 81.79%, and 17.01%, respectively, without accuracy degradation. Copyright © 2021, The Authors. All rights reserved.

关键词： Numbering systems