检索结果-内蒙古大学图书馆

Robust laser self-mixing displacement demodulation via continuous wavelet–Hilbert transform for high-precision accelerometer calibration

引用

Applied Optics 2025年第16期64卷 4559-4567页

作者： Chang, Zhikun Song, Song Xue, Dongbai He, Chunling Shi, Zhoumiao Yu, Jing Kong, Ming Xie, Yuying Deng, Xiao Li, Tongbao Cheng, Xinbin Institute of Precision Optical Engineering Tongji University Shanghai200092 China Tongji University Shanghai200092 China MOE Key Laboratory of Advanced Micro-Structured Materials Tongji University Shanghai200092 China Shanghai Frontiers Science Center of Digital Optics Tongji University Shanghai200092 China Shanghai Professional Technical Service Platform for Full-Spectrum and High-Performance Optical Thin Film Devices and Applications Tongji University Shanghai200092 China School of Physics Science and Engineering Tongji University Shanghai200092 China School of Computer Science and Technology Zhejiang University of Water Resources and Electric Power Hangzhou310018 China Shenzhen Institute of Technology Innovation NIM Shenzhen518107 China College of Metrology and Testing Engineering China Jiliang University Hangzhou310018 China

The laser self-mixing grating interferometer based on Cr atom lithography gratings has been applied to the primary calibration of accelerometers due to its compact structure, low cost, high accuracy, and direct on-site traceability. However, the high line density of Cr gratings (4700 l/mm) introduces dense outliers in interferometric signals, complicating displacement demodulation via conventional derivative-based methods and causing frequent phase jumps. To address this challenge, we propose a hybrid algorithm integrating the continuous wavelet transform and the Hilbert transform, which enables robust displacement demodulation under high-noise conditions. Experimental validation on a commercial MEMS accelerometer demonstrated exceptional accuracy: 100 consecutive data segments were successfully demodulated with a displacement fitting goodness of R2 = 0.9964 (σR2 = 0.0027), and the derived sensitivity deviated by only 0.1% from the nominal value. This algorithm establishes a paradigm for the high-accuracy dynamic calibration of inertial sensors in field applications. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Accelerometers

来源：评论

学校读者我要写书评

暂无评论

Optimal subsystem approach to multi-qubit quantum state discrimination and experimental investigation

引用

science China(Physics,Mechanics & Astronomy) 2018年第2期61卷 26-34页

作者： ShiChuan Xue JunJie Wu Ping Xu XueJun Yang State Key Laboratory of High Performance Computing National University of Defense Technology College of Computer National University of Defense Technology

Quantum computing is a significant computing capability which is superior to classical computing because of its superposition feature. Distinguishing several quantum states from quantum algorithm outputs is often a vital computational task. In most cases, the quantum states tend to be non-orthogonal due to superposition; quantum mechanics has proved that perfect outcomes could not be achieved by measurements, forcing repetitive measurement. Hence, it is important to determine the optimum measuring method which requires fewer repetitions and a lower error rate. However, extending current measurement approaches mainly aiming at quantum cryptography to multi-qubit situations for quantum computing confronts challenges, such as conducting global operations which has considerable costs in the experimental realm. Therefore, in this study, we have proposed an optimum subsystem method to avoid these difficulties. We have provided an analysis of the comparison between the reduced subsystem method and the global minimum error method for two-qubit problems; the conclusions have been verified experimentally. The results showed that the subsystem method could effectively discriminate non-orthogonal two-qubit states, such as separable states, entangled pure states, and mixed states; the cost of the experimental process had been significantly reduced, in most circumstances, with acceptable error rate. We believe the optimal subsystem method is the most valuable and promising approach for multi-qubit quantum computing applications.

关键词： quantum computing quantum measurement quantum state discrimination entanglement reduced density operators

来源：评论

学校读者我要写书评

暂无评论

A dynamic bypass approach to realize power efficient network-on-chip 21

A dynamic bypass approach to realize power efficient network...

引用

21st IEEE International Conference on high performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Wang, Peng Niknam, Sobhan Ma, Sheng Wamg, Zhiying Stefanov, Todor Leiden Institute of Advanced Computer Science Leiden University Netherlands State Key Laboratory of High Performance Computing National University of Defense Technology China

ISBN: (纸本)9781728120584

high power consumption becomes the major bottleneck that prevents applying Network-on-Chips (NoCs) on future many-core systems. Power gating is an effective way to reduce the power consumption of a NoC. However, conventional power gating approaches cause significant packet latency increase as well as additional power consumption overhead due to the power gating mechanism. One comprehensive way to reduce these negative impacts is to bypass the powered-off routers in a NoC to transfer packets. Therefore, in this paper, we propose a dynamic bypass (D-bypass) approach, which is based on a reservation mechanism to allow different upstream routers to forward packets through the same powered-off router at different times. With this feature, our D-bypass power gating approach overcomes the drawbacks in related power gating approaches. Compared with a conventional NoC without power gating, our D-bypass approach causes only 2.55% performance penalty, which is less than 28.67%, 19.26%, 7.24%, and 6.69% penalties in related approaches. With small hardware overhead, our approach just consumes on average 22.23% of total power consumption in a NoC, which is slightly better compared to 27.06%, 23.89%, 26.45%, and 24.70% total power consumption in related approaches. © 2019 IEEE.

关键词： Network-on-chip

来源：评论

学校读者我要写书评

暂无评论

Multi-UAV surveillance implementation under hierarchical dynamic task scheduling architecture

引用

Journal of Central South University 2020年第9期27卷 2614-2627页

作者： WU Wen-di WU Yun-long LI Jing-hua REN Xiao-guang SHI Dian-xi TANG Yu-hua State Key Laboratory of High Performance Computing(HPCL) College of ComputerNational University of Defense TechnologyChangsha 410073China Artificial Intelligence Research Center(AIRC) National Innovation Institute of Defense Technology(NIIDT)Beijing 100071China Tianjin Artificial Intelligence Innovation Center(TAIIC) Tianjin 300457China

In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground *** this scenario,we adopt the leader-follower control mode and propose a modified Lyapunov guidance vector field(LGVF)approach for improving the precision of surveillance trajectory ***,in order to adopt to poor communication conditions,we propose a prediction-based synchronization method for keeping the formation ***,in order to adapt the multi-UAV system to dynamic and uncertain environment,this paper proposes a hierarchical dynamic task scheduling *** this architecture,we firstly classify all the algorithms that perform tasks according to their functions,and then modularize the algorithms based on plugin ***,integrating the behavior model and plugin technique,this paper designs a three-layer control flow,which can efficiently achieve dynamic task *** order to verify the effectiveness of our architecture,we consider a multi-UAV traffic monitoring scenario and design several cases to demonstrate the online adjustment from three levels,respectively.

关键词： prediction-based synchronization dynamic task scheduling hierarchical software architecture

来源：评论

学校读者我要写书评

暂无评论

Variational quantum process tomography

arXiv

引用

arXiv 2021年

作者： Xue, Shichuan Liu, Yong Wang, Yang Zhu, Pingyu Guo, Chu Wu, Junjie Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China College of Information and Communication National University of Defense Technology Xi'an710006 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to 8 qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least 2 orders of magnitude less than required by the standard quantum process tomography. Copyright © 2021, The Authors. All rights reserved.

关键词： Tomography

来源：评论

学校读者我要写书评

暂无评论

ON THE OPPORTUNITIES OF GREEN computing: A SURVEY

arXiv

引用

arXiv 2023年

作者： Zhou, You Lin, Xiujing Zhang, Xiang Wang, Maolin Jiang, Gangwei Lu, Huakang Wu, Yupeng Zhang, Kai Yang, Zhe Wang, Kehang Sui, Yongduo Jia, Fengwei Tang, Zuoli Zhao, Yao Zhang, Hongxuan Yang, Tiannuo Chen, Weibo Mao, Yunong Li, Yi Bao, De Li, Yu Liao, Hongrui Liu, Ting Liu, Jingwen Guo, Jinchi Zhao, Xiangyu Wei, Ying Qian, Hong Liu, Qi Wang, Xiang Chan, Wai Kin Li, Chenliang Li, Yusen Yang, Shiyu Yan, Jining Mou, Chao Han, Shuai Jin, Wuxia Zhang, Guannan Zeng, Xiaodong Tsinghua University China Shanghai Jiaotong University China University of Science and Technology of China China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Huazhong University of Science and Technology China Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University China Xi’an Jiaotong University China College of Computer Science Nankai University China School of Computer Science China University of Geosciences China School of Information Science and Technology Beijing Forestry University China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration China Guangzhou University China Nanyang Technological University Singapore City University of Hong Kong Hong Kong Wuhan University China State Key Laboratory for Novel Software Technology Nanjing University China Ant Group China

Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series analysis, speech synthesis, etc. During the age of deep learning, especially with the arise of Large Language Models, a large majority of researchers’ attention is paid on pursuing new state-of-the-art (SOTA) results, resulting in ever increasing of model size and computational complexity. The needs for high computing power brings higher carbon emission and undermines research fairness by preventing small or medium-sized research institutions and companies with limited funding in participating in research. To tackle the challenges of computing resources and environmental impact of AI, Green computing has become a hot research topic. In this survey, we give a systematic overview of the technologies used in Green computing. We propose the framework of Green computing and devide it into four key components: (1) Measures of Greenness, (2) Energy-Efficient AI, (3) Energy-Efficient computing Systems and (4) AI Use Cases for Sustainability. For each components, we discuss the research progress made and the commonly used techniques to optimize the AI efficiency. We conclude that this new research direction has the potential to address the conflicts between resource constraints and AI development. We encourage more researchers to put attention on this direction and make AI more environmental friendly. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon footprint

来源：评论

学校读者我要写书评

暂无评论

Collaborative filtering recommendation algorithm based on interactive data classification

引用

The Journal of China Universities of Posts and Telecommunications 2020年第5期27卷 1-12页

作者： Ji Yimu Li Ke Liu Shangdong Liu Qiang Yao Haichang Li Kui School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and TelocommunicutionsNanjing 210003China Institue of High Performance Computing and Bigdata Nanjing University of Posts and TelecommunicationsNanjing 210003China Nanjing Center of HPC China Nanjing University of Posts and TelocommunicutionsNanjing 210003China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing University of Posts and Telocommunicutions Nanjing 210003China

In the matrix factorization(MF)based collaborative filtering recommendation method,the most critical part is to deal with the interaction between the features of users and *** mainstream approach is to use the inner product for MF to describe the user-item ***,as a shallow model,MF has its limitations in describing the relationship between *** addition,when the size of the data is large,the performance of MF is often poor due to data sparsity and *** paper presents a model called PIDC,short for potential interaction data clustering based deep learning ***,it uses classifiers to filter and cluster recommended items to solve the problem of sparse training ***,it combines MF and multi-layer perceptron(MLP)to optimize the prediction effect,and the limitation of inner product on the model expression ability is *** proposed model PIDC is tested on two *** experimental results show that compared with the existing benchmark algorithm,the model improved the recommendation effect.

关键词： personalized recommendation deep learning clustering collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Active hyperspectral imaging with a supercontinuum laser source in the dark

引用

Chinese Physics B 2019年第3期28卷 197-201页

作者： Zhongyuan Guo Yu Liu Xin Zheng Ke Yin State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 401173China College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology Academy of Military Sciences of the People’s Liberation Army of ChinaBeijing 100071China

An active hyperspectral imaging(HSI) system was built with a supercontinuum(SC) laser illuminator and a visible/near-infrared hyperspectral camera, which was used for object spectrum detection in the dark. It was demonstrated that the Gaussian-like distribution of the SC illuminator can still be used for accurate reflectance spectrum measurement once the illuminator was characterized in advance. The validity of active HSI results was demonstrated by comparison with passive results. Then, the active HSI system was used to acquire reflectance spectra of different objects in just one pushbroom measurement successfully. With algorithms of principal component analysis clustering and unsupervised K-means spectral classification, this active HSI system with high spectral and spatial resolutions was demonstrated to be efficient and applicable for specific spectrum detections.

关键词： laser spectroscopy active hyperspectral imaging supercontinuum laser

来源：评论

学校读者我要写书评

暂无评论

Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

arXiv

引用

arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Yu, Kai Xu, Yong Yang, Jian Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China The Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Terminus Group China

Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for fast MR imaging. However, current multi-contrast super-resolution (SR) methods tend to concatenate different contrasts directly, ignoring their relationships in different clues, e.g., in the high-intensity and low-intensity regions. In this study, we propose a separable attention network (comprising high-intensity priority attention and low-intensity separation attention), named SANet. Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image. SANet provides three appealing benefits: (1) It is the first model to explore a separable attention mechanism that uses the auxiliary contrast to predict the high-intensity and low-intensity regions regions, diverting more attention to refining any uncertain details between these regions and correcting the fine areas in the reconstructed results. (2) A multi-stage integration module is proposed to learn the response of multi-contrast fusion at multiple stages, get the dependency between the fused representations, and boost their representation ability. (3) Extensive experiments with various state-of-the-art multi-contrast SR methods on fastMRI and clinical in vivo datasets demonstrate the superiority of our model. © 2021, CC BY.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Measurement of very-high-energy diffuse gamma-ray emission from |b| ◦ of the Galactic plane with LHAASO-WCDA 38

Measurement of very-high-energy diffuse gamma-ray emission f...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Li, Huicai Zhang, Peipei Hu, Shicong Zha, Min Guo, Yiqing Yuan, Qiang Yao, Zhiguo Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China APC Universit

Galactic diffuse gamma ray emission (GDE) is introduced by the galactic cosmic rays (CR) interacting with the interstellar medium (ISM) and radiation fields (ISRF). The GDE is a very important probe of CR propagation and interaction. Different from the measurements of CR particles in the local vicinity, the GDE enables a direct measurement of CR distribution in the Milky Way, and can thus provide much more important information of the production and propagation of CRs. LHAASO-WCDA, with an large area of 78000 m2, has an excellent ability to study VHE gamma-ray astronomy and GDE. In this proceeding, method of background estimation and some techniques to reduce the contaminant of resolved gamma-ray sources are briefly introduced. At last, diffuse emissions from the inner (15◦ ◦, |b| ◦) and outer (125◦ ◦, |b| ◦) Galactic plane are detected with 27.9σ and 11.9σ significance, respectively. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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：