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第42届中国控制会议

作者： Pei Chen Zhongwei Lin Zihan Zhang Zhen Xie State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power University

Yaw misalignment is common in the yaw system control of wind turbines,resulting in reduced power generation efficiency and increased *** advantage of LiDAR is that the accuracy of wind measurement is greatly improved,while its disadvantage is that the cost remains *** this paper,an efficient machine learning method for estimating LiDAR measurement,XGBoost,is used to establish the data-driven wind turbine yaw misalignment compensation *** choose a suitable algorithm,four machine learning algorithms are studied,namely Linear Regression,Random Forest,Gradient Boosting and ***,to verify the effectiveness of the proposed method,a 2 MW WT was taken as the research object for filed testing,and the power generation was quantitatively evaluated under the three strategies of wind vane control,LiDAR control and ML algorithm *** results show that the proposed yaw misalignment compensation method is feasible for field *** this way,the yaw-to-wind error can be mitigated even after the LiDAR is removed.

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Subject-Independent Ankle Joint Power Estimation with Two IMUs During Flat and Inclined Walking

Subject-Independent Ankle Joint Power Estimation with Two IM...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Hong Wang Dongxuan Li Kairan Liang Peter Shull State Key Laboratory of Mechanical System and Vibration Shanghai Jiaotong University Shanghai China Department of Electrical and Computer Engineering Northeastern University Boston America

Assessing ankle joint power during real-life scenarios is crucial for analyzing human push-off and detecting abnormal gait patterns. However, traditional joint power monitoring methods require expensive and professional equipment, limiting their use to gait laboratories. To address this limitation, we propose a portable and robust two-stage approach that estimates ankle joint power using two inertial measurement units (IMU) sensors placed on the shank and foot, respectively. Our subject-independent CNN model accurately assessed ankle joint power during flat and inclined walking across 28 walking speeds and 6 ramp inclines. This solution facilitates ankle joint power assessment outside of gait laboratories and could serve as a foundation to enable gait abnormality evaluation in patients in hospitals, clinics, and home-based settings.

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers. © ... 详细信息

关键词： Light modulators

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Intelligent Intra- and Inter-Channel Nonlinearity Compensation for Terabit-Per-Lambda 16QAM Long-Haul Transmission

Intelligent Intra- and Inter-Channel Nonlinearity Compensati...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xiansong Fang Lingjun Zhou Yixiao Zhu Jiayu Zheng Xiang Cai Fan Zhang State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen China State Key Laboratory of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai China Pengcheng Laboratory Shenzhen China

We experimentally demonstrate the combination of learned modified DBP and adaptive equalizer to compensate for intra- and inter-channel nonlinearity, in an $8\times 125\text{Gbaud}$ 16QAM system over 1600km SSMF, sh...

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Next-generation machine vision systems incorporating two-dimensional materials: Progress and perspectives

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InfoMat 2022年第1期4卷 23-39页

作者： Peisong Wu Ting He He Zhu Yang Wang Qing Li Zhen Wang Xiao Fu Fang Wang Peng Wang Chongxin Shan Zhiyong Fan Lei Liao Peng Zhou Weida Hu State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghaiChina University of Chinese Academy of Sciences BeijingChina Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouChina State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityShanghaiChina Henan Key Laboratory of Diamond Optoelectronic Materials and Devices School of Physics and EngineeringZhengzhou UniversityZhengzhouChina Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina School of Physics and Electronics Hunan UniversityChangshaChina

Machine vision systems(MVSs)are an important component of intelligent systems,such as autonomous vehicles and ***,with the continuous increase in data and new application scenarios,new requirements are put forward for the next generation of *** is an urgent need to find new material systems to complement the existing semiconductor technology based on thin-film materials,and new architectures must be explored to improve *** of their unique physical properties,two-dimensional(2D)materials have received extensive attention for use in MVSs,especially in biomimetic ones:the human visual system,which can process complex visual information with low power consumption,provides a model for next-generation *** review paper summarizes the progress and challenges of applying 2D material photodetectors in sense-memory-computational integration and biomimetic image sensors for machine vision.

关键词： in-sensor computing machine vision systems photodetectors two-dimensional materials

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User-level failure detection and auto-recovery of parallel programs in HPC systems

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Frontiers of computer Science 2021年第6期15卷 31-42页

作者： Guozhen ZHANG Yi LIU Hailong YANG Jun XU Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China

As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.

关键词： high performance computing parallel program failure detection failure auto-recovery

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Learning or Self-aligning? Rethinking Instruction Fine-tuning

arXiv

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arXiv 2024年

作者： Ren, Mengjie Cao, Boxi Lin, Hongyu Liu, Cao Han, Xianpei Zeng, Ke Wan, Guanglu Cai, Xunliang Sun, Le Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Meituan China Key Laboratory of System Software Chinese Academy of Sciences China

Instruction Fine-tuning (IFT) is a crucial phase in building large language models (LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge intervention framework to decouple the potential underlying factors of IFT, thereby enabling individual analysis of different factors. Surprisingly, our experiments reveal that attempting to learn additional world knowledge through IFT often struggles to yield positive impacts and can even lead to markedly negative effects. Further, we discover that maintaining internal knowledge consistency before and after IFT is a critical factor for achieving successful IFT. Our findings reveal the underlying mechanisms of IFT and provide robust support for some very recent and potential future works. We release our experimental dataset and codes to facilitate future work. © 2024, CC BY.

关键词： Contrastive Learning

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UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs

arXiv

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arXiv 2024年

作者： Han, Xi Hou, Fei Qin, Hong Stony BrookNY11794 United States Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Numerical solvers of Partial Differential Equations (PDEs) are of fundamental significance to science and engineering. To date, the historical reliance on legacy techniques has circumscribed possible integration of big data knowledge and exhibits sub-optimal efficiency for certain PDE formulations, while data-driven neural methods typically lack mathematical guarantee of convergence and correctness. This paper articulates a mathematically rigorous neural solver for linear PDEs. The proposed UGrid solver, built upon the principled integration of U-Net and MultiGrid, manifests a mathematically rigorous proof of both convergence and correctness, and showcases high numerical accuracy, as well as strong generalization power to various input geometry/values and multiple PDE formulations. In addition, we devise a new residual loss metric, which enables self-supervised training and affords more stability and a larger solution space over the legacy losses. © 2024, CC BY-NC-ND.

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