检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Ding, Mengjie Xie, Jin-Han Wang, Jianchun Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing100871 China Joint Laboratory of Marine Hydrodynamics and Ocean Engineering Laoshan Laboratory Shandong 266237 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China

We study the statistically steady states of the forced dissipative three-dimensional homogeneous isotropic turbulence at scales larger than the forcing scale in real separation space. The probability density functions (PDFs) of longitudinal velocity difference at large separations are close to but deviate from Gaussian, measured by their non-zero odd parts. Under the assumption that forcing controls the large-scale dynamics, we propose a conjugate regime to Kolmogorov's inertial range, independent of the forcing scale, to capture the odd parts of PDFs. The analytical expressions of the third-order longitudinal structure functions derived from the Kármán-Howarth-Monin equation prove that the odd-part PDFs of velocity differences at large separations are small but non-zero, and show that the odd-order longitudinal structure functions have a universal power-law decay with exponent -2 as the separation tends to infinity regardless of the particular forcing form, implying a significant coupling between large and small scales. Thus, dynamics of large scales depart from the absolute equilibrium, and we can partially recover small-scale information without explicitly resolving small-scale dynamics. The departure from the statistical equilibrium is quantified and found to be viscosity independent. Even though this departure is small, it is significant and should be considered when studying the large scales of the forced three-dimensional homogeneous isotropic turbulence. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability density function

来源：评论

学校读者我要写书评

暂无评论

Network structure governs Drosophila brain functionality

引用

Fundamental Research 2025年

作者： Zhang, Xiaoyu Yang, Pengcheng Feng, Jiawei Wen, Kang Yan, Gang Luo, Qiang Lin, Wei Lu, Xin College of Systems Engineering National University of Defense Technology Changsha 410073 China Xiangjiang Laboratory Changsha 410205 China Research Institute of Intelligent Complex Systems Fudan University Shanghai 200433 China MOE Key Laboratory of Advanced Micro-Structured Materials School of Physical Science and Engineering Tongji University Shanghai 200092 China

How intelligence emerges in living beings is a fundamental but largely unanswered question in neuroscience. To address this challenge, we leveraged the largest available data set of adult Drosophila connectome, and constructed a comprehensive computational framework using simplified neuronal activation mechanisms to mimic the observed activation behavior within the connectome. The results revealed that even with rudimentary neuronal activation mechanisms, models grounded in real neuronal network structures can generate activation patterns strikingly analogous to those observed in the actual brain. A significant discovery was the consistency of activation patterns across various neuronal dynamic models with the same network structure. This consistency results therefore underscore the pivotal role of network topology in neural information processing, but challenge the prevailing view that solely relies on neuron count or complex individual neuron dynamics. Further analysis demonstrated a near-complete separation of the visual and the olfactory systems at the network level. Moreover, we found that the network distance, rather than spatial distance, is the primary determinant of activation patterns, and also that a reconnect rate of at least 1^o/_oo was sufficient to disrupt the previously observed activation patterns. We also observed synergistic effects between the brain hemispheres: Even with unilateral input stimuli, visual-related neurons in both hemispheres were activated, highlighting the importance of interhemispheric communication. All these findings suggest the crucial role of network structure in neural activation and offer novel insights into the fundamental principles governing brain functionality. © 2025 The Authors

关键词： Activation pattern Brain network Drosophila connectome Network communication model

来源：评论

学校读者我要写书评

暂无评论

In-Situ Synthesis of High-Quality Carbon Nanotubes on Cu Powder by Constructing an Al2o3 Barrier Layer

SSRN

引用

SSRN 2023年

作者： Zheng, Run Li, Shaofu Zhang, Lei Yang, Yafeng State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Science and Technology on Particle Materials Chinese Academy of Sciences Beijing China

The synthesis of high-quality carbon nanotubes (CNTs)-Cu composite powder by chemical vapor deposition remains a persistent challenge due to catalyst deactivation resulting from its diffusion into Cu powder at elevated temperatures. To overcome this obstacle, a novel approach involving pre-coating Cu powder with an Al2O3 barrier layer was proposed to facilitate the in-situ growth of CNTs. Experimental results demonstrated that the synthesized CNTs exhibited a desirable combination of uniform distribution, high aspect ratio and tunable structures achieved by utilizing different carbon source precursors. Notably, single/double-walled CNTs (S/DMCNTs) with exceptional structural integrity was achieved on Cu powder, which has not been reported previously. The Raman ID/IG ratio of present S/DMCNTs-Cu composite powder reached approximately 0.18, which was significantly superior compared to the relevant reported data. © 2023, The Authors. All rights reserved.

关键词： Chemical vapor deposition

来源：评论

学校读者我要写书评

暂无评论

EventHDR: from Event to High-Speed HDR Videos and Beyond

arXiv

引用

arXiv 2024年

作者： Zou, Yunhao Fu, Ying Takatani, Tsuyoshi Zheng, Yinqiang MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Systems and Information Engineering The University of Tsukuba Tsukuba Japan Next Generation Artificial Intelligence Research Center The University of Tokyo Tokyo113-8656 Japan

Event cameras are innovative neuromorphic sensors that asynchronously capture the scene dynamics. Due to the event-triggering mechanism, such cameras record event streams with much shorter response latency and higher intensity sensitivity compared to conventional cameras. On the basis of these features, previous works have attempted to reconstruct high dynamic range (HDR) videos from events, but have either suffered from unrealistic artifacts or failed to provide sufficiently high frame rates. In this paper, we present a recurrent convolutional neural network that reconstruct high-speed HDR videos from event sequences, with a key frame guidance to prevent potential error accumulation caused by the sparse event data. Additionally, to address the problem of severely limited real dataset, we develop a new optical system to collect a real-world dataset with paired high-speed HDR videos and event streams, facilitating future research in this field. Our dataset provides the first real paired dataset for event-to-HDR reconstruction, avoiding potential inaccuracies from simulation strategies. Experimental results demonstrate that our method can generate high-quality, high-speed HDR videos. We further explore the potential of our work in cross-camera reconstruction and downstream computer vision tasks, including object detection, panoramic segmentation, optical flow estimation, and monocular depth estimation under HDR scenarios. © 2024, CC BY.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-Enhanced Dynamic Multi-Objective Evolutionary Algorithm for Cooperative Shovel Scheduling in Open-Pit Mining

Reinforcement Learning-Enhanced Dynamic Multi-Objective Evol...

引用

Chinese Automation Congress (CAC)

作者： Zihao Zhang Changhe Li Shengxiang Yang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation China University of Geosciences Ministry of Education Wuhan China School of Artificial Intelligence Anhui University of Science & Technology Hefei China School of Computer Science and Informatics De Montfort University Leicester UK

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm.

关键词： Q-learning Ores Heuristic algorithms Process control Evolutionary computation Production Dynamic scheduling Real-time systems Windows Optimization

来源：评论

学校读者我要写书评

暂无评论

Modeling Neural Networks Training Process with Markov Decision Process 2

Modeling Neural Networks Training Process with Markov Decisi...

引用

2nd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2021

作者： Bai, Yantao Liu, Wanwei Mao, Xinjun Liang, Zhen National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha China College of Computer Science National University of Defense Technology Changsha China Institute for Quantum Information National University of Defense Technology State Key Laboratory of High Performance Computing Changsha China

ISBN: (纸本)9781665421867

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as 'black boxes'. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks. © 2021 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Crossband Generalized Memory Polynomial for Nonlinear Target Modeling and its Identification with Two-Tone Signals

Crossband Generalized Memory Polynomial for Nonlinear Target...

引用

2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021

作者： Li, Yuanzhe Hu, Weidong Du, Xiaoyong Zhu, Chang Fan, Hongqi National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China National University of Defense Technology State Key Lab. of Complex Electromagnetic Environment Effect on Electronics and Information Systems Changsha China

ISBN: (纸本)9781665429184

Nonlinear detection systems or radars have received sustained attention in recent years. However, the describing model for nonlinear scattering characterization remains to be the memoryless polynomial since the 1970s. For finer characterization of complicated nonlinear targets and future wideband applications, we introduced memorial structure in this field for the first time and proposed the crossband generalized memory polynomial model. Moreover, identification of the model coefficients based on two-Tone inputs was discussed, which demonstrated robustness to the unwanted nonlinear distortion compared with traditional white noisy inputs. Besides, we further explored the impact of frequency configuration, input SNR, output SNR and numbers of required and extra inputs on estimation performance. Several simulations were carried out to validate the analysis, which could serve as valuable guidance to further measuring experiment. © 2021 IEEE.

关键词： Polynomials

来源：评论

学校读者我要写书评

暂无评论

Implicit high-order gas-kinetic schemes for compressible flows on three-dimensional unstructured meshes I: steady flows

arXiv

引用

arXiv 2023年

作者： Yang, Yaqing Pan, Liang Xu, Kun Laboratory of Mathematics and Complex Systems School of Mathematical Sciences Beijing Normal University Beijing China Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Shenzhen Research Institute Hong Kong University of Science and Technology Shenzhen China

In the previous studies, the high-order gas-kinetic schemes (HGKS) have achieved successes for unsteady flows on three-dimensional unstructured meshes. In this paper, to accelerate the rate of convergence for steady flows, the implicit non-compact and compact HGKSs are developed. For non-compact scheme, the simple weighted essentially non-oscillatory (WENO) reconstruction is used to achieve the spatial accuracy, where the stencils for reconstruction contain two levels of neighboring cells. Incorporate with the nonlinear generalized minimal residual (GMRES) method, the implicit non-compact HGKS is developed. In order to improve the resolution and parallelism of non-compact HGKS, the implicit compact HGKS is developed with Hermite WENO (HWENO) reconstruction, in which the reconstruction stencils only contain one level of neighboring cells. The cell averaged conservative variable is also updated with GMRES method. Simultaneously, a simple strategy is used to update the cell averaged gradient by the time evolution of spatial-temporal coupled gas distribution function. To accelerate the computation, the implicit non-compact and compact HGKSs are implemented with the graphics processing unit (GPU) using compute unified device architecture (CUDA). A variety of numerical examples, from the subsonic to supersonic flows, are presented to validate the accuracy, robustness and efficiency of both inviscid and viscous flows. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Moving Target Shooting Control Policy Based on Deep Reinforcement Learning

Moving Target Shooting Control Policy Based on Deep Reinforc...

引用

2021 International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2021

作者： Li, Boyu Jin, Tao Zhu, Yuanheng Li, Haoran Wu, Yingnian Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Beijing Information Science and Technology University School of Automation Beijing100192 China

ISBN: (纸本)9781665402453

Robots are playing a more and more important role in people's production and life, recently. However, robot control in dynamic environment is still a difficulty. With the great breakthrough of deep reinforcement learning in the field of video games, this method is also extended to the field of robots. Due to the gap between the simulation environment and the real environment, the deep reinforcement learning algorithm trained in the simulation environment is difficult to be applied to the real environment. Aiming at the gimbal control with two degrees of freedom (DOF), a pipline combining system identification and deep reinforcement learning is proposed. On the one hand, the shooting accuracy of the gimbal to moving objects is improved through deep reinforcement learning algorithm. On the other hand, the gap between simulation and reality is reduced through system identification. The method is verified in the RoboMaster University AI Challenge (RMUA) shooting system. The results show that the shooting accuracy is better than the classical control method. © 2021 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

An Improved PC-Kriging Method for Efficient Robust Design Optimization

An Improved PC-Kriging Method for Efficient Robust Design Op...

引用

作者： Lin, Qizhang Chen, Chao Xiong, Fenfen Chen, Shishi Wang, Fenggang School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China Science and Technology on Complex Aviation Systems Simulation Laboratory Beijing100076 China Beijing Electromechanical Engineering Institute Beijing100074 China

The polynomial-chaos-kriging (PC-Kriging) method has been derived as a new uncertainty propagation approach and widely used for robust design optimization in a straightforward manner, of which the statistical moments would be estimated through directly conducting Monte Carlo simulation (MCS) on the PC-Kriging model. However, the computational cost still cannot be negligible because thousands of statistical moment estimations might be performed during robust optimization, especially for highly nonlinear and complicated engineering problems. An analytical statistical moment estimation method is derived for PC-Kriging in this work to reduce the computational cost rather than referring to MCS. Meanwhile, a sequential sampling strategy is applied for PC-Kriging model construction, in which the sample points are not generated all at once, but sequentially allocated in the region with the largest prediction uncertainty to improve the accuracy of PC-Kriging model as much as possible. Through testing on three mathematical examples and an airfoil robust optimization design problem, it is noticed that the improved PC-Kriging method with analytical statistical moment estimation and sequential sampling strategy is more efficient than the traditional ones, demonstrating its effectiveness and advantage. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Kriging

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：