检索结果-南通市图书馆

Modifying Lyapunov exponent of chaotic map by self-cascading

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

science China Technological sciences 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun School of Electronic and information Engineering Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing University of Information Science & Technology School of Computer science and technology Harbin Institute of Technology Shenzhen Graduate School

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos maps.Additionally, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

Self-supervised recalibration network for person re-identification

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Defence technology（防务技术） 2024年第1期31卷 163-178页

作者： Shaoqi Hou Zhiming Wang Zhihua Dong Ye Li Zhiguo Wang Guangqiang Yin Xinzhong Wang Shenzhen institute of information technology Shenzhen518172China University of Electronic science and technology of China Chengdu611731China Kash institute of Electronics and information Industry Kashi844099China

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

关键词： Person re-identification Attention mechanism Global information Local information Adaptive weighted fusion

Formation behaviors of rod-like reactive shaped charge penetrator and their effects on damage capability

在线全文

学校读者我要写书评

暂无评论

Defence technology（防务技术） 2024年第2期32卷 242-253页

作者： Tao Sun Haifu Wang Shipeng Wang Chao Ge Die Hu Pengwan Chen Yuanfeng Zheng Beijing institute of technology State Key Laboratory of Explosion Science and TechnologyBeijing100081China Xi'an institute of Electromechanical information technology Xi'an710065China

Formation behaviors of rod-like reactive shaped charge penetrator(RRSCP)and their effects on damage capability are investigated by experiments and numerical simulations.The pulsed X-ray technology and a spaced aluminum/steel plate with the thicknesses of 5 mm/100 mm are used.Three types of sphericalsegment aluminum-polytetrafluoroethylene-copper(Al-PTFE-Cu)reactive liners with Cu contents of 0%,46.6%,and 66%are fabricated and tested.The experimental results show that the reactive liners can form excellent rod-shaped penetrators with tail skirts under the shaped charge effect,but the tail skirts disappear over time.Moreover,rupturing damage to the aluminum plate and penetration to the steel plate are caused by the RRSCP impact.From simulation analysis,the RRSCP is formed by a mechanically and chemically coupled response with the reactive liner activated by shock in its outer walls and bottom and then backward overturning,forming a leading reactive penetrator and a following chemical energy cluster.The unique formation structure determines the damage modes of the aluminum plate and the steel plate.Further analysis indicates that the formation behaviors and damage capability of Al-PTFE-Cu RRSCP strongly depend on Cu content.With increasing Cu content,the velocity,activation extent,and reaction extent of Al-PTFE-Cu RRSCP decrease,which contribute to elongation and alleviate the negative effects of chemical reactions on elongation,significantly increasing the length-diameter ratio and thus enhancing the capability of steel plate penetration.However,the lower activation extent and energetic density will weaken the RRSCP's capability of causing rupturing damage to the aluminum plate.

关键词： Reactive materials Al-PTFE composites Reactive liner X-ray Penetration

Federated Meta Reinforcement Learning for Personalized Tasks

在线全文

学校读者我要写书评

暂无评论

Tsinghua science and technology 2024年第3期29卷 911-926页

作者： Wentao Liu Xiaolong Xu Jintao Wu Jielin Jiang School of Computer science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China

As an emerging privacy-preservation machine learning framework,Federated Learning(FL)facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and private.When this learning framework is applied to Deep Reinforcement Learning(DRL),the resultant Federated Reinforcement Learning(FRL)can circumvent the heavy data sampling required in conventional DRL and benefit from diversified training data,besides privacy preservation offered by FL.Existing FRL implementations presuppose that clients have compatible tasks which a single global model can cover.In practice,however,clients usually have incompatible(different but still similar)personalized tasks,which we called task shift.It may severely hinder the implementation of FRL for practical applications.In this paper,we propose a Federated Meta Reinforcement Learning(FMRL)framework by integrating Model-Agnostic Meta-Learning(MAML)and FRL.Specifically,we innovatively utilize Proximal Policy Optimization(PPO)to fulfil multi-step local training with a single round of sampling.Moreover,considering the sensitivity of learning rate selection in FRL,we reconstruct the aggregation optimizer with the Federated version of Adam(Fed-Adam)on the server side.The experiments demonstrate that,in different environments,FMRL outperforms other FL methods with high training efficiency brought by Fed-Adam.

关键词： federated learning reinforcement learning meta-learning personalization

Unleashing potentials with deep learning: decoding the complex events for distributed fiber optic sensing applications

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

science China(information sciences) 2024年第5期67卷 337-338页

作者： Yujiao LI Liqin HU Kuanglu YU institute of information science School of Computer and Information Technology Beijing Jiaotong University Beijing Key Laboratory of Advanced information science and Network technology Beijing Jiaotong University

After decades of research, distributed fiber optic sensors have become essential tools for precise long-distance measurements. They are applied across various sectors such as railway transportation, gas and petroleum, and smart grids. Signal processing, particularly in phase-sensitive optical time domain reflectometry(Φ-OTDR) event recognition and classification, is a primary research focus.

关键词： Deep learning

Method for triangular fuzzy multiple attribute decision making based on two-dimensional density operator method

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2024年第1期35卷 178-185页

作者： LIN Youliang LI Wu LIU Gang HUANG Dong School of Economics and Management Hunan Institute of Science and TechnologyYueyang 414000China Hunan Vocational College for Nationalities Yueyang 414000China School of information science and Engineering Hunan Institute of Science and TechnologyYueyang 414000China

Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.

关键词： fuzzy decision making clustering density operator multi-attribute decision making(MADM)

SAC-Based Computation Offloading for Reconfigurable Intelligent Surface-Aided Mobile Edge Networks

在线全文

学校读者我要写书评

暂无评论

China Communications 2024年第6期21卷 261-270页

作者： Li Bin Qian Zhen Fei Zesong School of Computer science Nanjing University of Information Science&TechnologyNanjing 210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing 210044 School of information and Electronics Beijing Institute of TechnologyBeijing 100081China

In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS,UEs’transmitting power,computing resources allocation,and the UEs’task offloading strategies for local computing and offloading.The formulated problem is a sequential decision making across multiple coherent time slots.Furthermore,the mobility of UEs brings uncertainties into the decision-making process.To cope with this challenging problem,the deep reinforcement learning-based Soft Actor-Critic(SAC)algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’task offloading strategies.Then,the transmitting power and computing resource allocation can be determined based on the action.Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately 14%lower than the deep deterministic policy gradient benchmark in terms of energy consumption.

关键词： MEC RIS soft actor-critic user mobility

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm:Designed for Dispatch of Integrated Energy System in Coal Mine

在线全文

学校读者我要写书评

暂无评论

Tsinghua science and technology 2024年第2期29卷 368-385页

作者： Canyun Dai Xiaoyan Sun Hejuan Hu Yong Zhang Dunwei Gong School of information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of information science and technology Qingdao University of Science and TechnologyQingdao 266061China

The dispatch of integrated energy systems in coal mines(IES-CM)with mine-associated supplies is vital for efficient energy utilization and carbon emissions reduction.However,IES-CM dispatch is highly challenging due to its feature as multi-objective and strong multi-constraint.Existing constrained multi-objective evolutionary algorithms often fall into locally feasible domains with poorly distributed Pareto front,which greatly deteriorates dispatch performance.To tackle this problem,we transform the traditional dispatch model of IES-CM into two tasks:the main task with all constraints and the helper task with constraint adaptive.Then we propose a constraint adaptive multi-tasking differential evolution algorithm(CA-MTDE)to optimize these two tasks effectively.The helper task with constraint adaptive is developed to obtain infeasible solutions near the feasible domain.The purpose of this infeasible solution is to transfer guiding knowledge to help the main task move away from local search.Additionally,a dynamic dual-learning strategy using DE/current-to-rand/1 and DE/current-to-best/1 is developed to maintain task diversity and convergence.Finally,we comprehensively evaluate the performance of CA-MTDE by applying it to a coal mine in Shanxi Province,considering two IES-CM scenarios.Results demonstrate the feasibility of CA-MTDE and its ability to generate a Pareto front with exceptional convergence,diversity,and distribution.

关键词： dispatch integrated energy system coal mine evolutionary multi-tasking constraint differential evolution

Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of Computer science and technology Beijing Institute of Technology Advanced Reasearch institute of Multidisciplinary science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Image denoising Image demosaicing Joint processing Guided attention Paired real dataset