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Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

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Applied Mathematics and Mechanics(English Edition) 2024年第4期45卷 603-618页

作者： Chen AN Jiaxi zhou Kai WANG College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China Department of Mechanical Engineering The Hong Kong Polytechnic UniversityHong KongChina

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter *** hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external ***,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial ***,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system ***,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and *** simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.

关键词： aerial robot hierarchical control strategy model-free iterative sliding mode controller(MFISMC) trajectory tracking reinforcement learning

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2613-2634页

作者： Ziqiao zhou Tianyang zhou Jinghao Xu Junhu Zhu Henan Key Laboratory of Information Security National Engineering Technology Research Center of the Digital Switching SystemZhengzhou450000China School of Cryptographic Engineering Information Engineering UniversityZhengzhou450000China

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test *** view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target *** RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the ***,most methods still rely on experts’*** address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic ***,the penetration testing problem is formally described in terms of reinforcement *** speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first ***,the method offers an exploration strategy based on episodic memory to guide the agents in *** design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning ***,comparison experiments are carried out with the existing RL-based *** results reveal that the proposed method has better convergence *** running time is reduced by more than 20%.

关键词： Intelligent penetration testing penetration testing path planning reinforcement learning episodic memory exploration strategy

Competition of Composition Fluctuation Modes in Weakly Segregated Salt-doped Symmetric Diblock Copolymers

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Chinese Journal of Polymer Science 2024年第9期42卷 1375-1385,I0008页

作者： Yuan-Xin zhou Xian Kong South China Advanced Institute for Soft Matter Science and Technology School of Emergent Soft MatterSouth China University of TechnologyGuangzhou510640China Guangdong Provincial Key Laboratory of Functional and Intelligent Hybrid Materials and Devices South China University of TechnologyGuangzhou510640China

Salt-doped block copolymers have widespread applications in batteries,fuel cells,semiconductors,and various industries,where their properties crucially depend on phase separation ***,investigations into salt-doped diblock copolymers have predominantly focused on microphase separation,overlooking the segregation between ionic and polymeric *** study employs weak segregation theory to explore the interplay between phase separation dominated by the polymer-modulated mode and the salt-out-modulated mode,corresponding to microscopic and macroscopic phase separations,*** comparing diblock copolymers doped with salts to those doped with neutral solvents,we elucidate the significant role of charged species in modulating phase *** phase separation mode exhibits a transition between the polymer-modulated and salt-out-modulated modes at different *** systems doped with neutral solvents,this transition is stepwise,while in salt-ion-doped systems,it is *** a sufficiently large Flory-Huggins parameter between ions and polymers,the salt-out-modulated mode becomes dominant,promoting macrophase *** to the solvation effect of salt ions,salt-doped systems are more inclined to undergo microphase ***,we explore factors influencing the critical wavenumber of phase separation,including doping level and the Flory-Huggins parameters between two blocks and between ions and polymeric *** findings reveal that in a neutral solvent environment,these factors alter only the boundary between micro-and macro-phase separations,leaving the critical wavenumber unchanged in microphase separation ***,in a salt-doped environment,the critical wavenumber of microphase separation varies with these *** provides valuable insights into the pivotal role of electrostatics in the phase separation of salt-doped block copolymers.

关键词： Block copolymer Phase separation Field theory Salt-doped

Consecutive narrow and broad quasi-periodic fast-propagating wave trains associated with a flare

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Science China(Physics,Mechanics & Astronomy) 2024年第5期67卷 200-211页

作者： Xinping zhou Yuandeng Shen Chengrui zhou Zehao Tang Ahmed Ahmed Ibrahim College of Physics and Electronic Engineering Sichuan Normal UniversityChengdu 610068China State Key Laboratory of Space Weather Chinese Academy of SciencesBeijing 100190China Yunnan Observatories Chinese Academy of SciencesKunming 650216China Department of Physics and Astronomy College of ScienceKing Saud UniversityP.O.Box 2455Riyadh 11451Saudi Arabia

The excitation mechanism of coronal quasi-period fast-propagating(QFP)wave trains remains *** Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory observations,we study a narrow and a broad QFP wave trains excited one after another during the successive eruptions of filaments hosted within a fan-spine magnetic system on October 20,*** consecutive occurrence of these two types of QFP wave trains in the same event provides an excellent opportunity to explore their excitation mechanisms and compare their physical *** observational results reveal that narrow and broad QFP wave trains exhibit distinct speeds,periods,energy fluxes,and relative intensity amplitudes,although originating from the same active region and being associated with the same GOES C2.9 *** wavelet analysis,we find that the narrow QFP wave train shares a similar period with the flare itself,suggesting its possible excitation through the pulsed energy release in the magnetic reconnection process that generated the accompanying *** the other hand,the broad QFP wave train appears to be associated with the energy pulses released by the successive expansion and unwinding of filament ***,it is plausible that the broad QFP wave train was also excited by the sequential stretching of closed magnetic field lines driven by the erupting *** findings shed light on the different excitation mechanisms and origins of the QFP wave trains.

关键词： narrow wave excitation

Predicting the International Roughness Index of JPCP and CRCP Rigid Pavement:A Random Forest(RF)Model Hybridized with Modified Beetle Antennae Search(MBAS)for Higher Accuracy

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1557-1582页

作者： zhou Ji Mengmeng zhou Qiang Wang Jiandong Huang College of Civil and Environmental Engineering Hunan University of Science and EngineeringYongzhou425006China School of Mines China University of Mining and TechnologyXuzhou221116China School of Civil Engineering Guangzhou UniversityGuangzhou510006China

To improve the prediction accuracy of the International Roughness Index(IRI)of Jointed PlainConcrete Pavements(JPCP)and Continuously Reinforced Concrete Pavements(CRCP),a machine learning approach is developed in this study for the modelling,combining an improved Beetle Antennae Search(MBAS)algorithm and Random Forest(RF)*** 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this *** importance scores of all input variables on the IRI of JPCP and CRCP were analysed as *** results by the comparative analysis showed the prediction accuracy of the IRI of the newly developed MBAS and RF hybrid machine learning model(RF-MBAS)in this study is higher,indicated by the RMSE and R values of 0.2732 and 0.9476 for the JPCP as well as the RMSE and R values of 0.1863 and 0.9182 for the *** accuracy of this obtained result far exceeds that of the IRI prediction model used in the traditional Mechanistic-Empirical Pavement Design Guide(MEPDG),indicating the great potential of this developed *** importance analysis showed that the IRI of JPCP and CRCP was proportional to the corresponding input variables in this study,including the total joint faulting cumulated per KM(TFAULT),percent subgrade material passing the 0.075-mm Sieve(P_(200))and pavement surface area with flexible and rigid patching(all Severities)(PATCH)which scored higher.

关键词： Cement pavement JPCP CRCP RF-MBAS IRI

Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 279-296页

作者： Hui Zhang Huaji zhou Li Wang Feng zhou The Ministry Key Laboratory of Electronic Information Countermeasure and Simulation Xidian UniversityXi’an710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033China

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open *** spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center *** designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during ***,this method allows unknown classes to occupy a larger space in the feature *** reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more ***,the feature comparator threshold can be used to reject unknown *** signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world *** experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open ***,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

关键词： Electromagnetic signal recognition deep learning feature extraction open set recognition

Patterns and mechanisms of belowground carbon responses to changes in precipitation

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Journal of Plant Ecology 2024年第2期17卷 1-17页

作者： Hongyang Chen Qi Zhang Lingyan zhou Xuhui zhou School of Forestry Key Laboratory of Sustainable Forest Ecosystem Management-Ministry ofEducationNortheast Forestry UniversityHarbin 150040China Research Center Shanghai Botanical Garden/Shanghai Engineering Research Center of Sustainable Plant InnovationShanghai 200231China

It is well known that aboveground productivity usually increases with ***,how belowground carbon(C)processes respond to changes in precipitation remains elusive,although belowground net primary productivity(BNPP)represents more than one-half of NPP and soil stores the largest terrestrial C in the *** paper reviews the patterns of belowground C processes(BNPP and soil C)in response to changes in precipitation from transect studies,manipulative experiments,modeling and data integration and *** results suggest the possible existence of nonlinear patterns of BNPP and soil C in response to changes in precipitation,which is largely different from linear response for aboveground productivity.C allocation,root turnover time and species composition may be three key processes underlying mechanisms of the nonlinear responses to changes in precipitation for belowground C *** addition,microbial community structure and long-term ecosystem processes(*** assemblage,soil texture,aggregate stability)may also affect patterns of belowground C processes in response to changes in *** last,we discuss implications and future perspectives for potential nonlinear responses of belowground C processes to changes in precipitation.

关键词： belowground processes BNPP nonlinear precipitation soil C

Historical Changes and Multi-scenario Prediction of Land Use and Terrestrial Ecosystem Carbon Storage in China

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Chinese Geographical Science 2024年第3期34卷 487-503页

作者： AN Yue TAN Xuelan REN Hui LI Yinqi zhou zhou College of Resources Hunan Agricultural UniversityChangsha 410128China School of Geographic Science Hunan Normal UniversityChangsha 410081China

Terrestrial carbon storage(CS)plays a crucial role in achieving carbon balance and mitigating global climate *** study employs the Shared Socioeconomic Pathways and Representative Concentration Pathways(SSPs-RCPs)published by the Intergovernmental Panel on Climate Change(IPCC)and incorporates the Policy Control Scenario(PCS)regulated by China’s land management *** Future Land Use Simulation(FLUS)model is employed to generate a 1 km resolution land use/cover change(LUCC)dataset for China in 2030 and *** on the carbon density dataset of China’s terrestrial ecosystems,the study analyses CS changes and their relationship with land use changes spanning from 1990 to *** findings indicate that the quantitative changes in land use in China from 1990 to 2020 are characterised by a reduction in the area proportion of cropland and grassland,along with an increase in the impervious surface and forest *** changing trend is projected to continue under the PCS from 2020 to *** the SSPs-RCPs scenario,the proportion of cropland and impervious surface predominantly increases,while the proportions of forest and grassland continuously *** loss in China’s carbon storage from 1990 to 2020 amounted to 0.53×10^(12)kg,primarily due to the reduced area of cropland and *** the SSPs-RCPs scenario,more significant carbon loss occurs,reaching a peak of8.07×10^(12)kg in the SSP4-RCP3.4 *** loss is mainly concentrated in the southeastern coastal area and the Beijing-TianjinHebei(BTH)region of China,with urbanisation and deforestation identified as the primary *** the future,it is advisable to enhance the protection of forests and grassland while stabilising cropland areas and improving the intensity of urban *** research findings offer valuable data support for China’s land management policy,land space optimisation,and the achievement of dual-carbon targets.

关键词： land use change Future Land Use Simulation(FLUS)model carbon storage carbon density dataset land use scenario China

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

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Journal of Geographical Sciences 2024年第7期34卷 1394-1414页

作者： LI Yinqi AN Yue zhou zhou REN Hui TAN Xuelan College of Resources Hunan Agricultural UniversityChangsha410128China College of Geographical Sciences Hunan Normal UniversityChangsha410081China

Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of *** makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling needs of large and complex geographic *** paper presents a CA model based on geographically optimal similarity(GOS)transition rules and similarly sized neighborhoods(SSN).By comparing the similarity in geographical configuration between samples and predicted points,the model enables a comprehensive characterization of the driving mechanism behind urban expansion and its self-organizing *** helps to mitigate the impact of sample selection and assumptions about spatial stationarity on simulation *** performance of GOS-SSN-CA simulation was tested by taking the urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration in China as an *** results show that GOS can derive more accurate and reliable urban transition rules with fewer samples,thereby significantly reducing spatial prediction errors compared with logistic ***,SSN selects different neighborhood sizes to represent the difference between the local self-organizing range and surrounding cells,thus further improving the simulation accuracy and restricting urban expansion ***,GOS-SSN-CA effectively characterizes the geographical similarity of urban expansion,improves simulation accuracy while constraining the urban expansion form,and enhances the practical application value of CA.

关键词： urban growth cellular automata geographic similarity model optimization spatial nonstationarity