A cable-driven redundant manipulator(CDRM)characterized by redundant degrees of freedom and a lightweight,slender design can perform tasks in confined and restricted spaces efficiently.However,the complex multistage c...A cable-driven redundant manipulator(CDRM)characterized by redundant degrees of freedom and a lightweight,slender design can perform tasks in confined and restricted spaces efficiently.However,the complex multistage coupling between drive cables and passive joints in CDRM leads to a challenging dynamic model with difficult parameter identification,complicating the efforts to achieve accurate modeling and control.To address these challenges,this paper proposes a dynamic modeling and adaptive control approach tailored for CDRM systems.A multilevel kinematic model of the cable-driven redundant manipulator is presented,and a screw theory is employed to represent the cable tension and cable contact forces as spatial wrenches,which are equivalently mapped to joint torque using the principle of virtual work.This approach simplifies the mapping process while maintaining the integrity of the dynamic model.A recursive method is used to compute cable tension section-by-section for enhancing the efficiency of inverse dynamics calculations and meeting the high-frequency demands of the controller,thereby avoiding large matrix operations.An adaptive control method is proposed building on this foundation,which involves the design of a dynamic parameter adaptive controller in the joint space to simplify the linearization process of the dynamic equations along with a closed-loop controller that incorporates motor parameters in the driving space.This approach improves the control accuracy and dynamic performance of the CDRM under dynamic uncertainties.The accuracy and computational efficiency of the dynamic model are validated through simulations,and the effectiveness of the proposed control method is demonstrated through control tests.This paper presents a dynamic modeling and adaptive control approach for CDRM to enhance accuracy and performance under dynamic uncertainties.展开更多
In this paper,a novel cooperative collision avoidance control strategy with relative velocity information for redundant robotic manipulators is derived to guarantee the behavioral safety of robots in the cooperative o...In this paper,a novel cooperative collision avoidance control strategy with relative velocity information for redundant robotic manipulators is derived to guarantee the behavioral safety of robots in the cooperative operational task.This strategy can generate the collision-free trajectory of the robotic links in real-time,which is to realize that the robot can avoid moving obstacles less conservatively and ensure tracking accuracy of terminal end-effector tasks in performing cooperative tasks.For the case where there is interference between the moving obstacle and the desired path of the robotic end-effector,the method inherits the null-space-based self-motion characteristics of the redundant manipulator,integrates the relative motion information,and uses the improved artificial potential field method to design the control items,which are used to generate the collision avoidance motion and carry out moving obstacles smoothly and less conservatively.At the same time,the strategy maintains the kinematic constraint relationship of dual-arm cooperatives,to meet the real-time collision avoidance task under collaborative tasks.Finally,the algorithm simulation indicates that the method can better ensure the tracking accuracy of the end-effector task and carry out moving obstacles smoothly.The experimental results show that the method can generate the real-time collision-free trajectory of the robot in the cooperative handling task,and the joint movement is continuous and stable.展开更多
Redundant techniques are widely adopted in vehicle management computer （VMC） to ensure that VMC has high reliability and safety. At the same time, it makes VMC have special characteristics, e.g., failure correlation...Redundant techniques are widely adopted in vehicle management computer （VMC） to ensure that VMC has high reliability and safety. At the same time, it makes VMC have special characteristics, e.g., failure correlation, event simultaneity, and failure self-recovery. Accordingly, the reliability and safety analysis to redundant VMC system （RVMCS） becomes more difficult. Aimed at the difficulties in RVMCS reliability modeling, this paper adopts generalized stochastic Petri nets to establish the reliability and safety models of RVMCS. Then this paper analyzes RVMCS oper- ating states and potential threats to flight control system. It is verified by simulation that the reli- ability of VMC is not the product of hardware reliability and software reliability, and the interactions between hardware and software faults can reduce the real reliability of VMC obviously. Furthermore, the failure undetected states and false alarming states inevitably exist in RVMCS due to the influences of limited fault monitoring coverage and false alarming probability of fault mon- itoring devices （FMD）. RVMCS operating in some failure undetected states will produce fatal threats to the safety of flight control system. RVMCS operating in some false alarming states will reduce utility of RVMCS obviously. The results abstracted in this paper can guide reliable VMC and efficient FMD designs. The methods adopted in this paper can also be used to analyze other intelligent systems＇ reliability.展开更多
With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for clou...With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.展开更多
A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble...A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.展开更多
Aim To define a mixed redundant model(MRM), improving the reliability of C 3I system. Methods The model combined the technology characters of two? unit system with one warm stand by unit and function substitute s...Aim To define a mixed redundant model(MRM), improving the reliability of C 3I system. Methods The model combined the technology characters of two? unit system with one warm stand by unit and function substitute system. The reliability and availability equations of MRM were deduced. Results and Conclusion Compared with several other reliability models, it has obvious effect upon improving the system reliability. The effect? cost rate is very high among these models. The model can be used in reliability design, evaluation and check of C 3I system. Only a little attached cost is needed to improve C 3I system reliability effectively.展开更多
The control method of highly redundant robot manipulators is introduced. A decentralized autonomous control scheme is used to guide the movement of robot manipulators so that the work done by manipulators is minimized...The control method of highly redundant robot manipulators is introduced. A decentralized autonomous control scheme is used to guide the movement of robot manipulators so that the work done by manipulators is minimized. The method of computing pseudoinverse which needs too many complicated calculation can be avoided. Then the calculation and control of robots are simplified. At the same time system robustness/fault tolerance is achieved.展开更多
[Objective]The construction of weirs changes the hydraulic characteristics of rivers and affects the structure of phytoplankton communities and the health of aquatic ecosystems in the river.This study aims to explore ...[Objective]The construction of weirs changes the hydraulic characteristics of rivers and affects the structure of phytoplankton communities and the health of aquatic ecosystems in the river.This study aims to explore the nonlinear response relationship between phytoplankton community structure and its driving factors in spring and autumn in Furong Creek under the construction of cascade weirs.[Methods]The structure of phytoplankton communities and related environmental factors were investigated in Furong Creek from 2023 to 2024.This study focused on the analysis of the changes of nutrient concentrations and biomass of phytoplankton in autumn and spring within the same dry season in Furong Creek.Redundancy analysis was used to identify the key factors influencing the structure of phytoplankton communities.The MIKE 11 model was employed to simulate the hydrodynamic changes in the river.Combined with total nitrogen and permanganate index,a GAM model of phytoplankton diversity index and hydrodynamic factors was developed,and the change of phytoplankton diversity after the optimized layout of the cascade weirs was fitted.[Results]The result showed that the annual average value of Shannon-Wiener diversity index of phytoplankton in Furong Creek was 2.79,which was in a state of mild pollution.A total of 239 species from 95 genera in 8 phyla were identified.Among the phytoplankton,Chlorophyta was the dominant group throughout the year in Furong Creek,followed by Bacillariophyta and Cyanophyta.The cell abundance of phytoplankton ranged from 3.11 to 20.64 mg/L and from 0.23 to 6.31 mg/L in spring and autumn,which indicated a clear seasonal succession of phytoplankton community structure.Compared with autumn,the relative abundance of Cyanophyta significantly decreased in spring across the whole river section,while Chrysophyta and Dinophyta showed significant increase at some monitoring sites,leading to water bloom phenomenon and a noticeable decline in the diversity of phytoplankton.The dominant species in the water bodies throughout the year were Cyclotella catenata,Chlorella vulgaris,Scenedesmus bijuga,Scenedesmus quadricauda,Chroomonas acuta,Cryptomonas ovata,and Cryptomonas erosa.Redundancy analysis(RDA)showed that hydrodynamic factors(v,h)and water environmental factors(TN,COD_(Mn))were the main influencing factors of phytoplankton community structure.[Conclusion]The result show that the nutrient concentration,phytoplankton biomass,and density in Furong Creek in spring are significantly higher than in autumn.The GAM model,constructed by combining hydrodynamic and environmental factors,can effectively reflect the nonlinear relationship between phytoplankton diversity index and its driving factors.In spring,with an increase in nutrient concentration,the habitat conditions of low flow speed and high water depths formed by overflow weirs will lead to a decrease in the Shannon-Wiener index of phytoplankton and an intensified risk of eutrophication.However,a reasonable layout scheme of cascade weirs will improve the diversity of phytoplankton and reduce the risk of eutrophication in the river.The findings of this study can help deepen the understanding of the ecological and environmental effects of cascade weir construction in the river.展开更多
Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that...Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform(DWT),Redundant Discrete Wavelet Transform(RDWT),and Möbius Transformations(MT),with optimization of transformation parameters achieved via a Genetic Algorithm(GA).By combining frequency and spatial domain techniques,the proposed method significantly enhances both the imper-ceptibility and robustness of watermark embedding.The approach leverages DWT and RDWT for multi-resolution decomposition,enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks.RDWT,in particular,offers shift-invariance,which improves performance under geometric transformations.Möbius transformations are employed for spatial manipulation,providing conformal mapping and spatial dispersion that fortify watermark resilience against rotation,scaling,and translation.The GA dynamically optimizes the Möbius parameters,selecting configurations that maximize robustness metrics such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),Bit Error Rate(BER),and Normalized Cross-Correlation(NCC).Extensive experiments conducted on medical and standard benchmark images demonstrate the efficacy of the proposed RDWT-MT scheme.Results show that PSNR exceeds 68 dB,SSIM approaches 1.0,and BER remains at 0.0000,indicating excellent imperceptibility and perfect watermark recovery.Moreover,the method exhibits exceptional resilience to a wide range of image processing attacks,including Gaussian noise,JPEG compression,histogram equalization,and cropping,achieving NCC values close to or equal to 1.0.Comparative evaluations with state-of-the-art watermarking techniques highlight the superiority of the proposed method in terms of robustness,fidelity,and computational efficiency.The hybrid framework ensures secure,adaptive watermark embedding,making it highly suitable for applications in digital rights management,content authentication,and medical image protection.The integration of spatial and frequency domain features with evolutionary optimization presents a promising direction for future watermarking technologies.展开更多
Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version...Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.展开更多
The interconnection between query processing and data partitioning is pivotal for the acceleration of massive data processing during query execution,primarily by minimizing the number of scanned block files.Existing p...The interconnection between query processing and data partitioning is pivotal for the acceleration of massive data processing during query execution,primarily by minimizing the number of scanned block files.Existing partitioning techniques predominantly focus on query accesses on numeric columns for constructing partitions,often overlooking non-numeric columns and thus limiting optimization potential.Additionally,these techniques,despite creating fine-grained partitions from representative queries to enhance system performance,experience from notable performance declines due to unpredictable fluctuations in future queries.To tackle these issues,we introduce LRP,a learned robust partitioning system for dynamic query processing.LRP first proposes a method for data and query encoding that captures comprehensive column access patterns from historical queries.It then employs Multi-Layer Perceptron and Long Short-Term Memory networks to predict shifts in the distribution of historical queries.To create high-quality,robust partitions based on these predictions,LRP adopts a greedy beam search algorithm for optimal partition division and implements a data redundancy mechanism to share frequently accessed data across partitions.Experimental evaluations reveal that LRP yields partitions with more stable performance under incoming queries and significantly surpasses state-of-the-art partitioning methods.展开更多
To explore the factors controlling human activity in Northeast Asia during the last deglaciation,this study synthesizes six pollen records from lakes and peatlands,alongside four paleotemperature records from terrestr...To explore the factors controlling human activity in Northeast Asia during the last deglaciation,this study synthesizes six pollen records from lakes and peatlands,alongside four paleotemperature records from terrestrial sedimentary sequences in this region.We simulated potential hunter-gatherer population densities using the Minimalist Terrestrial Resource Model(MTRM),and calculated vegetation openness,rate of change,and evenness based on pollen data.The results reveal a direct relationship between plant resources and hunter-gatherer populations from 20.9 to 10.2 ka BP.The synchronous increases in plant resources and population density from the Last Glacial(LG)to the B?lling-Aller?d(BA)warm period,as well as from the Younger Dryas(YD)to the early Holocene(EH),with stasis during the YD,suggest that resource availability was a key driver of human activity.Redundancy analysis(RDA)of pollen and paleotemperature records indicated that vegetation and plant resources were more closely linked to the mean annual air temperature,with winter characteristics,from the LG to the YD,whereas warm-season temperatures played a more significant role during the EH.This research emphasizes that variations in resource accessibility,rather than direct climate effects alone,were likely pivotal in shaping human activity responses to environmental changes.展开更多
Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection ...Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection systems.The complexity of real-world network data poses significant challenges in detection.Machine learning models have shown promise in detecting APTs;however,their performance often suffers when trained on large datasets with redundant or irrelevant features.This study presents a novel,hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data.It combines Mutual Information(MI),Symmetric Uncertainty(SU)and Minimum Redundancy Maximum Relevance(mRMR)to enhance feature selection.MI and SU assess feature relevance,while mRMR maximises relevance and minimises redundancy,ensuring that the most impactful features are prioritised.This method addresses redundancy among selected features,improving the overall efficiency and effectiveness of the detection model.Experiments on a real-world APT datasets were conducted to evaluate the proposed method.Multiple classifiers including,Random Forest,Support Vector Machine(SVM),Gradient Boosting,and Neural Networks were used to assess classification performance.The results demonstrate that the proposed feature selection method significantly enhances detection accuracy compared to baseline models trained on the full feature set.The Random Forest algorithm achieved the highest performance,with near-perfect accuracy,precision,recall,and F1 scores(99.97%).The proposed adaptive thresholding algorithm within the selection method allows each classifier to benefit from a reduced and optimised feature space,resulting in improved training and predictive performance.This research offers a scalable and classifier-agnostic solution for dimensionality reduction in cybersecurity applications.展开更多
In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, c...In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding(RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC(ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.展开更多
Grassland shrub encroachment is a phenomenon that is prevalent in arid and semi-arid regions worldwide,impacting grassland ecosystems in several ways.In the context of escalating climate change and human activities,ex...Grassland shrub encroachment is a phenomenon that is prevalent in arid and semi-arid regions worldwide,impacting grassland ecosystems in several ways.In the context of escalating climate change and human activities,examining the nutrient and stoichiometric characteristics of Spiraea shrubs in grassland ecosystems,along with their relationships with environmental factors,can yield valuable insights into the nutrient utilization and survival strategies of these shrubs.This,in turn,offers a scientific foundation for developing future conservation measures.This study was conducted in July 2023 in the Altay Mountains,Northwest China,where Spiraea shrubs thrive across five grassland types:temperate steppe desert,temperate desert steppe,temperate steppe,temperate meadow steppe,and mountain meadow.Leaf and soil samples were collected from each grassland type to analyze the concentrations of carbon(C),nitrogen(N),and phosphorus(P),as well as the stoichiometric characteristics of both the leaves and soil.Subsequently,correlation analysis and redundancy analysis(RDA)were conducted to investigate the variations in leaf C,N,and P concentrations and leaf stoichiometry of Spiraea shrubs as well as their influencing factors.The results indicated the presence of significant or highly significant differences(P<0.050)in the leaf C,N,and P concentrations and leaf stoichiometry(C:N,C:P,and N:P ratios)of Spiraea shrubs across the five grassland types.The N:P ratios of Spiraea shrub leaves in the five grassland types ranged from 7.37 to 11.77,suggesting that N availability generally limits the growth of Spiraea shrubs.Results of RDA revealed that the most significant contributors to the C,N,and P concentrations and stoichiometric characteristics of Spiraea shrub leaves were in the following order:soil total N>mean annual precipitation>elevation>soil pH>soil organic C>mean annual temperature.These factors had contribution rates of 35.32%,13.19%,10.20%,8.82%,8.34%,and 6.48%,respectively.It was determined that soil nutrients have a greater impact on the growth and nutrient accumulation of Spiraea shrubs compared to climatic factors.This study makes an important contribution to the theoretical basis and data support,enabling a deeper understanding of the response mechanisms of shrub species in the grassland ecosystems of the Altay Mountains to climate change.展开更多
Toxoplasma gondii is a single-celled parasite that infects nearly all warm-blooded animals,including humans(Montoya and Liesenfeld,2004).It occurs worldwide and can persist for a lifetime in mammals.Humans get infecte...Toxoplasma gondii is a single-celled parasite that infects nearly all warm-blooded animals,including humans(Montoya and Liesenfeld,2004).It occurs worldwide and can persist for a lifetime in mammals.Humans get infected by eating undercooked meat of animals containing the tissue cysts of this parasite.In immune-competent individuals,T.展开更多
Continuous cropping can lead to soil environment deterioration,cause plant health problems,and reduce crop productivity.However,the response mechanisms of soil microbial co-occurrence patterns to the duration of conti...Continuous cropping can lead to soil environment deterioration,cause plant health problems,and reduce crop productivity.However,the response mechanisms of soil microbial co-occurrence patterns to the duration of continuous melon cropping remain poorly understood.Here,we employed the metagenomic techniques to comparatively investigate the bulk and rhizosphere soil microbial communities of major melon-producing regions(where the duration of continuous melon cropping ranges from 1 to 30 a)in the eastern and southern parts of Xinjiang Uygur Autonomous Region,China.The results showed that soil pH clearly decreased with increasing melon cropping duration,while soil electrical conductivity(EC)and the other soil nutrient indices increased with increasing melon cropping duration(with the exception of AN and TK in the southern melon-producing region).The most dominant bacterial phyla were Proteobacteria and Actinobacteria,and the most abundant fungal phyla were Ascomycota and Mucoromycota.Redundancy analysis(RDA)indicated that soil pH and EC had no significant effects on the bacterial communities.However,after many years of continuous melon cropping in the southern melon-producing region,fungal communities were significantly negatively correlated with soil pH and significantly positively correlated with soil EC(P<0.050).Co-occurrence network analysis showed that continuous melon cropping increased the complexity but decreased the connectivity of the cross-domain microbial networks.Moreover,the enrichment patterns of microorganisms in the main microbial network modules varied significantly with the duration of continuous melon cropping.Based on the analysis of keystone taxa,we found that continuous melon cropping increased some plant pathogens(e.g.,Fusarium and Stagonospora)but decreased beneficial bacteria(e.g.,Mesorhizobium and Pseudoxanthomonas).In conclusion,this study has greatly enhanced the understanding of the effects of continuous melon cropping on alterations in the microbial community structure and ecological networks in Xinjiang.展开更多
A novel robust diagnostic system based on a linear fractional transform(LFT)representation combined with a static redundancy approach is proposed to design a residual generator for fault detection and localization in ...A novel robust diagnostic system based on a linear fractional transform(LFT)representation combined with a static redundancy approach is proposed to design a residual generator for fault detection and localization in a wind system using the doubly fed induction generator(DFIG).As a result,faults in DFIG-based grid-connected wind systems can be grouped into three classes of faults,namely,model uncertainty-related faults(FLMU),set point disturbance-related faults(FLDS)and parameter uncertainty-related faults(FLPU).Based on the parity-space residual generations,an artificial neural network(ANN)structure has been combined with the classification to enable the assessment of hidden,indistinguishable or small amplitude faults.The training validation with two data sizes of 1278*4 and 1278*1 respectively at the inputs and outputs of the proposed ANN,presents better performance for a mean squared error value(MSE=3.0532e 9),and a good correlation between outputs and targets for a regression value(R=1).It emerges that the proposed robust and complete diagnostic system for the optimal and sustainable integration of wind turbines into the grid,offers very great advan-tages,particularly with regard to the precise and rapid detection of faults,and the assessment of hidden faults and/or ambiguous fault states in the wind system based on DFIG.In addition,the proposed approach allows the use of a reduced number of data,sensors and actuators required.Consequently,the system maintenance difficulties,complexity and cost of the diagnostic system are reduced.展开更多
In an increasing number of area inspection applications,such as powerline inspection and sewage disposal monitoring,Unmanned Aerial Vehicles(UAVs)are used for capturing and transmitting on-site videos.Existing UAV vid...In an increasing number of area inspection applications,such as powerline inspection and sewage disposal monitoring,Unmanned Aerial Vehicles(UAVs)are used for capturing and transmitting on-site videos.Existing UAV video compressions employ Advanced Video Coding(AVC)or High Efficiency Video Coding(HEvC)encoders to eliminate intra-frame and short-term inter-frame redundancy,while these methods still face challenges in achieving high compression efficiency due to the high captured video bitrate and limited transmission capacity.In this paper,we further consider that UAVs revisit the same area and capture videos from different viewpoints,hence the Long-term Historical Background Redundancy(LHBR)exists among revisited video clips.Thus,we leverage the LHBR caused by UAV revisits,and propose a high-efficiency aerial video compression for UAVs.Our method comprises three steps:Firstly,we propose a lightweight method based on a spatial correlation model to select the most correlated reference frames from historical video database.Then,we design a Historical Reference Background Frame(HBRF)generation algorithm by alternately using the keypoint-based and telemetry-assisted alignments to align the selected frames with current frame.Finally,we use the generated HBRF as a reference frame to eliminate the LHBR within I-frames.Our proposed method has been experimentally proven to reduce Bjøntegaard-Delta bitrate(BD-bitrate)by 42.83%or enhance Bjøntegaard-Delta Peak Signal-to-Noise Ratio(BD-PSNR)by 2.98 dB over original HEVC,and take 29.3%of the encoding time needed for existing LHBR based compressions.展开更多
A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form ...A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form different lengths of robot arms for different application sites.The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space.This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator.In this integrated optimization,path planning is established on a Bezier curve,and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator.A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy.The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve.Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints.The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.52405040)Research Project of State Key Laboratory of Mechanical System and Vibration(Grant No.MSV202514)。
文摘A cable-driven redundant manipulator(CDRM)characterized by redundant degrees of freedom and a lightweight,slender design can perform tasks in confined and restricted spaces efficiently.However,the complex multistage coupling between drive cables and passive joints in CDRM leads to a challenging dynamic model with difficult parameter identification,complicating the efforts to achieve accurate modeling and control.To address these challenges,this paper proposes a dynamic modeling and adaptive control approach tailored for CDRM systems.A multilevel kinematic model of the cable-driven redundant manipulator is presented,and a screw theory is employed to represent the cable tension and cable contact forces as spatial wrenches,which are equivalently mapped to joint torque using the principle of virtual work.This approach simplifies the mapping process while maintaining the integrity of the dynamic model.A recursive method is used to compute cable tension section-by-section for enhancing the efficiency of inverse dynamics calculations and meeting the high-frequency demands of the controller,thereby avoiding large matrix operations.An adaptive control method is proposed building on this foundation,which involves the design of a dynamic parameter adaptive controller in the joint space to simplify the linearization process of the dynamic equations along with a closed-loop controller that incorporates motor parameters in the driving space.This approach improves the control accuracy and dynamic performance of the CDRM under dynamic uncertainties.The accuracy and computational efficiency of the dynamic model are validated through simulations,and the effectiveness of the proposed control method is demonstrated through control tests.This paper presents a dynamic modeling and adaptive control approach for CDRM to enhance accuracy and performance under dynamic uncertainties.
基金supported in part by the Advanced Equipment Manufacturing Technology Innovation Project of Hebei Province under Grant No.22311801D,23311807D,and 236Z1816Gin part by the National Natural Science Foundation of China under Grant No.U20A20283.
文摘In this paper,a novel cooperative collision avoidance control strategy with relative velocity information for redundant robotic manipulators is derived to guarantee the behavioral safety of robots in the cooperative operational task.This strategy can generate the collision-free trajectory of the robotic links in real-time,which is to realize that the robot can avoid moving obstacles less conservatively and ensure tracking accuracy of terminal end-effector tasks in performing cooperative tasks.For the case where there is interference between the moving obstacle and the desired path of the robotic end-effector,the method inherits the null-space-based self-motion characteristics of the redundant manipulator,integrates the relative motion information,and uses the improved artificial potential field method to design the control items,which are used to generate the collision avoidance motion and carry out moving obstacles smoothly and less conservatively.At the same time,the strategy maintains the kinematic constraint relationship of dual-arm cooperatives,to meet the real-time collision avoidance task under collaborative tasks.Finally,the algorithm simulation indicates that the method can better ensure the tracking accuracy of the end-effector task and carry out moving obstacles smoothly.The experimental results show that the method can generate the real-time collision-free trajectory of the robot in the cooperative handling task,and the joint movement is continuous and stable.
基金financed by the National Natural Science Foundation of China (No.61004022)111 Project of China Education Department
文摘Redundant techniques are widely adopted in vehicle management computer （VMC） to ensure that VMC has high reliability and safety. At the same time, it makes VMC have special characteristics, e.g., failure correlation, event simultaneity, and failure self-recovery. Accordingly, the reliability and safety analysis to redundant VMC system （RVMCS） becomes more difficult. Aimed at the difficulties in RVMCS reliability modeling, this paper adopts generalized stochastic Petri nets to establish the reliability and safety models of RVMCS. Then this paper analyzes RVMCS oper- ating states and potential threats to flight control system. It is verified by simulation that the reli- ability of VMC is not the product of hardware reliability and software reliability, and the interactions between hardware and software faults can reduce the real reliability of VMC obviously. Furthermore, the failure undetected states and false alarming states inevitably exist in RVMCS due to the influences of limited fault monitoring coverage and false alarming probability of fault mon- itoring devices （FMD）. RVMCS operating in some failure undetected states will produce fatal threats to the safety of flight control system. RVMCS operating in some false alarming states will reduce utility of RVMCS obviously. The results abstracted in this paper can guide reliable VMC and efficient FMD designs. The methods adopted in this paper can also be used to analyze other intelligent systems＇ reliability.
基金sponsored by the National Natural Science Foundation of China under grant number No. 62172353, No. 62302114, No. U20B2046 and No. 62172115Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1331007 and No. 1311022+1 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions Grant No. 17KJB520044Six Talent Peaks Project in Jiangsu Province No.XYDXX-108
文摘With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.
基金Project supported by the National Key Research and Development Program of China (Grant No.2021YFB3900701)the Science and Technology Plan Project of the State Administration for Market Regulation of China (Grant No.2023MK178)the National Natural Science Foundation of China (Grant No.42227802)。
文摘A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.
文摘Aim To define a mixed redundant model(MRM), improving the reliability of C 3I system. Methods The model combined the technology characters of two? unit system with one warm stand by unit and function substitute system. The reliability and availability equations of MRM were deduced. Results and Conclusion Compared with several other reliability models, it has obvious effect upon improving the system reliability. The effect? cost rate is very high among these models. The model can be used in reliability design, evaluation and check of C 3I system. Only a little attached cost is needed to improve C 3I system reliability effectively.
文摘The control method of highly redundant robot manipulators is introduced. A decentralized autonomous control scheme is used to guide the movement of robot manipulators so that the work done by manipulators is minimized. The method of computing pseudoinverse which needs too many complicated calculation can be avoided. Then the calculation and control of robots are simplified. At the same time system robustness/fault tolerance is achieved.
文摘[Objective]The construction of weirs changes the hydraulic characteristics of rivers and affects the structure of phytoplankton communities and the health of aquatic ecosystems in the river.This study aims to explore the nonlinear response relationship between phytoplankton community structure and its driving factors in spring and autumn in Furong Creek under the construction of cascade weirs.[Methods]The structure of phytoplankton communities and related environmental factors were investigated in Furong Creek from 2023 to 2024.This study focused on the analysis of the changes of nutrient concentrations and biomass of phytoplankton in autumn and spring within the same dry season in Furong Creek.Redundancy analysis was used to identify the key factors influencing the structure of phytoplankton communities.The MIKE 11 model was employed to simulate the hydrodynamic changes in the river.Combined with total nitrogen and permanganate index,a GAM model of phytoplankton diversity index and hydrodynamic factors was developed,and the change of phytoplankton diversity after the optimized layout of the cascade weirs was fitted.[Results]The result showed that the annual average value of Shannon-Wiener diversity index of phytoplankton in Furong Creek was 2.79,which was in a state of mild pollution.A total of 239 species from 95 genera in 8 phyla were identified.Among the phytoplankton,Chlorophyta was the dominant group throughout the year in Furong Creek,followed by Bacillariophyta and Cyanophyta.The cell abundance of phytoplankton ranged from 3.11 to 20.64 mg/L and from 0.23 to 6.31 mg/L in spring and autumn,which indicated a clear seasonal succession of phytoplankton community structure.Compared with autumn,the relative abundance of Cyanophyta significantly decreased in spring across the whole river section,while Chrysophyta and Dinophyta showed significant increase at some monitoring sites,leading to water bloom phenomenon and a noticeable decline in the diversity of phytoplankton.The dominant species in the water bodies throughout the year were Cyclotella catenata,Chlorella vulgaris,Scenedesmus bijuga,Scenedesmus quadricauda,Chroomonas acuta,Cryptomonas ovata,and Cryptomonas erosa.Redundancy analysis(RDA)showed that hydrodynamic factors(v,h)and water environmental factors(TN,COD_(Mn))were the main influencing factors of phytoplankton community structure.[Conclusion]The result show that the nutrient concentration,phytoplankton biomass,and density in Furong Creek in spring are significantly higher than in autumn.The GAM model,constructed by combining hydrodynamic and environmental factors,can effectively reflect the nonlinear relationship between phytoplankton diversity index and its driving factors.In spring,with an increase in nutrient concentration,the habitat conditions of low flow speed and high water depths formed by overflow weirs will lead to a decrease in the Shannon-Wiener index of phytoplankton and an intensified risk of eutrophication.However,a reasonable layout scheme of cascade weirs will improve the diversity of phytoplankton and reduce the risk of eutrophication in the river.The findings of this study can help deepen the understanding of the ecological and environmental effects of cascade weir construction in the river.
文摘Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform(DWT),Redundant Discrete Wavelet Transform(RDWT),and Möbius Transformations(MT),with optimization of transformation parameters achieved via a Genetic Algorithm(GA).By combining frequency and spatial domain techniques,the proposed method significantly enhances both the imper-ceptibility and robustness of watermark embedding.The approach leverages DWT and RDWT for multi-resolution decomposition,enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks.RDWT,in particular,offers shift-invariance,which improves performance under geometric transformations.Möbius transformations are employed for spatial manipulation,providing conformal mapping and spatial dispersion that fortify watermark resilience against rotation,scaling,and translation.The GA dynamically optimizes the Möbius parameters,selecting configurations that maximize robustness metrics such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),Bit Error Rate(BER),and Normalized Cross-Correlation(NCC).Extensive experiments conducted on medical and standard benchmark images demonstrate the efficacy of the proposed RDWT-MT scheme.Results show that PSNR exceeds 68 dB,SSIM approaches 1.0,and BER remains at 0.0000,indicating excellent imperceptibility and perfect watermark recovery.Moreover,the method exhibits exceptional resilience to a wide range of image processing attacks,including Gaussian noise,JPEG compression,histogram equalization,and cropping,achieving NCC values close to or equal to 1.0.Comparative evaluations with state-of-the-art watermarking techniques highlight the superiority of the proposed method in terms of robustness,fidelity,and computational efficiency.The hybrid framework ensures secure,adaptive watermark embedding,making it highly suitable for applications in digital rights management,content authentication,and medical image protection.The integration of spatial and frequency domain features with evolutionary optimization presents a promising direction for future watermarking technologies.
基金supported by the National Natural Science Foundation of China(No.62103298)the Natural Science Foundation of Hebei Province(No.F2018209289)。
文摘Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB4503600)the National Natural Science Foundation of China(Grant Nos.U23A20299,62072460,62172424,62276270,and 62322214).
文摘The interconnection between query processing and data partitioning is pivotal for the acceleration of massive data processing during query execution,primarily by minimizing the number of scanned block files.Existing partitioning techniques predominantly focus on query accesses on numeric columns for constructing partitions,often overlooking non-numeric columns and thus limiting optimization potential.Additionally,these techniques,despite creating fine-grained partitions from representative queries to enhance system performance,experience from notable performance declines due to unpredictable fluctuations in future queries.To tackle these issues,we introduce LRP,a learned robust partitioning system for dynamic query processing.LRP first proposes a method for data and query encoding that captures comprehensive column access patterns from historical queries.It then employs Multi-Layer Perceptron and Long Short-Term Memory networks to predict shifts in the distribution of historical queries.To create high-quality,robust partitions based on these predictions,LRP adopts a greedy beam search algorithm for optimal partition division and implements a data redundancy mechanism to share frequently accessed data across partitions.Experimental evaluations reveal that LRP yields partitions with more stable performance under incoming queries and significantly surpasses state-of-the-art partitioning methods.
基金National Key Research and Development Program of China,No.2020YFA0607700,No.2023YFF0804702National Natural Science Foundation of China,No.T2192954,No.42030507,No.42372352。
文摘To explore the factors controlling human activity in Northeast Asia during the last deglaciation,this study synthesizes six pollen records from lakes and peatlands,alongside four paleotemperature records from terrestrial sedimentary sequences in this region.We simulated potential hunter-gatherer population densities using the Minimalist Terrestrial Resource Model(MTRM),and calculated vegetation openness,rate of change,and evenness based on pollen data.The results reveal a direct relationship between plant resources and hunter-gatherer populations from 20.9 to 10.2 ka BP.The synchronous increases in plant resources and population density from the Last Glacial(LG)to the B?lling-Aller?d(BA)warm period,as well as from the Younger Dryas(YD)to the early Holocene(EH),with stasis during the YD,suggest that resource availability was a key driver of human activity.Redundancy analysis(RDA)of pollen and paleotemperature records indicated that vegetation and plant resources were more closely linked to the mean annual air temperature,with winter characteristics,from the LG to the YD,whereas warm-season temperatures played a more significant role during the EH.This research emphasizes that variations in resource accessibility,rather than direct climate effects alone,were likely pivotal in shaping human activity responses to environmental changes.
基金funded by Universiti Teknologi Malaysia under the UTM RA ICONIC Grant(Q.J130000.4351.09G61).
文摘Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection systems.The complexity of real-world network data poses significant challenges in detection.Machine learning models have shown promise in detecting APTs;however,their performance often suffers when trained on large datasets with redundant or irrelevant features.This study presents a novel,hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data.It combines Mutual Information(MI),Symmetric Uncertainty(SU)and Minimum Redundancy Maximum Relevance(mRMR)to enhance feature selection.MI and SU assess feature relevance,while mRMR maximises relevance and minimises redundancy,ensuring that the most impactful features are prioritised.This method addresses redundancy among selected features,improving the overall efficiency and effectiveness of the detection model.Experiments on a real-world APT datasets were conducted to evaluate the proposed method.Multiple classifiers including,Random Forest,Support Vector Machine(SVM),Gradient Boosting,and Neural Networks were used to assess classification performance.The results demonstrate that the proposed feature selection method significantly enhances detection accuracy compared to baseline models trained on the full feature set.The Random Forest algorithm achieved the highest performance,with near-perfect accuracy,precision,recall,and F1 scores(99.97%).The proposed adaptive thresholding algorithm within the selection method allows each classifier to benefit from a reduced and optimised feature space,resulting in improved training and predictive performance.This research offers a scalable and classifier-agnostic solution for dimensionality reduction in cybersecurity applications.
文摘In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding(RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC(ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.
基金supported by the National Natural Science Foundation of China(W2412123)the Youth Top Talents Project of"Tianshan Talent"Training Plan of Xinjiang Uygur Autonomous Region,China(2022TSYCCX0011)+1 种基金the Tianshan Talents Project of Xinjiang Uygur Autonomous Region,China(2022TSYCLJ0056)the Self-deployment Program of Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences(E350030401).
文摘Grassland shrub encroachment is a phenomenon that is prevalent in arid and semi-arid regions worldwide,impacting grassland ecosystems in several ways.In the context of escalating climate change and human activities,examining the nutrient and stoichiometric characteristics of Spiraea shrubs in grassland ecosystems,along with their relationships with environmental factors,can yield valuable insights into the nutrient utilization and survival strategies of these shrubs.This,in turn,offers a scientific foundation for developing future conservation measures.This study was conducted in July 2023 in the Altay Mountains,Northwest China,where Spiraea shrubs thrive across five grassland types:temperate steppe desert,temperate desert steppe,temperate steppe,temperate meadow steppe,and mountain meadow.Leaf and soil samples were collected from each grassland type to analyze the concentrations of carbon(C),nitrogen(N),and phosphorus(P),as well as the stoichiometric characteristics of both the leaves and soil.Subsequently,correlation analysis and redundancy analysis(RDA)were conducted to investigate the variations in leaf C,N,and P concentrations and leaf stoichiometry of Spiraea shrubs as well as their influencing factors.The results indicated the presence of significant or highly significant differences(P<0.050)in the leaf C,N,and P concentrations and leaf stoichiometry(C:N,C:P,and N:P ratios)of Spiraea shrubs across the five grassland types.The N:P ratios of Spiraea shrub leaves in the five grassland types ranged from 7.37 to 11.77,suggesting that N availability generally limits the growth of Spiraea shrubs.Results of RDA revealed that the most significant contributors to the C,N,and P concentrations and stoichiometric characteristics of Spiraea shrub leaves were in the following order:soil total N>mean annual precipitation>elevation>soil pH>soil organic C>mean annual temperature.These factors had contribution rates of 35.32%,13.19%,10.20%,8.82%,8.34%,and 6.48%,respectively.It was determined that soil nutrients have a greater impact on the growth and nutrient accumulation of Spiraea shrubs compared to climatic factors.This study makes an important contribution to the theoretical basis and data support,enabling a deeper understanding of the response mechanisms of shrub species in the grassland ecosystems of the Altay Mountains to climate change.
基金supported by the National Natural Sci ence Foundation of China(No.31672543)the Zhejiang Province“Sannongliufang”Science and Technology Coopera tion Project(No.2020SNLF007),China.
文摘Toxoplasma gondii is a single-celled parasite that infects nearly all warm-blooded animals,including humans(Montoya and Liesenfeld,2004).It occurs worldwide and can persist for a lifetime in mammals.Humans get infected by eating undercooked meat of animals containing the tissue cysts of this parasite.In immune-competent individuals,T.
基金funded by the Major Science and Technology Projects of Xinjiang Uygur Autonomous Region(2022A02007-4)the Xinjiang Uygur Autonomous Region Natural Science Foundation Youth Project(2024D01B31)the Graduate Student Research Innovation Project of Xinjiang Agricultural University(XJAUGRI2024033).
文摘Continuous cropping can lead to soil environment deterioration,cause plant health problems,and reduce crop productivity.However,the response mechanisms of soil microbial co-occurrence patterns to the duration of continuous melon cropping remain poorly understood.Here,we employed the metagenomic techniques to comparatively investigate the bulk and rhizosphere soil microbial communities of major melon-producing regions(where the duration of continuous melon cropping ranges from 1 to 30 a)in the eastern and southern parts of Xinjiang Uygur Autonomous Region,China.The results showed that soil pH clearly decreased with increasing melon cropping duration,while soil electrical conductivity(EC)and the other soil nutrient indices increased with increasing melon cropping duration(with the exception of AN and TK in the southern melon-producing region).The most dominant bacterial phyla were Proteobacteria and Actinobacteria,and the most abundant fungal phyla were Ascomycota and Mucoromycota.Redundancy analysis(RDA)indicated that soil pH and EC had no significant effects on the bacterial communities.However,after many years of continuous melon cropping in the southern melon-producing region,fungal communities were significantly negatively correlated with soil pH and significantly positively correlated with soil EC(P<0.050).Co-occurrence network analysis showed that continuous melon cropping increased the complexity but decreased the connectivity of the cross-domain microbial networks.Moreover,the enrichment patterns of microorganisms in the main microbial network modules varied significantly with the duration of continuous melon cropping.Based on the analysis of keystone taxa,we found that continuous melon cropping increased some plant pathogens(e.g.,Fusarium and Stagonospora)but decreased beneficial bacteria(e.g.,Mesorhizobium and Pseudoxanthomonas).In conclusion,this study has greatly enhanced the understanding of the effects of continuous melon cropping on alterations in the microbial community structure and ecological networks in Xinjiang.
文摘A novel robust diagnostic system based on a linear fractional transform(LFT)representation combined with a static redundancy approach is proposed to design a residual generator for fault detection and localization in a wind system using the doubly fed induction generator(DFIG).As a result,faults in DFIG-based grid-connected wind systems can be grouped into three classes of faults,namely,model uncertainty-related faults(FLMU),set point disturbance-related faults(FLDS)and parameter uncertainty-related faults(FLPU).Based on the parity-space residual generations,an artificial neural network(ANN)structure has been combined with the classification to enable the assessment of hidden,indistinguishable or small amplitude faults.The training validation with two data sizes of 1278*4 and 1278*1 respectively at the inputs and outputs of the proposed ANN,presents better performance for a mean squared error value(MSE=3.0532e 9),and a good correlation between outputs and targets for a regression value(R=1).It emerges that the proposed robust and complete diagnostic system for the optimal and sustainable integration of wind turbines into the grid,offers very great advan-tages,particularly with regard to the precise and rapid detection of faults,and the assessment of hidden faults and/or ambiguous fault states in the wind system based on DFIG.In addition,the proposed approach allows the use of a reduced number of data,sensors and actuators required.Consequently,the system maintenance difficulties,complexity and cost of the diagnostic system are reduced.
基金supported by the National Natural Science Foundation of China(No.62025110).
文摘In an increasing number of area inspection applications,such as powerline inspection and sewage disposal monitoring,Unmanned Aerial Vehicles(UAVs)are used for capturing and transmitting on-site videos.Existing UAV video compressions employ Advanced Video Coding(AVC)or High Efficiency Video Coding(HEvC)encoders to eliminate intra-frame and short-term inter-frame redundancy,while these methods still face challenges in achieving high compression efficiency due to the high captured video bitrate and limited transmission capacity.In this paper,we further consider that UAVs revisit the same area and capture videos from different viewpoints,hence the Long-term Historical Background Redundancy(LHBR)exists among revisited video clips.Thus,we leverage the LHBR caused by UAV revisits,and propose a high-efficiency aerial video compression for UAVs.Our method comprises three steps:Firstly,we propose a lightweight method based on a spatial correlation model to select the most correlated reference frames from historical video database.Then,we design a Historical Reference Background Frame(HBRF)generation algorithm by alternately using the keypoint-based and telemetry-assisted alignments to align the selected frames with current frame.Finally,we use the generated HBRF as a reference frame to eliminate the LHBR within I-frames.Our proposed method has been experimentally proven to reduce Bjøntegaard-Delta bitrate(BD-bitrate)by 42.83%or enhance Bjøntegaard-Delta Peak Signal-to-Noise Ratio(BD-PSNR)by 2.98 dB over original HEVC,and take 29.3%of the encoding time needed for existing LHBR based compressions.
基金Supported by National Natural Science Foundation of China(Grant No.61733017)Foundation of State Key Laboratory of Robotics of China(Grant No.2018O13)Shanghai Pujiang Program of China(Grant No.18PJD018).
文摘A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form different lengths of robot arms for different application sites.The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space.This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator.In this integrated optimization,path planning is established on a Bezier curve,and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator.A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy.The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve.Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints.The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.
