Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimi...The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimization MMTO approach may result in stress surpassing the material's tolerance limit,potentially culminating in failure.This research proposes a novel way for imposing stress constraints on each material to regulate their respective stress levels.The fundamental concept is that each material possesses its own interpolation function for the stress model.The maximum von Mises stress for each material can be established with the definition of an upper limit,ensuring that the materials will perform safely and effectively.This aids topological structures in resisting failure and augmenting strength.A multi-physics system including thermoelastic and self-weight loads is concurrently examined alongside stress limitations.The global stress constraint utilizes the p-norm function,and the adjoint method is used to derive sensitivity.This work employs a three-field strategy utilizing density filtering and Heaviside projection functions to mitigate the artificial stress in low density.The technique is assessed through two-dimensional(2D)and three-dimensional(3D)examples,illustrating the influence of stress limits on the compliance minimization under heat and self-weight loads.The optimized results indicate a substantial decrease in the stress levels accompanied by a minor gain in compliance,while maintaining the stress within the specified range for all materials.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
In machine vision,elliptical targets frequently appear within the camera's region of interest(ROI).Ellipse detection is essential for shape detection and geometric measurements in machine vision.However,existing e...In machine vision,elliptical targets frequently appear within the camera's region of interest(ROI).Ellipse detection is essential for shape detection and geometric measurements in machine vision.However,existing ellipse detection algorithms often face issues such as high computational complexity,strong dependence on initial conditions,sensitivity to noise,and lack of robustness to occlusions.In this paper,we propose a fast and robust ellipse detection method to address these challenges.This method first utilizes edge gradient and curvature information to segment the curve into circular arcs.Next,based on the convexity of the arcs,it divides them into different quadrants of the ellipse,groups and fits the arcs according to multiple geometric constraints at a low computational cost.Finally,it reduces the parameter space for hierarchical clustering and then segments the complete ellipse into several sectors for verification.We compare our method across seven datasets,including five public image datasets and two from industrial camera scenes.Experimental results show that our method achieves a precision ranging from 67.1%to 98.9%,a recall ranging from 48.1%to 92.9%,and an F-measure ranging from 58.0%to 95.8%.The average execution time per image ranges from 25 ms to 192 ms,demonstrating both high accuracy and efficiency.展开更多
This paper begins with a discussion of the trust issues that agricultural supply chain finance faces.It then examines the constraints of using blockchain technology to enhance trust in agricultural supply chain financ...This paper begins with a discussion of the trust issues that agricultural supply chain finance faces.It then examines the constraints of using blockchain technology to enhance trust in agricultural supply chain finance in accordance with the technological and institutional logic of combining blockchain with supply chains.This study then proposes the creation of an agricultural“blockchain+supply chain”information service platform and a financing trust mechanism that can effectively ensure the authenticity of the initial information input on the blockchain,consistency between on-chain transaction data and off-chain physical transactions,the controllability of risks in the set up and execution of smart contracts,and the removal of information constraints,resource allocation constraints,and institutional constraints in the agricultural supply chain financing.This aims to improve the efficiency of financing in agricultural supply chains and contribute to the industrial development of rural areas and rural revitalization.展开更多
This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data a...This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.展开更多
The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland im...The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.展开更多
Background As visual simultaneous localization and mapping(SLAM)is primarily based on the assumption of a static scene,the presence of dynamic objects in the frame causes problems such as a deterioration of system rob...Background As visual simultaneous localization and mapping(SLAM)is primarily based on the assumption of a static scene,the presence of dynamic objects in the frame causes problems such as a deterioration of system robustness and inaccurate position estimation.In this study,we propose a YGC-SLAM for indoor dynamic environments based on the ORB-SLAM2 framework combined with semantic and geometric constraints to improve the positioning accuracy and robustness of the system.Methods First,the recognition accuracy of YOLOv5 was improved by introducing the convolution block attention model and the improved EIOU loss function,whereby the prediction frame converges quickly for better detection.The improved YOLOv5 was then added to the tracking thread for dynamic target detection to eliminate dynamic points.Subsequently,multi-view geometric constraints were used for re-judging to further eliminate dynamic points while enabling more useful feature points to be retained and preventing the semantic approach from over-eliminating feature points,causing a failure of map building.The K-means clustering algorithm was used to accelerate this process and quickly calculate and determine the motion state of each cluster of pixel points.Finally,a strategy for drawing keyframes with de-redundancy was implemented to construct a clear 3D dense static point-cloud map.Results Through testing on TUM dataset and a real environment,the experimental results show that our algorithm reduces the absolute trajectory error by 98.22%and the relative trajectory error by 97.98%compared with the original ORBSLAM2,which is more accurate and has better real-time performance than similar algorithms,such as DynaSLAM and DS-SLAM.Conclusions The YGC-SLAM proposed in this study can effectively eliminate the adverse effects of dynamic objects,and the system can better complete positioning and map building tasks in complex environments.展开更多
This study aimed to identify and compensate for the geometric errors of the double swiveling axes in a five-axis computer numerical control(CNC)machining center.Hence,a three-dimensional coordinate calculation algorit...This study aimed to identify and compensate for the geometric errors of the double swiveling axes in a five-axis computer numerical control(CNC)machining center.Hence,a three-dimensional coordinate calculation algorithm for a measured point with additional rotational rigid body motion constraints is proposed.The motion constraints of the rotational rigid body were analyzed,and a mathematical model of the measured point algorithm in the swiveling axes was established.The Levenberg-Marquard method was used to solve the nonlinear superstatically determined equations.The spatial coordinate error was used to separate the spatial deviation of the measured point.An identification model of the position-independent and position-dependent geometric errors was established.The three-dimensional coordinate-solving algorithm of the measured point in the swiveling axis and geometric error identification method based on the Monte Carlo method were analyzed numerically.Geometric error measurement and cutting experiments were performed on a VMC25100U five-axis machining center,which integrated two swiveling axes.Geometric errors of the A-and B-axes were identified and measured experimentally.The angular positioning errors before and after compensation were measured using a laser interferometer,which verified the effectiveness of the proposed algorithm.A cutting experiment of a round table part was performed.The shape and position accuracy of the processed part before and after compensation were detected using a coordinate measuring machine.It verified that the geometric error of the swiveling axis was effectively compensated by the algorithm proposed herein.展开更多
This study examines the moderating role of entrepreneurs’creative cognitive styles in the relationship between resource constraints and bricolage.Drawing on insights from cognitive psychology and entrepreneurial rese...This study examines the moderating role of entrepreneurs’creative cognitive styles in the relationship between resource constraints and bricolage.Drawing on insights from cognitive psychology and entrepreneurial research,we explore how divergent and convergent thinking influence the extent to which entrepreneurs engage in bricolage under resource limitations.Bricolage refers to the creative recombination of available resources to address challenges and seize opportunities,a process often adopted by firms facing financial or knowledge constraints.Yet,individual cognitive differences may determine how effectively entrepreneurs can employ bricolage as a strategic response to scarcity.We propose that divergent thinking—the capacity to generate multiple creative solutions and identify novel resource combinations—strengthens the positive association between resource constraints and bricolage.In contrast,convergent thinking,which emphasizes logical analysis and the pursuit of a single optimal solution,weakens this association.To test these propositions,we collected survey data from 183 entrepreneurs in the United States and employed moderated regression analyses to examine the interactions among cognitive styles,resource constraints,and bricolage behaviors.Our findings reveal that divergent thinking significantly enhances the effect of both financial and knowledge constraints on bricolage,enabling entrepreneurs to creatively leverage limited resources.Conversely,convergent thinking appears to diminish the likelihood of engaging in bricolage when resources are scarce.These results highlight the importance of individual cognitive styles in shaping strategic responses to resource scarcity and contribute to a more nuanced understanding of entrepreneurial bricolage.The study offers practical implications for firms operating in resource-constrained environments by suggesting that enhancing divergent thinking abilities may facilitate more effective resource recombination.Future research should investigate additional cognitive factors and employ longitudinal designs to capture the dynamic nature of entrepreneurial decision-making.These insights open new avenues for further innovative entrepreneurial practices.展开更多
As an important tool to achieve sustainable economic and environmental development,green finance can effectively alleviate the financing constraints of small and medium-sized enterprises(SMEs),especially in promoting ...As an important tool to achieve sustainable economic and environmental development,green finance can effectively alleviate the financing constraints of small and medium-sized enterprises(SMEs),especially in promoting green transformation plays a key role.SMEs play an important role in economic growth,innovation,and job creation,but due to a lack of collateral,imperfect credit history,and opaque financial information,they face great obstacles in the financing process,especially in the early capital investment required for green transformation.Green finance,through innovative financial instruments such as green credit and green bonds,provides new financing channels for SMEs,helping them reduce financing costs,optimize financing structure,and promote their green transformation and sustainable development.This paper analyzes the current situation and root causes of SMEs’financing dilemma from the perspective of green finance,and probes into the influence of green finance policies on financing behavior.展开更多
Given the existence of real estate market bubbles and risks arising from high government debt,countries are faced with the challenge of preventing systemic risks.This study investigates the macroeconomic dynamics of t...Given the existence of real estate market bubbles and risks arising from high government debt,countries are faced with the challenge of preventing systemic risks.This study investigates the macroeconomic dynamics of the real estate market and local government debt risk from the perspective of liquidity constraints.We build a dynamic stochastic general equilibrium model with real estate and local government debt risk based on the New Keynesian-Dynamic Stochastic General Equilibrium Model(NK-DSGE)model to investigate the transmission path of local government debt risk under real estate regulation.In addition,we analyze the risk transmission between the real estate market and local government under different tax systems and investigate the shock to household welfare from a local government debt default.The results show monetary policy can effectively control the scale of local government debt to reduce default risk.An increase in property taxes that restrains housing demand can effectively regulate the real estate market.Although reducing taxes can increase macroeconomic output,reducing tax rates on consumption,capital,and labor weakens the liquidity of household assets.Further,lowering taxes increases local government default risk,which reduces household welfare and makes it more difficult for local governments to deleverage.Our findings provide important insights for countries seeking an effective real estate regulation mechanism to curb local government default risk.展开更多
Under the socialist market economic system of our country,the government,through the“invisible hand,”carries on macro regulation and control to improve the financing constraints that small and medium-sized enterpris...Under the socialist market economic system of our country,the government,through the“invisible hand,”carries on macro regulation and control to improve the financing constraints that small and medium-sized enterprises are facing.But because of the huge base number of small and medium-sized enterprises in our country,there are many kinds,and the problem of financing constraints is still puzzling the development of enterprises at present.With the continuous promotion of inclusive finance in our country,the problems plaguing SMEs in the last mile of financing are gradually improved.In this context,small and medium-sized enterprises in Hainan Free Trade Port are taken as the research object to study the role of digital inclusive finance on the financing constraints of SMEs.The research shows that,first of all,small and medium-sized enterprises in Hainan Free Trade Port generally have financing problems.The development of digital inclusive finance solves the“last kilometer”problem of traditional finance,enhances financial access ability,broadens the financial service group,provides convenience and diversified services for SMEs'financing,and provides inexhaustible impetus for the long-term healthy development of SMEs.Secondly,digital inclusive finance alleviates the financing difficulties faced by SMEs on the island by reducing financial costs and expanding the scale of credit.展开更多
The rapid development of digital financial inclusion is profoundly changing the financing environment for small and medium-sized enterprises(SMEs).As an important driver of economic growth and innovation,SMEs account ...The rapid development of digital financial inclusion is profoundly changing the financing environment for small and medium-sized enterprises(SMEs).As an important driver of economic growth and innovation,SMEs account for a significant share of employment and GDP globally.However,the traditional bank credit model has long failed to effectively meet the financing needs of SMEs due to issues such as information asymmetry,high cost,and difficulty in risk assessment,resulting in serious financing constraints.Digital financial inclusion,through technological innovation and big data analysis,has significantly reduced credit costs,alleviated information asymmetry,and provided SMEs with more flexible and efficient financing channels.Research shows that digital financial inclusion can not only ease the financing constraints of SMEs,but also promote their innovation and growth,providing important support for building a more inclusive and sustainable financial ecosystem.展开更多
Digital financial inclusion provides financial services through digital platforms,aiming to improve the ability of MSMEs and low-income groups to access financial resources,thereby easing their financing constraints a...Digital financial inclusion provides financial services through digital platforms,aiming to improve the ability of MSMEs and low-income groups to access financial resources,thereby easing their financing constraints and promoting economic growth and inclusive development.As an innovative financial model,digital financial inclusion utilizes modern technological means to significantly improve the accessibility and convenience of financial services,especially in areas where traditional banking services are under-covered.Digital finance has promoted the popularization of financial services such as micro-credit,micro-savings,and micro-insurance,and helped improve the financing environment of low-income groups and small and micro enterprises.At the same time,digital financial inclusion promotes financial literacy education and digital inclusion construction,and enhances the acceptance and use of digital financial instruments by the general public,which is the key to achieving sustainable development of digital financial inclusion.Therefore,digital financial inclusion can better ease the financing constraints of small and medium-sized enterprises and promote economic development.展开更多
Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While s...Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While semi-supervised fuzzy clustering(SSFC)methods leverage limited labeled data to enhance accuracy,they remain highly susceptible to inappropriate or mislabeled prior knowledge,especially in noisy or overlapping datasets where cluster boundaries are ambiguous.To enhance the effectiveness of clustering algorithms,it is essential to leverage labeled data while ensuring the safety of the previous knowledge.Existing solutions,such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method(TS3FCM),struggle with random centroid initialization,fixed neighbor radius formulas,and handling outliers or noise at cluster overlaps.A new framework called Active Safe Semi-Supervised Fuzzy Clustering with Pairwise Constraints Based on Cluster Boundary(AS3FCPC)is proposed in this paper to deal with these problems.It does this by combining pairwise constraints and active learning.AS3FCPC uses active learning to query only the most informative data instances close to the cluster boundaries.It also uses pairwise constraints to enforce the cluster structure,which makes the system more accurate and robust.Extensive test results on diverse datasets,including challenging noisy and overlapping scenarios,demonstrate that AS3FCPC consistently achieves superior performance compared to state-of-the-art methods like TS3FCM and other baselines,especially when the data is noisy and overlaps.This significant improvement underscores AS3FCPC’s potential for reliable and accurate semisupervised fuzzy clustering in complex,real-world applications,particularly by effectively managing mislabeled data and ambiguous cluster boundaries.展开更多
The biased allocation of emission reduction target constraints quantifies emission reduction responsibilities and reflects differences in pollutant reductions both across and within cities.This approach represents a s...The biased allocation of emission reduction target constraints quantifies emission reduction responsibilities and reflects differences in pollutant reductions both across and within cities.This approach represents a systematic innovation aims to enhance China’s green competitiveness and facilitate its economic transformation through localized and precise policymaking.Using panel data from 275 Chinese cities spanning 2000-2022,this study applies the difference-in-differences method to estimate the impact of biased allocation of emission reduction target constraints on urban green competitiveness.The findings indicate that such constraints-whether based on chemical oxygen demand or sulfur dioxide targets-significantly improve urban green competitiveness,with both pollutant-specific constraints producing comparable effects.Furthermore,these constraints exhibit significant spatial spillover effects within a 200-km geographical radius.Heterogeneity analysis reveals stronger policy impacts in resource-based cities,eastern regions,and cities designated as key areas for pollution prevention and control.Mechanism analysis demonstrates that the constraints enhance green competitiveness primarily by fostering green technological innovation and optimizing industrial structures.These conclusions provide a practical foundation for addressing China’s enduring conflict between environmental protection and economic development.展开更多
With the establishment of Hainan Free Trade Port,the small and medium-sized enterprises in Hainan Free Trade Port have developed and grown in the continuously optimized enterprise environment.The continuous establishm...With the establishment of Hainan Free Trade Port,the small and medium-sized enterprises in Hainan Free Trade Port have developed and grown in the continuously optimized enterprise environment.The continuous establishment of a large number of small and micro enterprises makes its social and economic development play a non-negligible role.However,due to the small size and insufficient economic strength of small and micro enterprises,their status in the financial system is often very humble.Therefore,under normal circumstances,small and micro enterprises are faced with financing difficulties and high costs,which has a great side effect on their development.The proposal and continuous development of digital inclusive finance,so that small and micro enterprises in access to a wide range of financing opportunities at the same time,their financing methods are more convenient than in the past,the cost is getting lower and lower.This paper deeply discusses the role of digital inclusive finance in easing the financing constraints of small and micro enterprises and puts forward corresponding suggestions[1].展开更多
Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in ...Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot.展开更多
In air combat,one effective way to counter an incoming missile attacking an aircraft is to launch a defense missile compared with traditional passive defense strategies such as decoy and electronic countermeasures.To ...In air combat,one effective way to counter an incoming missile attacking an aircraft is to launch a defense missile compared with traditional passive defense strategies such as decoy and electronic countermeasures.To address this issue,this paper proposes a three-body cooperative active defense guidance law with overload constraints from the perspective of a small speed ratio.First,a cooperative guidance-oriented model for active defense is established and linearized to provide a foundation for the design of the guidance law.Then,the essential quantity known as Zero-Effort-Miss(ZEM)is analyzed during the engagement process.In order to minimize the influence of inaccurate estimates of remaining flight time in the ZEM,the concept of Zero-Effort-Velocity(ZEV)is introduced.Subsequently,utilizing the sliding mode control method,the guidance law is designed by selecting the ZEM and ZEV as sliding mode surfaces,combined with the fast power reaching law,and its finite-time stability is analyzed using the Lyapunov method.Furthermore,to quantitatively evaluate the performance of the proposed active defense guidance law,the interception rendezvous angle index is introduced.The proposed active defense guidance law considers integrating information from the incoming missile,aircraft,and defense missile with fewer simplifications and assumptions,and ensures that the aircraft is effectively protected with less overload required for the defense missile.Finally,simulation experiments demonstrate the effectiveness and adaptability of the proposed active defense guidance law.展开更多
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
基金Project supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2025-02303676)。
文摘The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimization MMTO approach may result in stress surpassing the material's tolerance limit,potentially culminating in failure.This research proposes a novel way for imposing stress constraints on each material to regulate their respective stress levels.The fundamental concept is that each material possesses its own interpolation function for the stress model.The maximum von Mises stress for each material can be established with the definition of an upper limit,ensuring that the materials will perform safely and effectively.This aids topological structures in resisting failure and augmenting strength.A multi-physics system including thermoelastic and self-weight loads is concurrently examined alongside stress limitations.The global stress constraint utilizes the p-norm function,and the adjoint method is used to derive sensitivity.This work employs a three-field strategy utilizing density filtering and Heaviside projection functions to mitigate the artificial stress in low density.The technique is assessed through two-dimensional(2D)and three-dimensional(3D)examples,illustrating the influence of stress limits on the compliance minimization under heat and self-weight loads.The optimized results indicate a substantial decrease in the stress levels accompanied by a minor gain in compliance,while maintaining the stress within the specified range for all materials.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金supported by National Major Scientific Research Instrument Development Project of China(No.51927804)Science Fund for Shaanxi Provincial Department of Education's Youth Innovation Team Research Plan under Grant(No.23JP169).
文摘In machine vision,elliptical targets frequently appear within the camera's region of interest(ROI).Ellipse detection is essential for shape detection and geometric measurements in machine vision.However,existing ellipse detection algorithms often face issues such as high computational complexity,strong dependence on initial conditions,sensitivity to noise,and lack of robustness to occlusions.In this paper,we propose a fast and robust ellipse detection method to address these challenges.This method first utilizes edge gradient and curvature information to segment the curve into circular arcs.Next,based on the convexity of the arcs,it divides them into different quadrants of the ellipse,groups and fits the arcs according to multiple geometric constraints at a low computational cost.Finally,it reduces the parameter space for hierarchical clustering and then segments the complete ellipse into several sectors for verification.We compare our method across seven datasets,including five public image datasets and two from industrial camera scenes.Experimental results show that our method achieves a precision ranging from 67.1%to 98.9%,a recall ranging from 48.1%to 92.9%,and an F-measure ranging from 58.0%to 95.8%.The average execution time per image ranges from 25 ms to 192 ms,demonstrating both high accuracy and efficiency.
基金an initial outcome of the Research on the Trust Mechanism of Agricultural Supply Chain Financing in the Context of “Blockchain+Supply Chain” Integrated Governance (Project No:20AGL021)a key project under the National Social Science Fund of China (NSSFC)+3 种基金the Research on the Trust Mechanism of Online Bank Lending System Based on Online Social Capital of Long-tail Rural Households (Project No:19BGL155)a project under the NSSFCthe Research on the Cost Formation Mechanism of Data Factor Transactions and the Design of Transaction Mechanism (Project No:23CJY068)a youth project under the NSSFC
文摘This paper begins with a discussion of the trust issues that agricultural supply chain finance faces.It then examines the constraints of using blockchain technology to enhance trust in agricultural supply chain finance in accordance with the technological and institutional logic of combining blockchain with supply chains.This study then proposes the creation of an agricultural“blockchain+supply chain”information service platform and a financing trust mechanism that can effectively ensure the authenticity of the initial information input on the blockchain,consistency between on-chain transaction data and off-chain physical transactions,the controllability of risks in the set up and execution of smart contracts,and the removal of information constraints,resource allocation constraints,and institutional constraints in the agricultural supply chain financing.This aims to improve the efficiency of financing in agricultural supply chains and contribute to the industrial development of rural areas and rural revitalization.
基金This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124)Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.
文摘This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.
基金supported by National Natural Science Foundation of China(No.61761027)Gansu Young Doctor’s Fund for Higher Education Institutions(No.2021QB-053)。
文摘The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.
基金Supported by Jiangsu Key R&D Program(BE2021622)Jiangsu Postgraduate Practice and Innovation Program(SJCX23_0395).
文摘Background As visual simultaneous localization and mapping(SLAM)is primarily based on the assumption of a static scene,the presence of dynamic objects in the frame causes problems such as a deterioration of system robustness and inaccurate position estimation.In this study,we propose a YGC-SLAM for indoor dynamic environments based on the ORB-SLAM2 framework combined with semantic and geometric constraints to improve the positioning accuracy and robustness of the system.Methods First,the recognition accuracy of YOLOv5 was improved by introducing the convolution block attention model and the improved EIOU loss function,whereby the prediction frame converges quickly for better detection.The improved YOLOv5 was then added to the tracking thread for dynamic target detection to eliminate dynamic points.Subsequently,multi-view geometric constraints were used for re-judging to further eliminate dynamic points while enabling more useful feature points to be retained and preventing the semantic approach from over-eliminating feature points,causing a failure of map building.The K-means clustering algorithm was used to accelerate this process and quickly calculate and determine the motion state of each cluster of pixel points.Finally,a strategy for drawing keyframes with de-redundancy was implemented to construct a clear 3D dense static point-cloud map.Results Through testing on TUM dataset and a real environment,the experimental results show that our algorithm reduces the absolute trajectory error by 98.22%and the relative trajectory error by 97.98%compared with the original ORBSLAM2,which is more accurate and has better real-time performance than similar algorithms,such as DynaSLAM and DS-SLAM.Conclusions The YGC-SLAM proposed in this study can effectively eliminate the adverse effects of dynamic objects,and the system can better complete positioning and map building tasks in complex environments.
基金Supported by Shanxi Provincial Natural Science Foundation(Grant No.2021JM010)The Youth Innovation Team of Shaanxi Universities.
文摘This study aimed to identify and compensate for the geometric errors of the double swiveling axes in a five-axis computer numerical control(CNC)machining center.Hence,a three-dimensional coordinate calculation algorithm for a measured point with additional rotational rigid body motion constraints is proposed.The motion constraints of the rotational rigid body were analyzed,and a mathematical model of the measured point algorithm in the swiveling axes was established.The Levenberg-Marquard method was used to solve the nonlinear superstatically determined equations.The spatial coordinate error was used to separate the spatial deviation of the measured point.An identification model of the position-independent and position-dependent geometric errors was established.The three-dimensional coordinate-solving algorithm of the measured point in the swiveling axis and geometric error identification method based on the Monte Carlo method were analyzed numerically.Geometric error measurement and cutting experiments were performed on a VMC25100U five-axis machining center,which integrated two swiveling axes.Geometric errors of the A-and B-axes were identified and measured experimentally.The angular positioning errors before and after compensation were measured using a laser interferometer,which verified the effectiveness of the proposed algorithm.A cutting experiment of a round table part was performed.The shape and position accuracy of the processed part before and after compensation were detected using a coordinate measuring machine.It verified that the geometric error of the swiveling axis was effectively compensated by the algorithm proposed herein.
文摘This study examines the moderating role of entrepreneurs’creative cognitive styles in the relationship between resource constraints and bricolage.Drawing on insights from cognitive psychology and entrepreneurial research,we explore how divergent and convergent thinking influence the extent to which entrepreneurs engage in bricolage under resource limitations.Bricolage refers to the creative recombination of available resources to address challenges and seize opportunities,a process often adopted by firms facing financial or knowledge constraints.Yet,individual cognitive differences may determine how effectively entrepreneurs can employ bricolage as a strategic response to scarcity.We propose that divergent thinking—the capacity to generate multiple creative solutions and identify novel resource combinations—strengthens the positive association between resource constraints and bricolage.In contrast,convergent thinking,which emphasizes logical analysis and the pursuit of a single optimal solution,weakens this association.To test these propositions,we collected survey data from 183 entrepreneurs in the United States and employed moderated regression analyses to examine the interactions among cognitive styles,resource constraints,and bricolage behaviors.Our findings reveal that divergent thinking significantly enhances the effect of both financial and knowledge constraints on bricolage,enabling entrepreneurs to creatively leverage limited resources.Conversely,convergent thinking appears to diminish the likelihood of engaging in bricolage when resources are scarce.These results highlight the importance of individual cognitive styles in shaping strategic responses to resource scarcity and contribute to a more nuanced understanding of entrepreneurial bricolage.The study offers practical implications for firms operating in resource-constrained environments by suggesting that enhancing divergent thinking abilities may facilitate more effective resource recombination.Future research should investigate additional cognitive factors and employ longitudinal designs to capture the dynamic nature of entrepreneurial decision-making.These insights open new avenues for further innovative entrepreneurial practices.
文摘As an important tool to achieve sustainable economic and environmental development,green finance can effectively alleviate the financing constraints of small and medium-sized enterprises(SMEs),especially in promoting green transformation plays a key role.SMEs play an important role in economic growth,innovation,and job creation,but due to a lack of collateral,imperfect credit history,and opaque financial information,they face great obstacles in the financing process,especially in the early capital investment required for green transformation.Green finance,through innovative financial instruments such as green credit and green bonds,provides new financing channels for SMEs,helping them reduce financing costs,optimize financing structure,and promote their green transformation and sustainable development.This paper analyzes the current situation and root causes of SMEs’financing dilemma from the perspective of green finance,and probes into the influence of green finance policies on financing behavior.
基金supported by the National Natural Science Foundation of China(Nos.72271135,72141304,71901130)National Social Science Fund of China(22&ZD117)+3 种基金Laboratory of Computation and Analytics of Complex Management Systems(Tianjin University)Special Funds for Taishan Scholars(tsqn202211120)2024 Qingdao Finance Society Key Project2024 Qingdao Social Science Planning Project.
文摘Given the existence of real estate market bubbles and risks arising from high government debt,countries are faced with the challenge of preventing systemic risks.This study investigates the macroeconomic dynamics of the real estate market and local government debt risk from the perspective of liquidity constraints.We build a dynamic stochastic general equilibrium model with real estate and local government debt risk based on the New Keynesian-Dynamic Stochastic General Equilibrium Model(NK-DSGE)model to investigate the transmission path of local government debt risk under real estate regulation.In addition,we analyze the risk transmission between the real estate market and local government under different tax systems and investigate the shock to household welfare from a local government debt default.The results show monetary policy can effectively control the scale of local government debt to reduce default risk.An increase in property taxes that restrains housing demand can effectively regulate the real estate market.Although reducing taxes can increase macroeconomic output,reducing tax rates on consumption,capital,and labor weakens the liquidity of household assets.Further,lowering taxes increases local government default risk,which reduces household welfare and makes it more difficult for local governments to deleverage.Our findings provide important insights for countries seeking an effective real estate regulation mechanism to curb local government default risk.
文摘Under the socialist market economic system of our country,the government,through the“invisible hand,”carries on macro regulation and control to improve the financing constraints that small and medium-sized enterprises are facing.But because of the huge base number of small and medium-sized enterprises in our country,there are many kinds,and the problem of financing constraints is still puzzling the development of enterprises at present.With the continuous promotion of inclusive finance in our country,the problems plaguing SMEs in the last mile of financing are gradually improved.In this context,small and medium-sized enterprises in Hainan Free Trade Port are taken as the research object to study the role of digital inclusive finance on the financing constraints of SMEs.The research shows that,first of all,small and medium-sized enterprises in Hainan Free Trade Port generally have financing problems.The development of digital inclusive finance solves the“last kilometer”problem of traditional finance,enhances financial access ability,broadens the financial service group,provides convenience and diversified services for SMEs'financing,and provides inexhaustible impetus for the long-term healthy development of SMEs.Secondly,digital inclusive finance alleviates the financing difficulties faced by SMEs on the island by reducing financial costs and expanding the scale of credit.
文摘The rapid development of digital financial inclusion is profoundly changing the financing environment for small and medium-sized enterprises(SMEs).As an important driver of economic growth and innovation,SMEs account for a significant share of employment and GDP globally.However,the traditional bank credit model has long failed to effectively meet the financing needs of SMEs due to issues such as information asymmetry,high cost,and difficulty in risk assessment,resulting in serious financing constraints.Digital financial inclusion,through technological innovation and big data analysis,has significantly reduced credit costs,alleviated information asymmetry,and provided SMEs with more flexible and efficient financing channels.Research shows that digital financial inclusion can not only ease the financing constraints of SMEs,but also promote their innovation and growth,providing important support for building a more inclusive and sustainable financial ecosystem.
文摘Digital financial inclusion provides financial services through digital platforms,aiming to improve the ability of MSMEs and low-income groups to access financial resources,thereby easing their financing constraints and promoting economic growth and inclusive development.As an innovative financial model,digital financial inclusion utilizes modern technological means to significantly improve the accessibility and convenience of financial services,especially in areas where traditional banking services are under-covered.Digital finance has promoted the popularization of financial services such as micro-credit,micro-savings,and micro-insurance,and helped improve the financing environment of low-income groups and small and micro enterprises.At the same time,digital financial inclusion promotes financial literacy education and digital inclusion construction,and enhances the acceptance and use of digital financial instruments by the general public,which is the key to achieving sustainable development of digital financial inclusion.Therefore,digital financial inclusion can better ease the financing constraints of small and medium-sized enterprises and promote economic development.
文摘Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While semi-supervised fuzzy clustering(SSFC)methods leverage limited labeled data to enhance accuracy,they remain highly susceptible to inappropriate or mislabeled prior knowledge,especially in noisy or overlapping datasets where cluster boundaries are ambiguous.To enhance the effectiveness of clustering algorithms,it is essential to leverage labeled data while ensuring the safety of the previous knowledge.Existing solutions,such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method(TS3FCM),struggle with random centroid initialization,fixed neighbor radius formulas,and handling outliers or noise at cluster overlaps.A new framework called Active Safe Semi-Supervised Fuzzy Clustering with Pairwise Constraints Based on Cluster Boundary(AS3FCPC)is proposed in this paper to deal with these problems.It does this by combining pairwise constraints and active learning.AS3FCPC uses active learning to query only the most informative data instances close to the cluster boundaries.It also uses pairwise constraints to enforce the cluster structure,which makes the system more accurate and robust.Extensive test results on diverse datasets,including challenging noisy and overlapping scenarios,demonstrate that AS3FCPC consistently achieves superior performance compared to state-of-the-art methods like TS3FCM and other baselines,especially when the data is noisy and overlaps.This significant improvement underscores AS3FCPC’s potential for reliable and accurate semisupervised fuzzy clustering in complex,real-world applications,particularly by effectively managing mislabeled data and ambiguous cluster boundaries.
基金The funding was provided by National Office for Philosophy and Social ScienceThe authors express their gratitude to the research project titled“Study on Synergistic Governance and Optimization Path of Urban Environment under the Constraint of Biased Emission Reduction Target”[Grant No.23BGL222]this research is also supported by the Hubei Market Entity Vitality Research Center,a Key Research Institute of Humanities and Social Sciences in Hubei Universities[Grant No.00120721].
文摘The biased allocation of emission reduction target constraints quantifies emission reduction responsibilities and reflects differences in pollutant reductions both across and within cities.This approach represents a systematic innovation aims to enhance China’s green competitiveness and facilitate its economic transformation through localized and precise policymaking.Using panel data from 275 Chinese cities spanning 2000-2022,this study applies the difference-in-differences method to estimate the impact of biased allocation of emission reduction target constraints on urban green competitiveness.The findings indicate that such constraints-whether based on chemical oxygen demand or sulfur dioxide targets-significantly improve urban green competitiveness,with both pollutant-specific constraints producing comparable effects.Furthermore,these constraints exhibit significant spatial spillover effects within a 200-km geographical radius.Heterogeneity analysis reveals stronger policy impacts in resource-based cities,eastern regions,and cities designated as key areas for pollution prevention and control.Mechanism analysis demonstrates that the constraints enhance green competitiveness primarily by fostering green technological innovation and optimizing industrial structures.These conclusions provide a practical foundation for addressing China’s enduring conflict between environmental protection and economic development.
文摘With the establishment of Hainan Free Trade Port,the small and medium-sized enterprises in Hainan Free Trade Port have developed and grown in the continuously optimized enterprise environment.The continuous establishment of a large number of small and micro enterprises makes its social and economic development play a non-negligible role.However,due to the small size and insufficient economic strength of small and micro enterprises,their status in the financial system is often very humble.Therefore,under normal circumstances,small and micro enterprises are faced with financing difficulties and high costs,which has a great side effect on their development.The proposal and continuous development of digital inclusive finance,so that small and micro enterprises in access to a wide range of financing opportunities at the same time,their financing methods are more convenient than in the past,the cost is getting lower and lower.This paper deeply discusses the role of digital inclusive finance in easing the financing constraints of small and micro enterprises and puts forward corresponding suggestions[1].
基金Supported by Zhejiang Provincial Key Research and Development Program(Grant No.2021C04015)。
文摘Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot.
基金support provided by the National Natural Science Foundation of China(No.62173274)the National Key R&D Program of China(No.2019YFA0405300)+3 种基金the Natural Science Foundation of Hunan Province of China(No.2021JJ10045)Shanghai Aerospace Science and Technology Innovation Fund,China(No.SAST2020-004)Postdoctoral Fellowship Program of CPSF(No.GZB20240989)the Open Research Subject of State Key Laboratory of Intelligent Game,China(No.ZBKF-24-01).
文摘In air combat,one effective way to counter an incoming missile attacking an aircraft is to launch a defense missile compared with traditional passive defense strategies such as decoy and electronic countermeasures.To address this issue,this paper proposes a three-body cooperative active defense guidance law with overload constraints from the perspective of a small speed ratio.First,a cooperative guidance-oriented model for active defense is established and linearized to provide a foundation for the design of the guidance law.Then,the essential quantity known as Zero-Effort-Miss(ZEM)is analyzed during the engagement process.In order to minimize the influence of inaccurate estimates of remaining flight time in the ZEM,the concept of Zero-Effort-Velocity(ZEV)is introduced.Subsequently,utilizing the sliding mode control method,the guidance law is designed by selecting the ZEM and ZEV as sliding mode surfaces,combined with the fast power reaching law,and its finite-time stability is analyzed using the Lyapunov method.Furthermore,to quantitatively evaluate the performance of the proposed active defense guidance law,the interception rendezvous angle index is introduced.The proposed active defense guidance law considers integrating information from the incoming missile,aircraft,and defense missile with fewer simplifications and assumptions,and ensures that the aircraft is effectively protected with less overload required for the defense missile.Finally,simulation experiments demonstrate the effectiveness and adaptability of the proposed active defense guidance law.
