While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the se...While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or...Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or eliminate the effects of these uncertainties.However,the robustness to uncertainties of electrical drive systems has not been clearly defined.No systemic procedures have been proposed to evaluate a control system's robustness(how robust it is).This paper proposes a systemic method for evaluating control systems'robustness to uncertainties.The concept and fundamental theory of robust control are illustrated by considering a simple uncertain feedback control system.The effects of uncertainties on the control performance and stability are analyzed and discussed.The concept of design for six-sigma(a robust design method)is employed to numerically evaluate the robustness levels of control systems.To show the effectiveness of the proposed robustness evaluation method,case studies are conducted for second-order systems,DC motor drive systems,and PMSM drive systems.Besides the conventional predictive control of PMSM drive,three different robust predictive control methods are evaluated in terms of two different parametric uncertainty ranges and three application requirements against parametric uncertainties.展开更多
Software systems play increasing important roles in modern society,and the ability against attacks is of great practical importance to crucial software systems,resulting in that the structure and robustness of softwar...Software systems play increasing important roles in modern society,and the ability against attacks is of great practical importance to crucial software systems,resulting in that the structure and robustness of software systems have attracted a tremendous amount of interest in recent years.In this paper,based on the source code of Tar and MySQL,we propose an approach to generate coupled software networks and construct three kinds of directed software networks:The function call network,the weakly coupled network and the strongly coupled network.The structural properties of these complex networks are extensively investigated.It is found that the average influence and the average dependence for all functions are the same.Moreover,eight attacking strategies and two robustness indicators(the weakly connected indicator and the strongly connected indicator)are introduced to analyze the robustness of software networks.This shows that the strongly coupled network is just a weakly connected network rather than a strongly connected one.For MySQL,high in-degree strategy outperforms other attacking strategies when the weakly connected indicator is used.On the other hand,high out-degree strategy is a good choice when the strongly connected indicator is adopted.This work will highlight a better understanding of the structure and robustness of software networks.展开更多
Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably aff...Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably affect the surface transparency and limit the application of glass materials.To resolve the contradiction between the surface transparency and the robust superhydrophobicity,an efficient and low-cost laser-chemical surface functionalization process was utilized to fabricate superhydrophobic glass surface.The results show that the air can be effectively trapped in surface micro/nanostructure induced by laser texturing,thus reducing the solid-liquid contact area and interfacial tension.The deposition of hydrophobic carbon-containing groups on the surface can be accelerated by chemical treatment,and the surface energy is significantly reduced.The glass surface exhibits marvelous robust superhydrophobicity with a contact angle of 155.8°and a roll-off angle of 7.2°under the combination of hierarchical micro/nanostructure and low surface energy.Moreover,the surface transparency of the prepared superhydrophobic glass was only 5.42%lower than that of the untreated surface.This superhydrophobic glass with high transparency still maintains excellent superhydrophobicity after durability and stability tests.The facile fabrication of superhydrophobic glass with high transparency and robustness provides a strong reference for further expanding the application value of glass materials.展开更多
Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning method...Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
Time series anomaly detection is critical in domains such as manufacturing,finance,and cybersecurity.Recent generative AI models,particularly Transformer-and Autoencoder-based architectures,show strong accuracy but th...Time series anomaly detection is critical in domains such as manufacturing,finance,and cybersecurity.Recent generative AI models,particularly Transformer-and Autoencoder-based architectures,show strong accuracy but their robustness under noisy conditions is less understood.This study evaluates three representative models—AnomalyTransformer,TranAD,and USAD—on the Server Machine Dataset(SMD)and cross-domain benchmarks including the SoilMoisture Active Passive(SMAP)dataset,theMars Science Laboratory(MSL)dataset,and the Secure Water Treatment(SWaT)testbed.Seven noise settings(five canonical,two mixed)at multiple intensities are tested under fixed clean-data training,with variations in window,stride,and thresholding.Results reveal distinct robustness profiles:AnomalyTransformermaintains recall but loses precision under abrupt noise,TranAD balances sensitivity yet is vulnerable to structured anomalies,and USAD resists Gaussian perturbations but collapses under block anomalies.Quantitatively,F1 drops 60%–70%on noisy SMD,with severe collapse in SWaT(F1≤0.10,Drop up to 84%)but relative stability on SMAP/MSL(Drop within±10%).Overall,generative models exhibit complementary robustness patterns,highlighting noise-type dependent vulnerabilities and providing practical guidance for robust deployment.展开更多
Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and r...Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles.The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI.Although various adversarial attack and defense methods are actively being studied,there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks.An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall.Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks.In this paper,a Sophisticated Adversarial Robustness Score(SARS)is proposed for AI model robustness evaluation.SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process.This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators.Moreover,the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks.This paper proposed using SARS,calculated based on adversarial attacks,to identify data groups with robustness vulnerability and improve robustness through adversarial training.Through SARS,it is possible to evaluate the level of robustness,which can help developers identify areas for improvement.To validate the proposed method,experiments were conducted using a malware dataset.Through adversarial training,it was confirmed that SARS increased by 70.59%,and the recall reduction rate improved by 64.96%.Through SARS,it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types.In addition,it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training.展开更多
For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust op...For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust optimization model is constructed with both the center and halfwidth of the most important mechanical performance index described as objective functions and the other requirements on the mechanical performance indices described as constraint functions. To locate the optimal solution of objective and feasibility robustness, a new concept of interval violation vector and its calculation formulae corresponding to different constraint functions are proposed. The math?ematical formulae for calculating the feasibility and objective robustness indices and the robustness?based preferential guidelines are proposed for directly ranking various design vectors, which is realized by an algorithm integrating Kriging and nested genetic algorithm. The validity of the proposed method and its superiority to present interval optimization approaches are demonstrated by a numerical example. The robust optimization of the upper beam in a high?speed press with interval material properties demonstrated the applicability and effectiveness of the proposed method in engineering.展开更多
Unmanned Aerial Vehicle(UAV)swarms have been foreseen to play an important role in military applications in the future,wherein they will be frequently subjected to different disturbances and destructions such as attac...Unmanned Aerial Vehicle(UAV)swarms have been foreseen to play an important role in military applications in the future,wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults.Therefore,a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms.However,their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system.In this paper,a complex network theory-based robustness evaluation method for a UAV swarming system is proposed.This method takes into account the dynamic evolution of UAV swarms,including dynamic reconfiguration and information correlation.The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies.The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed.Finally,the validity of the proposed method is verified by conducting a case study analysis.The results can further provide some guidance and reference for the robust design,mission planning and decision-making of UAV swarms.展开更多
Robustness of transportation networks is one of the major challenges of the 21 st century.This paper investigates the resilience of global air transportation from a complex network point of view,with focus on attackin...Robustness of transportation networks is one of the major challenges of the 21 st century.This paper investigates the resilience of global air transportation from a complex network point of view,with focus on attacking strategies in the airport network,i.e.,to remove airports from the system and see what could affect the air traffic system from a passenger＇s perspective.Specifically,we identify commonalities and differences between several robustness measures and attacking strategies,proposing a novel notion of functional robustness：unaffected passengers with rerouting.We apply twelve attacking strategies to the worldwide airport network with three weights,and evaluate three robustness measures.We find that degree and Bonacich based attacks harm passenger weighted network most.Our evaluation is geared toward a unified view on air transportation network attack and serves as a foundation on how to develop effective mitigation strategies.展开更多
Nature-inspired superhydrophobic coatings with typical Cassie-Baxter contacts garner numerous interests for multifunctional applications.However,undesirable poor mechanical and thermal stability are still crucial bott...Nature-inspired superhydrophobic coatings with typical Cassie-Baxter contacts garner numerous interests for multifunctional applications.However,undesirable poor mechanical and thermal stability are still crucial bottlenecks for real-world employment.This work introduces a cost-effective,fluorine free and versatile strategy to achieve double-layered PDMS agglutinated candle soot coating with superior water-repellent superhydrophobicity.The surface morphologies,chemical compositions and wettability behaviors were investigated in detail.The mechanical stability,chemical stability and durable corrosion resistance of the fabricated PDMS-CS coating were evaluated through friction,calcination and electrochemical impedance spectroscopy.The results demonstrate a remarkably enhanced mechanical robustness and corrosion resistance,indicating PDMS units can act as an effective agglutinating agent between candle soot and underlying substrate.The synergistic effect of PDMS agglutination,porous network nanostructures and extremely low surface energy of incomplete combustion induced candle soot deposition contribute to the eventually robust corrosion resisting coating,which greatly increases the possibility for practical applications.展开更多
The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while...The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.Abstract： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transporta- tion network based on the most recent Automatic Identification System （AIS） data available. First, we subdivide three typical cargo ship transportation networks （i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, in- cluding random attack and three intentional attacks （i.e., degree-based attack, between- ness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） com- pared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the con- tainer network but a minor impact on the bulk carrier and oil tanker transportation networks.These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.展开更多
The robust magnesium surfaces with multi-functions are highly desirable,and the simple and scalable methods to construct such surfaces are urgently indispensable.Herein,we conducted a one-step spraying method to facil...The robust magnesium surfaces with multi-functions are highly desirable,and the simple and scalable methods to construct such surfaces are urgently indispensable.Herein,we conducted a one-step spraying method to facilely fabricate the robust coating with multi-functions on magnesium alloys.The as-sprayed magnesium alloys surface is superhydrophobic with a static water contact angle(WCA)of 157.0°and a roll-off angle of 6.0°.Such surface has excellent mechanical,chemical and thermal stabilities,even undergoing various physical and chemical damages,including sand impact(10 gmin^(-1),≥20 min),water impact(2 impacts s^(-1),≥180 min),abrasion(1.00 kPa,≥25 cycles),peeling(≥2.15 kPa),high temperature(200°C,≥24 h),strong acidic/salty/basic media(p H=113)and organic-solvent immersion(ethanol and n-hexane,≥24 h),demonstrating brilliant robustness.Notably,the surface displays multi-functions of corrosion protection,anti-fouling and heat insulation,which will undoubtedly promote the much wider applications of magnesium alloys.展开更多
Even under low external force,a few macromolecules of a polymer have to be much more highly stressed and fractured first due to the inherent heterogeneous microstructure.When the materials keep on working under loadin...Even under low external force,a few macromolecules of a polymer have to be much more highly stressed and fractured first due to the inherent heterogeneous microstructure.When the materials keep on working under loading,as is often the case,the minor damages would add up,endangering the safety of use.Here we show an innovative solution based on mechanochemically initiated reversible cascading variation of metal-ligand complexations.Upon loading,crosslinking density of the proof-of-concept metallopolymer networks autonomously increases,and recovers after unloading.Meanwhile,the stress-induced tiny fracture precursors are blocked to grow and then restored.The entire processes reversibly proceed free of manual intervention and catalyst.The proposed molecular-level internal equilibrium prevention mechanisms fundamentally enhance durability of polymers in service.展开更多
Robustness testing for safety-critical embedded software is still a challenge in its nascent stages. In this paper, we propose a practical methodology and implement an environment by employing model-based robustness t...Robustness testing for safety-critical embedded software is still a challenge in its nascent stages. In this paper, we propose a practical methodology and implement an environment by employing model-based robustness testing for embedded software systems. It is a system-level black-box testing approach in which the fault behaviors of embedded software is triggered with the aid of modelbased fault injection by the support of an executable model-driven hardware-in-loop （HIL） testing environment. The prototype implementation of the robustness testing environment based on the proposed approach is experimentally discussed and illustrated by industrial case studies based on several avionics-embedded software systems. The results show that our proposed and implemented robustness testing method and environment are effective to find more bugs, and reduce burdens of testing engineers to enhance efficiency of testing tasks, especially for testing complex embedded systems.展开更多
The system portfolio selection is a fundamental frontier issue in the development planning and demonstration of weapon equipment.The scientific and reasonable development of the weapon system portfolio is of great sig...The system portfolio selection is a fundamental frontier issue in the development planning and demonstration of weapon equipment.The scientific and reasonable development of the weapon system portfolio is of great significance for optimizing the design of equipment architecture,realizing effective resource allocation,and increasing the campaign effectiveness of integrated joint operations.From the perspective of system-ofsystems,this paper proposes a unified framework called structure-oriented weapon system portfolio selection(SWSPS)to solve the weapon system portfolio selection problem based on structural invulnerability.First,the types of equipment and the relationship between the equipment are sorted out based on the operation loop theory,and a heterogeneous combat network model of the weapon equipment system is established by abstracting the equipment and their relationships into different types of nodes and edges respectively.Then,based on the combat network model,the operation loop comprehensive evaluation index(OLCEI)is introduced to quantitatively describe the structural robustness of the combat network.Next,a weapon system combination selection model is established with the goal of maximizing the operation loop comprehensive evaluation index within the constraints of capability requirements and budget limitations.Finally,our proposed SWSPS is demonstrated through a case study of an armored infantry battalion.The results show that our proposed SWSPS can achieve excellent performance in solving the weapon system portfolio selection problem,which yields many meaningful insights and guidance to the future equipment development planning.展开更多
We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and geneti...We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm(GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background.展开更多
文摘While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or eliminate the effects of these uncertainties.However,the robustness to uncertainties of electrical drive systems has not been clearly defined.No systemic procedures have been proposed to evaluate a control system's robustness(how robust it is).This paper proposes a systemic method for evaluating control systems'robustness to uncertainties.The concept and fundamental theory of robust control are illustrated by considering a simple uncertain feedback control system.The effects of uncertainties on the control performance and stability are analyzed and discussed.The concept of design for six-sigma(a robust design method)is employed to numerically evaluate the robustness levels of control systems.To show the effectiveness of the proposed robustness evaluation method,case studies are conducted for second-order systems,DC motor drive systems,and PMSM drive systems.Besides the conventional predictive control of PMSM drive,three different robust predictive control methods are evaluated in terms of two different parametric uncertainty ranges and three application requirements against parametric uncertainties.
基金supported by the Beijing Education Commission Science and Technology Project(No.KM201811417005)the National Natural Science Foundation of China(No.62173237)+6 种基金the Aeronautical Science Foundation of China(No.20240055054001)the Open Fund of State Key Laboratory of Satellite Navigation System and Equipment Technology(No.CEPNT2023A01)Joint Fund of Ministry of Natural Resources Key Laboratory of Spatiotemporal Perception and Intelligent Processing(No.232203)the Civil Aviation Flight Technology and Flight Safety Engineering Technology Research Center of Sichuan(No.GY2024-02B)the Applied Basic Research Programs of Liaoning Province(No.2025JH2/101300011)the General Project of Liaoning Provincial Education Department(No.20250054)Research on Safety Intelligent Management Technology and Systems for Mixed Operations of General Aviation Aircraft in Low-Altitude Airspace(No.310125011).
文摘Software systems play increasing important roles in modern society,and the ability against attacks is of great practical importance to crucial software systems,resulting in that the structure and robustness of software systems have attracted a tremendous amount of interest in recent years.In this paper,based on the source code of Tar and MySQL,we propose an approach to generate coupled software networks and construct three kinds of directed software networks:The function call network,the weakly coupled network and the strongly coupled network.The structural properties of these complex networks are extensively investigated.It is found that the average influence and the average dependence for all functions are the same.Moreover,eight attacking strategies and two robustness indicators(the weakly connected indicator and the strongly connected indicator)are introduced to analyze the robustness of software networks.This shows that the strongly coupled network is just a weakly connected network rather than a strongly connected one.For MySQL,high in-degree strategy outperforms other attacking strategies when the weakly connected indicator is used.On the other hand,high out-degree strategy is a good choice when the strongly connected indicator is adopted.This work will highlight a better understanding of the structure and robustness of software networks.
基金Projects(52105175,52305149)supported by the National Natural Science Foundation of ChinaProject(2242024RCB0035)supported by the Zhishan Young Scholar Program of Southeast University,China+5 种基金Project(BK20210235)supported by the Natural Science Foundation of Jiangsu Province,ChinaProject(2023MK042)supported by the State Administration for Market Regulation,ChinaProject(KJ2023003)supported by the Jiangsu Administration for Market Regulation,ChinaProjects(KJ(Y)202429,KJ(YJ)2023001)supported by the Jiangsu Province Special Equipment Safety Supervision Inspection Institute,ChinaProject(JSSCBS20210121)supported by the Jiangsu Provincial Innovative and Entrepreneurial Doctor Program,ChinaProject(1102002310)supported by the Technology Innovation Project for Returnees in Nanjing,China。
文摘Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably affect the surface transparency and limit the application of glass materials.To resolve the contradiction between the surface transparency and the robust superhydrophobicity,an efficient and low-cost laser-chemical surface functionalization process was utilized to fabricate superhydrophobic glass surface.The results show that the air can be effectively trapped in surface micro/nanostructure induced by laser texturing,thus reducing the solid-liquid contact area and interfacial tension.The deposition of hydrophobic carbon-containing groups on the surface can be accelerated by chemical treatment,and the surface energy is significantly reduced.The glass surface exhibits marvelous robust superhydrophobicity with a contact angle of 155.8°and a roll-off angle of 7.2°under the combination of hierarchical micro/nanostructure and low surface energy.Moreover,the surface transparency of the prepared superhydrophobic glass was only 5.42%lower than that of the untreated surface.This superhydrophobic glass with high transparency still maintains excellent superhydrophobicity after durability and stability tests.The facile fabrication of superhydrophobic glass with high transparency and robustness provides a strong reference for further expanding the application value of glass materials.
基金supported by the Key Research and Development Program of Zhejiang Province(No.2024C01071)the Natural Science Foundation of Zhejiang Province(No.LQ15F030006).
文摘Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金supported by the“Regional Innovation System&Education(RISE)”through the Seoul RISE Center,funded by the Ministry of Education(MOE)the Seoul Metropolitan Government(2025-RISE-01-018-04)supported by the Korea Digital Forensic Center.
文摘Time series anomaly detection is critical in domains such as manufacturing,finance,and cybersecurity.Recent generative AI models,particularly Transformer-and Autoencoder-based architectures,show strong accuracy but their robustness under noisy conditions is less understood.This study evaluates three representative models—AnomalyTransformer,TranAD,and USAD—on the Server Machine Dataset(SMD)and cross-domain benchmarks including the SoilMoisture Active Passive(SMAP)dataset,theMars Science Laboratory(MSL)dataset,and the Secure Water Treatment(SWaT)testbed.Seven noise settings(five canonical,two mixed)at multiple intensities are tested under fixed clean-data training,with variations in window,stride,and thresholding.Results reveal distinct robustness profiles:AnomalyTransformermaintains recall but loses precision under abrupt noise,TranAD balances sensitivity yet is vulnerable to structured anomalies,and USAD resists Gaussian perturbations but collapses under block anomalies.Quantitatively,F1 drops 60%–70%on noisy SMD,with severe collapse in SWaT(F1≤0.10,Drop up to 84%)but relative stability on SMAP/MSL(Drop within±10%).Overall,generative models exhibit complementary robustness patterns,highlighting noise-type dependent vulnerabilities and providing practical guidance for robust deployment.
基金supported by an Institute of Information and Communications Technology Planning and Evaluation (IITP)grant funded by the Korean Government (MSIT) (No.2022-0-00089,Development of Clustering and Analysis Technology to Identify Cyber-Attack Groups Based on Life-Cycle)and MISP (Ministry of Science,ICT&Future Planning),Korea,under the National Program for Excellence in SW (2019-0-01834)supervised by the IITP (Institute of Information&Communications Technology Planning&Evaluation) (2019-0-01834).
文摘Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles.The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI.Although various adversarial attack and defense methods are actively being studied,there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks.An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall.Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks.In this paper,a Sophisticated Adversarial Robustness Score(SARS)is proposed for AI model robustness evaluation.SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process.This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators.Moreover,the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks.This paper proposed using SARS,calculated based on adversarial attacks,to identify data groups with robustness vulnerability and improve robustness through adversarial training.Through SARS,it is possible to evaluate the level of robustness,which can help developers identify areas for improvement.To validate the proposed method,experiments were conducted using a malware dataset.Through adversarial training,it was confirmed that SARS increased by 70.59%,and the recall reduction rate improved by 64.96%.Through SARS,it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types.In addition,it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training.
基金Supported by National Natural Science Foundation of China(Grant Nos.51775491,51475417,U1608256,51405433)
文摘For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust optimization model is constructed with both the center and halfwidth of the most important mechanical performance index described as objective functions and the other requirements on the mechanical performance indices described as constraint functions. To locate the optimal solution of objective and feasibility robustness, a new concept of interval violation vector and its calculation formulae corresponding to different constraint functions are proposed. The math?ematical formulae for calculating the feasibility and objective robustness indices and the robustness?based preferential guidelines are proposed for directly ranking various design vectors, which is realized by an algorithm integrating Kriging and nested genetic algorithm. The validity of the proposed method and its superiority to present interval optimization approaches are demonstrated by a numerical example. The robust optimization of the upper beam in a high?speed press with interval material properties demonstrated the applicability and effectiveness of the proposed method in engineering.
基金co-supported by the National Natural Science Foundation of China(No.51805016)Field Foundation of China(No.JZX7Y20190242012001).
文摘Unmanned Aerial Vehicle(UAV)swarms have been foreseen to play an important role in military applications in the future,wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults.Therefore,a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms.However,their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system.In this paper,a complex network theory-based robustness evaluation method for a UAV swarming system is proposed.This method takes into account the dynamic evolution of UAV swarms,including dynamic reconfiguration and information correlation.The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies.The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed.Finally,the validity of the proposed method is verified by conducting a case study analysis.The results can further provide some guidance and reference for the robust design,mission planning and decision-making of UAV swarms.
基金supported by the National Natural Science Foundation of China(Nos.61650110516,61601013 and 61521091)
文摘Robustness of transportation networks is one of the major challenges of the 21 st century.This paper investigates the resilience of global air transportation from a complex network point of view,with focus on attacking strategies in the airport network,i.e.,to remove airports from the system and see what could affect the air traffic system from a passenger＇s perspective.Specifically,we identify commonalities and differences between several robustness measures and attacking strategies,proposing a novel notion of functional robustness：unaffected passengers with rerouting.We apply twelve attacking strategies to the worldwide airport network with three weights,and evaluate three robustness measures.We find that degree and Bonacich based attacks harm passenger weighted network most.Our evaluation is geared toward a unified view on air transportation network attack and serves as a foundation on how to develop effective mitigation strategies.
基金financially supported by the National Natural Science Foundation of China(Nos.41806089 and 41827805)。
文摘Nature-inspired superhydrophobic coatings with typical Cassie-Baxter contacts garner numerous interests for multifunctional applications.However,undesirable poor mechanical and thermal stability are still crucial bottlenecks for real-world employment.This work introduces a cost-effective,fluorine free and versatile strategy to achieve double-layered PDMS agglutinated candle soot coating with superior water-repellent superhydrophobicity.The surface morphologies,chemical compositions and wettability behaviors were investigated in detail.The mechanical stability,chemical stability and durable corrosion resistance of the fabricated PDMS-CS coating were evaluated through friction,calcination and electrochemical impedance spectroscopy.The results demonstrate a remarkably enhanced mechanical robustness and corrosion resistance,indicating PDMS units can act as an effective agglutinating agent between candle soot and underlying substrate.The synergistic effect of PDMS agglutination,porous network nanostructures and extremely low surface energy of incomplete combustion induced candle soot deposition contribute to the eventually robust corrosion resisting coating,which greatly increases the possibility for practical applications.
基金Key Project of the Chinese Academy of Sciences,No.ZDRW-ZS-2016-6-3National Natural Science Foundation of China,No.41501490
文摘The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.Abstract： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transporta- tion network based on the most recent Automatic Identification System （AIS） data available. First, we subdivide three typical cargo ship transportation networks （i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, in- cluding random attack and three intentional attacks （i.e., degree-based attack, between- ness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） com- pared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the con- tainer network but a minor impact on the bulk carrier and oil tanker transportation networks.These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.
基金supported by the National Natural Science Foundation of China(21773019,21972012)the Graduate Research and Innovation Foundation of Chongqing(CYB18044)the sharing fund of Chongqing University s Large-scale Equipment
文摘The robust magnesium surfaces with multi-functions are highly desirable,and the simple and scalable methods to construct such surfaces are urgently indispensable.Herein,we conducted a one-step spraying method to facilely fabricate the robust coating with multi-functions on magnesium alloys.The as-sprayed magnesium alloys surface is superhydrophobic with a static water contact angle(WCA)of 157.0°and a roll-off angle of 6.0°.Such surface has excellent mechanical,chemical and thermal stabilities,even undergoing various physical and chemical damages,including sand impact(10 gmin^(-1),≥20 min),water impact(2 impacts s^(-1),≥180 min),abrasion(1.00 kPa,≥25 cycles),peeling(≥2.15 kPa),high temperature(200°C,≥24 h),strong acidic/salty/basic media(p H=113)and organic-solvent immersion(ethanol and n-hexane,≥24 h),demonstrating brilliant robustness.Notably,the surface displays multi-functions of corrosion protection,anti-fouling and heat insulation,which will undoubtedly promote the much wider applications of magnesium alloys.
基金supported by the National Natural Science Foundation of China(Nos.52033011,51773229,51873235).
文摘Even under low external force,a few macromolecules of a polymer have to be much more highly stressed and fractured first due to the inherent heterogeneous microstructure.When the materials keep on working under loading,as is often the case,the minor damages would add up,endangering the safety of use.Here we show an innovative solution based on mechanochemically initiated reversible cascading variation of metal-ligand complexations.Upon loading,crosslinking density of the proof-of-concept metallopolymer networks autonomously increases,and recovers after unloading.Meanwhile,the stress-induced tiny fracture precursors are blocked to grow and then restored.The entire processes reversibly proceed free of manual intervention and catalyst.The proposed molecular-level internal equilibrium prevention mechanisms fundamentally enhance durability of polymers in service.
基金the Aeronautics Science Foundation of China(No.2011ZD51055)Science and Technology on Reliability&Environmental Engineering Laboratory(No.302367)the National Pre-Research Foundation of China(No.51319080201)
文摘Robustness testing for safety-critical embedded software is still a challenge in its nascent stages. In this paper, we propose a practical methodology and implement an environment by employing model-based robustness testing for embedded software systems. It is a system-level black-box testing approach in which the fault behaviors of embedded software is triggered with the aid of modelbased fault injection by the support of an executable model-driven hardware-in-loop （HIL） testing environment. The prototype implementation of the robustness testing environment based on the proposed approach is experimentally discussed and illustrated by industrial case studies based on several avionics-embedded software systems. The results show that our proposed and implemented robustness testing method and environment are effective to find more bugs, and reduce burdens of testing engineers to enhance efficiency of testing tasks, especially for testing complex embedded systems.
基金This work was supported by the National Natural Science Foundation of China(71690233,71971213,71571185)Scientific Research Foundation of National University of Defense Technology(ZK19-16).
文摘The system portfolio selection is a fundamental frontier issue in the development planning and demonstration of weapon equipment.The scientific and reasonable development of the weapon system portfolio is of great significance for optimizing the design of equipment architecture,realizing effective resource allocation,and increasing the campaign effectiveness of integrated joint operations.From the perspective of system-ofsystems,this paper proposes a unified framework called structure-oriented weapon system portfolio selection(SWSPS)to solve the weapon system portfolio selection problem based on structural invulnerability.First,the types of equipment and the relationship between the equipment are sorted out based on the operation loop theory,and a heterogeneous combat network model of the weapon equipment system is established by abstracting the equipment and their relationships into different types of nodes and edges respectively.Then,based on the combat network model,the operation loop comprehensive evaluation index(OLCEI)is introduced to quantitatively describe the structural robustness of the combat network.Next,a weapon system combination selection model is established with the goal of maximizing the operation loop comprehensive evaluation index within the constraints of capability requirements and budget limitations.Finally,our proposed SWSPS is demonstrated through a case study of an armored infantry battalion.The results show that our proposed SWSPS can achieve excellent performance in solving the weapon system portfolio selection problem,which yields many meaningful insights and guidance to the future equipment development planning.
基金Supported by the National Key Research and Development Program of China under Grant No 2017YFB1104500the Natural Science Foundation of Beijing under Grant No 7182091,the National Natural Science Foundation of China under Grant No 21627813the Fundamental Research Funds for the Central Universities under Grant No PYBZ1801
文摘We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm(GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background.
