Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adja...Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.展开更多
In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in ...In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.展开更多
Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation(5G)networks.However,it leads to performance degradation and huge...Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation(5G)networks.However,it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand.To meet these access conditions and improve Quality of Service,resource allocation(RA)should be carefully optimized.Traditionally,RA problems are nonconvex optimizations,which are performed using heuristic methods,such as genetic algorithm,particle swarm optimization,and simulated annealing.However,the application of these approaches remains computationally expensive and unattractive for dense cellular networks.Therefore,artificial intelligence algorithms are used to improve traditional RA mechanisms.Deep learning is a promising tool for addressing resource management problems in wireless communication.In this study,we investigate a double deep Q-network-based RA framework that maximizes energy efficiency(EE)and total network throughput in unmanned aerial vehicle(UAV)-assisted terrestrial networks.Specifically,the system is studied under the constraints of interference.However,the optimization problem is formulated as a mixed integer nonlinear program.Within this framework,we evaluated the effect of height and the number of UAVs on EE and throughput.Then,in accordance with the experimental results,we compare the proposed algorithm with several artificial intelligence methods.Simulation results indicate that the proposed approach can increase EE with a considerable throughput.展开更多
An analysis of the radar backscattering from the ocean surface covered by oil spill is presented using a mi- crowave scattering model and Monte-Carlo simulation. In the analysis, a one-dimensional rough sea sur- face ...An analysis of the radar backscattering from the ocean surface covered by oil spill is presented using a mi- crowave scattering model and Monte-Carlo simulation. In the analysis, a one-dimensional rough sea sur- face is numerically generated with an ocean waveheight spectrum for a given wind velocity. A two-layered medium is then generated by adding a thin oil layer on the simulated rough sea surface. The electric fields backscattered from the sea surface with two-layered medium are computed with the method of moments （MoM）, and the backscattering coefficients are statistically obtained with N independent samples for each oil-spilled surface using the Monte-Carlo technique for various conditions of surface roughness, oil-layer thickness, frequency, polarization and incidence angle. The numerical simulation results are compared with theoretical models for clean sea surfaces and SAR images of an off-spilled sea surface caused by the Hebei （Hebei province, China） Spirit oil tanker in 2007. Further, conditions for better oil spill extraction are sought by the numerical simulation on the effects of wind speed and oil-layer thickness at different inci- dence angles on the backscattering coefficients.展开更多
The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the O...The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the OpenFlow version,clustering,modularity,platform,and partnership support,etc.They are regarded as vital when making a selection among a set of controllers.As such,the selection of the controller becomes a multi-criteria decision making(MCDM)problem with several features.Hence,an increase in this number will increase the computational complexity of the controller selection process.Previously,the selection of controllers based on features has been studied by the researchers.However,the prioritization of features has gotten less attention.Moreover,several features increase the computational complexity of the selection process.In this paper,we propose a mathematical modeling for feature prioritization with analytical network process(ANP)bridge model for SDN controllers.The results indicate that a prioritized features model lead to a reduction in the computational complexity of the selection of SDN controller.In addition,our model generates prioritized features for SDN controllers.展开更多
The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The integrated navigation sensor using DVL (Doppler Vel...The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The integrated navigation sensor using DVL (Doppler Velocity Log) is widely used to improve the underwater navigation performance. However, the DVL’s range of measuring varied depending on the characteristics of sensor. So, if the sea gets too deep suddenly, it cannot measure the velocity. To complement such a weak point, the VKF was additionally designed, which was made of DVL, RPM (Revolve Per Minutes) of motor, and ES (Echo Sounder). The proposed approach relies on a VKF, augmented by an altitude from ES based switching architecture to yield robust performance, even when DVL exceeds the measurement range and the measured value is unable to be valid. The proposed approach relies on two parts: 1) indirect feedback navigation Kalman filter design, 2) VKF design. To evaluate the proposed method, we compare the VKF aided navigation system with PINS (Pure Inertial Navigation System) and conventional INS-DVL navigation system through simulation results. Simulations illustrate the effectiveness of the underwater navigation system assisted by the additional DVL-RPM based VKF in underwater environment.展开更多
In this research work,a hierarchical controller has been designed for an autonomous navigation robot to avoid unexpected moving obstacles where the state and action spaces are continuous.The proposed scheme consists o...In this research work,a hierarchical controller has been designed for an autonomous navigation robot to avoid unexpected moving obstacles where the state and action spaces are continuous.The proposed scheme consists of two parts:1)a controller with a high-level approximate reinforcement learning(ARL)technique for choosing an optimal trajectory in autonomous navigation;and 2)a low-level,appearance-based visual servoing(ABVS)controller which controls and execute the motion of the robot.A novel approach for path planning and visual servoing has been proposed by the combined system framework.The characteristics of the on-board camera which is equipped on the robot is naturally suitable for conducting the reinforcement learning algorithm.Regarding the ARL controller,the computational overhead is quite low thanks to the fact that a knowledge of obstacle motion is not necessary.The developed scheme has been implemented and validated in a simulation system of obstacle avoidance.It is noted that findings of the proposed method are successfully verified by obtaining an optimal robotic plan motion strategy.展开更多
In modern physics and fabrication technology,simulation of projectile and target collision is vital to improve design in some critical applications,like;bulletproofing and medical applications.Graphene,the most promin...In modern physics and fabrication technology,simulation of projectile and target collision is vital to improve design in some critical applications,like;bulletproofing and medical applications.Graphene,the most prominent member of two dimensional materials presents ultrahigh tensile strength and stiffness.Moreover,polydimethylsiloxane(PDMS)is one of the most important elastomeric materials with a high extensive application area,ranging from medical,fabric,and interface material.In this work we considered graphene/PDMS structures to explore the bullet resistance of resulting nanocomposites.To this aim,extensive molecular dynamic simulations were carried out to identify the penetration of bullet through the graphene and PDMS composite structures.In this paper,we simulate the impact of a diamond bullet with different velocities on the composites made of single-or bi-layer graphene placed in different positions of PDMS polymers.The underlying mechanism concerning how the PDMS improves the resistance of graphene against impact loading is discussed.We discuss that with the same content of graphene,placing the graphene in between the PDMS result in enhanced bullet resistance.This work comparatively examines the enhancement in design of polymer nanocomposites to improve their bulletproofing response and the obtained results may serve as valuable guide for future experimental and theoretical studies.展开更多
Wildfire events are increasing globally which may be partly associated with climate change,resulting in significant adverse impacts on local,regional air quality and global climate.In September 2020,a small wildfire(b...Wildfire events are increasing globally which may be partly associated with climate change,resulting in significant adverse impacts on local,regional air quality and global climate.In September 2020,a small wildfire(burned area:36.3 ha)event occurred in Souesmes(Loiret-Cher,Sologne,France),and its plume spread out over 200 km on the following day as observed by the MODIS satellite.Based on measurements at a suburban site(~50 km northwest of the fire location)in Orléans and backward trajectory analysis,young wildfire plumes were characterized.Significant increases in gaseous pollutants(CO,CH_(4),N_(2)O,VOCs,etc.)and particles(including black carbon)were found within the wildfire plumes,leading to a reduced air quality.Emission factors,defined as EF(X)=ΔX/ΔCO(where,X represents the target species),of various trace gases and black carbon within the young wildfire plumes were determined accordingly and compared with previous studies.Changes in the ambient ions(such as ammonium,sulfate,nitrate,chloride,and nitrite in the particle-and gasphase)and aerosol properties(e.g.,aerosol water content,aerosol p H)were also quantified and discussed.Moreover,we estimated the total carbon and climate-related species(e.g.,CO_(2),CH_(4),N_(2)O,and BC)emissions and compared them with fire emission inventories.Current biomass burning emission inventories have uncertainties in estimating small fire burned areas and emissions.For instance,we found that the Global Fire Assimilation System(GFAS)may underestimate emissions(e.g.,CO)of this small wildfire while other inventories(GFED and FINN)showed significant overestimation.Considering that it is the first time to record wildfire plumes in this region,related atmospheric implications are presented and discussed.展开更多
For legged robots,collecting tactile information is essential for stable posture and efficient gait.However,mounting sensors on small robots weighing less than 1 kg remain challenges in terms of the sensor’s durabili...For legged robots,collecting tactile information is essential for stable posture and efficient gait.However,mounting sensors on small robots weighing less than 1 kg remain challenges in terms of the sensor’s durability,flexibility,sensitivity,and size.Crackbased sensors featuring ultra-sensitivity,small-size,and flexibility could be a promising candidate,but performance degradation due to crack growing by repeated use is a stumbling block.This paper presents an ultra-stable and tough bio-inspired crack-based sensor by controlling the crack depth using silver nanowire(Ag NW)mesh as a crack stop layer.The Ag NW mesh inspired by skin collagen structure effectively mitigated crack propagation.The sensor was very thin,lightweight,sensitive,and ultra-durable that maintains its sensitivity during 200,000 cycles of 0.5%strain.We demonstrate sensor’s feasibility by implementing the tactile sensation to bio-inspired robots,and propose statistical and deep learning-based analysis methods which successfully distinguished terrain type.展开更多
In-situ tensile experiments on pure Ti were performed in a transmission electron microscope at room temperature.The dynamic process of stress-induced hexagonal closed-packed(hcp)to face-centered cu-bic(fcc)structural ...In-situ tensile experiments on pure Ti were performed in a transmission electron microscope at room temperature.The dynamic process of stress-induced hexagonal closed-packed(hcp)to face-centered cu-bic(fcc)structural transformation ahead of a crack tip was captured at the atomic level.Intriguingly,a sliding behavior of the ensuing(0001)hcp/(1¯11)_(fcc) phase boundary was observed to further accommodate the plastic deformation until crack initiation.The sliding was accomplished via the successive conserva-tive glide of extended dislocations along the(0001)hcp/(1¯11)_(fcc) phase boundary.A molecular dynamics simulation was carried out to corroborate the experiments and the results confirm the new dislocation-mediated sliding mechanism.展开更多
While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisua...While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisually to the end user and how to automatize the process.In this paper,we first present vocabularies to describe at a high level the elements that make up a geovisualization.We then propose a method that describes at a semantic level how to obtain a geovisualization from an existing data model.This method is based on our vocabularies and on a set of semantic rules encoding rich and complex operations on data.This leads to the derivation of ontological knowledge,ready to be exploited to automate the creation of a geovisualization.The method is implemented in a framework that uses Semantic Web technologies.The singularity and the strength of our proposal is that it enables to describe a geovisualization through a RDF specification file,which once loaded in our system makes the geovisualization directly available for use from a Web browser.This result is obtained by extending a priori an application data model with ad hoc geovisualization semantics features and rules.展开更多
Compound eyes found in insects provide intriguing sources of biological inspiration for miniaturised imaging systems.Here,we report an ultrathin arrayed camera inspired by insect eye structures for high-contrast and s...Compound eyes found in insects provide intriguing sources of biological inspiration for miniaturised imaging systems.Here,we report an ultrathin arrayed camera inspired by insect eye structures for high-contrast and super-resolution imaging.The ultrathin camera features micro-optical elements(MOEs),i.e.,inverted microlenses,multilayered pinhole arrays,and gap spacers on an image sensor.The MOE was fabricated by using repeated photolithography and thermal reflow.The fully packaged camera shows a total track length of 740μm and a field-of-view(FOV)of 73°.The experimental results demonstrate that the multilayered pinhole of the MOE allows high-contrast imaging by eliminating the optical crosstalk between microlenses.The integral image reconstructed from array images clearly increases the modulation transfer function(MTF)by~1.57 times compared to that of a single channel image in the ultrathin camera.This ultrathin arrayed camera provides a novel and practical direction for diverse mobile,surveillance or medical applications.展开更多
The emergence of mutual knowledge is a major cognitive mechanism for the robustness of complex socio-technical systems. It has been extensively studied from an ethnomethodological point of view and empirically reprodu...The emergence of mutual knowledge is a major cognitive mechanism for the robustness of complex socio-technical systems. It has been extensively studied from an ethnomethodological point of view and empirically reproduced by multi-agent simulations. Whilst such simulations have been used to design real work settings the underlying theoretical grounding for the process is vague. The aim of this paper is to investigate whether the emergence of mutual knowledge(MK) in a group of colocated individuals can be explained as a percolation phenomenon. The followed methodology consists in coupling agent-based simulation with dynamic networks analysis to study information propagation phenomena: After using an agent-based simulation the authors generated and then analyzed its traces as networks where agents met and exchanged knowledge. Deep analysis of the resulting networks clearly shows that the emergence of MK is comparable to a percolation process. The authors specifically focus on how changes at the microscopic level in the proposed agent based simulator affect percolation and robustness. These results therefore provide theoretical basis for the analysis of social organizations.展开更多
In this study, we present a framework based on a prediction model that facilitates user access to a number of services in a smart living environment. Users must be able to access all available services continuously eq...In this study, we present a framework based on a prediction model that facilitates user access to a number of services in a smart living environment. Users must be able to access all available services continuously equipped with mobile devices or smart objects without being impacted by technical constraints such as performance or memory issues, regardless of their physical location and mobility. To achieve this goal, we propose the use of cloudlet-based architecture that serves as distributed cloud resources with specific ranges of influence and a realtime processing framework that tracks events and preferences of the end consumers, predicts their requirements,and recommends services to optimize resource utilization and service response time.展开更多
The observation of demographical,economical or environmental indicators over time through maps is crucial.It enables analysing territories and helps stakeholders to take decisions.However,the understanding of Territor...The observation of demographical,economical or environmental indicators over time through maps is crucial.It enables analysing territories and helps stakeholders to take decisions.However,the understanding of Territorial Statistical Information(TSI)is compromised unless comprehensive description of both the statistical methodology used and the spatial and temporal references are given.Thus,in this paper,we stress the importance of metadata descriptions and of their quality that helps assessing data reliability.Furthermore,time-series of such TSI are paramount.They enable analysing a territory over a long period of time and likewise judging the effectiveness of reforms.In light of these observations,we present Spatio-Temporal evolutive Data Infrastructure(STeDI)an innovative Spatial Data Infrastructure(SDI)that enriches the description of a Digital Earth,providing a virtual representation of territories and of their evolution through statistics and time.STeDI aims at managing a whole dataflow of multi-dimensional,multi-scale and multi-temporal TSI,from their acquisition to their dissemination to scientists and policy-makers.The content of this SDI evolves autonomously thanks to automated processes and to a Web platform that help improving the quality of datasets uploaded by experts.Then,STeDI allows visualizing up-to-date time-series reflecting the human activities on a given territory.It helps policy-makers in their decision-making process.展开更多
This paper presents an overview of research studies made at the COGIT laboratory of IGN France in the fields of generalisation and symbol specification,particularly considering evaluation aspects.It then discusses how...This paper presents an overview of research studies made at the COGIT laboratory of IGN France in the fields of generalisation and symbol specification,particularly considering evaluation aspects.It then discusses how generalisation and symbol specification interact.Finally it explores some possible adaptations of the presented works in generalisation and symbol specification to cartography in the context of crisis management.展开更多
This paper highlights the use of situated artificial institution(SAI) within a hybrid, interactive,normative multi-agent system to regulate human collaboration in crisis management. Norms regulate the actions of human...This paper highlights the use of situated artificial institution(SAI) within a hybrid, interactive,normative multi-agent system to regulate human collaboration in crisis management. Norms regulate the actions of human actors based on the dynamics of the environment in which they are situated. This dynamics results from both environment evolution and actors' actions. Our objective is to situate norms in the environment in order to provide a context-aware crisis regulation. However, this coupling must be a loose one to keep both levels independent and easyto-change in order to face the complex and changing crisis situations. To that aim, we introduce a constitutive level between environmental and normative states providing a loose coupling of normative regulation with environment evolution. Norms are thus no more referring to environmental facts but to status functions, i.e., the institutional interpretation of environmental facts through constitutive rules. We present how this declarative and distinct SAI modelling succeeds in managing the crisis with a context-aware crisis regulation.展开更多
基金supported by National Natural Science Foundation of China(52222215, 52272420, 52072051)。
文摘Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.
基金supported in part by the Korea Research Institute for Defense Technology Planning and Advancement(KRIT)funded by the Korean Government’s Defense Acquisition Program Administration(DAPA)under Grant KRIT-CT-21-037in part by the Ministry of Education,Republic of Koreain part by the National Research Foundation of Korea under Grant RS-2023-00211871.
文摘In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.
基金This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R323)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,and Taif University Researchers Supporting Project Number TURSP-2020/34),Taif,Saudi Arabia。
文摘Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation(5G)networks.However,it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand.To meet these access conditions and improve Quality of Service,resource allocation(RA)should be carefully optimized.Traditionally,RA problems are nonconvex optimizations,which are performed using heuristic methods,such as genetic algorithm,particle swarm optimization,and simulated annealing.However,the application of these approaches remains computationally expensive and unattractive for dense cellular networks.Therefore,artificial intelligence algorithms are used to improve traditional RA mechanisms.Deep learning is a promising tool for addressing resource management problems in wireless communication.In this study,we investigate a double deep Q-network-based RA framework that maximizes energy efficiency(EE)and total network throughput in unmanned aerial vehicle(UAV)-assisted terrestrial networks.Specifically,the system is studied under the constraints of interference.However,the optimization problem is formulated as a mixed integer nonlinear program.Within this framework,we evaluated the effect of height and the number of UAVs on EE and throughput.Then,in accordance with the experimental results,we compare the proposed algorithm with several artificial intelligence methods.Simulation results indicate that the proposed approach can increase EE with a considerable throughput.
基金The Project "Development of Korea Operational Oceanographic System (PM57041)" funded by the Ministry of Land, Transport and Maritime Affairs of Korean Governmentthe Project "Cooperation on the Development of Basic Technologies for the Yellow Sea and East China Sea Operational Oceanographic System (YOOS)" funded by CKJORC and the Basic Research Projects (PE98731, PG47770 and PE98732) of the Korea Institute Ocean Science and Technologysupport by the PASCO Corporation,Japan is also apreciated
文摘An analysis of the radar backscattering from the ocean surface covered by oil spill is presented using a mi- crowave scattering model and Monte-Carlo simulation. In the analysis, a one-dimensional rough sea sur- face is numerically generated with an ocean waveheight spectrum for a given wind velocity. A two-layered medium is then generated by adding a thin oil layer on the simulated rough sea surface. The electric fields backscattered from the sea surface with two-layered medium are computed with the method of moments （MoM）, and the backscattering coefficients are statistically obtained with N independent samples for each oil-spilled surface using the Monte-Carlo technique for various conditions of surface roughness, oil-layer thickness, frequency, polarization and incidence angle. The numerical simulation results are compared with theoretical models for clean sea surfaces and SAR images of an off-spilled sea surface caused by the Hebei （Hebei province, China） Spirit oil tanker in 2007. Further, conditions for better oil spill extraction are sought by the numerical simulation on the effects of wind speed and oil-layer thickness at different inci- dence angles on the backscattering coefficients.
基金This research was supported partially by LIG Nex1It was also supported partially by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning Evaluation).
文摘The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the OpenFlow version,clustering,modularity,platform,and partnership support,etc.They are regarded as vital when making a selection among a set of controllers.As such,the selection of the controller becomes a multi-criteria decision making(MCDM)problem with several features.Hence,an increase in this number will increase the computational complexity of the controller selection process.Previously,the selection of controllers based on features has been studied by the researchers.However,the prioritization of features has gotten less attention.Moreover,several features increase the computational complexity of the selection process.In this paper,we propose a mathematical modeling for feature prioritization with analytical network process(ANP)bridge model for SDN controllers.The results indicate that a prioritized features model lead to a reduction in the computational complexity of the selection of SDN controller.In addition,our model generates prioritized features for SDN controllers.
文摘The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The integrated navigation sensor using DVL (Doppler Velocity Log) is widely used to improve the underwater navigation performance. However, the DVL’s range of measuring varied depending on the characteristics of sensor. So, if the sea gets too deep suddenly, it cannot measure the velocity. To complement such a weak point, the VKF was additionally designed, which was made of DVL, RPM (Revolve Per Minutes) of motor, and ES (Echo Sounder). The proposed approach relies on a VKF, augmented by an altitude from ES based switching architecture to yield robust performance, even when DVL exceeds the measurement range and the measured value is unable to be valid. The proposed approach relies on two parts: 1) indirect feedback navigation Kalman filter design, 2) VKF design. To evaluate the proposed method, we compare the VKF aided navigation system with PINS (Pure Inertial Navigation System) and conventional INS-DVL navigation system through simulation results. Simulations illustrate the effectiveness of the underwater navigation system assisted by the additional DVL-RPM based VKF in underwater environment.
基金supported by research grants from the Natural Sciences and Engineering Research Council(NSERC)of Canadathe British Columbia Knowledge Development Fund(BCKDF)+1 种基金the Canada Foundation for Innovation(CFI)the Canada Research Chair in Mechatronics and Industrial Automation held by C.W.de Silva
文摘In this research work,a hierarchical controller has been designed for an autonomous navigation robot to avoid unexpected moving obstacles where the state and action spaces are continuous.The proposed scheme consists of two parts:1)a controller with a high-level approximate reinforcement learning(ARL)technique for choosing an optimal trajectory in autonomous navigation;and 2)a low-level,appearance-based visual servoing(ABVS)controller which controls and execute the motion of the robot.A novel approach for path planning and visual servoing has been proposed by the combined system framework.The characteristics of the on-board camera which is equipped on the robot is naturally suitable for conducting the reinforcement learning algorithm.Regarding the ARL controller,the computational overhead is quite low thanks to the fact that a knowledge of obstacle motion is not necessary.The developed scheme has been implemented and validated in a simulation system of obstacle avoidance.It is noted that findings of the proposed method are successfully verified by obtaining an optimal robotic plan motion strategy.
基金B.M.and X.Z.appreciate the funding by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD(EXC 2122,Project ID 390833453).
文摘In modern physics and fabrication technology,simulation of projectile and target collision is vital to improve design in some critical applications,like;bulletproofing and medical applications.Graphene,the most prominent member of two dimensional materials presents ultrahigh tensile strength and stiffness.Moreover,polydimethylsiloxane(PDMS)is one of the most important elastomeric materials with a high extensive application area,ranging from medical,fabric,and interface material.In this work we considered graphene/PDMS structures to explore the bullet resistance of resulting nanocomposites.To this aim,extensive molecular dynamic simulations were carried out to identify the penetration of bullet through the graphene and PDMS composite structures.In this paper,we simulate the impact of a diamond bullet with different velocities on the composites made of single-or bi-layer graphene placed in different positions of PDMS polymers.The underlying mechanism concerning how the PDMS improves the resistance of graphene against impact loading is discussed.We discuss that with the same content of graphene,placing the graphene in between the PDMS result in enhanced bullet resistance.This work comparatively examines the enhancement in design of polymer nanocomposites to improve their bulletproofing response and the obtained results may serve as valuable guide for future experimental and theoretical studies.
基金supported by the VOLTAIRE project (ANR-10-LABX-100-01)funded by the ANR and the PIVOTS project provided by the Region Centre−Val de Loire (ARD 2020 program and CPER 2015−2020).
文摘Wildfire events are increasing globally which may be partly associated with climate change,resulting in significant adverse impacts on local,regional air quality and global climate.In September 2020,a small wildfire(burned area:36.3 ha)event occurred in Souesmes(Loiret-Cher,Sologne,France),and its plume spread out over 200 km on the following day as observed by the MODIS satellite.Based on measurements at a suburban site(~50 km northwest of the fire location)in Orléans and backward trajectory analysis,young wildfire plumes were characterized.Significant increases in gaseous pollutants(CO,CH_(4),N_(2)O,VOCs,etc.)and particles(including black carbon)were found within the wildfire plumes,leading to a reduced air quality.Emission factors,defined as EF(X)=ΔX/ΔCO(where,X represents the target species),of various trace gases and black carbon within the young wildfire plumes were determined accordingly and compared with previous studies.Changes in the ambient ions(such as ammonium,sulfate,nitrate,chloride,and nitrite in the particle-and gasphase)and aerosol properties(e.g.,aerosol water content,aerosol p H)were also quantified and discussed.Moreover,we estimated the total carbon and climate-related species(e.g.,CO_(2),CH_(4),N_(2)O,and BC)emissions and compared them with fire emission inventories.Current biomass burning emission inventories have uncertainties in estimating small fire burned areas and emissions.For instance,we found that the Global Fire Assimilation System(GFAS)may underestimate emissions(e.g.,CO)of this small wildfire while other inventories(GFED and FINN)showed significant overestimation.Considering that it is the first time to record wildfire plumes in this region,related atmospheric implications are presented and discussed.
基金the Defense Acquisition Program Administration’s Critical Technology R&D program(No.UC190002D).
文摘For legged robots,collecting tactile information is essential for stable posture and efficient gait.However,mounting sensors on small robots weighing less than 1 kg remain challenges in terms of the sensor’s durability,flexibility,sensitivity,and size.Crackbased sensors featuring ultra-sensitivity,small-size,and flexibility could be a promising candidate,but performance degradation due to crack growing by repeated use is a stumbling block.This paper presents an ultra-stable and tough bio-inspired crack-based sensor by controlling the crack depth using silver nanowire(Ag NW)mesh as a crack stop layer.The Ag NW mesh inspired by skin collagen structure effectively mitigated crack propagation.The sensor was very thin,lightweight,sensitive,and ultra-durable that maintains its sensitivity during 200,000 cycles of 0.5%strain.We demonstrate sensor’s feasibility by implementing the tactile sensation to bio-inspired robots,and propose statistical and deep learning-based analysis methods which successfully distinguished terrain type.
基金The authors would like to acknowledge the financial sup-port of the National Key R&D Program of China(Grant No.2021YFA1200203)the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20210352,BK20200503,and BK20200019)+1 种基金the National Natural Science Foundation of China(Grant Nos.51905268,52101142,52001116,and 51871120)China Postdoc-toral Science Foundation(Grant No.2021M691581).
文摘In-situ tensile experiments on pure Ti were performed in a transmission electron microscope at room temperature.The dynamic process of stress-induced hexagonal closed-packed(hcp)to face-centered cu-bic(fcc)structural transformation ahead of a crack tip was captured at the atomic level.Intriguingly,a sliding behavior of the ensuing(0001)hcp/(1¯11)_(fcc) phase boundary was observed to further accommodate the plastic deformation until crack initiation.The sliding was accomplished via the successive conserva-tive glide of extended dislocations along the(0001)hcp/(1¯11)_(fcc) phase boundary.A molecular dynamics simulation was carried out to corroborate the experiments and the results confirm the new dislocation-mediated sliding mechanism.
基金financed by the French National Research Agency within the project ‘Heterogeneous data integration and spatial reasoning for locating victims in mountain areas–CHOUCAS’[ANR-16-CE23-0018].
文摘While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component,there is still a lack of semantics to describe how to present this knowledge geovisually to the end user and how to automatize the process.In this paper,we first present vocabularies to describe at a high level the elements that make up a geovisualization.We then propose a method that describes at a semantic level how to obtain a geovisualization from an existing data model.This method is based on our vocabularies and on a set of semantic rules encoding rich and complex operations on data.This leads to the derivation of ontological knowledge,ready to be exploited to automate the creation of a geovisualization.The method is implemented in a framework that uses Semantic Web technologies.The singularity and the strength of our proposal is that it enables to describe a geovisualization through a RDF specification file,which once loaded in our system makes the geovisualization directly available for use from a Web browser.This result is obtained by extending a priori an application data model with ad hoc geovisualization semantics features and rules.
基金financially supported by a grant from the National Research Foundation of Korea(NRF)(No.2019023700)Ministry of Health&Welfare,Republic of Korea(No.HI16C1111).
文摘Compound eyes found in insects provide intriguing sources of biological inspiration for miniaturised imaging systems.Here,we report an ultrathin arrayed camera inspired by insect eye structures for high-contrast and super-resolution imaging.The ultrathin camera features micro-optical elements(MOEs),i.e.,inverted microlenses,multilayered pinhole arrays,and gap spacers on an image sensor.The MOE was fabricated by using repeated photolithography and thermal reflow.The fully packaged camera shows a total track length of 740μm and a field-of-view(FOV)of 73°.The experimental results demonstrate that the multilayered pinhole of the MOE allows high-contrast imaging by eliminating the optical crosstalk between microlenses.The integral image reconstructed from array images clearly increases the modulation transfer function(MTF)by~1.57 times compared to that of a single channel image in the ultrathin camera.This ultrathin arrayed camera provides a novel and practical direction for diverse mobile,surveillance or medical applications.
文摘The emergence of mutual knowledge is a major cognitive mechanism for the robustness of complex socio-technical systems. It has been extensively studied from an ethnomethodological point of view and empirically reproduced by multi-agent simulations. Whilst such simulations have been used to design real work settings the underlying theoretical grounding for the process is vague. The aim of this paper is to investigate whether the emergence of mutual knowledge(MK) in a group of colocated individuals can be explained as a percolation phenomenon. The followed methodology consists in coupling agent-based simulation with dynamic networks analysis to study information propagation phenomena: After using an agent-based simulation the authors generated and then analyzed its traces as networks where agents met and exchanged knowledge. Deep analysis of the resulting networks clearly shows that the emergence of MK is comparable to a percolation process. The authors specifically focus on how changes at the microscopic level in the proposed agent based simulator affect percolation and robustness. These results therefore provide theoretical basis for the analysis of social organizations.
基金supported by the National Institute of Standards and Technologies(NIST)
文摘In this study, we present a framework based on a prediction model that facilitates user access to a number of services in a smart living environment. Users must be able to access all available services continuously equipped with mobile devices or smart objects without being impacted by technical constraints such as performance or memory issues, regardless of their physical location and mobility. To achieve this goal, we propose the use of cloudlet-based architecture that serves as distributed cloud resources with specific ranges of influence and a realtime processing framework that tracks events and preferences of the end consumers, predicts their requirements,and recommends services to optimize resource utilization and service response time.
基金This work was supported by the French region Rhône-Alpes[grant number REGION 2015-DRH-0367].
文摘The observation of demographical,economical or environmental indicators over time through maps is crucial.It enables analysing territories and helps stakeholders to take decisions.However,the understanding of Territorial Statistical Information(TSI)is compromised unless comprehensive description of both the statistical methodology used and the spatial and temporal references are given.Thus,in this paper,we stress the importance of metadata descriptions and of their quality that helps assessing data reliability.Furthermore,time-series of such TSI are paramount.They enable analysing a territory over a long period of time and likewise judging the effectiveness of reforms.In light of these observations,we present Spatio-Temporal evolutive Data Infrastructure(STeDI)an innovative Spatial Data Infrastructure(SDI)that enriches the description of a Digital Earth,providing a virtual representation of territories and of their evolution through statistics and time.STeDI aims at managing a whole dataflow of multi-dimensional,multi-scale and multi-temporal TSI,from their acquisition to their dissemination to scientists and policy-makers.The content of this SDI evolves autonomously thanks to automated processes and to a Web platform that help improving the quality of datasets uploaded by experts.Then,STeDI allows visualizing up-to-date time-series reflecting the human activities on a given territory.It helps policy-makers in their decision-making process.
文摘This paper presents an overview of research studies made at the COGIT laboratory of IGN France in the fields of generalisation and symbol specification,particularly considering evaluation aspects.It then discusses how generalisation and symbol specification interact.Finally it explores some possible adaptations of the presented works in generalisation and symbol specification to cartography in the context of crisis management.
基金supported by CAPES-PDSE(No.4926145)CNPq(Nos.448462/2014-1 and 306301/2012-1)ARC 6 Region Rh?ne-Alpes(No.ARC-13-009716-01)
文摘This paper highlights the use of situated artificial institution(SAI) within a hybrid, interactive,normative multi-agent system to regulate human collaboration in crisis management. Norms regulate the actions of human actors based on the dynamics of the environment in which they are situated. This dynamics results from both environment evolution and actors' actions. Our objective is to situate norms in the environment in order to provide a context-aware crisis regulation. However, this coupling must be a loose one to keep both levels independent and easyto-change in order to face the complex and changing crisis situations. To that aim, we introduce a constitutive level between environmental and normative states providing a loose coupling of normative regulation with environment evolution. Norms are thus no more referring to environmental facts but to status functions, i.e., the institutional interpretation of environmental facts through constitutive rules. We present how this declarative and distinct SAI modelling succeeds in managing the crisis with a context-aware crisis regulation.
