An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） s...An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.展开更多
This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algo...This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications.展开更多
The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigat...The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H_∞ Models(IMM-NFAH_∞) filtering technique for UAV is presented. The proposed IMM-NFAH_∞ strategy switches between two different Nonlinear Fuzzy Adaptive H_∞(NFAH_∞) filters and each NFAH_∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H_∞ filter based on different fuzzy inference systems via adaptive filter bounds(di),along with disturbance attenuation parameter c. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H_∞(NH_∞) filter and that with different NFAH_∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH_∞ filter.展开更多
An adaptive finite element method for high-speed flow-structure interaction is pre- sented.The cell-centered finite element method is combined with an adaptive meshing technique to solve the Navier-Stokes equations fo...An adaptive finite element method for high-speed flow-structure interaction is pre- sented.The cell-centered finite element method is combined with an adaptive meshing technique to solve the Navier-Stokes equations for high-speed compressible flow behavior.The energy equation and the quasi-static structural equations for aerodynamically heated structures are solved by applying the Galerkin finite element method.The finite element formulation and computational procedure are de- scribed.Interactions between the high-speed flow,structural heat transfer,and deformation are studied by two applications of Mach 10 flow over an inclined plate,and Mach 4 flow in a channel.展开更多
This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot...This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.展开更多
In this paper, an investigation into the propagation of far field explosion waves in water and their effects on nearby structures are carried out. For the far field structure, the motion of the fluid surrounding the s...In this paper, an investigation into the propagation of far field explosion waves in water and their effects on nearby structures are carried out. For the far field structure, the motion of the fluid surrounding the structure may be assumed small, allowing linearization of the governing fluid equations. A complete analysis of the problem must involve simultaneous solution of the dynamic response of the structure and the propagation of explosion wave in the surrounding fluid. In this study, a dynamic adaptive finite element procedure is proposed. Its application to the solution of a 2D fluid-structure interaction is investigated in the time domain. The research includes：a） calculation of the far-field scatter wave due to underwater explosion including solution of the time-depended acoustic wave equation, b） fluid-structure interaction analysis using coupled Euler-Lagrangian approach, and c） adaptive finite element procedures employing error estimates, and re-meshing. The temporal mesh adaptation is achieved by local regeneration of the grid using a time-dependent error indicator based on curvature of pressure function. As a result, the overall response is better predicted by a moving mesh than an equivalent uniform mesh. In addition, the cost of computation for large problems is reduced while the accuracy is improved.展开更多
With the rising adoption of blockchain technology due to its decentralized,secure,and transparent features,ensuring its resilience against network threats,especially Distributed Denial of Service(DDoS)attacks,is cruci...With the rising adoption of blockchain technology due to its decentralized,secure,and transparent features,ensuring its resilience against network threats,especially Distributed Denial of Service(DDoS)attacks,is crucial.This research addresses the vulnerability of blockchain systems to DDoS assaults,which undermine their core decentralized characteristics,posing threats to their security and reliability.We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks.To ensure the robustness and validity of our approach,a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset.Using this,our methodology was applied to detect DDoS threats and further classify them into seven unique attack subcategories.To cope with the broad spectrum of DDoS attack variations,a holistic framework has been pro-posed that seamlessly integrates five machine learning models:Gate Recurrent Unit(GRU),Convolutional Neural Networks(CNN),Long-Short Term Memory(LSTM),Deep Neural Networks(DNN),and Support Vector Machine(SVM).The innovative aspect of our framework is the introduction of a dynamic weight adjustment mechanism,enhancing the system’s adaptability.Experimental results substantiate the superiority of our ensemble method in comparison to singular models across various evaluation metrics.The framework displayed remarkable accuracy,with rates reaching 99.71%for detection and 87.62%for classification tasks.By developing a comprehensive and adaptive methodology,this study paves the way for strengthening the defense mechanisms of blockchain systems against DDoS attacks.The ensemble approach,combined with the dynamic weight adjustment,offers promise in ensuring blockchain’s enduring security and trustworthiness.展开更多
Adaptive locomotion in different types of surfaces is of critical importance for legged robots.The knowledge of various ground substrates,especially some geological properties,plays an essential role in ensuring the l...Adaptive locomotion in different types of surfaces is of critical importance for legged robots.The knowledge of various ground substrates,especially some geological properties,plays an essential role in ensuring the legged robots'safety.In this paper,the interaction between the robots and the environments is investigated through interaction dynamics with the closed-loop system model,the compliant contact model,and the friction model,which unveil the influence of environment's geological characteristics for legged robots'locomotion.The proposed method to classify substrates is based on the interaction dynamics and the sensory-motor coordination.The foot contact forces,joint position errors,and joint motor currents,which reflect body dynamics,are measured as the sensing variables.We train and classify the features extracted from the raw data with a multilevel weighted k-Nearest Neighbor(kNN) algorithm.According to the interaction dynamics,the strategy of adaptive walking is developed by adjusting the touchdown angles and foot trajectories while lifting up and dropping down the foot.Experiments are conducted on five different substrates with quadruped robot FROG-I.The comparison with other classification methods and adaptive walking between different substrates demonstrate the effectiveness of our approach.展开更多
This paper studies the problem of diagnosis strategy for a doubly fed induction motor (DFIM) sensor faults. This strategy is based on unknown input proportional integral (PI) multiobserver. Thecontribution of this pap...This paper studies the problem of diagnosis strategy for a doubly fed induction motor (DFIM) sensor faults. This strategy is based on unknown input proportional integral (PI) multiobserver. Thecontribution of this paper is on one hand the creation of a new DFIM model based on multi-model approach and, on the other hand, the synthesis of an adaptive PI multi-observer. The DFIM Volt per Hertz drive system behaves as a nonlinear complex system. It consists of a DFIM powered through a controlled PWM Voltage Source Inverter (VSI). The need of a sensorless drive requires soft sensors such as estimators or observers. In particular, an adaptive Proportional-Integral multi-observer is synthesized in order to estimate the DFIM’s outputs which are affected by different faults and to generate the different residual signals symptoms of sensor fault occurrence. The convergence of the estimation error is guaranteed by using the Lyapunov’s based theory. The proposed diagnosis approach is experimentally validated on a 1 kW Induction motor. Obtained simulation results confirm that the adaptive PI multiobserver consent to accomplish the detection, isolation and fault identification tasks with high dynamic performances.展开更多
Aiming at the invalidation of DS theory dealing with the evidence in a high conflict and reducing confidence level of DSm theory processing a low conflict,this paper proposes an interactive-adaptive combination rule. ...Aiming at the invalidation of DS theory dealing with the evidence in a high conflict and reducing confidence level of DSm theory processing a low conflict,this paper proposes an interactive-adaptive combination rule. Adopting the angle similarity based on hyper-power set as the weight of generalized Dempster rule and PCR rule,the new rule adaptively processes various evidence combination issues. In this way,the rule can obtain not only the better fusion of decision making effect in a low conflict,but also the solution to the synthesis in a high conflict. Simulation analysis demonstrates the validity and applicability from this rule of combination.展开更多
Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physica...Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.展开更多
Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information c...Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.展开更多
An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuz...An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.展开更多
In total 36 superior clones of Dalbergia sissoo Roxb., screened from 300 selections conducted in natural and growing range of India and Nepal, were multiplied using single nodal cuttings and estab- lished to evaluate ...In total 36 superior clones of Dalbergia sissoo Roxb., screened from 300 selections conducted in natural and growing range of India and Nepal, were multiplied using single nodal cuttings and estab- lished to evaluate genotypexenvironmental interactions for adaptability and stability at the age of 30 months in three geographical locations in the state of Punjab, India. Clone 124 had maximum adaptability and stability （bi = 1.04） to perform exceedingly well over the locations. Clones 36 and 1 were stable with mean regression coefficient of 0.84 and 1.22, respectively. Nonetheless, clone 4 1 performed exceedingly well for all the characters to attain maximum population mean, and the perform- ance varied substantially across the locations. Therefore, clone 41 was considered as productive but non-adaptive clone. Though some of the clones were sensitive to sites, 14 clones for height, 16 for collar diameter, 12 for DBH and 7 for volume were relatively un-sensitive with higher regression coefficient. Nonetheless, clone 124 was the most Stable with average bi value of 1.04 and productive, which could play an important role in future breeding and commercial deployment of stable and produc- tive planting stock of Dalbergia sissoo.展开更多
In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching th...In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.展开更多
Crowd evacuation in different situations is an important topic in the research field of safety. This paper presents a hybrid model for heterogeneous pedestrian evacuation simulation. Our adaptive agent-based model (AB...Crowd evacuation in different situations is an important topic in the research field of safety. This paper presents a hybrid model for heterogeneous pedestrian evacuation simulation. Our adaptive agent-based model (ABM) combines the strength of human crowd behavior description from classical social force models with discrete dynamics expression from cellular automaton models by extending the conception of floor field. Several important factors which may influence the results of decision-making of pedestrians are taken into consideration, such as the location of sign, the attraction of exit, and the interaction among pedestrians. To compare the effect of information on the pedestrians, we construct three decision-making mechanisms with different assumptions. To validate these three simulation models, we compare the numerical results from different perspectives with rational range in the case study where the Tampere Theater evacuation was carried out. The ABM framework is open for rules modification and could be applied to different building plans and has implication for architectural design of gates and signs in order to increase the evacuation efficiency.展开更多
The membrane method based on adaptive wettability shows great advantages in oil-water separation.At present,researches focus on the excellent application performance of the membrane material,while the quantitative ana...The membrane method based on adaptive wettability shows great advantages in oil-water separation.At present,researches focus on the excellent application performance of the membrane material,while the quantitative analysis of interactions in oil-water separation is rarely recognized.Herein,we constructed an adaptable wettability membrane with multiple polymer networks by polydopamine(PDA)and mussel-inspired amphiphilic polymer.Based on the Owens three-probe liquid method,the surface energy of the modified membrane was verified to meet the adaptive wettability conditions,with surface energies(γ-8)of 147.6 mJ m^(−2)(superhydrophilic/underwater superoleophobic)and 49.87 mJ m^(−2)(superhydrophobic/superoleophobic),respectively.The adhesion or repulsion of the membrane to the oil phase under different conditions during the separation process was quantified by the chemical probe AFM technique.In addition,the oil-water selective separation mechanism was further analyzed in a simplified membrane microchannel model.The results show that the different wetting produces capillary additional pressure in opposite directions,resulting in different energies to be overcome when the oil or water passes through the microchannels,thus achieving selective separation.展开更多
Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Bas...Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.展开更多
For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents ...For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents a novel interactive multiple model(IMM)algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles(NSHGV)with a fast adaptive sam-pling control logic.The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers,thus achieving a balance between tracking accuracy and resource consumption.Simulation results on typical NSHGV targets show that the proposed algo-rithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.展开更多
基金co-supported by the National Key Research and Development Program of China(No.2016YFC0803103)Beijing Advanced Innovation Center for Future Urban Design(No.UDC2016050100)Beijing Postdoctoral Research Foundation
文摘An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.
基金Foundation item： Supported by the National Nature Science Foundation of China （No. 61074053, 61374114） and the Applied Basic Research Program of Ministry of Transport of China （No. 2011-329-225 -390）.
文摘This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications.
基金supported by a grant from the National Natural Science Foundation of China(No.61375082)
文摘The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H_∞ Models(IMM-NFAH_∞) filtering technique for UAV is presented. The proposed IMM-NFAH_∞ strategy switches between two different Nonlinear Fuzzy Adaptive H_∞(NFAH_∞) filters and each NFAH_∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H_∞ filter based on different fuzzy inference systems via adaptive filter bounds(di),along with disturbance attenuation parameter c. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H_∞(NH_∞) filter and that with different NFAH_∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH_∞ filter.
基金The project supported by the Thailand Research Fund(TRF)
文摘An adaptive finite element method for high-speed flow-structure interaction is pre- sented.The cell-centered finite element method is combined with an adaptive meshing technique to solve the Navier-Stokes equations for high-speed compressible flow behavior.The energy equation and the quasi-static structural equations for aerodynamically heated structures are solved by applying the Galerkin finite element method.The finite element formulation and computational procedure are de- scribed.Interactions between the high-speed flow,structural heat transfer,and deformation are studied by two applications of Mach 10 flow over an inclined plate,and Mach 4 flow in a channel.
基金The work was supported by the National Science Foundation，the Office of Naval Research grant，the AFOSR (Air Force Office of Scientific Research) EOARD (European Office of Aerospace Research and Development) grant，the U.S. Army Research Office grant
文摘This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.
文摘In this paper, an investigation into the propagation of far field explosion waves in water and their effects on nearby structures are carried out. For the far field structure, the motion of the fluid surrounding the structure may be assumed small, allowing linearization of the governing fluid equations. A complete analysis of the problem must involve simultaneous solution of the dynamic response of the structure and the propagation of explosion wave in the surrounding fluid. In this study, a dynamic adaptive finite element procedure is proposed. Its application to the solution of a 2D fluid-structure interaction is investigated in the time domain. The research includes：a） calculation of the far-field scatter wave due to underwater explosion including solution of the time-depended acoustic wave equation, b） fluid-structure interaction analysis using coupled Euler-Lagrangian approach, and c） adaptive finite element procedures employing error estimates, and re-meshing. The temporal mesh adaptation is achieved by local regeneration of the grid using a time-dependent error indicator based on curvature of pressure function. As a result, the overall response is better predicted by a moving mesh than an equivalent uniform mesh. In addition, the cost of computation for large problems is reduced while the accuracy is improved.
基金supported by National Natural Science Foundation of China(Grant Nos.62162022,62162024)Hainan Provincial Natural Science Foundation of China(Grant Nos.723QN238,621RC612).
文摘With the rising adoption of blockchain technology due to its decentralized,secure,and transparent features,ensuring its resilience against network threats,especially Distributed Denial of Service(DDoS)attacks,is crucial.This research addresses the vulnerability of blockchain systems to DDoS assaults,which undermine their core decentralized characteristics,posing threats to their security and reliability.We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks.To ensure the robustness and validity of our approach,a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset.Using this,our methodology was applied to detect DDoS threats and further classify them into seven unique attack subcategories.To cope with the broad spectrum of DDoS attack variations,a holistic framework has been pro-posed that seamlessly integrates five machine learning models:Gate Recurrent Unit(GRU),Convolutional Neural Networks(CNN),Long-Short Term Memory(LSTM),Deep Neural Networks(DNN),and Support Vector Machine(SVM).The innovative aspect of our framework is the introduction of a dynamic weight adjustment mechanism,enhancing the system’s adaptability.Experimental results substantiate the superiority of our ensemble method in comparison to singular models across various evaluation metrics.The framework displayed remarkable accuracy,with rates reaching 99.71%for detection and 87.62%for classification tasks.By developing a comprehensive and adaptive methodology,this study paves the way for strengthening the defense mechanisms of blockchain systems against DDoS attacks.The ensemble approach,combined with the dynamic weight adjustment,offers promise in ensuring blockchain’s enduring security and trustworthiness.
文摘Adaptive locomotion in different types of surfaces is of critical importance for legged robots.The knowledge of various ground substrates,especially some geological properties,plays an essential role in ensuring the legged robots'safety.In this paper,the interaction between the robots and the environments is investigated through interaction dynamics with the closed-loop system model,the compliant contact model,and the friction model,which unveil the influence of environment's geological characteristics for legged robots'locomotion.The proposed method to classify substrates is based on the interaction dynamics and the sensory-motor coordination.The foot contact forces,joint position errors,and joint motor currents,which reflect body dynamics,are measured as the sensing variables.We train and classify the features extracted from the raw data with a multilevel weighted k-Nearest Neighbor(kNN) algorithm.According to the interaction dynamics,the strategy of adaptive walking is developed by adjusting the touchdown angles and foot trajectories while lifting up and dropping down the foot.Experiments are conducted on five different substrates with quadruped robot FROG-I.The comparison with other classification methods and adaptive walking between different substrates demonstrate the effectiveness of our approach.
文摘This paper studies the problem of diagnosis strategy for a doubly fed induction motor (DFIM) sensor faults. This strategy is based on unknown input proportional integral (PI) multiobserver. Thecontribution of this paper is on one hand the creation of a new DFIM model based on multi-model approach and, on the other hand, the synthesis of an adaptive PI multi-observer. The DFIM Volt per Hertz drive system behaves as a nonlinear complex system. It consists of a DFIM powered through a controlled PWM Voltage Source Inverter (VSI). The need of a sensorless drive requires soft sensors such as estimators or observers. In particular, an adaptive Proportional-Integral multi-observer is synthesized in order to estimate the DFIM’s outputs which are affected by different faults and to generate the different residual signals symptoms of sensor fault occurrence. The convergence of the estimation error is guaranteed by using the Lyapunov’s based theory. The proposed diagnosis approach is experimentally validated on a 1 kW Induction motor. Obtained simulation results confirm that the adaptive PI multiobserver consent to accomplish the detection, isolation and fault identification tasks with high dynamic performances.
基金supported by Pre-Research Foundation of PLA(LY200838014)supported by the PLA Research Program of Science and Technology (KJ08062)
文摘Aiming at the invalidation of DS theory dealing with the evidence in a high conflict and reducing confidence level of DSm theory processing a low conflict,this paper proposes an interactive-adaptive combination rule. Adopting the angle similarity based on hyper-power set as the weight of generalized Dempster rule and PCR rule,the new rule adaptively processes various evidence combination issues. In this way,the rule can obtain not only the better fusion of decision making effect in a low conflict,but also the solution to the synthesis in a high conflict. Simulation analysis demonstrates the validity and applicability from this rule of combination.
文摘Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.
文摘Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.
基金National Natural Science Foundation of China (No.60774023)
文摘An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.
文摘In total 36 superior clones of Dalbergia sissoo Roxb., screened from 300 selections conducted in natural and growing range of India and Nepal, were multiplied using single nodal cuttings and estab- lished to evaluate genotypexenvironmental interactions for adaptability and stability at the age of 30 months in three geographical locations in the state of Punjab, India. Clone 124 had maximum adaptability and stability （bi = 1.04） to perform exceedingly well over the locations. Clones 36 and 1 were stable with mean regression coefficient of 0.84 and 1.22, respectively. Nonetheless, clone 4 1 performed exceedingly well for all the characters to attain maximum population mean, and the perform- ance varied substantially across the locations. Therefore, clone 41 was considered as productive but non-adaptive clone. Though some of the clones were sensitive to sites, 14 clones for height, 16 for collar diameter, 12 for DBH and 7 for volume were relatively un-sensitive with higher regression coefficient. Nonetheless, clone 124 was the most Stable with average bi value of 1.04 and productive, which could play an important role in future breeding and commercial deployment of stable and produc- tive planting stock of Dalbergia sissoo.
基金National Natural Science Foundation of China !( No.69772 0 3 1)
文摘In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.
基金the Natural Science Foundation of Shanghai (No. 18ZR1420200)the National Natural Science Foundation of China (No. 61603253)the China Postdoctoral Science Foundation Funded Project (No. 2016M601598)。
文摘Crowd evacuation in different situations is an important topic in the research field of safety. This paper presents a hybrid model for heterogeneous pedestrian evacuation simulation. Our adaptive agent-based model (ABM) combines the strength of human crowd behavior description from classical social force models with discrete dynamics expression from cellular automaton models by extending the conception of floor field. Several important factors which may influence the results of decision-making of pedestrians are taken into consideration, such as the location of sign, the attraction of exit, and the interaction among pedestrians. To compare the effect of information on the pedestrians, we construct three decision-making mechanisms with different assumptions. To validate these three simulation models, we compare the numerical results from different perspectives with rational range in the case study where the Tampere Theater evacuation was carried out. The ABM framework is open for rules modification and could be applied to different building plans and has implication for architectural design of gates and signs in order to increase the evacuation efficiency.
基金We gratefully acknowledge the financial support from National Key Research and Development Project,China(2019YFA0708700)the National Natural Science Foundation of China(52222403,52074333)the Innovation Fund Project for graduate students of China University of Petroleum(East China)(22CX04049A).
文摘The membrane method based on adaptive wettability shows great advantages in oil-water separation.At present,researches focus on the excellent application performance of the membrane material,while the quantitative analysis of interactions in oil-water separation is rarely recognized.Herein,we constructed an adaptable wettability membrane with multiple polymer networks by polydopamine(PDA)and mussel-inspired amphiphilic polymer.Based on the Owens three-probe liquid method,the surface energy of the modified membrane was verified to meet the adaptive wettability conditions,with surface energies(γ-8)of 147.6 mJ m^(−2)(superhydrophilic/underwater superoleophobic)and 49.87 mJ m^(−2)(superhydrophobic/superoleophobic),respectively.The adhesion or repulsion of the membrane to the oil phase under different conditions during the separation process was quantified by the chemical probe AFM technique.In addition,the oil-water selective separation mechanism was further analyzed in a simplified membrane microchannel model.The results show that the different wetting produces capillary additional pressure in opposite directions,resulting in different energies to be overcome when the oil or water passes through the microchannels,thus achieving selective separation.
文摘Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.
文摘For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents a novel interactive multiple model(IMM)algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles(NSHGV)with a fast adaptive sam-pling control logic.The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers,thus achieving a balance between tracking accuracy and resource consumption.Simulation results on typical NSHGV targets show that the proposed algo-rithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.
