Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for...Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.展开更多
Nickel and nickel-ceria catalysts supported on high surface area silica, with 6 wt% Ni and 20 wt% CeO2 were prepared by microwave assisted(co) precipitation method. The catalysts were investigated by XRD,TPR and XPS a...Nickel and nickel-ceria catalysts supported on high surface area silica, with 6 wt% Ni and 20 wt% CeO2 were prepared by microwave assisted(co) precipitation method. The catalysts were investigated by XRD,TPR and XPS analyses and they were tested in partial oxidation of methane(CPO). The catalytic reaction was carried out at atmospheric pressure in a temperature range of 400–800℃ with a feed gas mixture containing methane and oxygen in a molecular ratio CH4/O2=2. The Ni catalyst exhibited 60% methane conversion with 60% selectivity to CO already at 500℃. On the contrary, the Ni–Ce catalyst was inert to CPO up to 700℃. Moreover, the former catalyst reproduced its activity at the descending temperatures maintaining a good stability at 600℃, over a reaction time of 80 h, whereas the latter one completely deactivated. Test of CH4 temperature programmed surface reaction(CH4-TPSR) revealed a higher methane activation temperature(> 100℃) for the Ni–Ce catalyst as compared to the Ni one. Noticeable improvement of the ceria containing catalyst occurred when the reaction test started at a temperature higher than the methane decomposition temperature. In this case, the sample achieved the same catalytic behavior of the Ni catalyst. As confirmed by XPS analyses, the distinct electronic state of the supported nickel was responsible for the differences in catalytic behavior.展开更多
Walking is the most basic and essential part of the activities of daily living. To enable the elderly and non-ambulatory gait-impaired patients, the repetitive practice of this task, a novel gait training robot(GTR) w...Walking is the most basic and essential part of the activities of daily living. To enable the elderly and non-ambulatory gait-impaired patients, the repetitive practice of this task, a novel gait training robot(GTR) was designed followed the end-effector principle, and an active partial body weight support(PBWS) system was introduced to facilitate successful gait training. For successful establishment of a walking gait on the GTR with PBWS, the motion laws of the GTR were planned to enable the phase distribution relationships of the cycle step, and the center of gravity(COG) trajectory of the human body during gait training on the GTR was measured. A coordinated control strategy was proposed based on the impedance control principle. A robotic prototype was developed as a platform for evaluating the design concepts and control strategies. Preliminary gait training with a healthy subject was implemented by the robotic-assisted gait training system and the experimental results are encouraging.展开更多
An experimental study of an active body-weight support（BWS） system for improving treadmill-based locomotion training is performed.The dynamical foundation of the proposed system is developed based on a simplified ca...An experimental study of an active body-weight support（BWS） system for improving treadmill-based locomotion training is performed.The dynamical foundation of the proposed system is developed based on a simplified cable suspended mass-spring-damping system which is used to mimic the vertical gait of a walking human.A specifically designed cable pulley suspended cam-slider system is used to mimic the walking gait of a human in vertical direction.A load cell is installed to connect the slider and the cable which is driven by a winch based on the acceleration feedback.The contact force between the slider and the cam is measured to evaluate the walking load of the system.The experimental results demonstrate that the proposed active BWS system can simultaneously reduce both gravitational and inertial load of the walking body,which implies that the walking body suspended in such a BWS system will dynamically behave as if certain amount of body mass had been removed.展开更多
Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accu...Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.展开更多
Considering the uncertainty of material performance, geometric characteristic and analysis method, the analysis of statistical parameters of the resistance of cast ball-and-socket support joints and the reliability an...Considering the uncertainty of material performance, geometric characteristic and analysis method, the analysis of statistical parameters of the resistance of cast ball-and-socket support joints and the reliability analysis are carried out by JC method （improved first-order second-moment method） based on the relevant experimental data. Results show that the resistance partial factor of the joint increases with the increase of the reliability index. The resis- tance partial factors are suggested corresponding to the reliability index under different load combinations. Moreover, the suggested resistance partial factor is adopted in reliability design, and it is found that the reliability index of the joint is larger than the target reliability index. The optimal load combination is the one in which live load plays an im- portant control role. Finally, based on the suggested resistance partial factors, the reliability analysis of cast bail-and- socket support ioints of a proiect is conducted.展开更多
In this work,the catalytic performance of vanadia-molybdena loaded on TiO_(2),MgO,ZSM-5,NaY and Mordenite was investigated in the selective oxidation of 2-methylnaphthalene(2-MN)to 2-naphthaldehyde(2-NA).Results show ...In this work,the catalytic performance of vanadia-molybdena loaded on TiO_(2),MgO,ZSM-5,NaY and Mordenite was investigated in the selective oxidation of 2-methylnaphthalene(2-MN)to 2-naphthaldehyde(2-NA).Results show that strong interactions between supports(TiO2,ZSM-5)and active components can promote the dispersion of active component.Monolayer VOx and MoOx are the main form on the catalyst surface,which is beneficial to the 2-NA selectivity.However,the corresponding weak interactions between Mordenite and active components may lead to the production of oxide crystals and the separation of active components,thus reducing the 2-NA selectivity.Due to the high Na content,new crystal NaVMoO6 forms on the NaY surface,which is inactive in the reaction.For V-Mo/TiO2,V and Mo can be inserted into the TiO2 lattice,changing the electronic structures of active components and support and improving the activity of surface oxygen species.This investigation highlights an important consideration on supports properties when designing supported catalysts.展开更多
In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
基金financially supported by the National Council for Scientific and Technological Development(CNPq,Brazil),Swedish-Brazilian Research and Innovation Centre(CISB),and Saab AB under Grant No.CNPq:200053/2022-1the National Council for Scientific and Technological Development(CNPq,Brazil)under Grants No.CNPq:312924/2017-8 and No.CNPq:314660/2020-8.
文摘Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.
基金The Executive Programme for Cooperation between Italy and India (Prot.No.MAE01054762017)。
文摘Nickel and nickel-ceria catalysts supported on high surface area silica, with 6 wt% Ni and 20 wt% CeO2 were prepared by microwave assisted(co) precipitation method. The catalysts were investigated by XRD,TPR and XPS analyses and they were tested in partial oxidation of methane(CPO). The catalytic reaction was carried out at atmospheric pressure in a temperature range of 400–800℃ with a feed gas mixture containing methane and oxygen in a molecular ratio CH4/O2=2. The Ni catalyst exhibited 60% methane conversion with 60% selectivity to CO already at 500℃. On the contrary, the Ni–Ce catalyst was inert to CPO up to 700℃. Moreover, the former catalyst reproduced its activity at the descending temperatures maintaining a good stability at 600℃, over a reaction time of 80 h, whereas the latter one completely deactivated. Test of CH4 temperature programmed surface reaction(CH4-TPSR) revealed a higher methane activation temperature(> 100℃) for the Ni–Ce catalyst as compared to the Ni one. Noticeable improvement of the ceria containing catalyst occurred when the reaction test started at a temperature higher than the methane decomposition temperature. In this case, the sample achieved the same catalytic behavior of the Ni catalyst. As confirmed by XPS analyses, the distinct electronic state of the supported nickel was responsible for the differences in catalytic behavior.
基金Project(61175128) supported by the National Natural Science Foundation of ChinaProject(2008AA040203) supported by the National High Technology Research and Development Program of China
文摘Walking is the most basic and essential part of the activities of daily living. To enable the elderly and non-ambulatory gait-impaired patients, the repetitive practice of this task, a novel gait training robot(GTR) was designed followed the end-effector principle, and an active partial body weight support(PBWS) system was introduced to facilitate successful gait training. For successful establishment of a walking gait on the GTR with PBWS, the motion laws of the GTR were planned to enable the phase distribution relationships of the cycle step, and the center of gravity(COG) trajectory of the human body during gait training on the GTR was measured. A coordinated control strategy was proposed based on the impedance control principle. A robotic prototype was developed as a platform for evaluating the design concepts and control strategies. Preliminary gait training with a healthy subject was implemented by the robotic-assisted gait training system and the experimental results are encouraging.
文摘An experimental study of an active body-weight support（BWS） system for improving treadmill-based locomotion training is performed.The dynamical foundation of the proposed system is developed based on a simplified cable suspended mass-spring-damping system which is used to mimic the vertical gait of a walking human.A specifically designed cable pulley suspended cam-slider system is used to mimic the walking gait of a human in vertical direction.A load cell is installed to connect the slider and the cable which is driven by a winch based on the acceleration feedback.The contact force between the slider and the cam is measured to evaluate the walking load of the system.The experimental results demonstrate that the proposed active BWS system can simultaneously reduce both gravitational and inertial load of the walking body,which implies that the walking body suspended in such a BWS system will dynamically behave as if certain amount of body mass had been removed.
基金Project(2010CB732004)supported by the National Basic Research Program of ChinaProjects(50934006,41272304)supported by the National Natural Science Foundation of China
文摘Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.
基金Supported by National Natural Science Foundation of China (No.51078259)Seed Foundation of Tianjin University (No.2010XG-0013)Program for New Century Excellent Talents in University (No.1102010)
文摘Considering the uncertainty of material performance, geometric characteristic and analysis method, the analysis of statistical parameters of the resistance of cast ball-and-socket support joints and the reliability analysis are carried out by JC method （improved first-order second-moment method） based on the relevant experimental data. Results show that the resistance partial factor of the joint increases with the increase of the reliability index. The resis- tance partial factors are suggested corresponding to the reliability index under different load combinations. Moreover, the suggested resistance partial factor is adopted in reliability design, and it is found that the reliability index of the joint is larger than the target reliability index. The optimal load combination is the one in which live load plays an im- portant control role. Finally, based on the suggested resistance partial factors, the reliability analysis of cast bail-and- socket support ioints of a proiect is conducted.
基金Henan Province Science and Technology Research Project(232102321041).
文摘In this work,the catalytic performance of vanadia-molybdena loaded on TiO_(2),MgO,ZSM-5,NaY and Mordenite was investigated in the selective oxidation of 2-methylnaphthalene(2-MN)to 2-naphthaldehyde(2-NA).Results show that strong interactions between supports(TiO2,ZSM-5)and active components can promote the dispersion of active component.Monolayer VOx and MoOx are the main form on the catalyst surface,which is beneficial to the 2-NA selectivity.However,the corresponding weak interactions between Mordenite and active components may lead to the production of oxide crystals and the separation of active components,thus reducing the 2-NA selectivity.Due to the high Na content,new crystal NaVMoO6 forms on the NaY surface,which is inactive in the reaction.For V-Mo/TiO2,V and Mo can be inserted into the TiO2 lattice,changing the electronic structures of active components and support and improving the activity of surface oxygen species.This investigation highlights an important consideration on supports properties when designing supported catalysts.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
