Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart hea...Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems.Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs,physical human action recognition(HAR)through body sensors gives useful information about the lifestyle and functionality of an individual.This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based BehaviorRecognition(ITSOMLBR)technique using body sensor data.The presented ITSOML-BR technique collects data from different body sensors namely electrocardiography(ECG),accelerometer,and magnetometer.In addition,the ITSOML-BR technique extract features like variance,mean,skewness,and standard deviation.Moreover,the presented ITSOML-BR technique executes a micro neural network(MNN)which can be employed for long term healthcare monitoring and classification.Furthermore,the parameters related to the MNN model are optimally selected via the ITSO algorithm.The experimental result analysis of the ITSOML-BR technique is tested on the MHEALTH dataset.The comprehensive comparison study reported a higher result for the ITSOMLBR approach over other existing approaches with maximum accuracy of 99.60%.展开更多
The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). To improve the design of BTRRC under continuous opera...The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). To improve the design of BTRRC under continuous operation, the nonlinear dynamic reliability optimization of disk radial deformation was implemented based on extremum response surface method(ERSM), including ERSM-based quadratic function(QF-ERSM) and ERSM-based support vector machine of regression(SR-ERSM). The mathematical models of the two methods were established and the framework of reliability-based dynamic design optimization was developed. The numerical experiments demonstrate that the proposed optimization methods have the promising potential in reducing additional design samples and improving computational efficiency with acceptable precision, in which the SR-ERSM emerges more obviously. Through the case study, we find that disk radial deformation is reduced by about 6.5×10–5 m; δ=1.31×10–3 m is optimal for turbine disk radial deformation design and the proposed methods are verified again. The presented efforts provide an effective optimization method for the nonlinear transient design of motion structures for further research, and enrich mechanical reliability design theory.展开更多
Linear transient growth of optimal perturbations in particle-laden turbulent channel flow is investigated in this work.The problem is formulated in the framework of a Eulerian-Eulerian approach,employing two-way coupl...Linear transient growth of optimal perturbations in particle-laden turbulent channel flow is investigated in this work.The problem is formulated in the framework of a Eulerian-Eulerian approach,employing two-way coupling between fine particles and fluid flow.The model is first validated in laminar cases,after which the transient growth of coherent perturbations in turbulent channel flow is investigated,where the mean particle concentration distribution is obtained by direct numerical simulation.It is shown that the optimal small-scale structures for particles are streamwise streaks just below the optimal streamwise velocity streaks,as was previously found in numerical simulations of particle-laden channel flow.This indicates that the optimal growth of perturbations is a dominant mechanism for the distribution of particles in the near-wall region.The current study also considers the transient growth of small-and large-scale perturbations at relatively high Reynolds numbers,which reveals that the optimal large-scale structures for particles are in the near-wall region while the optimal large-scale structures for fluid enter the outer region.展开更多
The optimal transient growth process of perturbations driven by the pressure gradient is studied in a turbulent pipe flow. A new computational method is proposed, based on the projection operators which project the go...The optimal transient growth process of perturbations driven by the pressure gradient is studied in a turbulent pipe flow. A new computational method is proposed, based on the projection operators which project the governing equations onto the sub- space spanned by the radial vorticity and radial velocity. The method is validated by comparing with the previous studies. Two peaks of the maximum transient growth am- plification curve are found at different Reynolds numbers ranging from 20 000 to 250 000. The optimal flow structures are obtained and compared with the experiments and DNS results. The location of the outer peak is at the azimuthal wave number n = 1, while the location of the inner peak is varying with the Reynolds number. It is observed that the velocity streaks in the buffer layer with a spacing of 100δv are the most amplified flow structures. Finally, we consider the optimal transient growth time and its dependence on the azimuthal wave length. It shows a self-similar behavior for perturbations of different scales in the optimal transient growth process.展开更多
Multi-threshold image segmentation divides an image into regions with distinct features. However,as the number of thresholds increases,its computational complexity grows exponentially. To address this issue,an improve...Multi-threshold image segmentation divides an image into regions with distinct features. However,as the number of thresholds increases,its computational complexity grows exponentially. To address this issue,an improved transient search optimization(ITSO) algorithm is proposed to overcome the limitations of the original transient search optimization(TSO) algorithm,such as susceptibility to local optima and low convergence accuracy. ITSO enhances the diversity of initial solutions through a dynamic reflection learning strategy based on the Beta distribution,improves exploration capability using a Cauchy inverse cumulative distribution operator,and balances exploration and exploitation through a dynamic perturbation strategy. Tests on CEC2022 demonstrate that ITSO outperforms the dandelion optimizer(DO),tunicate swarm algorithm(TSA), whale optimization algorithm(WOA),golden jackal optimization(GJO),TSO,goose algorithm(GOOSE),and love evolution algorithm(LEA). When applied to image segmentation,ITSO achieves superior performance in terms of Otsu fitness,peak signal-to-noise ratio(PSNR),structural similarity(SSIM),and feature similarity(FSIM),showcasing its strong research value and application potential.展开更多
Visual impairment is one of the major problems among people of all age groups across the globe.Visually Impaired Persons(VIPs)require help from others to carry out their day-to-day tasks.Since they experience several ...Visual impairment is one of the major problems among people of all age groups across the globe.Visually Impaired Persons(VIPs)require help from others to carry out their day-to-day tasks.Since they experience several problems in their daily lives,technical intervention can help them resolve the challenges.In this background,an automatic object detection tool is the need of the hour to empower VIPs with safe navigation.The recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it possible.The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help VIPs.The primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to them.For data acquisition,IoT devices are used in this study.Then,the Lightweight RetinaNet(LWR)model is applied to detect objects accurately.Next,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR model.Finally,the Long Short-Term Memory(LSTM)model is exploited for classifying objects.The performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct aspects.The comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs.展开更多
文摘Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems.Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs,physical human action recognition(HAR)through body sensors gives useful information about the lifestyle and functionality of an individual.This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based BehaviorRecognition(ITSOMLBR)technique using body sensor data.The presented ITSOML-BR technique collects data from different body sensors namely electrocardiography(ECG),accelerometer,and magnetometer.In addition,the ITSOML-BR technique extract features like variance,mean,skewness,and standard deviation.Moreover,the presented ITSOML-BR technique executes a micro neural network(MNN)which can be employed for long term healthcare monitoring and classification.Furthermore,the parameters related to the MNN model are optimally selected via the ITSO algorithm.The experimental result analysis of the ITSOML-BR technique is tested on the MHEALTH dataset.The comprehensive comparison study reported a higher result for the ITSOMLBR approach over other existing approaches with maximum accuracy of 99.60%.
基金Project(51275024)supported by the National Natural Science Foundations of ChinaProject(2015M580037)supported by China’s Postdoctoral Science FundingProjects(XJ2015002,G-YZ90)supported by Hong Kong Scholars Program Foundations,China
文摘The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). To improve the design of BTRRC under continuous operation, the nonlinear dynamic reliability optimization of disk radial deformation was implemented based on extremum response surface method(ERSM), including ERSM-based quadratic function(QF-ERSM) and ERSM-based support vector machine of regression(SR-ERSM). The mathematical models of the two methods were established and the framework of reliability-based dynamic design optimization was developed. The numerical experiments demonstrate that the proposed optimization methods have the promising potential in reducing additional design samples and improving computational efficiency with acceptable precision, in which the SR-ERSM emerges more obviously. Through the case study, we find that disk radial deformation is reduced by about 6.5×10–5 m; δ=1.31×10–3 m is optimal for turbine disk radial deformation design and the proposed methods are verified again. The presented efforts provide an effective optimization method for the nonlinear transient design of motion structures for further research, and enrich mechanical reliability design theory.
文摘Linear transient growth of optimal perturbations in particle-laden turbulent channel flow is investigated in this work.The problem is formulated in the framework of a Eulerian-Eulerian approach,employing two-way coupling between fine particles and fluid flow.The model is first validated in laminar cases,after which the transient growth of coherent perturbations in turbulent channel flow is investigated,where the mean particle concentration distribution is obtained by direct numerical simulation.It is shown that the optimal small-scale structures for particles are streamwise streaks just below the optimal streamwise velocity streaks,as was previously found in numerical simulations of particle-laden channel flow.This indicates that the optimal growth of perturbations is a dominant mechanism for the distribution of particles in the near-wall region.The current study also considers the transient growth of small-and large-scale perturbations at relatively high Reynolds numbers,which reveals that the optimal large-scale structures for particles are in the near-wall region while the optimal large-scale structures for fluid enter the outer region.
基金Project supported by the National Natural Science Foundation of China(Nos.11322221,11132005,and 11490551)
文摘The optimal transient growth process of perturbations driven by the pressure gradient is studied in a turbulent pipe flow. A new computational method is proposed, based on the projection operators which project the governing equations onto the sub- space spanned by the radial vorticity and radial velocity. The method is validated by comparing with the previous studies. Two peaks of the maximum transient growth am- plification curve are found at different Reynolds numbers ranging from 20 000 to 250 000. The optimal flow structures are obtained and compared with the experiments and DNS results. The location of the outer peak is at the azimuthal wave number n = 1, while the location of the inner peak is varying with the Reynolds number. It is observed that the velocity streaks in the buffer layer with a spacing of 100δv are the most amplified flow structures. Finally, we consider the optimal transient growth time and its dependence on the azimuthal wave length. It shows a self-similar behavior for perturbations of different scales in the optimal transient growth process.
基金supported by the National Key Research and Development Program of China (2022ZD0119000)
文摘Multi-threshold image segmentation divides an image into regions with distinct features. However,as the number of thresholds increases,its computational complexity grows exponentially. To address this issue,an improved transient search optimization(ITSO) algorithm is proposed to overcome the limitations of the original transient search optimization(TSO) algorithm,such as susceptibility to local optima and low convergence accuracy. ITSO enhances the diversity of initial solutions through a dynamic reflection learning strategy based on the Beta distribution,improves exploration capability using a Cauchy inverse cumulative distribution operator,and balances exploration and exploitation through a dynamic perturbation strategy. Tests on CEC2022 demonstrate that ITSO outperforms the dandelion optimizer(DO),tunicate swarm algorithm(TSA), whale optimization algorithm(WOA),golden jackal optimization(GJO),TSO,goose algorithm(GOOSE),and love evolution algorithm(LEA). When applied to image segmentation,ITSO achieves superior performance in terms of Otsu fitness,peak signal-to-noise ratio(PSNR),structural similarity(SSIM),and feature similarity(FSIM),showcasing its strong research value and application potential.
基金The authors extend their appreciation to the King Salman center for Disability Research for funding this work through Research Group no KSRG-2022-030。
文摘Visual impairment is one of the major problems among people of all age groups across the globe.Visually Impaired Persons(VIPs)require help from others to carry out their day-to-day tasks.Since they experience several problems in their daily lives,technical intervention can help them resolve the challenges.In this background,an automatic object detection tool is the need of the hour to empower VIPs with safe navigation.The recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it possible.The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help VIPs.The primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to them.For data acquisition,IoT devices are used in this study.Then,the Lightweight RetinaNet(LWR)model is applied to detect objects accurately.Next,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR model.Finally,the Long Short-Term Memory(LSTM)model is exploited for classifying objects.The performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct aspects.The comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs.
