Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex syst...Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints.This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization(MNEHHO)algorithm to address the allocation of HRES components.The proposed approach integrates key technical parameters,including charge-discharge efficiency,storage device configurations,and renewable energy fraction.We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability.The MNEHHO algorithm employs multiple neighborhood structures to enhance solution diversity and exploration capabilities.The model’s effectiveness is validated through case studies across four distinct institutional energy demand profiles.Results demonstrate that our approach successfully generates practically feasible HRES configurations while achieving significant reductions in costs and emissions compared to conventional methods.The enhanced search mechanisms of MNEHHO show superior performance in avoiding local optima and achieving consistent solutions.Experimental results demonstrate concrete improvements in solution quality(up to 46% improvement in objective value)and computational efficiency(average coefficient of variance of 24%-27%)across diverse institutional settings.This confirms the robustness and scalability of our method under various operational scenarios,providing a reliable framework for solving renewable energy allocation problems.展开更多
Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the node...Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).展开更多
The globe faces an urgent need to close the energy demand-supply gap.Addressing this difficulty requires constructing a Hybrid Renewable Energy System(HRES),which has proven to be the most appropriate solution.HRES al...The globe faces an urgent need to close the energy demand-supply gap.Addressing this difficulty requires constructing a Hybrid Renewable Energy System(HRES),which has proven to be the most appropriate solution.HRES allows for integrating two or more renewable energy resources,successfully addressing the issue of intermittent availability of non-conventional energy resources.Optimization is critical for improving the HRES’s performance parameters during implementation.This study focuses on HRES using solar and biomass as renewable energy supplies and appropriate energy storage technologies.However,energy fluctuations present a problem with the power quality of HRES.To address this issue,the research paper introduces the Generalized Dynamic Progressive Neural Fuzzy Controller(GDPNFC),which regulates power flow within the proposed HRES.Furthermore,a unique approach called Enhanced Multi-Objective Monarch Butterfly Optimization(EMMBO)is used to optimize technical parameters.The simulation tool used in the research work is HOMER(Hybrid Optimization of Multiple Energy Resources)-PRO,and the system’s power quality is assessed using MATLAB 2016.The research paper concludes with comparing the performance of existing systems to the proposed system in terms of power loss and Total Harmonic Distortion(THD).It was established that the proposed technique involving EMMBO outperformed existing methods in technical optimization.展开更多
Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of i...Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.展开更多
Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wol...Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.展开更多
For the past few years,wind energy is the most popular non-traditional resource among renewable energy resources and it’s significant to make full use of wind energy to realize a high level of generating power.Moreov...For the past few years,wind energy is the most popular non-traditional resource among renewable energy resources and it’s significant to make full use of wind energy to realize a high level of generating power.Moreover,diverse maximum power point tracking(MPPT)methods have been designed for varying speed operation of wind energy conversion system(WECS)applications to obtain optimal power extraction.Hence,a novel and metaheuristic technique,named enhanced atom search optimization(EASO),is designed for a permanent magnet synchronous generator(PMSG)based WECS,which can be employed to track the maximum power point.One of the most promising benefits of this technique is powerful global search capability that leads to fast response and high-quality optimal solution.Besides,in contrast with other conventional meta-heuristic techniques,EASO is extremely not relying on the original solution,which can avoid sinking into a low-quality local maximum power point(LMPP)by realizing an appropriate trade-off between global exploration and local exploitation.At last,simulations employing two case studies through Matlab/Simulink validate the practicability and effectiveness of the proposed techniques for optimal proportional-integral-derivative(PID)control parameters tuning of PMSG based WECS under a variety of wind conditions.展开更多
In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the e...In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.展开更多
Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of...Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.展开更多
The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer...The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.展开更多
To address the challenges for vibration suppression in the precision sensors of underwater vehicles,phononic crystals have attracted significant attention due to the superior capabilities in elastic wave manipulation ...To address the challenges for vibration suppression in the precision sensors of underwater vehicles,phononic crystals have attracted significant attention due to the superior capabilities in elastic wave manipulation and vibration suppression.Unlike conventional damping materials,the phononic crystals can effectively suppress the wave propagation in the specific frequency ranges due to the unique periodic microstructures.展开更多
In this dissertation, un-powered gliding aircraft＇s optimal extended rangeproblem is discussed. The aircraft movement model was built. According to the degree of coupling, the model can be classified into a simple mo...In this dissertation, un-powered gliding aircraft＇s optimal extended rangeproblem is discussed. The aircraft movement model was built. According to the degree of coupling, the model can be classified into a simple model or a complicated model. Using an optimal control method, two different movement models gave out the aircraft＇s attitude angle optimal flight path. Complicated model＇s optimal solution can be found by the genetic algorithm. This method can transfer the analytic solution of complicated model to a numerical value solution. Comparing the simulation results of different methods, it showed that the genetic algorithm combined with the complicated model＇s numerical value solution had the best performance in control strategy. This method solved the problem in which the highly coupling complicated model＇s analytic solution was hard to obtain. It verified that the genetic algorithm has validity in the field of extended range solution searching.展开更多
Unsustainable fossil fuel energy usage and its environmental impacts are the most significant scientific challenges in the scientific community.Two-dimensional(2D)materials have received a lot of attention recently be...Unsustainable fossil fuel energy usage and its environmental impacts are the most significant scientific challenges in the scientific community.Two-dimensional(2D)materials have received a lot of attention recently because of their great potential for application in addressing some of society’s most enduring issues with renewable energy.Transition metal-based nitrides,carbides,or carbonitrides,known as“MXenes”,are a relatively new and large family of 2D materials.Since the discovery of the first MXene,Ti_(3)C_(2) in 2011 has become one of the fastest-expanding families of 2D materials with unique physiochemical features.MXene surface terminations with hydroxyl,oxygen,fluorine,etc.,are invariably present in the so far reported materials,imparting hydrophilicity to their surfaces.The current finding of multi-transition metal-layered MXenes with controlled surface termination capacity opens the door to fabricating unique structures for producing renewable energy.MXene NMs-based flexible chemistry allows them to be tuned for energy-producing/storage,electromagnetic interference shielding,gas/biosensors,water distillation,nanocomposite reinforcement,lubrication,and photo/electro/chemical catalysis.This review will first discuss the advancement of MXenes synthesis methods,their properties/stability,and renewable energy applications.Secondly,we will highlight the constraints and challenges that impede the scientific community from synthesizing functional MXene with controlled composition and properties.We will further reveal the high-tech implementations for renewable energy storage applications along with future challenges and their solutions.展开更多
As an important indicator to measure the economic development and modern civilization of a country or region, the evaluation of urbanization development quality is a complex systematic project, which is helpful to cla...As an important indicator to measure the economic development and modern civilization of a country or region, the evaluation of urbanization development quality is a complex systematic project, which is helpful to clarify the strengths and weaknesses and regulate and guide the development strategies. Based on hierarchical analysis, geographically weighted regression analysis and cluster analysis, this paper takes Lishui City, Zhejiang Province as an example and selects 1 primary indicator, 4 secondary indicators and 17 tertiary indicators to construct an urbanization development evaluation index system and make an objective evaluation of the development status. The results show that: the level of urbanization development in Lishui City is relatively backward in the province, but the development trend is good;in terms of spatial correlation, the development of 9 counties and districts is relatively close, among which Qingtian County plays a key role in the development of urbanization in Lishui City;in terms of cluster analysis, the city’s 9 counties and districts can be divided into "agglomeration enhancement type" and "coordination In terms of cluster analysis, the city’s 9 counties and districts can be divided into three major types." Cluster Enhancement Type", "Coordination Promotion Type" and"Weak Development Type". In addition, countermeasures are proposed according to the development status to provide some reference for the future development of Lishui City and similar cities.展开更多
文摘Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints.This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization(MNEHHO)algorithm to address the allocation of HRES components.The proposed approach integrates key technical parameters,including charge-discharge efficiency,storage device configurations,and renewable energy fraction.We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability.The MNEHHO algorithm employs multiple neighborhood structures to enhance solution diversity and exploration capabilities.The model’s effectiveness is validated through case studies across four distinct institutional energy demand profiles.Results demonstrate that our approach successfully generates practically feasible HRES configurations while achieving significant reductions in costs and emissions compared to conventional methods.The enhanced search mechanisms of MNEHHO show superior performance in avoiding local optima and achieving consistent solutions.Experimental results demonstrate concrete improvements in solution quality(up to 46% improvement in objective value)and computational efficiency(average coefficient of variance of 24%-27%)across diverse institutional settings.This confirms the robustness and scalability of our method under various operational scenarios,providing a reliable framework for solving renewable energy allocation problems.
文摘Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).
文摘The globe faces an urgent need to close the energy demand-supply gap.Addressing this difficulty requires constructing a Hybrid Renewable Energy System(HRES),which has proven to be the most appropriate solution.HRES allows for integrating two or more renewable energy resources,successfully addressing the issue of intermittent availability of non-conventional energy resources.Optimization is critical for improving the HRES’s performance parameters during implementation.This study focuses on HRES using solar and biomass as renewable energy supplies and appropriate energy storage technologies.However,energy fluctuations present a problem with the power quality of HRES.To address this issue,the research paper introduces the Generalized Dynamic Progressive Neural Fuzzy Controller(GDPNFC),which regulates power flow within the proposed HRES.Furthermore,a unique approach called Enhanced Multi-Objective Monarch Butterfly Optimization(EMMBO)is used to optimize technical parameters.The simulation tool used in the research work is HOMER(Hybrid Optimization of Multiple Energy Resources)-PRO,and the system’s power quality is assessed using MATLAB 2016.The research paper concludes with comparing the performance of existing systems to the proposed system in terms of power loss and Total Harmonic Distortion(THD).It was established that the proposed technique involving EMMBO outperformed existing methods in technical optimization.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFF0708903)Ningbo Municipal Key Technology Research and Development Program of China(Grant No.2022Z006)Youth Fund of National Natural Science Foundation of China(Grant No.52205043)。
文摘Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.
基金We acknowledge the funding support from the National Natural Science Foundation of China(Grant No.51808462)the Natural Science Foundation Project of Sichuan Province,China(Grant No.2023NSFSC0346)the Science and Technology Project of Inner Mongolia Transportation Department,China(Grant No.NJ-2022-14).
文摘Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.
基金The authors appreciatively acknowledge the support of rapid device state variation based system device invention of a training device for live-work electricity meter installation without electric shocks(YNZC202003110011)National Natural Science Foundation of China(NSFC)under Grant(61902039).
文摘For the past few years,wind energy is the most popular non-traditional resource among renewable energy resources and it’s significant to make full use of wind energy to realize a high level of generating power.Moreover,diverse maximum power point tracking(MPPT)methods have been designed for varying speed operation of wind energy conversion system(WECS)applications to obtain optimal power extraction.Hence,a novel and metaheuristic technique,named enhanced atom search optimization(EASO),is designed for a permanent magnet synchronous generator(PMSG)based WECS,which can be employed to track the maximum power point.One of the most promising benefits of this technique is powerful global search capability that leads to fast response and high-quality optimal solution.Besides,in contrast with other conventional meta-heuristic techniques,EASO is extremely not relying on the original solution,which can avoid sinking into a low-quality local maximum power point(LMPP)by realizing an appropriate trade-off between global exploration and local exploitation.At last,simulations employing two case studies through Matlab/Simulink validate the practicability and effectiveness of the proposed techniques for optimal proportional-integral-derivative(PID)control parameters tuning of PMSG based WECS under a variety of wind conditions.
文摘In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.
基金supported by the Aeronautical Science Fund of Shaanxi Province of China(20145596025)
文摘Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.
文摘The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.
基金supported by the National Key Research and Development Programme of China(Grant No.2023YFB3406302)the National Natural Science Foundation of China(Grant No.52175120)Key Research,Development Plan of Shaanxi(Grant No.2024GH-ZDXM-29)。
文摘To address the challenges for vibration suppression in the precision sensors of underwater vehicles,phononic crystals have attracted significant attention due to the superior capabilities in elastic wave manipulation and vibration suppression.Unlike conventional damping materials,the phononic crystals can effectively suppress the wave propagation in the specific frequency ranges due to the unique periodic microstructures.
基金Supported by the National Natural Science Foundation of China(11172071)
文摘In this dissertation, un-powered gliding aircraft＇s optimal extended rangeproblem is discussed. The aircraft movement model was built. According to the degree of coupling, the model can be classified into a simple model or a complicated model. Using an optimal control method, two different movement models gave out the aircraft＇s attitude angle optimal flight path. Complicated model＇s optimal solution can be found by the genetic algorithm. This method can transfer the analytic solution of complicated model to a numerical value solution. Comparing the simulation results of different methods, it showed that the genetic algorithm combined with the complicated model＇s numerical value solution had the best performance in control strategy. This method solved the problem in which the highly coupling complicated model＇s analytic solution was hard to obtain. It verified that the genetic algorithm has validity in the field of extended range solution searching.
文摘Unsustainable fossil fuel energy usage and its environmental impacts are the most significant scientific challenges in the scientific community.Two-dimensional(2D)materials have received a lot of attention recently because of their great potential for application in addressing some of society’s most enduring issues with renewable energy.Transition metal-based nitrides,carbides,or carbonitrides,known as“MXenes”,are a relatively new and large family of 2D materials.Since the discovery of the first MXene,Ti_(3)C_(2) in 2011 has become one of the fastest-expanding families of 2D materials with unique physiochemical features.MXene surface terminations with hydroxyl,oxygen,fluorine,etc.,are invariably present in the so far reported materials,imparting hydrophilicity to their surfaces.The current finding of multi-transition metal-layered MXenes with controlled surface termination capacity opens the door to fabricating unique structures for producing renewable energy.MXene NMs-based flexible chemistry allows them to be tuned for energy-producing/storage,electromagnetic interference shielding,gas/biosensors,water distillation,nanocomposite reinforcement,lubrication,and photo/electro/chemical catalysis.This review will first discuss the advancement of MXenes synthesis methods,their properties/stability,and renewable energy applications.Secondly,we will highlight the constraints and challenges that impede the scientific community from synthesizing functional MXene with controlled composition and properties.We will further reveal the high-tech implementations for renewable energy storage applications along with future challenges and their solutions.
基金supported by"the National Student Innovation and Entrepreneurship Training Program Project"(Project No.202110353017)。
文摘As an important indicator to measure the economic development and modern civilization of a country or region, the evaluation of urbanization development quality is a complex systematic project, which is helpful to clarify the strengths and weaknesses and regulate and guide the development strategies. Based on hierarchical analysis, geographically weighted regression analysis and cluster analysis, this paper takes Lishui City, Zhejiang Province as an example and selects 1 primary indicator, 4 secondary indicators and 17 tertiary indicators to construct an urbanization development evaluation index system and make an objective evaluation of the development status. The results show that: the level of urbanization development in Lishui City is relatively backward in the province, but the development trend is good;in terms of spatial correlation, the development of 9 counties and districts is relatively close, among which Qingtian County plays a key role in the development of urbanization in Lishui City;in terms of cluster analysis, the city’s 9 counties and districts can be divided into "agglomeration enhancement type" and "coordination In terms of cluster analysis, the city’s 9 counties and districts can be divided into three major types." Cluster Enhancement Type", "Coordination Promotion Type" and"Weak Development Type". In addition, countermeasures are proposed according to the development status to provide some reference for the future development of Lishui City and similar cities.
