An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their level...An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm （ACO） is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage- efficiency and a longer lifetime is obtained.展开更多
UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we inve...UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.展开更多
Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility features.They play a crucial role in modern reconnaissance,inspection,intel...Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility features.They play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance missions.Coverage path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large region.Therefore,energy consumption is one of the most challenging issues that need to be optimized.In this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP problem.The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region.To do so,the flight path is optimized and the number of turns is reduced to minimize the energy consumption.The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint.Then,the coverage path planning problem is formulated,where the exact solution is determined using the CPLEX solver.For small-scale problems,the CPLEX shows a better solution in a reasonable time.However,the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems.Thus,to solve the model for large-scale problems,simulated annealing forCPP is developed.The results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX solver.Finally,we compare the simulated annealing against the greedy algorithm.The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.展开更多
Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large ext...Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large extent on the sensing coverage, and its lifetime is determined by its energy consumption. In this paper, an energy-efficient Area Coverage protocol for Heterogeneous Energy sensor networks (ACHE) is proposed. ACHE can achieve a good performance in terms of sensing area coverage, lifetime by minimizing energy consumption for control overhead, and balancing the energy load among all nodes. Adopting the hierarchical clustering idea, ACHE selects the active nodes based on the average residual energy of neighboring nodes and its own residual energy parameters. Our simulation demonstrates that ACHE not only provide the high quality of sensing coverage, but also has the good performance in the energy efficiency. In addition, ACHE can better adapt the applications with the great heterogeneous energy capacities in the sensor networks, as well as effectively reduce the control overhead.展开更多
Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, cluster...Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, clustering is considered a key technique in minimizing the consumed energy. However, few clustering protocols address the sensing coverage metric. This paper proposes a general framework that addresses both metrics for clustering algorithms in wireless sensor networks. The proposed framework is based on applying the principles of Virtual Field Force on each cluster within the network in order to move the sensor nodes towards proper locations that maximize the sensing coverage and minimize the transmitted energy. Two types of virtual forces are used: an attractive force that moves the nodes towards the cluster head in order to reduce the energy used for communication and a repulsive force that moves the overlapping nodes away from each other such that their sensing coverage is maximized. The performance of the proposed mechanism was evaluated by applying it to the well-known LEACH clustering algorithm. The simulation results demonstrate that the proposed mechanism improves the performance of the LEACH protocol considerably in terms of the achieved sensing coverage, and the network lifetime.展开更多
This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot ...This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.展开更多
Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemin...Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemination of critical information,which leads to insufficient incentives for market participants to trade.To solve these problems,this study constructs a three-party evolutionary game model with energy saving service companies(ESCO),homeowners,and trading information platforms as the main players,analyzes the interaction and evolution of the three parties'strategies under the scenario of government rewards and penalties,and explores the effects of the three parties'initial willingness and changes of model parameters on the evolution of their strategies.There are some findings as follows:first,the positive transactions of homeowners and ESCOs have less influence on the platform side;second,compared with homeowners,the government penalties have more obvious constraints on the platform side and ESCOs;third,government subsidies and EER revenues are the important factors influencing the speed of the evolution of three-party strategies,fourth,platform service compensation,the factors governing cost and benefit sharing are pivotal in determining the alignment of strategic choices among the three parties involved.Based on the research conclusions.This study offers theoretical guidance for the advancement of platform-based market transactions for EER.展开更多
The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to t...The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay.These generated enormous complex,high-dimensional,high-volume,and high-speed data also brings challenges on its storage,transmission,processing,and energy cost,due to the limited computing capabilities,battery capacity,memory,and energy utilization of current IoT networks.In this paper,a seamless architecture by combining mobile and cloud computing is proposed.It can agilely bargain with 5G-IoT devices,sensor nodes,and mobile computing in a distributed manner,enabling minimized energy cost,high interoperability,and high scalability as well as overcoming the memory constraints.An artificial intelligence(AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities.The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability,compression ratio,and energy saving.This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.展开更多
Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV...Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.展开更多
Research has shown considerable variability in whitecap coverage(W)under low to moderate wind conditions.During an expedition to the Northwestern Pacific,oceanographic variables and photographic measurements were coll...Research has shown considerable variability in whitecap coverage(W)under low to moderate wind conditions.During an expedition to the Northwestern Pacific,oceanographic variables and photographic measurements were collected to investigate the influence of wave-induced stress on W within these wind ranges.The friction velocity was recalculated based on turbulent stress,and wind profiles were modified to account for wave-induced stress and swell presence on the sea surface.The study examined W’s relationship with multiple parameters,including friction velocity(u*),breaking wave Reynolds numbers,wavesea Reynolds numbers,and wave age.The analysis utilized both conventional u*and turbulent stress-based friction velocity(u*turb).When utilizing u*turb rather than u*,the estimation model’s fitting results revealed an increase in correlation coefficient(R2)from 0.51 to 0.62,and a decrease in root mean square error(RMSE)from 0.0652 to 0.0574.Additionally,when parameterizing W using the windsea Reynolds number,with u_(*turb) replacing u*and wind wave height substituting mixed wave height,the R^(2) increased from 0.38 to 0.53,and the RMSE decreased from 0.0737 to 0.0668.The results demonstrate that calculating u*using the turbulent stress-based method,along with wind wave height and peak wave speed of mixed waves,yields stronger correlation with W.This correlation improvement stems from the inhibition of wave breaking by swell and wave-induced stress.The integration of turbulent stress and wind wave field measurements enhances the understanding of relationships between W and various parameters.However,swell effects on wind profiles do not substantially affect W estimation using wind speed-related parameters.展开更多
As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehi...As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.展开更多
Background:Globally,the use of community pharmacies and pharmacists in the delivery of vaccination services has been hampered by several factors,laws,and regulations that do not support pharmacists to participate in t...Background:Globally,the use of community pharmacies and pharmacists in the delivery of vaccination services has been hampered by several factors,laws,and regulations that do not support pharmacists to participate in the delivery of vaccination services.With the advent of COVID-19 pandemic,many countries have included community pharmacists and pharmacies in vaccination services to improve coverage.This study described the delivery of vaccination services in community pharmacies using the COVID-19 experience and how their involvement impacted vaccination coverage in Nigeria.It also exposed how this experience can be used to support policy revisions to formally recognize pharmacists in immunization delivery.Methods:A descriptive cross-sectional study was conducted among 474 community pharmacists in two southwestern States in Nigeria,using a semi-structured questionnaire.It determines the number of community pharmacists who have been trained in the delivery of vaccination services,the types of vaccination services provided,and vaccines administered in their pharmacies.Data were analyzed with descriptive and inferential statistics and p-value at≤0.05.Results:Response rate was 86.7%.Less than half of the respondents(40.1%)had undergone vaccination training.Of the 129(31.4%)respondents that provide vaccination services,72(55.8%)administer vaccines in their pharmacies.Out of these 72 respondents;45(62.5%)were administering vaccines before their involvement in COVID-19 vaccine administration;57(79.2%)of the health personnel who administer vaccines were pharmacists;60(83.3%)of them administer vaccines on request;22(30.6%)administered COVID-19 vaccines only;and only 7(9.7%)of the respondents had administered over 500 doses of COVID-19 vaccines.Training in vaccination was associated with the vaccination services provided(p<0.05).Respondents suggested government support through legal framework and policy review,training and empowering pharmacists in vaccine administration,and recognition of community pharmacists as PHC providers.展开更多
This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks(LAINs)to provide agile coverage tailored to active air routes and takeoff/landing spots.Traditional cellular...This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks(LAINs)to provide agile coverage tailored to active air routes and takeoff/landing spots.Traditional cellular networks struggle to meet the dynamic demands of low-altitude UAV communications due to their rigid structures.The hyper-cellular network(HCN)architecture separates control and traffic coverage,enabling flexible and energy-efficient operations.The key components include control base stations(CBSs)for wide-area signaling coverage and traffic base stations(TBSs)that can be dynamically activated based on traffic demands.The proposed solution also integrates space information networks(SINs)to enhance the coverage efficiency.Key technologies such as all-G CBS using RISC-V architecture,AI-powered radio maps for low-altitude environments,and agile TBS coverage adaptation are introduced with some preliminary studies.These designs aim to address challenges like mobility management,interference coordination,and the need for real-time spectrum sharing in blended satellite-terrestrial networks.The proposed solution offers a scalable and agile framework to support the rapidly growing demand for reliable,low-latency,and high-capacity UAV communications in urban environments.展开更多
In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel ...In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel links,high transceiver an-tenna height is required,which limits the number of geographically available sites for BS deployment,and imposes a high cost for realizing effective wide-area coverage.In this paper,the joint user association and power allocation(JUAPA)problem is investigated to enhance the coverage of offshore maritime systems.By exploiting the characteristics of network topology as well as vessels’motion in offshore communica-tions,a multi-period JUAPA problem is formulated to maximize the number of ships that can be simultane-ously served by the network.This JUAPA problem is intrinsically non-convex and subject to mixed-integer constraints,which is difficult to solve either analyt-ically or numerically.Hence,we propose an iterative augmentation based framework to efficiently select the active vessels,where the JUAPA scheme is iteratively optimized by the network for increasing the number of the selected vessels.More specifically,in each itera-tion,the user association variables and power alloca-tion variables are determined by solving two separate subproblems,so that the JUAPA strategy can be up-dated in a low-complexity manner.The performance of the proposed JUAPA method is evaluated by exten-sive simulation,and numerical results indicate that it can effectively increase the number of vessels served by the network,and thus enhances the coverage of off-shore systems.展开更多
Objective:To assess the complete vaccination coverage and timeliness of childhood vaccinations among Indigenous children in Peninsular Malaysia.Methods:The study utilized data from the 2022 Orang Asli Health Survey,a ...Objective:To assess the complete vaccination coverage and timeliness of childhood vaccinations among Indigenous children in Peninsular Malaysia.Methods:The study utilized data from the 2022 Orang Asli Health Survey,a cross-sectional survey conducted among a representative sample of Orang Asli in Peninsular Malaysia.A total of 68 villages were randomly selected from a pool of 853 villages,encompassing diverse geographic and sociodemographic contexts with a total of 15950 respondents Orang Asli successfully interviewed.However,this study only utilized data from surveyed children aged 12 to 59 months with a total of 1551 children included.Validated structured questionnaires were used to collect sociodemographic data and health status,with nurses verifying vaccination records.Children who received all nine primary vaccinations were defined as having complete vaccination while those who received vaccine within the recommended time were defined as having timely vaccination.Data analysis was conducted using IBM SPSS version 25.0,focusing on descriptive analyses of children's vaccination status.Results:The prevalence of overall complete vaccination among Indigenous children was 87.7%,while timely vaccination was only 40.3%.The prevalence of complete vaccination for Bacillus Calmette-Guérin(BCG),the first dose of hepatitis B,three doses of DTaP-IPV-Hib,and measles,mumps,and rubella(MMR)was above 95.0%,except for the second and third doses of hepatitis B.The prevalence of timely vaccination ranged from above 95.0%for vaccines given at birth,gradually decreasing with increasing age to 57.5%for the first dose of MMR.Moreover,the completion rates for three doses of DtaP-IPV-Hib and the initial dose of MMR surpassed 90%among Indigenous children aged 12-23 months,yet the timeliness remained at a moderate level.Conclusions:While the overall complete vaccination coverage among Indigenous children in Malaysia is relatively high,there are concerning disparities in the timeliness of vaccination,particularly as children age.展开更多
In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance pr...In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance problem is decomposed into one-on-one guidance problems against predictive interception points.To expand the coverage area of each missile,these one-on-one guidance problems are formulated as flight path angle tracking problems,and the optimal error dynamics is extended to derive the guidance law analytically.In addition,through the introduction of the coverage probability model,the dynamic coverage strategy is proposed.The predictive interception points are updated online by maximizing the coverage probability,which aims to achieve successful interception despite variations in target acceleration.Furthermore,a switching strategy of the guidance command is designed for collision avoidance.Simulation results demonstrate that the missile group can cooperatively intercept a highly maneuvering target under the proposed guidance law.展开更多
Influenza is a significant global public health challenge,with seasonal epidemics imposing substantial burdens on healthcare systems and vulnerable populations,causing 3 to 5 million severe cases and 290,000 to 650,00...Influenza is a significant global public health challenge,with seasonal epidemics imposing substantial burdens on healthcare systems and vulnerable populations,causing 3 to 5 million severe cases and 290,000 to 650,000 respiratory-related deaths worldwide each year[1].Vaccines are an effective means of preventing influenza.In recent years,China has made progress in vaccine development and immunization strategies.The population is recommended to receive influenza vaccines annually;however,their coverage remain suboptimal[2].The World Health Organization(WHO)highlights that all countries should consider implementing seasonal influenza immunization programs,with priority groups determined based on local epidemiological contexts.In alignment with the Immunization Agenda 2030,the use of seasonal influenza vaccines contributes to strengthening the life course of immunization and serves as a critical component for addressing influenza pandemics,as outlined in the WHO Global Influenza Strategy 2019-2030.展开更多
Urban vegetation plays a crucial role in regulating temperatures and heat waves in urban areas.However,the influence of vegetation coverage and its configuration on surface temperatures in different climate zones at a...Urban vegetation plays a crucial role in regulating temperatures and heat waves in urban areas.However,the influence of vegetation coverage and its configuration on surface temperatures in different climate zones at a national scale is unclear.To address this,we utilized high-resolution data to detect spatial patterns for 31 provincial capital cities in China.We integrated day and night surface temperatures to determine the influence of vegetative coverage and configuration on urban temperatures across different climate zones and city sizes.Our study revealed that a subtropical monsoon climate and medium-sized cities had the highest vegetative coverage and shape complexity.The best connectivity and agglomeration of vegetation were found in a temperate monsoon climate and large cities.In contrast,small cities,especially those under a temperate continental climate,had low vegetation coverage,high fragmentation,and weak agglomeration and connectivity.In addition,vegetative coverage had a negative impact on daytime surface temperatures,especially in large cities in a subtropical monsoon climate.However,an increase in vegetation coverage could result in warming at night in small cities in temperate continental climates.Although urban vegetation configuration also contributed to moderating surface temperatures,especially at night,they did not surpass the influence of vegetation coverage.The effect on nighttime temperatures of the configuration of vegetation increased by 3–6%relative to that of daytime temperatures,especially in large cities in a temperate monsoon climate.The contribution vegetation coverage and configuration interaction to cooling efficiency decreased at night,especially in medium-sized cities in a temperate continental climate by 3–5%.In addition,this study identified several moderating effects of natural and social factors on the relationship between urban vegetation coverage and surface temperatures.High duration of sunshine,low humidity and high wind speed significantly enhanced the negative impact of vegetation coverage on surface temperatures.In addition,the moderating effect of vegetation coverage was more pronounced in low population density cities and high gross domestic product.This study enhances understanding of the ecological functions of urban vegetation and provides a valuable scientific basis and strategic recommendations for optimizing urban vegetation and improving urban environmental quality.展开更多
In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing ...In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing to their exceptional flexibility and rapid deployment capabilities,unmanned aerial vehicles(UAVs)have emerged as the ideal platforms for accomplishing these tasks.This study proposes a swarm A^(*)-guided Deep Q-Network(SADQN)algorithm to address the coverage path planning(CPP)problem for UAV swarms in complex environments.Firstly,to overcome the dependency of traditional modeling methods on regular terrain environments,this study proposes an improved cellular decomposition method for map discretization.Simultaneously,a distributed UAV swarm system architecture is adopted,which,through the integration of multi-scale maps,addresses the issues of redundant operations and flight conflicts inmulti-UAV cooperative coverage.Secondly,the heuristic mechanism of the A^(*)algorithmis combinedwith full-coverage path planning,and this approach is incorporated at the initial stage ofDeep Q-Network(DQN)algorithm training to provide effective guidance in action selection,thereby accelerating convergence.Additionally,a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm.To evaluate the efficacy of the proposed algorithm,simulation experiments were conducted in several irregular environments and compared with several popular algorithms.Simulation results show that the SADQNalgorithmoutperforms othermethods,achieving performance comparable to that of the baseline prior algorithm,with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers.In addition,the algorithm demonstrates excellent generalization ability,enabling it to adapt to different environments.展开更多
Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken us...Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken using component failure rate data to predict PTC for a full stroke test and a partial stroke test.Given the subjective and uncertain aspects of the FMEDA approach,specifically the selection of component failure rates and the determination of the probability of detecting failure modes,a Fuzzy Inference System(FIS)was proposed to manage the data,addressing the inherent uncertainties.Fuzzy inference systems have been used previously for various FMEA type assessments,but this is the first time an FIS has been employed for use with FMEDA.ESDV PTC values were generated from both the standard FMEDA and the fuzzy-FMEDA approaches using data provided by FMEDA experts.This work demonstrates that fuzzy inference systems can address the subjectivity inherent in FMEDA data,enabling reliable estimates of ESDV proof test coverage for both full and partial stroke tests.This facilitates optimized maintenance planning while ensuring safety is not compromised.展开更多
基金The Natural Science Foundation of Jiangsu Province(NoBK2005409)
文摘An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm （ACO） is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage- efficiency and a longer lifetime is obtained.
基金supported by the National Natural Science Foundation of China under Grant No. 61771488in part by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034+1 种基金 in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratorythe Guang Xi Universities Key Laboratory Fund of Embedded Technology and Intelligent System (Guilin University of Technology)
文摘UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.
基金funded by Project Number INML2104 under the Interdisci-Plinary Center of Smart Mobility and Logistics,KFUPM.
文摘Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility features.They play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance missions.Coverage path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large region.Therefore,energy consumption is one of the most challenging issues that need to be optimized.In this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP problem.The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region.To do so,the flight path is optimized and the number of turns is reduced to minimize the energy consumption.The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint.Then,the coverage path planning problem is formulated,where the exact solution is determined using the CPLEX solver.For small-scale problems,the CPLEX shows a better solution in a reasonable time.However,the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems.Thus,to solve the model for large-scale problems,simulated annealing forCPP is developed.The results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX solver.Finally,we compare the simulated annealing against the greedy algorithm.The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.
文摘Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large extent on the sensing coverage, and its lifetime is determined by its energy consumption. In this paper, an energy-efficient Area Coverage protocol for Heterogeneous Energy sensor networks (ACHE) is proposed. ACHE can achieve a good performance in terms of sensing area coverage, lifetime by minimizing energy consumption for control overhead, and balancing the energy load among all nodes. Adopting the hierarchical clustering idea, ACHE selects the active nodes based on the average residual energy of neighboring nodes and its own residual energy parameters. Our simulation demonstrates that ACHE not only provide the high quality of sensing coverage, but also has the good performance in the energy efficiency. In addition, ACHE can better adapt the applications with the great heterogeneous energy capacities in the sensor networks, as well as effectively reduce the control overhead.
文摘Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, clustering is considered a key technique in minimizing the consumed energy. However, few clustering protocols address the sensing coverage metric. This paper proposes a general framework that addresses both metrics for clustering algorithms in wireless sensor networks. The proposed framework is based on applying the principles of Virtual Field Force on each cluster within the network in order to move the sensor nodes towards proper locations that maximize the sensing coverage and minimize the transmitted energy. Two types of virtual forces are used: an attractive force that moves the nodes towards the cluster head in order to reduce the energy used for communication and a repulsive force that moves the overlapping nodes away from each other such that their sensing coverage is maximized. The performance of the proposed mechanism was evaluated by applying it to the well-known LEACH clustering algorithm. The simulation results demonstrate that the proposed mechanism improves the performance of the LEACH protocol considerably in terms of the achieved sensing coverage, and the network lifetime.
文摘This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.
基金supported by the National Natural Science Foundation of China(Grant No.71872122)the Late-stage Subsidy Project of Humanities and Social Sciences of the Education Department of China(Grant No.20JHQ095).
文摘Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemination of critical information,which leads to insufficient incentives for market participants to trade.To solve these problems,this study constructs a three-party evolutionary game model with energy saving service companies(ESCO),homeowners,and trading information platforms as the main players,analyzes the interaction and evolution of the three parties'strategies under the scenario of government rewards and penalties,and explores the effects of the three parties'initial willingness and changes of model parameters on the evolution of their strategies.There are some findings as follows:first,the positive transactions of homeowners and ESCOs have less influence on the platform side;second,compared with homeowners,the government penalties have more obvious constraints on the platform side and ESCOs;third,government subsidies and EER revenues are the important factors influencing the speed of the evolution of three-party strategies,fourth,platform service compensation,the factors governing cost and benefit sharing are pivotal in determining the alignment of strategic choices among the three parties involved.Based on the research conclusions.This study offers theoretical guidance for the advancement of platform-based market transactions for EER.
文摘The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay.These generated enormous complex,high-dimensional,high-volume,and high-speed data also brings challenges on its storage,transmission,processing,and energy cost,due to the limited computing capabilities,battery capacity,memory,and energy utilization of current IoT networks.In this paper,a seamless architecture by combining mobile and cloud computing is proposed.It can agilely bargain with 5G-IoT devices,sensor nodes,and mobile computing in a distributed manner,enabling minimized energy cost,high interoperability,and high scalability as well as overcoming the memory constraints.An artificial intelligence(AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities.The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability,compression ratio,and energy saving.This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.
基金National Natural Science Foundation of China(Grant No.52472417)to provide fund for conducting experiments.
文摘Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.
基金supported by the National Natural Science Foundation of China(Grant No.42276001)2024 Qinhuangdao Social Science Development Research Project(Grant No.2024LX206)Hebei Agricultural University Research Project for Talented scholars(Grant No.YJ201835).
文摘Research has shown considerable variability in whitecap coverage(W)under low to moderate wind conditions.During an expedition to the Northwestern Pacific,oceanographic variables and photographic measurements were collected to investigate the influence of wave-induced stress on W within these wind ranges.The friction velocity was recalculated based on turbulent stress,and wind profiles were modified to account for wave-induced stress and swell presence on the sea surface.The study examined W’s relationship with multiple parameters,including friction velocity(u*),breaking wave Reynolds numbers,wavesea Reynolds numbers,and wave age.The analysis utilized both conventional u*and turbulent stress-based friction velocity(u*turb).When utilizing u*turb rather than u*,the estimation model’s fitting results revealed an increase in correlation coefficient(R2)from 0.51 to 0.62,and a decrease in root mean square error(RMSE)from 0.0652 to 0.0574.Additionally,when parameterizing W using the windsea Reynolds number,with u_(*turb) replacing u*and wind wave height substituting mixed wave height,the R^(2) increased from 0.38 to 0.53,and the RMSE decreased from 0.0737 to 0.0668.The results demonstrate that calculating u*using the turbulent stress-based method,along with wind wave height and peak wave speed of mixed waves,yields stronger correlation with W.This correlation improvement stems from the inhibition of wave breaking by swell and wave-induced stress.The integration of turbulent stress and wind wave field measurements enhances the understanding of relationships between W and various parameters.However,swell effects on wind profiles do not substantially affect W estimation using wind speed-related parameters.
基金supported in part by the National Natural Science Foundation of China under Grants 62071189,62201220 and 62171189by the Key Research and Development Program of Hubei Province under Grant 2021BAA026 and 2020BAB120。
文摘As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.
文摘Background:Globally,the use of community pharmacies and pharmacists in the delivery of vaccination services has been hampered by several factors,laws,and regulations that do not support pharmacists to participate in the delivery of vaccination services.With the advent of COVID-19 pandemic,many countries have included community pharmacists and pharmacies in vaccination services to improve coverage.This study described the delivery of vaccination services in community pharmacies using the COVID-19 experience and how their involvement impacted vaccination coverage in Nigeria.It also exposed how this experience can be used to support policy revisions to formally recognize pharmacists in immunization delivery.Methods:A descriptive cross-sectional study was conducted among 474 community pharmacists in two southwestern States in Nigeria,using a semi-structured questionnaire.It determines the number of community pharmacists who have been trained in the delivery of vaccination services,the types of vaccination services provided,and vaccines administered in their pharmacies.Data were analyzed with descriptive and inferential statistics and p-value at≤0.05.Results:Response rate was 86.7%.Less than half of the respondents(40.1%)had undergone vaccination training.Of the 129(31.4%)respondents that provide vaccination services,72(55.8%)administer vaccines in their pharmacies.Out of these 72 respondents;45(62.5%)were administering vaccines before their involvement in COVID-19 vaccine administration;57(79.2%)of the health personnel who administer vaccines were pharmacists;60(83.3%)of them administer vaccines on request;22(30.6%)administered COVID-19 vaccines only;and only 7(9.7%)of the respondents had administered over 500 doses of COVID-19 vaccines.Training in vaccination was associated with the vaccination services provided(p<0.05).Respondents suggested government support through legal framework and policy review,training and empowering pharmacists in vaccine administration,and recognition of community pharmacists as PHC providers.
基金Feng Wei was supported by the National Natural Science Foundation of China under Grant 62425110.
文摘This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks(LAINs)to provide agile coverage tailored to active air routes and takeoff/landing spots.Traditional cellular networks struggle to meet the dynamic demands of low-altitude UAV communications due to their rigid structures.The hyper-cellular network(HCN)architecture separates control and traffic coverage,enabling flexible and energy-efficient operations.The key components include control base stations(CBSs)for wide-area signaling coverage and traffic base stations(TBSs)that can be dynamically activated based on traffic demands.The proposed solution also integrates space information networks(SINs)to enhance the coverage efficiency.Key technologies such as all-G CBS using RISC-V architecture,AI-powered radio maps for low-altitude environments,and agile TBS coverage adaptation are introduced with some preliminary studies.These designs aim to address challenges like mobility management,interference coordination,and the need for real-time spectrum sharing in blended satellite-terrestrial networks.The proposed solution offers a scalable and agile framework to support the rapidly growing demand for reliable,low-latency,and high-capacity UAV communications in urban environments.
基金supported by the National Key Research and Development Program of China under Grant 2018YFA0701601by the Program of Jiangsu Province under Grant NTACT-2024-Z-001.
文摘In offshore maritime communication sys-tems,base stations(BSs)are employed along the coastline to provide high-speed data service for ves-sels in coastal sea areas.To ensure the line-of-sight propagation of BS-vessel links,high transceiver an-tenna height is required,which limits the number of geographically available sites for BS deployment,and imposes a high cost for realizing effective wide-area coverage.In this paper,the joint user association and power allocation(JUAPA)problem is investigated to enhance the coverage of offshore maritime systems.By exploiting the characteristics of network topology as well as vessels’motion in offshore communica-tions,a multi-period JUAPA problem is formulated to maximize the number of ships that can be simultane-ously served by the network.This JUAPA problem is intrinsically non-convex and subject to mixed-integer constraints,which is difficult to solve either analyt-ically or numerically.Hence,we propose an iterative augmentation based framework to efficiently select the active vessels,where the JUAPA scheme is iteratively optimized by the network for increasing the number of the selected vessels.More specifically,in each itera-tion,the user association variables and power alloca-tion variables are determined by solving two separate subproblems,so that the JUAPA strategy can be up-dated in a low-complexity manner.The performance of the proposed JUAPA method is evaluated by exten-sive simulation,and numerical results indicate that it can effectively increase the number of vessels served by the network,and thus enhances the coverage of off-shore systems.
文摘Objective:To assess the complete vaccination coverage and timeliness of childhood vaccinations among Indigenous children in Peninsular Malaysia.Methods:The study utilized data from the 2022 Orang Asli Health Survey,a cross-sectional survey conducted among a representative sample of Orang Asli in Peninsular Malaysia.A total of 68 villages were randomly selected from a pool of 853 villages,encompassing diverse geographic and sociodemographic contexts with a total of 15950 respondents Orang Asli successfully interviewed.However,this study only utilized data from surveyed children aged 12 to 59 months with a total of 1551 children included.Validated structured questionnaires were used to collect sociodemographic data and health status,with nurses verifying vaccination records.Children who received all nine primary vaccinations were defined as having complete vaccination while those who received vaccine within the recommended time were defined as having timely vaccination.Data analysis was conducted using IBM SPSS version 25.0,focusing on descriptive analyses of children's vaccination status.Results:The prevalence of overall complete vaccination among Indigenous children was 87.7%,while timely vaccination was only 40.3%.The prevalence of complete vaccination for Bacillus Calmette-Guérin(BCG),the first dose of hepatitis B,three doses of DTaP-IPV-Hib,and measles,mumps,and rubella(MMR)was above 95.0%,except for the second and third doses of hepatitis B.The prevalence of timely vaccination ranged from above 95.0%for vaccines given at birth,gradually decreasing with increasing age to 57.5%for the first dose of MMR.Moreover,the completion rates for three doses of DtaP-IPV-Hib and the initial dose of MMR surpassed 90%among Indigenous children aged 12-23 months,yet the timeliness remained at a moderate level.Conclusions:While the overall complete vaccination coverage among Indigenous children in Malaysia is relatively high,there are concerning disparities in the timeliness of vaccination,particularly as children age.
基金supported by the National Natural Science Foundation of China(Nos.61773142,62303136)China Postdoctoral Science Foundation(No.2023M740912)Postdoctoral Fellowship Program of CPSF,China(No.GZC20233447).
文摘In this article,a three-dimensional cooperative guidance problem for highly maneuvering targets is investigated under the assumption of perfect information.Inspired by the coverage strategy,the cooperative guidance problem is decomposed into one-on-one guidance problems against predictive interception points.To expand the coverage area of each missile,these one-on-one guidance problems are formulated as flight path angle tracking problems,and the optimal error dynamics is extended to derive the guidance law analytically.In addition,through the introduction of the coverage probability model,the dynamic coverage strategy is proposed.The predictive interception points are updated online by maximizing the coverage probability,which aims to achieve successful interception despite variations in target acceleration.Furthermore,a switching strategy of the guidance command is designed for collision avoidance.Simulation results demonstrate that the missile group can cooperatively intercept a highly maneuvering target under the proposed guidance law.
基金supported by the Chinese Association of Preventive Medicine-Vaccine and Immunization Youth Talent Support Project(CPMAQT-YM0314)。
文摘Influenza is a significant global public health challenge,with seasonal epidemics imposing substantial burdens on healthcare systems and vulnerable populations,causing 3 to 5 million severe cases and 290,000 to 650,000 respiratory-related deaths worldwide each year[1].Vaccines are an effective means of preventing influenza.In recent years,China has made progress in vaccine development and immunization strategies.The population is recommended to receive influenza vaccines annually;however,their coverage remain suboptimal[2].The World Health Organization(WHO)highlights that all countries should consider implementing seasonal influenza immunization programs,with priority groups determined based on local epidemiological contexts.In alignment with the Immunization Agenda 2030,the use of seasonal influenza vaccines contributes to strengthening the life course of immunization and serves as a critical component for addressing influenza pandemics,as outlined in the WHO Global Influenza Strategy 2019-2030.
基金supported by the National Natural Science Foundation of China(42171109,32130068)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2020237)National Key R&D Program of China(2023YFF1304604).
文摘Urban vegetation plays a crucial role in regulating temperatures and heat waves in urban areas.However,the influence of vegetation coverage and its configuration on surface temperatures in different climate zones at a national scale is unclear.To address this,we utilized high-resolution data to detect spatial patterns for 31 provincial capital cities in China.We integrated day and night surface temperatures to determine the influence of vegetative coverage and configuration on urban temperatures across different climate zones and city sizes.Our study revealed that a subtropical monsoon climate and medium-sized cities had the highest vegetative coverage and shape complexity.The best connectivity and agglomeration of vegetation were found in a temperate monsoon climate and large cities.In contrast,small cities,especially those under a temperate continental climate,had low vegetation coverage,high fragmentation,and weak agglomeration and connectivity.In addition,vegetative coverage had a negative impact on daytime surface temperatures,especially in large cities in a subtropical monsoon climate.However,an increase in vegetation coverage could result in warming at night in small cities in temperate continental climates.Although urban vegetation configuration also contributed to moderating surface temperatures,especially at night,they did not surpass the influence of vegetation coverage.The effect on nighttime temperatures of the configuration of vegetation increased by 3–6%relative to that of daytime temperatures,especially in large cities in a temperate monsoon climate.The contribution vegetation coverage and configuration interaction to cooling efficiency decreased at night,especially in medium-sized cities in a temperate continental climate by 3–5%.In addition,this study identified several moderating effects of natural and social factors on the relationship between urban vegetation coverage and surface temperatures.High duration of sunshine,low humidity and high wind speed significantly enhanced the negative impact of vegetation coverage on surface temperatures.In addition,the moderating effect of vegetation coverage was more pronounced in low population density cities and high gross domestic product.This study enhances understanding of the ecological functions of urban vegetation and provides a valuable scientific basis and strategic recommendations for optimizing urban vegetation and improving urban environmental quality.
文摘In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing to their exceptional flexibility and rapid deployment capabilities,unmanned aerial vehicles(UAVs)have emerged as the ideal platforms for accomplishing these tasks.This study proposes a swarm A^(*)-guided Deep Q-Network(SADQN)algorithm to address the coverage path planning(CPP)problem for UAV swarms in complex environments.Firstly,to overcome the dependency of traditional modeling methods on regular terrain environments,this study proposes an improved cellular decomposition method for map discretization.Simultaneously,a distributed UAV swarm system architecture is adopted,which,through the integration of multi-scale maps,addresses the issues of redundant operations and flight conflicts inmulti-UAV cooperative coverage.Secondly,the heuristic mechanism of the A^(*)algorithmis combinedwith full-coverage path planning,and this approach is incorporated at the initial stage ofDeep Q-Network(DQN)algorithm training to provide effective guidance in action selection,thereby accelerating convergence.Additionally,a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm.To evaluate the efficacy of the proposed algorithm,simulation experiments were conducted in several irregular environments and compared with several popular algorithms.Simulation results show that the SADQNalgorithmoutperforms othermethods,achieving performance comparable to that of the baseline prior algorithm,with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers.In addition,the algorithm demonstrates excellent generalization ability,enabling it to adapt to different environments.
文摘Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken using component failure rate data to predict PTC for a full stroke test and a partial stroke test.Given the subjective and uncertain aspects of the FMEDA approach,specifically the selection of component failure rates and the determination of the probability of detecting failure modes,a Fuzzy Inference System(FIS)was proposed to manage the data,addressing the inherent uncertainties.Fuzzy inference systems have been used previously for various FMEA type assessments,but this is the first time an FIS has been employed for use with FMEDA.ESDV PTC values were generated from both the standard FMEDA and the fuzzy-FMEDA approaches using data provided by FMEDA experts.This work demonstrates that fuzzy inference systems can address the subjectivity inherent in FMEDA data,enabling reliable estimates of ESDV proof test coverage for both full and partial stroke tests.This facilitates optimized maintenance planning while ensuring safety is not compromised.
