Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is con...Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centraliz...Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of incentives.To address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain scenarios.In particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains.Second,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain user.Furthermore,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain.Finally,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.展开更多
The shortage of freshwater has become a global challenge,exacerbated by global warming and the rapid growth of the world’s population.Researchers across various fields have made numerous attempts to efficiently colle...The shortage of freshwater has become a global challenge,exacerbated by global warming and the rapid growth of the world’s population.Researchers across various fields have made numerous attempts to efficiently collect freshwater for human use.These efforts include seawater desalination through reverse osmosis or distillation,sewage treatment technologies,and atmospheric water harvesting.However,after thoroughly exploring traditional freshwater harvesting methods,it has become clear that bio-inspired fog harvesting technology offers new prospects due to its unique advantages of efficiency and sustainability.This paper systematically introduces the current principles of fog harvesting and wettability mechanism found in nature.It reviews the research status of combining bionic fog harvesting materials with textile science from two distinct dimensions.Additionally,it describes the practical applications of fog harvesting materials in agriculture,industry,and domestic water use,analyzes their prospects and feasibility in engineering projects,discusses potential challenges in practical applications,and envisions future trends and directions for the development of these materials.展开更多
Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling...Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.展开更多
Fog is a highly complex weather phenomenon influenced by numerous factors.This study investigated the impact of the Changbai Mountains’topography on the formation and development of spring fog in the Bohai Sea.From 1...Fog is a highly complex weather phenomenon influenced by numerous factors.This study investigated the impact of the Changbai Mountains’topography on the formation and development of spring fog in the Bohai Sea.From 12 to 14 May 2021,the Bohai region experienced a sea fog event.Utilizing Himawari-8 satellite data,ERA5 reanalysis dataset,land and sea station observations,the WRF model,a topography sensitivity experiment,and backward trajectory tracking,the influence of the Changbai Mountains’topography on the evolution of this sea fog event was assessed.Results indicated that the Changbai Mountains’topography significantly impacted the propagation and concentration of the sea fog through dual effects—namely,the Venturi Effect and Foehn Clearance Effect.Comparative simulations incorporating and excluding the Changbai Mountains revealed that its topography favored weak convergence(Venturi Effect)of low-level airflow over the Bohai Sea induced by a high-pressure system,promoting westward fog expansion.Additionally,the backward trajectory analysis further indicated that the Foehn Clearance Effect of the Changbai Mountains extended its influence far beyond the immediate lee side,contributing to significant changes in atmospheric conditions such as reductions in relative humidity and increases in potential temperature.The dry,warm foehn contributed to a reduction in the liquid water content,ultimately leading to the weakening or even dissipation of the sea fog in the region close to the Changbai Mountains.This study emphasizes the crucial role of the Changbai Mountains’topography in the development and evolution of fog,providing valuable insights for forecasting fog in regions with complex terrain.展开更多
Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machin...Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.展开更多
The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation ...The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.展开更多
With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these t...With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these technologies face critical security challenges,exacerbated by the emergence of quantum computing,which threatens traditional encryption methods.The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust,quantum-resistant security solutions.To address these challenges,researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography,which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity.This paper reviews the current security practices in IoT and Edge/Fog environments,explores the latest advancements in QKD and PQC technologies,and discusses their integration into distributed computing systems.Additionally,this paper proposes an enhanced QKD protocol combining the Cascade protocol and Kyber algorithm to address existing limitations.Finally,we highlight future research directions aimed at improving the scalability,efficiency,and practicality of QKD and PQC for securing IoT and Edge/Fog networks against evolving quantum threats.展开更多
Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(...Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively.展开更多
Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resourc...Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.展开更多
The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devic...The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devices.However,the performance of current 6G network intelligence technologies and its level of integration with the architecture,along with the system-level requirements for the number of access devices and limitations on energy consumption,have impeded further improvements in the 6G smart F-RAN.To better analyze the root causes of the network problems and promote the practical development of the network,this study used structured methods such as segmentation to conduct a review of the topic.The research results reveal that there are still many problems in the current 6G smart F-RAN.Future research directions and difficulties are also discussed.展开更多
This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event wit...This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event with cold-front synoptic pattern(CFSP).An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting model was adopted with ensemble sensitivity analysis(ESA).By comparing observation impacts(estimated from a 40-member ensemble with ESA)among different meteorological observation variables and pressure levels,the temperature at 850 hPa and surface layer(850 hPa-and-surface temperature)was selected as the target observation type.Additionally,the area with large observation impacts for this observation type was predicted in the transition region of the surface low–high system.This area developed southward with the low and moved eastward with the low–high system,which could be explained by the main features of CFSP.Moreover,both experiments assimilating synthetic and real observations showed that assimilating 850 hPa-and-surface temperature observations generally yielded better fog coverage forecasts in areas with greater observation impacts than areas with smaller impacts.However,the effectiveness of adaptive observations was reduced when real observations rather than synthetic observations were assimilated,which is possibly due to factors such as observation and model errors.The main conclusions above were verified by another typical fog event with CFSP characteristics.Results of this study highlight the importance of improved initial conditions in the transition region of the low–high system for improving fog prediction and provide scientific guidance for implementing an observation network for fog forecasting over the Bohai Sea.展开更多
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.展开更多
Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/f...Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/fog computing traffic surveillance and monitoring systems.Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time.To evaluate vision-based vehicle detection performance in foggy weather conditions,state-of-the-art Vehicle Detection in Adverse Weather Nature(DAWN)and Foggy Driving(FD)datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle detection classes:cars,buses,motorcycles,and trucks.The state-of-the-art single-stage deep learning algorithms YOLO-V5,and YOLO-V8 are considered for the task of vehicle detection.Furthermore,YOLO-V5s is enhanced by introducing attention modules Convolutional Block Attention Module(CBAM),Normalized-based Attention Module(NAM),and Simple Attention Module(SimAM)after the SPPF module as well as YOLO-V5l with BiFPN.Their vehicle detection accuracy parameters and running speed is validated on cloud(Google Colab)and edge(local)systems.The mAP50 score of YOLO-V5n is 72.60%,YOLOV5s is 75.20%,YOLO-V5m is 73.40%,and YOLO-V5l is 77.30%;and YOLO-V8n is 60.20%,YOLO-V8s is 73.50%,YOLO-V8m is 73.80%,and YOLO-V8l is 72.60%on DAWN dataset.The mAP50 score of YOLO-V5n is 43.90%,YOLO-V5s is 40.10%,YOLO-V5m is 49.70%,and YOLO-V5l is 57.30%;and YOLO-V8n is 41.60%,YOLO-V8s is 46.90%,YOLO-V8m is 42.90%,and YOLO-V8l is 44.80%on FD dataset.The vehicle detection speed of YOLOV5n is 59 Frame Per Seconds(FPS),YOLO-V5s is 47 FPS,YOLO-V5m is 38 FPS,and YOLO-V5l is 30 FPS;and YOLO-V8n is 185 FPS,YOLO-V8s is 109 FPS,YOLO-V8m is 72 FPS,and YOLO-V8l is 63 FPS on DAWN dataset.The vehicle detection speed of YOLO-V5n is 26 FPS,YOLO-V5s is 24 FPS,YOLO-V5m is 22 FPS,and YOLO-V5l is 17 FPS;and YOLO-V8n is 313 FPS,YOLO-V8s is 182 FPS,YOLO-V8m is 99 FPS,and YOLO-V8l is 60 FPS on FD dataset.YOLO-V5s,YOLO-V5s variants and YOLO-V5l_BiFPN,and YOLO-V8 algorithms are efficient and cost-effective solution for real-time vision-based vehicle detection in foggy weather.展开更多
Synchronous or quasi-synchronous sea-land-air observations were conducted using advanced sea ice, atmospheric and marine instruments during China's First Arctic Expedition. Expedition.Based on the Precious data f...Synchronous or quasi-synchronous sea-land-air observations were conducted using advanced sea ice, atmospheric and marine instruments during China's First Arctic Expedition. Expedition.Based on the Precious data from the expedition, it was found that in the Arctic Ocean, most part of which is covered with ice or is mixed with ice, various kinds of sea fog formed such as advection fog, radiation fog and vapor fog. Each kind he its own characteristic and mechanics of creation. In the southern part of the Arctic Ocean, due to the sufficient warm and wet flow there, it is favorable for advection fog to form, which is dense and lasts a long time. On ice cap or vast floating ice, due to the strong radiation cooling effect, stable radiating fog is likely to form. In floating ice area there forms vapor fog with the appearance of masses of vapor from a boiling pot, which is different from short-lasting land fog. The study indicates that the reason why there are many kinds of sea fog form in the Arctic Ocean is because of the complicated cushion and the consequent sea-air interaction caused by the sea ice distribution and its unique physical characteristics. Sea fog is the atmospheric phenomenon of sea-air heat exchange. Especially, due to the high albedo of ice and snow surface, it is diffcult to absorb great amount of solar radiation during the polar days. Besides, ice is a poor conductor of heat; it blocks the sea-air heat exchange. The sea-air exchange is active in floating ice area where the ice is broken. The sea sends heat to the atmosphere in form of latent heat; vapor fog is a way of sea-air heat exchange influencing the climate and an indicator of the extent of the exchange. The study also indicates that the sea also transports heat to the atmosophere in form of sensible heat when vapor fog occurs.展开更多
Based on the principle of transient perturbation analysis,in this paper,a method to objectively determine the weather pattern formed by sea fog is provided.On the basis of the classification results,the circulation si...Based on the principle of transient perturbation analysis,in this paper,a method to objectively determine the weather pattern formed by sea fog is provided.On the basis of the classification results,the circulation situation,divergence and vertical velocity field,and the vertical profile of temperature and humidity are synthesized and analyzed.The basic characteristics of the circulation and physical field of sea fog under low pressure control(L type sea fog)are obtained,and the results are compared with the sea fog under the control of high pressure(H type sea fog):a)L type sea fogs potential height anomaly disturbance is mainly manifested in the low layer,and its average value is-65.66 gpm,gradually weakening upward;b)L type sea fogs inversion structure is weaker than H type sea fogs when it occurs,the fog layer is thicker and the high relative humidity level is high over the fog layer,while the H type sea fogs fog layer has a relatively obvious dry layer;c)L sea fog has three layers of structure at the vertical direction.The first layer 1000-950 hPa is convergence accompanied by weak rise and subsidence,the second layer 950-850 hPa is divergence accompanied by weak subsidence,and the third layer 850 to 500hPa is gradually strengthened.While there are two layer structures of the H type sea fog.1000 hPa is divergence accompanied by weak rising and sinking movement,950-500 hPa is a uniform subsidence movement.d)Probability density statistical analysis further quantified the vertical movement of L and H type sea fog and the distribution of relative humidity in each layer.These conclusions provide an important reference for forecasting the sea fog in the northwest of the Yellow Sea under the condition of low pressure circulation in summer.展开更多
Fog can adversely affect human activity directly and indirectly, resulting in large losses both in terms of the local economy and lives. Much effort has been devoted to studies of fog across many areas of China, and i...Fog can adversely affect human activity directly and indirectly, resulting in large losses both in terms of the local economy and lives. Much effort has been devoted to studies of fog across many areas of China, and in that context this paper aims to summarize climatic characteristics and review fog field experiments and their major results relating to fog mechanisms, physical properties and chemical characteristics. Progress in the application of remote sensing techniques and numerical simulation in fog research are also discussed. In particular, the effects of urbanization and industrialization on fog are highlighted. To end, perspectives on future fog research are outlined. The goal of this review paper is to introduce fog research in China to the global academic community and thus promote international collaboration on fog research. This is important because most papers on fog in China are published in Chinese, which are unreadable for the vast majority of non-Chinese researchers.展开更多
针对液晶显示器(LCD)面板的“Chip/FPC on Glass”(C/FOG)工艺生产制造过程中存在的计量延迟大、生产异常无法提前预测的问题,本文提出一种基于神经网络的C/FOG工艺生产制造虚拟计量方法。该方法利用生产机台上的传感器采集生产过程中...针对液晶显示器(LCD)面板的“Chip/FPC on Glass”(C/FOG)工艺生产制造过程中存在的计量延迟大、生产异常无法提前预测的问题,本文提出一种基于神经网络的C/FOG工艺生产制造虚拟计量方法。该方法利用生产机台上的传感器采集生产过程中的过程状态数据,构建基于多尺度一维卷积及通道注意力模型(MS1DC-CA)的虚拟计量模型。通过多个尺度的卷积核提取不同尺度范围内的状态数据特征。在对含有缺失值的原始数据预处理中,提出了基于粒子群算法改进的K近邻填补方法(PSO-KNN Imputation)进行缺失值填充,保留特征的同时,减少因填充值引入的干扰。最后在实际生产采集的数据上进行实验对比分析,实际不良率主要集中在0.1%~0.5%,该虚拟计量模型的拟合均方误差为0.397 7‱,低于其他现有拟合模型,在平均绝对误差、对称平均绝对百分比误差和拟合优度3种评价指标下也均优于其他现有的拟合模型,具有良好的预测性能。展开更多
基金supported in part by the Natural Science Foundation of China (62171110,U19B2028 and U20B2070)。
文摘Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFB3101100in part by the National Natural Science Foundation of China under Grant 62272123,62272102,62272124+2 种基金in part by the Project of High-level Innovative Talents of Guizhou Province under Grant[2020]6008in part by the Science and Technology Program of Guizhou Province under Grant No.[2020]5017,No.[2022]065in part by the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS202105。
文摘Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of incentives.To address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain scenarios.In particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains.Second,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain user.Furthermore,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain.Finally,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.
基金Shandong Provincial Key Research and Development Program(Major Scientific and Technological Innovation Project)(2021CXGC011001)Huafon Microfibre(Jiangsu)Co.Ltd.(2021120011000234)+1 种基金Textile Vision Basic Research Program(J202306)China Postdoctoral Science Foundation(No.2023M732103).
文摘The shortage of freshwater has become a global challenge,exacerbated by global warming and the rapid growth of the world’s population.Researchers across various fields have made numerous attempts to efficiently collect freshwater for human use.These efforts include seawater desalination through reverse osmosis or distillation,sewage treatment technologies,and atmospheric water harvesting.However,after thoroughly exploring traditional freshwater harvesting methods,it has become clear that bio-inspired fog harvesting technology offers new prospects due to its unique advantages of efficiency and sustainability.This paper systematically introduces the current principles of fog harvesting and wettability mechanism found in nature.It reviews the research status of combining bionic fog harvesting materials with textile science from two distinct dimensions.Additionally,it describes the practical applications of fog harvesting materials in agriculture,industry,and domestic water use,analyzes their prospects and feasibility in engineering projects,discusses potential challenges in practical applications,and envisions future trends and directions for the development of these materials.
基金the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1567).
文摘Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.
基金jointly supported by the National Natural Science Foundation of China[grant number 42205009]the Open Grants of the State Key Laboratory of Severe Weather[grant number 2024LASWB23]+1 种基金the Collaborative Innovation Project for Marine Meteorological Science and Technology in the Bohai Rim Region[grant number QYXM202315]the Research and Development Project of Hebei Provincial Meteorological Bureau[grant number 22ky26]。
文摘Fog is a highly complex weather phenomenon influenced by numerous factors.This study investigated the impact of the Changbai Mountains’topography on the formation and development of spring fog in the Bohai Sea.From 12 to 14 May 2021,the Bohai region experienced a sea fog event.Utilizing Himawari-8 satellite data,ERA5 reanalysis dataset,land and sea station observations,the WRF model,a topography sensitivity experiment,and backward trajectory tracking,the influence of the Changbai Mountains’topography on the evolution of this sea fog event was assessed.Results indicated that the Changbai Mountains’topography significantly impacted the propagation and concentration of the sea fog through dual effects—namely,the Venturi Effect and Foehn Clearance Effect.Comparative simulations incorporating and excluding the Changbai Mountains revealed that its topography favored weak convergence(Venturi Effect)of low-level airflow over the Bohai Sea induced by a high-pressure system,promoting westward fog expansion.Additionally,the backward trajectory analysis further indicated that the Foehn Clearance Effect of the Changbai Mountains extended its influence far beyond the immediate lee side,contributing to significant changes in atmospheric conditions such as reductions in relative humidity and increases in potential temperature.The dry,warm foehn contributed to a reduction in the liquid water content,ultimately leading to the weakening or even dissipation of the sea fog in the region close to the Changbai Mountains.This study emphasizes the crucial role of the Changbai Mountains’topography in the development and evolution of fog,providing valuable insights for forecasting fog in regions with complex terrain.
基金supported in part by the National Natural Science Foundation of China(62462053)the Science and Technology Foundation of Qinghai Province(2023-ZJ-731)+1 种基金the Open Project of the Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Area(2023-KF-12)the Open Research Fund of Guangdong Key Laboratory of Blockchain Security,Guangzhou University。
文摘Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.
基金the Deanship of Graduate Studies and Scientific Research at Najran University for funding this work under the Easy Funding Program grant code(NU/EFP/SERC/13/166).
文摘The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.
基金supported by the National Research Foundation of Korea(NRF)funded by theMinistry of Science and ICT(2022K1A3A1A61014825)。
文摘With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these technologies face critical security challenges,exacerbated by the emergence of quantum computing,which threatens traditional encryption methods.The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust,quantum-resistant security solutions.To address these challenges,researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography,which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity.This paper reviews the current security practices in IoT and Edge/Fog environments,explores the latest advancements in QKD and PQC technologies,and discusses their integration into distributed computing systems.Additionally,this paper proposes an enhanced QKD protocol combining the Cascade protocol and Kyber algorithm to address existing limitations.Finally,we highlight future research directions aimed at improving the scalability,efficiency,and practicality of QKD and PQC for securing IoT and Edge/Fog networks against evolving quantum threats.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively.
基金funded by Researchers Supporting Project Number(RSPD2025R947)King Saud University,Riyadh,Saudi Arabia.
文摘Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+2 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067).
文摘The 6G smart Fog Radio Access Network(F-RAN)is an integration of 6G network intelligence technologies and the F-RAN architecture.Its aim is to provide low-latency and high-performance services for massive access devices.However,the performance of current 6G network intelligence technologies and its level of integration with the architecture,along with the system-level requirements for the number of access devices and limitations on energy consumption,have impeded further improvements in the 6G smart F-RAN.To better analyze the root causes of the network problems and promote the practical development of the network,this study used structured methods such as segmentation to conduct a review of the topic.The research results reveal that there are still many problems in the current 6G smart F-RAN.Future research directions and difficulties are also discussed.
基金supported by the National Natural Science Foundation of China(Grant No.41705081)the Shandong Natural Science Foundation Project(Grant No.ZR2019ZD12)the Laoshan Laboratory(Grant No.LSKJ202202203).
文摘This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event with cold-front synoptic pattern(CFSP).An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting model was adopted with ensemble sensitivity analysis(ESA).By comparing observation impacts(estimated from a 40-member ensemble with ESA)among different meteorological observation variables and pressure levels,the temperature at 850 hPa and surface layer(850 hPa-and-surface temperature)was selected as the target observation type.Additionally,the area with large observation impacts for this observation type was predicted in the transition region of the surface low–high system.This area developed southward with the low and moved eastward with the low–high system,which could be explained by the main features of CFSP.Moreover,both experiments assimilating synthetic and real observations showed that assimilating 850 hPa-and-surface temperature observations generally yielded better fog coverage forecasts in areas with greater observation impacts than areas with smaller impacts.However,the effectiveness of adaptive observations was reduced when real observations rather than synthetic observations were assimilated,which is possibly due to factors such as observation and model errors.The main conclusions above were verified by another typical fog event with CFSP characteristics.Results of this study highlight the importance of improved initial conditions in the transition region of the low–high system for improving fog prediction and provide scientific guidance for implementing an observation network for fog forecasting over the Bohai Sea.
基金supported by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RG23129).
文摘Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/fog computing traffic surveillance and monitoring systems.Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time.To evaluate vision-based vehicle detection performance in foggy weather conditions,state-of-the-art Vehicle Detection in Adverse Weather Nature(DAWN)and Foggy Driving(FD)datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle detection classes:cars,buses,motorcycles,and trucks.The state-of-the-art single-stage deep learning algorithms YOLO-V5,and YOLO-V8 are considered for the task of vehicle detection.Furthermore,YOLO-V5s is enhanced by introducing attention modules Convolutional Block Attention Module(CBAM),Normalized-based Attention Module(NAM),and Simple Attention Module(SimAM)after the SPPF module as well as YOLO-V5l with BiFPN.Their vehicle detection accuracy parameters and running speed is validated on cloud(Google Colab)and edge(local)systems.The mAP50 score of YOLO-V5n is 72.60%,YOLOV5s is 75.20%,YOLO-V5m is 73.40%,and YOLO-V5l is 77.30%;and YOLO-V8n is 60.20%,YOLO-V8s is 73.50%,YOLO-V8m is 73.80%,and YOLO-V8l is 72.60%on DAWN dataset.The mAP50 score of YOLO-V5n is 43.90%,YOLO-V5s is 40.10%,YOLO-V5m is 49.70%,and YOLO-V5l is 57.30%;and YOLO-V8n is 41.60%,YOLO-V8s is 46.90%,YOLO-V8m is 42.90%,and YOLO-V8l is 44.80%on FD dataset.The vehicle detection speed of YOLOV5n is 59 Frame Per Seconds(FPS),YOLO-V5s is 47 FPS,YOLO-V5m is 38 FPS,and YOLO-V5l is 30 FPS;and YOLO-V8n is 185 FPS,YOLO-V8s is 109 FPS,YOLO-V8m is 72 FPS,and YOLO-V8l is 63 FPS on DAWN dataset.The vehicle detection speed of YOLO-V5n is 26 FPS,YOLO-V5s is 24 FPS,YOLO-V5m is 22 FPS,and YOLO-V5l is 17 FPS;and YOLO-V8n is 313 FPS,YOLO-V8s is 182 FPS,YOLO-V8m is 99 FPS,and YOLO-V8l is 60 FPS on FD dataset.YOLO-V5s,YOLO-V5s variants and YOLO-V5l_BiFPN,and YOLO-V8 algorithms are efficient and cost-effective solution for real-time vision-based vehicle detection in foggy weather.
基金the National Natural Science Foundation of China under contrast No.49776280 andby the Project of China's First Arctic Expedit
文摘Synchronous or quasi-synchronous sea-land-air observations were conducted using advanced sea ice, atmospheric and marine instruments during China's First Arctic Expedition. Expedition.Based on the Precious data from the expedition, it was found that in the Arctic Ocean, most part of which is covered with ice or is mixed with ice, various kinds of sea fog formed such as advection fog, radiation fog and vapor fog. Each kind he its own characteristic and mechanics of creation. In the southern part of the Arctic Ocean, due to the sufficient warm and wet flow there, it is favorable for advection fog to form, which is dense and lasts a long time. On ice cap or vast floating ice, due to the strong radiation cooling effect, stable radiating fog is likely to form. In floating ice area there forms vapor fog with the appearance of masses of vapor from a boiling pot, which is different from short-lasting land fog. The study indicates that the reason why there are many kinds of sea fog form in the Arctic Ocean is because of the complicated cushion and the consequent sea-air interaction caused by the sea ice distribution and its unique physical characteristics. Sea fog is the atmospheric phenomenon of sea-air heat exchange. Especially, due to the high albedo of ice and snow surface, it is diffcult to absorb great amount of solar radiation during the polar days. Besides, ice is a poor conductor of heat; it blocks the sea-air heat exchange. The sea-air exchange is active in floating ice area where the ice is broken. The sea sends heat to the atmosphere in form of latent heat; vapor fog is a way of sea-air heat exchange influencing the climate and an indicator of the extent of the exchange. The study also indicates that the sea also transports heat to the atmosophere in form of sensible heat when vapor fog occurs.
基金supported by National Natural Science Foundation of China(No.41576108 and No.41605006)Natural Science Foundation project of Shandong Province(No.ZR2016DB26).
文摘Based on the principle of transient perturbation analysis,in this paper,a method to objectively determine the weather pattern formed by sea fog is provided.On the basis of the classification results,the circulation situation,divergence and vertical velocity field,and the vertical profile of temperature and humidity are synthesized and analyzed.The basic characteristics of the circulation and physical field of sea fog under low pressure control(L type sea fog)are obtained,and the results are compared with the sea fog under the control of high pressure(H type sea fog):a)L type sea fogs potential height anomaly disturbance is mainly manifested in the low layer,and its average value is-65.66 gpm,gradually weakening upward;b)L type sea fogs inversion structure is weaker than H type sea fogs when it occurs,the fog layer is thicker and the high relative humidity level is high over the fog layer,while the H type sea fogs fog layer has a relatively obvious dry layer;c)L sea fog has three layers of structure at the vertical direction.The first layer 1000-950 hPa is convergence accompanied by weak rise and subsidence,the second layer 950-850 hPa is divergence accompanied by weak subsidence,and the third layer 850 to 500hPa is gradually strengthened.While there are two layer structures of the H type sea fog.1000 hPa is divergence accompanied by weak rising and sinking movement,950-500 hPa is a uniform subsidence movement.d)Probability density statistical analysis further quantified the vertical movement of L and H type sea fog and the distribution of relative humidity in each layer.These conclusions provide an important reference for forecasting the sea fog in the northwest of the Yellow Sea under the condition of low pressure circulation in summer.
基金supported by the National Natural Science Foundation of China under Grant No.40775012the Jiangsu Key Basic Science Research Project for Universities under Grant No.06KJA17021+1 种基金the Jiangsu Key Basic Science Research Project for Universities under Grant No.08KJA170002the Scientific Research Project for the Meteorological Administration of the Ministry of Science and Technology of China under Grant No.GYHY200706026
文摘Fog can adversely affect human activity directly and indirectly, resulting in large losses both in terms of the local economy and lives. Much effort has been devoted to studies of fog across many areas of China, and in that context this paper aims to summarize climatic characteristics and review fog field experiments and their major results relating to fog mechanisms, physical properties and chemical characteristics. Progress in the application of remote sensing techniques and numerical simulation in fog research are also discussed. In particular, the effects of urbanization and industrialization on fog are highlighted. To end, perspectives on future fog research are outlined. The goal of this review paper is to introduce fog research in China to the global academic community and thus promote international collaboration on fog research. This is important because most papers on fog in China are published in Chinese, which are unreadable for the vast majority of non-Chinese researchers.
文摘针对液晶显示器(LCD)面板的“Chip/FPC on Glass”(C/FOG)工艺生产制造过程中存在的计量延迟大、生产异常无法提前预测的问题,本文提出一种基于神经网络的C/FOG工艺生产制造虚拟计量方法。该方法利用生产机台上的传感器采集生产过程中的过程状态数据,构建基于多尺度一维卷积及通道注意力模型(MS1DC-CA)的虚拟计量模型。通过多个尺度的卷积核提取不同尺度范围内的状态数据特征。在对含有缺失值的原始数据预处理中,提出了基于粒子群算法改进的K近邻填补方法(PSO-KNN Imputation)进行缺失值填充,保留特征的同时,减少因填充值引入的干扰。最后在实际生产采集的数据上进行实验对比分析,实际不良率主要集中在0.1%~0.5%,该虚拟计量模型的拟合均方误差为0.397 7‱,低于其他现有拟合模型,在平均绝对误差、对称平均绝对百分比误差和拟合优度3种评价指标下也均优于其他现有的拟合模型,具有良好的预测性能。
