Summer Arctic sea fog

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.

Efficient and Cost-Effective Vehicle Detection in Foggy Weather for Edge/Fog-Enabled Traffic Surveillance and Collision Avoidance Systems

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.

Fog Research in China:An Overview

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.

LCD面板C/FOG工艺制造虚拟计量方法研究

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。

Recent Observations and Modeling Study About Sea Fog over the Yellow Sea and East China Sea

This review presents some of the latest achievements in sea fog research,including fog climatology,fog structure in the marine atmospheric boundary layer,and numerical simulations and forecasting of fog.With the development of atmospheric observational techniques and equipments,new facts about sea fog are revealed.The mechanisms involved in the formation,development and dissipation of sea fog are further explored with the help of advanced atmospheric models.

Influences of tree characters on throughfall and stemflow from rainfall and fog in Popa Mountain Park, Myanmar

Available water for communities is insufficient in the central part of Myanmar due to limited rainfall and surface water resources. Over the last two decades, afforestation and reforestation projects have been implemented in this region to provide sufficient water to local communities, expecting forested areas to store more rainwater than other land uses. However, there has been no research and very limited information on rainfall partitioning into throughfall(TF) and stemflow(SF), particularly concerning tree characters. Gross rainfall, TF under different canopy types, and SF of different tree types were measured in 2019. TF and SF were frequently observed even without rain but under foggy conditions. Therefore, both were partitioned into TF and SF from rainfall and fog individually. Sparser canopies resulted in larger TF from rainfall than denser canopies. However, a denser canopy delivered larger TF from fog than a sparser one. TF rates from rainfall in sparser and denser canopies were 54.5% and 51.5%, respectively, while those from fog were 15.2% and 27.2%, respectively. As a result, total TF rate in the denser canopy(70.7%) was significantly larger than that from the sparser one(64.3%). Short trees with small crown projection area and smooth bark(Type Ⅰ) resulted in larger SF from rainfall than taller trees with large crown projection area and rough bark(Type Ⅱ). However, Type Ⅱ trees resulted in larger SF from fog. SF rates by rainfall from Type Ⅰ and Ⅱ trees were 17.5% and 12.2%, respectively, while those by fog were 22.2% and 39.5%, respectively. No significant total SF rates were found for Type Ⅰ(22.5%) and Ⅱ trees(20.1%). A denser canopy results in larger TF, and Type Ⅰ trees result in larger SF. In an area where foggy conditions occur frequently and for a lengthy period, however, Type Ⅱ trees will result in larger SF. These three tree characters(dense canopies, short trees with small crown projection area and smooth bark, and tall trees with large crown projection area and rough bark) should be considered for afforestation and reforestation projects in the Popa Mountain Park to enhance net water input by forests.

Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber services.The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning ones.Federated Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacypreserved intelligence in IoT applications.Even though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d setting.FL is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors.This paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d data.PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each class.The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets.The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8%as accuracy improvements.

A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our lives.However,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and threats.Many interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this problem.Given the resource limitations of fog computing environments,a lightweight IDS is essential.This paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based IDS.We test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm rate.We compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate metrics.Our findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource use.The practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog node.The proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge devices.We prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting approaches.Extensive experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

Design and Develop Function for Research Based Application of Intelligent Internet-of-Vehicles Model Based on Fog Computing

The fast growth in Internet-of-Vehicles(IoV)applications is rendering energy efficiency management of vehicular networks a highly important challenge.Most of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous environments.Based on Large Energy-Aware Fog(LEAF)computing,this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network scenarios.The main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected devices.The proposed LEAF model enables researchers to perform simulations of thousands of streaming applications over distributed and heterogeneous infrastructures.Among the possible reasons is that it provides a realistic simulation environment in which compute nodes can dynamically join and leave,while different kinds of networking protocols-wired and wireless-can also be employed.The novelty of this work is threefold:for the first time,the LEAF model integrates online decision-making algorithms for energy-aware task placement and routing strategies that leverage power usage traces with efficiency optimization in mind.Unlike existing fog computing simulators,data flows and power consumption are modeled as parameterizable mathematical equations in LEAF to ensure scalability and ease of analysis across a wide range of devices and applications.The results of evaluation show that LEAF can cover up to 98.75%of the distance,with devices ranging between 1 and 1000,showing significant energy-saving potential through A wide-area network(WAN)usage reduction.These findings indicate great promise for fog computing in the future-in particular,models like LEAF for planning energy-efficient IoV infrastructures.

Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance

The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers.However,with the advancement of information and communication technology,new security and privacy challenges have emerged for AMI.To address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this paper.The proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid.The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection levels.Furthermore,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user data.Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.

Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

Monitoring Sea Fog over the Yellow Sea and Bohai Bay Based on Deep Convolutional Neural Network

In this paper,we utilized the deep convolutional neural network D-LinkNet,a model for semantic segmentation,to analyze the Himawari-8 satellite data captured from 16 channels at a spatial resolution of 0.5 km,with a focus on the area over the Yellow Sea and the Bohai Sea(32°-42°N,117°-127°E).The objective was to develop an algorithm for fusing and segmenting multi-channel images from geostationary meteorological satellites,specifically for monitoring sea fog in this region.Firstly,the extreme gradient boosting algorithm was adopted to evaluate the data from the 16 channels of the Himawari-8 satellite for sea fog detection,and we found that the top three channels in order of importance were channels 3,4,and 14,which were fused into false color daytime images,while channels 7,13,and 15 were fused into false color nighttime images.Secondly,the simple linear iterative super-pixel clustering algorithm was used for the pixel-level segmentation of false color images,and based on super-pixel blocks,manual sea-fog annotation was performed to obtain fine-grained annotation labels.The deep convolutional neural network D-LinkNet was built on the ResNet backbone and the dilated convolutional layers with direct connections were added in the central part to form a string-and-combine structure with five branches having different depths and receptive fields.Results show that the accuracy rate of fog area(proportion of detected real fog to detected fog)was 66.5%,the recognition rate of fog zone(proportion of detected real fog to real fog or cloud cover)was 51.9%,and the detection accuracy rate(proportion of samples detected correctly to total samples)was 93.2%.

FOG Bonding 电阻降低方案及机理探究

近年来,中小尺寸产品客户端上线TP问题频发,其中有一类TP不良由FOG Bonding电阻异常偏大导致,但二次因未知。FOG堆叠结构为PNL+ACF(异方性导电胶)+FPC,但PNL、FPC为走线电阻,本身电阻较小(<1Ω),故不考虑此影响。基于此,重点研究FOG Bonding接触电阻。首先建立机理模型并进行探究,然后通过调整FOG Bonding参数,使ACF导电粒子的爆破状态和胶体的固化状态达到最优,最终降低电阻。

Production of Artificial Fog in the PAVIN Fog and Rain Platform: In Search of Big Droplets Fog

In fog, visibility is reduced. This reduction in visibility is measured by the meteorological optical range (MOR), which is important for studying human perception and various sensors in foggy conditions. The Cerema PAVIN Fog & Rain platform is capable of producing calibrated fog in order to better analyses it and understand its consequences. The problem is that the droplets produced by the platform are not large enough to resemble real fog. This can have a major impact on measurements since the interaction between electromagnetic waves and fog depends on the wavelength and diameter of the droplets. To remedy this, Cerema is building a new platform with new equipment capable of generating fog. This study analyses different nozzles and associated usage parameters such as the type of water used and the pressure used. The aim is to select the best nozzle with the associated parameters for producing large-diameter droplets and therefore more realistic fog.

Development of a Thermal Fogger-specific Sedimentation Stabilizer

In order to solve the drifting away of thermal fog droplets during thermal spraying and the incompatibility between fog droplet carrier and conventional com- mercial agro-chemicals, the fog droplet carrier, surfactant, condensation nucleus ma- terial and antifreeze, dispersant, thickener and defoamer were screened and assem- bled to develop a thermal fog sedimentation stabilizer in this study, thereby provid- ing technical support for application and promotion of thermal spraying technology in pest and disease control in crops.

Examining the Experiences and Challenges of Haze-Fog Governance in China

With the rapid development of industrialisation and urbanisation, China is facing the challenge of severe HF (Haze-Fog) pollution. This essay compares the advantages and disadvantages of China’s HF management and summarizes the important lessons China can teach the rest of the world about applying this tactic. China’s capabilities in the digital economy, National Innovation Demonstration Zones, and urban innovation systems are examined in this article, along with its shortcomings in information mechanisms and pollution sources. This essay also summarizes China’s achievements, particularly regarding local autonomy. The essay goes on to say, however, that China is probably going to be under more pressure to manage HF in the future, both in terms of long-term solutions and the economy.