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。

Study of the spatial growth of stimulated Brillouin scattering in a gas-filled hohlraum via detecting the driven ion acoustic wave

In an experiment performed on the Shenguang-III prototype laser facility, collective Thomson scattering (TS) is used to study the spatialgrowth of stimulated Brillouin scattering (SBS) in a gas-filled hohlraum by detecting the SBS-driven ion acoustic wave. High-quality timeresolved SBS and TS spectra are obtained simultaneously in the experiment, and these are analyzed by a steady-state code based on theray-tracing model. The analysis indicates that ion–ion collisions may play an important role in suppressing SBS growth in the Au plasma;as aresult, the SBS excited in the filled gas region is dominant. In the early phase of the laser pulse, SBS originates primarily from the high-densityplasma at the edges of the interaction beam channel, which is piled up by the heating of the interaction beam. Throughout the duration of thelaser pulse, the presence of the TS probe beam might mitigate SBS by perturbing the density distribution around the region overlapping withthe interaction beam.

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.

Suppression of stimulated Brillouin and Raman scatterings using an alternating frequency laser and transverse magnetic fields

A novel scheme to suppress both stimulated Brillouin scattering(SBS) and stimulated Raman scattering(SRS) by combining an alternating frequency(AF) laser and a transverse magnetic field is proposed. The AF laser allows the laser frequency to change discretely and alternately over time. The suppression of SBS is significant as long as the AF difference is greater than the linear growth rate of SBS or the alternating time of the laser frequency is shorter than the linear growth time of SBS. However, the AF laser proves ineffective in suppressing SRS, which usually has a much higher linear growth rate than SBS. To remedy that, a transverse magnetic field is included to suppress the SRS instability. The electrons trapped in the electron plasma waves(EPWs) of SRS can be accelerated by the surfatron mechanism in a transverse magnetic field and eventually detrapped. While continuously extracting energy from EPWs, the EPWs are dissipated and the kinetic inflation of SRS is suppressed. The one-dimensional particle-in-cell simulation results show that both SBS and SRS can be effectively suppressed by combining the AF laser with a transverse magnetic field with tens of Tesla. The total reflectivity can be dramatically reduced by more than one order of magnitude. These results provide a potential reference for controlling SBS and SRS under the related parameters of inertial confinement fusion.

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.

Broadband bidirectional Brillouin–Raman random fiber laser with ultra-narrow linewidth

We present a Brillouin–Raman random fiber laser(BRRFL)with full-open linear cavity structure to generate broadband Brillouin frequency comb(BFC)with double Brillouin-frequency-shift spacing.The incorporation of a regeneration portion consisting of an erbium-doped fiber and a single-mode fiber enables the generation of broadband BFC.The dynamics of broadband BFC generation changing with the pump power(EDF and Raman)and Brillouin pump(BP)wavelength are investigated in detail,respectively.Under suitable conditions,the bidirectional BRRFL proposed can produce a flatamplitude BFC with 40.7-nm bandwidth ranging from 1531 nm to 1571.7 nm,and built-in 242-order Brillouin Stokes lines(BSLs)with double Brillouin-frequency-shift spacing.Moreover,the linewidth of single BSL is experimentally measured to be about 2.5 kHz.The broadband bidirectional narrow-linewidth BRRFL has great potential applications in optical communication,optical sensing,spectral measurement,and so on.

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.

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.

Brillouin scattering spectrum for liquid detection and applications in oceanography

The Brillouin scattering spectrum has been used to investigate the properties of a liquid medium.Here,we propose an improved method based on the double-edge technique to obtain the Brillouin spectrum of a liquid.We calculated the transmission ratios and deduced the Brillouin shift and linewidth to construct the Brillouin spectrum by extracting the Brillouin edge signal through filtered double-edge data.We built a detection system to test the performance of this method and measured the Brillouin spectrum for distilled water at different temperatures and compared it with the theoretical prediction.The observed difference between the experimental and theoretical values for Brillouin shift and linewidth is less than 4.3 MHz and 3.2 MHz,respectively.Moreover,based on the double-edge technique,the accuracy of the extracted temperatures and salinity is approximately 0.1°C and 0.5‰,respectively,indicating significant potential for application in water detection and oceanography.

Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of data.The major concerns in this domain are security and privacy.Therefore,attaining a reliable level of confidentiality in the fog computing environment is a pivotal task.Among different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing technologies.Hence,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog data.Chaotic Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,etc.To boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat map.The sequence generated by this map is used to transform the coordinates of the fog data.The improved range of the proposed map is depicted using bifurcation analysis.The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy.We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack.The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

Blockchain-Enabled Secure and Privacy-Preserving Data Aggregation for Fog-Based ITS

As an essential component of intelligent transportation systems(ITS),electric vehicles(EVs)can store massive amounts of electric power in their batteries and send power back to a charging station(CS)at peak hours to balance the power supply and generate profits.However,when the system collects the corresponding power data,several severe security and privacy issues are encountered.The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids.Existing approaches requiring high computational overhead render them unsuitable for the resource-constrained Internet of Things(IoT)environment.To address above problems,this paper proposes a blockchain-enabled secure and privacy-preserving data aggregation scheme for fog-based ITS.First,a fog computing and blockchain co-aware aggregation framework of power injection data is designed,which provides strong support for ITS to achieve secure and efficient power injection.Second,Paillier homomorphic encryption,the batch aggregation signature mechanism and a Bloom filter are effectively integrated with efficient aggregation of power injection data with security and privacy guarantees.In addition,the fine-grained homomorphic aggregation is designed for power injection data generated by all EVs,which provides solid data support for accurate power dispatching and supply management in ITS.Experiments show that the total computational cost is significantly reduced in the proposed scheme while providing security and privacy guarantees.The proposed scheme is more suitable for ITS with latency-sensitive applications and is also adapted to deploying devices with limited resources.

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Task offloading is a key strategy in Fog Computing (FC). Thedefinition of resource-constrained devices no longer applies to sensors andInternet of Things (IoT) embedded system devices alone. Smart and mobileunits can also be viewed as resource-constrained devices if the power, cloudapplications, and data cloud are included in the set of required resources. Ina cloud-fog-based architecture, a task instance running on an end device mayneed to be offloaded to a fog node to complete its execution. However, ina busy network, a second offloading decision is required when the fog nodebecomes overloaded. The possibility of offloading a task, for the second time,to a fog or a cloud node depends to a great extent on task importance, latencyconstraints, and required resources. This paper presents a dynamic service thatdetermines which tasks can endure a second offloading. The task type, latencyconstraints, and amount of required resources are used to select the offloadingdestination node. This study proposes three heuristic offloading algorithms.Each algorithm targets a specific task type. An overloaded fog node can onlyissue one offloading request to execute one of these algorithms accordingto the task offloading priority. Offloading requests are sent to a SoftwareDefined Networking (SDN) controller. The fog node and controller determinethe number of offloaded tasks. Simulation results show that the average timerequired to select offloading nodes was improved by 33% when compared tothe dynamic fog-to-fog offloading algorithm. The distribution of workloadconverges to a uniform distribution when offloading latency-sensitive nonurgenttasks. The lowest offloading priority is assigned to latency-sensitivetasks with hard deadlines. At least 70% of these tasks are offloaded to fognodes that are one to three hops away from the overloaded node.

Levy Flight Firefly Based Efficient Resource Allocation for Fog Environment

Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational and storage services between cloud and terminal devices.However,an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services.A large number of heterogeneous shareable resources makes fog computing a complex environment.In the sight of these issues,this paper has proposed an efficient levy flight firefly-based resource allocation technique.The levy flight algorithm is a metaheuristic algorithm.It offers high efficiency and success rate because of its longer step length and fast convergence rate.Thus,it treats global optimization problems more efficiently and naturally.A system framework for fog computing is presented,followed by the proposed resource allocation scheme in the fog computing environment.Experimental evaluation and comparison with the firefly algorithm(FA),particle swarm optimization(PSO),genetic algorithm(GA)and hybrid algorithm using GA and PSO(GAPSO)have been conducted to validate the effectiveness and efficiency of the proposed algorithm.Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service(QoS).The proposed algorithm reduces average waiting time,average execution time,average turnaround time,processing cost and energy consumption and increases resource utilization and task success rate compared to FA,GAPSO,PSO and GA.

Cache in fog computing design,concepts,contributions,and security issues in machine learning prospective

The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication performance.It brings on-demand services proximate to the end devices and delivers the requested data in a short time.Fog computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end devices.To this end,fog caching plays an imperative role in addressing data dissemination issues.This study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog computing.Moreover,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing.Besides,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog computing.We also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.

Using Metaheuristic OFA Algorithm for Service Placement in Fog Computing

The use of fog computing in the Internet of Things(IoT)has emerged as a crucial solution,bringing cloud services closer to end users to process large amounts of data generated within the system.Despite its advantages,the increasing task demands from IoT objects often overload fog devices with limited resources,resulting in system delays,high network usage,and increased energy consumption.One of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog clouds.To address this challenge,we propose a novel Optimal Foraging Algorithm(OFA)for task placement on appropriate fog devices,taking into account the limited resources of each fog node.The OFA algorithm optimizes task sharing between fog devices by evaluating incoming task requests based on their types and allocating the services to the most suitable fog nodes.In our study,we compare the performance of the OFA algorithm with two other popular algorithms:Genetic Algorithm(GA)and Randomized Search Algorithm(RA).Through extensive simulation experiments,our findings demonstrate significant improvements achieved by the OFA algorithm.Specifically,it leads to up to 39.06%reduction in energy consumption for the Elektroensefalografi(EEG)application,up to 25.86%decrease in CPU utilization for the Intelligent surveillance through distributed camera networks(DCNS)application,up to 57.94%reduction in network utilization,and up to 23.83%improvement in runtime,outperforming other algorithms.As a result,the proposed OFA algorithm enhances the system’s efficiency by effectively allocating incoming task requests to the appropriate fog devices,mitigating the challenges posed by resource limitations and contributing to a more optimized IoT ecosystem.

High efficiency of Brillouin scattering behavior in single-mode Ge–As–Se–Te fibers at 2μm

The Brillouin characteristics of step-index Ge–As–Se–Te(GAST)fibers at 2μm are designed and simulated on the basis of optical and acoustic properties.The refractive indexes of Ge_(20)As_(20)Se_(45)Te_(15)glass and Ge_(20)As_(20)Se_(43)Te_(17)glass serving as fiber core and cladding are 3.20 and 3.18 at 2μm,and their acoustic velocities are 2200 m/s and 2300 m/s,respectively.Numerical results indicate that the stimulated Brillouin scattering(SBS)efficiency is 248 m^(-1)·W^(-1),and the Brillouin threshold power is 66 m W when the core diameter of the 2-m-long GAST fiber is 4μm at 2-μm wavelength.The optic–acoustic coupling factor,the Brillouin frequency shift,and the Brillouin gain coefficient are 0.98,7.02 GHz,and 3.81×10^(-9)m/W,respectively.The SBS effect of GAST fibers simulated for the first time provides a new promising approach to selecting gain medium based on 2-μm-wavelength fiber laser.

Two-Way Approach for Improved Real-Time Transmission in Fog-IoT-Based Health Monitoring System for Critical Patients

Health monitoring systems are now required,particularly for essential patients,following the COVID-19 pandemic,which was followed by its variants and other epidemics of a similar nature.Effective procedures and strategies are required,though,to react promptly to the enormous volume of real-time data offered by monitoring equipment.Although fog-based designs for IoT health systems typically result in enhanced services,they also give rise to issues that need to be resolved.In this paper,we propose a two-way strategy to reduce network latency and usewhile increasing real-time data transmission of device gateways used for sensors by making educated judgments for connection setup with BS and task assignment.For this,a simulation using iFogSim in the Eclipse IDE showed how effective the suggested strategy for massive IoT healthmonitoring systems is.The algorithm is analyzed for network usage and latency,and the results reveal 20%–25%improvements compared to the existing methods regarding network usage and latency.

Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression:A case study in the Bohai Sea,China

Sea fog is a disastrous weather phenomenon,posing a risk to the safety of maritime transportation.Dense sea fogs reduce visibility at sea and have frequently caused ship collisions.This study used a geographically weighted regression(GWR)model to explore the spatial non-stationarity of near-miss collision risk,as detected by a vessel conflict ranking operator(VCRO)model from automatic identification system(AIS)data under the influence of sea fog in the Bohai Sea.Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data.The spatial distributions of near-miss collision risk,sea fog,and the parameters of GWR were mapped.The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea,in which near-miss collision risk in the fog season is significantly higher than that outside the fog season,especially in the northeast(the sea area near Yingkou Port and Bayuquan Port)and the southeast(the sea area near Yantai Port).GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season,with higher R-squared(0.890 in fog season,2018),than outside the fog season(0.723 in non-fog season,2018).GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally.Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk.

Federated learning based QoS-aware caching decisions in fog-enabled internet of things networks

Quality of Service(QoS)in the 6G application scenario is an important issue with the premise of the massive data transmission.Edge caching based on the fog computing network is considered as a potential solution to effectively reduce the content fetch delay for latency-sensitive services of Internet of Things(IoT)devices.Considering the time-varying scenario,the machine learning techniques could further reduce the content fetch delay by optimizing the caching decisions.In this paper,to minimize the content fetch delay and ensure the QoS of the network,a Device-to-Device(D2D)assisted fog computing network architecture is introduced,which supports federated learning and QoS-aware caching decisions based on time-varying user preferences.To release the network congestion and the risk of the user privacy leakage,federated learning,is enabled in the D2D-assisted fog computing network.Specifically,it has been observed that federated learning yields suboptimal results according to the Non-Independent Identical Distribution(Non-IID)of local users data.To address this issue,a distributed cluster-based user preference estimation algorithm is proposed to optimize the content caching placement,improve the cache hit rate,the content fetch delay and the convergence rate,which can effectively mitigate the impact of the Non-IID data set by clustering.The simulation results show that the proposed algorithm provides a considerable performance improvement with better learning results compared with the existing algorithms.