基于HSV空间的煤矿井下低光照图像增强方法

针对煤矿井下采集到的图像对比度低、光照不均和细节信息弱等问题,提出一种基于色相-饱和度-明度(Hue-Saturation-Value,HSV)颜色空间的煤矿井下低光照图像增强方法。该方法基于图像的HSV空间,通过对低光照图像的亮度通道V通道的主要结构和边缘细节分别进行对比度增强,这样可以更好地抑制图像细节丢失,同时可以较好地再现原图中的轮廓和纹理细节。首先,将输入的煤矿井下低光照图像转换到HSV空间,利用相对全变分滤波(RTV)与改进的边窗滤波(SWF),分别对提取的V通道图像进行主要结构提取和轮廓边缘保留,对其非线性灰度拉伸后利用主成分分析融合技术(PCA)重构V通道图像,即融合V通道图像的主要结构和精细结构,最后合成图像,完成图像增强。通过实验验证,提出的基于HSV空间的煤矿井下低光照图像增强方法,在色彩和边缘模糊处理等方面表现良好,在煤矿井下工作面等环境中,对图像进行定量和定性实验,结果表明,与6种方法相比,增强图像的对比度、自然度和图像细节方面表现更好。

贵阳市MSM人群中HIV、TP、HSV-2感染的实验结果分析

目的分析贵阳市男男性行为者(MSM)人群中HIV、TP及HSV-2感染的实验检测结果,为该人群的艾滋病防控工作提供参考依据。方法通过同伴推动抽样法招募MSM人员为调查对象,对其血液样本进行HIV、TP及HSV-2检测,并分析实验检测结果。结果577例研究对象中,共发现HIV抗体阳性者、TP特异性抗体阳性者及HSV-2抗体阳性者116例(20.1%)、107例(18.5%)、74例(12.8%);HIV^(+)TP、HIV^(+)HSV-2、TP^(+)HSV-2以及HIV^(+)TP^(+)HSV-2合并感染者24例(4.2%)、8例(1.4%)、11例(1.9%)、20例(3.5%)。实验检测结果分析表明,新发现HIV感染者的WB试验各个反应带型频率不一,带型组合多样化;CD4^(+)T淋巴细胞绝对值计数最小为10个/μL,最大为914个/μL,平均值为(316.2±160.6)个/μL。病毒载量最高值>7 log_(10)/mL,最低值为1.5 log_(10)/mL,平均值为(4.3±0.9)log_(10)/mL。结论贵阳市MSM人群HIV、TP及HSV-2的感染率较高,合并感染情况较为严重,防治形势严峻,应加强本地区MSM人群的HIV、TP及HSV-2防治工作;同时应结合检测结果制定相关的治疗及防治措施,提高感染者的健康水平。

基于Retinex和HSV颜色空间的低照度图像增强算法

针对低照度图像存在的对比度低、颜色失真、细节信息损失、噪声干扰等问题,提出一种基于Retinex的改进算法。首先,将原始图像转换到HSV颜色空间,采用改进算法和Gamma校正对V分量进行亮度调节;其次,基于对比度拉伸算法调整S分量,增强图像饱和度和对比度;最后,将融合后的图像转换回RGB颜色空间,输出图像。在MATLAB平台上选取不同场景低照度图像进行增强处理,采用图像信息熵(IE)、峰值信噪比(PSNR)和结构相似性指数(SSIM)三个客观指标进行均值对比,实验结果表明,新算法计算结果均优于传统的直方图均衡化算法(HE)和多尺度Retinex算法(MSR),有效保持图像边缘信息,提升图像质量。

人LIGHT和HSV-TK-EGFP基因共转染的MSCs抗肿瘤免疫的体内研究

目的研究与单纯疱疹病毒的糖蛋白D竞争结合单纯疱疹病毒进入介导物(herpes virus entry mediator,HVEM)的淋巴毒素类似物(homologous to lymphotoxins,exhibits inducible expression,and competes with HSV glycoprotein D for HVEM,a receptor expressed by T lymphocytes,LIGHT)基因和单纯疱疹病毒胸苷激酶(herpes simplex virus thymidine kinase,HSV-TK)基因共转染的骨髓间充质干细胞(mesenchymal stem cells,MSCs)在体内的抗肿瘤免疫功能。方法将pIRES2-LIGHT基因和HSV-TK-EGFP基因共转染小鼠骨髓间充质干细胞(MSCs/LT组),以转染空载体和转染HSV-TK-EGFP基因的骨髓间充质干细胞作对照。流式细胞仪检测LIGHT分子和HSV-TK-EGFP分子在稳定转染的骨髓间充质干细胞上的表达。体内迁移实验观察MSCs/LT在小鼠体内迁移情况。观察更昔洛韦注射前后MSCs/LT对荷瘤小鼠体内肿瘤的治疗作用。ELISA法检测小鼠肿瘤组织中IFN-γ,IL-2和IL-10的水平。结果流式细胞仪检测发现,MSCs/LT能稳定高表达LIGHT分子。MSCs/LT有特异地向肿瘤组织趋化的特性。MSCs/LT和MSCs/T有较好的抑制肿瘤生长的能力,但在更昔洛韦诱导后,MSCs/LT的抗肿瘤效应下降甚至消失。同时,MSCs/LT可促使T细胞进入肿瘤组织,并促进T细胞分泌IL-2、IFN-γ,抑制IL-10分泌(P<0.05)。结论共转染人LIGHT和HSV-TK-EGFP基因的骨髓间充质干细胞能稳定高表达LIGHT分子,能特异性地向荷瘤小鼠体内肿瘤组织趋化并抑制肿瘤的生长,这种体内抗肿瘤功能可能与促进T淋巴细胞IL-2、IFN-γ等细胞因子的分泌,改善局部免疫抑制环境有关。

HSV空间的明度对数斜率变换的低照图像增强

为了在保持图像颜色自然的同时,适宜地提升图像的亮度和对比度,丰富图像的细节信息,提出了HSV空间的明度对数斜率变换的低照图像增强方法。将低照图像转到HSV颜色空间,将明度分量V进行多尺度Retinex分解,得到多尺度的细节层图像和光照图像;对多尺度的细节层图像进行自适应分数拉普拉斯增强,而用自适应的对数斜率变换对光照图像进行增强;将增强后的明度分量V与色调H和饱和度S分量组合,得到亮度适宜、高对比度和高清晰度的图像。与部分现有方法进行比较,实验数据显示所提方法的增强图像的视觉效果更佳、信息熵和平均梯度更高,因此具有更优的图像增强性能。

基于HSV色彩空间的图像感兴趣区域提取方法研究

嵌入式设备中部署深度学习检测模型往往面临算力不足的问题,而感兴趣区域(ROI)提取可作为一种高效的性能优化手段。文章提出一种基于HSV(Hue,Saturation,Value)色彩空间模型的ROI提取的方法,将检测目标的像素信息转化到HSV色彩空间,在色相-饱和度(H-S)平面引入DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法,精确定位目标的主色彩像素在H-S平面上的分布位置,同时过滤杂乱色彩,然后通过Quickhull(快壳)凸包算法,从散点数据中拟合出主色彩的精确分布范围。根据获取的主色彩范围对像素进行遍历,可以根据色彩信息有效地提取ROI。实验结果表明,经过该方法优化后的Faster R-CNN(Faster Regions with Convolutional Neural Networks)算法,较原模型减少了57.08%的平均推理耗时,同时精确率提升了0.9百分点。这对于嵌入式设备中进行实时目标检测具有重要的现实意义。

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

基于改进HSV空间的机器视觉花生霉变检测方法

花生霉变产生的黄曲霉毒素具有强致癌性,严重影响食品安全。为精准快速的识别霉变花生,提出一种基于机器视觉的霉变花生检测方法。首先对花生图像进行双边滤波降噪,然后将图像转为色调、饱和度、亮度(HSV)空间,通过在色调、饱和度空间内提取的霉变颜色范围叠加亮度空间的开运算处理结果来实现对霉变花生的识别检测。实验结果表明,该方法对于霉变花生的识别精度达到95.3%,处理单帧花生图像耗时为0.6s,通过与其它算法对比,该方法具有快速、准确率高等优点,可以满足霉变花生的实时检测,对花生霉变的分级处理也更加实用。

HSV-1即刻早期基因的5′UTR对报告基因mRNA翻译的影响

mRNA疫苗的5′非翻译区(5’untranslated regions,5′UTR)对其表达起着重要作用,报告基因增强绿色荧光蛋白基因(enhanced green fluorescent protein,EGFP)用于检测5′UTR调控mRNA表达已经是一个较为成熟的实验方法.为研究单纯疱疹病毒1型(herpes simplex virus 1,HSV-1)即刻早期基因的5′UTR作为报告基因EGFP mRNA的5′UTR对其翻译的影响,本研究将4个即刻早期基因的5’UTR以及表达能力较强的人β珠蛋白基因(humanβGlobin,hBg)5′UTR插入报告基因EGFP 5’端,通过观察绿色荧光强度研究不同即刻早期基因5′UTR对mRNA翻译的影响.结果表明:经mRNA转染试剂和纳米脂质体颗粒分别递送进293T细胞和小鼠体内;RL2的5′UTR作为报告基因EGFP 5′UTR的表达水平与hBg 5′UTR相近,RS1、US1、US12表达水平明显弱于hBg 5′UTR.因此,RL2基因的5′UTR有潜力作为mRNA疫苗的5′UTR.

血型检测凝集反应结果P-HSV综合判定方法

由于传统血型检测结果的确定取决于有经验的工作人员,使检测效率低下且准确性有待提高。为此,针对微流控血样芯片,首次提出了一种基于图像识别技术的P-HSV(Perimeter-Hue,Saturation,Value)综合判读方法。该方法采用血液凝集反应后的血红细胞凝集尺寸和颜色对血液类型进行综合判读。尺寸判读依据反应腔内凝集块轮廓边缘周长和凝集块数量进行分级,颜色判读依据反应腔内凝集块颜色饱和度(S:Saturation)与明度(V:Value)比值进行分级。根据尺寸和颜色判读结果综合判断凝集反应的等级。由于该方法依据机器视觉原理进行血液凝集反应结果的判断,因此可以快速、准确、客观判断血液类型,杜绝了人为判断的主观性,极大提高了检测速度和准确率。

基于HSV色彩空间及改进二维伽马函数的井下图像增强算法

为解决井下光照条件不足、光照不均引起的视频图像质量低下的问题,构造了一种改进二维伽马函数并以此提出了一种基于HSV色彩空间,作用于图像明度和饱和度分量的井下图像质量增强算法。以综采工作面以及井下泵房处图像为增强对象,将算法与目前主流的图像增强算法进行对比验证。结果表明,在井下环境下该算法对井下低亮度区域有着较好的增强效果,一定程度上满足了井下视频图像质量增强需求。

Trigonometric Regularization and Continuation Method Based Time-Optimal Control of Hypersonic Vehicles

Aiming at the time-optimal control problem of hypersonic vehicles(HSV)in ascending stage,a trigonometric regularization method(TRM)is introduced based on the indirect method of optimal control.This method avoids analyzing the switching function and distinguishing between singular control and bang-bang control,where the singular control problem is more complicated.While in bang-bang control,the costate variables are unsmooth due to the control jumping,resulting in difficulty in solving the two-point boundary value problem(TPBVP)induced by the indirect method.Aiming at the easy divergence when solving the TPBVP,the continuation method is introduced.This method uses the solution of the simplified problem as the initial value of the iteration.Then through solving a series of TPBVP,it approximates to the solution of the original complex problem.The calculation results show that through the above two methods,the time-optimal control problem of HSV in ascending stage under the complex model can be solved conveniently.

基于HSV算法的弓片打磨工艺参数优化研究

针对目前机器人打磨弓片存在少打、漏打、打磨不均等导致弓片打磨效果较差、效率较低等问题,通过搭建的机器人打磨平台进行正交试验,然后通过极差分析法、方差分析法确定复合弓片打磨的最优工艺参数为砂带线速度30 m/s、砂带目数240、工件速度40 mm/s;根据最优工艺参数运用多元非线性回归分析法建立表面亮度和磨削厚度的数学回归模型,并对该模型进行试验验证,发现其最大相对误差不超过11.7%;最后利用机器人打磨测力平台对最优工艺参数下复合弓片打磨过程中的受力进行测试,发现最优打磨力为25 N,此时弓片表面胶层能够被完全清理,表面亮度为0.44,完全符合生产要求。

A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic management.Meanwhile,concerns have been raised over the security and privacy of the tons of traffic and vehicle data.In this regard,Federated Learning(FL)with privacy protection features is considered a highly promising solution.However,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global model.Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time.In this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of FL.We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving privacy-preservation.Additionally,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty.The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers

Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.

A credibility-aware swarm-federated deep learning framework in internet of vehicles

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.