融合IMR-WGAN的时序数据修复方法

工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.

转录组和代谢组联合分析阐释木薯叶片花青素合成机制

木薯是世界上第六大粮食作物,其块根富含淀粉但缺乏蛋白质、花青素、胡萝卜素等营养物质。为了探索木薯花青素生物合成机制,本研究选取了两种不同颜色木薯种质资源叶片(FL与PL)为材料,进行转录组和花青素靶向代谢组及其联合分析。转录组分析结果显示在FL和PL中6864个差异表达基因,其中包含4112个上调表达和2752个下调表达。代谢组分析结果显示26种显著差异代谢物在PL中显著高于FL,其中21种属于花青素类。联合分析结果显示,其中7个差异表达的基因与花青素生物合成相关,且花青素含量与差异基因的表达呈正相关,尤其是MeANS1的表达差异最大。本研究结果为阐明木薯花青素的生物合成机制提供了候选基因,同时也为提高木薯花青素含量奠定科学基础。

Mean Shift跟踪算法创新实验项目设计

视频跟踪算法是计算机视觉实践课程中比较受关注的实验项目。针对突变情况下传统Mean Shift跟踪算法无法实时准确跟踪的问题,设计了基于模板更新和线性预估的Mean Shift跟踪算法创新实验项目。在模板更新策略下,引入背景模板,通过将原目标模板和背景模板与设定的阈值进行比较来对干扰因素进行判定,当干扰因素判定目标受到遮挡时,引入线性预估方程进行目标位置预测,有效解决目标在遮挡情况下跟踪丢失的问题。通过对测试视频的跟踪效果和性能进行对比分析,验证了算法在突变情况下相较于传统算法具有更好的抗干扰能力。以算法创新设计为核心,通过开放性创新实验项目的选题、设计、答辩、反馈的闭环实验过程,有效提高了学生算法创新设计能力。

基于深度对比学习的文本聚类

无监督聚类的目的是根据表示空间中的距离将数据划分为有意义或有用的簇,但往往不同类别在表示空间中是相互重叠的,为了实现不同类别的良好分离,使用实例对比学习模型,修改模型的激活函数为Tanh,并将单层感知机修改为多层感知机,提出了深度对比学习聚类模型。模型首先将原始中文长文本数据集输入神经网络特征提取层BERT中,然后将提取到的全部特征输入实例对比学习层中,对特征进行优化,最终使用K⁃means进行聚类。深度对比学习聚类模型在中文长文本聚类方面的性能相比于无监督聚类,在THUCNews数据集上的准确度提高了10%~25%。能够更好地促进不同类别相互重叠的数据的有效分离,实验效果显著优于现有的其他相关模型。

京津冀地区碳排放时空格局变化及其驱动因子

人类对陆地生态系统的改变是碳排放增加的主要原因。在“双碳”目标背景下探索土地利用变化与碳排放的动态关系,有助于区域土地低碳可持续利用。研究基于土地利用转移视角,采用重心-标准差椭圆方法揭示了京津冀地区土地利用碳排放时空格局演化特征,评估了碳排放与生态环境、社会经济发展的协调程度,并借助改进的Kaya模型和LMDI分解模型定量分析了土地利用变化对碳排放的影响程度。结果表明:(1)建设用地的转入是土地利用碳排放增加的主要来源,引起碳排放量增加15844.36万t;耕地、草地向林地、水域的转变促进了地区固碳能力的提升。(2)土地利用碳排放空间分布格局呈现出东北-西南方向向中心进一步聚集的趋势,并且东-西向聚集趋势大于南-北向。(3)京津冀地区整体碳排放与生态环境的协调性呈向好趋势发展,但大部分地区碳排放与社会经济发展出现失衡现象,地区间碳生产力差异逐渐增大。(4)经济水平是促进碳排放增加的最显著因素,单位GDP用地强度是抑制碳排放增加的最主要因素。分析结果表明,严格控制建设用地的无序扩张是促进低碳土地利用的基础,低碳经济发展是促进地区减碳的重要途径。

Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature(CMST) Dataset

According to the latest version(version 2.0) of the China global Merged Surface Temperature(CMST2.0) dataset, the global mean surface temperature(GMST) in the first half of 2023 reached its third warmest value since the period of instrumental observation began, being only slightly lower than the values recorded in 2016 and 2020, and historically record-breaking GMST emerged from May to July 2023. Further analysis also indicates that if the surface temperature in the last five months of 2023 approaches the average level of the past five years, the annual average surface temperature anomaly in 2023 of approximately 1.26°C will break the previous highest surface temperature, which was recorded in 2016of approximately 1.25°C(both values relative to the global pre-industrialization period, i.e., the average value from 1850 to1900). With El Ni?o triggering a record-breaking hottest July, record-breaking average annual temperatures will most likely become a reality in 2023.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Genetics of biochemical attributes regulating morpho-physiology of upland cotton under high temperature conditions

Background Cotton is a strategically important fibre crop for global textile industry.It profoundly impacts several countries’industrial and agricultural sectors.Sustainable cotton production is continuously threatened by the unpre-dictable changes in climate,specifically high temperatures.Breeding heat-tolerant,high-yielding cotton cultivars with wide adaptability to be grown in the regions with rising temperatures is one of the primary objectives of modern cotton breeding programmes.Therefore,the main objective of the current study is to figure out the effective breed-ing approach to imparting heat tolerance as well as the judicious utilization of commercially significant and stress-tolerant attributes in cotton breeding.Initially,the two most notable heat-susceptible(FH-115 and NIAB Kiran)and tolerant(IUB-13 and GH-Mubarak)cotton cultivars were spotted to develop filial and backcross populations to accom-plish the preceding study objectives.The heat tolerant cultivars were screened on the basis of various morphological(seed cotton yield per plant,ginning turnout percentage),physiological(pollen viability,cell membrane thermostabil-ity)and biochemical(peroxidase activity,proline content,hydrogen peroxide content)parameters.Results The results clearly exhibited that heat stress consequently had a detrimental impact on every studied plant trait,as revealed by the ability of crossing and their backcross populations to tolerate high temperatures.However,when considering overall yield,biochemical,and physiological traits,the IUB-13×FH-115 cross went over particularly well at both normal and high temperature conditions.Moreover,overall seed cotton yield per plant exhibited a posi-tive correlation with both pollen viability and antioxidant levels(POD activity and proline content).Conclusions Selection from segregation population and criteria involving pollen viability and antioxidant levels concluded to be an effective strategy for the screening of heat-tolerant cotton germplasms.Therefore,understanding acquired from this study can assist breeders identifying traits that should be prioritized in order to develop climate resilient cotton cultivars.

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.

基于信令数据的中型城市通勤公交站点优化方法

大中型城市之间的手机通信基站密度和通勤出行结构不同,公交站点布设呈现出显著差异。在此背景下,研究了基于改进Mean Shift聚类算法的中型城市通勤公交站点优化方法。该方法采用荆州市中心城区信令数据中的通勤记录,以系统总成本(包括运营成本和乘客步行时间成本)作为主要评价指标。根据中心城区早高峰的通勤出行需求,制定通勤公交站点优化方案。通过对比优化结果和现有公交站点布局,验证了优化方法的有效性;比较不同聚类算法,证明改进的Mean Shift聚类算法的性能优越性;考虑基站和等时圈的影响,对比不同场景,证明了考虑二者影响的必要性。结果表明:①针对荆州市研究区域的早高峰出行需求,优化方法共设置28个公交站,乘客步行时间成本下降51.98%,系统总成本下降17.82%,表明本方法能够得到系统总成本更优的站点布设方案,有效减少研究区域内乘客步行时间成本;②与不同聚类算法的比较中,改进Mean Shift算法得到的方案有明显提升,系统总成本比K-means聚类算法下降8.73%,比近邻传播聚类算法(affinity propagation,AP)下降2.48%;③与未考虑基站和等时圈影响的情况相比,本算法步行时间成本有所下降。上述指标表明改进Mean Shift聚类方法在聚类质量上优于其他方法,可以获得更优的公交站点布设方案,为中型城市的公交线路规划提供基础。

基于关联规则的粗纱工序断头影响因素分析

为了降低粗纱在生产过程中的断头率,提高纱线生产质量和效率,通过生产实践收集纺制C 14.6 tex和JC 24.3 tex两个品种的粗纱工序断头影响因素。使用K⁃means聚类算法对影响因素指标分别进行聚类,然后使用Apriori算法将聚类后的断头影响因素指标数据集进行关联规则挖掘。结果表明:纺制C 14.6 tex品种的粗纱工序断头影响因素关联规则有粗纱回潮率和末并定量湿重,粗纱条干CV和末并定量湿重,末并定量湿重和粗纱条干CV;纺制JC 24.3 tex品种的粗纱工序断头影响因素关联规则有粗纱捻系数和粗纱定量湿重,粗纱捻系数、粗纱条干CV和粗纱定量湿重,粗纱定量湿重和粗纱捻系数;当关联规则中的影响因素同时升高时,粗纱工序断头率增加。通过关联规则中挖掘出的信息,可为纺纱企业减少粗纱工序断头、提高纱线质量提供帮助。

婴幼儿看护机器人的设计与开发

在现代科学育婴理念普及以及消费观念升级的影响下,智能母婴产品迎来巨大的发展空间。该文设计了一种婴幼儿看护机器人,基于Jetson Nano,通过激光雷达与ROS集成,可以获取机器人周围环境的信息,并进行地图构建、路径规划等任务。顶端将舵机二维云台与USB高清摄像头配合使用,结合Mean Shift目标追踪算法自动转动摄像头调整看护角度,并使用YOLO目标检测算法对监控的实时视频流中的婴幼儿人脸进行关键点的定位表情判断等,检测到婴幼儿啼哭、处于消极状态或者危险情况时,及时提醒看护人,看护人还可以通过前端页面实时查看婴幼儿状态。

FFDEZOA优化的SCARA机器人故障数据聚类分析

针对现有聚类方法对机器人故障数据聚类时对初始点选取依赖性大、收敛速度慢且精度低等问题,提出了一种FFDEZOA算法来对KMC聚类算法进行优化。ZOA算法具有寻优能力较强,收敛速度快,且在聚类时对初始点选取依赖性小,但其有几率会陷入到局部最优解。首先针对ZOA算法的缺点,提出了自由觅食策略、非线性收敛因子及斑马进化策略等来对其进行改进,能够有效提高算法搜索范围,从而避免局部最优;进而结合FFDEZOA和KMC算法的互补迭代,既加快了算法的搜索速度,也提升了精度。在多个公开数据集上的实验表明,FFDEZOA KMC在精确度和归一化互信息的指标上均优于ZOA KMC、AO KMC、KMC和MFO KMC,具有更好的收敛性能和聚类效果。最后依据各故障特征的主成分不同,利用FFDEZOA KMC对故障数据进行了聚类,可在多种工况下对机器人进行针对性的保养和维护。

MEAN SENSITIVITY AND BANACH MEAN SENSITIVITY FOR LINEAR OPERATORS

Let(X,T)be a linear dynamical system,where X is a Banach space and T:X→X is a bounded linear operator.This paper obtains that(X,T)is sensitive(Li-Yorke sensitive,mean sensitive,syndetically mean sensitive,respectively)if and only if(X,T)is Banach mean sensitive(Banach mean Li-Yorke sensitive,thickly multi-mean sensitive,thickly syndetically mean sensitive,respectively).Several examples are provided to distinguish between different notions of mean sensitivity,syndetic mean sensitivi`ty and mean Li-Yorke sensitivity.

A STABILITY RESULT FOR TRANSLATINGSPACELIKE GRAPHS IN LORENTZ MANIFOLDS

In this paper,we investigate spacelike graphs defined over a domain Ω⊂M^(n) in the Lorentz manifold M^(n)×ℝ with the metric−ds^(2)+σ,where M^(n) is a complete Riemannian n-manifold with the metricσ,Ωhas piecewise smooth boundary,and ℝ denotes the Euclidean 1-space.We prove an interesting stability result for translating spacelike graphs in M^(n)×ℝ under a conformal transformation.

A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network

The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4).

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

基于空间约束K⁃means模型的长三角地区生态系统服务时空格局及功能分区研究

科学认知生态服务功能的时空演变规律以及合理划分生态系统服务簇的空间分类对于生态系统管理和政策制定至关重要。旨在揭示长三角城市群2000—2020年间生物多样性维护、产水量、碳储存、土壤保持、植被净初级生产力、粮食产量的时空演变特征、权衡关系,并利用空间约束K⁃means模型(SC K⁃means)聚类识别生态系统服务簇,划分生态系统服务功能管理区。研究结果表明:(1)生物多样性维护、产水量、碳储存、土壤保持、植被净初级生产力整体呈“西南山地高,东北平原低”空间格局,粮食产量与之相反;(2)六类生态系统服务空间变化差异体现出明显人类活动与自然气候主导,且粮食产量与其他生态系统服务表现为权衡关系;(3)基于SC K⁃means识别生态系统服务簇的结果将研究区划分为为粮食主产区、人类活动密集区、皖浙生态保护区、大别山生态保护区、皖江生态过渡带、长三角核心保护区六类生态功能区,针对各分区提出差异化生态管控建议。研究可为国土空间规划分区引导提供理论依据与实践参考。